From efd9dbd98cc06c9f140d0dc10d9300dcc469381e Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Thu, 5 Apr 2018 11:47:22 +0200 Subject: [PATCH 1/8] cleaned up sf_tools --- .coveragerc | 9 + .gitignore | 7 +- .travis.yml | 10 +- README.txt | 11 - setup.cfg | 18 +- setup.py | 22 +- sf_tools/__init__.py | 13 +- sf_tools/base/__init__.py | 16 -- sf_tools/base/np_adjust.py | 317 --------------------- sf_tools/base/transform.py | 216 -------------- sf_tools/base/types.py | 246 ---------------- sf_tools/image/__init__.py | 6 +- sf_tools/image/convolve.py | 236 ---------------- sf_tools/info.py | 25 ++ sf_tools/interface/__init__.py | 17 -- sf_tools/interface/errors.py | 105 ------- sf_tools/interface/log.py | 84 ------ sf_tools/math/__init__.py | 17 -- sf_tools/math/angle.py | 122 -------- sf_tools/math/integrate.py | 66 ----- sf_tools/math/matrix.py | 245 ---------------- sf_tools/math/misc.py | 121 -------- sf_tools/math/stats.py | 266 ----------------- sf_tools/plot/__init__.py | 17 -- sf_tools/plot/cost_plot.py | 47 --- sf_tools/signal/__init__.py | 38 --- sf_tools/signal/cost.py | 160 ----------- sf_tools/signal/filter.py | 99 ------- sf_tools/signal/gradient.py | 69 ----- sf_tools/signal/linear.py | 204 -------------- sf_tools/signal/noise.py | 176 ------------ sf_tools/signal/optimisation.py | 486 -------------------------------- sf_tools/signal/positivity.py | 50 ---- sf_tools/signal/proximity.py | 275 ------------------ sf_tools/signal/regression.py | 195 ------------- sf_tools/signal/reweight.py | 56 ---- sf_tools/signal/svd.py | 162 ----------- sf_tools/signal/validation.py | 58 ---- sf_tools/signal/wavelet.py | 165 ----------- 39 files changed, 76 insertions(+), 4376 deletions(-) create mode 100644 .coveragerc delete mode 100644 README.txt delete mode 100644 sf_tools/base/__init__.py delete mode 100644 sf_tools/base/np_adjust.py delete mode 100644 sf_tools/base/transform.py delete mode 100644 sf_tools/base/types.py delete mode 100644 sf_tools/image/convolve.py create mode 100644 sf_tools/info.py delete mode 100644 sf_tools/interface/__init__.py delete mode 100644 sf_tools/interface/errors.py delete mode 100644 sf_tools/interface/log.py delete mode 100644 sf_tools/math/__init__.py delete mode 100644 sf_tools/math/angle.py delete mode 100644 sf_tools/math/integrate.py delete mode 100644 sf_tools/math/matrix.py delete mode 100644 sf_tools/math/misc.py delete mode 100644 sf_tools/math/stats.py delete mode 100644 sf_tools/plot/__init__.py delete mode 100644 sf_tools/plot/cost_plot.py delete mode 100644 sf_tools/signal/__init__.py delete mode 100644 sf_tools/signal/cost.py delete mode 100644 sf_tools/signal/filter.py delete mode 100644 sf_tools/signal/gradient.py delete mode 100644 sf_tools/signal/linear.py delete mode 100644 sf_tools/signal/noise.py delete mode 100644 sf_tools/signal/optimisation.py delete mode 100644 sf_tools/signal/positivity.py delete mode 100644 sf_tools/signal/proximity.py delete mode 100644 sf_tools/signal/regression.py delete mode 100644 sf_tools/signal/reweight.py delete mode 100644 sf_tools/signal/svd.py delete mode 100644 sf_tools/signal/validation.py delete mode 100644 sf_tools/signal/wavelet.py diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..5110937 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,9 @@ +[run] +omit= + *interface* + *plot* + +[report] +show_missing=True +exclude_lines = + pragma: no cover diff --git a/.gitignore b/.gitignore index 88c326d..f76522d 100644 --- a/.gitignore +++ b/.gitignore @@ -9,8 +9,13 @@ # Ignore coverage tests .coverage +.pytest_cache/ # Ignore installation .eggs/ -sf_tools.egg-info/ +modopt.egg-info/ dist/ +build/ + +# Ignore pypi config +.pypirc diff --git a/.travis.yml b/.travis.yml index 8a33b6f..9321cc6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,15 +10,19 @@ branches: # install package and dependencies install: - - pip install -r requirements.txt + - pip install coverage nose pytest pytest-cov + - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then pip install numpy future scipy astropy sphinx==1.6.5 sphinx-rtd-theme numpydoc travis-sphinx coveralls; fi # run unit tests script: - - python setup.py nosetests + - python setup.py test + - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then sphinx-apidoc -feo docs/source sf_tools/; fi + - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then travis-sphinx -v -o docs/build build -n -s docs/source; fi # run coverage tests after_success: - - coveralls + - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then coveralls; fi + - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then travis-sphinx -o docs/build deploy; fi # notification settings notification: diff --git a/README.txt b/README.txt deleted file mode 100644 index fa234b6..0000000 --- a/README.txt +++ /dev/null @@ -1,11 +0,0 @@ -# sf_tools - -Author: Samuel Farrens -Year: 2017 -Email: samuel.farrens@gmail.com -Website: https://sfarrens.github.io - -sf_tools is a series of Python modules with applications to image analysis, signal -processing and statistics. - -Full documentation available here: https://sfarrens.github.io/sf_tools/ \ No newline at end of file diff --git a/setup.cfg b/setup.cfg index dfbcf8d..227c0b6 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,12 +1,12 @@ -[metadata] -description-file = README.rst - -[nosetests] -verbosity=2 -nologcapture=0 -cover-package=sf_tools -with-coverage=1 +[aliases] +test=pytest [build_sphinx] -build-dir=docs/build all_files=1 +build-dir=docs/build + +[metadata] +description-file = README.rst + +[tool:pytest] +addopts = --verbose --cov=modopt/ modopt/tests/ diff --git a/setup.py b/setup.py index 0c2ffb6..f8e3f5a 100644 --- a/setup.py +++ b/setup.py @@ -1,17 +1,27 @@ +#! /usr/bin/env python +# -*- coding: utf-8 -*- + from setuptools import setup, find_packages -from sf_tools import __version__ +import os + +release_info = {} +infopath = os.path.abspath(os.path.join(os.path.dirname(__file__), + "sf_tools", "info.py")) +with open(infopath) as open_file: + exec(open_file.read(), release_info) setup( name='sf_tools', author='sfarrens', - author_email='samuel.farrens@gmail.com', - version=__version__, + author_email='samuel.farrens@cea.fr', + version=release_info["__version__"], url='https://github.com/sfarrens/sf_tools', download_url='https://github.com/sfarrens/sf_tools', packages=find_packages(), + install_requires=['numpy>=1.14.1', 'future>=0.16.0', 'scipy>=1.0.0', 'astropy==2.0.4'], license='MIT', description='Tools for image analysis, signal processing and statistics.', - long_description=open('README.txt').read(), - setup_requires=['pytest-runner'], - tests_require=['pytest'], + long_description=release_info["__about__"], + setup_requires=['pytest-runner', ], + tests_require=['pytest', 'pytest-cov', ], ) diff --git a/sf_tools/__init__.py b/sf_tools/__init__.py index 90481af..3d97f6d 100644 --- a/sf_tools/__init__.py +++ b/sf_tools/__init__.py @@ -5,18 +5,11 @@ SF_TOOLS is a series of Python modules with applications to image analysis, signal processing and statistics. -:Author: Samuel Farrens - -:Version: 1.0.1 - -:Date: 25/10/2017 +:Author: Samuel Farrens """ -__all__ = ['base', 'image', 'interface', 'math', 'plot', 'signal'] +__all__ = ['image'] from . import * -from .base import * - -version_info = (1, 0, 1) -__version__ = '.'.join(str(c) for c in version_info) +from .image import * diff --git a/sf_tools/base/__init__.py b/sf_tools/base/__init__.py deleted file mode 100644 index c29090e..0000000 --- a/sf_tools/base/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# -*- coding: utf-8 -*- - -"""BASE ROUTINES - -This module contains submodules for basic operations such as type -transformations and adjustments to the default output of Numpy functions. - -:Author: Samuel Farrens - -:Version: 1.1 - -:Date: 25/10/2017 - -""" - -__all__ = ['np_adjust', 'transform', 'types'] diff --git a/sf_tools/base/np_adjust.py b/sf_tools/base/np_adjust.py deleted file mode 100644 index e032a5b..0000000 --- a/sf_tools/base/np_adjust.py +++ /dev/null @@ -1,317 +0,0 @@ -# -*- coding: utf-8 -*- - -"""NUMPY ADJUSTMENT ROUTINES - -This module contains methods for adjusting the default output for certain -Numpy functions. - -:Author: Samuel Farrens - -:Version: 1.1 - -:Date: 03/04/2017 - -""" - -from __future__ import division -import numpy as np - - -def rotate(data): - """Rotate - - This method rotates an input numpy array by 180 degrees. - - Parameters - ---------- - data : np.ndarray - Input data array (at least 2D) - - Returns - ------- - np.ndarray rotated data - - Notes - ----- - Adjustment to numpy.rot90() - - Examples - -------- - >>> from sf_tools.base.np_adjust import rotate - >>> x = np.arange(9).reshape((3, 3)) - >>> x - array([[0, 1, 2], - [3, 4, 5], - [6, 7, 8]]) - >>> rotate(x) - array([[8, 7, 6], - [5, 4, 3], - [2, 1, 0]]) - - """ - - return np.rot90(data, 2) - - -def rotate_stack(data): - """Rotate stack - - This method rotates each array in a stack of arrays by 180 degrees. - - Parameters - ---------- - data : np.ndarray - Input data array (at least 3D) - - Returns - ------- - np.ndarray rotated data - - Examples - -------- - >>> from sf_tools.base.np_adjust import rotate_stack - >>> x = np.arange(18).reshape((2, 3, 3)) - >>> x - array([[[ 0, 1, 2], - [ 3, 4, 5], - [ 6, 7, 8]], - [[ 9, 10, 11], - [12, 13, 14], - [15, 16, 17 - >>> rotate_stack(x) - array([[[ 8, 7, 6], - [ 5, 4, 3], - [ 2, 1, 0]], - [[17, 16, 15], - [14, 13, 12], - [11, 10, 9]]]) - - """ - - return np.array([rotate(x) for x in data]) - - -def pad2d(data, padding): - """Pad array - - This method pads an input numpy array with zeros in all directions. - - Parameters - ---------- - data : np.ndarray - Input data array (at least 2D) - padding : int, tuple - Amount of padding in x and y directions, respectively - - Returns - ------- - np.ndarray padded data - - Notes - ----- - Adjustment to numpy.pad() - - Examples - -------- - >>> from sf_tools.base.np_adjust import pad2d - >>> x = np.arange(9).reshape((3, 3)) - >>> x - array([[0, 1, 2], - [3, 4, 5], - [6, 7, 8]]) - >>> pad2d(x, (1, 1)) - array([[0, 0, 0, 0, 0], - [0, 0, 1, 2, 0], - [0, 3, 4, 5, 0], - [0, 6, 7, 8, 0], - [0, 0, 0, 0, 0]]) - - """ - - data = np.array(data) - - if isinstance(padding, int): - padding = np.array([padding]) - elif isinstance(padding, (tuple, list)): - padding = np.array(padding) - elif isinstance(padding, np.ndarray): - pass - else: - raise ValueError('Padding must be an integer or a tuple (or list, ' - 'np.ndarray) of itegers') - - if padding.ndim == 1: - padding = np.repeat(padding, 2) - - return np.pad(data, ((padding[0], padding[0]), (padding[1], padding[1])), - 'constant') - - -def x_bins(vals): - """X-range bins - - This method sets the bin values for a histogram. - - Parameters - ---------- - vals : np.ndarray - X-range bins from np.histogram()[1] - - Returns - ------- - np.ndarray corrected x-range bin data - - Notes - ----- - Adjustment to numpy.histogram() - - Examples - -------- - >>> from sf_tools.base.np_adjust import x_bins - >>> data = np.array([1, 2, 1, 3, 1, 1]) - >>> hist, bins = np.histogram(data) - >>> hist - array([4, 0, 0, 0, 0, 1, 0, 0, 0, 1]) - >>> bins - array([ 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]) - >>> x_bins(bins) - array([ 1.1, 1.3, 1.5, 1.7, 1.9, 2.1, 2.3, 2.5, 2.7, 2.9]) - - """ - - return (vals[:-1] + vals[1:]) / 2.0 - - -def x_bins_step(vals): - """X-range bins (step function) - - This method sets the bin values for a histogram plotted as a step funciton. - - Parameters - ---------- - vals : np.ndarray - X-range bins from np.histogram()[1] - - Returns - ------- - np.ndarray corrected x-range bin data - - Notes - ----- - Adjustment to numpy.histogram() - - Examples - -------- - >>> from sf_tools.base.np_adjust import x_bins - >>> data = np.array([1, 2, 1, 3, 1, 1]) - >>> hist, bins = np.histogram(data) - >>> hist - array([4, 0, 0, 0, 0, 1, 0, 0, 0, 1]) - >>> bins - array([ 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]) - >>> x_bins_step(bins) - array([ 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]) - - """ - - return x_bins(vals) + (vals[1] - vals[0]) / 2.0 - - -def ftr(data): - """Fancy transpose right - - Apply fancy_transpose() to data with roll=1 - - Parameters - ---------- - data : np.ndarray - Input data array - - Returns - ------- - np.ndarray transposed data - - """ - - return fancy_transpose(data) - - -def ftl(data): - """Fancy transpose left - - Apply fancy_transpose() to data with roll=-1 - - Parameters - ---------- - data : np.ndarray - Input data array - - Returns - ------- - np.ndarray transposed data - - """ - - return fancy_transpose(data, -1) - - -def fancy_transpose(data, roll=1): - """Fancy transpose - - This method transposes a multidimensional matrix. - - Parameters - ---------- - data : np.ndarray - Input data array - roll : int - Roll direction and amount. Default (roll=1) - - Returns - ------- - np.ndarray transposed data - - Notes - ----- - Adjustment to numpy.transpose - - Examples - -------- - >>> from sf_tools.base.np_adjust import fancy_transpose - >>> x = np.arange(27).reshape(3, 3, 3) - >>> x - array([[[ 0, 1, 2], - [ 3, 4, 5], - [ 6, 7, 8]], - [[ 9, 10, 11], - [12, 13, 14], - [15, 16, 17]], - [[18, 19, 20], - [21, 22, 23], - [24, 25, 26]]]) - >>> fancy_transpose(x) - array([[[ 0, 3, 6], - [ 9, 12, 15], - [18, 21, 24]], - [[ 1, 4, 7], - [10, 13, 16], - [19, 22, 25]], - [[ 2, 5, 8], - [11, 14, 17], - [20, 23, 26]]]) - >>> fancy_transpose(x, roll=-1) - array([[[ 0, 9, 18], - [ 1, 10, 19], - [ 2, 11, 20]], - [[ 3, 12, 21], - [ 4, 13, 22], - [ 5, 14, 23]], - [[ 6, 15, 24], - [ 7, 16, 25], - [ 8, 17, 26]]]) - - """ - - axis_roll = np.roll(np.arange(data.ndim), roll) - - return np.transpose(data, axes=axis_roll) diff --git a/sf_tools/base/transform.py b/sf_tools/base/transform.py deleted file mode 100644 index a9aadfe..0000000 --- a/sf_tools/base/transform.py +++ /dev/null @@ -1,216 +0,0 @@ -# -*- coding: utf-8 -*- - -"""DATA TRANSFORM ROUTINES - -This module contains methods for transforming data. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 20/10/2017 - -""" - -from __future__ import division -from builtins import range -import numpy as np -from scipy.ndimage import gaussian_filter -from itertools import islice, product - - -def cube2map(data_cube, layout): - """Cube to Map - - This method transforms the input data from a 3D cube to a 2D map with a - specified layout - - Parameters - ---------- - data_cube : np.ndarray - Input data cube, 3D array of 2D images - Layout : tuple - 2D layout of 2D images - - Returns - ------- - np.ndarray 2D map - - Raises - ------ - ValueError - For invalid layout - - """ - - if data_cube.shape[0] != np.prod(layout): - raise ValueError('The desired layout must match the number of input ' - 'data layers.') - - return np.vstack([np.hstack(data_cube[slice(layout[1] * i, layout[1] * - (i + 1))]) for i in range(layout[0])]) - - -def map2cube(data_map, layout): - """Map to cube - - This method transforms the input data from a 2D map with given layout to - a 3D cube - - Parameters - ---------- - data_map : np.ndarray - Input data map, 2D array - layout : tuple - 2D layout of 2D images - - Returns - ------- - np.ndarray 3D cube - - Raises - ------ - ValueError - For invalid layout - - """ - - if np.all(np.array(data_map.shape) % np.array(layout)) != 0: - raise ValueError('The desired layout must be a multiple of the number ' - 'pixels in the data map.') - - d_shape = np.array(data_map.shape) // np.array(layout) - - return np.array([data_map[(slice(i * d_shape[0], (i + 1) * d_shape[0]), - slice(j * d_shape[1], (j + 1) * d_shape[1]))] for i in - range(layout[0]) for j in range(layout[1])]) - - -def map2matrix(data_map, layout): - """Map to Matrix - - This method transforms a 2D map to a 2D matrix - - Parameters - ---------- - data_map : np.ndarray - Input data map, 2D array - layout : tuple - 2D layout of 2D images - - Returns - ------- - np.ndarray 2D matrix - - Raises - ------ - ValueError - For invalid layout - - """ - - layout = np.array(layout) - - # Select n objects - n_obj = np.prod(layout) - - # Get the shape of the galaxy images - gal_shape = (np.array(data_map.shape) // layout)[0] - - # Stack objects from map - data_matrix = [] - - for i in range(n_obj): - lower = (gal_shape * (i // layout[1]), - gal_shape * (i % layout[1])) - upper = (gal_shape * (i // layout[1] + 1), - gal_shape * (i % layout[1] + 1)) - data_matrix.append((data_map[lower[0]:upper[0], - lower[1]:upper[1]]).reshape(gal_shape ** 2)) - - return np.array(data_matrix).T - - -def matrix2map(data_matrix, map_shape): - """Matrix to Map - - This method transforms a 2D matrix to a 2D map - - Parameters - ---------- - data_matrix : np.ndarray - Input data matrix, 2D array - map_shape : tuple - 2D shape of the output map - - Returns - ------- - np.ndarray 2D map - - Raises - ------ - ValueError - For invalid layout - - """ - - map_shape = np.array(map_shape) - - # Get the shape and layout of the galaxy images - gal_shape = np.sqrt(data_matrix.shape[0]) - layout = np.array(map_shape // np.repeat(gal_shape, 2), dtype='int') - - # Map objects from matrix - data_map = np.zeros(map_shape) - - temp = data_matrix.reshape(gal_shape, gal_shape, data_matrix.shape[1]) - - for i in range(data_matrix.shape[1]): - lower = (gal_shape * (i // layout[1]), - gal_shape * (i % layout[1])) - upper = (gal_shape * (i // layout[1] + 1), - gal_shape * (i % layout[1] + 1)) - data_map[lower[0]:upper[0], lower[1]:upper[1]] = temp[:, :, i] - - return data_map - - -def cube2matrix(data_cube): - """Cube to Matrix - - This method transforms a 3D cube to a 2D matrix - - Parameters - ---------- - data_cube : np.ndarray - Input data cube, 3D array - - Returns - ------- - np.ndarray 2D matrix - - """ - - return data_cube.reshape([data_cube.shape[0]] + - [np.prod(data_cube.shape[1:])]).T - - -def matrix2cube(data_matrix, im_shape): - """Matrix to Cube - - This method transforms a 2D matrix to a 3D cube - - Parameters - ---------- - data_matrix : np.ndarray - Input data cube, 2D array - im_shape : tuple - 2D shape of the individual images - - Returns - ------- - np.ndarray 3D cube - - """ - - return data_matrix.T.reshape([data_matrix.shape[1]] + list(im_shape)) diff --git a/sf_tools/base/types.py b/sf_tools/base/types.py deleted file mode 100644 index fce255e..0000000 --- a/sf_tools/base/types.py +++ /dev/null @@ -1,246 +0,0 @@ -# -*- coding: utf-8 -*- - -"""TYPE HANDLING ROUTINES - -This module contains methods for handing object types. - -:Author: Samuel Farrens - -:Version: 1.0 - -:Date: 04/04/2017 - -""" - - -import numpy as np - - -def check_float(val): - """Check if input value is a float or a np.ndarray of floats, if not - convert. - - Parameters - ---------- - val : any - Input value - - Returns - ------- - float or np.ndarray of floats - - """ - - if type(val) is float: - pass - elif type(val) is int: - val = float(val) - elif type(val) is list or type(val) is tuple: - val = np.array(val, dtype=float) - elif type(val) is np.ndarray and val.dtype is not 'float64': - val = val.astype(float) - else: - raise ValueError('Invalid input type.') - - return val - - -def check_int(val): - """Check if input value is an int or a np.ndarray of ints, if not convert. - - Parameters - ---------- - val : any - Input value - - Returns - ------- - int or np.ndarray of ints - - """ - - if type(val) is int: - pass - elif type(val) is float: - val = int(val) - elif type(val) is list or type(val) is tuple: - val = np.array(val, dtype=int) - elif type(val) is np.ndarray and val.dtype is not 'int64': - val = val.astype(int) - else: - raise ValueError('Invalid input type.') - - return val - - -def val2int(val): - """Convert to int - - This method checks if input value is an int and if not converts it. - - Parameters - ---------- - val : int, float, str, list, tuple or np.ndarray - Input value - - Returns - ------- - int interger value or np.ndarray array of ints - - Raises - ------ - ValueError - For invalid input type - - Examples - -------- - >>> from sf_tools.base.types import val2int - >>> x = np.arange(5).astype(float) - >>> x - array([ 0., 1., 2., 3., 4.]) - >>> val2int(x) - array([0, 1, 2, 3, 4]) - - """ - - if isinstance(val, int): - pass - elif isinstance(val, (float, str)): - val = int(float(val)) - elif isinstance(val, (list, tuple)): - val = np.array(val, dtype=float).astype(int) - elif isinstance(val, np.ndarray): - if np.issubdtype(val.dtype, 'int64'): - pass - else: - val = val.astype(float).astype(int) - else: - raise ValueError('Invalid input type.') - - return val - - -def val2float(val): - """Convert to float - - This method checks if input value is a float and if not converts it. - - Parameters - ---------- - val : int, float, str, list, tuple or np.ndarray - Input value - - Returns - ------- - float floating point value or np.ndarray array of floats - - Examples - -------- - >>> from sf_tools.base.types import val2float - >>> x = np.arange(5) - >>> x - array([0, 1, 2, 3, 4]) - >>> val2float(x) - array([ 0., 1., 2., 3., 4.]) - - """ - - if isinstance(val, float): - pass - elif isinstance(val, (int, str)): - val = float(val) - elif isinstance(val, (list, tuple)): - val = np.array(val, dtype=float) - elif isinstance(val, np.ndarray): - if np.issubdtype(val.dtype, 'float64'): - pass - else: - val = val.astype(float) - else: - raise ValueError('Invalid input type.') - - return val - - -def val2str(val): - """Convert to string - - This method checks if input value is a string and if not converts it. - - Parameters - ---------- - val : int, float, str, list, tuple or np.ndarray - Input value - - Returns - ------- - str string or np.ndarray array of strings - - - Examples - -------- - >>> from sf_tools.base.types import val2str - >>> x = np.arange(5) - >>> x - array([0, 1, 2, 3, 4]) - >>> val2str(x) - array(['0', '1', '2', '3', '4'], - dtype='|S21') - - """ - - if isinstance(val, str): - pass - elif isinstance(val, (int, float)): - val = str(val) - elif isinstance(val, (list, tuple)): - val = np.array(val, dtype=str) - elif isinstance(val, np.ndarray): - if np.issubdtype(val.dtype, 'S21'): - pass - else: - val = val.astype(str) - else: - raise ValueError('Invalid input type.') - - return val - - -def nan2val(array, val=0.0): - """Convert NAN to val - - This converts all NANs in an array to a specified value. - - Parameters - ---------- - array : np.ndarray, list or tuple - Input array - val : int or float, optional - Value to replace NANs. Default (val=0.0) - - Returns - ------- - np.ndarray array without NANs - - NOTES - ----- - Output data type defined by val type. - - Examples - -------- - >>> from sf_tools.base.types import nan2val - >>> x = [1., 2., np.nan, 4.] - >>> x - [1.0, 2.0, nan, 4.0] - >>> nan2val(x, 3.) - array([ 1., 2., 3., 4.]) - - """ - - new_array = np.copy(array) - new_array[np.isnan(new_array)] = val - - if isinstance(val, int): - new_array = val2int(new_array) - - return new_array diff --git a/sf_tools/image/__init__.py b/sf_tools/image/__init__.py index 3d26629..f9399a4 100644 --- a/sf_tools/image/__init__.py +++ b/sf_tools/image/__init__.py @@ -6,10 +6,6 @@ :Author: Samuel Farrens -:Version: 1.0 - -:Date: 06/04/2017 - References ---------- @@ -28,6 +24,6 @@ """ -__all__ = ['convolve', 'distort', 'quality', 'shape', 'stamp', 'stats'] +__all__ = ['distort', 'quality', 'shape', 'stamp', 'stats'] from . import * diff --git a/sf_tools/image/convolve.py b/sf_tools/image/convolve.py deleted file mode 100644 index 9b2f273..0000000 --- a/sf_tools/image/convolve.py +++ /dev/null @@ -1,236 +0,0 @@ -# -*- coding: utf-8 -*- - -"""CONVOLUTION ROUTINES - -This module contains methods for convolution. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 23/10/2017 - -""" - -from __future__ import division -from builtins import zip -import numpy as np -from scipy.signal import fftconvolve -from scipy.fftpack import fftn, ifftn, fftshift, ifftshift -from astropy.convolution import convolve_fft -from sf_tools.base.np_adjust import rotate, rotate_stack - - -def convolve_np(image, kernel): - """Convolve with Numpy FFT - - This method convolves the input image with the input kernel - - Parameters - ---------- - image : np.ndarray - 2D image array - kernel : np.ndarray - 2D kernel array - - Returns - ------- - np.ndarray 2D convolved image array - - """ - - x = np.fft.fftshift(np.fft.fftn(image)) - y = np.fft.fftshift(np.fft.fftn(kernel)) - - return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x * y)))) - - -def deconvolve_np(image, kernel): - """Deconvolve with Numpy FFT - - This method deconvolves the input image with the input kernel - - Parameters - ---------- - image : np.ndarray - 2D image array - kernel : np.ndarray - 2D kernel array - - Returns - ------- - np.ndarray 2D deconvolved image array - - """ - - x = np.fft.fftshift(np.fft.fftn(image)) - y = np.fft.fftshift(np.fft.fftn(kernel)) - - return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x / y)))) - - -def convolve(data, kernel, method='astropy'): - """Convolve data with kernel - - This method convolves the input data with a given kernel using FFT and - is the default convolution used for all routines - - Parameters - ---------- - data : np.ndarray - Input data array, normally a 2D image - kernel : np.ndarray - Input kernel array, normally a 2D kernel - method : str {'astropy', 'scipy'}, optional - Convolution method (default is 'astropy') - - 'astropy': - Uses the astropy.convolution.convolve_fft method provided in - Astropy (http://www.astropy.org/) - - 'scipy': - Uses the scipy.signal.fftconvolve method provided in SciPy - (https://www.scipy.org/) - - Returns - ------- - np.ndarray convolved data - - Raises - ------ - ValueError - If `data` and `kernel` do not have the same number of dimensions - ValueError - If `method` is not 'astropy' or 'scipy' - - """ - - if data.ndim != kernel.ndim: - raise ValueError('Data and kernel must have the same dimensions.') - - if method not in ('astropy', 'scipy'): - raise ValueError('Invalid method. Options are "astropy" or "scipy".') - - if method == 'astropy': - return convolve_fft(data, kernel, boundary='wrap', crop=False, - nan_treatment='fill', normalize_kernel=False) - - elif method == 'scipy': - return fftconvolve(data, kernel, mode='same') - - -def convolve_stack(data, kernel, rot_kernel=False, method='astropy'): - """Convolve stack of data with stack of kernels - - This method convolves the input data with a given kernel using FFT and - is the default convolution used for all routines - - Parameters - ---------- - data : np.ndarray - Input data array, normally a 2D image - kernel : np.ndarray - Input kernel array, normally a 2D kernel - rot_kernel : bool - Option to rotate kernels by 180 degrees - method : str {'astropy', 'scipy'}, optional - Convolution method (default is 'astropy') - - Returns - ------- - np.ndarray convolved data - - """ - - if rot_kernel: - kernel = rotate_stack(kernel) - - return np.array([convolve(data_i, kernel_i, method=method) for data_i, - kernel_i in zip(data, kernel)]) - - -def psf_convolve(data, psf, psf_rot=False, psf_type='fixed', method='astropy'): - """Convolve data with PSF - - This method convolves an image with a PSF - - Parameters - ---------- - data : np.ndarray - Input data array, normally an array of 2D images - psf : np.ndarray - Input PSF array, normally either a single 2D PSF or an array of 2D - PSFs - psf_rot: bool - Option to rotate PSF by 180 degrees - psf_type : str {'fixed', 'obj_var'}, optional - PSF type (default is 'fixed') - method : str {'astropy', 'scipy'}, optional - Convolution method (default is 'astropy') - - 'fixed': - The PSF is fixed, i.e. it is the same for each image - - 'obj_var': - The PSF is object variant, i.e. it is different for each image - - Returns - ------- - np.ndarray convolved data - - Raises - ------ - ValueError - If `psf_type` is not 'fixed' or 'obj_var' - - """ - - if psf_type not in ('fixed', 'obj_var'): - raise ValueError('Invalid PSF type. Options are "fixed" or "obj_var"') - - if psf_rot and psf_type == 'fixed': - psf = rotate(psf) - - elif psf_rot: - psf = rotate_stack(psf) - - if psf_type == 'fixed': - return np.array([convolve(data_i, psf, method=method) for data_i in - data]) - - elif psf_type == 'obj_var': - - return convolve_stack(data, psf) - - -def pseudo_inverse(image, kernel, weight=None): - """Pseudo inverse - - This method calculates the pseudo inverse of the input image for the given - kernel using FFT - - Parameters - ---------- - image : np.ndarray - Input image, 2D array - kernel : np.ndarray - Input kernel, 2D array - weight : np.ndarray, optional - Optional weights, 2D array - - Returns - ------- - np.ndarray result of the pseudo inverse - - """ - - y_hat = fftshift(fftn(image)) - h_hat = fftshift(fftn(kernel)) - h_hat_star = np.conj(h_hat) - - res = ((h_hat_star * y_hat) / (h_hat_star * h_hat)) - - if not isinstance(weight, type(None)): - res *= weight - - return np.real(fftshift(ifftn(ifftshift(res)))) diff --git a/sf_tools/info.py b/sf_tools/info.py new file mode 100644 index 0000000..0d2a03e --- /dev/null +++ b/sf_tools/info.py @@ -0,0 +1,25 @@ +# -*- coding: utf-8 -*- + +"""SF-TOOLS PACKAGE INFO + +This module provides some basic information about the sf_tools package. + +:Author: Samuel Farrens + +:Version: 2.0.0 + +""" + +# Package Version +version_info = (2, 0, 0) +__version__ = '.'.join(str(c) for c in version_info) + +__about__ = ('sf_tools \n\n ' + 'Author: Samuel Farrens \n ' + 'Year: 2018 \n ' + 'Email: samuel.farrens@cea.fr \n ' + 'Website: https://sfarrens.github.io \n\n ' + 'sf_tools is a series of Python modules with applications to ' + 'image analysis signal processing and statistics.. \n\n ' + 'Full documentation available here: ' + 'https://sfarrens.github.io/sf_tools/') diff --git a/sf_tools/interface/__init__.py b/sf_tools/interface/__init__.py deleted file mode 100644 index e1d4254..0000000 --- a/sf_tools/interface/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- - -"""INTERFACE ROUTINES - -This module contains submodules for error handling, logging and IO interaction. - -:Author: Samuel Farrens - -:Version: 1.0 - -:Date: 06/04/2017 - -""" - -__all__ = ['errors', 'log'] - -from . import * diff --git a/sf_tools/interface/errors.py b/sf_tools/interface/errors.py deleted file mode 100644 index c029cb0..0000000 --- a/sf_tools/interface/errors.py +++ /dev/null @@ -1,105 +0,0 @@ -# -*- coding: utf-8 -*- - -"""ERROR HANDLING ROUTINES - -This module contains methods for handing warnings and errors. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 23/10/2017 - -""" - - -import sys -import os.path -import warnings -try: - from termcolor import colored -except ImportError: - import_fail = True -else: - import_fail = False - - -def warn(warn_string, log=None): - """Warning - - This method creates custom warning messages. - - Parameters - ---------- - warn_string : str - Warning message string - log : instance, optional - Logging structure instance - - """ - - if import_fail: - warn_txt = 'WARNING' - else: - warn_txt = colored('WARNING', 'yellow') - - # Print warning to stdout. - sys.stderr.write(warn_txt + ': ' + warn_string + '\n') - - # Check if a logging structure is provided. - if not isinstance(log, type(None)): - warnings.warn(warn_string) - - -def catch_error(exception, log=None): - """Catch error - - This method catches errors and prints them to the terminal. It also saves - the errors to a log if provided. - - Parameters - ---------- - exception : str - Exception message string - log : instance, optional - Logging structure instance - - """ - - if import_fail: - err_txt = 'ERROR' - else: - err_txt = colored('ERROR', 'red') - - # Print exception to stdout. - stream_txt = err_txt + ': ' + str(exception) + '\n' - sys.stderr.write(stream_txt) - - # Check if a logging structure is provided. - if not isinstance(log, type(None)): - log_txt = 'ERROR: ' + str(exception) + '\n' - log.exception(log_txt) - - -def file_name_error(file_name): - """File name error - - This method checks if the input file name is valid. - - Parameters - ---------- - file_name : str - File name string - - Raises - ------ - IOError - If file name not specified or file not found - - """ - - if file_name == '' or file_name[0][0] == '-': - raise IOError('Input file name not specified.') - - elif not os.path.isfile(file_name): - raise IOError('Input file name [%s] not found!' % file_name) diff --git a/sf_tools/interface/log.py b/sf_tools/interface/log.py deleted file mode 100644 index 95f334d..0000000 --- a/sf_tools/interface/log.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- - -"""LOGGING ROUTINES - -This module contains methods for handing logging. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 20/10/2017 - -""" - -from __future__ import print_function -import sys -import logging - - -def set_up_log(filename, verbose=True): - """Set up log - - This method sets up a basic log. - - Parameters - ---------- - filename : str - Log file name - - Returns - ------- - logging.Logger instance - - """ - - # Add file extension. - filename += '.log' - - if verbose: - print('Preparing log file:', filename) - - # Capture warnings. - logging.captureWarnings(True) - - # Set output format. - formatter = logging.Formatter(fmt='%(asctime)s %(message)s', - datefmt='%d/%m/%Y %H:%M:%S') - - # Create file handler. - fh = logging.FileHandler(filename=filename, mode='w') - fh.setLevel(logging.DEBUG) - fh.setFormatter(formatter) - - # Create log. - log = logging.getLogger('log') - log.setLevel(logging.DEBUG) - log.addHandler(fh) - - # Send opening message. - log.info('The log file has been set-up.') - - return log - - -def close_log(log, verbose=True): - """Close log - - This method closes and active logging.Logger instance. - - Parameters - ---------- - log : logging.Logger - Logging instance - - """ - - if verbose: - print('Closing log') - - # Send closing message. - log.info('The log file has been closed.') - - # Remove all handlers from log. - [log.removeHandler(handler) for handler in log.handlers] diff --git a/sf_tools/math/__init__.py b/sf_tools/math/__init__.py deleted file mode 100644 index b06fe9b..0000000 --- a/sf_tools/math/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- - -"""MATHEMATICS ROUTINES - -This module contains submodules for mathematical applications. - -:Author: Samuel Farrens - -:Version: 1.0 - -:Date: 06/04/2017 - -""" - -__all__ = ['angle', 'integrate', 'matrix', 'misc', 'stats'] - -from . import * diff --git a/sf_tools/math/angle.py b/sf_tools/math/angle.py deleted file mode 100644 index 43a7a9b..0000000 --- a/sf_tools/math/angle.py +++ /dev/null @@ -1,122 +0,0 @@ -# -*- coding: utf-8 -*- - -"""ANGLE HANDLING ROUTINES - -This module contains methods for handing angles and trigonometry. - -:Author: Samuel Farrens - -:Version: 1.1 - -:Date: 20/10/2017 - -""" - -from __future__ import division -import numpy as np - - -def deg2rad(angle): - r"""Degrees to radians - - This method converts the angle from degrees to radians. - - Parameters - ---------- - angle : float or np.ndarray - Input angle in degrees - - Returns - ------- - float angle in radians or np.ndarray of angles - - Examples - -------- - >>> from sf_tools.math.angle import deg2rad - >>> deg2rad(45.) - 0.7853981633974483 - - Notes - ----- - Implements the following equation: - - .. math:: - \mathrm{radians} = \mathrm{degrees} \times \frac{\pi}{180} - - """ - - return angle * np.pi / 180.0 - - -def rad2deg(angle): - r"""Radians to degrees - - This method converts the angle from radians to degrees. - - Parameters - ---------- - angle : float or np.ndarray - Input angle in radians - - Returns - ------- - float angle in degrees or np.ndarray of angles - - Examples - -------- - >>> from sf_tools.math.angle import deg2rad - >>> rad2deg(1.) - 57.29577951308232 - - Notes - ----- - Implements the following equation: - - .. math:: - \mathrm{degrees} = \mathrm{radians} \times \frac{180}{\pi} - - """ - - return angle * 180.0 / np.pi - - -def ang_sep(point1, point2): - r"""Angular separation - - This method calculates the angular separation in degrees between two - points. - - Parameters - ---------- - point1 : tuple - Angular position of point 1 in degrees - point1 : tuple - Angular position of point 2 in degrees - - Returns - ------- - float angular separation in degrees - - Examples - -------- - >>> from sf_tools.math.angle import ang_sep - >>> ang_sep((30.0, 0.0), (47.0, 10.0)) - 19.647958606833164 - - Notes - ----- - Implements the following equation: - - .. math:: - \theta = \cos^{-1}[\sin(\delta_1)\sin(\delta_2)+ - \cos(\delta_1)\cos(\delta_2)\cos(\alpha_1-\alpha_2)] - - See https://en.wikipedia.org/wiki/Angular_distance - - """ - - dist = np.around(np.sin(deg2rad(point1[1])) * np.sin(deg2rad(point2[1])) + - np.cos(deg2rad(point1[1])) * np.cos(deg2rad(point2[1])) * - np.cos(deg2rad(point1[0]) - deg2rad(point2[0])), 10) - - return rad2deg(np.array(np.arccos(dist))) diff --git a/sf_tools/math/integrate.py b/sf_tools/math/integrate.py deleted file mode 100644 index 83060d1..0000000 --- a/sf_tools/math/integrate.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- - -"""INTEGRATION ROUTINES - -This module contains methods for integration. - -:Author: Samuel Farrens - -:Version: 1.0 - -:Date: 03/04/2017 - -""" - - -import numpy as np -from scipy.integrate import quad - - -def integrate(func, lim_low, lim_up, *args): - """Integrate - - This method integrates a given function, which has N additional arguments, - between the specified limits. - - Parameters - ---------- - func : function - Function to be integrated - lim_low : float - Lower limit - lim_up : float - Upper limit - - Returns - ------- - Result of the definite integral - - """ - - return quad(func, lim_low, lim_up, args=args)[0] - - -def vintegrate(func, lim_low, lim_up, *args): - """Vectorised integration - - This method implements a vectorised version of integrate(). - - Parameters - ---------- - func : function - Function to be integrated - lim_low : float - Lower limit - lim_up : float - Upper limit - - Returns - ------- - Result of the definite integral - - """ - - v_integ = np.vectorize(integrate) - - return v_integ(func, lim_low, lim_up, *args) diff --git a/sf_tools/math/matrix.py b/sf_tools/math/matrix.py deleted file mode 100644 index 68201f4..0000000 --- a/sf_tools/math/matrix.py +++ /dev/null @@ -1,245 +0,0 @@ -"""MATRIX ROUTINES - -This module contains methods for matrix operations. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 20/10/2017 - -""" - -from __future__ import division -from builtins import range, zip -import numpy as np -from itertools import product - - -def gram_schmidt(matrix, return_opt='orthonormal'): - r"""Gram-Schmit - - This method orthonormalizes the row vectors of the input matrix. - - Parameters - ---------- - matrix : np.ndarray - Input matrix array - return_opt : str {orthonormal, orthogonal, both} - Option to return u, e or both. - - Returns - ------- - Lists of orthogonal vectors, u, and/or orthonormal vectors, e - - TODO - ---- - Add citation and equation - - """ - - u = [] - e = [] - - for vector in matrix: - - if len(u) == 0: - u_now = vector - else: - u_now = vector - sum([project(u_i, vector) for u_i in u]) - - u.append(u_now) - e.append(u_now / np.linalg.norm(u_now, 2)) - - u = np.array(u) - e = np.array(e) - - if return_opt == 'orthonormal': - return e - elif return_opt == 'orthogonal': - return u - else: - return u, e - - -def nuclear_norm(data): - """Nuclear norm - - This method computes the nuclear (or trace) norm of the input data. - - Parameters - ---------- - data : np.ndarray - Input data array - - Returns - ------- - float nuclear norm value - - TODO - ---- - Add equation - - """ - - # Get SVD of the data. - u, s, v = np.linalg.svd(data) - - # Return nuclear norm. - return np.sum(s) - - -def project(u, v): - """Project vector - - This method projects vector v onto vector u. - - Parameters - ---------- - u : np.ndarray - Input vector - v : np.ndarray - Input vector - - Returns - ------- - np.ndarray projection - - TODO - ---- - Add equation - - """ - - return np.inner(v, u) / np.inner(u, u) * u - - -def rot_matrix(angle): - """Rotation matrix - - This method produces a 2x2 rotation matrix for the given input angle. - - Parameters - ---------- - angle : float - Rotation angle - - Returns - ------- - np.ndarray 2x2 rotation matrix - - """ - - return np.around(np.array([[np.cos(angle), -np.sin(angle)], - [np.sin(angle), np.cos(angle)]], dtype='float'), 10) - - -def rotate(matrix, angle): - """Rotate - - This method rotates an input matrix about the input angle. - - Parameters - ---------- - matrix : np.ndarray - Input matrix array - angle : float - Rotation angle - - Returns - ------- - np.ndarray rotated matrix - - Raises - ------ - ValueError - For invalid matrix shape - - """ - - shape = np.array(matrix.shape) - - if shape[0] != shape[1]: - raise ValueError('Input matrix must be square.') - - shift = (np.array(shape) - 1) // 2 - - index = np.array(list(product(*np.array([np.arange(val) for val in - shape])))) - shift - - new_index = np.array(np.dot(index, rot_matrix(angle)), dtype='int') + shift - new_index[new_index >= shape[0]] -= shape[0] - - return matrix[list(zip(new_index.T))].reshape(shape.T) - - -class PowerMethod(object): - """Power method class - - This method performs implements power method to calculate the spectral - radius of the input data - - Parameters - ---------- - operator : class - Operator class instance - data_shape : tuple - Shape of the data array - auto_run : bool - Option to automatically calcualte the spectral radius upon - initialisation - - """ - - def __init__(self, operator, data_shape, auto_run=True): - - self.op = operator - self.data_shape = data_shape - if auto_run: - self.get_spec_rad() - - def set_initial_x(self): - """Set initial value of x - - This method sets the initial value of x to an arrray of random values - - """ - - return np.random.random(self.data_shape) - - def get_spec_rad(self, tolerance=1e-6, max_iter=10): - """Get spectral radius - - This method calculates the spectral radius - - Parameters - ---------- - tolerance : float, optional - Tolerance threshold for convergence (default is "1e-6") - max_iter : int, optional - Maximum number of iterations - - """ - - # Set (or reset) values of x. - x_old = self.set_initial_x() - - # Iterate until the L2 norm of x converges. - for i in range(max_iter): - - x_new = self.op(x_old) / np.linalg.norm(x_old) - - if(np.abs(np.linalg.norm(x_new) - np.linalg.norm(x_old)) < - tolerance): - print(' - Power Method converged after %d iterations!' % - (i + 1)) - break - - elif i == max_iter - 1: - print(' - Power Method did not converge after %d ' - 'iterations!' % max_iter) - - np.copyto(x_old, x_new) - - self.spec_rad = np.linalg.norm(x_new) - self.inv_spec_rad = 1.0 / self.spec_rad diff --git a/sf_tools/math/misc.py b/sf_tools/math/misc.py deleted file mode 100644 index 44a7af0..0000000 --- a/sf_tools/math/misc.py +++ /dev/null @@ -1,121 +0,0 @@ -# -*- coding: utf-8 -*- - -"""MISCELLANOUS MATH ROUTINES - -This module contains methods for various mathematical operations. - -:Author: Samuel Farrens - -:Version: 1.1 - -:Date: 20/10/2017 - -""" - -from __future__ import division -import numpy as np - - -def factor(n): - """Factors of n - - This method finds factors of a number (n). - - Parameters - ---------- - n : int - Whole number - - Returns - ------- - np.ndarray factors of n - - """ - - factors = set() - - for x in range(1, int(np.sqrt(n)) + 1): - if n % x == 0: - factors.add(x) - factors.add(n // x) - - return np.array(sorted(factors)) - - -def mfactor(n): - """Middle factors of n - - This method finds the middle factor(s) of a number (n). - - Parameters - ---------- - n : int - Whole number - - Returns - ------- - np.ndarray middle factors of n - - """ - - f = factor(n) - - if f.size % 2: - return np.repeat(f[f.size // 2], 2) - - else: - return f[f.size // 2 - 1:f.size // 2 + 1] - - -def k_val(n, L): - """Spatial frequency - - This method returns k-values in the range L. - - Parameters - ---------- - n : float - Number - limit : float - Limit - - Returns - ------- - float k value - - TODO - ---- - Add equation and example - - """ - - return ((2.0 * np.pi / limit) * np.array(range(n / 2.0) + - range(-n / 2.0, 0.0))) - - -def fourier_derivative(func, k, order): - """Fourier derivative - - This method returns the derivative of the specified function to the given - order. - - Parameters - ---------- - func : function - Function - k : float - k-value - order : int - Oder of derivative - - Returns - ------- - Float derivative - - TODO - ---- - Add equation and example - - """ - - return np.real(np.fft.ifft((1.j * k) ** order * np.fft.fft(func))) diff --git a/sf_tools/math/stats.py b/sf_tools/math/stats.py deleted file mode 100644 index 370b26e..0000000 --- a/sf_tools/math/stats.py +++ /dev/null @@ -1,266 +0,0 @@ -# -*- coding: utf-8 -*- - -"""STATISTICS ROUTINES - -This module contains methods for basic statistics. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 20/10/2017 - -""" - -from __future__ import division -from builtins import zip -import numpy as np -from scipy.stats import chi2 -from astropy.convolution import Gaussian2DKernel - - -def chi2_gof(data_obs, data_exp, sigma, ddof=1): - """Chi-squared goodness-of-fit - - This method tests the chi^2 goodness of fit. - - Parameters - ---------- - data_obs : np.ndarray - Observed data array - data_exp : np.ndarray - Expected data array - sigma : float - Expected data error - ddof : input - Delta degrees of freedom. Default (ddof = 1). - - Returns - ------- - tuple of floats chi-squared and P values - - """ - - chi2 = np.sum(((data_obs - data_exp) / sigma) ** 2) - p_val = chi2.cdf(chi2, len(data_obs) - ddof) - - return chi2, p_val - - -def gaussian(point, mean, sigma, amplitude=None): - """Gaussian distribution - - Method under development... - - """ - - if isinstance(amplitude, type(None)): - amplitude = 1 - - val = np.array([((x - mu) / sig) ** 2 for x, mu, sig in - zip(point, mean, sigma)]) - - return amplitude * np.exp(-0.5 * val) - - -def gaussian_kernel(data_shape, sigma, norm='max'): - """Gaussian kernel - - This method produces a Gaussian kerenal of a specified size and dispersion - - Parameters - ---------- - data_shape : tuple - Desiered shape of the kernel - sigma : float - Standard deviation of the kernel - norm : str {'max', 'sum'}, optional - Normalisation of the kerenl (options are 'max' or 'sum') - - Returns - ------- - np.ndarray kernel - - """ - - kernel = np.array(Gaussian2DKernel(sigma, x_size=data_shape[1], - y_size=data_shape[0])) - - if norm is 'max': - return kernel / np.max(kernel) - - elif norm is 'sum': - return kernel / np.sum(kernel) - - else: - return kernel - - -def mad(data): - r"""Median absolute deviation - - This method calculates the median absolute deviation of the input data. - - Parameters - ---------- - data : np.ndarray - Input data array - - Returns - ------- - float MAD value - - Notes - ----- - The MAD is calculated as follows: - - .. math:: - - \mathrm{MAD} = \mathrm{median}\left(|X_i - \mathrm{median}(X)|\right) - - """ - - return np.median(np.abs(data - np.median(data))) - - -def mse(data1, data2): - """Mean Squared Error - - This method returns the Mean Squared Error (MSE) between two data sets. - - Parameters - ---------- - data1 : np.ndarray - First data set - data2 : np.ndarray - Second data set - - """ - - return np.mean((data1 - data2) ** 2) - - -def psnr2(image, noisy_image, max_pix=255): - r"""Peak Signal-to-Noise Ratio - - This method calculates the PSNR between an image and a noisy version - of that image - - Parameters - ---------- - image : np.ndarray - Input image, 2D array - noisy_image : np.ndarray - Noisy image, 2D array - max_pix : int - Maximum number of pixels. Default (max_pix=255) - - Returns - ------- - float PSNR value - - Notes - ----- - Implements PSNR equation on - https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio - - .. math:: - - \mathrm{PSNR} = 20\log_{10}(\mathrm{MAX}_I - 10\log_{10}(\mathrm{MSE})) - - """ - - return (20 * np.log10(max_pix) - 10 * - np.log10(mse(image, noisy_image))) - - -def psnr(image, recovered_image): - """Peak Signal-to-Noise Ratio - - This method calculates the PSNR between an image and the recovered version - of that image - - Parameters - ---------- - image : np.ndarray - Input image, 2D array - recovered_image : np.ndarray - Recovered image, 2D array - - Returns - ------- - float PSNR value - - Notes - ----- - Implements eq.3.7 from _[S2010] - - """ - - return (20 * np.log10((image.shape[0] * np.abs(np.max(image) - - np.min(image))) / np.linalg.norm(image - recovered_image))) - - -def psnr_stack(images, recoverd_images, metric=np.mean): - """Peak Signa-to-Noise for stack of images - - This method calculates the PSNRs for a stack of images and the - corresponding recovered images. By default the metod returns the mean - value of the PSNRs, but any other metric can be used. - - Parameters - ---------- - images : np.ndarray - Stack of images, 3D array - recovered_images : np.ndarray - Stack of recovered images, 3D array - metric : function - The desired metric to be applied to the PSNR values (default is - 'np.mean') - - Returns - ------- - float metric result of PSNR values - - Raises - ------ - ValueError - For invalid input data dimensions - - """ - - if images.ndim != 3 or recoverd_images.ndim != 3: - raise ValueError('Input data must be a 3D np.ndarray') - - return metric([psnr(i, j) for i, j in zip(images, recoverd_images)]) - - -def sigma_mad(data): - r"""Standard deviation from MAD - - This method calculates the standard deviation of the input data from the - MAD. - - Parameters - ---------- - data : np.ndarray - Input data array - - Returns - ------- - float sigma value - - Notes - ----- - This function can be used for estimating the standeviation of the noise in - imgaes. - - Sigma is calculated as follows: - - .. math:: - - \sigma = 1.4826 \mathrm{MAD}(X) - - """ - - return 1.4826 * mad(data) diff --git a/sf_tools/plot/__init__.py b/sf_tools/plot/__init__.py deleted file mode 100644 index 8756a67..0000000 --- a/sf_tools/plot/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- - -"""PLOTTING ROUTINES - -This module contains submodules for plotting applications. - -:Author: Samuel Farrens - -:Version: 1.0 - -:Date: 19/07/2017 - -""" - -__all__ = ['cost_plot'] - -from . import * diff --git a/sf_tools/plot/cost_plot.py b/sf_tools/plot/cost_plot.py deleted file mode 100644 index d9ed4f7..0000000 --- a/sf_tools/plot/cost_plot.py +++ /dev/null @@ -1,47 +0,0 @@ -# -*- coding: utf-8 -*- - -"""PLOTTING ROUTINES - -This module contains methods for making plots. - -:Author: Samuel Farrens - -:Version: 1.1 - -:Date: 05/01/2017 - -""" - -from __future__ import print_function -import numpy as np -import matplotlib.pyplot as plt - - -def plotCost(cost_list, output=None): - """Plot cost function - - Plot the final cost function - - Parameters - ---------- - cost_list : list - List of cost function values - output : str, optional - Output file name - - """ - - if isinstance(output, type(None)): - file_name = 'cost_function.png' - else: - file_name = output + '_cost_function.png' - - plt.figure() - plt.plot(np.log10(cost_list), 'r-') - plt.title('Cost Function') - plt.xlabel('Iteration') - plt.ylabel('$\log_{10}$ Cost') - plt.savefig(file_name) - plt.close() - - print(' - Saving cost function data to:', file_name) diff --git a/sf_tools/signal/__init__.py b/sf_tools/signal/__init__.py deleted file mode 100644 index 33fffc6..0000000 --- a/sf_tools/signal/__init__.py +++ /dev/null @@ -1,38 +0,0 @@ -# -*- coding: utf-8 -*- - -"""SIGNAL PROCESSING ROUTINES - -This module contains submodules for signal processing. - -:Author: Samuel Farrens - -:Version: 1.0 - -:Date: 06/04/2017 - -References ----------- - -.. [Con2013] Condat, A Primal-Dual Splitting Method for Convex Optimization - Involving Lipschitzian, Proximable and Linear Composite Terms, 2013, - Journal of Optimization Theory and Applications, 158, 2, 460. - [https://link.springer.com/article/10.1007/s10957-012-0245-9] - -.. [B2011] Bauschke et al., Fixed-Point Algorithms for Inverse Problems in - Science and Engineering, 2011, Chapter 10. - [http://rentals.springer.com/product/9781441995698] - -.. [R2012] Raguet et al., Generalized Forward-Backward Splitting, 2012, SIAM, - v6. [https://arxiv.org/abs/1108.4404] - -.. [CWB2007] Candes, Wakin and Boyd, Enhancing Sparsity by Reweighting l1 - Minimization, 2007, Journal of Fourier Analysis and Applications, - 14(5):877-905. [https://arxiv.org/abs/0711.1612] - -""" - -__all__ = ['cost', 'filter', 'gradient', 'linear', 'noise', 'optimisation', - 'positivity', 'proximity', 'regression', 'reweight', 'svd', - 'validation', 'wavelet'] - -from . import * diff --git a/sf_tools/signal/cost.py b/sf_tools/signal/cost.py deleted file mode 100644 index f6e9508..0000000 --- a/sf_tools/signal/cost.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding: utf-8 -*- - -"""COST FUNCTIONS - -This module contains classes of different cost functions for optimization. - -:Author: Samuel Farrens - -:Version: 2.2 - -:Date: 23/10/2017 - -""" - -from __future__ import division, print_function -import numpy as np -try: - from sf_tools.plot.cost_plot import plotCost -except ImportError: - import_fail = True -else: - import_fail = False - - -class costObj(object): - - """Generic cost function object - - This class updates the cost according to the input cost functio class and - tests for convergence - - Parameters - ---------- - costFunc : class - Class for calculating the cost - initial_cost : float, optional - Initial value of the cost (default is "1e6") - tolerance : float, optional - Tolerance threshold for convergence (default is "1e-4") - cost_interval : int, optional - Iteration interval to calculate cost (default is "1") - test_range : int, optional - Number of cost values to be used in test (default is "4") - verbose : bool, optional - Option for verbose output (default is "True") - plot_output : str, optional - Output file name for cost function plot - - Notes - ----- - The costFunc class must contain a method called `calc_cost()`. - - """ - - def __init__(self, costFunc, initial_cost=1e6, tolerance=1e-4, - cost_interval=1, test_range=4, verbose=True, - plot_output=None): - - if not hasattr(costFunc, 'calc_cost'): - raise ValueError('costFunc must contain "calc_cost" method.') - - self.costFunc = costFunc - self.cost = initial_cost - self._cost_list = [] - self._cost_interval = cost_interval - self._iteration = 1 - self._test_list = [] - self._test_range = test_range - self._tolerance = tolerance - self._plot_output = plot_output - self._verbose = verbose - - def _check_cost(self): - """Check cost function - - This method tests the cost function for convergence in the specified - interval of iterations using the last n (test_range) cost values - - Returns - ------- - bool result of the convergence test - - """ - - # Add current cost value to the test list - self._test_list.append(self.cost) - - # Check if enough cost values have been collected - if len(self._test_list) == self._test_range: - - # The mean of the first half of the test list - t1 = np.mean(self._test_list[len(self._test_list) // 2:], axis=0) - # The mean of the second half of the test list - t2 = np.mean(self._test_list[:len(self._test_list) // 2], axis=0) - # Calculate the change across the test list - cost_diff = (np.linalg.norm(t1 - t2) / np.linalg.norm(t1)) - # Reset the test list - self._test_list = [] - - if self._verbose: - print(' - CONVERGENCE TEST - ') - print(' - CHANGE IN COST:', cost_diff) - print('') - - # Check for convergence - return cost_diff <= self._tolerance - - else: - - return False - - def get_cost(self, *args, **kwargs): - """Get cost function - - This method calculates the current cost and tests for convergence - - Returns - ------- - bool result of the convergence test - - """ - - # Check if the cost should be calculated - if self._iteration % self._cost_interval: - - test_result = False - - else: - - if self._verbose: - print(' - ITERATION:', self._iteration) - - # Calculate the current cost - self.cost = self.costFunc.calc_cost(*args, **kwargs) - self._cost_list.append(self.cost) - - if self._verbose: - print(' - Log10 COST:', np.log10(self.cost)) - print('') - - # Test for convergence - test_result = self._check_cost() - - # Update the current iteration number - self._iteration += 1 - - return test_result - - def plot_cost(self): - """Plot the cost function - - This method plots the cost function as function of iteration number - - """ - - if import_fail: - pass - - else: - plotCost(self._cost_list, self._plot_output) diff --git a/sf_tools/signal/filter.py b/sf_tools/signal/filter.py deleted file mode 100644 index e14f090..0000000 --- a/sf_tools/signal/filter.py +++ /dev/null @@ -1,99 +0,0 @@ -# -*- coding: utf-8 -*- - -"""FILTER ROUTINES - -This module contains methods for distance measurements in cosmology. - -:Author: Samuel Farrens - -:Version: 1.1 - -:Date: 20/10/2017 - -""" - -from __future__ import division -import numpy as np -from sf_tools.base.types import check_float - - -def Gaussian_filter(x, sigma, norm=True): - """Gaussian filter - - This method implements a Gaussian filter. - - Parameters - ---------- - x : float - Input data point - sigma : float - Standard deviation (filter scale) - norm : bool - Option to return normalised data. Default (norm=True) - - Returns - ------- - float Gaussian filtered data point - - """ - - x = check_float(x) - sigma = check_float(sigma) - - val = np.exp(-0.5 * (x / sigma) ** 2) - - if norm: - return val / (np.sqrt(2 * np.pi) * sigma) - - else: - return val - - -def mex_hat(x, sigma): - """Mexican hat - - This method implements a Mexican hat (or Ricker) wavelet. - - Parameters - ---------- - x : float - Input data point - sigma : float - Standard deviation (filter scale) - - Returns - ------- - float Mexican hat filtered data point - - """ - - x = check_float(x) - sigma = check_float(sigma) - - xs = (x / sigma) ** 2 - val = 2 * (3 * sigma) ** -0.5 * np.pi ** -0.25 - - return val * (1 - xs) * np.exp(-0.5 * xs) - - -def mex_hat_dir(x, y, sigma): - """Directional Mexican hat - - This method implements a directional Mexican hat (or Ricker) wavelet. - - Parameters - ---------- - x : float - Input data point for Gaussian - y : float - Input data point for Mexican hat - sigma : float - Standard deviation (filter scale) - - Returns - ------- - float directional Mexican hat filtered data point - - """ - - return -0.5 * (x / sigma) ** 2 * mex_hat(y, sigma) diff --git a/sf_tools/signal/gradient.py b/sf_tools/signal/gradient.py deleted file mode 100644 index 7f55d52..0000000 --- a/sf_tools/signal/gradient.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- - -"""GRADIENT CLASSES - -This module contains classses for defining algorithm gradients. -Based on work by Yinghao Ge and Fred Ngole. - -:Author: Samuel Farrens - -:Version: 1.3 - -:Date: 19/07/2017 - -""" - - -class GradBasic(object): - """Basic gradient class - - This class defines the basic methods that will be inherited by specific - gradient classes - - """ - - def MtMX(self, x): - """M^T M X - - This method calculates the action of the transpose of the matrix M on - the action of the matrix M on the data X - - Parameters - ---------- - x : np.ndarray - Input data array - - Returns - ------- - np.ndarray result - - Notes - ----- - Calculates M^T (MX) - - """ - - return self.MtX(self.MX(x)) - - def get_grad(self, x): - """Get the gradient step - - This method calculates the gradient step from the input data - - Parameters - ---------- - x : np.ndarray - Input data array - - Returns - ------- - np.ndarray gradient value - - Notes - ----- - - Calculates M^T (MX - Y) - - """ - - self.grad = self.MtX(self.MX(x) - self.y) diff --git a/sf_tools/signal/linear.py b/sf_tools/signal/linear.py deleted file mode 100644 index 2189182..0000000 --- a/sf_tools/signal/linear.py +++ /dev/null @@ -1,204 +0,0 @@ -# -*- coding: utf-8 -*- - -"""LINEAR OPERATORS - -This module contains linear operator classes. - -:Author: Samuel Farrens - -:Version: 1.3 - -:Date: 19/07/2017 - -""" - -from builtins import range, zip -import numpy as np -from sf_tools.signal.wavelet import * -from sf_tools.math.matrix import rotate - - -class Identity(object): - """Identity operator class - - This is a dummy class that can be used in the optimisation classes - - """ - - def __init__(self): - - self.l1norm = 1.0 - - def op(self, data, **kwargs): - """Operator - - Returns the input data unchanged - - Parameters - ---------- - data : np.ndarray - Input data array - **kwargs - Arbitrary keyword arguments - - Returns - ------- - np.ndarray input data - - """ - - return data - - def adj_op(self, data): - """Adjoint operator - - Returns the input data unchanged - - Parameters - ---------- - data : np.ndarray - Input data array - - Returns - ------- - np.ndarray input data - - """ - - return data - - -class Wavelet(object): - """Wavelet class - - This class defines the wavelet transform operators - - Parameters - ---------- - data : np.ndarray - Input data array, normally an array of 2D images - wavelet_opt: str, optional - Additional options for `mr_transform` - - """ - - def __init__(self, data, wavelet_opt=None): - - self.y = data - self.data_shape = data.shape[-2:] - n = data.shape[0] - - self.filters = get_mr_filters(self.data_shape, opt=wavelet_opt) - self.l1norm = n * np.sqrt(sum((np.sum(np.abs(filter)) ** 2 for - filter in self.filters))) - - def op(self, data): - """Operator - - This method returns the input data convolved with the wavelet filters - - Parameters - ---------- - data : np.ndarray - Input data array, a 2D image - - Returns - ------- - np.ndarray wavelet convolved data - - """ - - return filter_convolve_stack(data, self.filters) - - def adj_op(self, data): - """Adjoint operator - - This method returns the input data convolved with the wavelet filters - rotated by 180 degrees - - Parameters - ---------- - data : np.ndarray - Input data array, a 3D of wavelet coefficients - - Returns - ------- - np.ndarray wavelet convolved data - - """ - - return filter_convolve_stack(data, self.filters, filter_rot=True) - - -class LinearCombo(object): - """Linear combination class - - This class defines a combination of linear transform operators - - Parameters - ---------- - operators : list - List of linear operator class instances - weights : list, optional - List of weights for combining the linear adjoint operator results - - """ - - def __init__(self, operators, weights=None): - - self.operators = operators - self.weights = weights - self.l1norm = np.array([operator.l1norm for operator in - self.operators]) - - def op(self, data): - """Operator - - This method returns the input data operated on by all of the operators - - Parameters - ---------- - data : np.ndarray - Input data array, a 2D image - - Returns - ------- - np.ndarray linear operation results - - """ - - res = np.empty(len(self.operators), dtype=np.ndarray) - - for i in range(len(self.operators)): - res[i] = self.operators[i].op(data) - - return res - - def adj_op(self, data): - """Adjoint operator - - This method returns the combination of the result of all of the - adjoint operators. If weights are provided the comibination is the sum - of the weighted results, otherwise the combination is the mean. - - Parameters - ---------- - data : np.ndarray - Input data array, an array of coefficients - - Returns - ------- - np.ndarray adjoint operation results - - """ - - if isinstance(self.weights, type(None)): - - return np.mean([operator.adj_op(x) for x, operator in - zip(data, self.operators)], axis=0) - - else: - - return np.sum([weight * operator.adj_op(x) for x, operator, - weight in zip(data, self.operators, weights)], - axis=0) diff --git a/sf_tools/signal/noise.py b/sf_tools/signal/noise.py deleted file mode 100644 index 01f1000..0000000 --- a/sf_tools/signal/noise.py +++ /dev/null @@ -1,176 +0,0 @@ -# -*- coding: utf-8 -*- - -"""NOISE ROUTINES - -This module contains methods for adding and removing noise from data. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 04/01/2017 - -""" - -from builtins import zip -import numpy as np - - -def add_noise(data, sigma=1.0, noise_type='gauss'): - r"""Add noise to data - - This method adds Gaussian or Poisson noise to the input data - - Parameters - ---------- - data : np.ndarray, list or tuple - Input data array - sigma : float or list, optional - Standard deviation of the noise to be added ('gauss' only) - noise_type : str {'gauss', 'poisson'} - Type of noise to be added (default is 'gauss') - - Returns - ------- - np.ndarray input data with added noise - - Raises - ------ - ValueError - If `noise_type` is not 'gauss' or 'poisson' - ValueError - If number of `sigma` values does not match the first dimension of the - input data - - Examples - -------- - >>> import numpy as np - >>> from sf_tools.signal.noise import add_noise - >>> x = np.arange(9).reshape(3, 3).astype(float) - >>> x - array([[ 0., 1., 2.], - [ 3., 4., 5.], - [ 6., 7., 8.]]) - >>> np.random.seed(1) - >>> add_noise(x, noise_type='poisson') - array([[ 0., 2., 2.], - [ 4., 5., 10.], - [ 11., 15., 18.]]) - - >>> import numpy as np - >>> from sf_tools.signal.noise import add_noise - >>> x = np.zeros(5) - >>> x - array([ 0., 0., 0., 0., 0.]) - >>> np.random.seed(1) - >>> add_noise(x, sigma=2.0) - array([ 3.24869073, -1.22351283, -1.0563435 , -2.14593724, 1.73081526]) - - """ - - data = np.array(data) - - if noise_type not in ('gauss', 'poisson'): - raise ValueError('Invalid noise type. Options are "gauss" or' - '"poisson"') - - if isinstance(sigma, (list, np.ndarray)): - if len(sigma) != data.shape[0]: - raise ValueError('Number of sigma values must match first ' - 'dimension of input data') - - if noise_type is 'gauss': - random = np.random.randn(*data.shape) - - elif noise_type is 'poisson': - random = np.random.poisson(np.abs(data)) - - if isinstance(sigma, float): - return data + sigma * random - - else: - return data + np.array([s * r for s, r in zip(sigma, random)]) - - -def thresh(data, threshold, threshold_type='hard'): - r"""Threshold data - - This method perfoms hard or soft thresholding on the input data - - Parameters - ---------- - data : np.ndarray, list or tuple - Input data array - threshold : float - Threshold level - threshold_type : str {'hard', 'soft'} - Type of noise to be added (default is 'hard') - - Returns - ------- - np.ndarray thresholded data - - Raises - ------ - ValueError - If `threshold_type` is not 'hard' or 'soft' - - - Notes - ----- - Implements one of the following two equations: - - * Hard Threshold - .. math:: - \mathrm{HT}_\lambda(x) = - \begin{cases} - x & \text{if } |x|\geq\lambda \\ - 0 & \text{otherwise} - \end{cases} - - * Soft Threshold - .. math:: - \mathrm{ST}_\lambda(x) = - \begin{cases} - x-\lambda\text{sign}(x) & \text{if } |x|\geq\lambda \\ - 0 & \text{otherwise} - \end{cases} - - Examples - -------- - >>> import numpy as np - >>> from sf_tools.signal.noise import thresh - >>> np.random.seed(1) - >>> x = np.random.randint(-9, 9, 10) - >>> x - array([-4, 2, 3, -1, 0, 2, -4, 6, -9, 7]) - >>> thresh(x, 4) - array([-4, 0, 0, 0, 0, 0, -4, 6, -9, 7]) - - >>> import numpy as np - >>> from sf_tools.signal.noise import thresh - >>> np.random.seed(1) - >>> x = np.random.ranf((3, 3)) - >>> x - array([[ 4.17022005e-01, 7.20324493e-01, 1.14374817e-04], - [ 3.02332573e-01, 1.46755891e-01, 9.23385948e-02], - [ 1.86260211e-01, 3.45560727e-01, 3.96767474e-01]]) - >>> thresh(x, 0.2, threshold_type='soft') - array([[ 0.217022 , 0.52032449, -0. ], - [ 0.10233257, -0. , -0. ], - [-0. , 0.14556073, 0.19676747]]) - - """ - - data = np.array(data) - - if threshold_type not in ('hard', 'soft'): - raise ValueError('Invalid threshold type. Options are "hard" or' - '"soft"') - - if threshold_type == 'soft': - return (np.sign(data) * (np.abs(data) - threshold) * - (np.abs(data) >= threshold)) - - else: - return data * (np.abs(data) >= threshold) diff --git a/sf_tools/signal/optimisation.py b/sf_tools/signal/optimisation.py deleted file mode 100644 index 1d8387d..0000000 --- a/sf_tools/signal/optimisation.py +++ /dev/null @@ -1,486 +0,0 @@ -# -*- coding: utf-8 -*- - -r"""OPTIMISATION CLASSES - -This module contains classes for optimisation algoritms. - -:Author: Samuel Farrens - -:Version: 1.3 - -:Date: 20/10/2017 - -NOTES ------ - -Input classes must have the following properties: - - * **Gradient Operators** - - Must have the following methods: - - * ``get_grad()`` - calculate the gradient - - Must have the following variables: - - * ``grad`` - the gradient - * ``inv_spec_rad`` - inverse spectral radius :math:`\frac{1}{\rho}` - - * **Linear Operators** - - Must have the following methods: - - * ``op()`` - operator - * ``adj_op()`` - adjoint operator - - Must have the following variables: - - * ``l1norm`` - the l1 norm of the operator - - * **Proximity Operators** - - Must have the following methods: - - * ``op()`` - operator - -The following notation is used to implement the algorithms: - - * x_old is used in place of :math:`x_{n}`. - * x_new is used in place of :math:`x_{n+1}`. - * x_prox is used in place of :math:`\tilde{x}_{n+1}`. - * x_temp is used for intermediate operations. - -""" - -from __future__ import division, print_function -from builtins import range, zip -import numpy as np - - -class FISTA(object): - r"""FISTA - - This class is inhereited by optimisation classes to speed up convergence - - Parameters - ---------- - lambda_init : float, optional - Initial value of the relaxation parameter - active : bool, optional - Option to activate FISTA convergence speed-up (default is ``True``) - - """ - - def __init__(self, lambda_init=None, active=True): - - self.lambda_now = lambda_init - self.t_now = 1.0 - self.t_prev = 1.0 - self.use_speed_up = active - - def speed_switch(self, turn_on=True): - r"""Speed swicth - - This method turns on or off the speed-up - - Parameters - ---------- - turn_on : bool - Option to turn on speed-up (default is ``True``) - - """ - - self.use_speed_up = turn_on - - def update_lambda(self): - r"""Update lambda - - This method updates the value of lambda - - Notes - ----- - Implements steps 3 and 4 from algoritm 10.7 in [B2011]_ - - """ - - self.t_prev = self.t_now - self.t_now = (1 + np.sqrt(4 * self.t_prev ** 2 + 1)) * 0.5 - self.lambda_now = 1 + (self.t_prev - 1) / self.t_now - - def speed_up(self): - r"""speed-up - - This method returns the update if the speed-up is active - - """ - - if self.use_speed_up: - self.update_lambda() - - -class ForwardBackward(FISTA): - r"""Forward-Backward optimisation - - This class implements standard forward-backward optimisation with an the - option to use the FISTA speed-up - - Parameters - ---------- - x : np.ndarray - Initial guess for the primal variable - grad : class - Gradient operator class - prox : class - Proximity operator class - cost : class, optional - Cost function class - lambda_init : float, optional - Initial value of the relaxation parameter - lambda_update : function, optional - Relaxation parameter update method - use_fista : bool, optional - Option to use FISTA (default is ``True``) - auto_iterate : bool, optional - Option to automatically begin iterations upon initialisation (default - is 'True') - - """ - - def __init__(self, x, grad, prox, cost=None, lambda_init=None, - lambda_update=None, use_fista=True, auto_iterate=True): - - FISTA.__init__(self, lambda_init, use_fista) - self.x_old = x - self.z_old = np.copy(self.x_old) - self.grad = grad - self.prox = prox - self.cost_func = cost - self.lambda_update = lambda_update - self.converge = False - if auto_iterate: - self.iterate() - - def update(self): - r"""Update - - This method updates the current reconstruction - - Notes - ----- - Implements algorithm 10.7 (or 10.5) from [B2011]_ - - """ - - # Step 1 from alg.10.7. - self.grad.get_grad(self.z_old) - y_old = self.z_old - self.grad.inv_spec_rad * self.grad.grad - - # Step 2 from alg.10.7. - self.x_new = self.prox.op(y_old) - - # Steps 3 and 4 from alg.10.7. - self.speed_up() - - # Step 5 from alg.10.7. - self.z_new = self.x_old + self.lambda_now * (self.x_new - self.x_old) - - # Test primal variable for convergence. - if np.sum(np.abs(self.z_old - self.z_new)) <= 1e-6: - print(' - converged!') - self.converge = True - - # Update old values for next iteration. - np.copyto(self.x_old, self.x_new) - np.copyto(self.z_old, self.z_new) - - # Update parameter values for next iteration. - if not isinstance(self.lambda_update, type(None)): - self.lambda_now = self.lambda_update(self.lambda_now) - - # Test cost function for convergence. - if not isinstance(self.cost_func, type(None)): - self.converge = self.cost_func.get_cost(self.z_new) - - def iterate(self, max_iter=150): - r"""Iterate - - This method calls update until either convergence criteria is met or - the maximum number of iterations is reached - - Parameters - ---------- - max_iter : int, optional - Maximum number of iterations (default is ``150``) - - """ - - for i in range(max_iter): - self.update() - - if self.converge: - print(' - Converged!') - break - - self.x_final = self.z_new - - -class GenForwardBackward(object): - r"""Generalized Forward-Backward optimisation - - This class implements algorithm 1 from [R2012]_ - - Parameters - ---------- - x : np.ndarray - Initial guess for the primal variable - grad : class - Gradient operator class - prox_list : list - List of proximity operator classes - cost : class, optional - Cost function class - lambda_init : float, optional - Initial value of the relaxation parameter - lambda_update : function, optional - Relaxation parameter update method - weights : np.ndarray, optional - Proximity operator weights - auto_iterate : bool, optional - Option to automatically begin iterations upon initialisation (default - is 'True') - - """ - - def __init__(self, x, grad, prox_list, cost=None, lambda_init=1.0, - lambda_update=None, weights=None, auto_iterate=True): - - self.x_old = x - self.grad = grad - self.prox_list = np.array(prox_list) - self.cost_func = cost - self.lambda_init = lambda_init - self.lambda_update = lambda_update - - if isinstance(weights, type(None)): - self.weights = np.repeat(1.0 / self.prox_list.size, - self.prox_list.size) - else: - self.weights = np.array(weights) - - # Check weights. - if np.sum(self.weights) != 1.0: - raise ValueError('Proximity operator weights must sum to 1.0.' - 'Current sum of weights = ' + - str(np.sum(self.weights))) - - self.z = np.array([self.x_old for i in range(self.prox_list.size)]) - - self.converge = False - if auto_iterate: - self.iterate() - - def update(self): - r"""Update - - This method updates the current reconstruction - - Notes - ----- - Implements algorithm 1 from [R2012]_ - - """ - - # Calculate gradient for current iteration. - self.grad.get_grad(self.x_old) - - # Update z values. - for i in range(self.prox_list.size): - z_temp = (2 * self.x_old - self.z[i] - self.grad.inv_spec_rad * - self.grad.grad) - z_prox = self.prox_list[i].op(z_temp, - extra_factor=self.grad.inv_spec_rad / - self.weights[i]) - self.z[i] += self.lambda_init * (z_prox - self.x_old) - - # Update current reconstruction. - self.x_new = np.sum((z_i * w_i for z_i, w_i in - zip(self.z, self.weights)), axis=0) - - # Update old values for next iteration. - np.copyto(self.x_old, self.x_new) - - # Update parameter values for next iteration. - if not isinstance(self.lambda_update, type(None)): - self.lambda_now = self.lambda_update(self.lambda_now) - - # Test cost function for convergence. - if not isinstance(self.cost_func, type(None)): - self.converge = self.cost_func.get_cost(self.x_new) - - def iterate(self, max_iter=150): - r"""Iterate - - This method calls update until either convergence criteria is met or - the maximum number of iterations is reached - - Parameters - ---------- - max_iter : int, optional - Maximum number of iterations (default is ``150``) - - """ - - for i in range(max_iter): - self.update() - - if self.converge: - print(' - Converged!') - break - - self.x_final = self.x_new - - -class Condat(object): - r"""Condat optimisation - - This class implements algorithm 10.7 from [Con2013]_ - - Parameters - ---------- - x : np.ndarray - Initial guess for the primal variable - y : np.ndarray - Initial guess for the dual variable - grad : class - Gradient operator class - prox : class - Proximity primal operator class - prox_dual : class - Proximity dual operator class - linear : class - Linear operator class - cost : class - Cost function class - rho : float - Relaxation parameter - sigma : float - Proximal dual parameter - tau : float - Proximal primal paramater - rho_update : function, optional - Relaxation parameter update method - sigma_update : function, optional - Proximal dual parameter update method - tau_update : function, optional - Proximal primal parameter update method - auto_iterate : bool, optional - Option to automatically begin iterations upon initialisation (default - is 'True') - - """ - - def __init__(self, x, y, grad, prox, prox_dual, linear, cost, - rho, sigma, tau, rho_update=None, sigma_update=None, - tau_update=None, auto_iterate=True): - - self.x_old = x - self.y_old = y - self.grad = grad - self.prox = prox - self.prox_dual = prox_dual - self.linear = linear - self.cost_func = cost - self.rho = rho - self.sigma = sigma - self.tau = tau - self.rho_update = rho_update - self.sigma_update = sigma_update - self.tau_update = tau_update - self.converge = False - if auto_iterate: - self.iterate() - - def update_param(self): - r"""Update parameters - - This method updates the values of ``rho``, ``sigma`` and ``tau`` with - the methods provided - - """ - - # Update relaxation parameter. - if not isinstance(self.rho_update, type(None)): - self.rho = self.rho_update(self.rho) - - # Update proximal dual parameter. - if not isinstance(self.sigma_update, type(None)): - self.sigma = self.sigma_update(self.sigma) - - # Update proximal primal parameter. - if not isinstance(self.tau_update, type(None)): - self.tau = self.tau_update(self.tau) - - def update(self): - r"""Update - - This method updates the current reconstruction - - Notes - ----- - Implements equation 9 (algorithm 3.1) from [Con2013]_ - - - primal proximity operator set up for positivity constraint - - """ - - # Step 1 from eq.9. - self.grad.get_grad(self.x_old) - - x_prox = self.prox.op(self.x_old - self.tau * self.grad.grad - - self.tau * self.linear.adj_op(self.y_old)) - - # Step 2 from eq.9. - y_temp = (self.y_old + self.sigma * - self.linear.op(2 * x_prox - self.x_old)) - - y_prox = (y_temp - self.sigma * self.prox_dual.op(y_temp / self.sigma, - extra_factor=(1.0 / self.sigma))) - - # Step 3 from eq.9. - self.x_new = self.rho * x_prox + (1 - self.rho) * self.x_old - self.y_new = self.rho * y_prox + (1 - self.rho) * self.y_old - - del x_prox, y_prox, y_temp - - # Update old values for next iteration. - np.copyto(self.x_old, self.x_new) - np.copyto(self.y_old, self.y_new) - - # Update parameter values for next iteration. - self.update_param() - - # Test cost function for convergence. - self.converge = self.cost_func.get_cost(self.x_new, self.y_new) - - def iterate(self, max_iter=150): - r"""Iterate - - This method calls update until either convergence criteria is met or - the maximum number of iterations is reached - - Parameters - ---------- - max_iter : int, optional - Maximum number of iterations (default is ``150``) - - """ - - for i in range(max_iter): - self.update() - - if self.converge: - print(' - Converged!') - break - - self.x_final = self.x_new - self.y_final = self.y_new diff --git a/sf_tools/signal/positivity.py b/sf_tools/signal/positivity.py deleted file mode 100644 index 73b8449..0000000 --- a/sf_tools/signal/positivity.py +++ /dev/null @@ -1,50 +0,0 @@ -# -*- coding: utf-8 -*- - -"""POSITIVITY - -This module contains a function that retains only positive coefficients in -an array - -:Author: Samuel Farrens - -:Version: 1.0 - -:Date: 26/07/2017 - -""" - -import numpy as np - - -def positive(data): - """Positivity operator - - This method preserves only the positive coefficients of the input data, all - negative coefficients are set to zero - - Parameters - ---------- - data : np.ndarray, list or tuple - Input data array - - Returns - ------- - np.ndarray array with only positive coefficients - - """ - - def pos_recursive(data): - - data = np.array(data) - - if not data.dtype == 'O': - - result = list(data * (data > 0)) - - else: - - result = [pos_recursive(x) for x in data] - - return result - - return np.array(pos_recursive(data)) diff --git a/sf_tools/signal/proximity.py b/sf_tools/signal/proximity.py deleted file mode 100644 index 8e61f07..0000000 --- a/sf_tools/signal/proximity.py +++ /dev/null @@ -1,275 +0,0 @@ -# -*- coding: utf-8 -*- - -"""PROXIMITY OPERATORS - -This module contains classes of proximity operators for optimisation - -:Author: Samuel Farrens - -:Version: 1.3 - -:Date: 19/07/2017 - -""" - -from builtins import range -import numpy as np -from sf_tools.signal.noise import thresh -from sf_tools.signal.svd import svd_thresh, svd_thresh_coef -from sf_tools.signal.optimisation import ForwardBackward -from sf_tools.signal.positivity import positive -from sf_tools.base.transform import * - - -class Positive(object): - """Positivity proximity operator - - This class defines the positivity proximity operator - - """ - - def __init__(self): - pass - - def op(self, data, **kwargs): - """Operator - - This method preserves only the positive coefficients of the input data - - Parameters - ---------- - data : np.ndarray - Input data array - - Returns - ------- - np.ndarray all positive elements from input data - - """ - - return positive(data) - - -class Threshold(object): - """Threshold proximity operator - - This class defines the threshold proximity operator - - Parameters - ---------- - weights : np.ndarray - Input array of weights - thresh_type : str {'hard', 'soft'}, optional - Threshold type (default is 'soft') - - """ - - def __init__(self, weights, thresh_type='soft'): - - self.weights = weights - self.thresh_type = thresh_type - - def op(self, data, extra_factor=1.0): - """Operator - - This method returns the input data thresholded by the weights - - Parameters - ---------- - data : np.ndarray - Input data array - extra_factor : float - Additional multiplication factor - - Returns - ------- - np.ndarray thresholded data - - """ - - threshold = self.weights * extra_factor - - return thresh(data, threshold, self.thresh_type) - - -class LowRankMatrix(object): - """Low-rank proximity operator - - This class defines the low-rank proximity operator - - Parameters - ---------- - thresh : float - Threshold value - treshold_type : str {'hard', 'soft'} - Threshold type (options are 'hard' or 'soft') - lowr_type : str {'standard', 'ngole'} - Low-rank implementation (options are 'standard' or 'ngole') - operator : class - Operator class ('ngole' only) - - """ - - def __init__(self, thresh, thresh_type='soft', - lowr_type='standard', operator=None): - - self.thresh = thresh - self.thresh_type = thresh_type - self.lowr_type = lowr_type - self.operator = operator - - def op(self, data, extra_factor=1.0): - """Operator - - This method returns the input data after the singular values have been - thresholded - - Parameters - ---------- - data : np.ndarray - Input data array - extra_factor : float - Additional multiplication factor - - Returns - ------- - np.ndarray SVD thresholded data - - """ - - # Update threshold with extra factor. - threshold = self.thresh * extra_factor - - if self.lowr_type == 'standard': - data_matrix = svd_thresh(cube2matrix(data), threshold, - thresh_type=self.thresh_type) - - elif self.lowr_type == 'ngole': - data_matrix = svd_thresh_coef(data, self.operator, - threshold, - thresh_type=self.thresh_type) - - new_data = matrix2cube(data_matrix, data.shape[1:]) - - # Return updated data. - return new_data - - -class ProximityCombo(object): - """Proximity Combo - - This class defines a combined proximity operator - - Parameters - ---------- - operators : list - List of proximity operator class instances - - """ - - def __init__(self, operators): - - self.operators = operators - - def op(self, data, extra_factor=1.0): - """Operator - - This method returns the result of applying all of the proximity - operators to the data - - Parameters - ---------- - data : np.ndarray - Input data array - extra_factor : float - Additional multiplication factor - - Returns - ------- - np.ndarray result - - """ - - res = np.empty(len(self.operators), dtype=np.ndarray) - - for i in range(len(self.operators)): - res[i] = self.operators[i].op(data[i], extra_factor=extra_factor) - - return res - - -class SubIter(object): - """Sub iteration operator - - This class defines the sub-iteration proximity operator - - Parameters - ---------- - data_shape : tuple - Shape of input data array - operator : class - Proximity operator class - weights : np.ndarray - Array of weights - u_init : np.ndarray - Initial estimate of u - - """ - - def __init__(self, data_shape, operator, weights=None, u_init=None): - - self.operator = operator - - if not isinstance(weights, type(None)): - self.weights = weights - - if isinstance(u_init, type(None)): - self.u = np.ones(data_shape) - - self.opt = ForwardBackward(self.u, self.operator, - Threshold(self.weights), auto_iterate=False, - indent_level=2) - - def update_weights(self, weights): - """Update weights - - This method updates the values of the weights - - Parameters - ---------- - weights : np.ndarray - Array of weights - - """ - - self.weights = weights - - def update_u(self): - """Update u - - This method updates the values of u - - """ - - self.opt.iterate(100) - self.u = self.opt.x_final - - def op(self, data): - """Operator - - Parameters - ---------- - data : np.ndarray - Input data array - - Returns - ------- - np.ndarray result - - """ - - self.update_u() - - new_data = data - self.operator.adj_op(self.u) - - return new_data diff --git a/sf_tools/signal/regression.py b/sf_tools/signal/regression.py deleted file mode 100644 index 6aa1b30..0000000 --- a/sf_tools/signal/regression.py +++ /dev/null @@ -1,195 +0,0 @@ -# -*- coding: utf-8 -*- - -"""REGRESSION ROUTINES - -This module contains methods for linear regression. - -:Author: Samuel Farrens - -:Version: 1.0 - -:Date: 04/04/2017 - -""" - -from builtins import zip -import numpy as np -from scipy.odr import * - - -def linear_fit(B, x): - r"""Linear fit - - This method defines the equation of a straight line. - - Parameters - ---------- - B : tuple - Slope (m) and intercept (b) of the line. - x : list or np.ndarray - The 1D data vector - - Returns - ------- - np.ndarray 1D array of corresponding y values - - NOTES - ----- - This equation of a stright line is given by - - .. math:: - - y = mx + b - - """ - - return B[0] * np.array(x) + B[1] - - -def polynomial(x, a): - r"""Polynomial - - This method defines the equation of a polynomial line. - - Parameters - ---------- - x : list or np.ndarray - The 1D data vector - a : list or np.ndarray - The 1D polynomial coefficient vector - - Returns - ------- - np.ndarray 1D array of corresponding y values - - NOTES - ----- - This equation of a stright line is given by - - .. math:: - - y = a_0 + a_1x + a_2x^2 + \dots + a_kx^k - - """ - - a = np.array(a) - x = np.array(x) - - return sum([(a_i * x ** n) for a_i, n in zip(a, range(a.size))]) - - -def polynomial_fit(x, y, k=1): - """Polynomial fit - - This method finds the coefficients for a polynomial line fit to the input - data using least squares. - - Parameters - ---------- - x : list or np.ndarray - The 1D independent data vector - y : list or np.ndarray - The 1D dependent data vector - k : int, optional - Number of degrees of freedom. Default (k=1) - - Returns - ------- - np.ndarray 1D array of coefficients a - - """ - - y = np.array(y) - - return least_squares(x_matrix(x, k), y) - - -def least_squares(X, y): - """Least squares - - This method performs an analytical least squares regression. Returns the - values of the coefficients, a, given the input matrix X and the - corresponding y values. - - Parameters - ---------- - X : np.ndarray - The 2D independent data matrix. - y : np.ndarray - The 1D dependent data vector - - Returns - ------- - np.ndarray 1D array of coefficients a - - Raises - ------ - ValueError - If inputs are not numpy arrays - - ToDo - ---- - Add equation and example - - """ - - if not np.all([isinstance(i, np.ndarray) for i in (X, y)]): - raise ValueError('Inputs must be numpy arrays.') - - return np.dot(np.dot(np.linalg.inv(np.dot(X.T, X)), X.T), y) - - -def x_matrix(x, k): - """Define X matrix - - This method defines the matrix X for a given vector x corresponding to a - polynomial with k degrees of freedom. - - Parameters - ---------- - x : list or np.ndarray - The 1D independent data vector - k : int - Number of degrees of freedom - - Returns - ------- - np.ndarray the 2D independent variable matrix X - - """ - - x = np.array(x) - - return np.vstack([x ** n for n in range(k + 1)]).T - - -def fit_odr(x, y, xerr, yerr, fit): - """Fit via ODR - - This method performs an orthogonal distance regression fit. - - Parameters - ---------- - x : list or np.ndarray - The 1D independent data vector - y : list or np.ndarray - The 1D dependent data vector - x_err : list or np.ndarray - 1D data vector of x value errors - y_err : list or np.ndarray - 1D data vector of y value errors - fit : function - Fitting function - - Returns - ------- - tuple best fit parameters - - """ - - model = Model(fit) - r_data = RealData(x, y, sx=xerr, sy=yerr) - odr = ODR(r_data, model, beta0=[1.0, 2.0]) - odr_out = odr.run() - - return odr_out.beta diff --git a/sf_tools/signal/reweight.py b/sf_tools/signal/reweight.py deleted file mode 100644 index 204dff6..0000000 --- a/sf_tools/signal/reweight.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- - -"""REWEIGHTING CLASSES - -This module contains classes for reweighting optimisation implementations - -:Author: Samuel Farrens - -:Version: 1.3 - -:Date: 20/10/2017 - -""" - -from __future__ import division -import numpy as np - - -class cwbReweight(object): - r"""Candes, Wakin and Boyd reweighting class - - This class implements the reweighting scheme described in [CWB2007]_ - - Parameters - ---------- - weights : np.ndarray - Array of weights - thresh_factor : float - Threshold factor - - """ - - def __init__(self, weights, thresh_factor=1): - - self.weights = weights - self.original_weights = np.copy(self.weights) - self.thresh_factor = thresh_factor - - def reweight(self, data): - r"""Reweight - - This method implements the reweighting from section 4 in [CWB2007]_ - - Notes - ----- - - Reweighting implemented as: - - .. math:: - - w = w \left( \frac{1}{1 + \frac{|x^w|}{n \sigma}} \right) - - """ - - self.weights *= (1.0 / (1.0 + np.abs(data) / (self.thresh_factor * - self.original_weights))) diff --git a/sf_tools/signal/svd.py b/sf_tools/signal/svd.py deleted file mode 100644 index c108e24..0000000 --- a/sf_tools/signal/svd.py +++ /dev/null @@ -1,162 +0,0 @@ -# -*- coding: utf-8 -*- - -"""SVD ROUTINES - -This module contains methods for thresholding singular values. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 20/10/2017 - -""" - -from __future__ import division -from builtins import zip -import numpy as np -from scipy.linalg import svd, diagsvd -from sf_tools.image.convolve import convolve -from sf_tools.signal.noise import thresh -from sf_tools.base.transform import cube2matrix, matrix2cube -from sf_tools.interface.errors import warn - - -def find_n_pc(u, factor=0.5): - """Find number of principal components - - This method finds the minimum number of principal components required - - Parameters - ---------- - u : np.ndarray - Left singular vector - factor : float, optional - Factor for testing the auto correlation (default is '0.5') - - Returns - ------- - int number of principal components - - """ - - # Get the shape of the galaxy images. - gal_shape = np.repeat(np.int(np.sqrt(u.shape[0])), 2) - - # Find the auto correlation of the left singular vector. - u_auto = [convolve(a.reshape(gal_shape), np.rot90(a.reshape(gal_shape), 2)) - for a in u.T] - - # Return the required number of principal components. - return np.sum(((a[list(zip(gal_shape // 2))] ** 2 <= factor * - np.sum(a ** 2)) for a in u_auto)) - - -def svd_thresh(data, threshold=None, n_pc=None, thresh_type='hard'): - """Threshold the singular values - - This method thresholds the input data using singular value decomposition - - Parameters - ---------- - data : np.ndarray - Input data array - threshold : float, optional - Threshold value - n_pc : int or str, optional - Number of principal components, specify an integer value or 'all' - threshold_type : str {'hard', 'soft'} - Type of noise to be added (default is 'hard') - - Returns - ------- - np.ndarray thresholded data - - Raises - ------ - ValueError - For invalid string entry for n_pc - - """ - - if isinstance(n_pc, str) and n_pc != 'all': - raise ValueError('Invalid value for "n_pc", specify an integer value ' - 'or "all"') - - # Get SVD of input data. - u, s, v = svd(data, check_finite=False, lapack_driver='gesvd') - - # Find the threshold if not provided. - if isinstance(threshold, type(None)): - - # Find the required number of principal components if not specified. - if isinstance(n_pc, type(None)): - n_pc = find_n_pc(u, factor=0.1) - - # If the number of PCs is too large use all of the singular values. - if n_pc >= s.size or n_pc == 'all': - n_pc = s.size - 1 - warn('Using all singular values.') - - threshold = s[n_pc] - - # Remove noise from singular values. - s_new = thresh(s, threshold, thresh_type) - - if np.all(s_new == s): - warn('No change to singular values.') - - # Reshape the singular values to the shape of the input image. - s_new = diagsvd(s_new, *data.shape) - - # Return the thresholded image. - return np.dot(u, np.dot(s_new, v)) - - -def svd_thresh_coef(data, operator, threshold, thresh_type='hard'): - """Threshold the singular values coefficients - - This method thresholds the input data using singular value decomposition - - Parameters - ---------- - data : np.ndarray - Input data array - operator : class - Operator class instance - threshold : float, optional - Threshold value - threshold_type : str {'hard', 'soft'} - Type of noise to be added (default is 'hard') - - Returns - ------- - np.ndarray thresholded data - - Raises - ------ - ValueError - For invalid string entry for n_pc - - """ - - # Convert data cube to matrix. - data_matrix = cube2matrix(data) - - # Get SVD of data matrix. - u, s, v = np.linalg.svd(data_matrix, full_matrices=False) - - # Compute coefficients. - a = np.dot(np.diag(s), v) - - # Compute threshold matrix. - u_cube = matrix2cube(u, data.shape[1:]) - ti = np.array([np.linalg.norm(x) for x in operator(u_cube)]) - ti = np.repeat(ti, a.shape[1]).reshape(a.shape) - threshold *= ti - - # Remove noise from coefficients. - a_new = thresh(a, threshold, thresh_type) - - # Return the thresholded image. - return np.dot(u, a_new) diff --git a/sf_tools/signal/validation.py b/sf_tools/signal/validation.py deleted file mode 100644 index 16a4191..0000000 --- a/sf_tools/signal/validation.py +++ /dev/null @@ -1,58 +0,0 @@ -"""VALIDATION ROUTINES - -This module contains methods for testing signal and operator properties. - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 06/12/2017 - -""" - -from __future__ import print_function -import numpy as np - - -def transpose_test(operator, operator_t, x_shape, x_args, y_shape=None, - y_args=None): - """Transpose test - - This method tests two operators to see if they are the transpose of each - other. - - Parameters - ---------- - operator : function - Operator function - operator_t : function - Transpose operator function - x_shape : tuple - Shape of operator input data - x_args : tuple - Arguments to be passed to operator - y_shape : tuple, optional - Shape of transpose operator input data - y_args : tuple, optional - Arguments to be passed to transpose operator - - """ - - if isinstance(y_shape, type(None)): - y_shape = x_shape - - if isinstance(y_args, type(None)): - y_args = x_args - - # Generate random arrays. - x = np.random.ranf(x_shape) - y = np.random.ranf(y_shape) - - # Calculate - mx_y = np.sum(np.multiply(operator(x, *x_args), y)) - - # Calculate - x_mty = np.sum(np.multiply(x, operator_t(y, *y_args))) - - # Test the difference between the two. - print(' - | - | =', np.abs(mx_y - x_mty)) diff --git a/sf_tools/signal/wavelet.py b/sf_tools/signal/wavelet.py deleted file mode 100644 index f807844..0000000 --- a/sf_tools/signal/wavelet.py +++ /dev/null @@ -1,165 +0,0 @@ -# -*- coding: utf-8 -*- - -"""WAVELET MODULE - -This module contains methods for performing wavelet transformations using iSAP - -:Author: Samuel Farrens - -:Version: 1.2 - -:Date: 20/10/2017 - -""" - -from __future__ import division -from builtins import zip -import numpy as np -from os import remove -from subprocess import check_call -from datetime import datetime -from astropy.io import fits -from sf_tools.image.convolve import convolve -from sf_tools.base.np_adjust import rotate_stack - - -def call_mr_transform(data, opt=None, path='./', remove_files=True): - """Call mr_transform - - This method calls the iSAP module mr_transform - - Parameters - ---------- - data : np.ndarray - Input data, 2D array - opt : list, optional - List of additonal mr_transform options - path : str, optional - Path for output files (default is './') - remove_files : bool, optional - Option to remove output files (default is 'True') - - Returns - ------- - np.ndarray results of transform - - """ - - # Create a unique string using the current date and time. - unique_string = datetime.now().strftime('%Y.%m.%d_%H.%M.%S') - - # Set the ouput file names. - file_name = path + 'mr_temp_' + unique_string - file_fits = file_name + '.fits' - file_mr = file_name + '.mr' - - # Write the input data to a fits file. - fits.writeto(file_fits, data) - - # Call mr_transform. - if isinstance(opt, type(None)): - check_call(['mr_transform', file_fits, file_mr]) - else: - check_call(['mr_transform'] + opt + [file_fits, file_mr]) - - # Retrieve wavelet transformed data. - result = fits.getdata(file_mr) - - # Return the mr_transform results (and the output file names). - if remove_files: - remove(file_fits) - remove(file_mr) - return result - else: - return result, file_mr - - -def get_mr_filters(data_shape, opt=None, coarse=False): - """Get mr_transform filters - - This method obtains wavelet filters by calling mr_transform - - Parameters - ---------- - data_shape : tuple - 2D data shape - opt : list, optional - List of additonal mr_transform options - coarse : bool, optional - Option to keep coarse scale (default is 'False') - - Returns - ------- - np.ndarray 3D array of wavelet filters - - """ - - # Adjust the shape of the input data. - data_shape = np.array(data_shape) - data_shape += data_shape % 2 - 1 - - # Create fake data. - fake_data = np.zeros(data_shape) - fake_data[list(zip(data_shape // 2))] = 1 - - # Call mr_transform. - mr_filters = call_mr_transform(fake_data, opt=opt) - - # Return filters - if coarse: - return mr_filters - else: - return mr_filters[:-1] - - -def filter_convolve(data, filters, filter_rot=False): - """Filter convolve - - This method convolves the input image with the wavelet filters - - Parameters - ---------- - data : np.ndarray - Input data, 2D array - filters : np.ndarray - Wavelet filters, 3D array - filter_rot : bool, optional - Option to rotate wavelet filters (default is 'False') - - Returns - ------- - np.ndarray convolved data - - """ - - if filter_rot: - return np.sum((convolve(coef, f) for coef, f in - zip(data, rotate_stack(filters))), axis=0) - - else: - return np.array([convolve(data, f) for f in filters]) - - -def filter_convolve_stack(data, filters, filter_rot=False): - """Filter convolve - - This method convolves the a stack of input images with the wavelet filters - - Parameters - ---------- - data : np.ndarray - Input data, 3D array - filters : np.ndarray - Wavelet filters, 3D array - filter_rot : bool, optional - Option to rotate wavelet filters (default is 'False') - - Returns - ------- - np.ndarray convolved data - - """ - - # Return the convolved data cube. - return np.array([filter_convolve(x, filters, filter_rot=filter_rot) - for x in data]) From 898469e009e3541d8a99b758bfc00971c2dec581 Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Thu, 5 Apr 2018 11:57:04 +0200 Subject: [PATCH 2/8] updated modopt dependency --- requirements.txt | 8 -------- setup.py | 2 +- sf_tools/image/distort.py | 2 +- sf_tools/image/stamp.py | 2 +- sf_tools/image/stats.py | 2 +- 5 files changed, 4 insertions(+), 12 deletions(-) delete mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 9b6b64e..0000000 --- a/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -numpy==1.13.3 -scipy==1.0.0 -future==0.16.0 -astropy==2.0.2 -matplotlib==2.1.0 -termcolor==1.1.0 -coverage==4.0.3 -coveralls==1.2.0 diff --git a/setup.py b/setup.py index f8e3f5a..343b1e7 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,7 @@ url='https://github.com/sfarrens/sf_tools', download_url='https://github.com/sfarrens/sf_tools', packages=find_packages(), - install_requires=['numpy>=1.14.1', 'future>=0.16.0', 'scipy>=1.0.0', 'astropy==2.0.4'], + install_requires=['numpy>=1.14.1', 'future>=0.16.0', 'scipy>=1.0.0', 'astropy==2.0.4', 'modopt>=1.1.4'], license='MIT', description='Tools for image analysis, signal processing and statistics.', long_description=release_info["__about__"], diff --git a/sf_tools/image/distort.py b/sf_tools/image/distort.py index f087d48..2aa7bc6 100644 --- a/sf_tools/image/distort.py +++ b/sf_tools/image/distort.py @@ -15,7 +15,7 @@ from __future__ import division import numpy as np from itertools import product -from sf_tools.base.np_adjust import pad2d +from modopt.base.np_adjust import pad2d def downsample(image, factor): diff --git a/sf_tools/image/stamp.py b/sf_tools/image/stamp.py index ce53013..bc2401e 100644 --- a/sf_tools/image/stamp.py +++ b/sf_tools/image/stamp.py @@ -17,7 +17,7 @@ from builtins import zip import numpy as np from itertools import product -from sf_tools.base.np_adjust import pad2d +from modopt.base.np_adjust import pad2d def patch_centres(data_shape, layout): diff --git a/sf_tools/image/stats.py b/sf_tools/image/stats.py index 4d75621..3252e55 100644 --- a/sf_tools/image/stats.py +++ b/sf_tools/image/stats.py @@ -14,7 +14,7 @@ from __future__ import division import numpy as np -from sf_tools.base.np_adjust import pad2d +from modopt.base.np_adjust import pad2d class SAT(): From 631364a5c6af865e35de761db3821dda72f7e12d Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Thu, 5 Apr 2018 13:08:10 +0200 Subject: [PATCH 3/8] updated docs --- 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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/base/np_adjust.html b/docs/build/html/_modules/sf_tools/base/np_adjust.html deleted file mode 100644 index adafb69..0000000 --- a/docs/build/html/_modules/sf_tools/base/np_adjust.html +++ /dev/null @@ -1,554 +0,0 @@ - - - - - - - - - - - sf_tools.base.np_adjust — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Source code for sf_tools.base.np_adjust

    -# -*- coding: utf-8 -*-
-
-"""NUMPY ADJUSTMENT ROUTINES
-
-This module contains methods for adjusting the default output for certain
-Numpy functions.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.1
-
-:Date: 03/04/2017
-
-"""
-
-from __future__ import division
-import numpy as np
-
-
-
    [docs]def rotate(data):
-    """Rotate
-
-    This method rotates an input numpy array by 180 degrees.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array (at least 2D)
-
-    Returns
-    -------
-    np.ndarray rotated data
-
-    Notes
-    -----
-    Adjustment to numpy.rot90()
-
-    Examples
-    --------
-    >>> from sf_tools.base.np_adjust import rotate
-    >>> x = np.arange(9).reshape((3, 3))
-    >>> x
-    array([[0, 1, 2],
-           [3, 4, 5],
-           [6, 7, 8]])
-    >>> rotate(x)
-    array([[8, 7, 6],
-           [5, 4, 3],
-           [2, 1, 0]])
-
-    """
-
-    return np.rot90(data, 2)
    
-
-
-
    [docs]def rotate_stack(data):
-    """Rotate stack
-
-    This method rotates each array in a stack of arrays by 180 degrees.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array (at least 3D)
-
-    Returns
-    -------
-    np.ndarray rotated data
-
-    Examples
-    --------
-    >>> from sf_tools.base.np_adjust import rotate_stack
-    >>> x = np.arange(18).reshape((2, 3, 3))
-    >>> x
-    array([[[ 0,  1,  2],
-            [ 3,  4,  5],
-            [ 6,  7,  8]],
-           [[ 9, 10, 11],
-            [12, 13, 14],
-            [15, 16, 17
-    >>> rotate_stack(x)
-    array([[[ 8,  7,  6],
-            [ 5,  4,  3],
-            [ 2,  1,  0]],
-           [[17, 16, 15],
-            [14, 13, 12],
-            [11, 10,  9]]])
-
-    """
-
-    return np.array([rotate(x) for x in data])
    
-
-
-
    [docs]def pad2d(data, padding):
-    """Pad array
-
-    This method pads an input numpy array with zeros in all directions.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array (at least 2D)
-    padding : int, tuple
-        Amount of padding in x and y directions, respectively
-
-    Returns
-    -------
-    np.ndarray padded data
-
-    Notes
-    -----
-    Adjustment to numpy.pad()
-
-    Examples
-    --------
-    >>> from sf_tools.base.np_adjust import pad2d
-    >>> x = np.arange(9).reshape((3, 3))
-    >>> x
-    array([[0, 1, 2],
-           [3, 4, 5],
-           [6, 7, 8]])
-    >>> pad2d(x, (1, 1))
-    array([[0, 0, 0, 0, 0],
-           [0, 0, 1, 2, 0],
-           [0, 3, 4, 5, 0],
-           [0, 6, 7, 8, 0],
-           [0, 0, 0, 0, 0]])
-
-    """
-
-    data = np.array(data)
-
-    if isinstance(padding, int):
-        padding = np.array([padding])
-    elif isinstance(padding, (tuple, list)):
-        padding = np.array(padding)
-    elif isinstance(padding, np.ndarray):
-        pass
-    else:
-        raise ValueError('Padding must be an integer or a tuple (or list, '
-                         'np.ndarray) of itegers')
-
-    if padding.ndim == 1:
-        padding = np.repeat(padding, 2)
-
-    return np.pad(data, ((padding[0], padding[0]), (padding[1], padding[1])),
-                  'constant')
    
-
-
-
    [docs]def x_bins(vals):
-    """X-range bins
-
-    This method sets the bin values for a histogram.
-
-    Parameters
-    ----------
-    vals : np.ndarray
-        X-range bins from np.histogram()[1]
-
-    Returns
-    -------
-    np.ndarray corrected x-range bin data
-
-    Notes
-    -----
-    Adjustment to numpy.histogram()
-
-    Examples
-    --------
-    >>> from sf_tools.base.np_adjust import x_bins
-    >>> data = np.array([1, 2, 1, 3, 1, 1])
-    >>> hist, bins = np.histogram(data)
-    >>> hist
-    array([4, 0, 0, 0, 0, 1, 0, 0, 0, 1])
-    >>> bins
-    array([ 1. ,  1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])
-    >>> x_bins(bins)
-    array([ 1.1,  1.3,  1.5,  1.7,  1.9,  2.1,  2.3,  2.5,  2.7,  2.9])
-
-    """
-
-    return (vals[:-1] + vals[1:]) / 2.0
    
-
-
-
    [docs]def x_bins_step(vals):
-    """X-range bins (step function)
-
-    This method sets the bin values for a histogram plotted as a step funciton.
-
-    Parameters
-    ----------
-    vals : np.ndarray
-        X-range bins from np.histogram()[1]
-
-    Returns
-    -------
-    np.ndarray corrected x-range bin data
-
-    Notes
-    -----
-    Adjustment to numpy.histogram()
-
-    Examples
-    --------
-    >>> from sf_tools.base.np_adjust import x_bins
-    >>> data = np.array([1, 2, 1, 3, 1, 1])
-    >>> hist, bins = np.histogram(data)
-    >>> hist
-    array([4, 0, 0, 0, 0, 1, 0, 0, 0, 1])
-    >>> bins
-    array([ 1. ,  1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])
-    >>> x_bins_step(bins)
-    array([ 1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])
-
-    """
-
-    return x_bins(vals) + (vals[1] - vals[0]) / 2.0
    
-
-
-
    [docs]def ftr(data):
-    """Fancy transpose right
-
-    Apply fancy_transpose() to data with roll=1
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-
-    Returns
-    -------
-    np.ndarray transposed data
-
-    """
-
-    return fancy_transpose(data)
    
-
-
-
    [docs]def ftl(data):
-    """Fancy transpose left
-
-    Apply fancy_transpose() to data with roll=-1
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-
-    Returns
-    -------
-    np.ndarray transposed data
-
-    """
-
-    return fancy_transpose(data, -1)
    
-
-
-
    [docs]def fancy_transpose(data, roll=1):
-    """Fancy transpose
-
-    This method transposes a multidimensional matrix.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-    roll : int
-        Roll direction and amount. Default (roll=1)
-
-    Returns
-    -------
-    np.ndarray transposed data
-
-    Notes
-    -----
-    Adjustment to numpy.transpose
-
-    Examples
-    --------
-    >>> from sf_tools.base.np_adjust import fancy_transpose
-    >>> x = np.arange(27).reshape(3, 3, 3)
-    >>> x
-    array([[[ 0,  1,  2],
-            [ 3,  4,  5],
-            [ 6,  7,  8]],
-           [[ 9, 10, 11],
-            [12, 13, 14],
-            [15, 16, 17]],
-           [[18, 19, 20],
-            [21, 22, 23],
-            [24, 25, 26]]])
-    >>> fancy_transpose(x)
-    array([[[ 0,  3,  6],
-            [ 9, 12, 15],
-            [18, 21, 24]],
-           [[ 1,  4,  7],
-            [10, 13, 16],
-            [19, 22, 25]],
-           [[ 2,  5,  8],
-            [11, 14, 17],
-            [20, 23, 26]]])
-    >>> fancy_transpose(x, roll=-1)
-    array([[[ 0,  9, 18],
-            [ 1, 10, 19],
-            [ 2, 11, 20]],
-           [[ 3, 12, 21],
-            [ 4, 13, 22],
-            [ 5, 14, 23]],
-           [[ 6, 15, 24],
-            [ 7, 16, 25],
-            [ 8, 17, 26]]])
-
-    """
-
-    axis_roll = np.roll(np.arange(data.ndim), roll)
-
-    return np.transpose(data, axes=axis_roll)
    
-
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    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/base/transform.html b/docs/build/html/_modules/sf_tools/base/transform.html deleted file mode 100644 index 5ed9a8f..0000000 --- a/docs/build/html/_modules/sf_tools/base/transform.html +++ /dev/null @@ -1,453 +0,0 @@ - - - - - - - - - - - sf_tools.base.transform — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Source code for sf_tools.base.transform

    -# -*- coding: utf-8 -*-
-
-"""DATA TRANSFORM ROUTINES
-
-This module contains methods for transforming data.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-from builtins import range
-import numpy as np
-from scipy.ndimage import gaussian_filter
-from itertools import islice, product
-
-
-
    [docs]def cube2map(data_cube, layout):
-    """Cube to Map
-
-    This method transforms the input data from a 3D cube to a 2D map with a
-    specified layout
-
-    Parameters
-    ----------
-    data_cube : np.ndarray
-        Input data cube, 3D array of 2D images
-    Layout : tuple
-        2D layout of 2D images
-
-    Returns
-    -------
-    np.ndarray 2D map
-
-    Raises
-    ------
-    ValueError
-        For invalid layout
-
-    """
-
-    if data_cube.shape[0] != np.prod(layout):
-        raise ValueError('The desired layout must match the number of input '
-                         'data layers.')
-
-    return np.vstack([np.hstack(data_cube[slice(layout[1] * i, layout[1] *
-                      (i + 1))]) for i in range(layout[0])])
    
-
-
-
    [docs]def map2cube(data_map, layout):
-    """Map to cube
-
-    This method transforms the input data from a 2D map with given layout to
-    a 3D cube
-
-    Parameters
-    ----------
-    data_map : np.ndarray
-        Input data map, 2D array
-    layout : tuple
-        2D layout of 2D images
-
-    Returns
-    -------
-    np.ndarray 3D cube
-
-    Raises
-    ------
-    ValueError
-        For invalid layout
-
-    """
-
-    if np.all(np.array(data_map.shape) % np.array(layout)) != 0:
-        raise ValueError('The desired layout must be a multiple of the number '
-                         'pixels in the data map.')
-
-    d_shape = np.array(data_map.shape) // np.array(layout)
-
-    return np.array([data_map[(slice(i * d_shape[0], (i + 1) * d_shape[0]),
-                    slice(j * d_shape[1], (j + 1) * d_shape[1]))] for i in
-                    range(layout[0]) for j in range(layout[1])])
    
-
-
-
    [docs]def map2matrix(data_map, layout):
-    """Map to Matrix
-
-    This method transforms a 2D map to a 2D matrix
-
-    Parameters
-    ----------
-    data_map : np.ndarray
-        Input data map, 2D array
-    layout : tuple
-        2D layout of 2D images
-
-    Returns
-    -------
-    np.ndarray 2D matrix
-
-    Raises
-    ------
-    ValueError
-        For invalid layout
-
-    """
-
-    layout = np.array(layout)
-
-    # Select n objects
-    n_obj = np.prod(layout)
-
-    # Get the shape of the galaxy images
-    gal_shape = (np.array(data_map.shape) // layout)[0]
-
-    # Stack objects from map
-    data_matrix = []
-
-    for i in range(n_obj):
-        lower = (gal_shape * (i // layout[1]),
-                 gal_shape * (i % layout[1]))
-        upper = (gal_shape * (i // layout[1] + 1),
-                 gal_shape * (i % layout[1] + 1))
-        data_matrix.append((data_map[lower[0]:upper[0],
-                            lower[1]:upper[1]]).reshape(gal_shape ** 2))
-
-    return np.array(data_matrix).T
    
-
-
-
    [docs]def matrix2map(data_matrix, map_shape):
-    """Matrix to Map
-
-    This method transforms a 2D matrix to a 2D map
-
-    Parameters
-    ----------
-    data_matrix : np.ndarray
-        Input data matrix, 2D array
-    map_shape : tuple
-        2D shape of the output map
-
-    Returns
-    -------
-    np.ndarray 2D map
-
-    Raises
-    ------
-    ValueError
-        For invalid layout
-
-    """
-
-    map_shape = np.array(map_shape)
-
-    # Get the shape and layout of the galaxy images
-    gal_shape = np.sqrt(data_matrix.shape[0])
-    layout = np.array(map_shape // np.repeat(gal_shape, 2), dtype='int')
-
-    # Map objects from matrix
-    data_map = np.zeros(map_shape)
-
-    temp = data_matrix.reshape(gal_shape, gal_shape, data_matrix.shape[1])
-
-    for i in range(data_matrix.shape[1]):
-        lower = (gal_shape * (i // layout[1]),
-                 gal_shape * (i % layout[1]))
-        upper = (gal_shape * (i // layout[1] + 1),
-                 gal_shape * (i % layout[1] + 1))
-        data_map[lower[0]:upper[0], lower[1]:upper[1]] = temp[:, :, i]
-
-    return data_map
    
-
-
-
    [docs]def cube2matrix(data_cube):
-    """Cube to Matrix
-
-    This method transforms a 3D cube to a 2D matrix
-
-    Parameters
-    ----------
-    data_cube : np.ndarray
-        Input data cube, 3D array
-
-    Returns
-    -------
-    np.ndarray 2D matrix
-
-    """
-
-    return data_cube.reshape([data_cube.shape[0]] +
-                             [np.prod(data_cube.shape[1:])]).T
    
-
-
-
    [docs]def matrix2cube(data_matrix, im_shape):
-    """Matrix to Cube
-
-    This method transforms a 2D matrix to a 3D cube
-
-    Parameters
-    ----------
-    data_matrix : np.ndarray
-        Input data cube, 2D array
-    im_shape : tuple
-        2D shape of the individual images
-
-    Returns
-    -------
-    np.ndarray 3D cube
-
-    """
-
-    return data_matrix.T.reshape([data_matrix.shape[1]] + list(im_shape))
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/base/types.html b/docs/build/html/_modules/sf_tools/base/types.html deleted file mode 100644 index 1e3267c..0000000 --- a/docs/build/html/_modules/sf_tools/base/types.html +++ /dev/null @@ -1,483 +0,0 @@ - - - - - - - - - - - sf_tools.base.types — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - - - - -
    -
    -
    -
    - -

    Source code for sf_tools.base.types

    -# -*- coding: utf-8 -*-
-
-"""TYPE HANDLING ROUTINES
-
-This module contains methods for handing object types.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.0
-
-:Date: 04/04/2017
-
-"""
-
-
-import numpy as np
-
-
-
    [docs]def check_float(val):
-    """Check if input value is a float or a np.ndarray of floats, if not
-    convert.
-
-    Parameters
-    ----------
-    val : any
-        Input value
-
-    Returns
-    -------
-    float or np.ndarray of floats
-
-    """
-
-    if type(val) is float:
-        pass
-    elif type(val) is int:
-        val = float(val)
-    elif type(val) is list or type(val) is tuple:
-        val = np.array(val, dtype=float)
-    elif type(val) is np.ndarray and val.dtype is not 'float64':
-        val = val.astype(float)
-    else:
-        raise ValueError('Invalid input type.')
-
-    return val
    
-
-
-
    [docs]def check_int(val):
-    """Check if input value is an int or a np.ndarray of ints, if not convert.
-
-    Parameters
-    ----------
-    val : any
-        Input value
-
-    Returns
-    -------
-    int or np.ndarray of ints
-
-    """
-
-    if type(val) is int:
-        pass
-    elif type(val) is float:
-        val = int(val)
-    elif type(val) is list or type(val) is tuple:
-        val = np.array(val, dtype=int)
-    elif type(val) is np.ndarray and val.dtype is not 'int64':
-        val = val.astype(int)
-    else:
-        raise ValueError('Invalid input type.')
-
-    return val
    
-
-
-
    [docs]def val2int(val):
-    """Convert to int
-
-    This method checks if input value is an int and if not converts it.
-
-    Parameters
-    ----------
-    val : int, float, str, list, tuple or np.ndarray
-        Input value
-
-    Returns
-    -------
-    int interger value or np.ndarray array of ints
-
-    Raises
-    ------
-    ValueError
-        For invalid input type
-
-    Examples
-    --------
-    >>> from sf_tools.base.types import val2int
-    >>> x = np.arange(5).astype(float)
-    >>> x
-    array([ 0.,  1.,  2.,  3.,  4.])
-    >>> val2int(x)
-    array([0, 1, 2, 3, 4])
-
-    """
-
-    if isinstance(val, int):
-        pass
-    elif isinstance(val, (float, str)):
-        val = int(float(val))
-    elif isinstance(val, (list, tuple)):
-        val = np.array(val, dtype=float).astype(int)
-    elif isinstance(val, np.ndarray):
-        if np.issubdtype(val.dtype, 'int64'):
-            pass
-        else:
-            val = val.astype(float).astype(int)
-    else:
-        raise ValueError('Invalid input type.')
-
-    return val
    
-
-
-
    [docs]def val2float(val):
-    """Convert to float
-
-    This method checks if input value is a float and if not converts it.
-
-    Parameters
-    ----------
-    val : int, float, str, list, tuple or np.ndarray
-        Input value
-
-    Returns
-    -------
-    float floating point value or np.ndarray array of floats
-
-    Examples
-    --------
-    >>> from sf_tools.base.types import val2float
-    >>> x = np.arange(5)
-    >>> x
-    array([0, 1, 2, 3, 4])
-    >>> val2float(x)
-    array([ 0.,  1.,  2.,  3.,  4.])
-
-    """
-
-    if isinstance(val, float):
-        pass
-    elif isinstance(val, (int, str)):
-        val = float(val)
-    elif isinstance(val, (list, tuple)):
-        val = np.array(val, dtype=float)
-    elif isinstance(val, np.ndarray):
-        if np.issubdtype(val.dtype, 'float64'):
-            pass
-        else:
-            val = val.astype(float)
-    else:
-        raise ValueError('Invalid input type.')
-
-    return val
    
-
-
-
    [docs]def val2str(val):
-    """Convert to string
-
-    This method checks if input value is a string and if not converts it.
-
-    Parameters
-    ----------
-    val : int, float, str, list, tuple or np.ndarray
-        Input value
-
-    Returns
-    -------
-    str string or np.ndarray array of strings
-
-
-    Examples
-    --------
-    >>> from sf_tools.base.types import val2str
-    >>> x = np.arange(5)
-    >>> x
-    array([0, 1, 2, 3, 4])
-    >>> val2str(x)
-    array(['0', '1', '2', '3', '4'],
-          dtype='|S21')
-
-    """
-
-    if isinstance(val, str):
-        pass
-    elif isinstance(val, (int, float)):
-        val = str(val)
-    elif isinstance(val, (list, tuple)):
-        val = np.array(val, dtype=str)
-    elif isinstance(val, np.ndarray):
-        if np.issubdtype(val.dtype, 'S21'):
-            pass
-        else:
-            val = val.astype(str)
-    else:
-        raise ValueError('Invalid input type.')
-
-    return val
    
-
-
-
    [docs]def nan2val(array, val=0.0):
-    """Convert NAN to val
-
-    This converts all NANs in an array to a specified value.
-
-    Parameters
-    ----------
-    array : np.ndarray, list or tuple
-        Input array
-    val : int or float, optional
-        Value to replace NANs. Default (val=0.0)
-
-    Returns
-    -------
-    np.ndarray array without NANs
-
-    NOTES
-    -----
-    Output data type defined by val type.
-
-    Examples
-    --------
-    >>> from sf_tools.base.types import nan2val
-    >>> x = [1., 2., np.nan, 4.]
-    >>> x
-    [1.0, 2.0, nan, 4.0]
-    >>> nan2val(x, 3.)
-    array([ 1.,  2.,  3.,  4.])
-
-    """
-
-    new_array = np.copy(array)
-    new_array[np.isnan(new_array)] = val
-
-    if isinstance(val, int):
-        new_array = val2int(new_array)
-
-    return new_array
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/image/convolve.html b/docs/build/html/_modules/sf_tools/image/convolve.html deleted file mode 100644 index 1701357..0000000 --- a/docs/build/html/_modules/sf_tools/image/convolve.html +++ /dev/null @@ -1,473 +0,0 @@ - - - - - - - - - - - sf_tools.image.convolve — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.image.convolve
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.image.convolve

    -# -*- coding: utf-8 -*-
-
-"""CONVOLUTION ROUTINES
-
-This module contains methods for convolution.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 23/10/2017
-
-"""
-
-from __future__ import division
-from builtins import zip
-import numpy as np
-from scipy.signal import fftconvolve
-from scipy.fftpack import fftn, ifftn, fftshift, ifftshift
-from astropy.convolution import convolve_fft
-from sf_tools.base.np_adjust import rotate, rotate_stack
-
-
-
    [docs]def convolve_np(image, kernel):
-    """Convolve with Numpy FFT
-
-    This method convolves the input image with the input kernel
-
-    Parameters
-    ----------
-    image : np.ndarray
-        2D image array
-    kernel : np.ndarray
-        2D kernel array
-
-    Returns
-    -------
-    np.ndarray 2D convolved image array
-
-    """
-
-    x = np.fft.fftshift(np.fft.fftn(image))
-    y = np.fft.fftshift(np.fft.fftn(kernel))
-
-    return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x * y))))
    
-
-
-
    [docs]def deconvolve_np(image, kernel):
-    """Deconvolve with Numpy FFT
-
-    This method deconvolves the input image with the input kernel
-
-    Parameters
-    ----------
-    image : np.ndarray
-        2D image array
-    kernel : np.ndarray
-        2D kernel array
-
-    Returns
-    -------
-    np.ndarray 2D deconvolved image array
-
-    """
-
-    x = np.fft.fftshift(np.fft.fftn(image))
-    y = np.fft.fftshift(np.fft.fftn(kernel))
-
-    return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x / y))))
    
-
-
-
    [docs]def convolve(data, kernel, method='astropy'):
-    """Convolve data with kernel
-
-    This method convolves the input data with a given kernel using FFT and
-    is the default convolution used for all routines
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array, normally a 2D image
-    kernel : np.ndarray
-        Input kernel array, normally a 2D kernel
-    method : str {'astropy', 'scipy'}, optional
-        Convolution method (default is 'astropy')
-
-        'astropy':
-            Uses the astropy.convolution.convolve_fft method provided in
-            Astropy (http://www.astropy.org/)
-
-        'scipy':
-            Uses the scipy.signal.fftconvolve method provided in SciPy
-            (https://www.scipy.org/)
-
-    Returns
-    -------
-    np.ndarray convolved data
-
-    Raises
-    ------
-    ValueError
-        If `data` and `kernel` do not have the same number of dimensions
-    ValueError
-        If `method` is not 'astropy' or 'scipy'
-
-    """
-
-    if data.ndim != kernel.ndim:
-        raise ValueError('Data and kernel must have the same dimensions.')
-
-    if method not in ('astropy', 'scipy'):
-        raise ValueError('Invalid method. Options are "astropy" or "scipy".')
-
-    if method == 'astropy':
-        return convolve_fft(data, kernel, boundary='wrap', crop=False,
-                            nan_treatment='fill', normalize_kernel=False)
-
-    elif method == 'scipy':
-        return fftconvolve(data, kernel, mode='same')
    
-
-
-
    [docs]def convolve_stack(data, kernel, rot_kernel=False, method='astropy'):
-    """Convolve stack of data with stack of kernels
-
-    This method convolves the input data with a given kernel using FFT and
-    is the default convolution used for all routines
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array, normally a 2D image
-    kernel : np.ndarray
-        Input kernel array, normally a 2D kernel
-    rot_kernel : bool
-        Option to rotate kernels by 180 degrees
-    method : str {'astropy', 'scipy'}, optional
-        Convolution method (default is 'astropy')
-
-    Returns
-    -------
-    np.ndarray convolved data
-
-    """
-
-    if rot_kernel:
-        kernel = rotate_stack(kernel)
-
-    return np.array([convolve(data_i, kernel_i, method=method) for data_i,
-                    kernel_i in zip(data, kernel)])
    
-
-
-
    [docs]def psf_convolve(data, psf, psf_rot=False, psf_type='fixed', method='astropy'):
-    """Convolve data with PSF
-
-    This method convolves an image with a PSF
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array, normally an array of 2D images
-    psf : np.ndarray
-        Input PSF array, normally either a single 2D PSF or an array of 2D
-        PSFs
-    psf_rot: bool
-        Option to rotate PSF by 180 degrees
-    psf_type : str {'fixed', 'obj_var'}, optional
-        PSF type (default is 'fixed')
-    method : str {'astropy', 'scipy'}, optional
-        Convolution method (default is 'astropy')
-
-        'fixed':
-            The PSF is fixed, i.e. it is the same for each image
-
-        'obj_var':
-            The PSF is object variant, i.e. it is different for each image
-
-    Returns
-    -------
-    np.ndarray convolved data
-
-    Raises
-    ------
-    ValueError
-        If `psf_type` is not 'fixed' or 'obj_var'
-
-    """
-
-    if psf_type not in ('fixed', 'obj_var'):
-        raise ValueError('Invalid PSF type. Options are "fixed" or "obj_var"')
-
-    if psf_rot and psf_type == 'fixed':
-        psf = rotate(psf)
-
-    elif psf_rot:
-        psf = rotate_stack(psf)
-
-    if psf_type == 'fixed':
-        return np.array([convolve(data_i, psf, method=method) for data_i in
-                        data])
-
-    elif psf_type == 'obj_var':
-
-        return convolve_stack(data, psf)
    
-
-
-
    [docs]def pseudo_inverse(image, kernel, weight=None):
-    """Pseudo inverse
-
-    This method calculates the pseudo inverse of the input image for the given
-    kernel using FFT
-
-    Parameters
-    ----------
-    image : np.ndarray
-        Input image, 2D array
-    kernel : np.ndarray
-        Input kernel, 2D array
-    weight : np.ndarray, optional
-        Optional weights, 2D array
-
-    Returns
-    -------
-    np.ndarray result of the pseudo inverse
-
-    """
-
-    y_hat = fftshift(fftn(image))
-    h_hat = fftshift(fftn(kernel))
-    h_hat_star = np.conj(h_hat)
-
-    res = ((h_hat_star * y_hat) / (h_hat_star * h_hat))
-
-    if not isinstance(weight, type(None)):
-        res *= weight
-
-    return np.real(fftshift(ifftn(ifftshift(res))))
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/image/distort.html b/docs/build/html/_modules/sf_tools/image/distort.html deleted file mode 100644 index 3f25c35..0000000 --- a/docs/build/html/_modules/sf_tools/image/distort.html +++ /dev/null @@ -1,504 +0,0 @@ - - - - - - - - - - - sf_tools.image.distort — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
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    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.image.distort
      • - - -
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    - - -
    -
    -
    -
    - -

    Source code for sf_tools.image.distort

    -# -*- coding: utf-8 -*-
-
-"""IMAGE DISTORTION ROUTINES
-
-This module contains methods for playing around with image properties.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.1
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-import numpy as np
-from itertools import product
-from sf_tools.base.np_adjust import pad2d
-
-
-
    [docs]def downsample(image, factor):
-    """Downsample
-
-    This method downsamples (decimates) an image.
-
-    Parameters
-    ----------
-    image : np.ndarray
-        Input image array
-    factor : int
-        Downsampling factor
-
-    Returns
-    -------
-    np.ndarray downsampled image array
-
-    Raises
-    ------
-    ValueError
-        For invalid downsampling factor
-
-    """
-
-    factor = np.array(factor)
-
-    if not np.all(factor > 0):
-        raise ValueError('The downsampling factor values must be > 0.')
-
-    if factor.size == 1:
-        return image[0::factor, 0::factor]
-
-    elif factor.size == 2:
-        return image[0::factor[0], 0::factor[1]]
-
-    else:
-        raise ValueError('The downsampling factor can only contain one or ' +
-                         'two values.')
    
-
-
-
    [docs]def resize_even_image(image):
-    """Resize even image
-
-    This method returns an image with odd dimensions.
-
-    Parameters
-    ----------
-    image : np.ndarray
-        Input image array
-
-    Returns
-    -------
-    np.ndarray resized image array
-
-    """
-
-    return image[[slice(x) for x in (np.array(image.shape) +
-                  np.array(image.shape) % 2 - 1)]]
    
-
-
-
    [docs]def roll_2d(data, roll_rad=(1, 1)):
-    """Roll in 2D
-
-    This method rolls an array in 2 dimensions.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input 2D data array
-    roll_rad : tuple
-        Roll radius in each dimension
-
-    Returns
-    -------
-    np.ndarray rolled array
-
-    """
-
-    return np.roll(np.roll(data, roll_rad[1], axis=1), roll_rad[0], axis=0)
    
-
-
-
    [docs]def rot_and_roll(data):
-    """Rotate and roll
-
-    This method rotates (by 180 deg) and rolls a 2D array.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input 2D data array
-
-    Returns
-    -------
-    np.ndarray rotated and rolled array
-
-    """
-
-    return roll_2d(np.rot90(data, 2), -(np.array(data.shape) // 2))
    
-
-
-
    [docs]def gen_mask(kernel_shape, image_shape):
-    """Generate mask
-
-    This method generates an image mask.
-
-    Parameters
-    ----------
-    kernel_shape : tuple
-        Shape of kernel
-    image_shape : tuple
-        Shape of image
-
-    Returns
-    -------
-    np.ndarray boolean mask
-
-    """
-
-    kernel_shape = np.array(kernel_shape)
-    image_shape = np.array(image_shape)
-
-    shape_diff = image_shape - kernel_shape
-
-    mask = np.ones(image_shape, dtype=bool)
-
-    if shape_diff[0] > 0:
-        mask[-shape_diff[0]:] = False
-    if shape_diff[1] > 0:
-        mask[:, -shape_diff[1]:] = False
-
-    return roll_2d(mask, -(kernel_shape // 2))
    
-
-
-
    [docs]def roll_sequence(data_shape):
-    """Roll sequence
-
-    This method generates the roll sequence for a 2D array.
-
-    Parameters
-    ----------
-    data_shape : tuple
-        Shape of data
-
-    Returns
-    -------
-    list of roll radii
-
-    """
-
-    data_shape = np.array(data_shape)
-
-    return list(product(*(range(data_shape[0]), range(data_shape[1]))))
    
-
-
-
    [docs]def kernel_pattern(kernel_shape, mask):
-    """Kernel pattern
-
-    This method generates the kernel pattern. Rather than padding the kernel
-    with zeroes to match the image size one simply extracts the series of
-    repitions of the base kernel patterns.
-
-    Parameters
-    ----------
-    kernel_shape : tuple
-        Shape of kernel
-    mask : np.ndarray
-        Boolean mask
-
-    Returns
-    -------
-    np.ndarray kernel pattern
-
-    """
-
-    kernel_shape = np.array(kernel_shape)
-
-    kernel_buffer = 1 - np.array(kernel_shape) % 2
-
-    n_rep_axis1 = sum(1 - mask[:, 0])
-    n_rep_axis2 = sum(1 - mask[0])
-
-    if np.any(mask[:, 0] is False):
-        pos_1 = np.where(mask[:, 0] is False)[0][0] - 1 + kernel_buffer[0]
-
-    if np.any(mask[0] is False):
-        pos_2 = np.where(mask[0] is False)[0][0] - 1 + kernel_buffer[1]
-
-    pattern = np.arange(np.prod(kernel_shape)).reshape(kernel_shape)
-
-    for i in range(n_rep_axis1):
-        pattern = np.insert(pattern, pos_1, pattern[pos_1], axis=0)
-
-    for i in range(n_rep_axis2):
-        pattern = np.insert(pattern, pos_2, pattern[:, pos_2], axis=1)
-
-    return pattern.reshape(pattern.size)
    
-
-
-
    [docs]def rearrange_kernel(kernel, data_shape=None):
-    """Rearrange kernel
-
-    This method rearanges the input kernel elements for vector multiplication.
-    The input kernel is padded with zeroes to match the image size.
-
-    Parameters
-    ----------
-    kernel : np.ndarray
-        Input kernel array
-    data_shape : tuple
-        Shape of the data
-
-    Returns
-    -------
-    np.ndarray rearanged matrix of kernel elements
-
-    """
-
-    # Define kernel shape.
-    kernel_shape = np.array(kernel.shape)
-
-    # Set data shape if not provided.
-    if isinstance(data_shape, type(None)):
-        data_shape = kernel_shape
-    else:
-        data_shape = np.array(data_shape)
-
-    # Set the length of the output matrix rows.
-    vec_length = np.prod(data_shape)
-
-    # Find the diffrence between the shape of the data and the kernel.
-    shape_diff = data_shape - kernel_shape
-
-    if np.any(shape_diff < 0):
-        raise ValueError('Kernel shape must be less than or equal to the '
-                         'data shape')
-
-    # Set the kernel radius.
-    kernel_rad = kernel_shape // 2
-
-    # Rotate, pad and roll the input kernel.
-    kernel_rot = np.pad(np.rot90(kernel, 2), ((0, shape_diff[0]),
-                        (0, shape_diff[1])), 'constant')
-    kernel_rot = np.roll(np.roll(kernel_rot, -kernel_rad[1], axis=1),
-                         -kernel_rad[0], axis=0)
-
-    return np.array([np.roll(np.roll(kernel_rot, i, axis=0), j,
-                    axis=1).reshape(vec_length) for i in range(data_shape[0])
-                    for j in range(data_shape[1])])
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/image/quality.html b/docs/build/html/_modules/sf_tools/image/quality.html deleted file mode 100644 index fa6722f..0000000 --- a/docs/build/html/_modules/sf_tools/image/quality.html +++ /dev/null @@ -1,389 +0,0 @@ - - - - - - - - - - - sf_tools.image.quality — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.image.quality
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.image.quality

    -# -*- coding: utf-8 -*-
-
-"""QUALITY ASSESSMENT ROUTINES
-
-This module contains methods and classes for assessing the quality of image
-reconstructions.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 20/10/2017
-
-Notes
------
-Some of the methods in this module are based on work by Fred Ngole.
-
-"""
-
-from __future__ import division
-import numpy as np
-from sf_tools.image.shape import ellipticity_atoms
-
-
-
    [docs]def nmse(image1, image2, metric=np.mean):
-    r"""Normalised Mean Square Error
-
-    This method computes the NMSE of two input images, or the result of the
-    input metric on a stack of input images.
-
-    Parameters
-    ----------
-    image1 : np.ndarray
-        First image (or stack of images) to be analysed (original image)
-    image2 : np.ndarray
-        Second image (or stack of images) to be analysed (reconstructed image)
-    metric : function
-        Metric to be apllied to NMSE results (default is 'np.mean')
-
-    Returns
-    -------
-    float NMSE value or metric value(s)
-
-    Raises
-    ------
-    ValueError
-        For invalid input data dimensions
-
-    See Also
-    --------
-    e_error : ellipticity error
-
-    Notes
-    -----
-    This method implements the following equation:
-
-        - Equations from [NS2016]_ sec 4.1:
-
-        .. math::
-
-            \text{NMSE} = \frac{1}{D}\sum_{i=1}^D
-                \frac{\|\hat{\text{Im}}_i - \text{Im}_i\|_2^2}
-                {\|\text{Im}_i\|_2^2}
-
-    Examples
-    --------
-    >>> from image.quality import nmse
-
-    """
-
-    if image1.ndim != image2.ndim:
-        raise ValueError('Input images must have the same dimensions')
-
-    if image1.ndim not in (2, 3):
-        raise ValueError('Input data must be single image or stack of images')
-
-    if image1.ndim == 2:
-
-        return (np.linalg.norm(image2 - image1) ** 2 /
-                np.linalg.norm(image1) ** 2)
-
-    else:
-
-        res = (np.array([np.linalg.norm(x) ** 2 for x in (image2 - image1)]) /
-               np.array([np.linalg.norm(x) ** 2 for x in image1]))
-
-        return metric(res)
    
-
-
-
    [docs]def e_error(image1, image2, metric=np.mean):
-    r"""Normalised Mean Square Error
-
-    This method computes the ellipticity error of two input images, or the
-    result of the input metric on the ellipticity values.
-
-    Parameters
-    ----------
-    image1 : np.ndarray
-        First image to be analysed (original image)
-    image2 : np.ndarray
-        Second image to be analysed (reconstructed image)
-    metric : function
-        Metric to be apllied to ellipticity error results (default is
-        'np.mean')
-
-    Returns
-    -------
-    float ellipticity error value or metric value(s)
-
-    Raises
-    ------
-    ValueError
-        For invalid input data dimensions
-
-    See Also
-    --------
-    nmse : nmse error
-
-    Notes
-    -----
-    This method implements the following equation:
-
-        - Equations from [NS2016]_ sec 4.1:
-
-        .. math::
-
-            \text{E}_\gamma = \frac{1}{D}\sum_{i=1}^D
-                \|\gamma(\text{Im}_i) - \gamma(\hat{\text{Im}}_i)\|_2
-
-    Examples
-    --------
-    >>> from image.quality import e_error
-
-    """
-
-    if image1.ndim != image2.ndim:
-        raise ValueError('Input images must have the same dimensions')
-
-    if image1.ndim not in (2, 3):
-        raise ValueError('Input data must be single image or stack of images')
-
-    if image1.ndim == 2:
-
-        return np.linalg.norm(ellipticity_atoms(image1) -
-                              ellipticity_atoms(image2))
-
-    else:
-
-        diff = (np.array([ellipticity_atoms(x) for x in image1]) -
-                np.array([ellipticity_atoms(x) for x in image2]))
-
-        return metric([np.linalg.norm(x) for x in diff])
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/image/shape.html b/docs/build/html/_modules/sf_tools/image/shape.html deleted file mode 100644 index 9a082dc..0000000 --- a/docs/build/html/_modules/sf_tools/image/shape.html +++ /dev/null @@ -1,660 +0,0 @@ - - - - - - - - - - - sf_tools.image.shape — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.image.shape
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.image.shape

    -# -*- coding: utf-8 -*-
-
-"""SHAPE ESTIMATION ROUTINES
-
-This module contains methods and classes for estimating galaxy shapes.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.4
-
-:Date: 20/10/2017
-
-Notes
------
-Some of the methods in this module are based on work by Fred Ngole.
-
-"""
-
-from __future__ import division
-import numpy as np
-
-
-
    [docs]def ellipticity_atoms(data, offset=0):
-    r"""Calculate ellipticity
-
-    This method calculates the ellipticity of an image from its shape
-    projection components.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array, the image to be analysed
-    offset : int, optional
-        Shape projection offset (default is '0')
-
-    Returns
-    -------
-    np.ndarray of the image ellipticity components
-
-    See Also
-    --------
-    shape_project : shape projection matrix
-
-    Notes
-    -----
-    This technique was developed by Fred Ngole and implements the following
-    equations:
-
-        - Equations C.1 and C.2 from [NS2016]_ appendix:
-
-        .. math::
-
-            e_1(\mathbf{X}_i) = \frac{<\mathbf{X}_i, \mathbf{U}_4>
-                                      <\mathbf{X}_i, \mathbf{U}_2> -
-                                      <\mathbf{X}_i, \mathbf{U}_0>^2 +
-                                      <\mathbf{X}_i, \mathbf{U}_1>^2}
-                                      {<\mathbf{X}_i, \mathbf{U}_3>
-                                      <\mathbf{X}_i, \mathbf{U}_2> -
-                                      <\mathbf{X}_i, \mathbf{U}_0>^2 -
-                                      <\mathbf{X}_i, \mathbf{U}_1>^2
-                                      }
-
-            e_2(\mathbf{X}_i) = \frac{2\left(<\mathbf{X}_i, \mathbf{U}_5>
-                                      <\mathbf{X}_i, \mathbf{U}_2> -
-                                      <\mathbf{X}_i, \mathbf{U}_0>
-                                      <\mathbf{X}_i, \mathbf{U}_1>\right)}
-                                      {<\mathbf{X}_i, \mathbf{U}_3>
-                                      <\mathbf{X}_i, \mathbf{U}_2> -
-                                      <\mathbf{X}_i, \mathbf{U}_0>^2 -
-                                      <\mathbf{X}_i, \mathbf{U}_1>^2
-                                      }
-
-    Examples
-    --------
-    >>> from image.shape import ellipticity_atoms
-    >>> import numpy as np
-    >>> a = np.zeros((5, 5))
-    >>> a[2, 1:4] += 1
-    >>> ellipticity_atoms(a)
-    array([-1.,  0.])
-
-    >>> b = np.zeros((5, 5))
-    >>> b[1:4, 2] += 1
-    >>> ellipticity_atoms(b)
-    array([ 1.,  0.])
-
-    """
-
-    XU = [np.sum(data * U) for U in shape_project(data.shape, offset)]
-
-    divisor = XU[3] * XU[2] - XU[0] ** 2 - XU[1] ** 2
-    e1 = (XU[4] * XU[2] - XU[0] ** 2 + XU[1] ** 2) / divisor
-    e2 = 2 * (XU[5] * XU[2] - XU[0] * XU[1]) / divisor
-
-    return np.array([e1, e2])
    
-
-
-
    [docs]def shape_project(shape, offset=0, return_norm=False):
-    r"""Shape projection matrix
-
-    This method generates a shape projection matrix for a given image.
-
-    Parameters
-    ----------
-    shape : list, tuple or np.ndarray
-        List of image dimensions
-    offset : int, optional
-        Shape projection offset (default is '0')
-    return_norm : bool, optional
-        Option to return l2 normalised shape projection components
-        (default is 'False')
-
-    Returns
-    -------
-    np.ndarray of shape projection components
-
-    See Also
-    --------
-    ellipticity_atoms : calculate ellipticity
-
-    Notes
-    -----
-    This technique was developed by Fred Ngole and implements the following
-    equations:
-
-        - Equations from [NS2016]_ appendix:
-
-        .. math::
-
-            U_1 &= (k)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\
-            U_2 &= (l)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\
-            U_3 &= (1)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\
-            U_4 &= (k^2 + l^2)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\
-            U_5 &= (k^2 - l^2)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\
-            U_6 &= (kl)_{1 \leq k \leq N_l, 1 \leq l \leq N_c}
-
-    Examples
-    --------
-    >>> from image.shape import shape_project
-    >>> shape_project([3, 3])
-    array([[[ 0.,  0.,  0.],
-            [ 1.,  1.,  1.],
-            [ 2.,  2.,  2.]],
-    <BLANKLINE>
-           [[ 0.,  1.,  2.],
-            [ 0.,  1.,  2.],
-            [ 0.,  1.,  2.]],
-    <BLANKLINE>
-           [[ 1.,  1.,  1.],
-            [ 1.,  1.,  1.],
-            [ 1.,  1.,  1.]],
-    <BLANKLINE>
-           [[ 0.,  1.,  4.],
-            [ 1.,  2.,  5.],
-            [ 4.,  5.,  8.]],
-    <BLANKLINE>
-           [[ 0., -1., -4.],
-            [ 1.,  0., -3.],
-            [ 4.,  3.,  0.]],
-    <BLANKLINE>
-           [[ 0.,  0.,  0.],
-            [ 0.,  1.,  2.],
-            [ 0.,  2.,  4.]]])
-
-    """
-
-    U = []
-    U.append(np.outer(np.arange(shape[0]) + offset, np.ones(shape[1])))
-    U.append(np.outer(np.ones(shape[0]), np.arange(shape[1]) + offset))
-    U.append(np.ones(shape))
-    U.append(U[0] ** 2 + U[1] ** 2)
-    U.append(U[0] ** 2 - U[1] ** 2)
-    U.append(U[0] * U[1])
-
-    if return_norm:
-        np.array([np.linalg.norm(x, 2) for x in U])
-    else:
-        return np.array(U)
    
-
-
-
    [docs]class Ellipticity():
-    """ Image ellipticity class
-
-    This class calculates image ellipticities from quadrupole moments.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array, the image to be analysed
-    sigma : int, optional
-        Estimation error (default is '1000')
-    centroid : np.ndarray, optional
-        Centroid positions [x, y] of the input image (defualt is 'None')
-    moments : np.ndarray, optional
-        Quadrupole moments [[q00, q01], [q10, q11]] of the input image
-        (defualt is 'None')
-    ellip_type : str {'chi', 'epsilon'}, optional
-        Ellipticity type (default is 'chi')
-
-    Examples
-    --------
-    >>> from image.shape import Ellipticity
-    >>> import numpy as np
-    >>> a = np.zeros((5, 5))
-    >>> a[2, 1:4] += 1
-    >>> Ellipticity(a).e
-    array([-1.,  0.])
-
-    >>> b = np.zeros((5, 5))
-    >>> b[1:4, 2] += 1
-    >>> Ellipticity(b).e
-    array([ 1.,  0.])
-
-    """
-
-    def __init__(self, data, sigma=1000, centroid=None, moments=None,
-                 ellip_type='chi'):
-
-        self._data = data
-        self._sigma = sigma
-        self._ranges = np.array([np.arange(i) for i in data.shape])
-        self._ellip_type = ellip_type
-        self._check_ellip_type()
-
-        if not isinstance(moments, type(None)):
-            self.moments = np.array(moments).astype('complex').reshape(2, 2)
-            self._get_ellipse()
-        elif isinstance(centroid, type(None)):
-            self._get_centroid()
-        else:
-            self.centroid = centroid
-            self._update_weights()
-            self._get_moments()
-
-
    [docs]    def _check_ellip_type(self):
-        """Check Ellipticity Type
-
-        This method raises an error if ellip_type is not 'chi' or 'epsilon'.
-
-        Raises
-        ------
-        ValueError for invalid ellip_type
-
-        """
-
-        if self._ellip_type not in ('chi', 'epsilon'):
-            raise ValueError('Invalid ellip_type, options are "chi" or '
-                             '"epsilon"')
    
-
-
    [docs]    def _update_xy(self):
-        """Update the x and y values
-
-        This method updates the values of x and y using the current centroid.
-
-        """
-
-        self._x = np.outer(self._ranges[0] - self.centroid[0],
-                           np.ones(self._data.shape[1]))
-        self._y = np.outer(np.ones(self._data.shape[0]),
-                           self._ranges[1] - self.centroid[1])
    
-
-
    [docs]    def _update_weights(self):
-        """Update the weights
-
-        This method updates the value of the weights using the current values
-        of x and y.
-
-        Notes
-        -----
-        This method implements the following equations:
-
-            - The exponential part of equation 1 from [BM2007]_ to calculate
-              the weights:
-
-            .. math::
-
-                w(x,y) = e^{-\\frac{\\left((x-x_c)^2+(y-y_c)^2\\right)}
-                    {2\\sigma^2}}
-
-        """
-
-        self._update_xy()
-        self._weights = np.exp(-(self._x ** 2 + self._y ** 2) /
-                               (2 * self._sigma ** 2))
    
-
-
    [docs]    def _update_centroid(self):
-        """Update the centroid
-
-        This method updates the centroid value using the current weights.
-
-        Notes
-        -----
-        This method implements the following equations:
-
-            - Equation 2a, 2b and 2c from [BM2007]_ to calculate the position
-              moments:
-
-            .. math::
-
-                S_w = \sum_{x,y} I(x,y)w(x,y)
-
-                S_x = \sum_{x,y} xI(x,y)w(x,y)
-
-                S_y = \sum_{x,y} yI(x,y)w(x,y)
-
-            - Equation 3 from [BM2007]_ to calculate the centroid:
-
-            .. math::
-
-                X_c = S_x/S_w,\\
-                Y_c = S_y/S_w
-
-        """
-
-        # Calculate the position moments.
-        iw = np.array([np.sum(self._data * self._weights, axis=i)
-                       for i in (1, 0)])
-        sw = np.sum(iw, axis=1)
-        sxy = np.sum(iw * self._ranges, axis=1)
-
-        # Update the centroid value.
-        self.centroid = sxy / sw
    
-
-
    [docs]    def _get_centroid(self, n_iter=10):
-        """Calculate centroid
-
-        This method iteratively calculates the centroid of the image.
-
-        Parameters
-        ----------
-        n_iter : int, optional
-            Number of iterations (deafult is '10')
-
-        """
-
-        # Set initial value for the weights.
-        self._weights = np.ones(self._data.shape)
-
-        # Iteratively calculate the centroid.
-        for i in range(n_iter):
-
-            # Update the centroid value.
-            self._update_centroid()
-
-            # Update the weights.
-            self._update_weights()
-
-        # Calculate the quadrupole moments.
-        self._get_moments()
    
-
-
    [docs]    def _get_moments(self):
-        """ Calculate the quadrupole moments
-
-        This method calculates the quadrupole moments.
-
-        Notes
-        -----
-        This method implements the following equations:
-
-            - Equation 10 from [C2013]_ to calculate the moments:
-
-            .. math::
-
-                Q_{ij}=\\frac{\\int\\int\\Phi(x_i,x_j) w(x_i,x_j)
-                    (x_i-\\bar{x_i})(x_j-\\bar{x_j}) dx_idx_j}
-                    {\\int\\int\\Phi(x_i,x_j)w(x_i,x_j)dx_idx_j}
-
-        """
-
-        # Calculate moments.
-        q = np.array([np.sum(self._data * self._weights * xi * xj) for xi in
-                      (self._x, self._y) for xj in (self._x, self._y)])
-
-        self.moments = (q / np.sum(self._data *
-                        self._weights)).reshape(2, 2).astype('complex')
-
-        # Calculate the ellipticities.
-        self._get_ellipse()
    
-
-
    [docs]    def _get_ellipse(self):
-        """Calculate the ellipticities
-
-        This method cacluates ellipticities from quadrupole moments.
-
-        Notes
-        -----
-        This method implements the following equations:
-
-            - Equation 11 from [C2013]_ to calculate the size:
-
-            .. math:: R^2 = Q_{00} + Q_{11}
-
-            - Equation 7 from [S2005]_ to calculate the ellipticities:
-
-            .. math::
-
-               \\chi = \\frac{Q_{00}-Q_{11}+iQ_{01}+iQ_{10}}{R^2}
-
-               \\epsilon = \\frac{Q_{00}-Q_{11}+iQ_{01}+iQ_{10}}{R^2 +
-                   2\\sqrt{(Q_{00}Q_{11} - Q_{01}Q_{10})}}
-
-        """
-
-        # Calculate the size.
-        self.r2 = self.moments[0, 0] + self.moments[1, 1]
-
-        # Calculate the numerator
-        numerator = (self.moments[0, 0] - self.moments[1, 1] + np.complex(0,
-                     self.moments[0, 1] + self.moments[1, 0]))
-
-        # Calculate the denominator
-        if self._ellip_type == 'epsilon':
-            denominator = (self.r2 + 2 * np.sqrt(self.moments[0, 0] *
-                           self.moments[1, 1] - self.moments[0, 1] *
-                           self.moments[1, 0]))
-
-        else:
-            denominator = self.r2
-
-        # Calculate the ellipticity
-        ellip = numerator / denominator
-
-        self.e = np.array([ellip.real, ellip.imag])
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/image/stamp.html b/docs/build/html/_modules/sf_tools/image/stamp.html deleted file mode 100644 index de4f95d..0000000 --- a/docs/build/html/_modules/sf_tools/image/stamp.html +++ /dev/null @@ -1,530 +0,0 @@ - - - - - - - - - - - sf_tools.image.stamp — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.image.stamp
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.image.stamp

    -# -*- coding: utf-8 -*-
-
-"""IMAGE STAMP SELECTION ROUTINES
-
-This module contains methods for selecting stamps or patches from
-images.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.0
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-from builtins import zip
-import numpy as np
-from itertools import product
-from sf_tools.base.np_adjust import pad2d
-
-
-
    [docs]def patch_centres(data_shape, layout):
-    """Image centres
-
-    This method inds the centres of the patches in a 2D map.
-
-    Parameters
-    ----------
-    data_shape : tuple
-        Shape of the 2D map
-    layout : tuple
-        Layout of the patches
-
-    Returns
-    -------
-    np.ndarray array of patch centres
-
-    """
-
-    data_size = np.array(data_size)
-    layout = np.array(layout)
-
-    if data_size.size != 2:
-        raise ValueError('The the data shape must be of size 2.')
-    if layout.size != 2:
-        raise ValueError('The the layout must be of size 2.')
-
-    ranges = np.array(list(product(*np.array([np.arange(x) for x in layout]))))
-    patch_shape = data_shape // layout
-    patch_centre = patch_shape // 2
-
-    return patch_centre + patch_size * ranges
    
-
-
-
    [docs]def postage_stamp(data, pos, pixel_rad):
-    """Postage stamp
-
-    This metho selects a postage stamp of a given size from a 2D-array.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-    pos : tuple
-        Position of postage stamp centre
-    pixel_rad : tuple
-        Pixel radius in each dimension (i.e. size of the stamp from centre)
-
-    Returns
-    -------
-    np.ndarray rearanged matrix of kernel elements
-
-    Notes
-    -----
-    The image edges are padded with zeros.
-
-    """
-
-    # Check that the input array has two dimensions.
-    data = np.array(data)
-    if data.ndim != 2:
-        raise ValueError('The input array must be 2D.')
-
-    # If the pixel radius size is one repat the value for the 2nd dimension.
-    pixel_rad = np.array(pixel_rad)
-    if pixel_rad.size == 1:
-        pixel_rad = np.repeat(pixel_rad, 2)
-    if pixel_rad.size not in (1, 2):
-        raise ValueError('The pixel radius must have a size of 1 or 2.')
-
-    # Check that the array position has a size of two.
-    pos = np.array(pos) % np.array(data.shape)
-    if pos.size != 2:
-        raise ValueError('The array position must have a size of 2.')
-
-    # Check if the pixel radius is within the bounds of the input array.
-    if (np.any(pixel_rad < 1) or np.any(np.array(data.shape) // 2 -
-                                        pixel_rad < 0)):
-        raise ValueError('The pixel radius values must have a value of at '
-                         'least 1 and at most half the size of the input '
-                         'array. Array size: ' + str(data.shape))
-
-    # Return postage stamp.
-    return pad2d(data, pixel_rad)[[slice(a, a + 2 * b + 1) for a, b in
-                                   zip(pos, pixel_rad)]]
    
-
-
-
    [docs]def pixel_pos(array_shape):
-    """Pixel positions
-
-    This method returns all of the pixel positions from a 2D-array.
-
-    Parameters
-    ----------
-    array_shape : tuple
-        Shape of array
-
-    Returns
-    -------
-    list of pixel positions
-
-    """
-
-    ranges = np.array([np.arange(x) for x in np.array(array_shape)])
-
-    return list(product(*ranges))
    
-
-
-
    [docs]class FetchStamps(object):
-    """Fetch postage stamps
-
-    This class returns a stack of postage stamps from a given 2D image array.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input 2D data array
-    pixel_rad : tuple
-        Pixel radius in each dimension
-    all : boolean, optional
-        Option to select all pixels. Default (all=False)
-
-    """
-
-    def __init__(self, data, pixel_rad, all=False):
-        self.data = np.array(data)
-        self.shape = np.array(self.data.shape)
-        self.pixel_rad = np.array(pixel_rad)
-        self._check_inputs()
-        self._pad_data()
-        if all:
-            self.n_pixels()
-
-
    [docs]    def _check_inputs(self):
-        """Check inputs
-
-        This method checks the class variable values.
-
-        Raises
-        ------
-        ValueError
-            For invalid array dimensions or pixel radius values
-
-        """
-
-        if self.data.ndim != 2:
-            raise ValueError('The input array must be 2D.')
-        if self.pixel_rad.size == 1:
-            self.pixel_rad = np.repeat(self.pixel_rad, 2)
-        elif self.pixel_rad.size not in (1, 2):
-            raise ValueError('The pixel radius must have a size of 1 or 2.')
    
-
-
    [docs]    def _pad_data(self):
-        """Pad data
-
-        This method pads the input array with zeros.
-
-        """
-
-        self.pad_data = pad2d(self.data, self.pixel_rad)
    
-
-
    [docs]    def _adjust_pixels(self):
-        """Adjust pixels
-
-        This method adjusts the pixel positions according to the pixel radius.
-
-        """
-
-        self.pixels = self.pixels % self.shape + self.pixel_rad
    
-
-
    [docs]    def get_pixels(self, pixels):
-        """Get pixels
-
-        This method gets the desired pixel positions.
-
-        Parameters
-        ----------
-        pixels : list or np.ndarray
-            List of pixel positions
-
-        Raises
-        ------
-        ValueError
-            For invalid number of dimensions for pixel position array
-
-        """
-
-        self.pixels = np.array(pixels)
-        if not 1 <= self.pixels.ndim <= 2:
-            raise ValueError('Invalid number of dimensions for pixels')
-        elif self.pixels.ndim == 2 and self.pixels.shape[1] != 2:
-            raise ValueError('The second dimension of pixels must have size 2')
-        self._adjust_pixels()
    
-
-
    [docs]    def n_pixels(self, n_pixels=None, random=False):
-        """Number of pixels
-
-        This method selects a specified number of pixel positions.
-
-        Parameters
-        ----------
-        n_pixels : int, optional
-            Number of pixels to keep. Default (n_pixels=None)
-        random : bool, optional
-            Option to select random pixel position
-
-        """
-
-        self.pixels = pixel_pos(self.shape)
-        if random:
-            np.random.shuffle(self.pixels)
-        self.pixels = self.pixels[:n_pixels]
-        self._adjust_pixels()
    
-
-
    [docs]    def _stamp(self, pos, func=None, *args):
-        """Stamp
-
-        This method retrieves a postage stamp from the padded input data at a
-        given position.
-
-        Parameters
-        ----------
-        pos : tuple
-            Pixel position in 2D padded array.
-        func : function, optional
-            Optional function to be applied to postage stamp.
-
-        Returns
-        -------
-        np.ndarray postage stamp array or result of func
-
-        Raises
-        ------
-        ValueError
-            For for invalid size of pixel position.
-
-        """
-
-        pos = np.array(pos)
-        if pos.size != 2:
-            raise ValueError('The pixel position must have a size of 2.')
-        stamp = self.pad_data[[slice(a - b, a + b + 1) for a, b in
-                              zip(pos, self.pixel_rad)]]
-        if isinstance(func, type(None)):
-            return stamp
-        else:
-            return func(stamp, *args)
    
-
-
    [docs]    def scan(self, func=None, *args, **kwargs):
-        """Scan stamps
-
-        This method scans the 2D padded input array and retrieves the postage
-        stamps at all the desired pixel positions.
-
-        Parameters
-        ----------
-        func : function, optional
-            Optional function to be applied to postage stamps.
-
-        Returns
-        -------
-        np.ndarray postage stamp arrays or results of func
-
-        """
-
-        if 'arg_type' in kwargs and kwargs['arg_type'] == 'list':
-            return np.array([self._stamp(pos, func, arg) for pos, arg in
-                             zip(self.pixels, *args)])
-
-        else:
-            return np.array([self._stamp(pos, func, *args) for pos in
-                             self.pixels])
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/image/stats.html b/docs/build/html/_modules/sf_tools/image/stats.html deleted file mode 100644 index 3fbf145..0000000 --- a/docs/build/html/_modules/sf_tools/image/stats.html +++ /dev/null @@ -1,377 +0,0 @@ - - - - - - - - - - - sf_tools.image.stats — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.image.stats
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.image.stats

    -# -*- coding: utf-8 -*-
-
-"""IMAGE STATISTICS ROUTINES
-
-This module contains methods for the statistical analysis of images.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.1
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-import numpy as np
-from sf_tools.base.np_adjust import pad2d
-
-
-
    [docs]class SAT():
-    """Summed Area Table
-
-    This class produces a Summed Area Table (SAT) for fast and efficient
-    statistics on image patches.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input 2D data array
-
-    Notes
-    -----
-    Also know as Itegral Image (i in the class features).
-
-    TODO
-    ----
-    Add equations and citations
-
-    """
-
-    def __init__(self, data):
-
-        self.x = data
-        self.x2 = self.x ** 2
-        self.i = self.x.cumsum(axis=0).cumsum(axis=1)
-        self.i2 = self.x2.cumsum(axis=0).cumsum(axis=1)
-        self.i_pad = pad2d(self.i, 1)
-        self.i2_pad = pad2d(self.i2, 1)
-
-
    [docs]    def get_area(self, data, corners):
-        """Get area
-
-        This method calculates the area of a patch.
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input 2D data array
-        corners : tuple
-            Positions of upper left and bottom right corners.
-
-        Returns
-        -------
-        float area
-
-        """
-
-        corners = np.array(corners)
-        corners[1] += 1
-
-        a = data[zip(corners[0])]
-        b = data[corners[0, 0], corners[1, 1]]
-        c = data[corners[1, 0], corners[0, 1]]
-        d = data[zip(corners[1])]
-
-        return float(a + d - b - c)
    
-
-
    [docs]    def get_npx(self, corners):
-        """Get number of pixels
-
-        This method calculates the number of pixels in a patch.
-
-        Parameters
-        ----------
-        corners : tuple
-            Positions of upper left and bottom right corners.
-
-        Returns
-        -------
-        int number of pixels
-
-        """
-
-        return np.prod(np.diff(corners, axis=0) + 1)
    
-
-
    [docs]    def get_var(self):
-        """Get variance
-
-        This method calculates the variance and standard deviation of a patch.
-
-        """
-
-        self.var = (self.area2 - (self.area ** 2 / self.npx)) / self.npx
-        self.std = np.sqrt(self.var)
    
-
-
    [docs]    def set_patch(self, corners):
-        """Set patch
-
-        This method sets the corner positions of a single patch.
-
-        Parameters
-        ----------
-        corners : tuple
-            Positions of upper left and bottom right corners.
-
-        """
-
-        self.area = self.get_area(self.i_pad, corners)
-        self.area2 = self.get_area(self.i2_pad, corners)
-        self.npx = self.get_npx(corners)
-        self.get_var()
    
-
-
    [docs]    def set_patches(self, corners):
-        """Set patches
-
-        This method sets the corner positions for multiple patches.
-
-        Parameters
-        ----------
-        corners : list
-            List of the positions of upper left and bottom right corners.
-
-        """
-
-        self.area = np.array([self.get_area(self.i_pad, corner)
-                              for corner in corners])
-        self.area2 = np.array([self.get_area(self.i2_pad, corner)
-                               for corner in corners])
-        self.npx = np.array([self.get_npx(corner) for corner in corners])
-        self.get_var()
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/interface/errors.html b/docs/build/html/_modules/sf_tools/interface/errors.html deleted file mode 100644 index 7321b87..0000000 --- a/docs/build/html/_modules/sf_tools/interface/errors.html +++ /dev/null @@ -1,342 +0,0 @@ - - - - - - - - - - - sf_tools.interface.errors — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.interface.errors
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.interface.errors

    -# -*- coding: utf-8 -*-
-
-"""ERROR HANDLING ROUTINES
-
-This module contains methods for handing warnings and errors.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 23/10/2017
-
-"""
-
-
-import sys
-import os.path
-import warnings
-try:
-    from termcolor import colored
-except ImportError:
-    import_fail = True
-else:
-    import_fail = False
-
-
-
    [docs]def warn(warn_string, log=None):
-    """Warning
-
-    This method creates custom warning messages.
-
-    Parameters
-    ----------
-    warn_string : str
-        Warning message string
-    log : instance, optional
-        Logging structure instance
-
-    """
-
-    if import_fail:
-        warn_txt = 'WARNING'
-    else:
-        warn_txt = colored('WARNING', 'yellow')
-
-    # Print warning to stdout.
-    sys.stderr.write(warn_txt + ': ' + warn_string + '\n')
-
-    # Check if a logging structure is provided.
-    if not isinstance(log, type(None)):
-        warnings.warn(warn_string)
    
-
-
-
    [docs]def catch_error(exception, log=None):
-    """Catch error
-
-    This method catches errors and prints them to the terminal. It also saves
-    the errors to a log if provided.
-
-    Parameters
-    ----------
-    exception : str
-        Exception message string
-    log : instance, optional
-        Logging structure instance
-
-    """
-
-    if import_fail:
-        err_txt = 'ERROR'
-    else:
-        err_txt = colored('ERROR', 'red')
-
-    # Print exception to stdout.
-    stream_txt = err_txt + ': ' + str(exception) + '\n'
-    sys.stderr.write(stream_txt)
-
-    # Check if a logging structure is provided.
-    if not isinstance(log, type(None)):
-        log_txt = 'ERROR: ' + str(exception) + '\n'
-        log.exception(log_txt)
    
-
-
-
    [docs]def file_name_error(file_name):
-    """File name error
-
-    This method checks if the input file name is valid.
-
-    Parameters
-    ----------
-    file_name : str
-        File name string
-
-    Raises
-    ------
-    IOError
-        If file name not specified or file not found
-
-    """
-
-    if file_name == '' or file_name[0][0] == '-':
-        raise IOError('Input file name not specified.')
-
-    elif not os.path.isfile(file_name):
-        raise IOError('Input file name [%s] not found!' % file_name)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/interface/log.html b/docs/build/html/_modules/sf_tools/interface/log.html deleted file mode 100644 index ae33271..0000000 --- a/docs/build/html/_modules/sf_tools/interface/log.html +++ /dev/null @@ -1,321 +0,0 @@ - - - - - - - - - - - sf_tools.interface.log — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.interface.log
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.interface.log

    -# -*- coding: utf-8 -*-
-
-"""LOGGING ROUTINES
-
-This module contains methods for handing logging.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import print_function
-import sys
-import logging
-
-
-
    [docs]def set_up_log(filename, verbose=True):
-    """Set up log
-
-    This method sets up a basic log.
-
-    Parameters
-    ----------
-    filename : str
-        Log file name
-
-    Returns
-    -------
-    logging.Logger instance
-
-    """
-
-    # Add file extension.
-    filename += '.log'
-
-    if verbose:
-        print('Preparing log file:', filename)
-
-    # Capture warnings.
-    logging.captureWarnings(True)
-
-    # Set output format.
-    formatter = logging.Formatter(fmt='%(asctime)s %(message)s',
-                                  datefmt='%d/%m/%Y %H:%M:%S')
-
-    # Create file handler.
-    fh = logging.FileHandler(filename=filename, mode='w')
-    fh.setLevel(logging.DEBUG)
-    fh.setFormatter(formatter)
-
-    # Create log.
-    log = logging.getLogger('log')
-    log.setLevel(logging.DEBUG)
-    log.addHandler(fh)
-
-    # Send opening message.
-    log.info('The log file has been set-up.')
-
-    return log
    
-
-
-
    [docs]def close_log(log, verbose=True):
-    """Close log
-
-    This method closes and active logging.Logger instance.
-
-    Parameters
-    ----------
-    log : logging.Logger
-        Logging instance
-
-    """
-
-    if verbose:
-        print('Closing log')
-
-    # Send closing message.
-    log.info('The log file has been closed.')
-
-    # Remove all handlers from log.
-    [log.removeHandler(handler) for handler in log.handlers]
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/math/angle.html b/docs/build/html/_modules/sf_tools/math/angle.html deleted file mode 100644 index c54fbd3..0000000 --- a/docs/build/html/_modules/sf_tools/math/angle.html +++ /dev/null @@ -1,359 +0,0 @@ - - - - - - - - - - - sf_tools.math.angle — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - - - - -
    -
    -
    -
    - -

    Source code for sf_tools.math.angle

    -# -*- coding: utf-8 -*-
-
-"""ANGLE HANDLING ROUTINES
-
-This module contains methods for handing angles and trigonometry.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.1
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-import numpy as np
-
-
-
    [docs]def deg2rad(angle):
-    r"""Degrees to radians
-
-    This method converts the angle from degrees to radians.
-
-    Parameters
-    ----------
-    angle : float or np.ndarray
-        Input angle in degrees
-
-    Returns
-    -------
-    float angle in radians or np.ndarray of angles
-
-    Examples
-    --------
-    >>> from sf_tools.math.angle import deg2rad
-    >>> deg2rad(45.)
-    0.7853981633974483
-
-    Notes
-    -----
-    Implements the following equation:
-
-    .. math::
-        \mathrm{radians} = \mathrm{degrees} \times \frac{\pi}{180}
-
-    """
-
-    return angle * np.pi / 180.0
    
-
-
-
    [docs]def rad2deg(angle):
-    r"""Radians to degrees
-
-    This method converts the angle from radians to degrees.
-
-    Parameters
-    ----------
-    angle : float or np.ndarray
-        Input angle in radians
-
-    Returns
-    -------
-    float angle in degrees or np.ndarray of angles
-
-    Examples
-    --------
-    >>> from sf_tools.math.angle import deg2rad
-    >>> rad2deg(1.)
-    57.29577951308232
-
-    Notes
-    -----
-    Implements the following equation:
-
-    .. math::
-        \mathrm{degrees} = \mathrm{radians} \times \frac{180}{\pi}
-
-    """
-
-    return angle * 180.0 / np.pi
    
-
-
-
    [docs]def ang_sep(point1, point2):
-    r"""Angular separation
-
-    This method calculates the angular separation in degrees between two
-    points.
-
-    Parameters
-    ----------
-    point1 : tuple
-        Angular position of point 1 in degrees
-    point1 : tuple
-        Angular position of point 2 in degrees
-
-    Returns
-    -------
-    float angular separation in degrees
-
-    Examples
-    --------
-    >>> from sf_tools.math.angle import ang_sep
-    >>> ang_sep((30.0, 0.0), (47.0, 10.0))
-    19.647958606833164
-
-    Notes
-    -----
-    Implements the following equation:
-
-    .. math::
-        \theta = \cos^{-1}[\sin(\delta_1)\sin(\delta_2)+
-        \cos(\delta_1)\cos(\delta_2)\cos(\alpha_1-\alpha_2)]
-
-    See https://en.wikipedia.org/wiki/Angular_distance
-
-    """
-
-    dist = np.around(np.sin(deg2rad(point1[1])) * np.sin(deg2rad(point2[1])) +
-                     np.cos(deg2rad(point1[1])) * np.cos(deg2rad(point2[1])) *
-                     np.cos(deg2rad(point1[0]) - deg2rad(point2[0])), 10)
-
-    return rad2deg(np.array(np.arccos(dist)))
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/math/integrate.html b/docs/build/html/_modules/sf_tools/math/integrate.html deleted file mode 100644 index 601214a..0000000 --- a/docs/build/html/_modules/sf_tools/math/integrate.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - - - - - sf_tools.math.integrate — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.math.integrate
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.math.integrate

    -# -*- coding: utf-8 -*-
-
-"""INTEGRATION ROUTINES
-
-This module contains methods for integration.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.0
-
-:Date: 03/04/2017
-
-"""
-
-
-import numpy as np
-from scipy.integrate import quad
-
-
-
    [docs]def integrate(func, lim_low, lim_up, *args):
-    """Integrate
-
-    This method integrates a given function, which has N additional arguments,
-    between the specified limits.
-
-    Parameters
-    ----------
-    func : function
-        Function to be integrated
-    lim_low : float
-        Lower limit
-    lim_up : float
-        Upper limit
-
-    Returns
-    -------
-    Result of the definite integral
-
-    """
-
-    return quad(func, lim_low, lim_up, args=args)[0]
    
-
-
-
    [docs]def vintegrate(func, lim_low, lim_up, *args):
-    """Vectorised integration
-
-    This method implements a vectorised version of integrate().
-
-    Parameters
-    ----------
-    func : function
-        Function to be integrated
-    lim_low : float
-        Lower limit
-    lim_up : float
-        Upper limit
-
-    Returns
-    -------
-    Result of the definite integral
-
-    """
-
-    v_integ = np.vectorize(integrate)
-
-    return v_integ(func, lim_low, lim_up, *args)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/math/matrix.html b/docs/build/html/_modules/sf_tools/math/matrix.html deleted file mode 100644 index 25234fa..0000000 --- a/docs/build/html/_modules/sf_tools/math/matrix.html +++ /dev/null @@ -1,482 +0,0 @@ - - - - - - - - - - - sf_tools.math.matrix — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
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    • Docs »
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      • - -
    • sf_tools.math.matrix
      • - - -
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    -
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    - -

    Source code for sf_tools.math.matrix

    -"""MATRIX ROUTINES
-
-This module contains methods for matrix operations.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-from builtins import range, zip
-import numpy as np
-from itertools import product
-
-
-
    [docs]def gram_schmidt(matrix, return_opt='orthonormal'):
-    r"""Gram-Schmit
-
-    This method orthonormalizes the row vectors of the input matrix.
-
-    Parameters
-    ----------
-    matrix : np.ndarray
-        Input matrix array
-    return_opt : str {orthonormal, orthogonal, both}
-        Option to return u, e or both.
-
-    Returns
-    -------
-    Lists of orthogonal vectors, u, and/or orthonormal vectors, e
-
-    TODO
-    ----
-    Add citation and equation
-
-    """
-
-    u = []
-    e = []
-
-    for vector in matrix:
-
-        if len(u) == 0:
-            u_now = vector
-        else:
-            u_now = vector - sum([project(u_i, vector) for u_i in u])
-
-        u.append(u_now)
-        e.append(u_now / np.linalg.norm(u_now, 2))
-
-    u = np.array(u)
-    e = np.array(e)
-
-    if return_opt == 'orthonormal':
-        return e
-    elif return_opt == 'orthogonal':
-        return u
-    else:
-        return u, e
    
-
-
-
    [docs]def nuclear_norm(data):
-    """Nuclear norm
-
-    This method computes the nuclear (or trace) norm of the input data.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-
-    Returns
-    -------
-    float nuclear norm value
-
-    TODO
-    ----
-    Add equation
-
-    """
-
-    # Get SVD of the data.
-    u, s, v = np.linalg.svd(data)
-
-    # Return nuclear norm.
-    return np.sum(s)
    
-
-
-
    [docs]def project(u, v):
-    """Project vector
-
-    This method projects vector v onto vector u.
-
-    Parameters
-    ----------
-    u : np.ndarray
-        Input vector
-    v : np.ndarray
-        Input vector
-
-    Returns
-    -------
-    np.ndarray projection
-
-    TODO
-    ----
-    Add equation
-
-    """
-
-    return np.inner(v, u) / np.inner(u, u) * u
    
-
-
-
    [docs]def rot_matrix(angle):
-    """Rotation matrix
-
-    This method produces a 2x2 rotation matrix for the given input angle.
-
-    Parameters
-    ----------
-    angle : float
-        Rotation angle
-
-    Returns
-    -------
-    np.ndarray 2x2 rotation matrix
-
-    """
-
-    return np.around(np.array([[np.cos(angle), -np.sin(angle)],
-                     [np.sin(angle), np.cos(angle)]], dtype='float'), 10)
    
-
-
-
    [docs]def rotate(matrix, angle):
-    """Rotate
-
-    This method rotates an input matrix about the input angle.
-
-    Parameters
-    ----------
-    matrix : np.ndarray
-        Input matrix array
-    angle : float
-        Rotation angle
-
-    Returns
-    -------
-    np.ndarray rotated matrix
-
-    Raises
-    ------
-    ValueError
-        For invalid matrix shape
-
-    """
-
-    shape = np.array(matrix.shape)
-
-    if shape[0] != shape[1]:
-        raise ValueError('Input matrix must be square.')
-
-    shift = (np.array(shape) - 1) // 2
-
-    index = np.array(list(product(*np.array([np.arange(val) for val in
-                     shape])))) - shift
-
-    new_index = np.array(np.dot(index, rot_matrix(angle)), dtype='int') + shift
-    new_index[new_index >= shape[0]] -= shape[0]
-
-    return matrix[list(zip(new_index.T))].reshape(shape.T)
    
-
-
-
    [docs]class PowerMethod(object):
-    """Power method class
-
-    This method performs implements power method to calculate the spectral
-    radius of the input data
-
-    Parameters
-    ----------
-    operator : class
-        Operator class instance
-    data_shape : tuple
-        Shape of the data array
-    auto_run : bool
-        Option to automatically calcualte the spectral radius upon
-        initialisation
-
-    """
-
-    def __init__(self, operator, data_shape, auto_run=True):
-
-        self.op = operator
-        self.data_shape = data_shape
-        if auto_run:
-            self.get_spec_rad()
-
-
    [docs]    def set_initial_x(self):
-        """Set initial value of x
-
-        This method sets the initial value of x to an arrray of random values
-
-        """
-
-        return np.random.random(self.data_shape)
    
-
-
    [docs]    def get_spec_rad(self, tolerance=1e-6, max_iter=10):
-        """Get spectral radius
-
-        This method calculates the spectral radius
-
-        Parameters
-        ----------
-        tolerance : float, optional
-            Tolerance threshold for convergence (default is "1e-6")
-        max_iter : int, optional
-            Maximum number of iterations
-
-        """
-
-        # Set (or reset) values of x.
-        x_old = self.set_initial_x()
-
-        # Iterate until the L2 norm of x converges.
-        for i in range(max_iter):
-
-            x_new = self.op(x_old) / np.linalg.norm(x_old)
-
-            if(np.abs(np.linalg.norm(x_new) - np.linalg.norm(x_old)) <
-               tolerance):
-                print(' - Power Method converged after %d iterations!' %
-                      (i + 1))
-                break
-
-            elif i == max_iter - 1:
-                print(' - Power Method did not converge after %d '
-                      'iterations!' % max_iter)
-
-            np.copyto(x_old, x_new)
-
-        self.spec_rad = np.linalg.norm(x_new)
-        self.inv_spec_rad = 1.0 / self.spec_rad
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/math/misc.html b/docs/build/html/_modules/sf_tools/math/misc.html deleted file mode 100644 index 4578ada..0000000 --- a/docs/build/html/_modules/sf_tools/math/misc.html +++ /dev/null @@ -1,358 +0,0 @@ - - - - - - - - - - - sf_tools.math.misc — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
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    -
    -
    -
    - -

    Source code for sf_tools.math.misc

    -# -*- coding: utf-8 -*-
-
-"""MISCELLANOUS MATH ROUTINES
-
-This module contains methods for various mathematical operations.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.1
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-import numpy as np
-
-
-
    [docs]def factor(n):
-    """Factors of n
-
-    This method finds factors of a number (n).
-
-    Parameters
-    ----------
-    n : int
-        Whole number
-
-    Returns
-    -------
-    np.ndarray factors of n
-
-    """
-
-    factors = set()
-
-    for x in range(1, int(np.sqrt(n)) + 1):
-        if n % x == 0:
-            factors.add(x)
-            factors.add(n // x)
-
-    return np.array(sorted(factors))
    
-
-
-
    [docs]def mfactor(n):
-    """Middle factors of n
-
-    This method finds the middle factor(s) of a number (n).
-
-    Parameters
-    ----------
-    n : int
-        Whole number
-
-    Returns
-    -------
-    np.ndarray middle factors of n
-
-    """
-
-    f = factor(n)
-
-    if f.size % 2:
-        return np.repeat(f[f.size // 2], 2)
-
-    else:
-        return f[f.size // 2 - 1:f.size // 2 + 1]
    
-
-
-
    [docs]def k_val(n, L):
-    """Spatial frequency
-
-    This method returns k-values in the range L.
-
-    Parameters
-    ----------
-    n : float
-        Number
-    limit : float
-        Limit
-
-    Returns
-    -------
-    float k value
-
-    TODO
-    ----
-    Add equation and example
-
-    """
-
-    return ((2.0 * np.pi / limit) * np.array(range(n / 2.0) +
-            range(-n / 2.0, 0.0)))
    
-
-
-
    [docs]def fourier_derivative(func, k, order):
-    """Fourier derivative
-
-    This method returns the derivative of the specified function to the given
-    order.
-
-    Parameters
-    ----------
-    func : function
-        Function
-    k : float
-        k-value
-    order : int
-        Oder of derivative
-
-    Returns
-    -------
-        Float derivative
-
-    TODO
-    ----
-    Add equation and example
-
-    """
-
-    return np.real(np.fft.ifft((1.j * k) ** order * np.fft.fft(func)))
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/math/stats.html b/docs/build/html/_modules/sf_tools/math/stats.html deleted file mode 100644 index 1ab6fdb..0000000 --- a/docs/build/html/_modules/sf_tools/math/stats.html +++ /dev/null @@ -1,503 +0,0 @@ - - - - - - - - - - - sf_tools.math.stats — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - - - - -
    -
    -
    -
    - -

    Source code for sf_tools.math.stats

    -# -*- coding: utf-8 -*-
-
-"""STATISTICS ROUTINES
-
-This module contains methods for basic statistics.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-from builtins import zip
-import numpy as np
-from scipy.stats import chi2
-from astropy.convolution import Gaussian2DKernel
-
-
-
    [docs]def chi2_gof(data_obs, data_exp, sigma, ddof=1):
-    """Chi-squared goodness-of-fit
-
-    This method tests the chi^2 goodness of fit.
-
-    Parameters
-    ----------
-    data_obs : np.ndarray
-        Observed data array
-    data_exp : np.ndarray
-        Expected data array
-    sigma : float
-        Expected data error
-    ddof : input
-        Delta degrees of freedom. Default (ddof = 1).
-
-    Returns
-    -------
-    tuple of floats chi-squared and P values
-
-    """
-
-    chi2 = np.sum(((data_obs - data_exp) / sigma) ** 2)
-    p_val = chi2.cdf(chi2, len(data_obs) - ddof)
-
-    return chi2, p_val
    
-
-
-
    [docs]def gaussian(point, mean, sigma, amplitude=None):
-    """Gaussian distribution
-
-    Method under development...
-
-    """
-
-    if isinstance(amplitude, type(None)):
-        amplitude = 1
-
-    val = np.array([((x - mu) / sig) ** 2 for x, mu, sig in
-                   zip(point, mean, sigma)])
-
-    return amplitude * np.exp(-0.5 * val)
    
-
-
-
    [docs]def gaussian_kernel(data_shape, sigma, norm='max'):
-    """Gaussian kernel
-
-    This method produces a Gaussian kerenal of a specified size and dispersion
-
-    Parameters
-    ----------
-    data_shape : tuple
-        Desiered shape of the kernel
-    sigma : float
-        Standard deviation of the kernel
-    norm : str {'max', 'sum'}, optional
-        Normalisation of the kerenl (options are 'max' or 'sum')
-
-    Returns
-    -------
-    np.ndarray kernel
-
-    """
-
-    kernel = np.array(Gaussian2DKernel(sigma, x_size=data_shape[1],
-                      y_size=data_shape[0]))
-
-    if norm is 'max':
-        return kernel / np.max(kernel)
-
-    elif norm is 'sum':
-        return kernel / np.sum(kernel)
-
-    else:
-        return kernel
    
-
-
-
    [docs]def mad(data):
-    r"""Median absolute deviation
-
-    This method calculates the median absolute deviation of the input data.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-
-    Returns
-    -------
-    float MAD value
-
-    Notes
-    -----
-    The MAD is calculated as follows:
-
-    .. math::
-
-        \mathrm{MAD} = \mathrm{median}\left(|X_i - \mathrm{median}(X)|\right)
-
-    """
-
-    return np.median(np.abs(data - np.median(data)))
    
-
-
-
    [docs]def mse(data1, data2):
-    """Mean Squared Error
-
-    This method returns the Mean Squared Error (MSE) between two data sets.
-
-    Parameters
-    ----------
-    data1 : np.ndarray
-        First data set
-    data2 : np.ndarray
-        Second data set
-
-    """
-
-    return np.mean((data1 - data2) ** 2)
    
-
-
-
    [docs]def psnr2(image, noisy_image, max_pix=255):
-    r"""Peak Signal-to-Noise Ratio
-
-    This method calculates the PSNR between an image and a noisy version
-    of that image
-
-    Parameters
-    ----------
-    image : np.ndarray
-        Input image, 2D array
-    noisy_image : np.ndarray
-        Noisy image, 2D array
-    max_pix : int
-        Maximum number of pixels. Default (max_pix=255)
-
-    Returns
-    -------
-    float PSNR value
-
-    Notes
-    -----
-    Implements PSNR equation on
-    https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
-
-    .. math::
-
-        \mathrm{PSNR} = 20\log_{10}(\mathrm{MAX}_I - 10\log_{10}(\mathrm{MSE}))
-
-    """
-
-    return (20 * np.log10(max_pix) - 10 *
-            np.log10(mse(image, noisy_image)))
    
-
-
-
    [docs]def psnr(image, recovered_image):
-    """Peak Signal-to-Noise Ratio
-
-    This method calculates the PSNR between an image and the recovered version
-    of that image
-
-    Parameters
-    ----------
-    image : np.ndarray
-        Input image, 2D array
-    recovered_image : np.ndarray
-        Recovered image, 2D array
-
-    Returns
-    -------
-    float PSNR value
-
-    Notes
-    -----
-    Implements eq.3.7 from _[S2010]
-
-    """
-
-    return (20 * np.log10((image.shape[0] * np.abs(np.max(image) -
-            np.min(image))) / np.linalg.norm(image - recovered_image)))
    
-
-
-
    [docs]def psnr_stack(images, recoverd_images, metric=np.mean):
-    """Peak Signa-to-Noise for stack of images
-
-    This method calculates the PSNRs for a stack of images and the
-    corresponding recovered images. By default the metod returns the mean
-    value of the PSNRs, but any other metric can be used.
-
-    Parameters
-    ----------
-    images : np.ndarray
-        Stack of images, 3D array
-    recovered_images : np.ndarray
-        Stack of recovered images, 3D array
-    metric : function
-        The desired metric to be applied to the PSNR values (default is
-        'np.mean')
-
-    Returns
-    -------
-    float metric result of PSNR values
-
-    Raises
-    ------
-    ValueError
-        For invalid input data dimensions
-
-    """
-
-    if images.ndim != 3 or recoverd_images.ndim != 3:
-        raise ValueError('Input data must be a 3D np.ndarray')
-
-    return metric([psnr(i, j) for i, j in zip(images, recoverd_images)])
    
-
-
-
    [docs]def sigma_mad(data):
-    r"""Standard deviation from MAD
-
-    This method calculates the standard deviation of the input data from the
-    MAD.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-
-    Returns
-    -------
-    float sigma value
-
-    Notes
-    -----
-    This function can be used for estimating the standeviation of the noise in
-    imgaes.
-
-    Sigma is calculated as follows:
-
-    .. math::
-
-        \sigma = 1.4826 \mathrm{MAD}(X)
-
-    """
-
-    return 1.4826 * mad(data)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/plot/cost_plot.html b/docs/build/html/_modules/sf_tools/plot/cost_plot.html deleted file mode 100644 index 248a910..0000000 --- a/docs/build/html/_modules/sf_tools/plot/cost_plot.html +++ /dev/null @@ -1,284 +0,0 @@ - - - - - - - - - - - sf_tools.plot.cost_plot — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.plot.cost_plot
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.plot.cost_plot

    -# -*- coding: utf-8 -*-
-
-"""PLOTTING ROUTINES
-
-This module contains methods for making plots.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.1
-
-:Date: 05/01/2017
-
-"""
-
-from __future__ import print_function
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-
    [docs]def plotCost(cost_list, output=None):
-    """Plot cost function
-
-    Plot the final cost function
-
-    Parameters
-    ----------
-    cost_list : list
-        List of cost function values
-    output : str, optional
-        Output file name
-
-    """
-
-    if isinstance(output, type(None)):
-        file_name = 'cost_function.png'
-    else:
-        file_name = output + '_cost_function.png'
-
-    plt.figure()
-    plt.plot(np.log10(cost_list), 'r-')
-    plt.title('Cost Function')
-    plt.xlabel('Iteration')
-    plt.ylabel('$\log_{10}$ Cost')
-    plt.savefig(file_name)
-    plt.close()
-
-    print(' - Saving cost function data to:', file_name)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/cost.html b/docs/build/html/_modules/sf_tools/signal/cost.html deleted file mode 100644 index 37dcdf5..0000000 --- a/docs/build/html/_modules/sf_tools/signal/cost.html +++ /dev/null @@ -1,397 +0,0 @@ - - - - - - - - - - - sf_tools.signal.cost — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.signal.cost
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.signal.cost

    -# -*- coding: utf-8 -*-
-
-"""COST FUNCTIONS
-
-This module contains classes of different cost functions for optimization.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 2.2
-
-:Date: 23/10/2017
-
-"""
-
-from __future__ import division, print_function
-import numpy as np
-try:
-    from sf_tools.plot.cost_plot import plotCost
-except ImportError:
-    import_fail = True
-else:
-    import_fail = False
-
-
-
    [docs]class costObj(object):
-
-    """Generic cost function object
-
-    This class updates the cost according to the input cost functio class and
-    tests for convergence
-
-    Parameters
-    ----------
-    costFunc : class
-        Class for calculating the cost
-    initial_cost : float, optional
-        Initial value of the cost (default is "1e6")
-    tolerance : float, optional
-        Tolerance threshold for convergence (default is "1e-4")
-    cost_interval : int, optional
-        Iteration interval to calculate cost (default is "1")
-    test_range : int, optional
-        Number of cost values to be used in test (default is "4")
-    verbose : bool, optional
-        Option for verbose output (default is "True")
-    plot_output : str, optional
-        Output file name for cost function plot
-
-    Notes
-    -----
-    The costFunc class must contain a method called `calc_cost()`.
-
-    """
-
-    def __init__(self, costFunc, initial_cost=1e6, tolerance=1e-4,
-                 cost_interval=1, test_range=4, verbose=True,
-                 plot_output=None):
-
-        if not hasattr(costFunc, 'calc_cost'):
-            raise ValueError('costFunc must contain "calc_cost" method.')
-
-        self.costFunc = costFunc
-        self.cost = initial_cost
-        self._cost_list = []
-        self._cost_interval = cost_interval
-        self._iteration = 1
-        self._test_list = []
-        self._test_range = test_range
-        self._tolerance = tolerance
-        self._plot_output = plot_output
-        self._verbose = verbose
-
-
    [docs]    def _check_cost(self):
-        """Check cost function
-
-        This method tests the cost function for convergence in the specified
-        interval of iterations using the last n (test_range) cost values
-
-        Returns
-        -------
-        bool result of the convergence test
-
-        """
-
-        # Add current cost value to the test list
-        self._test_list.append(self.cost)
-
-        # Check if enough cost values have been collected
-        if len(self._test_list) == self._test_range:
-
-            # The mean of the first half of the test list
-            t1 = np.mean(self._test_list[len(self._test_list) // 2:], axis=0)
-            # The mean of the second half of the test list
-            t2 = np.mean(self._test_list[:len(self._test_list) // 2], axis=0)
-            # Calculate the change across the test list
-            cost_diff = (np.linalg.norm(t1 - t2) / np.linalg.norm(t1))
-            # Reset the test list
-            self._test_list = []
-
-            if self._verbose:
-                print(' - CONVERGENCE TEST - ')
-                print(' - CHANGE IN COST:', cost_diff)
-                print('')
-
-            # Check for convergence
-            return cost_diff <= self._tolerance
-
-        else:
-
-            return False
    
-
-
    [docs]    def get_cost(self, *args, **kwargs):
-        """Get cost function
-
-        This method calculates the current cost and tests for convergence
-
-        Returns
-        -------
-        bool result of the convergence test
-
-        """
-
-        # Check if the cost should be calculated
-        if self._iteration % self._cost_interval:
-
-            test_result = False
-
-        else:
-
-            if self._verbose:
-                print(' - ITERATION:', self._iteration)
-
-            # Calculate the current cost
-            self.cost = self.costFunc.calc_cost(*args, **kwargs)
-            self._cost_list.append(self.cost)
-
-            if self._verbose:
-                print(' - Log10 COST:', np.log10(self.cost))
-                print('')
-
-            # Test for convergence
-            test_result = self._check_cost()
-
-        # Update the current iteration number
-        self._iteration += 1
-
-        return test_result
    
-
-
    [docs]    def plot_cost(self):
-        """Plot the cost function
-
-        This method plots the cost function as function of iteration number
-
-        """
-
-        if import_fail:
-            pass
-
-        else:
-            plotCost(self._cost_list, self._plot_output)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/filter.html b/docs/build/html/_modules/sf_tools/signal/filter.html deleted file mode 100644 index d4cac42..0000000 --- a/docs/build/html/_modules/sf_tools/signal/filter.html +++ /dev/null @@ -1,336 +0,0 @@ - - - - - - - - - - - sf_tools.signal.filter — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
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      • - -
    • Module code »
      • - -
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      • - - -
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    - - -
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    -
    -
    - -

    Source code for sf_tools.signal.filter

    -# -*- coding: utf-8 -*-
-
-"""FILTER ROUTINES
-
-This module contains methods for distance measurements in cosmology.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.1
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-import numpy as np
-from sf_tools.base.types import check_float
-
-
-
    [docs]def Gaussian_filter(x, sigma, norm=True):
-    """Gaussian filter
-
-    This method implements a Gaussian filter.
-
-    Parameters
-    ----------
-    x : float
-        Input data point
-    sigma : float
-        Standard deviation (filter scale)
-    norm : bool
-        Option to return normalised data. Default (norm=True)
-
-    Returns
-    -------
-    float Gaussian filtered data point
-
-    """
-
-    x = check_float(x)
-    sigma = check_float(sigma)
-
-    val = np.exp(-0.5 * (x / sigma) ** 2)
-
-    if norm:
-        return val / (np.sqrt(2 * np.pi) * sigma)
-
-    else:
-        return val
    
-
-
-
    [docs]def mex_hat(x, sigma):
-    """Mexican hat
-
-    This method implements a Mexican hat (or Ricker) wavelet.
-
-    Parameters
-    ----------
-    x : float
-        Input data point
-    sigma : float
-        Standard deviation (filter scale)
-
-    Returns
-    -------
-    float Mexican hat filtered data point
-
-    """
-
-    x = check_float(x)
-    sigma = check_float(sigma)
-
-    xs = (x / sigma) ** 2
-    val = 2 * (3 * sigma) ** -0.5 * np.pi ** -0.25
-
-    return val * (1 - xs) * np.exp(-0.5 * xs)
    
-
-
-
    [docs]def mex_hat_dir(x, y, sigma):
-    """Directional Mexican hat
-
-    This method implements a directional Mexican hat (or Ricker) wavelet.
-
-    Parameters
-    ----------
-    x : float
-        Input data point for Gaussian
-    y : float
-        Input data point for Mexican hat
-    sigma : float
-        Standard deviation (filter scale)
-
-    Returns
-    -------
-    float directional Mexican hat filtered data point
-
-    """
-
-    return -0.5 * (x / sigma) ** 2 * mex_hat(y, sigma)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/gradient.html b/docs/build/html/_modules/sf_tools/signal/gradient.html deleted file mode 100644 index 72cf51d..0000000 --- a/docs/build/html/_modules/sf_tools/signal/gradient.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - - - - - - sf_tools.signal.gradient — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.signal.gradient
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.signal.gradient

    -# -*- coding: utf-8 -*-
-
-"""GRADIENT CLASSES
-
-This module contains classses for defining algorithm gradients.
-Based on work by Yinghao Ge and Fred Ngole.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.3
-
-:Date: 19/07/2017
-
-"""
-
-
-
    [docs]class GradBasic(object):
-    """Basic gradient class
-
-    This class defines the basic methods that will be inherited by specific
-    gradient classes
-
-    """
-
-
    [docs]    def MtMX(self, x):
-        """M^T M X
-
-        This method calculates the action of the transpose of the matrix M on
-        the action of the matrix M on the data X
-
-        Parameters
-        ----------
-        x : np.ndarray
-            Input data array
-
-        Returns
-        -------
-        np.ndarray result
-
-        Notes
-        -----
-        Calculates  M^T (MX)
-
-        """
-
-        return self.MtX(self.MX(x))
    
-
-
    [docs]    def get_grad(self, x):
-        """Get the gradient step
-
-        This method calculates the gradient step from the input data
-
-        Parameters
-        ----------
-        x : np.ndarray
-            Input data array
-
-        Returns
-        -------
-        np.ndarray gradient value
-
-        Notes
-        -----
-
-        Calculates M^T (MX - Y)
-
-        """
-
-        self.grad = self.MtX(self.MX(x) - self.y)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/linear.html b/docs/build/html/_modules/sf_tools/signal/linear.html deleted file mode 100644 index 8d7e36c..0000000 --- a/docs/build/html/_modules/sf_tools/signal/linear.html +++ /dev/null @@ -1,441 +0,0 @@ - - - - - - - - - - - sf_tools.signal.linear — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.signal.linear
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.signal.linear

    -# -*- coding: utf-8 -*-
-
-"""LINEAR OPERATORS
-
-This module contains linear operator classes.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.3
-
-:Date: 19/07/2017
-
-"""
-
-from builtins import range, zip
-import numpy as np
-from sf_tools.signal.wavelet import *
-from sf_tools.math.matrix import rotate
-
-
-
    [docs]class Identity(object):
-    """Identity operator class
-
-    This is a dummy class that can be used in the optimisation classes
-
-    """
-
-    def __init__(self):
-
-        self.l1norm = 1.0
-
-
    [docs]    def op(self, data, **kwargs):
-        """Operator
-
-        Returns the input data unchanged
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array
-        **kwargs
-            Arbitrary keyword arguments
-
-        Returns
-        -------
-        np.ndarray input data
-
-        """
-
-        return data
    
-
-
    [docs]    def adj_op(self, data):
-        """Adjoint operator
-
-        Returns the input data unchanged
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array
-
-        Returns
-        -------
-        np.ndarray input data
-
-        """
-
-        return data
    
-
-
-
    [docs]class Wavelet(object):
-    """Wavelet class
-
-    This class defines the wavelet transform operators
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array, normally an array of 2D images
-    wavelet_opt: str, optional
-        Additional options for `mr_transform`
-
-    """
-
-    def __init__(self, data, wavelet_opt=None):
-
-        self.y = data
-        self.data_shape = data.shape[-2:]
-        n = data.shape[0]
-
-        self.filters = get_mr_filters(self.data_shape, opt=wavelet_opt)
-        self.l1norm = n * np.sqrt(sum((np.sum(np.abs(filter)) ** 2 for
-                                       filter in self.filters)))
-
-
    [docs]    def op(self, data):
-        """Operator
-
-        This method returns the input data convolved with the wavelet filters
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array, a 2D image
-
-        Returns
-        -------
-        np.ndarray wavelet convolved data
-
-        """
-
-        return filter_convolve_stack(data, self.filters)
    
-
-
    [docs]    def adj_op(self, data):
-        """Adjoint operator
-
-        This method returns the input data convolved with the wavelet filters
-        rotated by 180 degrees
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array, a 3D of wavelet coefficients
-
-        Returns
-        -------
-        np.ndarray wavelet convolved data
-
-        """
-
-        return filter_convolve_stack(data, self.filters, filter_rot=True)
    
-
-
-
    [docs]class LinearCombo(object):
-    """Linear combination class
-
-    This class defines a combination of linear transform operators
-
-    Parameters
-    ----------
-    operators : list
-        List of linear operator class instances
-    weights : list, optional
-        List of weights for combining the linear adjoint operator results
-
-    """
-
-    def __init__(self, operators, weights=None):
-
-        self.operators = operators
-        self.weights = weights
-        self.l1norm = np.array([operator.l1norm for operator in
-                                self.operators])
-
-
    [docs]    def op(self, data):
-        """Operator
-
-        This method returns the input data operated on by all of the operators
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array, a 2D image
-
-        Returns
-        -------
-        np.ndarray linear operation results
-
-        """
-
-        res = np.empty(len(self.operators), dtype=np.ndarray)
-
-        for i in range(len(self.operators)):
-            res[i] = self.operators[i].op(data)
-
-        return res
    
-
-
    [docs]    def adj_op(self, data):
-        """Adjoint operator
-
-        This method returns the combination of the result of all of the
-        adjoint operators. If weights are provided the comibination is the sum
-        of the weighted results, otherwise the combination is the mean.
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array, an array of coefficients
-
-        Returns
-        -------
-        np.ndarray adjoint operation results
-
-        """
-
-        if isinstance(self.weights, type(None)):
-
-            return np.mean([operator.adj_op(x) for x, operator in
-                           zip(data, self.operators)], axis=0)
-
-        else:
-
-            return np.sum([weight * operator.adj_op(x) for x, operator,
-                          weight in zip(data, self.operators, weights)],
-                          axis=0)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/noise.html b/docs/build/html/_modules/sf_tools/signal/noise.html deleted file mode 100644 index 79db50c..0000000 --- a/docs/build/html/_modules/sf_tools/signal/noise.html +++ /dev/null @@ -1,413 +0,0 @@ - - - - - - - - - - - sf_tools.signal.noise — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.signal.noise
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.signal.noise

    -# -*- coding: utf-8 -*-
-
-"""NOISE ROUTINES
-
-This module contains methods for adding and removing noise from data.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 04/01/2017
-
-"""
-
-from builtins import zip
-import numpy as np
-
-
-
    [docs]def add_noise(data, sigma=1.0, noise_type='gauss'):
-    r"""Add noise to data
-
-    This method adds Gaussian or Poisson noise to the input data
-
-    Parameters
-    ----------
-    data : np.ndarray, list or tuple
-        Input data array
-    sigma : float or list, optional
-        Standard deviation of the noise to be added ('gauss' only)
-    noise_type : str {'gauss', 'poisson'}
-        Type of noise to be added (default is 'gauss')
-
-    Returns
-    -------
-    np.ndarray input data with added noise
-
-    Raises
-    ------
-    ValueError
-        If `noise_type` is not 'gauss' or 'poisson'
-    ValueError
-        If number of `sigma` values does not match the first dimension of the
-        input data
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from sf_tools.signal.noise import add_noise
-    >>> x = np.arange(9).reshape(3, 3).astype(float)
-    >>> x
-    array([[ 0.,  1.,  2.],
-           [ 3.,  4.,  5.],
-           [ 6.,  7.,  8.]])
-    >>> np.random.seed(1)
-    >>> add_noise(x, noise_type='poisson')
-    array([[  0.,   2.,   2.],
-           [  4.,   5.,  10.],
-           [ 11.,  15.,  18.]])
-
-    >>> import numpy as np
-    >>> from sf_tools.signal.noise import add_noise
-    >>> x = np.zeros(5)
-    >>> x
-    array([ 0.,  0.,  0.,  0.,  0.])
-    >>> np.random.seed(1)
-    >>> add_noise(x, sigma=2.0)
-    array([ 3.24869073, -1.22351283, -1.0563435 , -2.14593724,  1.73081526])
-
-    """
-
-    data = np.array(data)
-
-    if noise_type not in ('gauss', 'poisson'):
-        raise ValueError('Invalid noise type. Options are "gauss" or'
-                         '"poisson"')
-
-    if isinstance(sigma, (list, np.ndarray)):
-        if len(sigma) != data.shape[0]:
-            raise ValueError('Number of sigma values must match first '
-                             'dimension of input data')
-
-    if noise_type is 'gauss':
-        random = np.random.randn(*data.shape)
-
-    elif noise_type is 'poisson':
-        random = np.random.poisson(np.abs(data))
-
-    if isinstance(sigma, float):
-        return data + sigma * random
-
-    else:
-        return data + np.array([s * r for s, r in zip(sigma, random)])
    
-
-
-
    [docs]def thresh(data, threshold, threshold_type='hard'):
-    r"""Threshold data
-
-    This method perfoms hard or soft thresholding on the input data
-
-    Parameters
-    ----------
-    data : np.ndarray, list or tuple
-        Input data array
-    threshold : float
-        Threshold level
-    threshold_type : str {'hard', 'soft'}
-        Type of noise to be added (default is 'hard')
-
-    Returns
-    -------
-    np.ndarray thresholded data
-
-    Raises
-    ------
-    ValueError
-        If `threshold_type` is not 'hard' or 'soft'
-
-
-    Notes
-    -----
-    Implements one of the following two equations:
-
-    * Hard Threshold
-        .. math::
-            \mathrm{HT}_\lambda(x) =
-            \begin{cases}
-            x & \text{if } |x|\geq\lambda \\
-            0 & \text{otherwise}
-            \end{cases}
-
-    * Soft Threshold
-        .. math::
-            \mathrm{ST}_\lambda(x) =
-            \begin{cases}
-            x-\lambda\text{sign}(x) & \text{if } |x|\geq\lambda \\
-            0 & \text{otherwise}
-            \end{cases}
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from sf_tools.signal.noise import thresh
-    >>> np.random.seed(1)
-    >>> x = np.random.randint(-9, 9, 10)
-    >>> x
-    array([-4,  2,  3, -1,  0,  2, -4,  6, -9,  7])
-    >>> thresh(x, 4)
-    array([-4,  0,  0,  0,  0,  0, -4,  6, -9,  7])
-
-    >>> import numpy as np
-    >>> from sf_tools.signal.noise import thresh
-    >>> np.random.seed(1)
-    >>> x = np.random.ranf((3, 3))
-    >>> x
-    array([[  4.17022005e-01,   7.20324493e-01,   1.14374817e-04],
-           [  3.02332573e-01,   1.46755891e-01,   9.23385948e-02],
-           [  1.86260211e-01,   3.45560727e-01,   3.96767474e-01]])
-    >>> thresh(x, 0.2, threshold_type='soft')
-    array([[ 0.217022  ,  0.52032449, -0.        ],
-           [ 0.10233257, -0.        , -0.        ],
-           [-0.        ,  0.14556073,  0.19676747]])
-
-    """
-
-    data = np.array(data)
-
-    if threshold_type not in ('hard', 'soft'):
-        raise ValueError('Invalid threshold type. Options are "hard" or'
-                         '"soft"')
-
-    if threshold_type == 'soft':
-        return (np.sign(data) * (np.abs(data) - threshold) *
-                (np.abs(data) >= threshold))
-
-    else:
-        return data * (np.abs(data) >= threshold)
    
-
    - -
    -
    - -
    -
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    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/optimisation.html b/docs/build/html/_modules/sf_tools/signal/optimisation.html deleted file mode 100644 index d5466bb..0000000 --- a/docs/build/html/_modules/sf_tools/signal/optimisation.html +++ /dev/null @@ -1,723 +0,0 @@ - - - - - - - - - - - sf_tools.signal.optimisation — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Source code for sf_tools.signal.optimisation

    -# -*- coding: utf-8 -*-
-
-r"""OPTIMISATION CLASSES
-
-This module contains classes for optimisation algoritms.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.3
-
-:Date: 20/10/2017
-
-NOTES
------
-
-Input classes must have the following properties:
-
-    * **Gradient Operators**
-
-    Must have the following methods:
-
-        * ``get_grad()`` - calculate the gradient
-
-    Must have the following variables:
-
-        * ``grad`` - the gradient
-        * ``inv_spec_rad`` - inverse spectral radius :math:`\frac{1}{\rho}`
-
-    * **Linear Operators**
-
-    Must have the following methods:
-
-        * ``op()`` - operator
-        * ``adj_op()`` - adjoint operator
-
-    Must have the following variables:
-
-        * ``l1norm`` - the l1 norm of the operator
-
-    * **Proximity Operators**
-
-    Must have the following methods:
-
-        * ``op()`` - operator
-
-The following notation is used to implement the algorithms:
-
-    * x_old is used in place of :math:`x_{n}`.
-    * x_new is used in place of :math:`x_{n+1}`.
-    * x_prox is used in place of :math:`\tilde{x}_{n+1}`.
-    * x_temp is used for intermediate operations.
-
-"""
-
-from __future__ import division, print_function
-from builtins import range, zip
-import numpy as np
-
-
-
    [docs]class FISTA(object):
-    r"""FISTA
-
-    This class is inhereited by optimisation classes to speed up convergence
-
-    Parameters
-    ----------
-    lambda_init : float, optional
-        Initial value of the relaxation parameter
-    active : bool, optional
-        Option to activate FISTA convergence speed-up (default is ``True``)
-
-    """
-
-    def __init__(self, lambda_init=None, active=True):
-
-        self.lambda_now = lambda_init
-        self.t_now = 1.0
-        self.t_prev = 1.0
-        self.use_speed_up = active
-
-
    [docs]    def speed_switch(self, turn_on=True):
-        r"""Speed swicth
-
-        This method turns on or off the speed-up
-
-        Parameters
-        ----------
-        turn_on : bool
-            Option to turn on speed-up (default is ``True``)
-
-        """
-
-        self.use_speed_up = turn_on
    
-
-
    [docs]    def update_lambda(self):
-        r"""Update lambda
-
-        This method updates the value of lambda
-
-        Notes
-        -----
-        Implements steps 3 and 4 from algoritm 10.7 in [B2011]_
-
-        """
-
-        self.t_prev = self.t_now
-        self.t_now = (1 + np.sqrt(4 * self.t_prev ** 2 + 1)) * 0.5
-        self.lambda_now = 1 + (self.t_prev - 1) / self.t_now
    
-
-
    [docs]    def speed_up(self):
-        r"""speed-up
-
-        This method returns the update if the speed-up is active
-
-        """
-
-        if self.use_speed_up:
-            self.update_lambda()
    
-
-
-
    [docs]class ForwardBackward(FISTA):
-    r"""Forward-Backward optimisation
-
-    This class implements standard forward-backward optimisation with an the
-    option to use the FISTA speed-up
-
-    Parameters
-    ----------
-    x : np.ndarray
-        Initial guess for the primal variable
-    grad : class
-        Gradient operator class
-    prox : class
-        Proximity operator class
-    cost : class, optional
-        Cost function class
-    lambda_init : float, optional
-        Initial value of the relaxation parameter
-    lambda_update : function, optional
-        Relaxation parameter update method
-    use_fista : bool, optional
-        Option to use FISTA (default is ``True``)
-    auto_iterate : bool, optional
-        Option to automatically begin iterations upon initialisation (default
-        is 'True')
-
-    """
-
-    def __init__(self, x, grad, prox, cost=None, lambda_init=None,
-                 lambda_update=None, use_fista=True, auto_iterate=True):
-
-        FISTA.__init__(self, lambda_init, use_fista)
-        self.x_old = x
-        self.z_old = np.copy(self.x_old)
-        self.grad = grad
-        self.prox = prox
-        self.cost_func = cost
-        self.lambda_update = lambda_update
-        self.converge = False
-        if auto_iterate:
-            self.iterate()
-
-
    [docs]    def update(self):
-        r"""Update
-
-        This method updates the current reconstruction
-
-        Notes
-        -----
-        Implements algorithm 10.7 (or 10.5) from [B2011]_
-
-        """
-
-        # Step 1 from alg.10.7.
-        self.grad.get_grad(self.z_old)
-        y_old = self.z_old - self.grad.inv_spec_rad * self.grad.grad
-
-        # Step 2 from alg.10.7.
-        self.x_new = self.prox.op(y_old)
-
-        # Steps 3 and 4 from alg.10.7.
-        self.speed_up()
-
-        # Step 5 from alg.10.7.
-        self.z_new = self.x_old + self.lambda_now * (self.x_new - self.x_old)
-
-        # Test primal variable for convergence.
-        if np.sum(np.abs(self.z_old - self.z_new)) <= 1e-6:
-            print(' - converged!')
-            self.converge = True
-
-        # Update old values for next iteration.
-        np.copyto(self.x_old, self.x_new)
-        np.copyto(self.z_old, self.z_new)
-
-        # Update parameter values for next iteration.
-        if not isinstance(self.lambda_update, type(None)):
-            self.lambda_now = self.lambda_update(self.lambda_now)
-
-        # Test cost function for convergence.
-        if not isinstance(self.cost_func, type(None)):
-            self.converge = self.cost_func.get_cost(self.z_new)
    
-
-
    [docs]    def iterate(self, max_iter=150):
-        r"""Iterate
-
-        This method calls update until either convergence criteria is met or
-        the maximum number of iterations is reached
-
-        Parameters
-        ----------
-        max_iter : int, optional
-            Maximum number of iterations (default is ``150``)
-
-        """
-
-        for i in range(max_iter):
-            self.update()
-
-            if self.converge:
-                print(' - Converged!')
-                break
-
-        self.x_final = self.z_new
    
-
-
-
    [docs]class GenForwardBackward(object):
-    r"""Generalized Forward-Backward optimisation
-
-    This class implements algorithm 1 from [R2012]_
-
-    Parameters
-    ----------
-    x : np.ndarray
-        Initial guess for the primal variable
-    grad : class
-        Gradient operator class
-    prox_list : list
-        List of proximity operator classes
-    cost : class, optional
-        Cost function class
-    lambda_init : float, optional
-        Initial value of the relaxation parameter
-    lambda_update : function, optional
-        Relaxation parameter update method
-    weights : np.ndarray, optional
-        Proximity operator weights
-    auto_iterate : bool, optional
-        Option to automatically begin iterations upon initialisation (default
-        is 'True')
-
-    """
-
-    def __init__(self, x, grad, prox_list, cost=None, lambda_init=1.0,
-                 lambda_update=None, weights=None, auto_iterate=True):
-
-        self.x_old = x
-        self.grad = grad
-        self.prox_list = np.array(prox_list)
-        self.cost_func = cost
-        self.lambda_init = lambda_init
-        self.lambda_update = lambda_update
-
-        if isinstance(weights, type(None)):
-            self.weights = np.repeat(1.0 / self.prox_list.size,
-                                     self.prox_list.size)
-        else:
-            self.weights = np.array(weights)
-
-        # Check weights.
-        if np.sum(self.weights) != 1.0:
-            raise ValueError('Proximity operator weights must sum to 1.0.'
-                             'Current sum of weights = ' +
-                             str(np.sum(self.weights)))
-
-        self.z = np.array([self.x_old for i in range(self.prox_list.size)])
-
-        self.converge = False
-        if auto_iterate:
-            self.iterate()
-
-
    [docs]    def update(self):
-        r"""Update
-
-        This method updates the current reconstruction
-
-        Notes
-        -----
-        Implements algorithm 1 from [R2012]_
-
-        """
-
-        # Calculate gradient for current iteration.
-        self.grad.get_grad(self.x_old)
-
-        # Update z values.
-        for i in range(self.prox_list.size):
-            z_temp = (2 * self.x_old - self.z[i] - self.grad.inv_spec_rad *
-                      self.grad.grad)
-            z_prox = self.prox_list[i].op(z_temp,
-                                          extra_factor=self.grad.inv_spec_rad /
-                                          self.weights[i])
-            self.z[i] += self.lambda_init * (z_prox - self.x_old)
-
-        # Update current reconstruction.
-        self.x_new = np.sum((z_i * w_i for z_i, w_i in
-                            zip(self.z, self.weights)), axis=0)
-
-        # Update old values for next iteration.
-        np.copyto(self.x_old, self.x_new)
-
-        # Update parameter values for next iteration.
-        if not isinstance(self.lambda_update, type(None)):
-            self.lambda_now = self.lambda_update(self.lambda_now)
-
-        # Test cost function for convergence.
-        if not isinstance(self.cost_func, type(None)):
-            self.converge = self.cost_func.get_cost(self.x_new)
    
-
-
    [docs]    def iterate(self, max_iter=150):
-        r"""Iterate
-
-        This method calls update until either convergence criteria is met or
-        the maximum number of iterations is reached
-
-        Parameters
-        ----------
-        max_iter : int, optional
-            Maximum number of iterations (default is ``150``)
-
-        """
-
-        for i in range(max_iter):
-            self.update()
-
-            if self.converge:
-                print(' - Converged!')
-                break
-
-        self.x_final = self.x_new
    
-
-
-
    [docs]class Condat(object):
-    r"""Condat optimisation
-
-    This class implements algorithm 10.7 from [Con2013]_
-
-    Parameters
-    ----------
-    x : np.ndarray
-        Initial guess for the primal variable
-    y : np.ndarray
-        Initial guess for the dual variable
-    grad : class
-        Gradient operator class
-    prox : class
-        Proximity primal operator class
-    prox_dual : class
-        Proximity dual operator class
-    linear : class
-        Linear operator class
-    cost : class
-        Cost function class
-    rho : float
-        Relaxation parameter
-    sigma : float
-        Proximal dual parameter
-    tau : float
-        Proximal primal paramater
-    rho_update : function, optional
-        Relaxation parameter update method
-    sigma_update : function, optional
-        Proximal dual parameter update method
-    tau_update : function, optional
-        Proximal primal parameter update method
-    auto_iterate : bool, optional
-        Option to automatically begin iterations upon initialisation (default
-        is 'True')
-
-    """
-
-    def __init__(self, x, y, grad, prox, prox_dual, linear, cost,
-                 rho,  sigma, tau, rho_update=None, sigma_update=None,
-                 tau_update=None, auto_iterate=True):
-
-        self.x_old = x
-        self.y_old = y
-        self.grad = grad
-        self.prox = prox
-        self.prox_dual = prox_dual
-        self.linear = linear
-        self.cost_func = cost
-        self.rho = rho
-        self.sigma = sigma
-        self.tau = tau
-        self.rho_update = rho_update
-        self.sigma_update = sigma_update
-        self.tau_update = tau_update
-        self.converge = False
-        if auto_iterate:
-            self.iterate()
-
-
    [docs]    def update_param(self):
-        r"""Update parameters
-
-        This method updates the values of ``rho``, ``sigma`` and ``tau`` with
-        the methods provided
-
-        """
-
-        # Update relaxation parameter.
-        if not isinstance(self.rho_update, type(None)):
-            self.rho = self.rho_update(self.rho)
-
-        # Update proximal dual parameter.
-        if not isinstance(self.sigma_update, type(None)):
-            self.sigma = self.sigma_update(self.sigma)
-
-        # Update proximal primal parameter.
-        if not isinstance(self.tau_update, type(None)):
-            self.tau = self.tau_update(self.tau)
    
-
-
    [docs]    def update(self):
-        r"""Update
-
-        This method updates the current reconstruction
-
-        Notes
-        -----
-        Implements equation 9 (algorithm 3.1) from [Con2013]_
-
-        - primal proximity operator set up for positivity constraint
-
-        """
-
-        # Step 1 from eq.9.
-        self.grad.get_grad(self.x_old)
-
-        x_prox = self.prox.op(self.x_old - self.tau * self.grad.grad -
-                              self.tau * self.linear.adj_op(self.y_old))
-
-        # Step 2 from eq.9.
-        y_temp = (self.y_old + self.sigma *
-                  self.linear.op(2 * x_prox - self.x_old))
-
-        y_prox = (y_temp - self.sigma * self.prox_dual.op(y_temp / self.sigma,
-                  extra_factor=(1.0 / self.sigma)))
-
-        # Step 3 from eq.9.
-        self.x_new = self.rho * x_prox + (1 - self.rho) * self.x_old
-        self.y_new = self.rho * y_prox + (1 - self.rho) * self.y_old
-
-        del x_prox, y_prox, y_temp
-
-        # Update old values for next iteration.
-        np.copyto(self.x_old, self.x_new)
-        np.copyto(self.y_old, self.y_new)
-
-        # Update parameter values for next iteration.
-        self.update_param()
-
-        # Test cost function for convergence.
-        self.converge = self.cost_func.get_cost(self.x_new)
    
-
-
    [docs]    def iterate(self, max_iter=150):
-        r"""Iterate
-
-        This method calls update until either convergence criteria is met or
-        the maximum number of iterations is reached
-
-        Parameters
-        ----------
-        max_iter : int, optional
-            Maximum number of iterations (default is ``150``)
-
-        """
-
-        for i in range(max_iter):
-            self.update()
-
-            if self.converge:
-                print(' - Converged!')
-                break
-
-        self.x_final = self.x_new
-        self.y_final = self.y_new
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/positivity.html b/docs/build/html/_modules/sf_tools/signal/positivity.html deleted file mode 100644 index 1074772..0000000 --- a/docs/build/html/_modules/sf_tools/signal/positivity.html +++ /dev/null @@ -1,287 +0,0 @@ - - - - - - - - - - - sf_tools.signal.positivity — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
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    - - - - - - - - - - - - - - - - -
    - -
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    Source code for sf_tools.signal.positivity

    -# -*- coding: utf-8 -*-
-
-"""POSITIVITY
-
-This module contains a function that retains only positive coefficients in
-an array
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.0
-
-:Date: 26/07/2017
-
-"""
-
-import numpy as np
-
-
-
    [docs]def positive(data):
-    """Positivity operator
-
-    This method preserves only the positive coefficients of the input data, all
-    negative coefficients are set to zero
-
-    Parameters
-    ----------
-    data : np.ndarray, list or tuple
-        Input data array
-
-    Returns
-    -------
-    np.ndarray array with only positive coefficients
-
-    """
-
-    def pos_recursive(data):
-
-        data = np.array(data)
-
-        if not data.dtype == 'O':
-
-            result = list(data * (data > 0))
-
-        else:
-
-            result = [pos_recursive(x) for x in data]
-
-        return result
-
-    return np.array(pos_recursive(data))
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/proximity.html b/docs/build/html/_modules/sf_tools/signal/proximity.html deleted file mode 100644 index e6d0f16..0000000 --- a/docs/build/html/_modules/sf_tools/signal/proximity.html +++ /dev/null @@ -1,512 +0,0 @@ - - - - - - - - - - - sf_tools.signal.proximity — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Source code for sf_tools.signal.proximity

    -# -*- coding: utf-8 -*-
-
-"""PROXIMITY OPERATORS
-
-This module contains classes of proximity operators for optimisation
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.3
-
-:Date: 19/07/2017
-
-"""
-
-from builtins import range
-import numpy as np
-from sf_tools.signal.noise import thresh
-from sf_tools.signal.svd import svd_thresh, svd_thresh_coef
-from sf_tools.signal.optimisation import ForwardBackward
-from sf_tools.signal.positivity import positive
-from sf_tools.base.transform import *
-
-
-
    [docs]class Positive(object):
-    """Positivity proximity operator
-
-    This class defines the positivity proximity operator
-
-    """
-
-    def __init__(self):
-        pass
-
-
    [docs]    def op(self, data, **kwargs):
-        """Operator
-
-        This method preserves only the positive coefficients of the input data
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array
-
-        Returns
-        -------
-        np.ndarray all positive elements from input data
-
-        """
-
-        return positive(data)
    
-
-
-
    [docs]class Threshold(object):
-    """Threshold proximity operator
-
-    This class defines the threshold proximity operator
-
-    Parameters
-    ----------
-    weights : np.ndarray
-        Input array of weights
-    thresh_type : str {'hard', 'soft'}, optional
-        Threshold type (default is 'soft')
-
-    """
-
-    def __init__(self, weights, thresh_type='soft'):
-
-        self.weights = weights
-        self.thresh_type = thresh_type
-
-
    [docs]    def op(self, data, extra_factor=1.0):
-        """Operator
-
-        This method returns the input data thresholded by the weights
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array
-        extra_factor : float
-            Additional multiplication factor
-
-        Returns
-        -------
-        np.ndarray thresholded data
-
-        """
-
-        threshold = self.weights * extra_factor
-
-        return thresh(data, threshold, self.thresh_type)
    
-
-
-
    [docs]class LowRankMatrix(object):
-    """Low-rank proximity operator
-
-    This class defines the low-rank proximity operator
-
-    Parameters
-    ----------
-    thresh : float
-        Threshold value
-    treshold_type : str {'hard', 'soft'}
-        Threshold type (options are 'hard' or 'soft')
-    lowr_type : str {'standard', 'ngole'}
-        Low-rank implementation (options are 'standard' or 'ngole')
-    operator : class
-        Operator class ('ngole' only)
-
-    """
-
-    def __init__(self, thresh, thresh_type='soft',
-                 lowr_type='standard', operator=None):
-
-        self.thresh = thresh
-        self.thresh_type = thresh_type
-        self.lowr_type = lowr_type
-        self.operator = operator
-
-
    [docs]    def op(self, data, extra_factor=1.0):
-        """Operator
-
-        This method returns the input data after the singular values have been
-        thresholded
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array
-        extra_factor : float
-            Additional multiplication factor
-
-        Returns
-        -------
-        np.ndarray SVD thresholded data
-
-        """
-
-        # Update threshold with extra factor.
-        threshold = self.thresh * extra_factor
-
-        if self.lowr_type == 'standard':
-            data_matrix = svd_thresh(cube2matrix(data), threshold,
-                                     thresh_type=self.thresh_type)
-
-        elif self.lowr_type == 'ngole':
-            data_matrix = svd_thresh_coef(data, self.operator,
-                                          threshold,
-                                          thresh_type=self.thresh_type)
-
-        new_data = matrix2cube(data_matrix, data.shape[1:])
-
-        # Return updated data.
-        return new_data
    
-
-
-
    [docs]class ProximityCombo(object):
-    """Proximity Combo
-
-    This class defines a combined proximity operator
-
-    Parameters
-    ----------
-    operators : list
-        List of proximity operator class instances
-
-    """
-
-    def __init__(self, operators):
-
-        self.operators = operators
-
-
    [docs]    def op(self, data, extra_factor=1.0):
-        """Operator
-
-        This method returns the result of applying all of the proximity
-        operators to the data
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array
-        extra_factor : float
-            Additional multiplication factor
-
-        Returns
-        -------
-        np.ndarray result
-
-        """
-
-        res = np.empty(len(self.operators), dtype=np.ndarray)
-
-        for i in range(len(self.operators)):
-            res[i] = self.operators[i].op(data[i], extra_factor=extra_factor)
-
-        return res
    
-
-
-
    [docs]class SubIter(object):
-    """Sub iteration operator
-
-    This class defines the sub-iteration proximity operator
-
-    Parameters
-    ----------
-    data_shape : tuple
-        Shape of input data array
-    operator : class
-        Proximity operator class
-    weights : np.ndarray
-        Array of weights
-    u_init : np.ndarray
-        Initial estimate of u
-
-    """
-
-    def __init__(self, data_shape, operator, weights=None, u_init=None):
-
-        self.operator = operator
-
-        if not isinstance(weights, type(None)):
-            self.weights = weights
-
-        if isinstance(u_init, type(None)):
-            self.u = np.ones(data_shape)
-
-        self.opt = ForwardBackward(self.u, self.operator,
-                                   Threshold(self.weights), auto_iterate=False,
-                                   indent_level=2)
-
-
    [docs]    def update_weights(self, weights):
-        """Update weights
-
-        This method updates the values of the weights
-
-        Parameters
-        ----------
-        weights : np.ndarray
-            Array of weights
-
-        """
-
-        self.weights = weights
    
-
-
    [docs]    def update_u(self):
-        """Update u
-
-        This method updates the values of u
-
-        """
-
-        self.opt.iterate(100)
-        self.u = self.opt.x_final
    
-
-
    [docs]    def op(self, data):
-        """Operator
-
-        Parameters
-        ----------
-        data : np.ndarray
-            Input data array
-
-        Returns
-        -------
-        np.ndarray result
-
-        """
-
-        self.update_u()
-
-        new_data = data - self.operator.adj_op(self.u)
-
-        return new_data
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/regression.html b/docs/build/html/_modules/sf_tools/signal/regression.html deleted file mode 100644 index b7e6969..0000000 --- a/docs/build/html/_modules/sf_tools/signal/regression.html +++ /dev/null @@ -1,432 +0,0 @@ - - - - - - - - - - - sf_tools.signal.regression — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
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      • - -
    • Module code »
      • - -
    • sf_tools.signal.regression
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    -
    -
    - -

    Source code for sf_tools.signal.regression

    -# -*- coding: utf-8 -*-
-
-"""REGRESSION ROUTINES
-
-This module contains methods for linear regression.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.0
-
-:Date: 04/04/2017
-
-"""
-
-from builtins import zip
-import numpy as np
-from scipy.odr import *
-
-
-
    [docs]def linear_fit(B, x):
-    r"""Linear fit
-
-    This method defines the equation of a straight line.
-
-    Parameters
-    ----------
-    B : tuple
-        Slope (m) and intercept (b) of the line.
-    x : list or np.ndarray
-        The 1D data vector
-
-    Returns
-    -------
-    np.ndarray 1D array of corresponding y values
-
-    NOTES
-    -----
-    This equation of a stright line is given by
-
-    .. math::
-
-        y = mx + b
-
-    """
-
-    return B[0] * np.array(x) + B[1]
    
-
-
-
    [docs]def polynomial(x, a):
-    r"""Polynomial
-
-    This method defines the equation of a polynomial line.
-
-    Parameters
-    ----------
-    x : list or np.ndarray
-        The 1D data vector
-    a : list or np.ndarray
-        The 1D polynomial coefficient vector
-
-    Returns
-    -------
-    np.ndarray 1D array of corresponding y values
-
-    NOTES
-    -----
-    This equation of a stright line is given by
-
-    .. math::
-
-        y = a_0 + a_1x + a_2x^2 + \dots + a_kx^k
-
-    """
-
-    a = np.array(a)
-    x = np.array(x)
-
-    return sum([(a_i * x ** n) for a_i, n in zip(a, range(a.size))])
    
-
-
-
    [docs]def polynomial_fit(x, y, k=1):
-    """Polynomial fit
-
-    This method finds the coefficients for a polynomial line fit to the input
-    data using least squares.
-
-    Parameters
-    ----------
-    x : list or np.ndarray
-        The 1D independent data vector
-    y : list or np.ndarray
-        The 1D dependent data vector
-    k : int, optional
-        Number of degrees of freedom. Default (k=1)
-
-    Returns
-    -------
-    np.ndarray 1D array of coefficients a
-
-    """
-
-    y = np.array(y)
-
-    return least_squares(x_matrix(x, k), y)
    
-
-
-
    [docs]def least_squares(X, y):
-    """Least squares
-
-    This method performs an analytical least squares regression. Returns the
-    values of the coefficients, a, given the input matrix X and the
-    corresponding y values.
-
-    Parameters
-    ----------
-    X : np.ndarray
-        The 2D independent data matrix.
-    y : np.ndarray
-        The 1D dependent data vector
-
-    Returns
-    -------
-    np.ndarray 1D array of coefficients a
-
-    Raises
-    ------
-    ValueError
-        If inputs are not numpy arrays
-
-    ToDo
-    ----
-    Add equation and example
-
-    """
-
-    if not np.all([isinstance(i, np.ndarray) for i in (X, y)]):
-        raise ValueError('Inputs must be numpy arrays.')
-
-    return np.dot(np.dot(np.linalg.inv(np.dot(X.T, X)), X.T), y)
    
-
-
-
    [docs]def x_matrix(x, k):
-    """Define X matrix
-
-    This method defines the matrix X for a given vector x corresponding to a
-    polynomial with k degrees of freedom.
-
-    Parameters
-    ----------
-    x : list or np.ndarray
-        The 1D independent data vector
-    k : int
-        Number of degrees of freedom
-
-    Returns
-    -------
-    np.ndarray the 2D independent variable matrix X
-
-    """
-
-    x = np.array(x)
-
-    return np.vstack([x ** n for n in range(k + 1)]).T
    
-
-
-
    [docs]def fit_odr(x, y, xerr, yerr, fit):
-    """Fit via ODR
-
-    This method performs an orthogonal distance regression fit.
-
-    Parameters
-    ----------
-    x : list or np.ndarray
-        The 1D independent data vector
-    y : list or np.ndarray
-        The 1D dependent data vector
-    x_err : list or np.ndarray
-        1D data vector of x value errors
-    y_err : list or np.ndarray
-        1D data vector of y value errors
-    fit : function
-        Fitting function
-
-    Returns
-    -------
-    tuple best fit parameters
-
-    """
-
-    model = Model(fit)
-    r_data = RealData(x, y, sx=xerr, sy=yerr)
-    odr = ODR(r_data, model, beta0=[1.0, 2.0])
-    odr_out = odr.run()
-
-    return odr_out.beta
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/reweight.html b/docs/build/html/_modules/sf_tools/signal/reweight.html deleted file mode 100644 index eebefd8..0000000 --- a/docs/build/html/_modules/sf_tools/signal/reweight.html +++ /dev/null @@ -1,293 +0,0 @@ - - - - - - - - - - - sf_tools.signal.reweight — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.signal.reweight
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.signal.reweight

    -# -*- coding: utf-8 -*-
-
-"""REWEIGHTING CLASSES
-
-This module contains classes for reweighting optimisation implementations
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.3
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-import numpy as np
-
-
-
    [docs]class cwbReweight(object):
-    r"""Candes, Wakin and Boyd reweighting class
-
-    This class implements the reweighting scheme described in [CWB2007]_
-
-    Parameters
-    ----------
-    weights : np.ndarray
-        Array of weights
-    thresh_factor : float
-        Threshold factor
-
-    """
-
-    def __init__(self, weights, thresh_factor=1):
-
-        self.weights = weights
-        self.original_weights = np.copy(self.weights)
-        self.thresh_factor = thresh_factor
-
-
    [docs]    def reweight(self, data):
-        r"""Reweight
-
-        This method implements the reweighting from section 4 in [CWB2007]_
-
-        Notes
-        -----
-
-        Reweighting implemented as:
-
-        .. math::
-
-            w = w \left( \frac{1}{1 + \frac{|x^w|}{n \sigma}} \right)
-
-        """
-
-        self.weights *= (1.0 / (1.0 + np.abs(data) / (self.thresh_factor *
-                         self.original_weights)))
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/svd.html b/docs/build/html/_modules/sf_tools/signal/svd.html deleted file mode 100644 index 9c67c33..0000000 --- a/docs/build/html/_modules/sf_tools/signal/svd.html +++ /dev/null @@ -1,399 +0,0 @@ - - - - - - - - - - - sf_tools.signal.svd — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
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    - - - - - - - - - - - - - - - - -
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    Source code for sf_tools.signal.svd

    -# -*- coding: utf-8 -*-
-
-"""SVD ROUTINES
-
-This module contains methods for thresholding singular values.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-from builtins import zip
-import numpy as np
-from scipy.linalg import svd, diagsvd
-from sf_tools.image.convolve import convolve
-from sf_tools.signal.noise import thresh
-from sf_tools.base.transform import cube2matrix, matrix2cube
-from sf_tools.interface.errors import warn
-
-
-
    [docs]def find_n_pc(u, factor=0.5):
-    """Find number of principal components
-
-    This method finds the minimum number of principal components required
-
-    Parameters
-    ----------
-    u : np.ndarray
-        Left singular vector
-    factor : float, optional
-        Factor for testing the auto correlation (default is '0.5')
-
-    Returns
-    -------
-    int number of principal components
-
-    """
-
-    # Get the shape of the galaxy images.
-    gal_shape = np.repeat(np.int(np.sqrt(u.shape[0])), 2)
-
-    # Find the auto correlation of the left singular vector.
-    u_auto = [convolve(a.reshape(gal_shape), np.rot90(a.reshape(gal_shape), 2))
-              for a in u.T]
-
-    # Return the required number of principal components.
-    return np.sum(((a[list(zip(gal_shape // 2))] ** 2 <= factor *
-                  np.sum(a ** 2)) for a in u_auto))
    
-
-
-
    [docs]def svd_thresh(data, threshold=None, n_pc=None, thresh_type='hard'):
-    """Threshold the singular values
-
-    This method thresholds the input data using singular value decomposition
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-    threshold : float, optional
-        Threshold value
-    n_pc : int or str, optional
-        Number of principal components, specify an integer value or 'all'
-    threshold_type : str {'hard', 'soft'}
-        Type of noise to be added (default is 'hard')
-
-    Returns
-    -------
-    np.ndarray thresholded data
-
-    Raises
-    ------
-    ValueError
-        For invalid string entry for n_pc
-
-    """
-
-    if isinstance(n_pc, str) and n_pc != 'all':
-        raise ValueError('Invalid value for "n_pc", specify an integer value '
-                         'or "all"')
-
-    # Get SVD of input data.
-    u, s, v = svd(data, check_finite=False, lapack_driver='gesvd')
-
-    # Find the threshold if not provided.
-    if isinstance(threshold, type(None)):
-
-        # Find the required number of principal components if not specified.
-        if isinstance(n_pc, type(None)):
-            n_pc = find_n_pc(u, factor=0.1)
-
-        # If the number of PCs is too large use all of the singular values.
-        if n_pc >= s.size or n_pc == 'all':
-            n_pc = s.size - 1
-            warn('Using all singular values.')
-
-        threshold = s[n_pc]
-
-    # Remove noise from singular values.
-    s_new = thresh(s, threshold, thresh_type)
-
-    if np.all(s_new == s):
-        warn('No change to singular values.')
-
-    # Reshape the singular values to the shape of the input image.
-    s_new = diagsvd(s_new, *data.shape)
-
-    # Return the thresholded image.
-    return np.dot(u, np.dot(s_new, v))
    
-
-
-
    [docs]def svd_thresh_coef(data, operator, threshold, thresh_type='hard'):
-    """Threshold the singular values coefficients
-
-    This method thresholds the input data using singular value decomposition
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data array
-    operator : class
-        Operator class instance
-    threshold : float, optional
-        Threshold value
-    threshold_type : str {'hard', 'soft'}
-        Type of noise to be added (default is 'hard')
-
-    Returns
-    -------
-    np.ndarray thresholded data
-
-    Raises
-    ------
-    ValueError
-        For invalid string entry for n_pc
-
-    """
-
-    # Convert data cube to matrix.
-    data_matrix = cube2matrix(data)
-
-    # Get SVD of data matrix.
-    u, s, v = np.linalg.svd(data_matrix, full_matrices=False)
-
-    # Compute coefficients.
-    a = np.dot(np.diag(s), v)
-
-    # Compute threshold matrix.
-    u_cube = matrix2cube(u, data.shape[1:])
-    ti = np.array([np.linalg.norm(x) for x in operator(u_cube)])
-    ti = np.repeat(ti, a.shape[1]).reshape(a.shape)
-    threshold *= ti
-
-    # Remove noise from coefficients.
-    a_new = thresh(a, threshold, thresh_type)
-
-    # Return the thresholded image.
-    return np.dot(u, a_new)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/validation.html b/docs/build/html/_modules/sf_tools/signal/validation.html deleted file mode 100644 index d93e260..0000000 --- a/docs/build/html/_modules/sf_tools/signal/validation.html +++ /dev/null @@ -1,295 +0,0 @@ - - - - - - - - - - - sf_tools.signal.validation — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
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    -
    - -

    Source code for sf_tools.signal.validation

    -"""VALIDATION ROUTINES
-
-This module contains methods for testing signal and operator properties.
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 06/12/2017
-
-"""
-
-from __future__ import print_function
-import numpy as np
-
-
-
    [docs]def transpose_test(operator, operator_t, x_shape, x_args, y_shape=None,
-                   y_args=None):
-    """Transpose test
-
-    This method tests two operators to see if they are the transpose of each
-    other.
-
-    Parameters
-    ----------
-    operator : function
-        Operator function
-    operator_t : function
-        Transpose operator function
-    x_shape : tuple
-        Shape of operator input data
-    x_args : tuple
-        Arguments to be passed to operator
-    y_shape : tuple, optional
-        Shape of transpose operator input data
-    y_args : tuple, optional
-        Arguments to be passed to transpose operator
-
-    """
-
-    if isinstance(y_shape, type(None)):
-        y_shape = x_shape
-
-    if isinstance(y_args, type(None)):
-        y_args = x_args
-
-    # Generate random arrays.
-    x = np.random.ranf(x_shape)
-    y = np.random.ranf(y_shape)
-
-    # Calculate <MX, Y>
-    mx_y = np.sum(np.multiply(operator(x, *x_args), y))
-
-    # Calculate <X, M.TY>
-    x_mty = np.sum(np.multiply(x, operator_t(y, *y_args)))
-
-    # Test the difference between the two.
-    print(' - |<MX, Y> - <X, M.TY>| =', np.abs(mx_y - x_mty))
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_modules/sf_tools/signal/wavelet.html b/docs/build/html/_modules/sf_tools/signal/wavelet.html deleted file mode 100644 index 4c5c1f2..0000000 --- a/docs/build/html/_modules/sf_tools/signal/wavelet.html +++ /dev/null @@ -1,402 +0,0 @@ - - - - - - - - - - - sf_tools.signal.wavelet — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
      - -
    • Docs »
      • - -
    • Module code »
      • - -
    • sf_tools.signal.wavelet
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for sf_tools.signal.wavelet

    -# -*- coding: utf-8 -*-
-
-"""WAVELET MODULE
-
-This module contains methods for performing wavelet transformations using iSAP
-
-:Author: Samuel Farrens <samuel.farrens@gmail.com>
-
-:Version: 1.2
-
-:Date: 20/10/2017
-
-"""
-
-from __future__ import division
-from builtins import zip
-import numpy as np
-from os import remove
-from subprocess import check_call
-from datetime import datetime
-from astropy.io import fits
-from sf_tools.image.convolve import convolve
-from sf_tools.base.np_adjust import rotate_stack
-
-
-
    [docs]def call_mr_transform(data, opt=None, path='./', remove_files=True):
-    """Call mr_transform
-
-    This method calls the iSAP module mr_transform
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data, 2D array
-    opt : list, optional
-        List of additonal mr_transform options
-    path : str, optional
-        Path for output files (default is './')
-    remove_files : bool, optional
-        Option to remove output files (default is 'True')
-
-    Returns
-    -------
-    np.ndarray results of transform
-
-    """
-
-    # Create a unique string using the current date and time.
-    unique_string = datetime.now().strftime('%Y.%m.%d_%H.%M.%S')
-
-    # Set the ouput file names.
-    file_name = path + 'mr_temp_' + unique_string
-    file_fits = file_name + '.fits'
-    file_mr = file_name + '.mr'
-
-    # Write the input data to a fits file.
-    fits.writeto(file_fits, data)
-
-    # Call mr_transform.
-    if isinstance(opt, type(None)):
-        check_call(['mr_transform', file_fits, file_mr])
-    else:
-        check_call(['mr_transform'] + opt + [file_fits, file_mr])
-
-    # Retrieve wavelet transformed data.
-    result = fits.getdata(file_mr)
-
-    # Return the mr_transform results (and the output file names).
-    if remove_files:
-        remove(file_fits)
-        remove(file_mr)
-        return result
-    else:
-        return result, file_mr
    
-
-
-
    [docs]def get_mr_filters(data_shape, opt=None, coarse=False):
-    """Get mr_transform filters
-
-    This method obtains wavelet filters by calling mr_transform
-
-    Parameters
-    ----------
-    data_shape : tuple
-        2D data shape
-    opt : list, optional
-        List of additonal mr_transform options
-    coarse : bool, optional
-        Option to keep coarse scale (default is 'False')
-
-    Returns
-    -------
-    np.ndarray 3D array of wavelet filters
-
-    """
-
-    # Adjust the shape of the input data.
-    data_shape = np.array(data_shape)
-    data_shape += data_shape % 2 - 1
-
-    # Create fake data.
-    fake_data = np.zeros(data_shape)
-    fake_data[list(zip(data_shape // 2))] = 1
-
-    # Call mr_transform.
-    mr_filters = call_mr_transform(fake_data, opt=opt)
-
-    # Return filters
-    if coarse:
-        return mr_filters
-    else:
-        return mr_filters[:-1]
    
-
-
-
    [docs]def filter_convolve(data, filters, filter_rot=False):
-    """Filter convolve
-
-    This method convolves the input image with the wavelet filters
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data, 2D array
-    filters : np.ndarray
-        Wavelet filters, 3D array
-    filter_rot : bool, optional
-        Option to rotate wavelet filters (default is 'False')
-
-    Returns
-    -------
-    np.ndarray convolved data
-
-    """
-
-    if filter_rot:
-        return np.sum((convolve(coef, f) for coef, f in
-                      zip(data, rotate_stack(filters))), axis=0)
-
-    else:
-        return np.array([convolve(data, f) for f in filters])
    
-
-
-
    [docs]def filter_convolve_stack(data, filters, filter_rot=False):
-    """Filter convolve
-
-    This method convolves the a stack of input images with the wavelet filters
-
-    Parameters
-    ----------
-    data : np.ndarray
-        Input data, 3D array
-    filters : np.ndarray
-        Wavelet filters, 3D array
-    filter_rot : bool, optional
-        Option to rotate wavelet filters (default is 'False')
-
-    Returns
-    -------
-    np.ndarray convolved data
-
-    """
-
-    # Return the convolved data cube.
-    return np.array([filter_convolve(x, filters, filter_rot=filter_rot)
-                     for x in data])
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, Samuel Farrens. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/_sources/examples.rst.txt b/docs/build/html/_sources/examples.rst.txt deleted file mode 100644 index d623e26..0000000 --- a/docs/build/html/_sources/examples.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -sf_tools Examples -================= - -Coming soon! diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt deleted file mode 100644 index f210ad1..0000000 --- a/docs/build/html/_sources/index.rst.txt +++ /dev/null @@ -1,122 +0,0 @@ -.. sf_tools documentation master file, created by - sphinx-quickstart on Mon Oct 24 16:46:22 2016. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -SF_TOOLS Documentation -====================== - -:Author: Samuel Farrens - -:Version: 1.0.1 - -:Date: 25/10/2017 - -SF_TOOLS is a series of Python 2.7 modules with applications to image analysis, -signal processing and statistics. - -Contents -======== - -1. `Dependencies`_ - - 1. `Required Packages`_ - 2. `Optional Packages`_ - -2. `Installation`_ - -3. `Package Contents`_ - -Dependencies -============ - -Required Packages ------------------ - -In order to run the code in this repository the following packages must be -installed: - -* |link-to-python| [Tested with v 2.7.11 and 3.6.3] - -* |link-to-numpy| [Tested with v 1.13.3] - -* |link-to-scipy| [Tested with v 0.18.1] - -* |link-to-future| [Tested with v 0.16.0] - -* |link-to-astropy| [Tested with v 1.3] - -.. |link-to-python| raw:: html - - Python - -.. |link-to-numpy| raw:: html - - Numpy - -.. |link-to-scipy| raw:: html - - Scipy - -.. |link-to-future| raw:: html - - Future - -.. |link-to-astropy| raw:: html - - Astropy - -Optional Packages ------------------ - -The following packages can optionally be installed to add extra functionality: - -* |link-to-matplotlib| [Tested with v 2.0.2] - -* |link-to-termcolor| [Tested with v 1.1.0] - -.. |link-to-matplotlib| raw:: html - - Matplotlib - -.. |link-to-termcolor| raw:: html - - Termcolor - -Installation -============ - -To clone the sf_tools repository from GitHub run the following command: - -.. code-block:: bash - - $ git clone https://github.com/sfarrens/sf_tools - -To install using `easy_install` run the following command: - -.. code-block:: bash - - $ easy_install sf_tools - -To install using `pip` run the following command: - -.. code-block:: bash - - $ pip install sf_tools - -Package Contents -================ - -.. toctree:: - :numbered: - :maxdepth: 3 - - examples - sf_tools diff --git a/docs/build/html/_sources/modules.rst.txt b/docs/build/html/_sources/modules.rst.txt deleted file mode 100644 index f69bd2b..0000000 --- a/docs/build/html/_sources/modules.rst.txt +++ /dev/null @@ -1,8 +0,0 @@ -sf_tools -======== - -.. toctree:: - :maxdepth: 4 - - setup - sf_tools diff --git a/docs/build/html/_sources/setup.rst.txt b/docs/build/html/_sources/setup.rst.txt deleted file mode 100644 index 31789b1..0000000 --- a/docs/build/html/_sources/setup.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -setup module -============ - -.. automodule:: setup - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.base.np_adjust.rst.txt b/docs/build/html/_sources/sf_tools.base.np_adjust.rst.txt deleted file mode 100644 index 66d73bc..0000000 --- a/docs/build/html/_sources/sf_tools.base.np_adjust.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.base\.np\_adjust module -================================== - -.. automodule:: sf_tools.base.np_adjust - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.base.rst.txt b/docs/build/html/_sources/sf_tools.base.rst.txt deleted file mode 100644 index aaf37f3..0000000 --- a/docs/build/html/_sources/sf_tools.base.rst.txt +++ /dev/null @@ -1,19 +0,0 @@ -sf\_tools\.base package -======================= - -Submodules ----------- - -.. toctree:: - - sf_tools.base.np_adjust - sf_tools.base.transform - sf_tools.base.types - -Module contents ---------------- - -.. automodule:: sf_tools.base - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.base.transform.rst.txt b/docs/build/html/_sources/sf_tools.base.transform.rst.txt deleted file mode 100644 index ec5f1f3..0000000 --- a/docs/build/html/_sources/sf_tools.base.transform.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.base\.transform module -================================= - -.. automodule:: sf_tools.base.transform - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.base.types.rst.txt b/docs/build/html/_sources/sf_tools.base.types.rst.txt deleted file mode 100644 index 770a9e3..0000000 --- a/docs/build/html/_sources/sf_tools.base.types.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.base\.types module -============================= - -.. automodule:: sf_tools.base.types - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.image.convolve.rst.txt b/docs/build/html/_sources/sf_tools.image.convolve.rst.txt deleted file mode 100644 index 73d9c72..0000000 --- a/docs/build/html/_sources/sf_tools.image.convolve.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.convolve module -================================= - -.. automodule:: sf_tools.image.convolve - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.image.distort.rst.txt b/docs/build/html/_sources/sf_tools.image.distort.rst.txt deleted file mode 100644 index 3c2aa36..0000000 --- a/docs/build/html/_sources/sf_tools.image.distort.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.distort module -================================ - -.. automodule:: sf_tools.image.distort - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.image.quality.rst.txt b/docs/build/html/_sources/sf_tools.image.quality.rst.txt deleted file mode 100644 index 6219c53..0000000 --- a/docs/build/html/_sources/sf_tools.image.quality.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.quality module -================================ - -.. automodule:: sf_tools.image.quality - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.image.rst.txt b/docs/build/html/_sources/sf_tools.image.rst.txt deleted file mode 100644 index e283833..0000000 --- a/docs/build/html/_sources/sf_tools.image.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -sf\_tools\.image package -======================== - -Submodules ----------- - -.. toctree:: - - sf_tools.image.convolve - sf_tools.image.distort - sf_tools.image.quality - sf_tools.image.shape - sf_tools.image.stamp - sf_tools.image.stats - -Module contents ---------------- - -.. automodule:: sf_tools.image - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.image.shape.rst.txt b/docs/build/html/_sources/sf_tools.image.shape.rst.txt deleted file mode 100644 index e426acf..0000000 --- a/docs/build/html/_sources/sf_tools.image.shape.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.shape module -============================== - -.. automodule:: sf_tools.image.shape - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.image.stamp.rst.txt b/docs/build/html/_sources/sf_tools.image.stamp.rst.txt deleted file mode 100644 index 646ffba..0000000 --- a/docs/build/html/_sources/sf_tools.image.stamp.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.stamp module -============================== - -.. automodule:: sf_tools.image.stamp - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.image.stats.rst.txt b/docs/build/html/_sources/sf_tools.image.stats.rst.txt deleted file mode 100644 index 64ea1cf..0000000 --- a/docs/build/html/_sources/sf_tools.image.stats.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.stats module -============================== - -.. automodule:: sf_tools.image.stats - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.interface.errors.rst.txt b/docs/build/html/_sources/sf_tools.interface.errors.rst.txt deleted file mode 100644 index 55fff77..0000000 --- a/docs/build/html/_sources/sf_tools.interface.errors.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.interface\.errors module -=================================== - -.. automodule:: sf_tools.interface.errors - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.interface.log.rst.txt b/docs/build/html/_sources/sf_tools.interface.log.rst.txt deleted file mode 100644 index afcbb0d..0000000 --- a/docs/build/html/_sources/sf_tools.interface.log.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.interface\.log module -================================ - -.. automodule:: sf_tools.interface.log - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.interface.rst.txt b/docs/build/html/_sources/sf_tools.interface.rst.txt deleted file mode 100644 index 4f55993..0000000 --- a/docs/build/html/_sources/sf_tools.interface.rst.txt +++ /dev/null @@ -1,18 +0,0 @@ -sf\_tools\.interface package -============================ - -Submodules ----------- - -.. toctree:: - - sf_tools.interface.errors - sf_tools.interface.log - -Module contents ---------------- - -.. automodule:: sf_tools.interface - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.math.angle.rst.txt b/docs/build/html/_sources/sf_tools.math.angle.rst.txt deleted file mode 100644 index 405930b..0000000 --- a/docs/build/html/_sources/sf_tools.math.angle.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.angle module -============================= - -.. automodule:: sf_tools.math.angle - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.math.integrate.rst.txt b/docs/build/html/_sources/sf_tools.math.integrate.rst.txt deleted file mode 100644 index 527fadc..0000000 --- a/docs/build/html/_sources/sf_tools.math.integrate.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.integrate module -================================= - -.. automodule:: sf_tools.math.integrate - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.math.matrix.rst.txt b/docs/build/html/_sources/sf_tools.math.matrix.rst.txt deleted file mode 100644 index 3a34404..0000000 --- a/docs/build/html/_sources/sf_tools.math.matrix.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.matrix module -============================== - -.. automodule:: sf_tools.math.matrix - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.math.misc.rst.txt b/docs/build/html/_sources/sf_tools.math.misc.rst.txt deleted file mode 100644 index b1a541d..0000000 --- a/docs/build/html/_sources/sf_tools.math.misc.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.misc module -============================ - -.. automodule:: sf_tools.math.misc - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.math.rst.txt b/docs/build/html/_sources/sf_tools.math.rst.txt deleted file mode 100644 index 0ddf616..0000000 --- a/docs/build/html/_sources/sf_tools.math.rst.txt +++ /dev/null @@ -1,21 +0,0 @@ -sf\_tools\.math package -======================= - -Submodules ----------- - -.. toctree:: - - sf_tools.math.angle - sf_tools.math.integrate - sf_tools.math.matrix - sf_tools.math.misc - sf_tools.math.stats - -Module contents ---------------- - -.. automodule:: sf_tools.math - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.math.stats.rst.txt b/docs/build/html/_sources/sf_tools.math.stats.rst.txt deleted file mode 100644 index 6263488..0000000 --- a/docs/build/html/_sources/sf_tools.math.stats.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.stats module -============================= - -.. automodule:: sf_tools.math.stats - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.plot.cost_plot.rst.txt b/docs/build/html/_sources/sf_tools.plot.cost_plot.rst.txt deleted file mode 100644 index f634444..0000000 --- a/docs/build/html/_sources/sf_tools.plot.cost_plot.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.plot\.cost\_plot module -================================== - -.. automodule:: sf_tools.plot.cost_plot - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.plot.rst.txt b/docs/build/html/_sources/sf_tools.plot.rst.txt deleted file mode 100644 index 63a26e9..0000000 --- a/docs/build/html/_sources/sf_tools.plot.rst.txt +++ /dev/null @@ -1,17 +0,0 @@ -sf\_tools\.plot package -======================= - -Submodules ----------- - -.. toctree:: - - sf_tools.plot.cost_plot - -Module contents ---------------- - -.. automodule:: sf_tools.plot - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.rst.txt b/docs/build/html/_sources/sf_tools.rst.txt deleted file mode 100644 index 782f6f2..0000000 --- a/docs/build/html/_sources/sf_tools.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -sf\_tools package -================= - -Subpackages ------------ - -.. toctree:: - - sf_tools.base - sf_tools.image - sf_tools.interface - sf_tools.math - sf_tools.plot - sf_tools.signal - -Module contents ---------------- - -.. automodule:: sf_tools - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.cost.rst.txt b/docs/build/html/_sources/sf_tools.signal.cost.rst.txt deleted file mode 100644 index bcf75b8..0000000 --- a/docs/build/html/_sources/sf_tools.signal.cost.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.cost module -============================== - -.. automodule:: sf_tools.signal.cost - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.filter.rst.txt b/docs/build/html/_sources/sf_tools.signal.filter.rst.txt deleted file mode 100644 index c24084d..0000000 --- a/docs/build/html/_sources/sf_tools.signal.filter.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.filter module -================================ - -.. automodule:: sf_tools.signal.filter - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.gradient.rst.txt b/docs/build/html/_sources/sf_tools.signal.gradient.rst.txt deleted file mode 100644 index 521794d..0000000 --- a/docs/build/html/_sources/sf_tools.signal.gradient.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.gradient module -================================== - -.. automodule:: sf_tools.signal.gradient - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.linear.rst.txt b/docs/build/html/_sources/sf_tools.signal.linear.rst.txt deleted file mode 100644 index 48dc126..0000000 --- a/docs/build/html/_sources/sf_tools.signal.linear.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.linear module -================================ - -.. automodule:: sf_tools.signal.linear - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.noise.rst.txt b/docs/build/html/_sources/sf_tools.signal.noise.rst.txt deleted file mode 100644 index d0f6215..0000000 --- a/docs/build/html/_sources/sf_tools.signal.noise.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.noise module -=============================== - -.. automodule:: sf_tools.signal.noise - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.optimisation.rst.txt b/docs/build/html/_sources/sf_tools.signal.optimisation.rst.txt deleted file mode 100644 index 6d9c60b..0000000 --- a/docs/build/html/_sources/sf_tools.signal.optimisation.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.optimisation module -====================================== - -.. automodule:: sf_tools.signal.optimisation - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.positivity.rst.txt b/docs/build/html/_sources/sf_tools.signal.positivity.rst.txt deleted file mode 100644 index 619f0c5..0000000 --- a/docs/build/html/_sources/sf_tools.signal.positivity.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.positivity module -==================================== - -.. automodule:: sf_tools.signal.positivity - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.proximity.rst.txt b/docs/build/html/_sources/sf_tools.signal.proximity.rst.txt deleted file mode 100644 index b96ec5e..0000000 --- a/docs/build/html/_sources/sf_tools.signal.proximity.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.proximity module -=================================== - -.. automodule:: sf_tools.signal.proximity - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.regression.rst.txt b/docs/build/html/_sources/sf_tools.signal.regression.rst.txt deleted file mode 100644 index 24c3364..0000000 --- a/docs/build/html/_sources/sf_tools.signal.regression.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.regression module -==================================== - -.. automodule:: sf_tools.signal.regression - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.reweight.rst.txt b/docs/build/html/_sources/sf_tools.signal.reweight.rst.txt deleted file mode 100644 index 401cc5b..0000000 --- a/docs/build/html/_sources/sf_tools.signal.reweight.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.reweight module -================================== - -.. automodule:: sf_tools.signal.reweight - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.rst.txt b/docs/build/html/_sources/sf_tools.signal.rst.txt deleted file mode 100644 index fead595..0000000 --- a/docs/build/html/_sources/sf_tools.signal.rst.txt +++ /dev/null @@ -1,29 +0,0 @@ -sf\_tools\.signal package -========================= - -Submodules ----------- - -.. toctree:: - - sf_tools.signal.cost - sf_tools.signal.filter - sf_tools.signal.gradient - sf_tools.signal.linear - sf_tools.signal.noise - sf_tools.signal.optimisation - sf_tools.signal.positivity - sf_tools.signal.proximity - sf_tools.signal.regression - sf_tools.signal.reweight - sf_tools.signal.svd - sf_tools.signal.validation - sf_tools.signal.wavelet - -Module contents ---------------- - -.. automodule:: sf_tools.signal - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.svd.rst.txt b/docs/build/html/_sources/sf_tools.signal.svd.rst.txt deleted file mode 100644 index 511982f..0000000 --- a/docs/build/html/_sources/sf_tools.signal.svd.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.svd module -============================= - -.. automodule:: sf_tools.signal.svd - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/html/_sources/sf_tools.signal.validation.rst.txt b/docs/build/html/_sources/sf_tools.signal.validation.rst.txt deleted file mode 100644 index baa2371..0000000 --- a/docs/build/html/_sources/sf_tools.signal.validation.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.validation module -==================================== - -.. automodule:: sf_tools.signal.validation - :members: - :undoc-members: - :show-inheritance: diff --git 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(such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var document = window.document; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var concat = arr.concat; - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - - - - function DOMEval( code, doc ) { - doc = doc || document; - - var script = doc.createElement( "script" ); - - script.text = code; - doc.head.appendChild( script ).parentNode.removeChild( script ); - } -/* global Symbol */ -// Defining this global in .eslintrc would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.1.0", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }, - - // Support: Android <=4.0 only - // Make sure we trim BOM and NBSP - rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, - - // Matches dashed string for camelizing - rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g, - - // Used by jQuery.camelCase as callback to replace() - fcamelCase = function( all, letter ) { - return letter.toUpperCase(); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - return num != null ? - - // Return just the one element from the set - ( num < 0 ? this[ num + this.length ] : this[ num ] ) : - - // Return all the elements in a clean array - slice.call( this ); - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !jQuery.isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - src = target[ name ]; - copy = options[ name ]; - - // Prevent never-ending loop - if ( target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = jQuery.isArray( copy ) ) ) ) { - - if ( copyIsArray ) { - copyIsArray = false; - clone = src && jQuery.isArray( src ) ? src : []; - - } else { - clone = src && jQuery.isPlainObject( src ) ? src : {}; - } - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isFunction: function( obj ) { - return jQuery.type( obj ) === "function"; - }, - - isArray: Array.isArray, - - isWindow: function( obj ) { - return obj != null && obj === obj.window; - }, - - isNumeric: function( obj ) { - - // As of jQuery 3.0, isNumeric is limited to - // strings and numbers (primitives or objects) - // that can be coerced to finite numbers (gh-2662) - var type = jQuery.type( obj ); - return ( type === "number" || type === "string" ) && - - // parseFloat NaNs numeric-cast false positives ("") - // ...but misinterprets leading-number strings, particularly hex literals ("0x...") - // subtraction forces infinities to NaN - !isNaN( obj - parseFloat( obj ) ); - }, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - - /* eslint-disable no-unused-vars */ - // See https://github.com/eslint/eslint/issues/6125 - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - type: function( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; - }, - - // Evaluates a script in a global context - globalEval: function( code ) { - DOMEval( code ); - }, - - // Convert dashed to camelCase; used by the css and data modules - // Support: IE <=9 - 11, Edge 12 - 13 - // Microsoft forgot to hump their vendor prefix (#9572) - camelCase: function( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); - }, - - nodeName: function( elem, name ) { - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // Support: Android <=4.0 only - trim: function( text ) { - return text == null ? - "" : - ( text + "" ).replace( rtrim, "" ); - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return concat.apply( [], ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // Bind a function to a context, optionally partially applying any - // arguments. - proxy: function( fn, context ) { - var tmp, args, proxy; - - if ( typeof context === "string" ) { - tmp = fn[ context ]; - context = fn; - fn = tmp; - } - - // Quick check to determine if target is callable, in the spec - // this throws a TypeError, but we will just return undefined. - if ( !jQuery.isFunction( fn ) ) { - return undefined; - } - - // Simulated bind - args = slice.call( arguments, 2 ); - proxy = function() { - return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); - }; - - // Set the guid of unique handler to the same of original handler, so it can be removed - proxy.guid = fn.guid = fn.guid || jQuery.guid++; - - return proxy; - }, - - now: Date.now, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = jQuery.type( obj ); - - if ( type === "function" || jQuery.isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.0 - * https://sizzlejs.com/ - * - * Copyright jQuery Foundation and other contributors - * Released under the MIT license - * http://jquery.org/license - * - * Date: 2016-01-04 - */ -(function( window ) { - -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ({}).hasOwnProperty, - arr = [], - pop = arr.pop, - push_native = arr.push, - push = arr.push, - slice = arr.slice, - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[i] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier - identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + - "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), - - rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + - "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + - "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + - whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), - funescape = function( _, escaped, escapedWhitespace ) { - var high = "0x" + escaped - 0x10000; - // NaN means non-codepoint - // Support: Firefox<24 - // Workaround erroneous numeric interpretation of +"0x" - return high !== high || escapedWhitespace ? - escaped : - high < 0 ? - // BMP codepoint - String.fromCharCode( high + 0x10000 ) : - // Supplemental Plane codepoint (surrogate pair) - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\x80-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - disabledAncestor = addCombinator( - function( elem ) { - return elem.disabled === true; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - (arr = slice.call( preferredDoc.childNodes )), - preferredDoc.childNodes - ); - // Support: Android<4.0 - // Detect silently failing push.apply - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - push_native.apply( target, slice.call(els) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - // Can't trust NodeList.length - while ( (target[j++] = els[i++]) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - - if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { - setDocument( context ); - } - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { - - // ID selector - if ( (m = match[1]) ) { - - // Document context - if ( nodeType === 9 ) { - if ( (elem = context.getElementById( m )) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && (elem = newContext.getElementById( m )) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[2] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( (m = match[3]) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !compilerCache[ selector + " " ] && - (!rbuggyQSA || !rbuggyQSA.test( selector )) ) { - - if ( nodeType !== 1 ) { - newContext = context; - newSelector = selector; - - // qSA looks outside Element context, which is not what we want - // Thanks to Andrew Dupont for this workaround technique - // Support: IE <=8 - // Exclude object elements - } else if ( context.nodeName.toLowerCase() !== "object" ) { - - // Capture the context ID, setting it first if necessary - if ( (nid = context.getAttribute( "id" )) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", (nid = expando) ); - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[i] = "#" + nid + " " + toSelector( groups[i] ); - } - newSelector = groups.join( "," ); - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - } - - if ( newSelector ) { - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return (cache[ key + " " ] = value); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement("fieldset"); - - try { - return !!fn( el ); - } catch (e) { - return false; - } finally { - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split("|"), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[i] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( (cur = cur.nextSibling) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return (name === "input" || name === "button") && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - // Known :disabled false positives: - // IE: *[disabled]:not(button, input, select, textarea, optgroup, option, menuitem, fieldset) - // not IE: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Check form elements and option elements for explicit disabling - return "label" in elem && elem.disabled === disabled || - "form" in elem && elem.disabled === disabled || - - // Check non-disabled form elements for fieldset[disabled] ancestors - "form" in elem && elem.disabled === false && ( - // Support: IE6-11+ - // Ancestry is covered for us - elem.isDisabled === disabled || - - // Otherwise, assume any non-" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll("[msallowcapture^='']").length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll("[selected]").length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push("~="); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll(":checked").length ) { - rbuggyQSA.push(":checked"); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push(".#.+[+~]"); - } - }); - - assert(function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement("input"); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll("[name=d]").length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll(":enabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll(":disabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll("*,:x"); - rbuggyQSA.push(",.*:"); - }); - } - - if ( (support.matchesSelector = rnative.test( (matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector) )) ) { - - assert(function( el ) { - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - }); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - )); - } : - function( a, b ) { - if ( b ) { - while ( (b = b.parentNode) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) { - - // Choose the first element that is related to our preferred document - if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) { - return -1; - } - if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - return a === document ? -1 : - b === document ? 1 : - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( (cur = cur.parentNode) ) { - ap.unshift( cur ); - } - cur = b; - while ( (cur = cur.parentNode) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[i] === bp[i] ) { - i++; - } - - return i ? - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[i], bp[i] ) : - - // Otherwise nodes in our document sort first - ap[i] === preferredDoc ? -1 : - bp[i] === preferredDoc ? 1 : - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - // Make sure that attribute selectors are quoted - expr = expr.replace( rattributeQuotes, "='$1']" ); - - if ( support.matchesSelector && documentIsHTML && - !compilerCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch (e) {} - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - // Set document vars if needed - if ( ( context.ownerDocument || context ) !== document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - (val = elem.getAttributeNode(name)) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return (sel + "").replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( (elem = results[i++]) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - // If no nodeType, this is expected to be an array - while ( (node = elem[i++]) ) { - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[1] = match[1].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape ); - - if ( match[2] === "~=" ) { - match[3] = " " + match[3] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[1] = match[1].toLowerCase(); - - if ( match[1].slice( 0, 3 ) === "nth" ) { - // nth-* requires argument - if ( !match[3] ) { - Sizzle.error( match[0] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) ); - match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" ); - - // other types prohibit arguments - } else if ( match[3] ) { - Sizzle.error( match[0] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[6] && match[2]; - - if ( matchExpr["CHILD"].test( match[0] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[3] ) { - match[2] = match[4] || match[5] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - // Get excess from tokenize (recursively) - (excess = tokenize( unquoted, true )) && - // advance to the next closing parenthesis - (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) { - - // excess is a negative index - match[0] = match[0].slice( 0, excess ); - match[2] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { return true; } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - (pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) && - classCache( className, function( elem ) { - return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" ); - }); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - }; - }, - - "CHILD": function( type, what, argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( (node = node[ dir ]) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( (node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - (diff = nodeIndex = 0) || start.pop()) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - // Use previously-cached element index if available - if ( useCache ) { - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - // Use the same loop as above to seek `elem` from the start - while ( (node = ++nodeIndex && node && node[ dir ] || - (diff = nodeIndex = 0) || start.pop()) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction(function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[i] ); - seed[ idx ] = !( matches[ idx ] = matched[i] ); - } - }) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - // Potentially complex pseudos - "not": markFunction(function( selector ) { - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction(function( seed, matches, context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( (elem = unmatched[i]) ) { - seed[i] = !(matches[i] = elem); - } - } - }) : - function( elem, context, xml ) { - input[0] = elem; - matcher( input, null, xml, results ); - // Don't keep the element (issue #299) - input[0] = null; - return !results.pop(); - }; - }), - - "has": markFunction(function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - }), - - "contains": markFunction(function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1; - }; - }), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - // lang value must be a valid identifier - if ( !ridentifier.test(lang || "") ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( (elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( (elem = elem.parentNode) && elem.nodeType === 1 ); - return false; - }; - }), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected); - }, - - "selected": function( elem ) { - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos["empty"]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo(function() { - return [ 0 ]; - }), - - "last": createPositionalPseudo(function( matchIndexes, length ) { - return [ length - 1 ]; - }), - - "eq": createPositionalPseudo(function( matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - }), - - "even": createPositionalPseudo(function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "odd": createPositionalPseudo(function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "lt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "gt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }) - } -}; - -Expr.pseudos["nth"] = Expr.pseudos["eq"]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || (match = rcomma.exec( soFar )) ) { - if ( match ) { - // Don't consume trailing commas as valid - soFar = soFar.slice( match[0].length ) || soFar; - } - groups.push( (tokens = []) ); - } - - matched = false; - - // Combinators - if ( (match = rcombinators.exec( soFar )) ) { - matched = match.shift(); - tokens.push({ - value: matched, - // Cast descendant combinators to space - type: match[0].replace( rtrim, " " ) - }); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] || - (match = preFilters[ type ]( match ))) ) { - matched = match.shift(); - tokens.push({ - value: matched, - type: type, - matches: match - }); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[i].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || (elem[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {}); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( (oldCache = uniqueCache[ key ]) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return (newCache[ 2 ] = oldCache[ 2 ]); - } else { - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) { - return true; - } - } - } - } - } - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[i]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[0]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[i], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( (elem = unmatched[i]) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction(function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( (elem = temp[i]) ) { - matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) ) { - // Restore matcherIn since elem is not yet a final match - temp.push( (matcherIn[i] = elem) ); - } - } - postFinder( null, (matcherOut = []), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) && - (temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) { - - seed[temp] = !(results[temp] = elem); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - }); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[0].type ], - implicitRelative = leadingRelative || Expr.relative[" "], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - (checkContext = context).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( (matcher = Expr.relative[ tokens[i].type ]) ) { - matchers = [ addCombinator(elementMatcher( matchers ), matcher) ]; - } else { - matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[j].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" }) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( (tokens = tokens.slice( j )) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find["TAG"]( "*", outermost ), - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1), - len = elems.length; - - if ( outermost ) { - outermostContext = context === document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && (elem = elems[i]) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - if ( !context && elem.ownerDocument !== document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( (matcher = elementMatchers[j++]) ) { - if ( matcher( elem, context || document, xml) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - // They will have gone through all possible matchers - if ( (elem = !matcher && elem) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( (matcher = setMatchers[j++]) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !(unmatched[i] || setMatched[i]) ) { - setMatched[i] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[i] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( (selector = compiled.selector || selector) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[0] = match[0].slice( 0 ); - if ( tokens.length > 2 && (token = tokens[0]).type === "ID" && - support.getById && context.nodeType === 9 && documentIsHTML && - Expr.relative[ tokens[1].type ] ) { - - context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[i]; - - // Abort if we hit a combinator - if ( Expr.relative[ (type = token.type) ] ) { - break; - } - if ( (find = Expr.find[ type ]) ) { - // Search, expanding context for leading sibling combinators - if ( (seed = find( - token.matches[0].replace( runescape, funescape ), - rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context - )) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split("").sort( sortOrder ).join("") === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert(function( el ) { - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement("fieldset") ) & 1; -}); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert(function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute("href") === "#" ; -}) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - }); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert(function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -}) ) { - addHandle( "value", function( elem, name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - }); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert(function( el ) { - return el.getAttribute("disabled") == null; -}) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - (val = elem.getAttributeNode( name )) && val.specified ? - val.value : - null; - } - }); -} - -return Sizzle; - -})( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -var risSimple = /^.[^:#\[\.,]*$/; - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( jQuery.isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - - } - - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - - } - - if ( typeof qualifier === "string" ) { - if ( risSimple.test( qualifier ) ) { - return jQuery.filter( qualifier, elements, not ); - } - - qualifier = jQuery.filter( qualifier, elements ); - } - - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1; - } ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - return elems.length === 1 && elem.nodeType === 1 ? - jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [] : - jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( jQuery.isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( jQuery.isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - return elem.contentDocument || jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnotwhite = ( /\S+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnotwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( jQuery.isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && jQuery.type( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && jQuery.isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && jQuery.isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - resolve.call( undefined, value ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.call( undefined, value ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && jQuery.isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( jQuery.isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - jQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Hold (or release) the ready event - holdReady: function( hold ) { - if ( hold ) { - jQuery.readyWait++; - } else { - jQuery.ready( true ); - } - }, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( jQuery.type( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !jQuery.isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - return chainable ? - elems : - - // Gets - bulk ? - fn.call( elems ) : - len ? fn( elems[ 0 ], key ) : emptyGet; -}; -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ jQuery.camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ jQuery.camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( jQuery.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( jQuery.camelCase ); - } else { - key = jQuery.camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnotwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = data === "true" ? true : - data === "false" ? false : - data === "null" ? null : - - // Only convert to a number if it doesn't change the string - +data + "" === data ? +data : - rbrace.test( data ) ? JSON.parse( data ) : - data; - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = jQuery.camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || jQuery.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - jQuery.contains( elem.ownerDocument, elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - -var swap = function( elem, options, callback, args ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.apply( elem, args || [] ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, - scale = 1, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - do { - - // If previous iteration zeroed out, double until we get *something*. - // Use string for doubling so we don't accidentally see scale as unchanged below - scale = scale || ".5"; - - // Adjust and apply - initialInUnit = initialInUnit / scale; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Update scale, tolerating zero or NaN from tween.cur() - // Break the loop if scale is unchanged or perfect, or if we've just had enough. - } while ( - scale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations - ); - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ), - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]+)/i ); - -var rscriptType = ( /^$|\/(?:java|ecma)script/i ); - - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // Support: IE <=9 only - option: [ 1, "" ], - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
    " ], - col: [ 2, "", "
    " ], - tr: [ 2, "", "
    " ], - td: [ 3, "", "
    " ], - - _default: [ 0, "", "" ] -}; - -// Support: IE <=9 only -wrapMap.optgroup = wrapMap.option; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret = typeof context.getElementsByTagName !== "undefined" ? - context.getElementsByTagName( tag || "*" ) : - typeof context.querySelectorAll !== "undefined" ? - context.querySelectorAll( tag || "*" ) : - []; - - return tag === undefined || tag && jQuery.nodeName( context, tag ) ? - jQuery.merge( [ context ], ret ) : - ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, contains, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( jQuery.type( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - contains = jQuery.contains( elem.ownerDocument, elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( contains ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; -} )(); -var documentElement = document.documentElement; - - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 only -// See #13393 for more info -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Don't attach events to noData or text/comment nodes (but allow plain objects) - if ( !elemData ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = {}; - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnotwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnotwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - // Make a writable jQuery.Event from the native event object - var event = jQuery.event.fix( nativeEvent ); - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // Triggered event must either 1) have no namespace, or 2) have namespace(s) - // a subset or equal to those in the bound event (both can have no namespace). - if ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, matches, sel, handleObj, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Support: IE <=9 - // Find delegate handlers - // Black-hole SVG instance trees (#13180) - // - // Support: Firefox <=42 - // Avoid non-left-click in FF but don't block IE radio events (#3861, gh-2343) - if ( delegateCount && cur.nodeType && - ( event.type !== "click" || isNaN( event.button ) || event.button < 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && ( cur.disabled !== true || event.type !== "click" ) ) { - matches = []; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matches[ sel ] === undefined ) { - matches[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matches[ sel ] ) { - matches.push( handleObj ); - } - } - if ( matches.length ) { - handlerQueue.push( { elem: cur, handlers: matches } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: this, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: jQuery.isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - focus: { - - // Fire native event if possible so blur/focus sequence is correct - trigger: function() { - if ( this !== safeActiveElement() && this.focus ) { - this.focus(); - return false; - } - }, - delegateType: "focusin" - }, - blur: { - trigger: function() { - if ( this === safeActiveElement() && this.blur ) { - this.blur(); - return false; - } - }, - delegateType: "focusout" - }, - click: { - - // For checkbox, fire native event so checked state will be right - trigger: function() { - if ( this.type === "checkbox" && this.click && jQuery.nodeName( this, "input" ) ) { - this.click(); - return false; - } - }, - - // For cross-browser consistency, don't fire native .click() on links - _default: function( event ) { - return jQuery.nodeName( event.target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || jQuery.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - return ( button & 1 ? 1 : ( button & 2 ? 3 : ( button & 4 ? 2 : 0 ) ) ); - } - - return event.which; - } -}, jQuery.event.addProp ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - /* eslint-disable max-len */ - - // See https://github.com/eslint/eslint/issues/3229 - rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi, - - /* eslint-enable */ - - // Support: IE <=10 - 11, Edge 12 - 13 - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -function manipulationTarget( elem, content ) { - if ( jQuery.nodeName( elem, "table" ) && - jQuery.nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return elem.getElementsByTagName( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - var match = rscriptTypeMasked.exec( elem.type ); - - if ( match ) { - elem.type = match[ 1 ]; - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.access( src ); - pdataCur = dataPriv.set( dest, pdataOld ); - events = pdataOld.events; - - if ( events ) { - delete pdataCur.handle; - pdataCur.events = {}; - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = concat.apply( [], args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - isFunction = jQuery.isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( isFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( isFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl ) { - jQuery._evalUrl( node.src ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && jQuery.contains( node.ownerDocument, node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html.replace( rxhtmlTag, "<$1>" ); - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = jQuery.contains( elem.ownerDocument, elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rmargin = ( /^margin/ ); - -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - div.style.cssText = - "box-sizing:border-box;" + - "position:relative;display:block;" + - "margin:auto;border:1px;padding:1px;" + - "top:1%;width:50%"; - div.innerHTML = ""; - documentElement.appendChild( container ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = divStyle.marginLeft === "2px"; - boxSizingReliableVal = divStyle.width === "4px"; - - // Support: Android 4.0 - 4.3 only - // Some styles come back with percentage values, even though they shouldn't - div.style.marginRight = "50%"; - pixelMarginRightVal = divStyle.marginRight === "4px"; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - var pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - container.style.cssText = "border:0;width:8px;height:0;top:0;left:-9999px;" + - "padding:0;margin-top:1px;position:absolute"; - container.appendChild( div ); - - jQuery.extend( support, { - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelMarginRight: function() { - computeStyleTests(); - return pixelMarginRightVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - style = elem.style; - - computed = computed || getStyles( elem ); - - // Support: IE <=9 only - // getPropertyValue is only needed for .css('filter') (#12537) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !jQuery.contains( elem.ownerDocument, elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }, - - cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style; - -// Return a css property mapped to a potentially vendor prefixed property -function vendorPropName( name ) { - - // Shortcut for names that are not vendor prefixed - if ( name in emptyStyle ) { - return name; - } - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -function setPositiveNumber( elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) { - var i = extra === ( isBorderBox ? "border" : "content" ) ? - - // If we already have the right measurement, avoid augmentation - 4 : - - // Otherwise initialize for horizontal or vertical properties - name === "width" ? 1 : 0, - - val = 0; - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin, so add it if we want it - if ( extra === "margin" ) { - val += jQuery.css( elem, extra + cssExpand[ i ], true, styles ); - } - - if ( isBorderBox ) { - - // border-box includes padding, so remove it if we want content - if ( extra === "content" ) { - val -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // At this point, extra isn't border nor margin, so remove border - if ( extra !== "margin" ) { - val -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } else { - - // At this point, extra isn't content, so add padding - val += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // At this point, extra isn't content nor padding, so add border - if ( extra !== "padding" ) { - val += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - return val; -} - -function getWidthOrHeight( elem, name, extra ) { - - // Start with offset property, which is equivalent to the border-box value - var val, - valueIsBorderBox = true, - styles = getStyles( elem ), - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - if ( elem.getClientRects().length ) { - val = elem.getBoundingClientRect()[ name ]; - } - - // Some non-html elements return undefined for offsetWidth, so check for null/undefined - // svg - https://bugzilla.mozilla.org/show_bug.cgi?id=649285 - // MathML - https://bugzilla.mozilla.org/show_bug.cgi?id=491668 - if ( val <= 0 || val == null ) { - - // Fall back to computed then uncomputed css if necessary - val = curCSS( elem, name, styles ); - if ( val < 0 || val == null ) { - val = elem.style[ name ]; - } - - // Computed unit is not pixels. Stop here and return. - if ( rnumnonpx.test( val ) ) { - return val; - } - - // Check for style in case a browser which returns unreliable values - // for getComputedStyle silently falls back to the reliable elem.style - valueIsBorderBox = isBorderBox && - ( support.boxSizingReliable() || val === elem.style[ name ] ); - - // Normalize "", auto, and prepare for extra - val = parseFloat( val ) || 0; - } - - // Use the active box-sizing model to add/subtract irrelevant styles - return ( val + - augmentWidthOrHeight( - elem, - name, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: { - "float": "cssFloat" - }, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = jQuery.camelCase( name ), - style = elem.style; - - name = jQuery.cssProps[ origName ] || - ( jQuery.cssProps[ origName ] = vendorPropName( origName ) || origName ); - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - if ( type === "number" ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - style[ name ] = value; - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = jQuery.camelCase( name ); - - // Make sure that we're working with the right name - name = jQuery.cssProps[ origName ] || - ( jQuery.cssProps[ origName ] = vendorPropName( origName ) || origName ); - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( i, name ) { - jQuery.cssHooks[ name ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, name, extra ); - } ) : - getWidthOrHeight( elem, name, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = extra && getStyles( elem ), - subtract = extra && augmentWidthOrHeight( - elem, - name, - extra, - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - styles - ); - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ name ] = value; - value = jQuery.css( elem, name ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( !rmargin.test( prefix ) ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( jQuery.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && - ( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null || - jQuery.cssHooks[ tween.prop ] ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, timerId, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function raf() { - if ( timerId ) { - window.requestAnimationFrame( raf ); - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = jQuery.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 13 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = jQuery.camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( jQuery.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - if ( percent < 1 && length ) { - return remaining; - } else { - deferred.resolveWith( elem, [ animation ] ); - return false; - } - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( jQuery.isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - jQuery.proxy( result.stop, result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( jQuery.isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - // attach callbacks from options - return animation.progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( jQuery.isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnotwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - jQuery.isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !jQuery.isFunction( easing ) && easing - }; - - // Go to the end state if fx are off or if document is hidden - if ( jQuery.fx.off || document.hidden ) { - opt.duration = 0; - - } else { - opt.duration = typeof opt.duration === "number" ? - opt.duration : opt.duration in jQuery.fx.speeds ? - jQuery.fx.speeds[ opt.duration ] : jQuery.fx.speeds._default; - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( jQuery.isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue && type !== false ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = jQuery.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Checks the timer has not already been removed - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - if ( timer() ) { - jQuery.fx.start(); - } else { - jQuery.timers.pop(); - } -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( !timerId ) { - timerId = window.requestAnimationFrame ? - window.requestAnimationFrame( raf ) : - window.setInterval( jQuery.fx.tick, jQuery.fx.interval ); - } -}; - -jQuery.fx.stop = function() { - if ( window.cancelAnimationFrame ) { - window.cancelAnimationFrame( timerId ); - } else { - window.clearInterval( timerId ); - } - - timerId = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - jQuery.nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - attrNames = value && value.match( rnotwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - return tabindex ? - parseInt( tabindex, 10 ) : - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && elem.href ? - 0 : - -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - -var rclass = /[\t\r\n\f]/g; - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( jQuery.isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( typeof value === "string" && value ) { - classes = value.match( rnotwhite ) || []; - - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && - ( " " + curValue + " " ).replace( rclass, " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = jQuery.trim( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( jQuery.isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - if ( typeof value === "string" && value ) { - classes = value.match( rnotwhite ) || []; - - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && - ( " " + curValue + " " ).replace( rclass, " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = jQuery.trim( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value; - - if ( typeof stateVal === "boolean" && type === "string" ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( jQuery.isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( type === "string" ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = value.match( rnotwhite ) || []; - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + getClass( elem ) + " " ).replace( rclass, " " ) - .indexOf( className ) > -1 - ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g, - rspaces = /[\x20\t\r\n\f]+/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, isFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - return typeof ret === "string" ? - - // Handle most common string cases - ret.replace( rreturn, "" ) : - - // Handle cases where value is null/undef or number - ret == null ? "" : ret; - } - - return; - } - - isFunction = jQuery.isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( isFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( jQuery.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - jQuery.trim( jQuery.text( elem ) ).replace( rspaces, " " ); - } - }, - select: { - get: function( elem ) { - var value, option, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length, - i = index < 0 ? - max : - one ? index : 0; - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !jQuery.nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( jQuery.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - elem[ type ](); - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " + - "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " + - "change select submit keydown keypress keyup contextmenu" ).split( " " ), - function( i, name ) { - - // Handle event binding - jQuery.fn[ name ] = function( data, fn ) { - return arguments.length > 0 ? - this.on( name, null, data, fn ) : - this.trigger( name ); - }; -} ); - -jQuery.fn.extend( { - hover: function( fnOver, fnOut ) { - return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver ); - } -} ); - - - - -support.focusin = "onfocusin" in window; - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - var doc = this.ownerDocument || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = jQuery.now(); - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( jQuery.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && jQuery.type( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = jQuery.isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - // If an array was passed in, assume that it is an array of form elements. - if ( jQuery.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( i, elem ) { - var val = jQuery( this ).val(); - - return val == null ? - null : - jQuery.isArray( val ) ? - jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ) : - { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rts = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnotwhite ) || []; - - if ( jQuery.isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ]; - } - } - match = responseHeaders[ key.toLowerCase() ]; - } - return match == null ? null : match; - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnotwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 13 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available, append data to url - if ( s.data ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add anti-cache in uncached url if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rts, "" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? 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    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0.1
    Date:25/10/2017
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    SF_TOOLS is a series of Python 2.7 modules with applications to image analysis, -signal processing and statistics.

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    In order to run the code in this repository the following packages must be -installed:

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100644 index b5d2779..0000000 --- a/docs/build/html/setup.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - - - - setup module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    setup module

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    - © Copyright 2017, Samuel Farrens. - -

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    2.1.1. sf_tools.base package

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    2.1.1.2. Module contents

    -

    BASE ROUTINES

    -

    This module contains submodules for basic operations such as type -transformations and adjustments to the default output of Numpy functions.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.1
    Date:25/10/2017
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    2.1.1.1.1. sf_tools.base.np_adjust module

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    NUMPY ADJUSTMENT ROUTINES

    -

    This module contains methods for adjusting the default output for certain -Numpy functions.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.1
    Date:03/04/2017
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    Rotate

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    This method rotates an input numpy array by 180 degrees.

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array (at least 2D)
    Returns:
    Return type:np.ndarray rotated data
    -

    Notes

    -

    Adjustment to numpy.rot90()

    -

    Examples

    -
    >>> from sf_tools.base.np_adjust import rotate
->>> x = np.arange(9).reshape((3, 3))
->>> x
-array([[0, 1, 2],
-       [3, 4, 5],
-       [6, 7, 8]])
->>> rotate(x)
-array([[8, 7, 6],
-       [5, 4, 3],
-       [2, 1, 0]])
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    -sf_tools.base.np_adjust.rotate_stack(data)[source]
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    Rotate stack

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    This method rotates each array in a stack of arrays by 180 degrees.

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array (at least 3D)
    Returns:
    Return type:np.ndarray rotated data
    -

    Examples

    -
    >>> from sf_tools.base.np_adjust import rotate_stack
->>> x = np.arange(18).reshape((2, 3, 3))
->>> x
-array([[[ 0,  1,  2],
-        [ 3,  4,  5],
-        [ 6,  7,  8]],
-       [[ 9, 10, 11],
-        [12, 13, 14],
-        [15, 16, 17
->>> rotate_stack(x)
-array([[[ 8,  7,  6],
-        [ 5,  4,  3],
-        [ 2,  1,  0]],
-       [[17, 16, 15],
-        [14, 13, 12],
-        [11, 10,  9]]])
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    Pad array

    -

    This method pads an input numpy array with zeros in all directions.

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array (at least 2D)
      • -
    • padding (int, tuple) – Amount of padding in x and y directions, respectively
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray padded data

    -
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    Notes

    -

    Adjustment to numpy.pad()

    -

    Examples

    -
    >>> from sf_tools.base.np_adjust import pad2d
->>> x = np.arange(9).reshape((3, 3))
->>> x
-array([[0, 1, 2],
-       [3, 4, 5],
-       [6, 7, 8]])
->>> pad2d(x, (1, 1))
-array([[0, 0, 0, 0, 0],
-       [0, 0, 1, 2, 0],
-       [0, 3, 4, 5, 0],
-       [0, 6, 7, 8, 0],
-       [0, 0, 0, 0, 0]])
-
    -
    -
    - -
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    -sf_tools.base.np_adjust.x_bins(vals)[source]
    -

    X-range bins

    -

    This method sets the bin values for a histogram.

    - --- - - - - - - - -
    Parameters:vals (np.ndarray) – X-range bins from np.histogram()[1]
    Returns:
    Return type:np.ndarray corrected x-range bin data
    -

    Notes

    -

    Adjustment to numpy.histogram()

    -

    Examples

    -
    >>> from sf_tools.base.np_adjust import x_bins
->>> data = np.array([1, 2, 1, 3, 1, 1])
->>> hist, bins = np.histogram(data)
->>> hist
-array([4, 0, 0, 0, 0, 1, 0, 0, 0, 1])
->>> bins
-array([ 1. ,  1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])
->>> x_bins(bins)
-array([ 1.1,  1.3,  1.5,  1.7,  1.9,  2.1,  2.3,  2.5,  2.7,  2.9])
-
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    -

    X-range bins (step function)

    -

    This method sets the bin values for a histogram plotted as a step funciton.

    - --- - - - - - - - -
    Parameters:vals (np.ndarray) – X-range bins from np.histogram()[1]
    Returns:
    Return type:np.ndarray corrected x-range bin data
    -

    Notes

    -

    Adjustment to numpy.histogram()

    -

    Examples

    -
    >>> from sf_tools.base.np_adjust import x_bins
->>> data = np.array([1, 2, 1, 3, 1, 1])
->>> hist, bins = np.histogram(data)
->>> hist
-array([4, 0, 0, 0, 0, 1, 0, 0, 0, 1])
->>> bins
-array([ 1. ,  1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])
->>> x_bins_step(bins)
-array([ 1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])
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    Fancy transpose right

    -

    Apply fancy_transpose() to data with roll=1

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array
    Returns:
    Return type:np.ndarray transposed data
    -
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    -sf_tools.base.np_adjust.ftl(data)[source]
    -

    Fancy transpose left

    -

    Apply fancy_transpose() to data with roll=-1

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array
    Returns:
    Return type:np.ndarray transposed data
    -
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    -sf_tools.base.np_adjust.fancy_transpose(data, roll=1)[source]
    -

    Fancy transpose

    -

    This method transposes a multidimensional matrix.

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array
      • -
    • roll (int) – Roll direction and amount. Default (roll=1)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray transposed data

    -
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    Notes

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    Adjustment to numpy.transpose

    -

    Examples

    -
    >>> from sf_tools.base.np_adjust import fancy_transpose
->>> x = np.arange(27).reshape(3, 3, 3)
->>> x
-array([[[ 0,  1,  2],
-        [ 3,  4,  5],
-        [ 6,  7,  8]],
-       [[ 9, 10, 11],
-        [12, 13, 14],
-        [15, 16, 17]],
-       [[18, 19, 20],
-        [21, 22, 23],
-        [24, 25, 26]]])
->>> fancy_transpose(x)
-array([[[ 0,  3,  6],
-        [ 9, 12, 15],
-        [18, 21, 24]],
-       [[ 1,  4,  7],
-        [10, 13, 16],
-        [19, 22, 25]],
-       [[ 2,  5,  8],
-        [11, 14, 17],
-        [20, 23, 26]]])
->>> fancy_transpose(x, roll=-1)
-array([[[ 0,  9, 18],
-        [ 1, 10, 19],
-        [ 2, 11, 20]],
-       [[ 3, 12, 21],
-        [ 4, 13, 22],
-        [ 5, 14, 23]],
-       [[ 6, 15, 24],
-        [ 7, 16, 25],
-        [ 8, 17, 26]]])
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    DATA TRANSFORM ROUTINES

    -

    This module contains methods for transforming data.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:20/10/2017
    -
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    -

    Cube to Map

    -

    This method transforms the input data from a 3D cube to a 2D map with a -specified layout

    - --- - - - - - - - - - -
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      -
    • data_cube (np.ndarray) – Input data cube, 3D array of 2D images
      • -
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      • -
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    np.ndarray 2D map

    -
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    ValueError – For invalid layout

    -
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    -

    Map to cube

    -

    This method transforms the input data from a 2D map with given layout to -a 3D cube

    - --- - - - - - - - - - -
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      -
    • data_map (np.ndarray) – Input data map, 2D array
      • -
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      • -
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    -
    Return type:

    np.ndarray 3D cube

    -
    Raises:

    ValueError – For invalid layout

    -
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    -

    Map to Matrix

    -

    This method transforms a 2D map to a 2D matrix

    - --- - - - - - - - - - -
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      -
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      • -
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      • -
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    np.ndarray 2D matrix

    -
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    ValueError – For invalid layout

    -
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    -

    Matrix to Map

    -

    This method transforms a 2D matrix to a 2D map

    - --- - - - - - - - - - -
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      -
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      • -
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    Return type:

    np.ndarray 2D map

    -
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    ValueError – For invalid layout

    -
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    -

    Cube to Matrix

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    This method transforms a 3D cube to a 2D matrix

    - --- - - - - - - - -
    Parameters:data_cube (np.ndarray) – Input data cube, 3D array
    Returns:
    Return type:np.ndarray 2D matrix
    -
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    -
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    -

    Matrix to Cube

    -

    This method transforms a 2D matrix to a 3D cube

    - --- - - - - - - - -
    Parameters:
      -
    • data_matrix (np.ndarray) – Input data cube, 2D array
      • -
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    -

    TYPE HANDLING ROUTINES

    -

    This module contains methods for handing object types.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:04/04/2017
    -
    -
    -sf_tools.base.types.check_float(val)[source]
    -

    Check if input value is a float or a np.ndarray of floats, if not -convert.

    - --- - - - - - - - -
    Parameters:val (any) – Input value
    Returns:
    Return type:float or np.ndarray of floats
    -
    - -
    -
    -sf_tools.base.types.check_int(val)[source]
    -

    Check if input value is an int or a np.ndarray of ints, if not convert.

    - --- - - - - - - - -
    Parameters:val (any) – Input value
    Returns:
    Return type:int or np.ndarray of ints
    -
    - -
    -
    -sf_tools.base.types.val2int(val)[source]
    -

    Convert to int

    -

    This method checks if input value is an int and if not converts it.

    - --- - - - - - - - - - -
    Parameters:val (int, float, str, list, tuple or np.ndarray) – Input value
    Returns:
    Return type:int interger value or np.ndarray array of ints
    Raises:ValueError – For invalid input type
    -

    Examples

    -
    >>> from sf_tools.base.types import val2int
->>> x = np.arange(5).astype(float)
->>> x
-array([ 0.,  1.,  2.,  3.,  4.])
->>> val2int(x)
-array([0, 1, 2, 3, 4])
-
    -
    -
    - -
    -
    -sf_tools.base.types.val2float(val)[source]
    -

    Convert to float

    -

    This method checks if input value is a float and if not converts it.

    - --- - - - - - - - -
    Parameters:val (int, float, str, list, tuple or np.ndarray) – Input value
    Returns:
    Return type:float floating point value or np.ndarray array of floats
    -

    Examples

    -
    >>> from sf_tools.base.types import val2float
->>> x = np.arange(5)
->>> x
-array([0, 1, 2, 3, 4])
->>> val2float(x)
-array([ 0.,  1.,  2.,  3.,  4.])
-
    -
    -
    - -
    -
    -sf_tools.base.types.val2str(val)[source]
    -

    Convert to string

    -

    This method checks if input value is a string and if not converts it.

    - --- - - - - - - - -
    Parameters:val (int, float, str, list, tuple or np.ndarray) – Input value
    Returns:
    Return type:str string or np.ndarray array of strings
    -

    Examples

    -
    >>> from sf_tools.base.types import val2str
->>> x = np.arange(5)
->>> x
-array([0, 1, 2, 3, 4])
->>> val2str(x)
-array(['0', '1', '2', '3', '4'],
-      dtype='|S21')
-
    -
    -
    - -
    -
    -sf_tools.base.types.nan2val(array, val=0.0)[source]
    -

    Convert NAN to val

    -

    This converts all NANs in an array to a specified value.

    - --- - - - - - - - -
    Parameters:
      -
    • array (np.ndarray, list or tuple) – Input array
      • -
    • val (int or float, optional) – Value to replace NANs. Default (val=0.0)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray array without NANs

    -
    -

    Notes

    -

    Output data type defined by val type.

    -

    Examples

    -
    >>> from sf_tools.base.types import nan2val
->>> x = [1., 2., np.nan, 4.]
->>> x
-[1.0, 2.0, nan, 4.0]
->>> nan2val(x, 3.)
-array([ 1.,  2.,  3.,  4.])
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    2. sf_tools package

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    2.1. Subpackages

    -
    - -
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    2.2. Module contents

    -

    SF-TOOLS PACKAGE

    -

    SF_TOOLS is a series of Python modules with applications to image analysis, -signal processing and statistics.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0.1
    Date:25/10/2017
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    2.1.2.1.1. sf_tools.image.convolve module

    -

    CONVOLUTION ROUTINES

    -

    This module contains methods for convolution.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:23/10/2017
    -
    -
    -sf_tools.image.convolve.convolve_np(image, kernel)[source]
    -

    Convolve with Numpy FFT

    -

    This method convolves the input image with the input kernel

    - --- - - - - - - - -
    Parameters:
      -
    • image (np.ndarray) – 2D image array
      • -
    • kernel (np.ndarray) – 2D kernel array
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray 2D convolved image array

    -
    -
    - -
    -
    -sf_tools.image.convolve.deconvolve_np(image, kernel)[source]
    -

    Deconvolve with Numpy FFT

    -

    This method deconvolves the input image with the input kernel

    - --- - - - - - - - -
    Parameters:
      -
    • image (np.ndarray) – 2D image array
      • -
    • kernel (np.ndarray) – 2D kernel array
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray 2D deconvolved image array

    -
    -
    - -
    -
    -sf_tools.image.convolve.convolve(data, kernel, method='astropy')[source]
    -

    Convolve data with kernel

    -

    This method convolves the input data with a given kernel using FFT and -is the default convolution used for all routines

    - --- - - - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array, normally a 2D image
      • -
    • kernel (np.ndarray) – Input kernel array, normally a 2D kernel
      • -
    • method (str {'astropy', 'scipy'}, optional) –

      Convolution method (default is ‘astropy’)

      -
      -
      ’astropy’:
      -
      Uses the astropy.convolution.convolve_fft method provided in -Astropy (http://www.astropy.org/)
      -
      ’scipy’:
      -
      Uses the scipy.signal.fftconvolve method provided in SciPy -(https://www.scipy.org/)
      -
      -
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray convolved data

    -
    Raises:
      -
    • ValueError – If data and kernel do not have the same number of dimensions
      • -
    • ValueError – If method is not ‘astropy’ or ‘scipy’
      • -
    -
    -
    - -
    -
    -sf_tools.image.convolve.convolve_stack(data, kernel, rot_kernel=False, method='astropy')[source]
    -

    Convolve stack of data with stack of kernels

    -

    This method convolves the input data with a given kernel using FFT and -is the default convolution used for all routines

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array, normally a 2D image
      • -
    • kernel (np.ndarray) – Input kernel array, normally a 2D kernel
      • -
    • rot_kernel (bool) – Option to rotate kernels by 180 degrees
      • -
    • method (str {'astropy', 'scipy'}, optional) – Convolution method (default is ‘astropy’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray convolved data

    -
    -
    - -
    -
    -sf_tools.image.convolve.psf_convolve(data, psf, psf_rot=False, psf_type='fixed', method='astropy')[source]
    -

    Convolve data with PSF

    -

    This method convolves an image with a PSF

    - --- - - - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array, normally an array of 2D images
      • -
    • psf (np.ndarray) – Input PSF array, normally either a single 2D PSF or an array of 2D -PSFs
      • -
    • psf_rot (bool) – Option to rotate PSF by 180 degrees
      • -
    • psf_type (str {'fixed', 'obj_var'}, optional) – PSF type (default is ‘fixed’)
      • -
    • method (str {'astropy', 'scipy'}, optional) –

      Convolution method (default is ‘astropy’)

      -
      -
      ’fixed’:
      -
      The PSF is fixed, i.e. it is the same for each image
      -
      ’obj_var’:
      -
      The PSF is object variant, i.e. it is different for each image
      -
      -
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray convolved data

    -
    Raises:

    ValueError – If psf_type is not ‘fixed’ or ‘obj_var’

    -
    -
    - -
    -
    -sf_tools.image.convolve.pseudo_inverse(image, kernel, weight=None)[source]
    -

    Pseudo inverse

    -

    This method calculates the pseudo inverse of the input image for the given -kernel using FFT

    - --- - - - - - - - -
    Parameters:
      -
    • image (np.ndarray) – Input image, 2D array
      • -
    • kernel (np.ndarray) – Input kernel, 2D array
      • -
    • weight (np.ndarray, optional) – Optional weights, 2D array
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray result of the pseudo inverse

    -
    -
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    2.1.2.1.2. sf_tools.image.distort module

    -

    IMAGE DISTORTION ROUTINES

    -

    This module contains methods for playing around with image properties.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.1
    Date:20/10/2017
    -
    -
    -sf_tools.image.distort.downsample(image, factor)[source]
    -

    Downsample

    -

    This method downsamples (decimates) an image.

    - --- - - - - - - - - - -
    Parameters:
      -
    • image (np.ndarray) – Input image array
      • -
    • factor (int) – Downsampling factor
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray downsampled image array

    -
    Raises:

    ValueError – For invalid downsampling factor

    -
    -
    - -
    -
    -sf_tools.image.distort.resize_even_image(image)[source]
    -

    Resize even image

    -

    This method returns an image with odd dimensions.

    - --- - - - - - - - -
    Parameters:image (np.ndarray) – Input image array
    Returns:
    Return type:np.ndarray resized image array
    -
    - -
    -
    -sf_tools.image.distort.roll_2d(data, roll_rad=(1, 1))[source]
    -

    Roll in 2D

    -

    This method rolls an array in 2 dimensions.

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input 2D data array
      • -
    • roll_rad (tuple) – Roll radius in each dimension
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray rolled array

    -
    -
    - -
    -
    -sf_tools.image.distort.rot_and_roll(data)[source]
    -

    Rotate and roll

    -

    This method rotates (by 180 deg) and rolls a 2D array.

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input 2D data array
    Returns:
    Return type:np.ndarray rotated and rolled array
    -
    - -
    -
    -sf_tools.image.distort.gen_mask(kernel_shape, image_shape)[source]
    -

    Generate mask

    -

    This method generates an image mask.

    - --- - - - - - - - -
    Parameters:
      -
    • kernel_shape (tuple) – Shape of kernel
      • -
    • image_shape (tuple) – Shape of image
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray boolean mask

    -
    -
    - -
    -
    -sf_tools.image.distort.roll_sequence(data_shape)[source]
    -

    Roll sequence

    -

    This method generates the roll sequence for a 2D array.

    - --- - - - - - - - -
    Parameters:data_shape (tuple) – Shape of data
    Returns:
    Return type:list of roll radii
    -
    - -
    -
    -sf_tools.image.distort.kernel_pattern(kernel_shape, mask)[source]
    -

    Kernel pattern

    -

    This method generates the kernel pattern. Rather than padding the kernel -with zeroes to match the image size one simply extracts the series of -repitions of the base kernel patterns.

    - --- - - - - - - - -
    Parameters:
      -
    • kernel_shape (tuple) – Shape of kernel
      • -
    • mask (np.ndarray) – Boolean mask
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray kernel pattern

    -
    -
    - -
    -
    -sf_tools.image.distort.rearrange_kernel(kernel, data_shape=None)[source]
    -

    Rearrange kernel

    -

    This method rearanges the input kernel elements for vector multiplication. -The input kernel is padded with zeroes to match the image size.

    - --- - - - - - - - -
    Parameters:
      -
    • kernel (np.ndarray) – Input kernel array
      • -
    • data_shape (tuple) – Shape of the data
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray rearanged matrix of kernel elements

    -
    -
    - -
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    2.1.2. sf_tools.image package

    - -
    -

    2.1.2.2. Module contents

    -

    IMAGE ROUTINES

    -

    This module contains submodules for image analysis.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:06/04/2017
    -

    References

    - - - - - -
    [C20138]Cropper et al., Defining a Weak Lensing Experiment in Space, 2013, -MNRAS, 431, 3103C. [https://arxiv.org/abs/1210.7691]
    - - - - - -
    [BM20078]Baker and Moallem, Iteratively weighted centroiding for -Shack-Hartman wave-front sensors, 2007n, Optics Express, 15, 8, 5147. -[https://www.osapublishing.org/oe/abstract.cfm?uri=oe-15-8-5147]
    - - - - - -
    [NS20168]Ngolé and Starck, PSFs field learning based on Optimal Transport -distances, 2016,
    - - - - - -
    [S20058]Schneider, Weak Gravitational Lensing, 2005, arXiv. -[https://arxiv.org/abs/astro-ph/0509252]
    -
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    2.1.2.1.3. sf_tools.image.quality module

    -

    QUALITY ASSESSMENT ROUTINES

    -

    This module contains methods and classes for assessing the quality of image -reconstructions.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:20/10/2017
    -

    Notes

    -

    Some of the methods in this module are based on work by Fred Ngole.

    -
    -
    -sf_tools.image.quality.nmse(image1, image2, metric=<function mean>)[source]
    -

    Normalised Mean Square Error

    -

    This method computes the NMSE of two input images, or the result of the -input metric on a stack of input images.

    - --- - - - - - - - - - -
    Parameters:
      -
    • image1 (np.ndarray) – First image (or stack of images) to be analysed (original image)
      • -
    • image2 (np.ndarray) – Second image (or stack of images) to be analysed (reconstructed image)
      • -
    • metric (function) – Metric to be apllied to NMSE results (default is ‘np.mean’)
      • -
    -
    Returns:

    -
    Return type:

    float NMSE value or metric value(s)

    -
    Raises:

    ValueError – For invalid input data dimensions

    -
    -
    -

    See also

    -
    -
    e_error()
    -
    ellipticity error
    -
    -
    -

    Notes

    -

    This method implements the following equation:

    -
    -
      -
    • Equations from [NS2016] sec 4.1:
      • -
    -
    -\[\text{NMSE} = \frac{1}{D}\sum_{i=1}^D - \frac{\|\hat{\text{Im}}_i - \text{Im}_i\|_2^2} - {\|\text{Im}_i\|_2^2}\]
    -
    -

    Examples

    -
    >>> from image.quality import nmse
-
    -
    -
    - -
    -
    -sf_tools.image.quality.e_error(image1, image2, metric=<function mean>)[source]
    -

    Normalised Mean Square Error

    -

    This method computes the ellipticity error of two input images, or the -result of the input metric on the ellipticity values.

    - --- - - - - - - - - - -
    Parameters:
      -
    • image1 (np.ndarray) – First image to be analysed (original image)
      • -
    • image2 (np.ndarray) – Second image to be analysed (reconstructed image)
      • -
    • metric (function) – Metric to be apllied to ellipticity error results (default is -‘np.mean’)
      • -
    -
    Returns:

    -
    Return type:

    float ellipticity error value or metric value(s)

    -
    Raises:

    ValueError – For invalid input data dimensions

    -
    -
    -

    See also

    -
    -
    nmse()
    -
    nmse error
    -
    -
    -

    Notes

    -

    This method implements the following equation:

    -
    -
      -
    • Equations from [NS2016] sec 4.1:
      • -
    -
    -\[\text{E}_\gamma = \frac{1}{D}\sum_{i=1}^D - \|\gamma(\text{Im}_i) - \gamma(\hat{\text{Im}}_i)\|_2\]
    -
    -

    Examples

    -
    >>> from image.quality import e_error
-
    -
    -
    - -
    - - -
    -
    - -
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    - -
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    - - - - -
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    2.1.2.1.4. sf_tools.image.shape module

    -

    SHAPE ESTIMATION ROUTINES

    -

    This module contains methods and classes for estimating galaxy shapes.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.4
    Date:20/10/2017
    -

    Notes

    -

    Some of the methods in this module are based on work by Fred Ngole.

    -
    -
    -sf_tools.image.shape.ellipticity_atoms(data, offset=0)[source]
    -

    Calculate ellipticity

    -

    This method calculates the ellipticity of an image from its shape -projection components.

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array, the image to be analysed
      • -
    • offset (int, optional) – Shape projection offset (default is ‘0’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray of the image ellipticity components

    -
    -
    -

    See also

    -
    -
    shape_project()
    -
    shape projection matrix
    -
    -
    -

    Notes

    -

    This technique was developed by Fred Ngole and implements the following -equations:

    -
    -
      -
    • Equations C.1 and C.2 from [NS2016] appendix:
      • -
    -
    -\[ \begin{align}\begin{aligned}e_1(\mathbf{X}_i) = \frac{<\mathbf{X}_i, \mathbf{U}_4> - <\mathbf{X}_i, \mathbf{U}_2> - - <\mathbf{X}_i, \mathbf{U}_0>^2 + - <\mathbf{X}_i, \mathbf{U}_1>^2} - {<\mathbf{X}_i, \mathbf{U}_3> - <\mathbf{X}_i, \mathbf{U}_2> - - <\mathbf{X}_i, \mathbf{U}_0>^2 - - <\mathbf{X}_i, \mathbf{U}_1>^2 - }\\e_2(\mathbf{X}_i) = \frac{2\left(<\mathbf{X}_i, \mathbf{U}_5> - <\mathbf{X}_i, \mathbf{U}_2> - - <\mathbf{X}_i, \mathbf{U}_0> - <\mathbf{X}_i, \mathbf{U}_1>\right)} - {<\mathbf{X}_i, \mathbf{U}_3> - <\mathbf{X}_i, \mathbf{U}_2> - - <\mathbf{X}_i, \mathbf{U}_0>^2 - - <\mathbf{X}_i, \mathbf{U}_1>^2 - }\end{aligned}\end{align} \]
    -
    -

    Examples

    -
    >>> from image.shape import ellipticity_atoms
->>> import numpy as np
->>> a = np.zeros((5, 5))
->>> a[2, 1:4] += 1
->>> ellipticity_atoms(a)
-array([-1.,  0.])
-
    -
    -
    >>> b = np.zeros((5, 5))
->>> b[1:4, 2] += 1
->>> ellipticity_atoms(b)
-array([ 1.,  0.])
-
    -
    -
    - -
    -
    -sf_tools.image.shape.shape_project(shape, offset=0, return_norm=False)[source]
    -

    Shape projection matrix

    -

    This method generates a shape projection matrix for a given image.

    - --- - - - - - - - -
    Parameters:
      -
    • shape (list, tuple or np.ndarray) – List of image dimensions
      • -
    • offset (int, optional) – Shape projection offset (default is ‘0’)
      • -
    • return_norm (bool, optional) – Option to return l2 normalised shape projection components -(default is ‘False’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray of shape projection components

    -
    -
    -

    See also

    -
    -
    ellipticity_atoms()
    -
    calculate ellipticity
    -
    -
    -

    Notes

    -

    This technique was developed by Fred Ngole and implements the following -equations:

    -
    -
      -
    • Equations from [NS2016] appendix:
      • -
    -
    -\[\begin{split}U_1 &= (k)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\ -U_2 &= (l)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\ -U_3 &= (1)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\ -U_4 &= (k^2 + l^2)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\ -U_5 &= (k^2 - l^2)_{1 \leq k \leq N_l, 1 \leq l \leq N_c} \\ -U_6 &= (kl)_{1 \leq k \leq N_l, 1 \leq l \leq N_c}\end{split}\]
    -
    -

    Examples

    -
    >>> from image.shape import shape_project
->>> shape_project([3, 3])
-array([[[ 0.,  0.,  0.],
-        [ 1.,  1.,  1.],
-        [ 2.,  2.,  2.]],
-
-       [[ 0.,  1.,  2.],
-        [ 0.,  1.,  2.],
-        [ 0.,  1.,  2.]],
-
-       [[ 1.,  1.,  1.],
-        [ 1.,  1.,  1.],
-        [ 1.,  1.,  1.]],
-
-       [[ 0.,  1.,  4.],
-        [ 1.,  2.,  5.],
-        [ 4.,  5.,  8.]],
-
-       [[ 0., -1., -4.],
-        [ 1.,  0., -3.],
-        [ 4.,  3.,  0.]],
-
-       [[ 0.,  0.,  0.],
-        [ 0.,  1.,  2.],
-        [ 0.,  2.,  4.]]])
-
    -
    -
    - -
    -
    -class sf_tools.image.shape.Ellipticity(data, sigma=1000, centroid=None, moments=None, ellip_type='chi')[source]
    -

    Image ellipticity class

    -

    This class calculates image ellipticities from quadrupole moments.

    - --- - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array, the image to be analysed
      • -
    • sigma (int, optional) – Estimation error (default is ‘1000’)
      • -
    • centroid (np.ndarray, optional) – Centroid positions [x, y] of the input image (defualt is ‘None’)
      • -
    • moments (np.ndarray, optional) – Quadrupole moments [[q00, q01], [q10, q11]] of the input image -(defualt is ‘None’)
      • -
    • ellip_type (str {'chi', 'epsilon'}, optional) – Ellipticity type (default is ‘chi’)
      • -
    -
    -

    Examples

    -
    >>> from image.shape import Ellipticity
->>> import numpy as np
->>> a = np.zeros((5, 5))
->>> a[2, 1:4] += 1
->>> Ellipticity(a).e
-array([-1.,  0.])
-
    -
    -
    >>> b = np.zeros((5, 5))
->>> b[1:4, 2] += 1
->>> Ellipticity(b).e
-array([ 1.,  0.])
-
    -
    -
    -
    -_check_ellip_type()[source]
    -

    Check Ellipticity Type

    -

    This method raises an error if ellip_type is not ‘chi’ or ‘epsilon’.

    - --- - - - -
    Raises:ValueError for invalid ellip_type
    -
    - -
    -
    -_update_xy()[source]
    -

    Update the x and y values

    -

    This method updates the values of x and y using the current centroid.

    -
    - -
    -
    -_update_weights()[source]
    -

    Update the weights

    -

    This method updates the value of the weights using the current values -of x and y.

    -

    Notes

    -

    This method implements the following equations:

    -
    -
      -
    • The exponential part of equation 1 from [BM2007] to calculate -the weights:
      • -
    -
    -\[w(x,y) = e^{-\frac{\left((x-x_c)^2+(y-y_c)^2\right)} - {2\sigma^2}}\]
    -
    -
    - -
    -
    -_update_centroid()[source]
    -

    Update the centroid

    -

    This method updates the centroid value using the current weights.

    -

    Notes

    -

    This method implements the following equations:

    -
    -
      -
    • Equation 2a, 2b and 2c from [BM2007] to calculate the position -moments:
      • -
    -
    -\[ \begin{align}\begin{aligned}S_w = \sum_{x,y} I(x,y)w(x,y)\\S_x = \sum_{x,y} xI(x,y)w(x,y)\\S_y = \sum_{x,y} yI(x,y)w(x,y)\end{aligned}\end{align} \]
    -
      -
    • Equation 3 from [BM2007] to calculate the centroid:
      • -
    -
    -\[X_c = S_x/S_w,\ -Y_c = S_y/S_w\]
    -
    -
    - -
    -
    -_get_centroid(n_iter=10)[source]
    -

    Calculate centroid

    -

    This method iteratively calculates the centroid of the image.

    - --- - - - -
    Parameters:n_iter (int, optional) – Number of iterations (deafult is ‘10’)
    -
    - -
    -
    -_get_moments()[source]
    -

    Calculate the quadrupole moments

    -

    This method calculates the quadrupole moments.

    -

    Notes

    -

    This method implements the following equations:

    -
    -
      -
    • Equation 10 from [C2013] to calculate the moments:
      • -
    -
    -\[Q_{ij}=\frac{\int\int\Phi(x_i,x_j) w(x_i,x_j) - (x_i-\bar{x_i})(x_j-\bar{x_j}) dx_idx_j} - {\int\int\Phi(x_i,x_j)w(x_i,x_j)dx_idx_j}\]
    -
    -
    - -
    -
    -_get_ellipse()[source]
    -

    Calculate the ellipticities

    -

    This method cacluates ellipticities from quadrupole moments.

    -

    Notes

    -

    This method implements the following equations:

    -
    -
      -
    • Equation 11 from [C2013] to calculate the size:
      • -
    -
    -\[R^2 = Q_{00} + Q_{11}\]
    -
      -
    • Equation 7 from [S2005] to calculate the ellipticities:
      • -
    -
    -\[ \begin{align}\begin{aligned}\chi = \frac{Q_{00}-Q_{11}+iQ_{01}+iQ_{10}}{R^2}\\\epsilon = \frac{Q_{00}-Q_{11}+iQ_{01}+iQ_{10}}{R^2 + - 2\sqrt{(Q_{00}Q_{11} - Q_{01}Q_{10})}}\end{aligned}\end{align} \]
    -
    -
    - -
    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.image.stamp.html b/docs/build/html/sf_tools.image.stamp.html deleted file mode 100644 index 43a6e08..0000000 --- a/docs/build/html/sf_tools.image.stamp.html +++ /dev/null @@ -1,476 +0,0 @@ - - - - - - - - - - - 2.1.2.1.5. sf_tools.image.stamp module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.2.1.5. sf_tools.image.stamp module

    -

    IMAGE STAMP SELECTION ROUTINES

    -

    This module contains methods for selecting stamps or patches from -images.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:20/10/2017
    -
    -
    -sf_tools.image.stamp.patch_centres(data_shape, layout)[source]
    -

    Image centres

    -

    This method inds the centres of the patches in a 2D map.

    - --- - - - - - - - -
    Parameters:
      -
    • data_shape (tuple) – Shape of the 2D map
      • -
    • layout (tuple) – Layout of the patches
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray array of patch centres

    -
    -
    - -
    -
    -sf_tools.image.stamp.postage_stamp(data, pos, pixel_rad)[source]
    -

    Postage stamp

    -

    This metho selects a postage stamp of a given size from a 2D-array.

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array
      • -
    • pos (tuple) – Position of postage stamp centre
      • -
    • pixel_rad (tuple) – Pixel radius in each dimension (i.e. size of the stamp from centre)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray rearanged matrix of kernel elements

    -
    -

    Notes

    -

    The image edges are padded with zeros.

    -
    - -
    -
    -sf_tools.image.stamp.pixel_pos(array_shape)[source]
    -

    Pixel positions

    -

    This method returns all of the pixel positions from a 2D-array.

    - --- - - - - - - - -
    Parameters:array_shape (tuple) – Shape of array
    Returns:
    Return type:list of pixel positions
    -
    - -
    -
    -class sf_tools.image.stamp.FetchStamps(data, pixel_rad, all=False)[source]
    -

    Bases: object

    -

    Fetch postage stamps

    -

    This class returns a stack of postage stamps from a given 2D image array.

    - --- - - - -
    Parameters:
      -
    • data (np.ndarray) – Input 2D data array
      • -
    • pixel_rad (tuple) – Pixel radius in each dimension
      • -
    • all (boolean, optional) – Option to select all pixels. Default (all=False)
      • -
    -
    -
    -
    -_check_inputs()[source]
    -

    Check inputs

    -

    This method checks the class variable values.

    - --- - - - -
    Raises:ValueError – For invalid array dimensions or pixel radius values
    -
    - -
    -
    -_pad_data()[source]
    -

    Pad data

    -

    This method pads the input array with zeros.

    -
    - -
    -
    -_adjust_pixels()[source]
    -

    Adjust pixels

    -

    This method adjusts the pixel positions according to the pixel radius.

    -
    - -
    -
    -get_pixels(pixels)[source]
    -

    Get pixels

    -

    This method gets the desired pixel positions.

    - --- - - - - - -
    Parameters:pixels (list or np.ndarray) – List of pixel positions
    Raises:ValueError – For invalid number of dimensions for pixel position array
    -
    - -
    -
    -n_pixels(n_pixels=None, random=False)[source]
    -

    Number of pixels

    -

    This method selects a specified number of pixel positions.

    - --- - - - -
    Parameters:
      -
    • n_pixels (int, optional) – Number of pixels to keep. Default (n_pixels=None)
      • -
    • random (bool, optional) – Option to select random pixel position
      • -
    -
    -
    - -
    -
    -_stamp(pos, func=None, *args)[source]
    -

    Stamp

    -

    This method retrieves a postage stamp from the padded input data at a -given position.

    - --- - - - - - - - - - -
    Parameters:
      -
    • pos (tuple) – Pixel position in 2D padded array.
      • -
    • func (function, optional) – Optional function to be applied to postage stamp.
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray postage stamp array or result of func

    -
    Raises:

    ValueError – For for invalid size of pixel position.

    -
    -
    - -
    -
    -scan(func=None, *args, **kwargs)[source]
    -

    Scan stamps

    -

    This method scans the 2D padded input array and retrieves the postage -stamps at all the desired pixel positions.

    - --- - - - - - - - -
    Parameters:func (function, optional) – Optional function to be applied to postage stamps.
    Returns:
    Return type:np.ndarray postage stamp arrays or results of func
    -
    - -
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    2.1.2.1.6. sf_tools.image.stats module

    -

    IMAGE STATISTICS ROUTINES

    -

    This module contains methods for the statistical analysis of images.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.1
    Date:20/10/2017
    -
    -
    -class sf_tools.image.stats.SAT(data)[source]
    -

    Summed Area Table

    -

    This class produces a Summed Area Table (SAT) for fast and efficient -statistics on image patches.

    - --- - - - -
    Parameters:data (np.ndarray) – Input 2D data array
    -

    Notes

    -

    Also know as Itegral Image (i in the class features).

    -
    -

    Todo

    -

    Add equations and citations

    -
    -
    -
    -get_area(data, corners)[source]
    -

    Get area

    -

    This method calculates the area of a patch.

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input 2D data array
      • -
    • corners (tuple) – Positions of upper left and bottom right corners.
      • -
    -
    Returns:

    -
    Return type:

    float area

    -
    -
    - -
    -
    -get_npx(corners)[source]
    -

    Get number of pixels

    -

    This method calculates the number of pixels in a patch.

    - --- - - - - - - - -
    Parameters:corners (tuple) – Positions of upper left and bottom right corners.
    Returns:
    Return type:int number of pixels
    -
    - -
    -
    -get_var()[source]
    -

    Get variance

    -

    This method calculates the variance and standard deviation of a patch.

    -
    - -
    -
    -set_patch(corners)[source]
    -

    Set patch

    -

    This method sets the corner positions of a single patch.

    - --- - - - -
    Parameters:corners (tuple) – Positions of upper left and bottom right corners.
    -
    - -
    -
    -set_patches(corners)[source]
    -

    Set patches

    -

    This method sets the corner positions for multiple patches.

    - --- - - - -
    Parameters:corners (list) – List of the positions of upper left and bottom right corners.
    -
    - -
    - -
    - - -
    -
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    -
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    -
    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.interface.errors.html b/docs/build/html/sf_tools.interface.errors.html deleted file mode 100644 index 22b018a..0000000 --- a/docs/build/html/sf_tools.interface.errors.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - - - - - - - 2.1.3.1.1. sf_tools.interface.errors module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - -
    -

    2.1.3.1.1. sf_tools.interface.errors module

    -

    ERROR HANDLING ROUTINES

    -

    This module contains methods for handing warnings and errors.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:23/10/2017
    -
    -
    -sf_tools.interface.errors.warn(warn_string, log=None)[source]
    -

    Warning

    -

    This method creates custom warning messages.

    - --- - - - -
    Parameters:
      -
    • warn_string (str) – Warning message string
      • -
    • log (instance, optional) – Logging structure instance
      • -
    -
    -
    - -
    -
    -sf_tools.interface.errors.catch_error(exception, log=None)[source]
    -

    Catch error

    -

    This method catches errors and prints them to the terminal. It also saves -the errors to a log if provided.

    - --- - - - -
    Parameters:
      -
    • exception (str) – Exception message string
      • -
    • log (instance, optional) – Logging structure instance
      • -
    -
    -
    - -
    -
    -sf_tools.interface.errors.file_name_error(file_name)[source]
    -

    File name error

    -

    This method checks if the input file name is valid.

    - --- - - - - - -
    Parameters:file_name (str) – File name string
    Raises:IOError – If file name not specified or file not found
    -
    - -
    - - -
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    -
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    - -
    -

    2.1.3. sf_tools.interface package

    - -
    -

    2.1.3.2. Module contents

    -

    INTERFACE ROUTINES

    -

    This module contains submodules for error handling, logging and IO interaction.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:06/04/2017
    -
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    2.1.3.1.2. sf_tools.interface.log module

    -

    LOGGING ROUTINES

    -

    This module contains methods for handing logging.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:20/10/2017
    -
    -
    -sf_tools.interface.log.set_up_log(filename, verbose=True)[source]
    -

    Set up log

    -

    This method sets up a basic log.

    - --- - - - - - - - -
    Parameters:filename (str) – Log file name
    Returns:
    Return type:logging.Logger instance
    -
    - -
    -
    -sf_tools.interface.log.close_log(log, verbose=True)[source]
    -

    Close log

    -

    This method closes and active logging.Logger instance.

    - --- - - - -
    Parameters:log (logging.Logger) – Logging instance
    -
    - -
    - - -
    -
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    2.1.4.1.1. sf_tools.math.angle module

    -

    ANGLE HANDLING ROUTINES

    -

    This module contains methods for handing angles and trigonometry.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.1
    Date:20/10/2017
    -
    -
    -sf_tools.math.angle.deg2rad(angle)[source]
    -

    Degrees to radians

    -

    This method converts the angle from degrees to radians.

    - --- - - - - - - - -
    Parameters:angle (float or np.ndarray) – Input angle in degrees
    Returns:
    Return type:float angle in radians or np.ndarray of angles
    -

    Examples

    -
    >>> from sf_tools.math.angle import deg2rad
->>> deg2rad(45.)
-0.7853981633974483
-
    -
    -

    Notes

    -

    Implements the following equation:

    -
    -\[\mathrm{radians} = \mathrm{degrees} \times \frac{\pi}{180}\]
    -
    - -
    -
    -sf_tools.math.angle.rad2deg(angle)[source]
    -

    Radians to degrees

    -

    This method converts the angle from radians to degrees.

    - --- - - - - - - - -
    Parameters:angle (float or np.ndarray) – Input angle in radians
    Returns:
    Return type:float angle in degrees or np.ndarray of angles
    -

    Examples

    -
    >>> from sf_tools.math.angle import deg2rad
->>> rad2deg(1.)
-57.29577951308232
-
    -
    -

    Notes

    -

    Implements the following equation:

    -
    -\[\mathrm{degrees} = \mathrm{radians} \times \frac{180}{\pi}\]
    -
    - -
    -
    -sf_tools.math.angle.ang_sep(point1, point2)[source]
    -

    Angular separation

    -

    This method calculates the angular separation in degrees between two -points.

    - --- - - - - - - - -
    Parameters:
      -
    • point1 (tuple) – Angular position of point 1 in degrees
      • -
    • point1 – Angular position of point 2 in degrees
      • -
    -
    Returns:

    -
    Return type:

    float angular separation in degrees

    -
    -

    Examples

    -
    >>> from sf_tools.math.angle import ang_sep
->>> ang_sep((30.0, 0.0), (47.0, 10.0))
-19.647958606833164
-
    -
    -

    Notes

    -

    Implements the following equation:

    -
    -\[\theta = \cos^{-1}[\sin(\delta_1)\sin(\delta_2)+ -\cos(\delta_1)\cos(\delta_2)\cos(\alpha_1-\alpha_2)]\]
    -

    See https://en.wikipedia.org/wiki/Angular_distance

    -
    - -
    - - -
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    2.1.4. sf_tools.math package

    - -
    -

    2.1.4.2. Module contents

    -

    MATHEMATICS ROUTINES

    -

    This module contains submodules for mathematical applications.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:06/04/2017
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.math.integrate.html b/docs/build/html/sf_tools.math.integrate.html deleted file mode 100644 index d6cc083..0000000 --- a/docs/build/html/sf_tools.math.integrate.html +++ /dev/null @@ -1,321 +0,0 @@ - - - - - - - - - - - 2.1.4.1.2. sf_tools.math.integrate module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.4.1.2. sf_tools.math.integrate module

    -

    INTEGRATION ROUTINES

    -

    This module contains methods for integration.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:03/04/2017
    -
    -
    -sf_tools.math.integrate.integrate(func, lim_low, lim_up, *args)[source]
    -

    Integrate

    -

    This method integrates a given function, which has N additional arguments, -between the specified limits.

    - --- - - - - - - - -
    Parameters:
      -
    • func (function) – Function to be integrated
      • -
    • lim_low (float) – Lower limit
      • -
    • lim_up (float) – Upper limit
      • -
    -
    Returns:

    -
    Return type:

    Result of the definite integral

    -
    -
    - -
    -
    -sf_tools.math.integrate.vintegrate(func, lim_low, lim_up, *args)[source]
    -

    Vectorised integration

    -

    This method implements a vectorised version of integrate().

    - --- - - - - - - - -
    Parameters:
      -
    • func (function) – Function to be integrated
      • -
    • lim_low (float) – Lower limit
      • -
    • lim_up (float) – Upper limit
      • -
    -
    Returns:

    -
    Return type:

    Result of the definite integral

    -
    -
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    2.1.4.1.3. sf_tools.math.matrix module

    -

    MATRIX ROUTINES

    -

    This module contains methods for matrix operations.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:20/10/2017
    -
    -
    -sf_tools.math.matrix.gram_schmidt(matrix, return_opt='orthonormal')[source]
    -

    Gram-Schmit

    -

    This method orthonormalizes the row vectors of the input matrix.

    - --- - - - - - - - -
    Parameters:
      -
    • matrix (np.ndarray) – Input matrix array
      • -
    • return_opt (str {orthonormal, orthogonal, both}) – Option to return u, e or both.
      • -
    -
    Returns:

    -
    Return type:

    Lists of orthogonal vectors, u, and/or orthonormal vectors, e

    -
    -
    -

    Todo

    -

    Add citation and equation

    -
    -
    - -
    -
    -sf_tools.math.matrix.nuclear_norm(data)[source]
    -

    Nuclear norm

    -

    This method computes the nuclear (or trace) norm of the input data.

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array
    Returns:
    Return type:float nuclear norm value
    -
    -

    Todo

    -

    Add equation

    -
    -
    - -
    -
    -sf_tools.math.matrix.project(u, v)[source]
    -

    Project vector

    -

    This method projects vector v onto vector u.

    - --- - - - - - - - -
    Parameters:
      -
    • u (np.ndarray) – Input vector
      • -
    • v (np.ndarray) – Input vector
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray projection

    -
    -
    -

    Todo

    -

    Add equation

    -
    -
    - -
    -
    -sf_tools.math.matrix.rot_matrix(angle)[source]
    -

    Rotation matrix

    -

    This method produces a 2x2 rotation matrix for the given input angle.

    - --- - - - - - - - -
    Parameters:angle (float) – Rotation angle
    Returns:
    Return type:np.ndarray 2x2 rotation matrix
    -
    - -
    -
    -sf_tools.math.matrix.rotate(matrix, angle)[source]
    -

    Rotate

    -

    This method rotates an input matrix about the input angle.

    - --- - - - - - - - - - -
    Parameters:
      -
    • matrix (np.ndarray) – Input matrix array
      • -
    • angle (float) – Rotation angle
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray rotated matrix

    -
    Raises:

    ValueError – For invalid matrix shape

    -
    -
    - -
    -
    -class sf_tools.math.matrix.PowerMethod(operator, data_shape, auto_run=True)[source]
    -

    Bases: object

    -

    Power method class

    -

    This method performs implements power method to calculate the spectral -radius of the input data

    - --- - - - -
    Parameters:
      -
    • operator (class) – Operator class instance
      • -
    • data_shape (tuple) – Shape of the data array
      • -
    • auto_run (bool) – Option to automatically calcualte the spectral radius upon -initialisation
      • -
    -
    -
    -
    -set_initial_x()[source]
    -

    Set initial value of x

    -

    This method sets the initial value of x to an arrray of random values

    -
    - -
    -
    -get_spec_rad(tolerance=1e-06, max_iter=10)[source]
    -

    Get spectral radius

    -

    This method calculates the spectral radius

    - --- - - - -
    Parameters:
      -
    • tolerance (float, optional) – Tolerance threshold for convergence (default is “1e-6”)
      • -
    • max_iter (int, optional) – Maximum number of iterations
      • -
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    2.1.4.1.4. sf_tools.math.misc module

    -

    MISCELLANOUS MATH ROUTINES

    -

    This module contains methods for various mathematical operations.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.1
    Date:20/10/2017
    -
    -
    -sf_tools.math.misc.factor(n)[source]
    -

    Factors of n

    -

    This method finds factors of a number (n).

    - --- - - - - - - - -
    Parameters:n (int) – Whole number
    Returns:
    Return type:np.ndarray factors of n
    -
    - -
    -
    -sf_tools.math.misc.mfactor(n)[source]
    -

    Middle factors of n

    -

    This method finds the middle factor(s) of a number (n).

    - --- - - - - - - - -
    Parameters:n (int) – Whole number
    Returns:
    Return type:np.ndarray middle factors of n
    -
    - -
    -
    -sf_tools.math.misc.k_val(n, L)[source]
    -

    Spatial frequency

    -

    This method returns k-values in the range L.

    - --- - - - - - - - -
    Parameters: -
    Returns:

    -
    Return type:

    float k value

    -
    -
    -

    Todo

    -

    Add equation and example

    -
    -
    - -
    -
    -sf_tools.math.misc.fourier_derivative(func, k, order)[source]
    -

    Fourier derivative

    -

    This method returns the derivative of the specified function to the given -order.

    - --- - - - - - - - -
    Parameters:
      -
    • func (function) – Function
      • -
    • k (float) – k-value
      • -
    • order (int) – Oder of derivative
      • -
    -
    Returns:

    -
    Return type:

    Float derivative

    -
    -
    -

    Todo

    -

    Add equation and example

    -
    -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.math.stats.html b/docs/build/html/sf_tools.math.stats.html deleted file mode 100644 index 342ff9c..0000000 --- a/docs/build/html/sf_tools.math.stats.html +++ /dev/null @@ -1,488 +0,0 @@ - - - - - - - - - - - 2.1.4.1.5. sf_tools.math.stats module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.4.1.5. sf_tools.math.stats module

    -

    STATISTICS ROUTINES

    -

    This module contains methods for basic statistics.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:20/10/2017
    -
    -
    -sf_tools.math.stats.chi2_gof(data_obs, data_exp, sigma, ddof=1)[source]
    -

    Chi-squared goodness-of-fit

    -

    This method tests the chi^2 goodness of fit.

    - --- - - - - - - - -
    Parameters:
      -
    • data_obs (np.ndarray) – Observed data array
      • -
    • data_exp (np.ndarray) – Expected data array
      • -
    • sigma (float) – Expected data error
      • -
    • ddof (input) – Delta degrees of freedom. Default (ddof = 1).
      • -
    -
    Returns:

    -
    Return type:

    tuple of floats chi-squared and P values

    -
    -
    - -
    -
    -sf_tools.math.stats.gaussian(point, mean, sigma, amplitude=None)[source]
    -

    Gaussian distribution

    -

    Method under development…

    -
    - -
    -
    -sf_tools.math.stats.gaussian_kernel(data_shape, sigma, norm='max')[source]
    -

    Gaussian kernel

    -

    This method produces a Gaussian kerenal of a specified size and dispersion

    - --- - - - - - - - -
    Parameters:
      -
    • data_shape (tuple) – Desiered shape of the kernel
      • -
    • sigma (float) – Standard deviation of the kernel
      • -
    • norm (str {'max', 'sum'}, optional) – Normalisation of the kerenl (options are ‘max’ or ‘sum’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray kernel

    -
    -
    - -
    -
    -sf_tools.math.stats.mad(data)[source]
    -

    Median absolute deviation

    -

    This method calculates the median absolute deviation of the input data.

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array
    Returns:
    Return type:float MAD value
    -

    Notes

    -

    The MAD is calculated as follows:

    -
    -\[\mathrm{MAD} = \mathrm{median}\left(|X_i - \mathrm{median}(X)|\right)\]
    -
    - -
    -
    -sf_tools.math.stats.mse(data1, data2)[source]
    -

    Mean Squared Error

    -

    This method returns the Mean Squared Error (MSE) between two data sets.

    - --- - - - -
    Parameters:
      -
    • data1 (np.ndarray) – First data set
      • -
    • data2 (np.ndarray) – Second data set
      • -
    -
    -
    - -
    -
    -sf_tools.math.stats.psnr2(image, noisy_image, max_pix=255)[source]
    -

    Peak Signal-to-Noise Ratio

    -

    This method calculates the PSNR between an image and a noisy version -of that image

    - --- - - - - - - - -
    Parameters:
      -
    • image (np.ndarray) – Input image, 2D array
      • -
    • noisy_image (np.ndarray) – Noisy image, 2D array
      • -
    • max_pix (int) – Maximum number of pixels. Default (max_pix=255)
      • -
    -
    Returns:

    -
    Return type:

    float PSNR value

    -
    -

    Notes

    -

    Implements PSNR equation on -https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

    -
    -\[\mathrm{PSNR} = 20\log_{10}(\mathrm{MAX}_I - 10\log_{10}(\mathrm{MSE}))\]
    -
    - -
    -
    -sf_tools.math.stats.psnr(image, recovered_image)[source]
    -

    Peak Signal-to-Noise Ratio

    -

    This method calculates the PSNR between an image and the recovered version -of that image

    - --- - - - - - - - -
    Parameters:
      -
    • image (np.ndarray) – Input image, 2D array
      • -
    • recovered_image (np.ndarray) – Recovered image, 2D array
      • -
    -
    Returns:

    -
    Return type:

    float PSNR value

    -
    -

    Notes

    -

    Implements eq.3.7 from _[S2010]

    -
    - -
    -
    -sf_tools.math.stats.psnr_stack(images, recoverd_images, metric=<function mean>)[source]
    -

    Peak Signa-to-Noise for stack of images

    -

    This method calculates the PSNRs for a stack of images and the -corresponding recovered images. By default the metod returns the mean -value of the PSNRs, but any other metric can be used.

    - --- - - - - - - - - - -
    Parameters:
      -
    • images (np.ndarray) – Stack of images, 3D array
      • -
    • recovered_images (np.ndarray) – Stack of recovered images, 3D array
      • -
    • metric (function) – The desired metric to be applied to the PSNR values (default is -‘np.mean’)
      • -
    -
    Returns:

    -
    Return type:

    float metric result of PSNR values

    -
    Raises:

    ValueError – For invalid input data dimensions

    -
    -
    - -
    -
    -sf_tools.math.stats.sigma_mad(data)[source]
    -

    Standard deviation from MAD

    -

    This method calculates the standard deviation of the input data from the -MAD.

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array
    Returns:
    Return type:float sigma value
    -

    Notes

    -

    This function can be used for estimating the standeviation of the noise in -imgaes.

    -

    Sigma is calculated as follows:

    -
    -\[\sigma = 1.4826 \mathrm{MAD}(X)\]
    -
    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.plot.cost_plot.html b/docs/build/html/sf_tools.plot.cost_plot.html deleted file mode 100644 index 000fac6..0000000 --- a/docs/build/html/sf_tools.plot.cost_plot.html +++ /dev/null @@ -1,287 +0,0 @@ - - - - - - - - - - - 2.1.5.1.1. sf_tools.plot.cost_plot module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.5.1.1. sf_tools.plot.cost_plot module

    -

    PLOTTING ROUTINES

    -

    This module contains methods for making plots.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.1
    Date:05/01/2017
    -
    -
    -sf_tools.plot.cost_plot.plotCost(cost_list, output=None)[source]
    -

    Plot cost function

    -

    Plot the final cost function

    - --- - - - -
    Parameters:
      -
    • cost_list (list) – List of cost function values
      • -
    • output (str, optional) – Output file name
      • -
    -
    -
    - -
    - - -
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    2.1.5. sf_tools.plot package

    -
    -

    2.1.5.1. Submodules

    -
    - -
    -
    -
    -

    2.1.5.2. Module contents

    -

    PLOTTING ROUTINES

    -

    This module contains submodules for plotting applications.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:19/07/2017
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.signal.cost.html b/docs/build/html/sf_tools.signal.cost.html deleted file mode 100644 index cb2c545..0000000 --- a/docs/build/html/sf_tools.signal.cost.html +++ /dev/null @@ -1,338 +0,0 @@ - - - - - - - - - - - 2.1.6.1.1. sf_tools.signal.cost module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.6.1.1. sf_tools.signal.cost module

    -

    COST FUNCTIONS

    -

    This module contains classes of different cost functions for optimization.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:2.2
    Date:23/10/2017
    -
    -
    -class sf_tools.signal.cost.costObj(costFunc, initial_cost=1000000.0, tolerance=0.0001, cost_interval=1, test_range=4, verbose=True, plot_output=None)[source]
    -

    Bases: object

    -

    Generic cost function object

    -

    This class updates the cost according to the input cost functio class and -tests for convergence

    - --- - - - -
    Parameters:
      -
    • costFunc (class) – Class for calculating the cost
      • -
    • initial_cost (float, optional) – Initial value of the cost (default is “1e6”)
      • -
    • tolerance (float, optional) – Tolerance threshold for convergence (default is “1e-4”)
      • -
    • cost_interval (int, optional) – Iteration interval to calculate cost (default is “1”)
      • -
    • test_range (int, optional) – Number of cost values to be used in test (default is “4”)
      • -
    • verbose (bool, optional) – Option for verbose output (default is “True”)
      • -
    • plot_output (str, optional) – Output file name for cost function plot
      • -
    -
    -

    Notes

    -

    The costFunc class must contain a method called calc_cost().

    -
    -
    -_check_cost()[source]
    -

    Check cost function

    -

    This method tests the cost function for convergence in the specified -interval of iterations using the last n (test_range) cost values

    - --- - - - - - -
    Returns:
    Return type:bool result of the convergence test
    -
    - -
    -
    -get_cost(*args, **kwargs)[source]
    -

    Get cost function

    -

    This method calculates the current cost and tests for convergence

    - --- - - - - - -
    Returns:
    Return type:bool result of the convergence test
    -
    - -
    -
    -plot_cost()[source]
    -

    Plot the cost function

    -

    This method plots the cost function as function of iteration number

    -
    - -
    - -
    - - -
    -
    - -
    -
    - - -
    -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.signal.filter.html b/docs/build/html/sf_tools.signal.filter.html deleted file mode 100644 index ab99f77..0000000 --- a/docs/build/html/sf_tools.signal.filter.html +++ /dev/null @@ -1,345 +0,0 @@ - - - - - - - - - - - 2.1.6.1.2. sf_tools.signal.filter module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.6.1.2. sf_tools.signal.filter module

    -

    FILTER ROUTINES

    -

    This module contains methods for distance measurements in cosmology.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.1
    Date:20/10/2017
    -
    -
    -sf_tools.signal.filter.Gaussian_filter(x, sigma, norm=True)[source]
    -

    Gaussian filter

    -

    This method implements a Gaussian filter.

    - --- - - - - - - - -
    Parameters:
      -
    • x (float) – Input data point
      • -
    • sigma (float) – Standard deviation (filter scale)
      • -
    • norm (bool) – Option to return normalised data. Default (norm=True)
      • -
    -
    Returns:

    -
    Return type:

    float Gaussian filtered data point

    -
    -
    - -
    -
    -sf_tools.signal.filter.mex_hat(x, sigma)[source]
    -

    Mexican hat

    -

    This method implements a Mexican hat (or Ricker) wavelet.

    - --- - - - - - - - -
    Parameters:
      -
    • x (float) – Input data point
      • -
    • sigma (float) – Standard deviation (filter scale)
      • -
    -
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    -
    Return type:

    float Mexican hat filtered data point

    -
    -
    - -
    -
    -sf_tools.signal.filter.mex_hat_dir(x, y, sigma)[source]
    -

    Directional Mexican hat

    -

    This method implements a directional Mexican hat (or Ricker) wavelet.

    - --- - - - - - - - -
    Parameters:
      -
    • x (float) – Input data point for Gaussian
      • -
    • y (float) – Input data point for Mexican hat
      • -
    • sigma (float) – Standard deviation (filter scale)
      • -
    -
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    -
    Return type:

    float directional Mexican hat filtered data point

    -
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    2.1.6.1.3. sf_tools.signal.gradient module

    -

    GRADIENT CLASSES

    -

    This module contains classses for defining algorithm gradients. -Based on work by Yinghao Ge and Fred Ngole.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.3
    Date:19/07/2017
    -
    -
    -class sf_tools.signal.gradient.GradBasic[source]
    -

    Bases: object

    -

    Basic gradient class

    -

    This class defines the basic methods that will be inherited by specific -gradient classes

    -
    -
    -MtMX(x)[source]
    -

    M^T M X

    -

    This method calculates the action of the transpose of the matrix M on -the action of the matrix M on the data X

    - --- - - - - - - - -
    Parameters:x (np.ndarray) – Input data array
    Returns:
    Return type:np.ndarray result
    -

    Notes

    -

    Calculates M^T (MX)

    -
    - -
    -
    -get_grad(x)[source]
    -

    Get the gradient step

    -

    This method calculates the gradient step from the input data

    - --- - - - - - - - -
    Parameters:x (np.ndarray) – Input data array
    Returns:
    Return type:np.ndarray gradient value
    -

    Notes

    -

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    2.1.6. sf_tools.signal package

    - -
    -

    2.1.6.2. Module contents

    -

    SIGNAL PROCESSING ROUTINES

    -

    This module contains submodules for signal processing.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:06/04/2017
    -

    References

    - - - - - -
    [Con201312]Condat, A Primal-Dual Splitting Method for Convex Optimization -Involving Lipschitzian, Proximable and Linear Composite Terms, 2013, -Journal of Optimization Theory and Applications, 158, 2, 460. -[https://link.springer.com/article/10.1007/s10957-012-0245-9]
    - - - - - -
    [B201112]Bauschke et al., Fixed-Point Algorithms for Inverse Problems in -Science and Engineering, 2011, Chapter 10. -[http://rentals.springer.com/product/9781441995698]
    - - - - - -
    [R201212]Raguet et al., Generalized Forward-Backward Splitting, 2012, SIAM, -v6. [https://arxiv.org/abs/1108.4404]
    - - - - - -
    [CWB200712]Candes, Wakin and Boyd, Enhancing Sparsity by Reweighting l1 -Minimization, 2007, Journal of Fourier Analysis and Applications, -14(5):877-905. [https://arxiv.org/abs/0711.1612]
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    2.1.6.1.4. sf_tools.signal.linear module

    -

    LINEAR OPERATORS

    -

    This module contains linear operator classes.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.3
    Date:19/07/2017
    -
    -
    -class sf_tools.signal.linear.Identity[source]
    -

    Bases: object

    -

    Identity operator class

    -

    This is a dummy class that can be used in the optimisation classes

    -
    -
    -op(data, **kwargs)[source]
    -

    Operator

    -

    Returns the input data unchanged

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array
      • -
    • **kwargs – Arbitrary keyword arguments
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray input data

    -
    -
    - -
    -
    -adj_op(data)[source]
    -

    Adjoint operator

    -

    Returns the input data unchanged

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array
    Returns:
    Return type:np.ndarray input data
    -
    - -
    - -
    -
    -class sf_tools.signal.linear.Wavelet(data, wavelet_opt=None)[source]
    -

    Bases: object

    -

    Wavelet class

    -

    This class defines the wavelet transform operators

    - --- - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array, normally an array of 2D images
      • -
    • wavelet_opt (str, optional) – Additional options for mr_transform
      • -
    -
    -
    -
    -op(data)[source]
    -

    Operator

    -

    This method returns the input data convolved with the wavelet filters

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array, a 2D image
    Returns:
    Return type:np.ndarray wavelet convolved data
    -
    - -
    -
    -adj_op(data)[source]
    -

    Adjoint operator

    -

    This method returns the input data convolved with the wavelet filters -rotated by 180 degrees

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array, a 3D of wavelet coefficients
    Returns:
    Return type:np.ndarray wavelet convolved data
    -
    - -
    - -
    -
    -class sf_tools.signal.linear.LinearCombo(operators, weights=None)[source]
    -

    Bases: object

    -

    Linear combination class

    -

    This class defines a combination of linear transform operators

    - --- - - - -
    Parameters:
      -
    • operators (list) – List of linear operator class instances
      • -
    • weights (list, optional) – List of weights for combining the linear adjoint operator results
      • -
    -
    -
    -
    -op(data)[source]
    -

    Operator

    -

    This method returns the input data operated on by all of the operators

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array, a 2D image
    Returns:
    Return type:np.ndarray linear operation results
    -
    - -
    -
    -adj_op(data)[source]
    -

    Adjoint operator

    -

    This method returns the combination of the result of all of the -adjoint operators. If weights are provided the comibination is the sum -of the weighted results, otherwise the combination is the mean.

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array, an array of coefficients
    Returns:
    Return type:np.ndarray adjoint operation results
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    2.1.6.1.5. sf_tools.signal.noise module

    -

    NOISE ROUTINES

    -

    This module contains methods for adding and removing noise from data.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:04/01/2017
    -
    -
    -sf_tools.signal.noise.add_noise(data, sigma=1.0, noise_type='gauss')[source]
    -

    Add noise to data

    -

    This method adds Gaussian or Poisson noise to the input data

    - --- - - - - - - - - - -
    Parameters:
      -
    • data (np.ndarray, list or tuple) – Input data array
      • -
    • sigma (float or list, optional) – Standard deviation of the noise to be added (‘gauss’ only)
      • -
    • noise_type (str {'gauss', 'poisson'}) – Type of noise to be added (default is ‘gauss’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray input data with added noise

    -
    Raises:
      -
    • ValueError – If noise_type is not ‘gauss’ or ‘poisson’
      • -
    • ValueError – If number of sigma values does not match the first dimension of the -input data
      • -
    -
    -

    Examples

    -
    >>> import numpy as np
->>> from sf_tools.signal.noise import add_noise
->>> x = np.arange(9).reshape(3, 3).astype(float)
->>> x
-array([[ 0.,  1.,  2.],
-       [ 3.,  4.,  5.],
-       [ 6.,  7.,  8.]])
->>> np.random.seed(1)
->>> add_noise(x, noise_type='poisson')
-array([[  0.,   2.,   2.],
-       [  4.,   5.,  10.],
-       [ 11.,  15.,  18.]])
-
    -
    -
    >>> import numpy as np
->>> from sf_tools.signal.noise import add_noise
->>> x = np.zeros(5)
->>> x
-array([ 0.,  0.,  0.,  0.,  0.])
->>> np.random.seed(1)
->>> add_noise(x, sigma=2.0)
-array([ 3.24869073, -1.22351283, -1.0563435 , -2.14593724,  1.73081526])
-
    -
    -
    - -
    -
    -sf_tools.signal.noise.thresh(data, threshold, threshold_type='hard')[source]
    -

    Threshold data

    -

    This method perfoms hard or soft thresholding on the input data

    - --- - - - - - - - - - -
    Parameters:
      -
    • data (np.ndarray, list or tuple) – Input data array
      • -
    • threshold (float) – Threshold level
      • -
    • threshold_type (str {'hard', 'soft'}) – Type of noise to be added (default is ‘hard’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray thresholded data

    -
    Raises:

    ValueError – If threshold_type is not ‘hard’ or ‘soft’

    -
    -

    Notes

    -

    Implements one of the following two equations:

    -
      -
    • -
      Hard Threshold
      -
      -\[\begin{split}\mathrm{HT}_\lambda(x) = -\begin{cases} -x & \text{if } |x|\geq\lambda \\ -0 & \text{otherwise} -\end{cases}\end{split}\]
      -
      -
      -
      • -
    • -
      Soft Threshold
      -
      -\[\begin{split}\mathrm{ST}_\lambda(x) = -\begin{cases} -x-\lambda\text{sign}(x) & \text{if } |x|\geq\lambda \\ -0 & \text{otherwise} -\end{cases}\end{split}\]
      -
      -
      -
      • -
    -

    Examples

    -
    >>> import numpy as np
->>> from sf_tools.signal.noise import thresh
->>> np.random.seed(1)
->>> x = np.random.randint(-9, 9, 10)
->>> x
-array([-4,  2,  3, -1,  0,  2, -4,  6, -9,  7])
->>> thresh(x, 4)
-array([-4,  0,  0,  0,  0,  0, -4,  6, -9,  7])
-
    -
    -
    >>> import numpy as np
->>> from sf_tools.signal.noise import thresh
->>> np.random.seed(1)
->>> x = np.random.ranf((3, 3))
->>> x
-array([[  4.17022005e-01,   7.20324493e-01,   1.14374817e-04],
-       [  3.02332573e-01,   1.46755891e-01,   9.23385948e-02],
-       [  1.86260211e-01,   3.45560727e-01,   3.96767474e-01]])
->>> thresh(x, 0.2, threshold_type='soft')
-array([[ 0.217022  ,  0.52032449, -0.        ],
-       [ 0.10233257, -0.        , -0.        ],
-       [-0.        ,  0.14556073,  0.19676747]])
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    2.1.6.1.6. sf_tools.signal.optimisation module

    -

    OPTIMISATION CLASSES

    -

    This module contains classes for optimisation algoritms.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.3
    Date:20/10/2017
    -

    Notes

    -

    Input classes must have the following properties:

    -
    -
      -
    • Gradient Operators
      • -
    -

    Must have the following methods:

    -
    -
      -
    • get_grad() - calculate the gradient
      • -
    -
    -

    Must have the following variables:

    -
    -
      -
    • grad - the gradient
      • -
    • inv_spec_rad - inverse spectral radius \(\frac{1}{\rho}\)
      • -
    -
    -
      -
    • Linear Operators
      • -
    -

    Must have the following methods:

    -
    -
      -
    • op() - operator
      • -
    • adj_op() - adjoint operator
      • -
    -
    -

    Must have the following variables:

    -
    -
      -
    • l1norm - the l1 norm of the operator
      • -
    -
    -
      -
    • Proximity Operators
      • -
    -

    Must have the following methods:

    -
    -
      -
    • op() - operator
      • -
    -
    -
    -

    The following notation is used to implement the algorithms:

    -
    -
      -
    • x_old is used in place of \(x_{n}\).
      • -
    • x_new is used in place of \(x_{n+1}\).
      • -
    • x_prox is used in place of \(\tilde{x}_{n+1}\).
      • -
    • x_temp is used for intermediate operations.
      • -
    -
    -
    -
    -class sf_tools.signal.optimisation.FISTA(lambda_init=None, active=True)[source]
    -

    Bases: object

    -

    This class is inhereited by optimisation classes to speed up convergence

    - --- - - - -
    Parameters:
      -
    • lambda_init (float, optional) – Initial value of the relaxation parameter
      • -
    • active (bool, optional) – Option to activate FISTA convergence speed-up (default is True)
      • -
    -
    -
    -
    -speed_switch(turn_on=True)[source]
    -

    Speed swicth

    -

    This method turns on or off the speed-up

    - --- - - - -
    Parameters:turn_on (bool) – Option to turn on speed-up (default is True)
    -
    - -
    -
    -update_lambda()[source]
    -

    Update lambda

    -

    This method updates the value of lambda

    -

    Notes

    -

    Implements steps 3 and 4 from algoritm 10.7 in [B2011]

    -
    - -
    -
    -speed_up()[source]
    -

    speed-up

    -

    This method returns the update if the speed-up is active

    -
    - -
    - -
    -
    -class sf_tools.signal.optimisation.ForwardBackward(x, grad, prox, cost=None, lambda_init=None, lambda_update=None, use_fista=True, auto_iterate=True)[source]
    -

    Bases: sf_tools.signal.optimisation.FISTA

    -

    Forward-Backward optimisation

    -

    This class implements standard forward-backward optimisation with an the -option to use the FISTA speed-up

    - --- - - - -
    Parameters:
      -
    • x (np.ndarray) – Initial guess for the primal variable
      • -
    • grad (class) – Gradient operator class
      • -
    • prox (class) – Proximity operator class
      • -
    • cost (class, optional) – Cost function class
      • -
    • lambda_init (float, optional) – Initial value of the relaxation parameter
      • -
    • lambda_update (function, optional) – Relaxation parameter update method
      • -
    • use_fista (bool, optional) – Option to use FISTA (default is True)
      • -
    • auto_iterate (bool, optional) – Option to automatically begin iterations upon initialisation (default -is ‘True’)
      • -
    -
    -
    -
    -update()[source]
    -

    Update

    -

    This method updates the current reconstruction

    -

    Notes

    -

    Implements algorithm 10.7 (or 10.5) from [B2011]

    -
    - -
    -
    -iterate(max_iter=150)[source]
    -

    Iterate

    -

    This method calls update until either convergence criteria is met or -the maximum number of iterations is reached

    - --- - - - -
    Parameters:max_iter (int, optional) – Maximum number of iterations (default is 150)
    -
    - -
    - -
    -
    -class sf_tools.signal.optimisation.GenForwardBackward(x, grad, prox_list, cost=None, lambda_init=1.0, lambda_update=None, weights=None, auto_iterate=True)[source]
    -

    Bases: object

    -

    Generalized Forward-Backward optimisation

    -

    This class implements algorithm 1 from [R2012]

    - --- - - - -
    Parameters:
      -
    • x (np.ndarray) – Initial guess for the primal variable
      • -
    • grad (class) – Gradient operator class
      • -
    • prox_list (list) – List of proximity operator classes
      • -
    • cost (class, optional) – Cost function class
      • -
    • lambda_init (float, optional) – Initial value of the relaxation parameter
      • -
    • lambda_update (function, optional) – Relaxation parameter update method
      • -
    • weights (np.ndarray, optional) – Proximity operator weights
      • -
    • auto_iterate (bool, optional) – Option to automatically begin iterations upon initialisation (default -is ‘True’)
      • -
    -
    -
    -
    -update()[source]
    -

    Update

    -

    This method updates the current reconstruction

    -

    Notes

    -

    Implements algorithm 1 from [R2012]

    -
    - -
    -
    -iterate(max_iter=150)[source]
    -

    Iterate

    -

    This method calls update until either convergence criteria is met or -the maximum number of iterations is reached

    - --- - - - -
    Parameters:max_iter (int, optional) – Maximum number of iterations (default is 150)
    -
    - -
    - -
    -
    -class sf_tools.signal.optimisation.Condat(x, y, grad, prox, prox_dual, linear, cost, rho, sigma, tau, rho_update=None, sigma_update=None, tau_update=None, auto_iterate=True)[source]
    -

    Bases: object

    -

    Condat optimisation

    -

    This class implements algorithm 10.7 from [Con2013]

    - --- - - - -
    Parameters:
      -
    • x (np.ndarray) – Initial guess for the primal variable
      • -
    • y (np.ndarray) – Initial guess for the dual variable
      • -
    • grad (class) – Gradient operator class
      • -
    • prox (class) – Proximity primal operator class
      • -
    • prox_dual (class) – Proximity dual operator class
      • -
    • linear (class) – Linear operator class
      • -
    • cost (class) – Cost function class
      • -
    • rho (float) – Relaxation parameter
      • -
    • sigma (float) – Proximal dual parameter
      • -
    • tau (float) – Proximal primal paramater
      • -
    • rho_update (function, optional) – Relaxation parameter update method
      • -
    • sigma_update (function, optional) – Proximal dual parameter update method
      • -
    • tau_update (function, optional) – Proximal primal parameter update method
      • -
    • auto_iterate (bool, optional) – Option to automatically begin iterations upon initialisation (default -is ‘True’)
      • -
    -
    -
    -
    -update_param()[source]
    -

    Update parameters

    -

    This method updates the values of rho, sigma and tau with -the methods provided

    -
    - -
    -
    -update()[source]
    -

    Update

    -

    This method updates the current reconstruction

    -

    Notes

    -

    Implements equation 9 (algorithm 3.1) from [Con2013]

    -
      -
    • primal proximity operator set up for positivity constraint
      • -
    -
    - -
    -
    -iterate(max_iter=150)[source]
    -

    Iterate

    -

    This method calls update until either convergence criteria is met or -the maximum number of iterations is reached

    - --- - - - -
    Parameters:max_iter (int, optional) – Maximum number of iterations (default is 150)
    -
    - -
    - -
    - - -
    -
    - -
    -
    - - -
    -
    - -
    - -
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    2.1.6.1.7. sf_tools.signal.positivity module

    -

    POSITIVITY

    -

    This module contains a function that retains only positive coefficients in -an array

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:26/07/2017
    -
    -
    -sf_tools.signal.positivity.positive(data)[source]
    -

    Positivity operator

    -

    This method preserves only the positive coefficients of the input data, all -negative coefficients are set to zero

    - --- - - - - - - - -
    Parameters:data (np.ndarray, list or tuple) – Input data array
    Returns:
    Return type:np.ndarray array with only positive coefficients
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    2.1.6.1.8. sf_tools.signal.proximity module

    -

    PROXIMITY OPERATORS

    -

    This module contains classes of proximity operators for optimisation

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.3
    Date:19/07/2017
    -
    -
    -class sf_tools.signal.proximity.Positive[source]
    -

    Bases: object

    -

    Positivity proximity operator

    -

    This class defines the positivity proximity operator

    -
    -
    -op(data, **kwargs)[source]
    -

    Operator

    -

    This method preserves only the positive coefficients of the input data

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array
    Returns:
    Return type:np.ndarray all positive elements from input data
    -
    - -
    - -
    -
    -class sf_tools.signal.proximity.Threshold(weights, thresh_type='soft')[source]
    -

    Bases: object

    -

    Threshold proximity operator

    -

    This class defines the threshold proximity operator

    - --- - - - -
    Parameters:
      -
    • weights (np.ndarray) – Input array of weights
      • -
    • thresh_type (str {'hard', 'soft'}, optional) – Threshold type (default is ‘soft’)
      • -
    -
    -
    -
    -op(data, extra_factor=1.0)[source]
    -

    Operator

    -

    This method returns the input data thresholded by the weights

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array
      • -
    • extra_factor (float) – Additional multiplication factor
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray thresholded data

    -
    -
    - -
    - -
    -
    -class sf_tools.signal.proximity.LowRankMatrix(thresh, thresh_type='soft', lowr_type='standard', operator=None)[source]
    -

    Bases: object

    -

    Low-rank proximity operator

    -

    This class defines the low-rank proximity operator

    - --- - - - -
    Parameters:
      -
    • thresh (float) – Threshold value
      • -
    • treshold_type (str {'hard', 'soft'}) – Threshold type (options are ‘hard’ or ‘soft’)
      • -
    • lowr_type (str {'standard', 'ngole'}) – Low-rank implementation (options are ‘standard’ or ‘ngole’)
      • -
    • operator (class) – Operator class (‘ngole’ only)
      • -
    -
    -
    -
    -op(data, extra_factor=1.0)[source]
    -

    Operator

    -

    This method returns the input data after the singular values have been -thresholded

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array
      • -
    • extra_factor (float) – Additional multiplication factor
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray SVD thresholded data

    -
    -
    - -
    - -
    -
    -class sf_tools.signal.proximity.ProximityCombo(operators)[source]
    -

    Bases: object

    -

    Proximity Combo

    -

    This class defines a combined proximity operator

    - --- - - - -
    Parameters:operators (list) – List of proximity operator class instances
    -
    -
    -op(data, extra_factor=1.0)[source]
    -

    Operator

    -

    This method returns the result of applying all of the proximity -operators to the data

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array
      • -
    • extra_factor (float) – Additional multiplication factor
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray result

    -
    -
    - -
    - -
    -
    -class sf_tools.signal.proximity.SubIter(data_shape, operator, weights=None, u_init=None)[source]
    -

    Bases: object

    -

    Sub iteration operator

    -

    This class defines the sub-iteration proximity operator

    - --- - - - -
    Parameters:
      -
    • data_shape (tuple) – Shape of input data array
      • -
    • operator (class) – Proximity operator class
      • -
    • weights (np.ndarray) – Array of weights
      • -
    • u_init (np.ndarray) – Initial estimate of u
      • -
    -
    -
    -
    -update_weights(weights)[source]
    -

    Update weights

    -

    This method updates the values of the weights

    - --- - - - -
    Parameters:weights (np.ndarray) – Array of weights
    -
    - -
    -
    -update_u()[source]
    -

    Update u

    -

    This method updates the values of u

    -
    - -
    -
    -op(data)[source]
    -

    Operator

    - --- - - - - - - - -
    Parameters:data (np.ndarray) – Input data array
    Returns:
    Return type:np.ndarray result
    -
    - -
    - -
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    2.1.6.1.9. sf_tools.signal.regression module

    -

    REGRESSION ROUTINES

    -

    This module contains methods for linear regression.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.0
    Date:04/04/2017
    -
    -
    -sf_tools.signal.regression.linear_fit(B, x)[source]
    -

    Linear fit

    -

    This method defines the equation of a straight line.

    - --- - - - - - - - -
    Parameters:
      -
    • B (tuple) – Slope (m) and intercept (b) of the line.
      • -
    • x (list or np.ndarray) – The 1D data vector
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray 1D array of corresponding y values

    -
    -

    Notes

    -

    This equation of a stright line is given by

    -
    -\[y = mx + b\]
    -
    - -
    -
    -sf_tools.signal.regression.polynomial(x, a)[source]
    -

    Polynomial

    -

    This method defines the equation of a polynomial line.

    - --- - - - - - - - -
    Parameters:
      -
    • x (list or np.ndarray) – The 1D data vector
      • -
    • a (list or np.ndarray) – The 1D polynomial coefficient vector
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray 1D array of corresponding y values

    -
    -

    Notes

    -

    This equation of a stright line is given by

    -
    -\[y = a_0 + a_1x + a_2x^2 + \dots + a_kx^k\]
    -
    - -
    -
    -sf_tools.signal.regression.polynomial_fit(x, y, k=1)[source]
    -

    Polynomial fit

    -

    This method finds the coefficients for a polynomial line fit to the input -data using least squares.

    - --- - - - - - - - -
    Parameters:
      -
    • x (list or np.ndarray) – The 1D independent data vector
      • -
    • y (list or np.ndarray) – The 1D dependent data vector
      • -
    • k (int, optional) – Number of degrees of freedom. Default (k=1)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray 1D array of coefficients a

    -
    -
    - -
    -
    -sf_tools.signal.regression.least_squares(X, y)[source]
    -

    Least squares

    -

    This method performs an analytical least squares regression. Returns the -values of the coefficients, a, given the input matrix X and the -corresponding y values.

    - --- - - - - - - - - - -
    Parameters:
      -
    • X (np.ndarray) – The 2D independent data matrix.
      • -
    • y (np.ndarray) – The 1D dependent data vector
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray 1D array of coefficients a

    -
    Raises:

    ValueError – If inputs are not numpy arrays

    -
    -
    -

    Todo

    -

    Add equation and example

    -
    -
    - -
    -
    -sf_tools.signal.regression.x_matrix(x, k)[source]
    -

    Define X matrix

    -

    This method defines the matrix X for a given vector x corresponding to a -polynomial with k degrees of freedom.

    - --- - - - - - - - -
    Parameters:
      -
    • x (list or np.ndarray) – The 1D independent data vector
      • -
    • k (int) – Number of degrees of freedom
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray the 2D independent variable matrix X

    -
    -
    - -
    -
    -sf_tools.signal.regression.fit_odr(x, y, xerr, yerr, fit)[source]
    -

    Fit via ODR

    -

    This method performs an orthogonal distance regression fit.

    - --- - - - - - - - -
    Parameters:
      -
    • x (list or np.ndarray) – The 1D independent data vector
      • -
    • y (list or np.ndarray) – The 1D dependent data vector
      • -
    • x_err (list or np.ndarray) – 1D data vector of x value errors
      • -
    • y_err (list or np.ndarray) – 1D data vector of y value errors
      • -
    • fit (function) – Fitting function
      • -
    -
    Returns:

    -
    Return type:

    tuple best fit parameters

    -
    -
    - -
    - - -
    -
    - -
    -
    - - -
    -
    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.signal.reweight.html b/docs/build/html/sf_tools.signal.reweight.html deleted file mode 100644 index 1f04c1d..0000000 --- a/docs/build/html/sf_tools.signal.reweight.html +++ /dev/null @@ -1,299 +0,0 @@ - - - - - - - - - - - 2.1.6.1.10. sf_tools.signal.reweight module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.6.1.10. sf_tools.signal.reweight module

    -

    REWEIGHTING CLASSES

    -

    This module contains classes for reweighting optimisation implementations

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.3
    Date:20/10/2017
    -
    -
    -class sf_tools.signal.reweight.cwbReweight(weights, thresh_factor=1)[source]
    -

    Bases: object

    -

    Candes, Wakin and Boyd reweighting class

    -

    This class implements the reweighting scheme described in [CWB2007]

    - --- - - - -
    Parameters:
      -
    • weights (np.ndarray) – Array of weights
      • -
    • thresh_factor (float) – Threshold factor
      • -
    -
    -
    -
    -reweight(data)[source]
    -

    Reweight

    -

    This method implements the reweighting from section 4 in [CWB2007]

    -

    Notes

    -

    Reweighting implemented as:

    -
    -\[w = w \left( \frac{1}{1 + \frac{|x^w|}{n \sigma}} \right)\]
    -
    - -
    - -
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    2.1.6.1.11. sf_tools.signal.svd module

    -

    SVD ROUTINES

    -

    This module contains methods for thresholding singular values.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:20/10/2017
    -
    -
    -sf_tools.signal.svd.find_n_pc(u, factor=0.5)[source]
    -

    Find number of principal components

    -

    This method finds the minimum number of principal components required

    - --- - - - - - - - -
    Parameters:
      -
    • u (np.ndarray) – Left singular vector
      • -
    • factor (float, optional) – Factor for testing the auto correlation (default is ‘0.5’)
      • -
    -
    Returns:

    -
    Return type:

    int number of principal components

    -
    -
    - -
    -
    -sf_tools.signal.svd.svd_thresh(data, threshold=None, n_pc=None, thresh_type='hard')[source]
    -

    Threshold the singular values

    -

    This method thresholds the input data using singular value decomposition

    - --- - - - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array
      • -
    • threshold (float, optional) – Threshold value
      • -
    • n_pc (int or str, optional) – Number of principal components, specify an integer value or ‘all’
      • -
    • threshold_type (str {'hard', 'soft'}) – Type of noise to be added (default is ‘hard’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray thresholded data

    -
    Raises:

    ValueError – For invalid string entry for n_pc

    -
    -
    - -
    -
    -sf_tools.signal.svd.svd_thresh_coef(data, operator, threshold, thresh_type='hard')[source]
    -

    Threshold the singular values coefficients

    -

    This method thresholds the input data using singular value decomposition

    - --- - - - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data array
      • -
    • operator (class) – Operator class instance
      • -
    • threshold (float, optional) – Threshold value
      • -
    • threshold_type (str {'hard', 'soft'}) – Type of noise to be added (default is ‘hard’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray thresholded data

    -
    Raises:

    ValueError – For invalid string entry for n_pc

    -
    -
    - -
    - - -
    -
    - -
    -
    - - -
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    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.signal.validation.html b/docs/build/html/sf_tools.signal.validation.html deleted file mode 100644 index 2980f85..0000000 --- a/docs/build/html/sf_tools.signal.validation.html +++ /dev/null @@ -1,292 +0,0 @@ - - - - - - - - - - - 2.1.6.1.12. sf_tools.signal.validation module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.6.1.12. sf_tools.signal.validation module

    -

    VALIDATION ROUTINES

    -

    This module contains methods for testing signal and operator properties.

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:06/12/2017
    -
    -
    -sf_tools.signal.validation.transpose_test(operator, operator_t, x_shape, x_args, y_shape=None, y_args=None)[source]
    -

    Transpose test

    -

    This method tests two operators to see if they are the transpose of each -other.

    - --- - - - -
    Parameters:
      -
    • operator (function) – Operator function
      • -
    • operator_t (function) – Transpose operator function
      • -
    • x_shape (tuple) – Shape of operator input data
      • -
    • x_args (tuple) – Arguments to be passed to operator
      • -
    • y_shape (tuple, optional) – Shape of transpose operator input data
      • -
    • y_args (tuple, optional) – Arguments to be passed to transpose operator
      • -
    -
    -
    - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/sf_tools.signal.wavelet.html b/docs/build/html/sf_tools.signal.wavelet.html deleted file mode 100644 index 6032ea2..0000000 --- a/docs/build/html/sf_tools.signal.wavelet.html +++ /dev/null @@ -1,370 +0,0 @@ - - - - - - - - - - - 2.1.6.1.13. sf_tools.signal.wavelet module — sf_tools 1.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    2.1.6.1.13. sf_tools.signal.wavelet module

    -

    WAVELET MODULE

    -

    This module contains methods for performing wavelet transformations using iSAP

    - --- - - - - - - - -
    Author:Samuel Farrens <samuel.farrens@gmail.com>
    Version:1.2
    Date:20/10/2017
    -
    -
    -sf_tools.signal.wavelet.call_mr_transform(data, opt=None, path='./', remove_files=True)[source]
    -

    Call mr_transform

    -

    This method calls the iSAP module mr_transform

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data, 2D array
      • -
    • opt (list, optional) – List of additonal mr_transform options
      • -
    • path (str, optional) – Path for output files (default is ‘./’)
      • -
    • remove_files (bool, optional) – Option to remove output files (default is ‘True’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray results of transform

    -
    -
    - -
    -
    -sf_tools.signal.wavelet.get_mr_filters(data_shape, opt=None, coarse=False)[source]
    -

    Get mr_transform filters

    -

    This method obtains wavelet filters by calling mr_transform

    - --- - - - - - - - -
    Parameters:
      -
    • data_shape (tuple) – 2D data shape
      • -
    • opt (list, optional) – List of additonal mr_transform options
      • -
    • coarse (bool, optional) – Option to keep coarse scale (default is ‘False’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray 3D array of wavelet filters

    -
    -
    - -
    -
    -sf_tools.signal.wavelet.filter_convolve(data, filters, filter_rot=False)[source]
    -

    Filter convolve

    -

    This method convolves the input image with the wavelet filters

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data, 2D array
      • -
    • filters (np.ndarray) – Wavelet filters, 3D array
      • -
    • filter_rot (bool, optional) – Option to rotate wavelet filters (default is ‘False’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray convolved data

    -
    -
    - -
    -
    -sf_tools.signal.wavelet.filter_convolve_stack(data, filters, filter_rot=False)[source]
    -

    Filter convolve

    -

    This method convolves the a stack of input images with the wavelet filters

    - --- - - - - - - - -
    Parameters:
      -
    • data (np.ndarray) – Input data, 3D array
      • -
    • filters (np.ndarray) – Wavelet filters, 3D array
      • -
    • filter_rot (bool, optional) – Option to rotate wavelet filters (default is ‘False’)
      • -
    -
    Returns:

    -
    Return type:

    np.ndarray convolved data

    -
    -
    - -
    - - -
    -
    - -
    -
    - - -
    -
    - -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/source/sf_tools.base.np_adjust.rst b/docs/source/sf_tools.base.np_adjust.rst deleted file mode 100644 index 66d73bc..0000000 --- a/docs/source/sf_tools.base.np_adjust.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.base\.np\_adjust module -================================== - -.. automodule:: sf_tools.base.np_adjust - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.base.rst b/docs/source/sf_tools.base.rst deleted file mode 100644 index aaf37f3..0000000 --- a/docs/source/sf_tools.base.rst +++ /dev/null @@ -1,19 +0,0 @@ -sf\_tools\.base package -======================= - -Submodules ----------- - -.. toctree:: - - sf_tools.base.np_adjust - sf_tools.base.transform - sf_tools.base.types - -Module contents ---------------- - -.. automodule:: sf_tools.base - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.base.transform.rst b/docs/source/sf_tools.base.transform.rst deleted file mode 100644 index ec5f1f3..0000000 --- a/docs/source/sf_tools.base.transform.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.base\.transform module -================================= - -.. automodule:: sf_tools.base.transform - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.base.types.rst b/docs/source/sf_tools.base.types.rst deleted file mode 100644 index 770a9e3..0000000 --- a/docs/source/sf_tools.base.types.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.base\.types module -============================= - -.. automodule:: sf_tools.base.types - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.image.convolve.rst b/docs/source/sf_tools.image.convolve.rst deleted file mode 100644 index 73d9c72..0000000 --- a/docs/source/sf_tools.image.convolve.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.convolve module -================================= - -.. automodule:: sf_tools.image.convolve - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.image.distort.rst b/docs/source/sf_tools.image.distort.rst deleted file mode 100644 index 3c2aa36..0000000 --- a/docs/source/sf_tools.image.distort.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.distort module -================================ - -.. automodule:: sf_tools.image.distort - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.image.quality.rst b/docs/source/sf_tools.image.quality.rst deleted file mode 100644 index 6219c53..0000000 --- a/docs/source/sf_tools.image.quality.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.quality module -================================ - -.. automodule:: sf_tools.image.quality - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.image.rst b/docs/source/sf_tools.image.rst deleted file mode 100644 index e283833..0000000 --- a/docs/source/sf_tools.image.rst +++ /dev/null @@ -1,22 +0,0 @@ -sf\_tools\.image package -======================== - -Submodules ----------- - -.. toctree:: - - sf_tools.image.convolve - sf_tools.image.distort - sf_tools.image.quality - sf_tools.image.shape - sf_tools.image.stamp - sf_tools.image.stats - -Module contents ---------------- - -.. automodule:: sf_tools.image - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.image.shape.rst b/docs/source/sf_tools.image.shape.rst deleted file mode 100644 index e426acf..0000000 --- a/docs/source/sf_tools.image.shape.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.shape module -============================== - -.. automodule:: sf_tools.image.shape - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.image.stamp.rst b/docs/source/sf_tools.image.stamp.rst deleted file mode 100644 index 646ffba..0000000 --- a/docs/source/sf_tools.image.stamp.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.stamp module -============================== - -.. automodule:: sf_tools.image.stamp - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.image.stats.rst b/docs/source/sf_tools.image.stats.rst deleted file mode 100644 index 64ea1cf..0000000 --- a/docs/source/sf_tools.image.stats.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.image\.stats module -============================== - -.. automodule:: sf_tools.image.stats - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.interface.errors.rst b/docs/source/sf_tools.interface.errors.rst deleted file mode 100644 index 55fff77..0000000 --- a/docs/source/sf_tools.interface.errors.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.interface\.errors module -=================================== - -.. automodule:: sf_tools.interface.errors - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.interface.log.rst b/docs/source/sf_tools.interface.log.rst deleted file mode 100644 index afcbb0d..0000000 --- a/docs/source/sf_tools.interface.log.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.interface\.log module -================================ - -.. automodule:: sf_tools.interface.log - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.interface.rst b/docs/source/sf_tools.interface.rst deleted file mode 100644 index 4f55993..0000000 --- a/docs/source/sf_tools.interface.rst +++ /dev/null @@ -1,18 +0,0 @@ -sf\_tools\.interface package -============================ - -Submodules ----------- - -.. toctree:: - - sf_tools.interface.errors - sf_tools.interface.log - -Module contents ---------------- - -.. automodule:: sf_tools.interface - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.math.angle.rst b/docs/source/sf_tools.math.angle.rst deleted file mode 100644 index 405930b..0000000 --- a/docs/source/sf_tools.math.angle.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.angle module -============================= - -.. automodule:: sf_tools.math.angle - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.math.integrate.rst b/docs/source/sf_tools.math.integrate.rst deleted file mode 100644 index 527fadc..0000000 --- a/docs/source/sf_tools.math.integrate.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.integrate module -================================= - -.. automodule:: sf_tools.math.integrate - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.math.matrix.rst b/docs/source/sf_tools.math.matrix.rst deleted file mode 100644 index 3a34404..0000000 --- a/docs/source/sf_tools.math.matrix.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.matrix module -============================== - -.. automodule:: sf_tools.math.matrix - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.math.misc.rst b/docs/source/sf_tools.math.misc.rst deleted file mode 100644 index b1a541d..0000000 --- a/docs/source/sf_tools.math.misc.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.misc module -============================ - -.. automodule:: sf_tools.math.misc - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.math.rst b/docs/source/sf_tools.math.rst deleted file mode 100644 index 0ddf616..0000000 --- a/docs/source/sf_tools.math.rst +++ /dev/null @@ -1,21 +0,0 @@ -sf\_tools\.math package -======================= - -Submodules ----------- - -.. toctree:: - - sf_tools.math.angle - sf_tools.math.integrate - sf_tools.math.matrix - sf_tools.math.misc - sf_tools.math.stats - -Module contents ---------------- - -.. automodule:: sf_tools.math - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.math.stats.rst b/docs/source/sf_tools.math.stats.rst deleted file mode 100644 index 6263488..0000000 --- a/docs/source/sf_tools.math.stats.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.math\.stats module -============================= - -.. automodule:: sf_tools.math.stats - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.plot.cost_plot.rst b/docs/source/sf_tools.plot.cost_plot.rst deleted file mode 100644 index f634444..0000000 --- a/docs/source/sf_tools.plot.cost_plot.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.plot\.cost\_plot module -================================== - -.. automodule:: sf_tools.plot.cost_plot - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.plot.rst b/docs/source/sf_tools.plot.rst deleted file mode 100644 index 63a26e9..0000000 --- a/docs/source/sf_tools.plot.rst +++ /dev/null @@ -1,17 +0,0 @@ -sf\_tools\.plot package -======================= - -Submodules ----------- - -.. toctree:: - - sf_tools.plot.cost_plot - -Module contents ---------------- - -.. automodule:: sf_tools.plot - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.rst b/docs/source/sf_tools.rst deleted file mode 100644 index 782f6f2..0000000 --- a/docs/source/sf_tools.rst +++ /dev/null @@ -1,22 +0,0 @@ -sf\_tools package -================= - -Subpackages ------------ - -.. toctree:: - - sf_tools.base - sf_tools.image - sf_tools.interface - sf_tools.math - sf_tools.plot - sf_tools.signal - -Module contents ---------------- - -.. automodule:: sf_tools - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.cost.rst b/docs/source/sf_tools.signal.cost.rst deleted file mode 100644 index bcf75b8..0000000 --- a/docs/source/sf_tools.signal.cost.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.cost module -============================== - -.. automodule:: sf_tools.signal.cost - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.filter.rst b/docs/source/sf_tools.signal.filter.rst deleted file mode 100644 index c24084d..0000000 --- a/docs/source/sf_tools.signal.filter.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.filter module -================================ - -.. automodule:: sf_tools.signal.filter - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.gradient.rst b/docs/source/sf_tools.signal.gradient.rst deleted file mode 100644 index 521794d..0000000 --- a/docs/source/sf_tools.signal.gradient.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.gradient module -================================== - -.. automodule:: sf_tools.signal.gradient - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.linear.rst b/docs/source/sf_tools.signal.linear.rst deleted file mode 100644 index 48dc126..0000000 --- a/docs/source/sf_tools.signal.linear.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.linear module -================================ - -.. automodule:: sf_tools.signal.linear - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.noise.rst b/docs/source/sf_tools.signal.noise.rst deleted file mode 100644 index d0f6215..0000000 --- a/docs/source/sf_tools.signal.noise.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.noise module -=============================== - -.. automodule:: sf_tools.signal.noise - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.optimisation.rst b/docs/source/sf_tools.signal.optimisation.rst deleted file mode 100644 index 6d9c60b..0000000 --- a/docs/source/sf_tools.signal.optimisation.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.optimisation module -====================================== - -.. automodule:: sf_tools.signal.optimisation - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.positivity.rst b/docs/source/sf_tools.signal.positivity.rst deleted file mode 100644 index 619f0c5..0000000 --- a/docs/source/sf_tools.signal.positivity.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.positivity module -==================================== - -.. automodule:: sf_tools.signal.positivity - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.proximity.rst b/docs/source/sf_tools.signal.proximity.rst deleted file mode 100644 index b96ec5e..0000000 --- a/docs/source/sf_tools.signal.proximity.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.proximity module -=================================== - -.. automodule:: sf_tools.signal.proximity - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.regression.rst b/docs/source/sf_tools.signal.regression.rst deleted file mode 100644 index 24c3364..0000000 --- a/docs/source/sf_tools.signal.regression.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.regression module -==================================== - -.. automodule:: sf_tools.signal.regression - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.reweight.rst b/docs/source/sf_tools.signal.reweight.rst deleted file mode 100644 index 401cc5b..0000000 --- a/docs/source/sf_tools.signal.reweight.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.reweight module -================================== - -.. automodule:: sf_tools.signal.reweight - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.rst b/docs/source/sf_tools.signal.rst deleted file mode 100644 index fead595..0000000 --- a/docs/source/sf_tools.signal.rst +++ /dev/null @@ -1,29 +0,0 @@ -sf\_tools\.signal package -========================= - -Submodules ----------- - -.. toctree:: - - sf_tools.signal.cost - sf_tools.signal.filter - sf_tools.signal.gradient - sf_tools.signal.linear - sf_tools.signal.noise - sf_tools.signal.optimisation - sf_tools.signal.positivity - sf_tools.signal.proximity - sf_tools.signal.regression - sf_tools.signal.reweight - sf_tools.signal.svd - sf_tools.signal.validation - sf_tools.signal.wavelet - -Module contents ---------------- - -.. automodule:: sf_tools.signal - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.svd.rst b/docs/source/sf_tools.signal.svd.rst deleted file mode 100644 index 511982f..0000000 --- a/docs/source/sf_tools.signal.svd.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.svd module -============================= - -.. automodule:: sf_tools.signal.svd - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.validation.rst b/docs/source/sf_tools.signal.validation.rst deleted file mode 100644 index baa2371..0000000 --- a/docs/source/sf_tools.signal.validation.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.validation module -==================================== - -.. automodule:: sf_tools.signal.validation - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/sf_tools.signal.wavelet.rst b/docs/source/sf_tools.signal.wavelet.rst deleted file mode 100644 index e617d8b..0000000 --- a/docs/source/sf_tools.signal.wavelet.rst +++ /dev/null @@ -1,7 +0,0 @@ -sf\_tools\.signal\.wavelet module -================================= - -.. automodule:: sf_tools.signal.wavelet - :members: - :undoc-members: - :show-inheritance: From 0a1a897ca400b6c85161bc19a76d09b700241a01 Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Thu, 5 Apr 2018 13:11:05 +0200 Subject: [PATCH 4/8] updated travis --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 9321cc6..d2faddc 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,7 +11,7 @@ branches: # install package and dependencies install: - pip install coverage nose pytest pytest-cov - - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then pip install numpy future scipy astropy sphinx==1.6.5 sphinx-rtd-theme numpydoc travis-sphinx coveralls; fi + - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then pip install numpy future scipy astropy modopt sphinx==1.6.5 sphinx-rtd-theme numpydoc travis-sphinx coveralls; fi # run unit tests script: From 88decc93d5a2da148add463df79d8f35e85b4b13 Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Thu, 5 Apr 2018 13:39:10 +0200 Subject: [PATCH 5/8] fixed bug --- dist/sf_tools-0.1.dev0.tar.gz | Bin 29973 -> 0 bytes dist/sf_tools-0.1.dev1.tar.gz | Bin 30225 -> 0 bytes dist/sf_tools-0.2.dev0.tar.gz | Bin 29978 -> 0 bytes dist/sf_tools-0.3.dev0.tar.gz | Bin 29971 -> 0 bytes dist/sf_tools-1.0.0.tar.gz | Bin 30657 -> 0 bytes dist/sf_tools-1.0.1.tar.gz | Bin 30674 -> 0 bytes dist/sf_tools-1.0.tar.gz | Bin 29930 -> 0 bytes dist/sf_tools-1.1.tar.gz | Bin 30075 -> 0 bytes setup.cfg | 2 +- sf_tools.egg-info/PKG-INFO | 22 ----------- sf_tools.egg-info/SOURCES.txt | 45 ---------------------- sf_tools.egg-info/dependency_links.txt | 1 - sf_tools.egg-info/top_level.txt | 1 - sf_tools/tests/test_image.py | 51 ------------------------- 14 files changed, 1 insertion(+), 121 deletions(-) delete mode 100644 dist/sf_tools-0.1.dev0.tar.gz 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z$XV}tVab-obLwncgSSD}Y3&(>2q~xul8jChgpdDJOC~H8dpi`wZ}}DwI5~=w%mVqe z2&Oc$W)VWzw6^ptn9c3!S)s?=__L(f0!(c~OMugOqCvp%fW1Nj38KjqnaRW=2$YLV z5Yu`#)5CORrkcZ?PvgwqAp#-s>ETTSG1V4pF$RNzwsgv^u|e Date: Thu, 5 Apr 2018 13:58:00 +0200 Subject: [PATCH 6/8] fixed another bug --- .gitignore | 2 +- setup.cfg | 2 +- setup.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index f76522d..0acf821 100644 --- a/.gitignore +++ b/.gitignore @@ -13,7 +13,7 @@ # Ignore installation .eggs/ -modopt.egg-info/ +sf_tools.egg-info/ dist/ build/ diff --git a/setup.cfg b/setup.cfg index 79ecfa8..0d93ed6 100644 --- a/setup.cfg +++ b/setup.cfg @@ -9,4 +9,4 @@ build-dir=docs/build description-file = README.rst [tool:pytest] -addopts = --verbose --cov=modopt/ sf_tools/tests/ +addopts = --verbose --cov=sf_tools/ sf_tools/tests/ diff --git a/setup.py b/setup.py index 343b1e7..e5038dd 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,7 @@ url='https://github.com/sfarrens/sf_tools', download_url='https://github.com/sfarrens/sf_tools', packages=find_packages(), - install_requires=['numpy>=1.14.1', 'future>=0.16.0', 'scipy>=1.0.0', 'astropy==2.0.4', 'modopt>=1.1.4'], + install_requires=['numpy>=1.14.1', 'future>=0.16.0', 'modopt>=1.1.4'], license='MIT', description='Tools for image analysis, signal processing and statistics.', long_description=release_info["__about__"], From c76bdef53ef3be949af6e66f6218d9673bf34db8 Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Thu, 5 Apr 2018 15:08:24 +0200 Subject: [PATCH 7/8] testing sphinx version --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index d2faddc..f00fcc3 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,7 +11,7 @@ branches: # install package and dependencies install: - pip install coverage nose pytest pytest-cov - - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then pip install numpy future scipy astropy modopt sphinx==1.6.5 sphinx-rtd-theme numpydoc travis-sphinx coveralls; fi + - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then pip install numpy future scipy astropy modopt sphinx sphinx-rtd-theme numpydoc travis-sphinx coveralls; fi # run unit tests script: From e78cdb96980f1af68889a6fdac903f3bd0e5b6ac Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Thu, 5 Apr 2018 15:14:52 +0200 Subject: [PATCH 8/8] testing sphinx version --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index f00fcc3..b114702 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,7 +11,7 @@ branches: # install package and dependencies install: - pip install coverage nose pytest pytest-cov - - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then pip install numpy future scipy astropy modopt sphinx sphinx-rtd-theme numpydoc travis-sphinx coveralls; fi + - if [[ $TRAVIS_PYTHON_VERSION == '2.7' ]]; then pip install numpy future scipy astropy modopt sphinx==1.5.6 sphinx-rtd-theme numpydoc travis-sphinx coveralls; fi # run unit tests script:

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