Mercurial > hg > octave-nkf
annotate scripts/general/gradient.m @ 12575:d0b799dafede
Grammarcheck files for 3.4.1 release.
author | Rik <octave@nomad.inbox5.com> |
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date | Mon, 04 Apr 2011 15:33:46 -0700 |
parents | b0084095098e |
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11523 | 1 ## Copyright (C) 2000-2011 Kai Habel |
5837 | 2 ## |
3 ## This file is part of Octave. | |
4 ## | |
5 ## Octave is free software; you can redistribute it and/or modify it | |
6 ## under the terms of the GNU General Public License as published by | |
7016 | 7 ## the Free Software Foundation; either version 3 of the License, or (at |
8 ## your option) any later version. | |
5837 | 9 ## |
10 ## Octave is distributed in the hope that it will be useful, but | |
11 ## WITHOUT ANY WARRANTY; without even the implied warranty of | |
12 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU | |
13 ## General Public License for more details. | |
14 ## | |
15 ## You should have received a copy of the GNU General Public License | |
7016 | 16 ## along with Octave; see the file COPYING. If not, see |
17 ## <http://www.gnu.org/licenses/>. | |
5837 | 18 |
19 ## -*- texinfo -*- | |
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20 ## @deftypefn {Function File} {@var{dx} =} gradient (@var{m}) |
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21 ## @deftypefnx {Function File} {[@var{dx}, @var{dy}, @var{dz}, @dots{}] =} gradient (@var{m}) |
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22 ## @deftypefnx {Function File} {[@dots{}] =} gradient (@var{m}, @var{s}) |
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23 ## @deftypefnx {Function File} {[@dots{}] =} gradient (@var{m}, @var{x}, @var{y}, @var{z}, @dots{}) |
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24 ## @deftypefnx {Function File} {[@dots{}] =} gradient (@var{f}, @var{x0}) |
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25 ## @deftypefnx {Function File} {[@dots{}] =} gradient (@var{f}, @var{x0}, @var{s}) |
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26 ## @deftypefnx {Function File} {[@dots{}] =} gradient (@var{f}, @var{x0}, @var{x}, @var{y}, @dots{}) |
5837 | 27 ## |
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28 ## Calculate the gradient of sampled data or a function. If @var{m} |
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29 ## is a vector, calculate the one-dimensional gradient of @var{m}. If |
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30 ## @var{m} is a matrix the gradient is calculated for each dimension. |
5837 | 31 ## |
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32 ## @code{[@var{dx}, @var{dy}] = gradient (@var{m})} calculates the one |
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33 ## dimensional gradient for @var{x} and @var{y} direction if @var{m} is a |
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34 ## matrix. Additional return arguments can be use for multi-dimensional |
5837 | 35 ## matrices. |
36 ## | |
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37 ## A constant spacing between two points can be provided by the |
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38 ## @var{s} parameter. If @var{s} is a scalar, it is assumed to be the spacing |
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39 ## for all dimensions. |
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40 ## Otherwise, separate values of the spacing can be supplied by |
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41 ## the @var{x}, @dots{} arguments. Scalar values specify an equidistant |
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42 ## spacing. |
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43 ## Vector values for the @var{x}, @dots{} arguments specify the coordinate for |
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44 ## that |
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45 ## dimension. The length must match their respective dimension of @var{m}. |
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46 ## |
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47 ## At boundary points a linear extrapolation is applied. Interior points |
5837 | 48 ## are calculated with the first approximation of the numerical gradient |
49 ## | |
50 ## @example | |
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51 ## y'(i) = 1/(x(i+1)-x(i-1)) * (y(i-1)-y(i+1)). |
5837 | 52 ## @end example |
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53 ## |
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54 ## If the first argument @var{f} is a function handle, the gradient of the |
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55 ## function at the points in @var{x0} is approximated using central |
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56 ## difference. For example, @code{gradient (@@cos, 0)} approximates the |
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57 ## gradient of the cosine function in the point @math{x0 = 0}. As with |
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58 ## sampled data, the spacing values between the points from which the |
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59 ## gradient is estimated can be set via the @var{s} or @var{dx}, |
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60 ## @var{dy}, @dots{} arguments. By default a spacing of 1 is used. |
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61 ## @seealso{diff, del2} |
5837 | 62 ## @end deftypefn |
63 | |
64 ## Author: Kai Habel <kai.habel@gmx.de> | |
65 ## Modified: David Bateman <dbateman@free.fr> Added NDArray support | |
66 | |
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67 function varargout = gradient (m, varargin) |
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68 |
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70 print_usage (); |
5837 | 71 endif |
72 | |
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73 nargout_with_ans = max(1,nargout); |
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74 if (ismatrix (m)) |
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75 [varargout{1:nargout_with_ans}] = matrix_gradient (m, varargin{:}); |
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76 elseif (isa (m, "function_handle")) |
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77 [varargout{1:nargout_with_ans}] = handle_gradient (m, varargin{:}); |
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78 elseif (ischar(m)) |
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79 [varargout{1:nargout_with_ans}] = handle_gradient (str2func (m), varargin{:}); |
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80 else |
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81 error ("gradient: first input must be an array or a function"); |
5837 | 82 endif |
83 | |
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84 endfunction |
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85 |
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86 function varargout = matrix_gradient (m, varargin) |
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87 transposed = false; |
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88 if (isvector (m)) |
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89 ## make a row vector. |
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90 transposed = (size (m, 2) == 1); |
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91 m = m(:)'; |
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92 endif |
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93 |
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94 nd = ndims (m); |
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95 sz = size (m); |
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96 if (length(sz) > 1) |
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97 tmp = sz(1); sz(1) = sz(2); sz(2) = tmp; |
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98 endif |
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99 |
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101 print_usage (); |
5837 | 102 endif |
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103 |
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104 ## cell d stores a spacing vector for each dimension |
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5837 | 106 if (nargin == 1) |
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107 ## no spacing given - assume 1.0 for all dimensions |
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109 d{i} = ones (sz(i) - 1, 1); |
5837 | 110 endfor |
111 elseif (nargin == 2) | |
112 if (isscalar (varargin{1})) | |
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113 ## single scalar value for all dimensions |
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115 d{i} = varargin{1} * ones (sz(i) - 1, 1); |
5837 | 116 endfor |
117 else | |
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118 ## vector for one-dimensional derivative |
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119 d{1} = diff (varargin{1}(:)); |
5837 | 120 endif |
121 else | |
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122 ## have spacing value for each dimension |
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123 if (length(varargin) != nd) |
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124 error ("gradient: dimensions and number of spacing values do not match"); |
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125 endif |
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7120 | 127 if (isscalar (varargin{i})) |
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128 d{i} = varargin{i} * ones (sz(i) - 1, 1); |
5837 | 129 else |
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130 d{i} = diff (varargin{i}(:)); |
5837 | 131 endif |
132 endfor | |
133 endif | |
134 | |
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135 m = shiftdim (m, 1); |
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136 for i = 1:min (nd, nargout) |
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137 mr = rows (m); |
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138 mc = numel (m) / mr; |
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139 Y = zeros (size (m), class (m)); |
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140 |
5837 | 141 if (mr > 1) |
8507 | 142 ## Top and bottom boundary. |
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143 Y(1,:) = diff (m(1:2, :)) / d{i}(1); |
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144 Y(mr,:) = diff (m(mr-1:mr, :) / d{i}(mr - 1)); |
5837 | 145 endif |
146 | |
147 if (mr > 2) | |
8507 | 148 ## Interior points. |
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149 Y(2:mr-1,:) = ((m(3:mr,:) - m(1:mr-2,:)) |
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150 ./ kron (d{i}(1:mr-2) + d{i}(2:mr-1), ones (1, mc))); |
5837 | 151 endif |
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152 |
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153 ## turn multi-dimensional matrix in a way, that gradient |
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154 ## along x-direction is calculated first then y, z, ... |
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155 |
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156 if (i == 1) |
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157 varargout{i} = shiftdim (Y, nd - 1); |
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158 m = shiftdim (m, nd - 1); |
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159 elseif (i == 2) |
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160 varargout{i} = Y; |
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161 m = shiftdim (m, 2); |
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162 else |
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163 varargout{i} = shiftdim (Y, nd - i + 1); |
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164 m = shiftdim (m, 1); |
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165 endif |
5837 | 166 endfor |
167 | |
168 if (transposed) | |
169 varargout{1} = varargout{1}.'; | |
170 endif | |
171 endfunction | |
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172 |
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173 function varargout = handle_gradient (f, p0, varargin) |
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174 ## Input checking |
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175 p0_size = size (p0); |
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176 |
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177 if (numel (p0_size) != 2) |
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178 error ("gradient: the second input argument should either be a vector or a matrix"); |
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179 endif |
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180 |
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181 if (any (p0_size == 1)) |
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182 p0 = p0 (:); |
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183 dim = 1; |
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184 num_points = numel (p0); |
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185 else |
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186 num_points = p0_size (1); |
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187 dim = p0_size (2); |
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188 endif |
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189 |
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190 if (length (varargin) == 0) |
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191 delta = 1; |
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192 elseif (length (varargin) == 1 || length (varargin) == dim) |
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193 try |
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194 delta = [varargin{:}]; |
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195 catch |
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196 error ("gradient: spacing parameters must be scalars or a vector"); |
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197 end_try_catch |
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198 else |
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199 error ("gradient: incorrect number of spacing parameters"); |
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200 endif |
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201 |
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202 if (isscalar (delta)) |
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203 delta = repmat (delta, 1, dim); |
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204 elseif (!isvector (delta)) |
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205 error ("gradient: spacing values must be scalars or a vector"); |
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206 endif |
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207 |
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208 ## Calculate the gradient |
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209 p0 = mat2cell (p0, num_points, ones (1, dim)); |
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210 varargout = cell (1, dim); |
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211 for d = 1:dim |
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212 s = delta (d); |
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213 df_dx = (f (p0{1:d-1}, p0{d}+s, p0{d+1:end}) |
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214 - f (p0{1:d-1}, p0{d}-s, p0{d+1:end})) ./ (2*s); |
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215 if (dim == 1) |
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216 varargout{d} = reshape (df_dx, p0_size); |
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217 else |
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218 varargout{d} = df_dx; |
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219 endif |
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220 endfor |
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221 endfunction |
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222 |
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223 %!test |
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224 %! data = [1, 2, 4, 2]; |
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225 %! dx = gradient (data); |
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226 %! dx2 = gradient (data, 0.25); |
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227 %! dx3 = gradient (data, [0.25, 0.5, 1, 3]); |
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228 %! assert (dx, [1, 3/2, 0, -2]); |
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229 %! assert (dx2, [4, 6, 0, -8]); |
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230 %! assert (dx3, [4, 4, 0, -1]); |
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231 %! assert (size_equal(data, dx)); |
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232 |
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233 %!test |
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234 %! [Y,X,Z,U] = ndgrid (2:2:8,1:5,4:4:12,3:5:30); |
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235 %! [dX,dY,dZ,dU] = gradient (X); |
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236 %! assert (all(dX(:)==1)); |
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237 %! assert (all(dY(:)==0)); |
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238 %! assert (all(dZ(:)==0)); |
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239 %! assert (all(dU(:)==0)); |
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240 %! [dX,dY,dZ,dU] = gradient (Y); |
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241 %! assert (all(dX(:)==0)); |
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242 %! assert (all(dY(:)==2)); |
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243 %! assert (all(dZ(:)==0)); |
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244 %! assert (all(dU(:)==0)); |
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245 %! [dX,dY,dZ,dU] = gradient (Z); |
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246 %! assert (all(dX(:)==0)); |
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247 %! assert (all(dY(:)==0)); |
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248 %! assert (all(dZ(:)==4)); |
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249 %! assert (all(dU(:)==0)); |
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250 %! [dX,dY,dZ,dU] = gradient (U); |
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251 %! assert (all(dX(:)==0)); |
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252 %! assert (all(dY(:)==0)); |
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253 %! assert (all(dZ(:)==0)); |
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254 %! assert (all(dU(:)==5)); |
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255 %! assert (size_equal(dX, dY, dZ, dU, X, Y, Z, U)); |
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256 %! [dX,dY,dZ,dU] = gradient (U, 5.0); |
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257 %! assert (all(dU(:)==1)); |
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258 %! [dX,dY,dZ,dU] = gradient (U, 1.0, 2.0, 3.0, 2.5); |
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259 %! assert (all(dU(:)==2)); |
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260 |
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261 %!test |
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262 %! x = 0:10; |
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263 %! f = @cos; |
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264 %! df_dx = @(x) -sin (x); |
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265 %! assert (gradient (f, x), df_dx (x), 0.2); |
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266 %! assert (gradient (f, x, 0.5), df_dx (x), 0.1); |
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267 |
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268 %!test |
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269 %! xy = reshape (1:10, 5, 2); |
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270 %! f = @(x,y) sin (x) .* cos (y); |
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271 %! df_dx = @(x, y) cos (x) .* cos (y); |
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272 %! df_dy = @(x, y) -sin (x) .* sin (y); |
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273 %! [dx, dy] = gradient (f, xy); |
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274 %! assert (dx, df_dx (xy (:, 1), xy (:, 2)), 0.1) |
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275 %! assert (dy, df_dy (xy (:, 1), xy (:, 2)), 0.1) |
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276 |