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comet.m: Speed up animation by using low-level graphic commands. * scripts/plot/comet.m: Speed up animation by using low-level graphic commands.
author Rik <rik@octave.org>
date Mon, 05 Aug 2013 15:44:02 -0700
parents 6e8c621c3496
children bc924baa2c4e
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 ## Copyright (C) 2007-2012 David Bateman
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2 ## Copyright (C) 2003 Alberto Terruzzi
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3 ##
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4 ## This file is part of Octave.
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5 ##
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6 ## Octave is free software; you can redistribute it and/or modify it
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7 ## under the terms of the GNU General Public License as published by
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8 ## the Free Software Foundation; either version 3 of the License, or (at
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9 ## your option) any later version.
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10 ##
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11 ## Octave is distributed in the hope that it will be useful, but
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12 ## WITHOUT ANY WARRANTY; without even the implied warranty of
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13 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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14 ## General Public License for more details.
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15 ##
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16 ## You should have received a copy of the GNU General Public License
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17 ## along with Octave; see the file COPYING. If not, see
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18 ## <http://www.gnu.org/licenses/>.
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19
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20 ## -*- texinfo -*-
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21 ## @deftypefn {Function File} {} pareto (@var{y})
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22 ## @deftypefnx {Function File} {} pareto (@var{y}, @var{x})
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23 ## @deftypefnx {Function File} {} pareto (@var{hax}, @dots{})
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24 ## @deftypefnx {Function File} {@var{h} =} pareto (@dots{})
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25 ## Draw a Pareto chart.
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26 ##
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27 ## A Pareto chart is a bar graph that arranges information in such a way
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28 ## that priorities for process improvement can be established; It organizes
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29 ## and displays information to show the relative importance of data. The chart
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30 ## is similar to the histogram or bar chart, except that the bars are arranged
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31 ## in decreasing magnitude from left to right along the x-axis.
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32 ##
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33 ## The fundamental idea (Pareto principle) behind the use of Pareto
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34 ## diagrams is that the majority of an effect is due to a small subset of the
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35 ## causes. For quality improvement, the first few contributing causes
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36 ## (leftmost bars as presented on the diagram) to a problem usually account for
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37 ## the majority of the result. Thus, targeting these "major causes" for
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38 ## elimination results in the most cost-effective improvement scheme.
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39 ##
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40 ## Typically only the magnitude data @var{y} is present in which case
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41 ## @var{x} is taken to be the range @code{1 : length (@var{y})}. If @var{x}
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42 ## is given it may be a string array, a cell array of strings, or a numerical
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43 ## vector.
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44 ##
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45 ## If the first argument @var{hax} is an axes handle, then plot into this axis,
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46 ## rather than the current axes returned by @code{gca}.
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47 ##
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48 ## The optional return value @var{h} is a 2-element vector with a graphics
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49 ## handle for the created bar plot and a second handle for the created line
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50 ## plot.
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51 ##
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52 ## An example of the use of @code{pareto} is
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53 ##
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54 ## @example
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55 ## @group
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56 ## Cheese = @{"Cheddar", "Swiss", "Camembert", ...
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57 ## "Munster", "Stilton", "Blue"@};
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58 ## Sold = [105, 30, 70, 10, 15, 20];
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59 ## pareto (Sold, Cheese);
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60 ## @end group
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61 ## @end example
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62 ## @seealso{bar, barh, hist, pie, plot}
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63 ## @end deftypefn
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64
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65 function h = pareto (varargin)
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66
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67 [hax, varargin, nargin] = __plt_get_axis_arg__ ("pareto", varargin{:});
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68
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69 if (nargin != 1 && nargin != 2)
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70 print_usage ();
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71 endif
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72
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73 x = varargin {1}(:).';
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74 if (nargin == 2)
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75 y = varargin {2}(:).';
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76 if (! iscell (y))
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77 if (ischar (y))
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78 y = cellstr (y);
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79 else
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80 y = cellfun ("num2str", num2cell (y), "uniformoutput", false);
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81 endif
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82 endif
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83 else
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84 y = cellfun ("int2str", num2cell (1 : numel (x)),
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85 "uniformoutput", false);
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86 endif
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87
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88 [x, idx] = sort (x, "descend");
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89 y = y(idx);
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90 cdf = cumsum (x);
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91 maxcdf = max (cdf);
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92 cdf = cdf ./ maxcdf;
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93 cdf95 = cdf - 0.95;
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94 idx95 = find (sign (cdf95(1:end-1)) != sign (cdf95(2:end)))(1);
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95
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96 if (isempty (hax))
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97 [ax, hbar, hline] = plotyy (1 : idx95, x (1 : idx95),
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98 1 : length (cdf), 100 .* cdf,
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99 @bar, @plot);
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100 else
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101 [ax, hbar, hline] = plotyy (hax, 1 : idx95, x (1 : idx95),
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102 1 : length (cdf), 100 .* cdf,
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103 @bar, @plot);
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104 endif
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105
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106 axis (ax(1), [1 - 0.6, idx95 + 0.6, 0, maxcdf]);
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107 axis (ax(2), [1 - 0.6, idx95 + 0.6, 0, 100]);
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108 set (ax(2), "ytick", [0, 20, 40, 60, 80, 100],
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109 "yticklabel", {"0%", "20%", "40%", "60%", "80%", "100%"});
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110 set (ax(1), "xtick", 1:idx95, "xticklabel", y(1:idx95));
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111 set (ax(2), "xtick", 1:idx95, "xticklabel", y(1:idx95));
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112
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113 if (nargout > 0)
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114 h = [hbar; hline];
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115 endif
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116
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117 endfunction
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118
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119
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120 %!demo
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121 %! clf;
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122 %! colormap (jet (64));
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123 %! Cheese = {'Cheddar', 'Swiss', 'Camembert', 'Munster', 'Stilton', 'Blue'};
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124 %! Sold = [105, 30, 70, 10, 15, 20];
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125 %! pareto (Sold, Cheese);
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126
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127 %!demo
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128 %! clf;
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129 %! % Suppose that we want establish which products makes 80% of turnover.
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130 %! Codes = {'AB4','BD7','CF8','CC5','AD11','BB5','BB3','AD8','DF3','DE7'};
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131 %! Value = [2.35 7.9 2.45 1.1 0.15 13.45 5.4 2.05 0.85 1.65]';
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132 %! SoldUnits = [54723 41114 16939 1576091 168000 687197 120222 168195, ...
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133 %! 1084118 55576]';
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134 %! pareto (Value.*SoldUnits, Codes);
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135