gusucode.com > bigdata 工具箱 matlab源码程序 > bigdata/@tall/histogram.m
function h = histogram(varargin)
%HISTOGRAM Plots a Histogram.
% Supported syntaxes for tall X:
% HISTOGRAM(X)
% HISTOGRAM(X,M)
% HISTOGRAM(X,EDGES)
% HISTOGRAM(...,'BinWidth',BW)
% HISTOGRAM(...,'BinLimits',[BMIN,BMAX])
% HISTOGRAM(...,'Normalization',NM)
% HISTOGRAM(...,'DisplayStyle',STYLE)
% HISTOGRAM(...,'BinMethod',BM) - BM can be 'auto' (default),
% 'scott', 'integers', 'sturges', 'sqrt'
% HISTOGRAM(...,NAME,VALUE) set the property NAME to VALUE.
% HISTOGRAM(AX,...) plots into AX instead of the current axes
% H = HISTOGRAM(...) also returns a histogram object.
%
% Limitation: Properties and methods of the histogram output object that
% require recomputing the bins are not supported for tall arrays. These
% include the methods morebins and fewerbins and the properties BinWidth
% and BinLimits.
%
% Supported syntaxes for tall categorical C:
% HISTOGRAM(C)
% HISTOGRAM(C, CATEGORIES)
% HISTOGRAM(...,'Normalization',NM)
%
% See also HISTOGRAM, CATEGORICAL/HISTOGRAM
% Copyright 2016 The MathWorks, Inc.
[cax,args] = matlab.bigdata.internal.util.axesCheck(varargin{:});
narginchk(1,inf);
x = args{1};
args(1) = [];
x = tall.validateType(x,mfilename,{'numeric','logical', 'categorical'},1);
checkNotTall(upper(mfilename), 1, args{:});
isCat = isequal(x.Adaptor.Class, 'categorical');
opts = parseinput(args,isCat);
[countArgs, plotArgs] = iSplitCountAndPlotArgs(opts, isCat);
if isCat
% For categoricals, the edges are categories.
[N, cats] = histcounts(x, countArgs{:});
[N, cats] = gather(N, cats);
cax = newplot(cax);
h1 = histogram(cax, 'Categories', cats, 'BinCounts', N, plotArgs{:});
else
[N, edges] = histcounts(x, countArgs{:});
[N, edges] = gather(N, edges);
cax = newplot(cax);
h1 = histogram(cax, 'BinEdges', edges, 'BinCounts', N, plotArgs{:});
end
if nargout > 0
h = h1;
end
end
function [countArgs, plotArgs] = iSplitCountAndPlotArgs(opts, isCat)
if isCat
countNames = {'Categories'};
opts = rmfield(opts, 'BinMethod');
else
countNames = {'NumBins', 'MaxNumBins', 'BinEdges','BinLimits',...
'BinWidth','BinMethod'};
end
names = fieldnames(opts);
values = struct2cell(opts);
% remove null args
notEmpty = ~cellfun(@isempty, values);
names = names(notEmpty);
values = values(notEmpty);
% Extract the name value pairs for histcounts
isCountArg = ismember(names, countNames);
countNames = names(isCountArg);
countValues = values(isCountArg);
countArgs = iMergeNameValuePairs(countNames, countValues);
% The rest of the args must be plot visualization arguments
plotNames = names(~isCountArg);
plotValues = values(~isCountArg);
plotArgs = iMergeNameValuePairs(plotNames, plotValues);
end
function nameValuePairs = iMergeNameValuePairs(names, values)
nameValuePairs = [names values]';
nameValuePairs = nameValuePairs(:)';
end
function opts = parseinput(input, isCat)
opts = struct('Categories', [], 'NumBins',[],'BinEdges',[],'BinLimits',[],'BinWidth',[],...
'Normalization','count','BinMethod','auto','DisplayStyle','bar',...
'EdgeAlpha',1,'EdgeColor',[],'FaceAlpha',0.6,'FaceColor',[],...
'LineStyle','-','LineWidth',0.5,'Orientation','vertical');
funcname = mfilename;
% Parse second input in the function call
if ~isempty(input)
in = input{1};
inputoffset = 0;
if isnumeric(in) || islogical(in) || iscategorical(in) || iscellstr(in)
if iscategorical(in) || iscellstr(in)
opts.Categories = in;
elseif isscalar(in)
validateattributes(in,{'numeric','logical'},{'integer', 'positive'}, ...
funcname, 'm', inputoffset+2)
opts.NumBins = in;
opts.BinMethod = [];
else
validateattributes(in,{'numeric','logical'},{'vector','nonempty', ...
'real', 'nondecreasing'}, funcname, 'edges', inputoffset+2)
opts.BinEdges = in;
opts.BinMethod = [];
end
input(1) = [];
inputoffset = 1;
end
% All the rest are name-value pairs
inputlen = length(input);
if rem(inputlen,2) ~= 0
error(message('MATLAB:histcounts:ArgNameValueMismatch'))
end
for i = 1:2:inputlen
name = validatestring(input{i}, {'Categories','NumBins','BinEdges','BinWidth',...
'BinLimits','Normalization','DisplayStyle','BinMethod',...
'EdgeAlpha','EdgeColor','FaceAlpha','FaceColor','LineStyle',...
'LineWidth','Orientation'}, i+1+inputoffset);
value = input{i+1};
switch name
case 'Categories'
validateattributes(value,{'cell','categorical'},{}, ...
funcname, 'categories', i+2+inputoffset);
opts.Categories = value;
case 'NumBins'
validateattributes(value,{'numeric','logical'},{'scalar', 'integer', ...
'positive'}, funcname, 'NumBins', i+2+inputoffset)
opts.NumBins = value;
if ~isempty(opts.BinEdges)
error(message('MATLAB:histcounts:InvalidMixedBinInputs'))
end
opts.BinMethod = [];
opts.BinWidth = [];
case 'BinEdges'
validateattributes(value,{'numeric','logical'},{'vector', ...
'real', 'nondecreasing'}, funcname, 'BinEdges', i+2+inputoffset);
if length(value) < 2
error(message('MATLAB:histcounts:EmptyOrScalarBinEdges'));
end
opts.BinEdges = value;
opts.BinMethod = [];
opts.NumBins = [];
opts.BinWidth = [];
opts.BinLimits = [];
case 'BinWidth'
validateattributes(value, {'numeric','logical'}, {'scalar', 'real', ...
'positive', 'finite'}, funcname, 'BinWidth', i+2+inputoffset);
opts.BinWidth = value;
if ~isempty(opts.BinEdges)
error(message('MATLAB:histcounts:InvalidMixedBinInputs'))
end
opts.BinMethod = [];
opts.NumBins = [];
case 'BinLimits'
validateattributes(value, {'numeric','logical'}, {'numel', 2, 'vector', 'real', ...
'nondecreasing', 'finite'}, funcname, 'BinLimits', i+2+inputoffset)
opts.BinLimits = value;
if ~isempty(opts.BinEdges)
error(message('MATLAB:histcounts:InvalidMixedBinInputs'))
end
case 'Normalization'
opts.Normalization = validatestring(value, {'count', 'countdensity', 'cumcount',...
'probability', 'pdf', 'cdf'}, funcname, 'Normalization', i+2+inputoffset);
case 'DisplayStyle'
opts.DisplayStyle = validatestring(value, {'bar','stairs'},...
funcname, 'DisplayStyle', i+2+inputoffset);
case 'BinMethod'
opts.BinMethod = validatestring(value, {'auto','scott', ...
'integers', 'sturges', 'sqrt'}, funcname, 'BinMethod', i+2+inputoffset);
if ~isempty(opts.BinEdges)
error(message('MATLAB:histcounts:InvalidMixedBinInputs'))
end
opts.BinWidth = [];
opts.NumBins = [];
case 'EdgeAlpha'
validateattributes(value,{'numeric'},{'scalar','<=',1,'nonnegative'},...
funcname,'EdgeAlpha',i+2+inputoffset);
opts.EdgeAlpha = value;
case 'EdgeColor'
try
opts.EdgeColor = hgcastvalue('matlab.graphics.datatype.RGBAutoNoneColor',value);
catch
error(message('MATLAB:bigdata:array:BadEdgeColor'));
end
case 'FaceAlpha'
validateattributes(value,{'numeric'},{'scalar','<=',1,'nonnegative'},...
funcname,'FaceAlpha',i+2+inputoffset);
opts.FaceAlpha = value;
case 'FaceColor'
try
opts.FaceColor = hgcastvalue('matlab.graphics.datatype.RGBAutoNoneColor',value);
catch
error(message('MATLAB:bigdata:array:BadFaceColor'));
end
case 'LineStyle'
opts.LineStyle = validatestring(value, {'-','--',':','-.','none'},...
funcname, 'LineStyle', i+2+inputoffset);
case 'LineWidth'
validateattributes(value,{'numeric','logical'},{'scalar', ...
'positive'}, funcname, 'LineWidth', i+2+inputoffset);
opts.LineWidth = value;
case 'Orientation'
opts.Orientation = validatestring(value, {'vertical','horizontal'},...
funcname, 'Orientation', i+2+inputoffset);
end
end
end
% Set some dependent options
if isempty(opts.FaceColor)
if strcmpi(opts.DisplayStyle,'stairs')
opts.FaceColor = 'none';
else
opts.FaceColor = 'auto';
end
end
if isempty(opts.EdgeColor)
if strcmpi(opts.DisplayStyle,'stairs')
opts.EdgeColor = 'auto';
else
opts.EdgeColor = [0 0 0];
end
end
if ~isCat && strcmpi(opts.BinMethod, 'auto') && isempty(opts.NumBins) &&...
isempty(opts.BinWidth) && isempty(opts.BinEdges)
% Force histcounts to use the bin method that uses the auto rule with a
% maximum allowed number of bins.
opts.MaxNumBins = 100;
opts.BinMethod = '';
end
end