From 132870231c9eab8bbe5dd552cbfa4e62decb125c Mon Sep 17 00:00:00 2001 From: Pierre Morel Date: Tue, 5 Jul 2016 23:20:36 +0200 Subject: [PATCH] Added corner histogram of x-y difference stat_cornerhist() Improved speed on large datasets with many non-unique x values --- @gramm/private/dodge_comp.m | 8 + @gramm/private/my_histplot.m | 191 +++++++++++++++++++++++ @gramm/stat_bin.m | 180 +--------------------- @gramm/stat_cornerhist.m | 283 ++++++++++------------------------- README.md | 8 +- examples.m | 38 +++++ gramm cheat sheet.pdf | Bin 101995 -> 105426 bytes html/examples.html | 147 +++++++++++++++--- html/examples.png | Bin 1619 -> 1619 bytes html/examples_01.png | Bin 36107 -> 36107 bytes html/examples_02.png | Bin 73250 -> 73250 bytes html/examples_03.png | Bin 44146 -> 44288 bytes html/examples_04.png | Bin 53087 -> 53087 bytes html/examples_05.png | Bin 56583 -> 71090 bytes html/examples_06.png | Bin 22861 -> 22902 bytes html/examples_07.png | Bin 55523 -> 57220 bytes html/examples_08.png | Bin 111238 -> 96452 bytes html/examples_09.png | Bin 37249 -> 37728 bytes html/examples_10.png | Bin 80815 -> 76945 bytes html/examples_11.png | Bin 33183 -> 37492 bytes html/examples_12.png | Bin 28849 -> 32276 bytes html/examples_13.png | Bin 40023 -> 40267 bytes html/examples_14.png | Bin 58370 -> 42222 bytes html/examples_15.png | Bin 60505 -> 58442 bytes html/examples_16.png | Bin 56549 -> 64683 bytes html/examples_17.png | Bin 10802 -> 56291 bytes html/examples_18.png | Bin 28390 -> 10802 bytes html/examples_19.png | Bin 44484 -> 28390 bytes html/examples_20.png | Bin 30465 -> 44484 bytes html/examples_21.png | Bin 28179 -> 30465 bytes html/examples_22.png | Bin 37411 -> 28179 bytes html/examples_23.png | Bin 9354 -> 37411 bytes html/examples_24.png | Bin 8455 -> 9354 bytes html/examples_25.png | Bin 8455 -> 8455 bytes html/examples_26.png | Bin 8079 -> 8455 bytes html/examples_27.png | Bin 8455 -> 8079 bytes html/examples_28.png | Bin 8079 -> 8455 bytes html/examples_29.png | Bin 8151 -> 8079 bytes html/examples_30.png | Bin 0 -> 8151 bytes 39 files changed, 449 insertions(+), 406 deletions(-) create mode 100644 @gramm/private/my_histplot.m create mode 100644 html/examples_30.png diff --git a/@gramm/private/dodge_comp.m b/@gramm/private/dodge_comp.m index e5b0961..8167f83 100644 --- a/@gramm/private/dodge_comp.m +++ b/@gramm/private/dodge_comp.m @@ -11,6 +11,14 @@ %Here we need to implement a loose 'unique' because of %potential numerical errors uni_x(diff(uni_x)<1e-10)=[]; + + %Fallback if there are too many unique x values (dodging only makes + %sense for discrete x values)... 4000 x unique values is already a lot + %but quick enough to compute below + if numel(uni_x)>4000 + fallback=true; + uni_x=1; + end end N=length(uni_x)*length(uni_color)*length(uni_lightness); diff --git a/@gramm/private/my_histplot.m b/@gramm/private/my_histplot.m new file mode 100644 index 0000000..263445e --- /dev/null +++ b/@gramm/private/my_histplot.m @@ -0,0 +1,191 @@ +function [results] = my_histplot(obj,draw_data,params,setylim) + +%Presets for dodge parameter +if isempty(params.dodge) + if strcmp(params.geom,'bar') && draw_data.n_colors>1 %With 'bar' do we dodge by default if there are several colors + params.dodge=0.8; + else + params.dodge=0; %With all others we put don't dodge + end +end + +%Presets for width parameter +if isempty(params.width) + if params.dodge>0 + params.width=params.dodge; %If there is a dodge we modify the width accordingly + else + params.width=1; %Otherwise we use the full with (histogram). + end +end + + +%Set up default fill options for the different geoms +if isempty(params.fill) + switch params.geom + case 'bar' + params.fill='face'; + case 'line' + params.fill='edge'; + case 'overlaid_bar' + params.fill='transparent'; + case 'stacked_bar' + params.fill='face'; + case 'stairs' + params.fill='edge'; + case 'point' + params.fill='edge'; + end +end + +%Compute bins +if obj.x_factor %For categorical Xs (placed at integer values), we override and make the bins 0.5 1.5 2.5 ... + binranges=(0:length(obj.x_ticks))+0.5; + bincenters=1:length(obj.x_ticks); +else + if obj.polar.is_polar %For polar coordinates we make bin around 0:2pi + %Make data modulo 2pi + draw_data.x=mod(comb(draw_data.x),2*pi); + if isempty(params.edges) + binranges=linspace(0,2*pi,params.nbins+1); + else + if max(params.edges)>2*pi || min(params.edges)<0 + warning('Bin edges exceed the polar ranges (O-2pi)') + end + binranges=params.edges; + end + else + if isempty(params.edges) + binranges=linspace(obj.var_lim.minx,obj.var_lim.maxx,params.nbins+1); + else + binranges=params.edges; + end + end + bincenters=(binranges(1:(end-1))+binranges(2:end))/2; +end + + + + + +%Find actual counts +if iscell(draw_data.x) %If data was provided as Cell/Matrix + %We do the count individually for each element and average + %the counts + bincounts=zeros(1,length(binranges)-1); + for k=1:length(draw_data.x) + bincounts=bincounts+my_histcounts(draw_data.x{k},binranges,params.normalization); + end + bincounts=bincounts/length(draw_data.x); +else + %If data was provided as vector we just count + bincounts = my_histcounts(comb(draw_data.x),binranges,params.normalization); +end + +bincounts=shiftdim(bincounts); + +results.edges=binranges; +results.centers=bincenters; +results.counts=bincounts; + + +if setylim + %Set axis limits + obj.plot_lim.miny(obj.current_row,obj.current_column)=0; + if obj.firstrun(obj.current_row,obj.current_column) + obj.plot_lim.maxy(obj.current_row,obj.current_column)=max(bincounts); + + if ~isempty(params.edges) %If edges are specified we use those for x scale + obj.plot_lim.minx(obj.current_row,obj.current_column)=binranges(1)-nanmean(diff(binranges)); + obj.plot_lim.maxx(obj.current_row,obj.current_column)=binranges(end)+nanmean(diff(binranges)); + end + + %obj.firstrun(obj.current_row,obj.current_column)=0; + obj.aes_names.y=params.normalization; + %Initialize stacked bar + obj.extra.stacked_bar_height=zeros(size(bincenters)); + else + if max(obj.extra.stacked_bar_height+bincounts(1:end)')>obj.plot_lim.maxy(obj.current_row,obj.current_column) + obj.plot_lim.maxy(obj.current_row,obj.current_column)=max(bincounts(1:end)'+obj.extra.stacked_bar_height); + end + if max(bincounts)>obj.plot_lim.maxy(obj.current_row,obj.current_column) + obj.plot_lim.maxy(obj.current_row,obj.current_column)=max(bincounts); + end + end + +end + + +%Set up colors according to fill +[face_color , face_alpha , edge_color , edge_alpha] = parse_fill (params.fill,draw_data.color); + + + +%Set up dodging & width for bars +avl_w=diff(binranges); +dodging=avl_w.*params.dodge./(draw_data.n_colors); +if params.dodge>0 + bar_width=avl_w.*params.width./(draw_data.n_colors); +else + bar_width=avl_w.*params.width; +end +bar_mid=bincenters-0.5*dodging*draw_data.n_colors+dodging*0.5+(draw_data.color_index-1)*dodging; +bar_left=bar_mid-0.5*bar_width; +bar_right=bar_mid+0.5*bar_width; + + +spacing=1-params.width; + +%overlaid_bar is just a bar without dodging +if strcmp(params.geom,'overlaid_bar') + params.geom='bar'; +end + +switch params.geom + case 'bar' + xpatch=[bar_right ; bar_left ; bar_left ; bar_right]; + ypatch=[zeros(1,length(bincounts)) ; zeros(1,length(bincounts)) ; bincounts' ; bincounts']; + [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); + results.bar_handle=patch(xpatch,... + ypatch,... + [1 1 1],'FaceColor',face_color,'EdgeColor',edge_color,'FaceAlpha',face_alpha,'EdgeAlpha',edge_alpha); + + case 'line' + xtemp=bar_mid; + ytemp=bincounts(1:end)'; + [xtemp,ytemp]=to_polar(obj,xtemp,ytemp); + results.line_handle=plot(xtemp,ytemp,'LineStyle',draw_data.line_style,'Color',edge_color,'lineWidth',draw_data.size/4); + xpatch=[bar_mid(1:end-1) ; bar_mid(2:end) ; bar_mid(2:end);bar_mid(1:end-1)]; + ypatch=[zeros(1,length(bincounts)-1) ; zeros(1,length(bincounts)-1) ; bincounts(2:end)' ; bincounts(1:end-1)']; + [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); + results.fill_handle=patch(xpatch,ypatch,[1 1 1],'FaceColor',face_color,'EdgeColor','none','FaceAlpha',face_alpha); + + case 'stacked_bar' + xpatch=[binranges(1:end-1)+spacing ; binranges(2:end)-spacing ; binranges(2:end)-spacing ; binranges(1:end-1)+spacing]; + ypatch=[obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height+bincounts' ; obj.extra.stacked_bar_height+bincounts']; + [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); + results.bar_handle=patch(xpatch,... + ypatch,... + [1 1 1],'FaceColor',face_color,'EdgeColor',edge_color,'FaceAlpha',face_alpha,'EdgeAlpha',edge_alpha); + obj.extra.stacked_bar_height=obj.extra.stacked_bar_height+bincounts'; + case 'stairs' + xtemp=[binranges(1:end-1) ; binranges(2:end)]; + ytemp=[bincounts' ; bincounts']; + [xtemp,ytemp]=to_polar(obj,xtemp(:),ytemp(:)); + results.line_handle=plot(xtemp,ytemp,'LineStyle',draw_data.line_style,'Color',edge_color,'lineWidth',draw_data.size/4); + + xpatch=[binranges(1:end-1) ; binranges(2:end) ; binranges(2:end) ; binranges(1:end-1)]; + ypatch=[obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height+bincounts' ; obj.extra.stacked_bar_height+bincounts']; + [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); + + results.fill_handle=patch(xpatch,ypatch,[1 1 1],'FaceColor',face_color,'EdgeColor','none','FaceAlpha',face_alpha); + + case 'point' + xtemp=bar_mid; + ytemp=bincounts(1:end)'; + [xtemp,ytemp]=to_polar(obj,xtemp,ytemp); + results.point_handle=plot(xtemp,ytemp,draw_data.marker,'MarkerEdgeColor','none','markerSize',draw_data.size,'MarkerFaceColor',draw_data.color); +end + + +end + diff --git a/@gramm/stat_bin.m b/@gramm/stat_bin.m index bd603f3..af71d95 100644 --- a/@gramm/stat_bin.m +++ b/@gramm/stat_bin.m @@ -31,184 +31,6 @@ function hndl=my_bin(obj,draw_data,params) -%Presets for dodge parameter -if isempty(params.dodge) - if strcmp(params.geom,'bar') && draw_data.n_colors>1 %With 'bar' do we dodge by default if there are several colors - params.dodge=0.8; - else - params.dodge=0; %With all others we put don't dodge - end -end - -%Presets for width parameter -if isempty(params.width) - if params.dodge>0 - params.width=params.dodge; %If there is a dodge we modify the width accordingly - else - params.width=1; %Otherwise we use the full with (histogram). - end -end - - -%Set up default fill options for the different geoms -if isempty(params.fill) - switch params.geom - case 'bar' - params.fill='face'; - case 'line' - params.fill='edge'; - case 'overlaid_bar' - params.fill='transparent'; - case 'stacked_bar' - params.fill='face'; - case 'stairs' - params.fill='edge'; - case 'point' - params.fill='edge'; - end -end - - -%Compute bins -if obj.x_factor %For categorical Xs (placed at integer values), we override and make the bins 0.5 1.5 2.5 ... - binranges=(0:length(obj.x_ticks))+0.5; - bincenters=1:length(obj.x_ticks); -else - if obj.polar.is_polar %For polar coordinates we make bin around 0:2pi - %Make data modulo 2pi - draw_data.x=mod(comb(draw_data.x),2*pi); - if isempty(params.edges) - binranges=linspace(0,2*pi,params.nbins+1); - else - if max(params.edges)>2*pi || min(params.edges)<0 - warning('Bin edges exceed the polar ranges (O-2pi)') - end - binranges=params.edges; - end - else - if isempty(params.edges) - binranges=linspace(obj.var_lim.minx,obj.var_lim.maxx,params.nbins+1); - else - binranges=params.edges; - end - end - bincenters=(binranges(1:(end-1))+binranges(2:end))/2; -end - -%Find actual counts -if iscell(draw_data.x) %If data was provided as Cell/Matrix - %We do the count individually for each element and average - %the counts - bincounts=zeros(1,length(binranges)-1); - for k=1:length(draw_data.x) - bincounts=bincounts+my_histcounts(draw_data.x{k},binranges,params.normalization); - end - bincounts=bincounts/length(draw_data.x); -else - %If data was provided as vector we just count - bincounts = my_histcounts(comb(draw_data.x),binranges,params.normalization); -end - -bincounts=shiftdim(bincounts); - -obj.results.stat_bin{obj.result_ind,1}.edges=bincenters; -obj.results.stat_bin{obj.result_ind,1}.counts=bincounts; - -%Set axis limits -obj.plot_lim.miny(obj.current_row,obj.current_column)=0; -if obj.firstrun(obj.current_row,obj.current_column) - obj.plot_lim.maxy(obj.current_row,obj.current_column)=max(bincounts); - - if ~isempty(params.edges) %If edges are specified we use those for x scale - obj.plot_lim.minx(obj.current_row,obj.current_column)=binranges(1)-nanmean(diff(binranges)); - obj.plot_lim.maxx(obj.current_row,obj.current_column)=binranges(end)+nanmean(diff(binranges)); - end - - %obj.firstrun(obj.current_row,obj.current_column)=0; - obj.aes_names.y=params.normalization; - %Initialize stacked bar - obj.extra.stacked_bar_height=zeros(size(bincenters)); -else - if max(obj.extra.stacked_bar_height+bincounts(1:end)')>obj.plot_lim.maxy(obj.current_row,obj.current_column) - obj.plot_lim.maxy(obj.current_row,obj.current_column)=max(bincounts(1:end)'+obj.extra.stacked_bar_height); - end - if max(bincounts)>obj.plot_lim.maxy(obj.current_row,obj.current_column) - obj.plot_lim.maxy(obj.current_row,obj.current_column)=max(bincounts); - end -end - - - -%Set up colors according to fill -[face_color , face_alpha , edge_color , edge_alpha] = parse_fill (params.fill,draw_data.color); - - - - -%Set up dodging & width for bars -avl_w=diff(binranges); -dodging=avl_w.*params.dodge./(draw_data.n_colors); -if params.dodge>0 - bar_width=avl_w.*params.width./(draw_data.n_colors); -else - bar_width=avl_w.*params.width; -end -bar_mid=bincenters-0.5*dodging*draw_data.n_colors+dodging*0.5+(draw_data.color_index-1)*dodging; -bar_left=bar_mid-0.5*bar_width; -bar_right=bar_mid+0.5*bar_width; - - -spacing=1-params.width; - -%overlaid_bar is just a bar without dodging -if strcmp(params.geom,'overlaid_bar') - params.geom='bar'; -end - -switch params.geom - case 'bar' - xpatch=[bar_right ; bar_left ; bar_left ; bar_right]; - ypatch=[zeros(1,length(bincounts)) ; zeros(1,length(bincounts)) ; bincounts' ; bincounts']; - [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); - obj.results.stat_bin{obj.result_ind,1}.bar_handle=patch(xpatch,... - ypatch,... - [1 1 1],'FaceColor',face_color,'EdgeColor',edge_color,'FaceAlpha',face_alpha,'EdgeAlpha',edge_alpha); - - case 'line' - xtemp=bar_mid; - ytemp=bincounts(1:end)'; - [xtemp,ytemp]=to_polar(obj,xtemp,ytemp); - obj.results.stat_bin{obj.result_ind,1}.line_handle=plot(xtemp,ytemp,'LineStyle',draw_data.line_style,'Color',edge_color,'lineWidth',draw_data.size/4); - xpatch=[bar_mid(1:end-1) ; bar_mid(2:end) ; bar_mid(2:end);bar_mid(1:end-1)]; - ypatch=[zeros(1,length(bincounts)-1) ; zeros(1,length(bincounts)-1) ; bincounts(2:end)' ; bincounts(1:end-1)']; - [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); - obj.results.stat_bin{obj.result_ind,1}.fill_handle=patch(xpatch,ypatch,[1 1 1],'FaceColor',face_color,'EdgeColor','none','FaceAlpha',face_alpha); - - case 'stacked_bar' - xpatch=[binranges(1:end-1)+spacing ; binranges(2:end)-spacing ; binranges(2:end)-spacing ; binranges(1:end-1)+spacing]; - ypatch=[obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height+bincounts' ; obj.extra.stacked_bar_height+bincounts']; - [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); - obj.results.stat_bin{obj.result_ind,1}.bar_handle=patch(xpatch,... - ypatch,... - [1 1 1],'FaceColor',face_color,'EdgeColor',edge_color,'FaceAlpha',face_alpha,'EdgeAlpha',edge_alpha); - obj.extra.stacked_bar_height=obj.extra.stacked_bar_height+bincounts'; - case 'stairs' - xtemp=[binranges(1:end-1) ; binranges(2:end)]; - ytemp=[bincounts' ; bincounts']; - [xtemp,ytemp]=to_polar(obj,xtemp(:),ytemp(:)); - obj.results.stat_bin{obj.result_ind,1}.line_handle=plot(xtemp,ytemp,'LineStyle',draw_data.line_style,'Color',edge_color,'lineWidth',draw_data.size/4); - - xpatch=[binranges(1:end-1) ; binranges(2:end) ; binranges(2:end) ; binranges(1:end-1)]; - ypatch=[obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height+bincounts' ; obj.extra.stacked_bar_height+bincounts']; - [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); - - obj.results.stat_bin{obj.result_ind,1}.fill_handle=patch(xpatch,ypatch,[1 1 1],'FaceColor',face_color,'EdgeColor','none','FaceAlpha',face_alpha); - - case 'point' - xtemp=bar_mid; - ytemp=bincounts(1:end)'; - [xtemp,ytemp]=to_polar(obj,xtemp,ytemp); - obj.results.stat_bin{obj.result_ind,1}.point_handle=plot(xtemp,ytemp,draw_data.marker,'MarkerEdgeColor','none','markerSize',draw_data.size,'MarkerFaceColor',draw_data.color); -end +obj.results.stat_bin{obj.result_ind,1}=my_histplot(obj,draw_data,params,true); end diff --git a/@gramm/stat_cornerhist.m b/@gramm/stat_cornerhist.m index 359b425..8a7cac7 100644 --- a/@gramm/stat_cornerhist.m +++ b/@gramm/stat_cornerhist.m @@ -4,13 +4,23 @@ p=inputParser; my_addParameter(p,'nbins',30); my_addParameter(p,'edges',[]); -my_addParameter(p,'geom','bar'); %line, bar, overlaid_bar, stacked_bar,stairs, point +my_addParameter(p,'geom','overlaid_bar'); %line, bar, overlaid_bar, stacked_bar,stairs, point my_addParameter(p,'normalization','count'); my_addParameter(p,'fill',[]); %edge,face,all,transparent my_addParameter(p,'width',[]); my_addParameter(p,'dodge',[]); +my_addParameter(p,'aspect',0.3); %Aspect ratio of the inset axis +my_addParameter(p,'location',[]); %X/Y location in the main axis of the inset axis parse(p,varargin{:}); +if isempty(p.Results.edges) + warning('Specifying ''edges'' is recommended with stat_cornerhist()') +end + +if strcmp('geom','stacked_bar') + error('stacked_bar unsupported in stat_cornerhist()') +end + obj.geom=vertcat(obj.geom,{@(dd)my_cornerhist(obj,dd,p.Results)}); obj.results.stat_cornerhist={}; end @@ -18,9 +28,9 @@ function hndl=my_cornerhist(obj,draw_data,params) -%If this is the first call ever, we initialize an array of handles +%If this is the first call ever, we initialize an array of handles for the +%child axes if isempty(obj.results.stat_cornerhist) - disp('Corner axes array initialized') if obj.handle_graphics obj.extra.cornerhist_child_axe=gobjects(size(obj.facet_axes_handles,1),size(obj.facet_axes_handles,2)); else @@ -33,6 +43,11 @@ parent_axe_pos=get(parent_axe,'Position'); set(parent_axe,'color','none') +%Compute default location of the child axis if needed +if isempty(params.location) + params.location=max(obj.var_lim.maxx,obj.var_lim.maxy); +end + new_child=false; %Create child axis if necessary if (obj.handle_graphics &&... @@ -40,25 +55,25 @@ || (obj.extra.cornerhist_child_axe(obj.current_row,obj.current_column)==0) - %If first run, create inset (weird, if cha is not used as intermediary + %If first run, create inset axis(weird, if cha is not used as intermediary %variable, the resize callback doesn't seem to work properly) cha=axes('Position',[parent_axe_pos(1) parent_axe_pos(2) parent_axe_pos(3) parent_axe_pos(4)],... - 'Clipping','off'); + 'Clipping','off','NextPlot','add'); obj.extra.cornerhist_child_axe(obj.current_row,obj.current_column)=cha; - child_x=max(obj.var_lim.maxx,obj.var_lim.maxy); + - % Add callback to that will be called by redraw() on figure resize - obj.redraw_fun=vertcat(obj.redraw_fun,{@()cornerhist_redraw(parent_axe,cha,child_x)}); + % Add callback for child axis update that will be called by redraw() on figure resize + obj.redraw_fun=vertcat(obj.redraw_fun,{@()cornerhist_redraw(parent_axe,cha,params.location,params.aspect)}); %Set properties - set(cha,'color','none'); + set(cha,'color','none','Units','Pixels'); if obj.handle_graphics - set(cha,'Ycolor','none'); + set(cha,'Ycolor','none','Box','off'); else %No 'none' value possible for older matlabs - set(cha,'Ycolor',[1 1 1]); + set(cha,'Ycolor',[1 1 1],'Box','off'); end new_child=true; @@ -67,169 +82,40 @@ axes(obj.extra.cornerhist_child_axe(obj.current_row,obj.current_column)); end - - -%Presets for dodge parameter -if isempty(params.dodge) - if strcmp(params.geom,'bar') && draw_data.n_colors>1 %With 'bar' do we dodge by default if there are several colors - params.dodge=0.8; - else - params.dodge=0; %With all others we put don't dodge - end -end - -%Presets for width parameter -if isempty(params.width) - if params.dodge>0 - params.width=params.dodge; %If there is a dodge we modify the width accordingly - else - params.width=1; %Otherwise we use the full with (histogram). - end -end - - -%Set up default fill options for the different geoms -if isempty(params.fill) - switch params.geom - case 'bar' - params.fill='face'; - case 'line' - params.fill='edge'; - case 'overlaid_bar' - params.fill='transparent'; - case 'stacked_bar' - params.fill='face'; - case 'stairs' - params.fill='edge'; - case 'point' - params.fill='edge'; - end -end - %Draw data is x-y draw_data.x=draw_data.x-draw_data.y; -%Compute bins +%Compute edges here (my_histplot automatic edge computation is based on x +%only) if isempty(params.edges) - binranges=linspace(min(draw_data.x),max(draw_data.x),params.nbins+1); -else - binranges=params.edges; + params.edges=linspace(min(draw_data.x),max(draw_data.x),params.nbins+1); end -bincenters=(binranges(1:(end-1))+binranges(2:end))/2; - - -%Find actual counts -if iscell(draw_data.x) %If data was provided as Cell/Matrix - %We do the count individually for each element and average - %the counts - bincounts=zeros(1,length(binranges)-1); - for k=1:length(draw_data.x) - bincounts=bincounts+my_histcounts(draw_data.x{k},binranges,params.normalization); - end - bincounts=bincounts/length(draw_data.x); -else - %If data was provided as vector we just count - bincounts = my_histcounts(comb(draw_data.x),binranges,params.normalization); -end - -bincounts=shiftdim(bincounts); - -obj.results.stat_cornerhist{obj.result_ind,1}.edges=bincenters; -obj.results.stat_cornerhist{obj.result_ind,1}.counts=bincounts; - - -%Set up colors according to fill -[face_color , face_alpha , edge_color , edge_alpha] = parse_fill (params.fill,draw_data.color); - - - -%Set up dodging & width for bars -avl_w=diff(binranges); -dodging=avl_w.*params.dodge./(draw_data.n_colors); -if params.dodge>0 - bar_width=avl_w.*params.width./(draw_data.n_colors); -else - bar_width=avl_w.*params.width; -end -bar_mid=bincenters-0.5*dodging*draw_data.n_colors+dodging*0.5+(draw_data.color_index-1)*dodging; -bar_left=bar_mid-0.5*bar_width; -bar_right=bar_mid+0.5*bar_width; - - -spacing=1-params.width; - -%overlaid_bar is just a bar without dodging -if strcmp(params.geom,'overlaid_bar') - params.geom='bar'; -end - -switch params.geom - case 'bar' - xpatch=[bar_right ; bar_left ; bar_left ; bar_right]; - ypatch=[zeros(1,length(bincounts)) ; zeros(1,length(bincounts)) ; bincounts' ; bincounts']; - [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); - obj.results.stat_cornerhist{obj.result_ind,1}.bar_handle=patch(xpatch,... - ypatch,... - [1 1 1],'FaceColor',face_color,'EdgeColor',edge_color,'FaceAlpha',face_alpha,'EdgeAlpha',edge_alpha); - - case 'line' - xtemp=bar_mid; - ytemp=bincounts(1:end)'; - [xtemp,ytemp]=to_polar(obj,xtemp,ytemp); - obj.results.stat_cornerhist{obj.result_ind,1}.line_handle=plot(xtemp,ytemp,'LineStyle',draw_data.line_style,'Color',edge_color,'lineWidth',draw_data.size/4); - xpatch=[bar_mid(1:end-1) ; bar_mid(2:end) ; bar_mid(2:end);bar_mid(1:end-1)]; - ypatch=[zeros(1,length(bincounts)-1) ; zeros(1,length(bincounts)-1) ; bincounts(2:end)' ; bincounts(1:end-1)']; - [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); - obj.results.stat_cornerhist{obj.result_ind,1}.fill_handle=patch(xpatch,ypatch,[1 1 1],'FaceColor',face_color,'EdgeColor','none','FaceAlpha',face_alpha); - - case 'stacked_bar' - xpatch=[binranges(1:end-1)+spacing ; binranges(2:end)-spacing ; binranges(2:end)-spacing ; binranges(1:end-1)+spacing]; - ypatch=[obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height+bincounts' ; obj.extra.stacked_bar_height+bincounts']; - [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); - obj.results.stat_cornerhist{obj.result_ind,1}.bar_handle=patch(xpatch,... - ypatch,... - [1 1 1],'FaceColor',face_color,'EdgeColor',edge_color,'FaceAlpha',face_alpha,'EdgeAlpha',edge_alpha); - obj.extra.stacked_bar_height=obj.extra.stacked_bar_height+bincounts'; - case 'stairs' - xtemp=[binranges(1:end-1) ; binranges(2:end)]; - ytemp=[bincounts' ; bincounts']; - [xtemp,ytemp]=to_polar(obj,xtemp(:),ytemp(:)); - obj.results.stat_cornerhist{obj.result_ind,1}.line_handle=plot(xtemp,ytemp,'LineStyle',draw_data.line_style,'Color',edge_color,'lineWidth',draw_data.size/4); - - xpatch=[binranges(1:end-1) ; binranges(2:end) ; binranges(2:end) ; binranges(1:end-1)]; - ypatch=[obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height ; obj.extra.stacked_bar_height+bincounts' ; obj.extra.stacked_bar_height+bincounts']; - [xpatch,ypatch]=to_polar(obj,xpatch,ypatch); - - obj.results.stat_cornerhist{obj.result_ind,1}.fill_handle=patch(xpatch,ypatch,[1 1 1],'FaceColor',face_color,'EdgeColor','none','FaceAlpha',face_alpha); - - case 'point' - xtemp=bar_mid; - ytemp=bincounts(1:end)'; - [xtemp,ytemp]=to_polar(obj,xtemp,ytemp); - obj.results.stat_cornerhist{obj.result_ind,1}.point_handle=plot(xtemp,ytemp,draw_data.marker,'MarkerEdgeColor','none','markerSize',draw_data.size,'MarkerFaceColor',draw_data.color); -end +%Draw histogram with my_histplot() (common with stat_bin) +obj.results.stat_cornerhist{obj.result_ind,1}=my_histplot(obj,draw_data,params,false); +obj.results.stat_cornerhist{obj.result_ind,1}.child_axe_handle=obj.extra.cornerhist_child_axe(obj.current_row,obj.current_column); %Set limits of child axis -set(obj.extra.cornerhist_child_axe(obj.current_row,obj.current_column),'XLim',[min(binranges) max(binranges)]); +set(obj.extra.cornerhist_child_axe(obj.current_row,obj.current_column),'XLim',[min(params.edges) max(params.edges)]); if new_child %Change limits of parent axis - if min(binranges)<0 - %obj.plot_lim.maxy(obj.current_row,obj.current_column)=obj.plot_lim.maxy(obj.current_row,obj.current_column)+sqrt(0.6*min(binranges).^2); - obj.plot_lim.maxy(obj.current_row,obj.current_column)=child_x+sqrt(0.6*min(binranges).^2); + if min(params.edges)<0 + obj.plot_lim.maxy(obj.current_row,obj.current_column)=max(obj.plot_lim.maxy(obj.current_row,obj.current_column),... + params.location+sqrt(0.6*min(params.edges).^2)); end - if max(binranges)>0 - %obj.plot_lim.maxx(obj.current_row,obj.current_column)=obj.plot_lim.maxx(obj.current_row,obj.current_column)+sqrt(0.6*max(binranges).^2); - obj.plot_lim.maxx(obj.current_row,obj.current_column)=child_x+sqrt(0.6*max(binranges).^2); + if max(params.edges)>0 + obj.plot_lim.maxx(obj.current_row,obj.current_column)=max(obj.plot_lim.maxx(obj.current_row,obj.current_column),... + params.location+sqrt(0.6*max(params.edges).^2)); end end axes(parent_axe); + end -function cornerhist_redraw(parent_axe,child_axe,x_pos) +function cornerhist_redraw(parent_axe,child_axe,x_pos,aspect_ratio) %Get info about updated parent axis @@ -238,22 +124,24 @@ function cornerhist_redraw(parent_axe,child_axe,x_pos) pa_xlim=get(parent_axe,'XLim'); pa_ylim=get(parent_axe,'YLim'); +%Get info about child axis +ch_xlim=get(child_axe,'XLim'); +ch_ylim=get(child_axe,'YLim'); +c_tar=get(child_axe,'CameraTarget'); +c_pos=get(child_axe,'CameraPosition'); -%To place on unity line -pa_halfxpos=pa_pos(1)+(x_pos-pa_xlim(1))*pa_pos(3)/diff(pa_xlim); -pa_halfypos=pa_pos(2)+(x_pos-pa_ylim(1))*pa_pos(4)/diff(pa_ylim); - -angle=atan2(pa_pos(4)/diff(pa_ylim),pa_pos(3)/diff(pa_xlim)); +%Compute the desired location at x=x_pos on the unity line in the main axis in (pixel) figure coordinates +pa_loc(1)=pa_pos(1)+(x_pos-pa_xlim(1))*pa_pos(3)/diff(pa_xlim); +pa_loc(2)=pa_pos(2)+(x_pos-pa_ylim(1))*pa_pos(4)/diff(pa_ylim); +% We want to center data point (0,0) of the child axe on the computed +% location +ch_origin=[0 0]; -set(child_axe,'Units','Pixels') -ch_pos=get(child_axe,'Position'); -ch_xlim=get(child_axe,'XLim'); -ch_ylim=get(child_axe,'YLim'); -ch_dar=get(child_axe,'DataAspectRatio'); -ch_pbar=get(child_axe,'PlotBoxAspectRatio'); -ch_va=get(child_axe,'CameraViewAngle') +% Compute the absolute angle (using pixels) of the unity line in the main +% axis of the figure +angle=atan2(pa_pos(4)/diff(pa_ylim),pa_pos(3)/diff(pa_xlim)); %Projection of (0,1) from parent axis in pixel coordinates on the line @@ -261,45 +149,40 @@ function cornerhist_redraw(parent_axe,child_axe,x_pos) new_w=diff(ch_xlim)*cos(angle)*pa_pos(4)/diff(pa_ylim); -%DAR [diff(ch_xlim)/(ch_dar(1)*diff(ch_ylim)) 1 1] -pbar=0.2; +%Compute new child axis position +ch_newpos=zeros(1,4); +%Set size according to computed x length and plot box aspect ratio +ch_newpos(3)=new_w; +ch_newpos(4)=new_w*aspect_ratio; -set(child_axe,'Position',[ch_pos(1) ch_pos(2) new_w new_w*pbar],'DataAspectRatio',[diff(ch_xlim)/(diff(ch_ylim)/pbar) 1 1],'PlotBoxAspectRatio',[1/pbar 1 1],'Xlim',ch_xlim,'Ylim',ch_ylim) +%Positions are chosen position in pixels + origin offset compensation (relative to +%bottom left edge) + camera target offset compensation (relative to center) +ch_newpos(1)=pa_loc(1)-(ch_origin(1)-ch_xlim(1))*ch_newpos(3)/diff(ch_xlim)+(ch_origin(1)-mean(ch_xlim))*ch_newpos(3)/diff(ch_xlim); +ch_newpos(2)=pa_loc(2)-(ch_origin(2)-ch_ylim(1))*ch_newpos(4)/diff(ch_ylim)+(ch_origin(2)-mean(ch_ylim))*ch_newpos(4)/diff(ch_ylim); -place_axes(child_axe,[0 0],[pa_halfxpos pa_halfypos],angle-pi/2); -set(parent_axe,'Units','Normalized') +%Camera view angle is often reported/set incorrectly by Matlab (bug reported). +%Computing it here to work around the issue +dir_len=norm(c_tar-c_pos); %Distance between camera and axes +c_va=2*atand(1/2*diff(ch_ylim)/dir_len); %Angle is the vertical angle -end +%Compute data aspect ratio +dar=[diff(ch_xlim)/(diff(ch_ylim)/aspect_ratio) 1 1]; -function place_axes(h,origin,position,angle) -%UNTITLED Summary of this function goes here -% Detailed explanation goes here - -xlim=get(h,'XLim') -ylim=get(h,'YLim') -pos=get(h,'Position') -c_tar=get(h,'CameraTarget') -c_pos=get(h,'CameraPosition') -c_va=get(h,'CameraViewAngle') -c_uv=get(h,'CameraUpVector') -dar=get(h,'DataAspectRatio') -pbar=get(h,'PlotBoxAspectRatio') - -%Positions are chosen position + origin offset compensation (relative to -%bottom left edge) + camera target offset compensation (relative to center) -%Setting the data aspect ratio prevents glitches WORKING -set(h,'Position',[position(1)-(origin(1)-xlim(1))*pos(3)/diff(xlim)+(origin(1)-mean(xlim))*pos(3)/diff(xlim)... - position(2)-(origin(2)-ylim(1))*pos(4)/diff(ylim)+(origin(2)-mean(ylim))*pos(4)/diff(ylim)... - pos(3) pos(4)],... - 'CameraPosition',[origin(1) origin(2) c_pos(3)],... - 'CameraTarget',[origin(1) origin(2) c_tar(3)],'CameraViewAngle',c_va,'PlotBoxAspectRatio',pbar,'DataAspectRatio',dar,'CameraUpVector',[sin(angle)*dar(1) cos(angle)*dar(2) 0]) - -%If dar was not set in the previous call, it could be updated so getting it -%again was needed -%dar=get(h,'DataAspectRatio'); -%set(h,'CameraUpVector',[sin(angle)*dar(1) cos(angle)*dar(2) 0]) +%Update child axis properties +set(child_axe,'Position',ch_newpos,... + 'CameraPosition',[ch_origin(1) ch_origin(2) c_pos(3)],... + 'CameraTarget',[ch_origin(1) ch_origin(2) c_tar(3)],... + 'CameraUpVector',[sin(angle-pi/2)*dar(1) cos(angle-pi/2)*dar(2) 0],... + 'Xlim',ch_xlim,'Ylim',ch_ylim,... + 'CameraViewAngle',c_va,... + 'DataAspectRatio',dar,... + 'PlotBoxAspectRatio',[1/aspect_ratio 1 1]); + +%Set parent axes back in normalized coordinates +set(parent_axe,'Units','Normalized') end + diff --git a/README.md b/README.md index 5e5a344..afa2e41 100644 --- a/README.md +++ b/README.md @@ -147,23 +147,23 @@ Using stat_bin() it is possible to construct peristimulus time hist ### Colormap customization ### With set_color_options() -Colormaps example +Colormaps example ### Continuous colors -Continuous colors +Continuous colors ###Reordering of categorical variables With set_order_options() -Reordering +Reordering ### Superimposition of gramm objects on the same axes By making calling the update() method after a first draw, the same axes can be reused for another gramm plot. Here this allows to use different groupings for the points and for the glm fit. -gramm superimposition +gramm superimposition ## Acknowledgements gramm was inspired and/or used code from: diff --git a/examples.m b/examples.m index ffb2a46..71fe163 100644 --- a/examples.m +++ b/examples.m @@ -210,6 +210,15 @@ g(2,2).set_title('stat_summary(''bin_in'',10)'); g.set_names('x','Horsepower','y','Acceleration','color','# Cylinders'); + +%Corner histogram +g(2,3)=gramm('x',(cars.Horsepower-nanmean(cars.Horsepower))/nanstd(cars.Horsepower),'y',-(cars.Acceleration-nanmean(cars.Acceleration))/nanstd(cars.Acceleration),'color',cars.Cylinders,'subset',cars.Cylinders~=3 & cars.Cylinders~=5); +g(2,3).geom_point(); +g(2,3).stat_cornerhist('edges',-4:0.2:4,'aspect',0.6) +g(2,3).geom_abline() +g(2,3).set_title('stat_cornerhist()'); +g(2,3).set_names('x','z(Horsepower)','y','-z(Acceleration)') + g.set_title('Visualization of Y~X relationship with both X and Y as continuous variables'); figure('Position',[100 100 800 550]); g.draw(); @@ -599,6 +608,35 @@ figure('Position',[100 100 800 600]); g7.draw(); +%% Visualize x-y difference with inset histogram using stat_cornerhist() + + +%Generate sample data +N=200; +x=randn(1,N*4); +y=x+randn(1,N*4)/2; +c=repmat([1 2],1,N*2); +b=repmat([1 2 2 2],1,N); +y(c==1 & b==2)=y(c==1 & b==2)+2; + + +clear g +g=gramm('x',x,'y',y,'color',c); +g.facet_grid([],b); +g.geom_point(); +g.stat_cornerhist('edges',-4:0.1:2,'aspect',0.5); +g.geom_abline(); + +g.set_title('Visualize x-y with stat_cornerhist()'); +figure('Position',[100 100 800 600]); +g.draw(); + +%Possibility to use axe handles of the inset axes to add elements or change +%properties +plot(g.results.stat_cornerhist(2).child_axe_handle,[-2 -2],[0 50],'k:','LineWidth',2) +%set([g.results.stat_cornerhist.child_axe_handle],'XTick',[]) + + %% Graphic and normalization options in stat_violin() clear g diff --git a/gramm cheat sheet.pdf b/gramm cheat sheet.pdf index 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z`~Ty9J{~^4KLvsxr+*|SKNlzeKlr%*;RWz)!1bqbziIsc&3?qc;twWrbN#QF{8sfh zjqeZ7fN9`t{jVji&c@cZ=FW&hLgZ}f*52S5i=UiL&B4Ky{CALkPhGP177pa#$>*>C zl3PAzs(cmzV{U#Eet;RDi8%)!mk8oNc>cliFYF{2SMW^j W>1=L+2v&>-z=KFbBc=Qf@&5sA`i_zS diff --git a/html/examples.html b/html/examples.html index c396729..61a2e68 100644 --- a/html/examples.html +++ b/html/examples.html @@ -6,7 +6,7 @@ gramm examples

gramm examples

Examples and how-tos for gramm

Contents

Example from the readme

Here we plot the evolution of fuel economy of new cars bewteen 1970 and 1980 (carbig dataset). Gramm is used to easily separate groups on the basis of the number of cylinders of the cars (color), and on the basis of the region of origin of the cars (subplot columns). Both the raw data (points) and a glm fit with 95% confidence interval (line+shaded area) are plotted.

We stat by loading the sample data (structure created from the carbig dataset)

load example_data;
+  

gramm examples

Examples and how-tos for gramm

Contents

Example from the readme

Here we plot the evolution of fuel economy of new cars bewteen 1970 and 1980 (carbig dataset). Gramm is used to easily separate groups on the basis of the number of cylinders of the cars (color), and on the basis of the region of origin of the cars (subplot columns). Both the raw data (points) and a glm fit with 95% confidence interval (line+shaded area) are plotted.

We stat by loading the sample data (structure created from the carbig dataset)

load example_data;
 

Create a gramm object, provide x (year of production) and y (fuel economy) data, color grouping data (number of cylinders) and select a subset of the data

g=gramm('x',cars.Model_Year,'y',cars.MPG,'color',cars.Cylinders,'subset',cars.Cylinders~=3 & cars.Cylinders~=5);
 

Subdivide the data in subplots horizontally by region of origin using facet_grid()

g.facet_grid([],cars.Origin_Region);
 

Plot raw data as points

g.geom_point();
@@ -216,10 +216,49 @@
 g(2,2).set_title('stat_summary(''bin_in'',10)');
 
 g.set_names('x','Horsepower','y','Acceleration','color','# Cylinders');
+
+%Corner histogram
+g(2,3)=gramm('x',(cars.Horsepower-nanmean(cars.Horsepower))/nanstd(cars.Horsepower),'y',-(cars.Acceleration-nanmean(cars.Acceleration))/nanstd(cars.Acceleration),'color',cars.Cylinders,'subset',cars.Cylinders~=3 & cars.Cylinders~=5);
+g(2,3).geom_point();
+g(2,3).stat_cornerhist('edges',-4:0.2:4,'aspect',0.6)
+g(2,3).geom_abline()
+g(2,3).set_title('stat_cornerhist()');
+g(2,3).set_names('x','z(Horsepower)','y','-z(Acceleration)')
+
 g.set_title('Visualization of Y~X relationship with both X and Y as continuous variables');
 figure('Position',[100 100 800 550]);
 g.draw();
-
Warning: Start point not provided, choosing random start point. 
+
+ans = 
+
+  gramm with properties:
+
+     legend_axe_handle: []
+      title_axe_handle: []
+    facet_axes_handles: []
+               results: [1x1 struct]
+
+
+ans = 
+
+  gramm with properties:
+
+     legend_axe_handle: []
+      title_axe_handle: []
+    facet_axes_handles: []
+               results: [1x1 struct]
+
+
+ans = 
+
+  gramm with properties:
+
+     legend_axe_handle: []
+      title_axe_handle: []
+    facet_axes_handles: []
+               results: [1x1 struct]
+
+Warning: Start point not provided, choosing random start point. 
 Warning: Start point not provided, choosing random start point. 
 Warning: Start point not provided, choosing random start point. 
 

Methods for visualizing custom confidence intervals

With geom_interval() it is possible to plot custom confidence intervals by provinding 'ymin' and 'ymax' values to gramm(). All options to display confidence intervals in stat_summary() are available, including dodging.

cars_table=struct2table(cars);
@@ -533,7 +572,31 @@
 
 figure('Position',[100 100 800 600]);
 g7.draw();
-

Graphic and normalization options in stat_violin()

clear g
+

Visualize x-y difference with inset histogram using stat_cornerhist()

%Generate sample data
+N=200;
+x=randn(1,N*4);
+y=x+randn(1,N*4)/2;
+c=repmat([1 2],1,N*2);
+b=repmat([1 2 2 2],1,N);
+y(c==1 & b==2)=y(c==1 & b==2)+2;
+
+
+clear g
+g=gramm('x',x,'y',y,'color',c);
+g.facet_grid([],b);
+g.geom_point();
+g.stat_cornerhist('edges',-4:0.1:2,'aspect',0.5);
+g.geom_abline();
+
+g.set_title('Visualize x-y with stat_cornerhist()');
+figure('Position',[100 100 800 600]);
+g.draw();
+
+%Possibility to use axe handles of the inset axes to add elements or change
+%properties
+plot(g.results.stat_cornerhist(2).child_axe_handle,[-2 -2],[0 50],'k:','LineWidth',2)
+%set([g.results.stat_cornerhist.child_axe_handle],'XTick',[])
+

Graphic and normalization options in stat_violin()

clear g
 g(1,1)=gramm('x',cars.Origin_Region,'y',cars.Horsepower,'color',cars.Cylinders,'subset',cars.Cylinders~=3 & cars.Cylinders~=5);
 g(1,1).set_names('x','Origin','y','Horsepower','color','# Cyl');
 g(1,2)=copy(g(1,1));
@@ -568,7 +631,7 @@
 g.set_title('Options for stat_violin()');
 figure('Position',[100 100 800 600]);
 g.draw();
-

Options for dodging and spacing graphic elements in stat_summary() and stat_boxplot()

stat_summary() and stat_boxplot(), as well as stat_bin(), use a pair of options for setting the width of graphical elements ('width') and setting how elements of different colors can be dodged to the side to avoid overlap ('dodge')

%Create data
+

Options for dodging and spacing graphic elements in stat_summary() and stat_boxplot()

stat_summary() and stat_boxplot(), as well as stat_bin(), use a pair of options for setting the width of graphical elements ('width') and setting how elements of different colors can be dodged to the side to avoid overlap ('dodge')

%Create data
 x=repmat(1:10,1,100);
 catx=repmat({'A' 'B' 'C' 'F' 'E' 'D' 'G' 'H' 'I' 'J'},1,100);
 y=randn(1,1000)*3;
@@ -601,14 +664,14 @@
 g(4,1).set_title('''width'',0.6,''dodge'',0.4');
 
 g(5,1).facet_grid([],c);
-g(5,1).stat_boxplot('width',0.5,'dodge',0);
-g(5,1).set_title('''width'',0.5,''dodge'',0');
+g(5,1).stat_boxplot('width',0.5,'dodge',0,'notch',true);
+g(5,1).set_title('''width'',0.5,''dodge'',0,''notch'',true');
 
 g.set_title('Dodge and spacing options for stat_boxplot()');
 
 figure('Position',[100 100 800 1000]);
 g.draw();
-

With stat_summary(), 'width' controls the width of bars and error bars.

clear g
+

With stat_summary(), 'width' controls the width of bars and error bars.

clear g
 g(1,1)=gramm('x',catx,'y',y,'color',c);
 g(2,1)=copy(g(1));
 g(3,1)=copy(g(1));
@@ -639,7 +702,7 @@
 
 figure('Position',[100 100 800 1000]);
 g.draw();
-

Using different groups for different stat_ and geom_ methods by superimposing gramm plots

By using the method update() after a first draw() call of a gramm object, it is possible to add or remove grouping variables. Here in a first gramm plot we make a glm fit of cars Acceleration as a function of Horsepower, across all countries and number of cylinders, and change the color options so that the fit appears in grey

clear g10
+

Using different groups for different stat_ and geom_ methods by superimposing gramm plots

By using the method update() after a first draw() call of a gramm object, it is possible to add or remove grouping variables. Here in a first gramm plot we make a glm fit of cars Acceleration as a function of Horsepower, across all countries and number of cylinders, and change the color options so that the fit appears in grey

clear g10
 figure('Position',[100 100 600 450]);
 g10=gramm('x',cars.Horsepower,'y',cars.Acceleration,'subset',cars.Cylinders~=3 & cars.Cylinders~=5);
 g10.set_names('color','# Cylinders','x','Horsepower','y','Acceleration','Column','Origin');
@@ -657,12 +720,12 @@
     facet_axes_handles: [1x1 Axes]
                results: [1x1 struct]
 
-

After the first draw() call (optional), we call the update() method by specifying a new grouping variable determining colors. We also change the facet_grid() options, which will duplicate the fit made earlier across all new facets. Last, color options are reinitialized to default values

g10.update('color',cars.Cylinders);
+

After the first draw() call (optional), we call the update() method by specifying a new grouping variable determining colors. We also change the facet_grid() options, which will duplicate the fit made earlier across all new facets. Last, color options are reinitialized to default values

g10.update('color',cars.Cylinders);
 g10.facet_grid([],cars.Origin_Region);
 g10.set_color_options();
 g10.geom_point();
 g10.draw();
-

Customizing color maps with set_color_options()

With the method set_color_options(), automatic color generation for color and lightness groups can be tweaked

%Default: LCH-based colormap with hue variation
+

Customizing color maps with set_color_options()

With the method set_color_options(), automatic color generation for color and lightness groups can be tweaked

%Default: LCH-based colormap with hue variation
 clear g
 g(1,1)=gramm('x',cars.Origin,'y',cars.Horsepower,'color',cars.Origin);
 g(1,2)=copy(g(1));
@@ -710,7 +773,7 @@
 
 figure('Position',[100 100 800 600])
 g.draw();
-

Using a continuous color scale

When the variable provided as 'color' contains too many different values (>15), or when set_continuous_color is used, gramm switches from a categorical color scale to a gradient-based continuous color scale.

load spectra.mat
+

Using a continuous color scale

When the variable provided as 'color' contains too many different values (>15), or when set_continuous_color is used, gramm switches from a categorical color scale to a gradient-based continuous color scale.

load spectra.mat
 
 %Here we create x as a 1xN array (see example above), and use a MxN matrix
 %for y. Color applies to the M rows of y.
@@ -720,7 +783,7 @@
 g18.geom_line;
 figure('Position',[100 100 800 450]);
 g18.draw();
-

Changing the order of elements with set_order_options()

By default, gramm uses grouping data in increasing order of the group value (alphabetical for cellstr, numerical for arrays). Using set_order_options(), it is possible to fine tweak the orders of color, lightness, facet rows and columns, as well as categorical X

y=[36 38 40 42 44 46];
+

Changing the order of elements with set_order_options()

By default, gramm uses grouping data in increasing order of the group value (alphabetical for cellstr, numerical for arrays). Using set_order_options(), it is possible to fine tweak the orders of color, lightness, facet rows and columns, as well as categorical X

y=[36 38 40 42 44 46];
 x={'XS' 'S' 'M' 'L' 'XL' 'XXL'};
 
 
@@ -769,7 +832,7 @@
 g.draw();
 
ordering given as values
 ordering given as indices
-

Advanced customization of gramm figures

The options for the geom_ and stat_ methods, as well as the axe_property() method allow for high-level customization of gramm figures. Since the gramm object allows access to all handles for graphical objects, it's also possible to do more precise customizations and modifications of a gramm figure once it's drawn. In this figure:

  • Y grid is turned on on all facets with axe_property('YGrid','on')
  • A vertical line and text is added to only one of the facets by using the facet_axes_handles public property of gramm objects
  • All points are made smaller by using their handles results.grom_point_handle in the set() function
  • Similarly, all confidence areas are made grey g.results.stat_glm.area_handle
  • A subset of the glm lines are made thicker by calling set() on a subset teir handles |g.results.stat_glm(g.results.color==4).line_handle]

It is also possible to set where the gramm axes are drawn by using the set_parent(parent_handle) function, which receives the handle of a figure/uipanel/uitab object to use as parent as argument.

f=figure('Position',[100 100 800 500]);
+

Advanced customization of gramm figures

The options for the geom_ and stat_ methods, as well as the axe_property() method allow for high-level customization of gramm figures. Since the gramm object allows access to all handles for graphical objects, it's also possible to do more precise customizations and modifications of a gramm figure once it's drawn. In this figure:

  • Y grid is turned on on all facets with axe_property('YGrid','on')
  • A vertical line and text is added to only one of the facets by using the facet_axes_handles public property of gramm objects
  • All points are made smaller by using their handles results.grom_point_handle in the set() function
  • Similarly, all confidence areas are made grey g.results.stat_glm.area_handle
  • A subset of the glm lines are made thicker by calling set() on a subset teir handles |g.results.stat_glm(g.results.color==4).line_handle]

It is also possible to set where the gramm axes are drawn by using the set_parent(parent_handle) function, which receives the handle of a figure/uipanel/uitab object to use as parent as argument.

f=figure('Position',[100 100 800 500]);
 %Create fake button
 c=uicontrol('Style','pushbutton','String','Dummy','Units','normalized','Position',[0.8 0.45 0.15 0.1]);
 %Create uipanel to put our gramm plots
@@ -797,31 +860,31 @@
 set([g.results.stat_glm.area_handle],'FaceColor',[0.4 0.4 0.4]);
 %Or on a subset of them (here only for the lines of glms of 4-cylinder cars)
 set([g.results.stat_glm(g.results.color==4).line_handle],'LineWidth',3);
-

Using different input formats for x and y (1D arrays, cells of arrays, 2D arrays)

Standard ggplot-like input (arrays for everything) Note the continuous line connecting all blue data points, gramm can't know when to start a new line in this case

Y=[1 2 3 4 5 2 3 4 5 6 3 4 5 6 7];
+

Using different input formats for x and y (1D arrays, cells of arrays, 2D arrays)

Standard ggplot-like input (arrays for everything) Note the continuous line connecting all blue data points, gramm can't know when to start a new line in this case

Y=[1 2 3 4 5 2 3 4 5 6 3 4 5 6 7];
 X=[1 2 3 4 5 0 1 2 3 4 -1 0 1 2 3];
 C=[1 1 1 1 1 2 2 2 2 2 2 2 2 2 2];
 figure
 g11=gramm('x',X,'y',Y,'color',C);
 g11.geom_line();
 g11.draw();
-

Adding a group variable solves the problem in a ggplot-like way

G=[1 1 1 1 1 2 2 2 2 2 3 3 3 3 3];
+

Adding a group variable solves the problem in a ggplot-like way

G=[1 1 1 1 1 2 2 2 2 2 3 3 3 3 3];
 figure
 g12=gramm('x',X,'y',Y,'color',C,'group',G);
 g12.geom_line();
 g12.draw();
-

For a more matlab-like solution, Y and X can be 2D arrays, rows will automatically be considered as groups. as a consequence grouping data (color, etc...) are provided for the rows !

Y=[1 2 3 4 5;2 3 4 5 6; 3 4 5 6 7];
+

For a more matlab-like solution, Y and X can be 2D arrays, rows will automatically be considered as groups. as a consequence grouping data (color, etc...) are provided for the rows !

Y=[1 2 3 4 5;2 3 4 5 6; 3 4 5 6 7];
 X=[1 2 3 4 5; 0 1 2 3 4; -1 0 1 2 3];
 C=[1 2 2];
 figure
 g13=gramm('x',X,'y',Y,'color',C);
 g13.geom_line();
 g13.draw();
-

If all X values are the same, it's possible to provide X as a single row

X=[1 2 3 4 5];
+

If all X values are the same, it's possible to provide X as a single row

X=[1 2 3 4 5];
 figure
 g14=gramm('x',X,'y',Y,'color',C);
 g14.geom_line();
 g14.draw();
-

Similar results can be obtained with cells of arrays

Y={[1 2 3 4 5] [2 3 4 5 6] [3 4 5 6 7]};
+

Similar results can be obtained with cells of arrays

Y={[1 2 3 4 5] [2 3 4 5 6] [3 4 5 6 7]};
 X={[1 2 3 4 5] [0 1 2 3 4] [-1 0 1 2 3]};
 figure
 g15=gramm('x',X,'y',Y,'color',C);
@@ -834,13 +897,13 @@
 g16=gramm('x',X,'y',Y,'color',C);
 g16.geom_line();
 g16.draw();
-

With cells of arrays, there is the opportunity to have different lengths for different groups

Y={[1 2 3 4 5] [3 4 5] [3 4 5 6 7]};
+

With cells of arrays, there is the opportunity to have different lengths for different groups

Y={[1 2 3 4 5] [3 4 5] [3 4 5 6 7]};
 X={[1 2 3 4 5] [1 2 3] [-1 0 1 2 3]};
 figure
 g17=gramm('x',X,'y',Y,'color',C);
 g17.geom_line();
 g17.draw();
-
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