File @dac/dac.m changed (mode: 100644) (index 5626ce4..a981b86)
...	...	classdef dac < Singleton
11	11	% configuration	% configuration
12	12	quiet % no output	quiet % no output
13	13	prngseed % seed PRNG	prngseed % seed PRNG
14
	14
15	15	Ntmax % maximum numer of points to load	Ntmax % maximum numer of points to load
16	16	Ntsim % maximum numer of points to simulate	Ntsim % maximum numer of points to simulate
17
	17
18	18	psdnwin % pwelch window number	psdnwin % pwelch window number
19	19	psdwinfunc % pwelch window function	psdwinfunc % pwelch window function
20	20	psdoverlap % pwelch window overlap	psdoverlap % pwelch window overlap
...	...	classdef dac < Singleton
23	23	refF % reference frequency	refF % reference frequency
24	24	expFs % sample frequency	expFs % sample frequency
25	25	Fs % Fs rescaled to refF	Fs % Fs rescaled to refF
26
	26
27	27	postprocess % run postprocessing (true/false)	postprocess % run postprocessing (true/false)
28	28	postp % postprocessing (struct)	postp % postprocessing (struct)
29
	29
30	30	user % userinterface	user % userinterface
31	31	end	end
32	32
...	...	classdef dac < Singleton
53	53	newObj.refF = [];	newObj.refF = [];
54	54	newObj.expFs = [];	newObj.expFs = [];
55	55	newObj.Fs = [];	newObj.Fs = [];
56
	56
57	57	% user interface	% user interface
58		newObj.user = [];
	58		newObj.user = [];
59	59	end	end
60	60	end	end
61	61
...	...	classdef dac < Singleton
141	141	Value = obj.Fs;	Value = obj.Fs;
142	142	end	end
143	143	end	end
144
	144
145	145	end	end
146	146	end	end

File @dadac/filter.m changed (mode: 100644) (index 4c7909c..f64a77e)
...	...	function dsout = filter(ds,varargin)
2	2	% dsout = filter(ds)	% dsout = filter(ds)
3	3	%	%
4	4	% Filters time data (dadac object) with specified bandwidth and filter	% Filters time data (dadac object) with specified bandwidth and filter
5		% order. Uses internal function 'filter', but with different arguments.
	5		% order. Uses internal function 'filter', but with different arguments.
6	6	% arguments:	% arguments:
7	7	% - ds: time data (dadac object)	% - ds: time data (dadac object)
8	8	% optional arguments:	% optional arguments:
...	...	forder=512;
22	22
23	23	% parse varargs	% parse varargs
24	24	for ii=1:2:nargs	for ii=1:2:nargs
25		switch args{ii}
26		case 'fresponse', fresponse = args{ii+1};
27		case 'frange', frange = args{ii+1};
28		case 'forder', forder = args{ii+1};
	25		switch args{ii}
	26		case 'fresponse', fresponse = args{ii+1};
	27		case 'frange', frange = args{ii+1};
	28		case 'forder', forder = args{ii+1};
29	29	otherwise, warning(['unrecognized argument ' args{ii}])	otherwise, warning(['unrecognized argument ' args{ii}])
30	30	end	end
31	31	end	end
...	...	dsout = dadac(numel(ds),'type','fr');
53	53
54	54	% calculate spectrum for each entry in ds	% calculate spectrum for each entry in ds
55	55	for ii = 1:numel(ds)	for ii = 1:numel(ds)
56
57		% calculate filter for each row
	56
	57		% calculate filter for each row
58	58	for jj = 1:size(ds(ii).yData,2)	for jj = 1:size(ds(ii).yData,2)
59	59	tf = filter(b,1,ds(ii).yData(:,jj));	tf = filter(b,1,ds(ii).yData(:,jj));
60	60	dsout(ii).yData(:,jj) = tf(:);	dsout(ii).yData(:,jj) = tf(:);
61	61	end	end
62
	62
63	63	% copy other properties	% copy other properties
64	64	dsout(ii).id = ds(ii).id;	dsout(ii).id = ds(ii).id;
65	65	dsout(ii).label = ds(ii).label;	dsout(ii).label = ds(ii).label;

File @dadac/plot.m changed (mode: 100644) (index 133b59b..9710634)
...	...	function varargout = plot(ds,varargin)
6	6
7	7	for ii = 1:numel(ds)	for ii = 1:numel(ds)
8	8	ph = figure();	ph = figure();
9
	9
10	10	xvec = ds(ii).xData;	xvec = ds(ii).xData;
11	11	if isempty(xvec)	if isempty(xvec)
12	12	tempPh = plot(ds(ii).yData,varargin{:});	tempPh = plot(ds(ii).yData,varargin{:});

File @dadac/postprocess.m changed (mode: 100644) (index 2da57f2..e24a144)
...	...	end
50	50	% $$$ if ~strcmp(pps.method,'none')	% $$$ if ~strcmp(pps.method,'none')
51	51	if ~isempty(pps)	if ~isempty(pps)
52	52	Hd = design(pps.fdesign,pps.method);	Hd = design(pps.fdesign,pps.method);
53		%Hd = pps.hd;
	53		%Hd = pps.hd;
54	54	for jj = part	for jj = part
55		if ~ds(jj).postpd
	55		if ~ds(jj).postpd
56	56	for ii = 1:size(ds(jj).yData,2)	for ii = 1:size(ds(jj).yData,2)
57	57	ds(jj).yData(:,ii)=filter(Hd,ds(jj).yData(:,ii));	ds(jj).yData(:,ii)=filter(Hd,ds(jj).yData(:,ii));
58	58	ds(jj).postpd = true;	ds(jj).postpd = true;
...	...	else
75	75	pps.fdesign.Fpass2=Fpass2Scaled;	pps.fdesign.Fpass2=Fpass2Scaled;
76	76	end	end
77	77	pps.fdesign.Fs=FsScaled;	pps.fdesign.Fs=FsScaled;
78

File @dadac/pwelch.m changed (mode: 100644) (index d3cebfa..f463ff4)
...	...	for ii = 1:numel(ds)
42	42	win = PWELCH_WIN_FUNCTION(winlen);	win = PWELCH_WIN_FUNCTION(winlen);
43	43
44	44	% calculate psd for each row	% calculate psd for each row
45		fr = [];
	45		fr = [];
46	46	for jj = 1:size(ds(ii).yData,2)	for jj = 1:size(ds(ii).yData,2)
47	47	[power,fr] = pwelch(ds(ii).yData(:,jj),win,PWELCH_NOVERLAP,[],dao.expFs,PWELCH_RANGE);	[power,fr] = pwelch(ds(ii).yData(:,jj),win,PWELCH_NOVERLAP,[],dao.expFs,PWELCH_RANGE);
48	48	dsout(ii).yData(:,jj) = power(:);	dsout(ii).yData(:,jj) = power(:);

File @dadac/pwelchplot.m changed (mode: 100644) (index 3b7ad41..325d96e)
1	1	function pwelchplot(frdata)	function pwelchplot(frdata)
2	2	% pwelchplot(frdata)	% pwelchplot(frdata)
3		%
	3		%
4	4	% Plots PSD. This is a helper for ess/pwelch and dadac/pwelch and is	% Plots PSD. This is a helper for ess/pwelch and dadac/pwelch and is
5	5	% not supposed to be called directly!	% not supposed to be called directly!
6	6	%	%

File @daddc/daddc.m changed (mode: 100644) (index debcb09..c4a0ac8)
...	...	classdef daddc
103	103	obj.basedir = Value;	obj.basedir = Value;
104	104	end	end
105	105	end	end
106
	106
107	107	function obj = set.config(obj,Value)	function obj = set.config(obj,Value)
108	108	if isempty(Value)	if isempty(Value)
109	109	obj.config = '';	obj.config = '';
...	...	classdef daddc
121	121	% $$$ % create directory and add it to datadir	% $$$ % create directory and add it to datadir
122	122	% $$$ function obj = makeDatadir(obj,varargin)	% $$$ function obj = makeDatadir(obj,varargin)
123	123	% $$$ args = varargs2cell(varargin);	% $$$ args = varargs2cell(varargin);
124		% $$$ if
125		% $$$
	124		% $$$ if
	125		% $$$
126	126	% $$$ end	% $$$ end
127	127	% $$$ end	% $$$ end
128	128

File @daddc/loadconfig.m changed (mode: 100644) (index 008f4b5..3694d55)
...	...	function varargout = loadconfig(ddir,varargin)
4	4	%	%
5	5	% Call configuration function in ddir.basedir and pass arguments. If	% Call configuration function in ddir.basedir and pass arguments. If
6	6	% second argument is 'help' print help text of configuration function.	% second argument is 'help' print help text of configuration function.
7		%
	7		%
8	8	% Note: Only use FULL filenames for this to work correctly!	% Note: Only use FULL filenames for this to work correctly!
9	9
10	10	varargout = cell(1,max(1,nargout)); % always return result	varargout = cell(1,max(1,nargout)); % always return result
...	...	end
30	30	clear(funcString);	clear(funcString);
31	31
32	32	% to fix:	% to fix:
33		% problems with 'dirNotInPath' check: search order in variable 'path'
	33		% problems with 'dirNotInPath' check: search order in variable 'path'
34	34	% yields problems for finding correct parameter file	% yields problems for finding correct parameter file
35	35
36	36	% add basedir to path if needed	% add basedir to path if needed
...	...	if ischar(varargin{1})
49	49	elseif isstruct(varargin{1}) || isempty(varargin{1})	elseif isstruct(varargin{1}) || isempty(varargin{1})
50	50	% load parameters	% load parameters
51	51	argsStruct = varargs2struct(varargin);	argsStruct = varargs2struct(varargin);
52		[varargout{:}] = confFunc(argsStruct);
	52		[varargout{:}] = confFunc(argsStruct);
53	53	end	end
54	54	% comment see above	% comment see above
55	55	% only remove basedir from path if we added it before	% only remove basedir from path if we added it before

File @ess/append.m changed (mode: 100644) (index d425f80..6b5cb14)
...	...	function ssa=append(varargin)
3	3	%	%
4	4	% Concatenate models sys1,sys2,... to form one model, without	% Concatenate models sys1,sys2,... to form one model, without
5	5	% making a connection between the different models.	% making a connection between the different models.
6
	6
7	7	sss = cell(1,length(nargin));	sss = cell(1,length(nargin));
8	8	ssl = {}; ssr = {}; ssq = {}; ssm = {};	ssl = {}; ssr = {}; ssq = {}; ssm = {};
9	9	nvar = 0; nnvar = 0; jj = 1;	nvar = 0; nnvar = 0; jj = 1;
10
	10
11	11	for ii = 1:nargin	for ii = 1:nargin
12	12	% dynamics	% dynamics
13	13	sss{ii} = ss(varargin{ii});	sss{ii} = ss(varargin{ii});
...	...	function ssa=append(varargin)
43	43	% incorrect.	% incorrect.
44	44	ssr{ii} = rnotempty(varargin{ii});	ssr{ii} = rnotempty(varargin{ii});
45	45	end	end
46
	46
47	47	% append deterministic (ss) part	% append deterministic (ss) part
48	48	ssa = ess(append(sss{:}));	ssa = ess(append(sss{:}));
49	49

File @ess/c2d.m changed (mode: 100644) (index e52350c..9a5d660)
...	...	if sys_c.Ts == 0
37	37	sys_d.c = H; sys_d.d = D;	sys_d.c = H; sys_d.d = D;
38	38	sys_d.q = Q; sys_d.r = R; sys_d.m = M;	sys_d.q = Q; sys_d.r = R; sys_d.m = M;
39	39	sys_d.Ts = Ts;	sys_d.Ts = Ts;
40
	40
41	41	case {'zoh','foh','impulse','tustin'} % 'matched' is broken	case {'zoh','foh','impulse','tustin'} % 'matched' is broken
42	42	% convert ss part	% convert ss part
43	43	% prescaling might be applied by ss/c2d	% prescaling might be applied by ss/c2d

File @ess/compactify.m changed (mode: 100644) (index e4c88ef..b8d2b32)
...	...	switch method
65	65	% delete InputGroup and OutputGroup	% delete InputGroup and OutputGroup
66	66	sys.InputGroup = struct();	sys.InputGroup = struct();
67	67	sys.OutputGroup = struct();	sys.OutputGroup = struct();
68
	68
69	69	% create new model and copy meta-properties	% create new model and copy meta-properties
70	70	if sys.isnoisy()	if sys.isnoisy()
71	71	nrm=intersect(intersect(hhor,dhor),nhor);	nrm=intersect(intersect(hhor,dhor),nhor);

File @ess/display.m changed (mode: 100644) (index a0669fb..452a575)
1	1	function display(sys)	function display(sys)
2	2	%DISPLAY Pretty-print for ess models.	%DISPLAY Pretty-print for ess models.
3	3
4
	4
5	5	%% Code adapted from @ss/display.m	%% Code adapted from @ss/display.m
6	6
7	7	% Extract state-space data and sampling/delay times	% Extract state-space data and sampling/delay times
...	...	end
101	101	%% print l matrix	%% print l matrix
102	102	function displm(sys,LeftMargin)	function displm(sys,LeftMargin)
103	103	% this has to be changed if ss gets qlabel property	% this has to be changed if ss gets qlabel property
104		wlabels = '';
	104		wlabels = '';
105	105	xlabels = sys.StateName;	xlabels = sys.StateName;
106	106	[nx,nq] = size(sys.l);	[nx,nq] = size(sys.l);
107	107
...	...	function displm(sys,LeftMargin)
114	114	if isempty(xlabels{i}), xlabels{i} = '?'; end	if isempty(xlabels{i}), xlabels{i} = '?'; end
115	115	end	end
116	116	end	end
117
	117
118	118	if isempty(wlabels) || isequal('',wlabels{:}),	if isempty(wlabels) || isequal('',wlabels{:}),
119	119	for i=1:nq,	for i=1:nq,
120	120	wlabels{i} = sprintf('w%d',i);	wlabels{i} = sprintf('w%d',i);
121	121	end	end
122	122	else	else
123		for i=1:nq,
	123		for i=1:nq,
124	124	if isempty(wlabels{i}), wlabels{i} = '?'; end	if isempty(wlabels{i}), wlabels{i} = '?'; end
125	125	end	end
126	126	end	end

File @ess/ess.m changed (mode: 100644) (index fc6df34..631f40a)
...	...	classdef ess < ss
12	12	%	%
13	13	% This class extends ss. See 'help ss' for a list of additional	% This class extends ss. See 'help ss' for a list of additional
14	14	% arguments.	% arguments.
15
	15
16	16	% adapt number of meta-properties in getMetaProperties if	% adapt number of meta-properties in getMetaProperties if
17	17	% datastructure is changed!	% datastructure is changed!
18	18	properties	properties
...	...	classdef ess < ss
26	26	isnoisy % system has noisy input	isnoisy % system has noisy input
27	27	% (boolean)	% (boolean)
28	28	end	end
29
	29
30	30
31	31	methods	methods
32
	32
33	33	%%% constructor	%%% constructor
34	34	function obj = ess(varargin)	function obj = ess(varargin)
35	35	% parse arguments	% parse arguments
...	...	classdef ess < ss
37	37	essvarlen = 8;	essvarlen = 8;
38	38	addvarlen = essvarlen-ssvarlen;	addvarlen = essvarlen-ssvarlen;
39	39	newArgs = cell(1,addvarlen); superArgs = {};	newArgs = cell(1,addvarlen); superArgs = {};
40
	40
41	41	% copy from ss object	% copy from ss object
42	42	if nargin>0 && isa(varargin{1},'StateSpaceModel') || isa(varargin{1},'tf')	if nargin>0 && isa(varargin{1},'StateSpaceModel') || isa(varargin{1},'tf')
43	43	% create empty ss object and call copy constructor	% create empty ss object and call copy constructor
...	...	classdef ess < ss
52	52	% already ess model	% already ess model
53	53	S = varargin{1};	S = varargin{1};
54	54	end	end
55
	55
56	56	superArgs{1} = [];	superArgs{1} = [];
57	57	[copyNames,copyArgs] = getProperties(S);	[copyNames,copyArgs] = getProperties(S);
58
	58
59	59	% fields not set in ss object	% fields not set in ss object
60	60	if nargin > 1	if nargin > 1
61	61	newArgs(1:nargin-1) = varargin(2:nargin);	newArgs(1:nargin-1) = varargin(2:nargin);
...	...	classdef ess < ss
86	86	newArgs = varargin(ssvarlen+1:essvarlen);	newArgs = varargin(ssvarlen+1:essvarlen);
87	87	end	end
88	88	end	end
89
	89
90	90	% call superclass constructor	% call superclass constructor
91	91	obj = obj@ss(superArgs{:});	obj = obj@ss(superArgs{:});
92
	92
93	93	% copy properties	% copy properties
94	94	clen = length(copyArgs);	clen = length(copyArgs);
95	95	if clen > 0	if clen > 0
...	...	classdef ess < ss
97	97	obj.(copyNames{ii}) = copyArgs{ii};	obj.(copyNames{ii}) = copyArgs{ii};
98	98	end	end
99	99	end	end
100
	100
101	101	% set new fields	% set new fields
102	102	if length(newArgs) > 0	if length(newArgs) > 0
103	103	[obj.l,obj.q,obj.r,obj.m] = newArgs{:};	[obj.l,obj.q,obj.r,obj.m] = newArgs{:};
...	...	classdef ess < ss
111	111	if ~h	if ~h
112	112	error('Model dimensions are not valid!')	error('Model dimensions are not valid!')
113	113	end	end
114
	114
115	115	end	end
116
	116
117	117	function nobj = cleanNoise(obj)	function nobj = cleanNoise(obj)
118	118	% empty all noise matrices if noiseTest fails	% empty all noise matrices if noiseTest fails
119	119	nobj = obj;	nobj = obj;
...	...	classdef ess < ss
121	121	nobj.l = []; nobj.q = []; nobj.r = []; nobj.m = [];	nobj.l = []; nobj.q = []; nobj.r = []; nobj.m = [];
122	122	end	end
123	123	end	end
124
125		%% meta-properties
	124
	125		%% meta-properties
126	126	% extract object meta-properties = all except matrices	% extract object meta-properties = all except matrices
127	127	% adapt number of meta-properties if datastructure is changed!	% adapt number of meta-properties if datastructure is changed!
128	128	function props = getMetaProperties(ssObj)	function props = getMetaProperties(ssObj)
...	...	classdef ess < ss
142	142	ssObjout.(names{ii}) = props.(names{ii});	ssObjout.(names{ii}) = props.(names{ii});
143	143	end	end
144	144	end	end
145
146
	145
	146
147	147	%% set: these toggle isnoisy if required	%% set: these toggle isnoisy if required
148	148	function obj = set.q(obj,Value)	function obj = set.q(obj,Value)
149	149	tmpq = obj.q;	tmpq = obj.q;
...	...	classdef ess < ss
183	183	S.ess = localEssData(obj); % extra ess matrices (l,q,r,m)	S.ess = localEssData(obj); % extra ess matrices (l,q,r,m)
184	184	S.prop = getMetaProperties(obj); % meta properties	S.prop = getMetaProperties(obj); % meta properties
185	185	end	end
186
	186
187	187	end	end
188
	188
189	189	methods (Static)	methods (Static)
190	190	% extend ss saveobj	% extend ss saveobj
191	191	function obj = loadobj(S)	function obj = loadobj(S)
...	...	function c = extraEssProperties(obj)
251	251	% takes ess obj as input	% takes ess obj as input
252	252	sp = properties('ss'); ep = properties(obj);	sp = properties('ss'); ep = properties(obj);
253	253	% note order of input: ep must be before!!	% note order of input: ep must be before!!
254		% 'stable' is needed such that original order
	254		% 'stable' is needed such that original order
255	255	% of arguments is preserved for the ess constructor	% of arguments is preserved for the ess constructor
256		c = setdiff(ep,sp,'stable');
	256		c = setdiff(ep,sp,'stable');
257	257	end	end
258	258
259	259	% local essdata	% local essdata

File @ess/initial.m changed (mode: 100644) (index 6d3d125..9a72643)
...	...	if isempty(xinit)
31	31	xinit = zeros(size(sys.a,1),1);	xinit = zeros(size(sys.a,1),1);
32	32	end	end
33	33
34		varargout = cell(1,nargout);
	34		varargout = cell(1,nargout);
35	35
36	36	% create trajectory	% create trajectory
37	37	if sys.isnoisy()	if sys.isnoisy()

File @ess/isphysmeas.m changed (mode: 100644) (index 0e18116..e44ff41)
1	1	function bool = isphysmeas(sys,varargin)	function bool = isphysmeas(sys,varargin)
2	2	% bool = isphysmeas(sys,...)	% bool = isphysmeas(sys,...)
3		%
	3		%
4	4	% optional arguments:	% optional arguments:
5	5	% - 'eigs': show eigenvalues true/false	% - 'eigs': show eigenvalues true/false
6	6	% - 'statevars': select relevant state variables (integer range)	% - 'statevars': select relevant state variables (integer range)
...	...	mat = L*(Q-M*inv(R)*M')*L'-(symm*C'*inv(R)*C*symm')/4;
31	31	bool = (num==0);	bool = (num==0);
32	32
33	33	if showeigs	if showeigs
34		fprintf('eigenvalues:\n')
35		eig(mat)
	34		fprintf('eigenvalues:\n')
	35		eig(mat)
36	36	end	end

File @ess/kalman.m changed (mode: 100644) (index b5382d0..cb5bbc0)
...	...	Q = sys.q; R = sys.r; M = sys.m;
17	17	% help ess/ss says:	% help ess/ss says:
18	18	% 'augmented' is equivalent to SS(A,[B,L,0],C,[D,0,1])	% 'augmented' is equivalent to SS(A,[B,L,0],C,[D,0,1])
19	19	asys = ss(sys,'augmented');	asys = ss(sys,'augmented');
20
	20
21	21	[kest,L,P,varargout{:}] = kalman(asys(:,{'KnownInput','ProcessNoise'}),Q,R,M,varargin{:});	[kest,L,P,varargout{:}] = kalman(asys(:,{'KnownInput','ProcessNoise'}),Q,R,M,varargin{:});

File @ess/kfss.m changed (mode: 100644) (index fe6cb05..3f43b73)
...	...	function [ksys,kss,pss,psu] = kfss(sys)
8	8	%	%
9	9	% The discrete Kalman filter is given by	% The discrete Kalman filter is given by
10	10	% x[n|n] = (1-K)F x[n-1|n-1] + K z[n] + B u[n],	% x[n|n] = (1-K)F x[n-1|n-1] + K z[n] + B u[n],
11		% z[n|n] = C x[n|n] + D u[n],
	11		% z[n|n] = C x[n|n] + D u[n],
12	12	% where x[n|n] is the a posterior state.	% where x[n|n] is the a posterior state.
13	13	%	%
14	14	% The prior and posterior estimation error covariance matrices are	% The prior and posterior estimation error covariance matrices are
...	...	else
55	55	% ss gain equations see Simon p. 194, Heijden p.271	% ss gain equations see Simon p. 194, Heijden p.271
56	56	id = eye(sn);	id = eye(sn);
57	57	kss = (pss*H'+M)/(H*pss*H'+R+H*M+(H*M)');% kalman gain	kss = (pss*H'+M)/(H*pss*H'+R+H*M+(H*M)');% kalman gain
58
	58
59	59	% a posteriori state x[n|n]	% a posteriori state x[n|n]
60	60	A = (F-kss*H*F); % filter dynamics	A = (F-kss*H*F); % filter dynamics
61	61

File @ess/lsim.m changed (mode: 100644) (index b01d5ee..e97e27d)
...	...	function varargout = lsim(sys,varargin)
8	8	% observation-noise used to simulate the response. Note that the input	% observation-noise used to simulate the response. Note that the input
9	9	% U now has modified dimensions! If this is not desired, use	% U now has modified dimensions! If this is not desired, use
10	10	% lsim(ss(sys),...) instead.	% lsim(ss(sys),...) instead.
11		%
	11		%
12	12	% Wrapper function: See also SS/LSIM.	% Wrapper function: See also SS/LSIM.
13	13
14	14	% reference varargin in struct (no copies are created!)	% reference varargin in struct (no copies are created!)
...	...	else
29	29	uaugmented = [argS.u,zeros(length(argS.t),sn+on)];	uaugmented = [argS.u,zeros(length(argS.t),sn+on)];
30	30	end	end
31	31
32		varargout = cell(1,nargout);
	32		varargout = cell(1,nargout);
33	33
34	34	% check if system is noisy, if not call ss/lsim on unmodified system	% check if system is noisy, if not call ss/lsim on unmodified system
35		if sys.isnoisy()
	35		if sys.isnoisy()
36	36	% generate and concatenate noise to deterministic input	% generate and concatenate noise to deterministic input
37	37	% [argS.u,statenoise,obsnoise]	% [argS.u,statenoise,obsnoise]
38	38	uaugmented(:,un+1:end) = gennoise(sys,argS.t,'combinednoise','true');	uaugmented(:,un+1:end) = gennoise(sys,argS.t,'combinednoise','true');

File @ess/parallel.m changed (mode: 100644) (index 921f2b1..2903dd5)
1	1	function sys_cc = parallel(varargin)	function sys_cc = parallel(varargin)
2	2	% sys=parallel(sys1,sys2,...)	% sys=parallel(sys1,sys2,...)
3	3	%	%
4		% Connect sys1, sys2, ... in parallel, i.e. inputs of sys1 and sys2
	4		% Connect sys1, sys2, ... in parallel, i.e. inputs of sys1 and sys2
5	5	% get the same determinsitic input u, outputs of sys1 and sys 2 are	% get the same determinsitic input u, outputs of sys1 and sys 2 are
6	6	% directly summed.	% directly summed.
7	7	%	%
...	...	function sys_cc = parallel(varargin)
24	24	% connect systems	% connect systems
25	25	sys_cc=varargin{1};	sys_cc=varargin{1};
26	26	if nargin == 2	if nargin == 2
27		sys_cc=tparallel(sys_cc,varargin{2});
	27		sys_cc=tparallel(sys_cc,varargin{2});
28	28	else	else
29	29	error('Parallel connection of more than two models is currently not supported')	error('Parallel connection of more than two models is currently not supported')
30	30	end	end
...	...	ec = @(x) cellfun(@(x)'',x,'UniformOutput',false); % return cell array {'',...}
53	53	sys_c.q = blkdiag(sys1.q,sys2.q);	sys_c.q = blkdiag(sys1.q,sys2.q);
54	54	sys_c.r = sys1.r+sys2.r;	sys_c.r = sys1.r+sys2.r;
55	55	sys_c.m = [sys1.m;sys2.m];	sys_c.m = [sys1.m;sys2.m];
56
	56
57	57	elseif sys1.isnoisy()	elseif sys1.isnoisy()
58	58	sys_c.l = blkdiag(sys1.l,zeros(size(sys2.a,1),1));	sys_c.l = blkdiag(sys1.l,zeros(size(sys2.a,1),1));
59	59	sys_c.q = blkdiag(sys1.q,zeros(1,1));	sys_c.q = blkdiag(sys1.q,zeros(1,1));
60	60	sys_c.r = sys1.r;	sys_c.r = sys1.r;
61	61	sys_c.m = [sys1.m;zeros(1,size(sys2.c,1))];	sys_c.m = [sys1.m;zeros(1,size(sys2.c,1))];
62
	62
63	63	elseif sys2.isnoisy()	elseif sys2.isnoisy()
64	64	sys_c.l = blkdiag(zeros(size(sys1.a,1),1),sys2.l);	sys_c.l = blkdiag(zeros(size(sys1.a,1),1),sys2.l);
65	65	sys_c.q = blkdiag(zeros(1,1),sys2.q);	sys_c.q = blkdiag(zeros(1,1),sys2.q);

File @ess/prescale.m changed (mode: 100644) (index a3a2b84..f09684c)
1	1	function varargout = prescale(sys,varargin)	function varargout = prescale(sys,varargin)
2	2	% Wrapper for prescale for ess objects.	% Wrapper for prescale for ess objects.
3		%
	3		%
4	4	% Converts ss to ess objects before passing all arguments to	% Converts ss to ess objects before passing all arguments to
5	5	% ss/prescale. Specifically, the noise input (l) is appended to the	% ss/prescale. Specifically, the noise input (l) is appended to the
6	6	% deterministic input (b). If this is not desired, use	% deterministic input (b). If this is not desired, use

File @ess/selectIO.m changed (mode: 100644) (index 16eeb02..ace13f3)
1	1	function [ssys,opos] = selectIO(sys,varargin)	function [ssys,opos] = selectIO(sys,varargin)
2	2	% ssys = selectIO(sys,...)	% ssys = selectIO(sys,...)
3		%
	3		%
4	4	% Select input/output channels from sys. Syntax as for ss models.	% Select input/output channels from sys. Syntax as for ss models.
5	5	% See also: SS	% See also: SS
6
	6
7	7	is = size(sys.b,2); % input number	is = size(sys.b,2); % input number
8	8	os = size(sys.c,1); % output number	os = size(sys.c,1); % output number
9	9
...	...	function [ssys,opos] = selectIO(sys,varargin)
19	19	else	else
20	20	error('too many arguments')	error('too many arguments')
21	21	end	end
22
	22
23	23	% if arguments are empty, use all channels	% if arguments are empty, use all channels
24	24	if isempty(partO), partO = 1:os; end	if isempty(partO), partO = 1:os; end
25	25	if isempty(partI), partI = 1:is; end	if isempty(partI), partI = 1:is; end
...	...	function [ssys,opos] = selectIO(sys,varargin)
29	29	ssys = ss(sys); ssys = ssys(partO,partI);	ssys = ss(sys); ssys = ssys(partO,partI);
30	30	% modify R and M to match new output	% modify R and M to match new output
31	31	% input need not be modified	% input need not be modified
32		opos = findPosition('output',partO);
	32		opos = findPosition('output',partO);
33	33	ssys = ess(ssys,sys.l,sys.q,sys.r(opos,opos),sys.m(:,opos));	ssys = ess(ssys,sys.l,sys.q,sys.r(opos,opos),sys.m(:,opos));
34
	34
35	35	%% private	%% private
36	36
37	37	function pos = findPosition(searchWhere,searchExp)	function pos = findPosition(searchWhere,searchExp)

File @ess/sscov.m changed (mode: 100644) (index 9ab4733..2fc9017)
...	...	end
12	12	cov = lyap(sys.a,sys.l*sys.q*sys.l');	cov = lyap(sys.a,sys.l*sys.q*sys.l');
13	13
14	14	end	end
15

File PXItraces/@PXItrace/PXI_running_variance.m changed (mode: 100644) (index c998b31..b5a5ac1)
1	1	%% PXI_running_variance: takes a PXI trace and calculates its variance	%% PXI_running_variance: takes a PXI trace and calculates its variance
2	2	function [vvar, mmean] = PXI_running_variance(PXItrace)	function [vvar, mmean] = PXI_running_variance(PXItrace)
3		sample_no = PXItrace.sample_no;
4		samples_per_run = 1E7;
5		runs = floor(sample_no/samples_per_run);
	3		sample_no = PXItrace.sample_no;
	4		samples_per_run = 1E7;
	5		runs = floor(sample_no/samples_per_run);
6	6
7		PXItrace.fopen()
8		c = onCleanup(@()PXItrace.fclose());
9		tic;
10		for k=1:runs
11		dat = PXItrace.fread(samples_per_run);
12		if k==1
13		vvar = var(dat);
14		mmean = mean(dat);
15		else
16		mmean2 = mean(dat);
17		vvar2 = var(dat);
	7		PXItrace.fopen()
	8		c = onCleanup(@()PXItrace.fclose());
	9		tic;
	10		for k=1:runs
	11		dat = PXItrace.fread(samples_per_run);
	12		if k==1
	13		vvar = var(dat);
	14		mmean = mean(dat);
	15		else
	16		mmean2 = mean(dat);
	17		vvar2 = var(dat);
18	18
19		vvar = varupdate(...
20		mmean,vvar,k*samples_per_run,...
21		mmean2,vvar2,samples_per_run);
22		mmean = meanupdate(...
23		mmean,k*samples_per_run,...
24		mmean2,samples_per_run);
25		end
26		if k==5
27		t = toc;
28		exp_dur = t*runs/5;
29		disp(['Expected duration: ', num2str(round(exp_dur)), ' seconds'])
30		disp(['Expected duration: ', num2str(round(exp_dur/60)), ' minutes'])
31		end
32		end
	19		vvar = varupdate(...
	20		mmean,vvar,k*samples_per_run,...
	21		mmean2,vvar2,samples_per_run);
	22		mmean = meanupdate(...
	23		mmean,k*samples_per_run,...
	24		mmean2,samples_per_run);
	25		end
	26		if k==5
	27		t = toc;
	28		exp_dur = t*runs/5;
	29		disp(['Expected duration: ', num2str(round(exp_dur)), ' seconds'])
	30		disp(['Expected duration: ', num2str(round(exp_dur/60)), ' minutes'])
	31		end
	32		end
33	33	end	end

File PXItraces/@PXItrace/PXItrace.m changed (mode: 100644) (index 3fb2f14..eae64e3)
...	...	classdef PXItrace < class_conveniences
32	32	pseudohash % quick pseudohash computed with pseudo_hash	pseudohash % quick pseudohash computed with pseudo_hash
33	33	chunk_index % for long measurements where traces are split into several chunks	chunk_index % for long measurements where traces are split into several chunks
34	34	dummy = false; % dummy = true creates a short dummy file with random numbers to play around with	dummy = false; % dummy = true creates a short dummy file with random numbers to play around with
35		stream = false % stream = true <==> PXItrace does not rely on existing file but on a stream
	35		stream = false % stream = true <==> PXItrace does not rely on existing file but on a stream
36	36	% as created e.g. by simulation_data_stream.m	% as created e.g. by simulation_data_stream.m
37	37	end	end
38	38	properties(Dependent)	properties(Dependent)
...	...	classdef PXItrace < class_conveniences
42	42	bytes_per_sample	bytes_per_sample
43	43	sample_no	sample_no
44	44	end	end
45		properties(Hidden)
	45		properties(Hidden)
46	46	fileID % needed for fopen, fclose, fseek and fread	fileID % needed for fopen, fclose, fseek and fread
47	47	NI_voltage_gain	NI_voltage_gain
48	48	NI_voltage_offset	NI_voltage_offset
...	...	classdef PXItrace < class_conveniences
73	73	obj.pseudohash = pseudo_hash(filepath);	obj.pseudohash = pseudo_hash(filepath);
74	74	end	end
75	75	end	end
76
	76
77	77	%destructor	%destructor
78	78	function delete(obj)	function delete(obj)
79	79	%disp('destructor called');	%disp('destructor called');
...	...	classdef PXItrace < class_conveniences
84	84	%disp(['deleted the dummy file ', obj.filename]);	%disp(['deleted the dummy file ', obj.filename]);
85	85	delete(obj.filepath);	delete(obj.filepath);
86	86	end	end
87		end
	87		end
88	88
89	89	function dir = get.directory(obj)	function dir = get.directory(obj)
90	90	dir = fileparts(obj.filepath);	dir = fileparts(obj.filepath);

File PXItraces/@PXItrace/blocked_cross_correlation.m changed (mode: 100644) (index 93da534..f340548)
...	...	function [cf, cf_a, cf_b] = blocked_cross_correlation(tr_a, tr_b, block_size, va
8	8	elseif length(varargin)==1	elseif length(varargin)==1
9	9	scaleopt = varargin{1};	scaleopt = varargin{1};
10	10	assert(any(ismember(scaleopt, {'none', 'biased', 'unbiased', 'coeff'})),...	assert(any(ismember(scaleopt, {'none', 'biased', 'unbiased', 'coeff'})),...
11		'scaleopt must be one of: "none", "biased", "unbiased", "coeff"!')
	11		'scaleopt must be one of: "none", "biased", "unbiased", "coeff"!')
12	12	end	end
13	13	disp(['calculating crosscorrelation between ',...	disp(['calculating crosscorrelation between ',...
14	14	tr_a.filename,' and ',tr_b.filename,'.']);	tr_a.filename,' and ',tr_b.filename,'.']);
15
	15
16	16	tr_a.fopen; read_a = @()tr_a.fread(block_size);	tr_a.fopen; read_a = @()tr_a.fread(block_size);
17	17	tr_b.fopen; read_b = @()tr_b.fread(block_size);	tr_b.fopen; read_b = @()tr_b.fread(block_size);
18
	18
19	19	sample_no = min(tr_a.sample_no, tr_b.sample_no);	sample_no = min(tr_a.sample_no, tr_b.sample_no);
20	20	block_no = floor(sample_no/block_size);	block_no = floor(sample_no/block_size);
21	21
...	...	function [cf, cf_a, cf_b] = blocked_cross_correlation(tr_a, tr_b, block_size, va
42	42	a3 = zeropad;	a3 = zeropad;
43	43	b3 = zeropad;	b3 = zeropad;
44	44	elseif k==10	elseif k==10
45		print_eta(k, block_no)
	45		print_eta(k, block_no)
46	46	a3 = read_a();	a3 = read_a();
47	47	b3 = read_b();	b3 = read_b();
48	48	else	else
49	49	a3 = read_a();	a3 = read_a();
50	50	b3 = read_b();	b3 = read_b();
51	51	end	end
52
	52
53	53	cf = cf + myxcorr(a1,a2,a3, b1,b2,b3,k);	cf = cf + myxcorr(a1,a2,a3, b1,b2,b3,k);
54	54	cf_a = cf_a + myxcorr(a1,a2,a3, a1,a2,a3,k);	cf_a = cf_a + myxcorr(a1,a2,a3, a1,a2,a3,k);
55	55	cf_b = cf_b + myxcorr(b1,b2,b3, b1,b2,b3,k);	cf_b = cf_b + myxcorr(b1,b2,b3, b1,b2,b3,k);

File PXItraces/@timetrace/show_snapshot.m changed (mode: 100644) (index c86a246..b9387fd)
1	1	function fig = show_snapshot(obj)	function fig = show_snapshot(obj)
2		sample_no = 1000;
3		obj.fopen;
4		data_vec = obj.fread(sample_no);
5		obj.fclose;
	2		sample_no = 1000;
	3		obj.fopen;
	4		data_vec = obj.fread(sample_no);
	5		obj.fclose;
6	6	time_vec = obj.timevec_from_t1_to_t2(0,sample_no/obj.samplingrate);	time_vec = obj.timevec_from_t1_to_t2(0,sample_no/obj.samplingrate);
7	7	fig = gcf;	fig = gcf;
8	8	plt = plot(time_vec, data_vec,'.-',...	plt = plot(time_vec, data_vec,'.-',...

File PXItraces/Felix/READhomotrace.m changed (mode: 100644) (index bd6d517..64c7442)
1	1	function data = READhomotrace(path,samplingrate)	function data = READhomotrace(path,samplingrate)
2	2	temp_data = homotrace(path);	temp_data = homotrace(path);
3	3	ttpath = char(path);	ttpath = char(path);
4
	4
5	5	%READhomotrace('F:\Experiment\2016\20160707 - LT SiN Norcada 500mum glued\P20muW\P20muW_1_amp_Range0200mV_Amplification30dB.dat',8e+7)	%READhomotrace('F:\Experiment\2016\20160707 - LT SiN Norcada 500mum glued\P20muW\P20muW_1_amp_Range0200mV_Amplification30dB.dat',8e+7)
6
	6
7	7	j = 1;	j = 1;
8	8	k = 1;	k = 1;
9	9	n = 1;	n = 1;
10	10	u = 1;	u = 1;
11	11	prev = 0;	prev = 0;
12
	12
13	13	for i = 1:length(ttpath)	for i = 1:length(ttpath)
14	14	if ttpath(i) == '\'	if ttpath(i) == '\'
15	15	prev = i + 1;	prev = i + 1;
16	16	end	end
17	17	end	end
18
	18
19	19	newpath = [];	newpath = [];
20
	20
21	21	for i = prev:length(ttpath)	for i = prev:length(ttpath)
22	22	if ttpath(i) == '_' || ttpath(i) == '.'	if ttpath(i) == '_' || ttpath(i) == '.'
23	23	if ttpath(i) == '.'	if ttpath(i) == '.'
...	...	function data = READhomotrace(path,samplingrate)
27	27	n = 1;	n = 1;
28	28	k = 1;	k = 1;
29	29	u = 1;	u = 1;
30
	30
31	31	else	else
32	32	switch j	switch j
33	33	case 1	case 1
...	...	function data = READhomotrace(path,samplingrate)
72	72	k = k + 1;	k = k + 1;
73	73	end	end
74	74	end	end
75
	75
76	76	if voltsU == 'mV'	if voltsU == 'mV'
77	77	volts = str2num(volts)/1000;	volts = str2num(volts)/1000;
78	78	voltsU = 'V';	voltsU = 'V';
79	79	end	end
80
	80
81	81	if wattsU == 'muW'	if wattsU == 'muW'
82	82	temp_data.signal_power_muW = str2num(watts);	temp_data.signal_power_muW = str2num(watts);
83	83	end	end
84
	84
85	85	if ampU == 'dB'	if ampU == 'dB'
86	86	amp = 10^(str2num(amp)/10);	amp = 10^(str2num(amp)/10);
87	87	ampU = 'linear';	ampU = 'linear';
88	88	end	end
89
	89
90	90	if voltsU == 'V'	if voltsU == 'V'
91	91	temp_data.DAQrange = volts;	temp_data.DAQrange = volts;
92	92	else	else
93	93	temp_data.DAQrange = DAQrange;	temp_data.DAQrange = DAQrange;
94	94	end	end
95
	95
96	96	if ampU == 'linear'	if ampU == 'linear'
97	97	temp_data.amplification = amp;	temp_data.amplification = amp;
98	98	else	else
99	99	temp_data.amplification = amplification;	temp_data.amplification = amplification;
100	100	end	end
101
	101
102	102	if strcmp(ampORphase,'amp')	if strcmp(ampORphase,'amp')
103	103	ampORphase = 'Amplitude';	ampORphase = 'Amplitude';
104	104	temp_data.quadrature = ampORphase;	temp_data.quadrature = ampORphase;
...	...	function data = READhomotrace(path,samplingrate)
106	106	ampORphase = 'Phase';	ampORphase = 'Phase';
107	107	temp_data.quadrature = ampORphase;	temp_data.quadrature = ampORphase;
108	108	end	end
109
	109
110	110	temp_data.samplingrate = samplingrate;	temp_data.samplingrate = samplingrate;
111	111	data = temp_data;	data = temp_data;
112	112	end	end

File PXItraces/Felix/check_range.m changed (mode: 100644) (index c4971bd..7945305)
...	...	function [data, chunk_number, error_chunks] = check_range(obj,inv_scalar)
4	4	chunk_size = 1e+6;	chunk_size = 1e+6;
5	5	chunk_number = floor(obj.sample_no/(chunk_size*inv_scalar));	chunk_number = floor(obj.sample_no/(chunk_size*inv_scalar));
6	6	%chunk_number = 1e+3;	%chunk_number = 1e+3;
7
8
	7
	8
9	9	precision = obj.precision;	precision = obj.precision;
10
	10
11	11	minrange = intmin(precision);	minrange = intmin(precision);
12	12	maxrange = intmax(precision);	maxrange = intmax(precision);
13
14
	13
	14
15	15	temp_valids = zeros(chunk_number,1);	temp_valids = zeros(chunk_number,1);
16	16	%temp_data = zeros(chunk_size,chunk_number);	%temp_data = zeros(chunk_size,chunk_number);
17	17	%avgmax = zeros(1,chunk_number);	%avgmax = zeros(1,chunk_number);
18	18	%avgmin = zeros(1,chunk_number);	%avgmin = zeros(1,chunk_number);
19
	19
20	20	k = 1;	k = 1;
21	21	for i = 1:chunk_number	for i = 1:chunk_number
22	22	s1 = (i-1) * chunk_size + 1;	s1 = (i-1) * chunk_size + 1;
23
	23
24	24	obj.fopen;	obj.fopen;
25	25	obj.fseek(s1);	obj.fseek(s1);
26	26	tmp = fread_raw(obj,chunk_size);	tmp = fread_raw(obj,chunk_size);
27	27	obj.fclose;	obj.fclose;
28
	28
29	29	%do calculations with tmp	%do calculations with tmp
30
	30
31	31
32	32	valid = true;	valid = true;
33
	33
34	34	if max(tmp) > maxrange || min(tmp) < minrange	if max(tmp) > maxrange || min(tmp) < minrange
35	35	valid = false;	valid = false;
36	36	end	end
37
38
	37
	38
39	39	temp_valids(i) = valid;	temp_valids(i) = valid;
40
	40
41	41	if not(valid)	if not(valid)
42	42	disp(['chunk number ' num2str(i) ' out of range']);	disp(['chunk number ' num2str(i) ' out of range']);
43	43	error_chunks(k) = tmp;	error_chunks(k) = tmp;
44	44	k = k+1;	k = k+1;
45	45	end	end
46
47
48
49
	46
	47
	48
	49
50	50	%temp_data(:,i) = tmp;	%temp_data(:,i) = tmp;
51	51	end	end
52
53
54
	52
	53
	54
55	55	data = temp_valids;	data = temp_valids;
56	56	end	end

File PXItraces/Felix/fread_raw.m changed (mode: 100644) (index d239c7e..a7b0713)
...	...	function data = fread_raw(obj, size, varargin)
6	6	%to fread after the precision-argument, ie varargin{1}=SKIP,	%to fread after the precision-argument, ie varargin{1}=SKIP,
7	7	%varargin{2}=MACHINEFORMAT etc	%varargin{2}=MACHINEFORMAT etc
8	8	rawdata = fread(obj.fileID, size, obj.precision,varargin{:});	rawdata = fread(obj.fileID, size, obj.precision,varargin{:});
9
	9
10	10
11	11	data = rawdata;	data = rawdata;
12	12	end	end

File PXItraces/Felix/new_fft.m changed (mode: 100644) (index a059a4f..d569812)
...	...	function fig = new_fft(obj,time)
23	23	plot(f,mx);	plot(f,mx);
24	24	title('Power Spectrum of the Vector');	title('Power Spectrum of the Vector');
25	25	xlabel('Frequency (Hz)');	xlabel('Frequency (Hz)');
26		ylabel('Power');
	26		ylabel('Power');
27	27	end	end

File PXItraces/Felix/sanity_check_short.m changed (mode: 100644) (index e112cdf..6b90577)
...	...	function data = sanity_check_short(obj,inv_scalar)
4	4
5	5	%inv_scalar details how much of the file should be read	%inv_scalar details how much of the file should be read
6	6	%1 is everything, 2 is half of the file, 100 is 1 percent etc...	%1 is everything, 2 is half of the file, 100 is 1 percent etc...
7
	7
8	8	[out, chunk_number] = check_range(obj,inv_scalar);	[out, chunk_number] = check_range(obj,inv_scalar);
9
	9
10	10	%add function to check for lock problems	%add function to check for lock problems
11
	11
12	12	if out == ones(chunk_number,1)	if out == ones(chunk_number,1)
13	13	disp('no range errors detected');	disp('no range errors detected');
14	14	data = true;	data = true;
15	15	else	else
16		data = false;
	16		data = false;
17	17	end	end
18
19
20		end
21	18
	19
	20		end

File PXItraces/Felix/show_fourier_transform.m changed (mode: 100644) (index 92fe781..261f0b8)
1	1	function fig = show_fourier_transform(obj)	function fig = show_fourier_transform(obj)
2
	2
3	3	Fs = obj.samplingrate;	Fs = obj.samplingrate;
4	4	T = 1/Fs;	T = 1/Fs;
5
	5
6	6
7	7	obj.fopen;	obj.fopen;
8	8	X = obj.fread(Fs);	X = obj.fread(Fs);
9	9	obj.fclose;	obj.fclose;
10
	10
11	11	L = length(X);	L = length(X);
12		n = pow2(nextpow2(L));
13
	12		n = pow2(nextpow2(L));
	13
14	14	Y = fft(X(1:L),n);	Y = fft(X(1:L),n);
15	15	t = (0:T:(L-1)*T);	t = (0:T:(L-1)*T);
16	16	f = (0:n-1)*(Fs/n);	f = (0:n-1)*(Fs/n);
17	17	power = Y.*conj(Y)/n;	power = Y.*conj(Y)/n;
18
19
20
	18
	19
	20
21	21	fig = gcf;	fig = gcf;
22	22	%plot(t,X(1:L));	%plot(t,X(1:L));
23	23	plot(f,power);	plot(f,power);
24
25
26
	24
	25
	26
27	27	end	end

File PXItraces/Felix/show_integral.m changed (mode: 100644) (index f74c7c5..1198335)
1	1	function fig = show_integral(obj)	function fig = show_integral(obj)
2
	2
3	3	obj.fopen;	obj.fopen;
4	4	temp_data = obj.fread(1e+4);	temp_data = obj.fread(1e+4);
5	5	obj.fclose;	obj.fclose;
6
	6
7	7	Fs = obj.samplingrate;	Fs = obj.samplingrate;
8	8	T = 1/Fs;	T = 1/Fs;
9	9	L = length(temp_data);	L = length(temp_data);
10
	10
11	11	temp_data_size = temp_data(1);	temp_data_size = temp_data(1);
12
	12
13	13	for i = 1:L-1	for i = 1:L-1
14	14	data(i) = int(temp_data(i:i+1));	data(i) = int(temp_data(i:i+1));
15	15	end	end
16
	16
17	17	t = (0:T:(L-2)*T);	t = (0:T:(L-2)*T);
18	18	fig = gcf;	fig = gcf;
19	19	plot(t,data);	plot(t,data);
20
	20
21	21	end	end

File PXItraces/Felix/show_pwelch.m changed (mode: 100644) (index 7f9cdc9..53598f8)
1	1	function fig = show_pwelch(obj,time)	function fig = show_pwelch(obj,time)
2	2	Fs = obj.samplingrate; % Sampling frequency	Fs = obj.samplingrate; % Sampling frequency
3
	3
4	4	obj.fopen;	obj.fopen;
5	5	x = obj.fread(Fs*time);	x = obj.fread(Fs*time);
6	6	obj.fclose;	obj.fclose;
7
	7
8	8	[pxx,f] = pwelch(x,[],[],[],Fs);	[pxx,f] = pwelch(x,[],[],[],Fs);
9	9	%pmax = pwelch(x,[],[],[],Fs,'maxhold');	%pmax = pwelch(x,[],[],[],Fs,'maxhold');
10	10	%pmin = pwelch(x,[],[],[],Fs,'minhold');	%pmin = pwelch(x,[],[],[],Fs,'minhold');
11
	11
12	12	fig = gcf;	fig = gcf;
13	13	plot(f,pow2db(pxx))	plot(f,pow2db(pxx))
14
	14
15	15	end	end

File PXItraces/PXIcalibration.m changed (mode: 100644) (index 3fc88b9..f2f0d65)
...	...	for k=1:2
57	57	%mytrace.fseek(randi(mytrace.sample_no));	%mytrace.fseek(randi(mytrace.sample_no));
58	58	data = mytrace.fread(sample_no)';	data = mytrace.fread(sample_no)';
59	59	mytrace.fclose();	mytrace.fclose();
60
	60
61	61
62	62	% fit with fixed frequency cosine	% fit with fixed frequency cosine
63	63	% x(1) = amp, x(2) = offset, x(3) = shift	% x(1) = amp, x(2) = offset, x(3) = shift
...	...	for k=1:2
69	69
70	70
71	71	fit_values = lsqcurvefit(cosfit,guess,timevec,data,lbound,ubound);	fit_values = lsqcurvefit(cosfit,guess,timevec,data,lbound,ubound);
72
	72
73	73	figure(fig_orig);	figure(fig_orig);
74	74	plot(timevec, data);	plot(timevec, data);
75	75	plot(timevec, cosfit(fit_values, timevec));	plot(timevec, cosfit(fit_values, timevec));
76
	76
77	77	gain = fit_values(1);	gain = fit_values(1);
78	78	offset = fit_values(2);	offset = fit_values(2);
79
	79
80	80	figure(fig_cali);	figure(fig_cali);
81	81	plot(timevec, (data-offset)/gain)	plot(timevec, (data-offset)/gain)
82	82	end	end
83
84
85
	83
	84
	85
86	86	%% define meas and fit traces	%% define meas and fit traces
87	87	sample_no = 1e4;	sample_no = 1e4;
88	88

File PXItraces/SanityChecks/sanity_check_header_v2.m changed (mode: 100644) (index d8bea7e..7187a63)
...	...	fprintf('\nOptomechanics:\n');
71	71	if isempty(del_OM)	if isempty(del_OM)
72	72	fprintf('All data is usable!!!\n');	fprintf('All data is usable!!!\n');
73	73	else	else
74		fprintf('%.2f%% of data not usable!!!\n',100*length(del_OM)*read_nr/testobj.sample_no);
	74		fprintf('%.2f%% of data not usable!!!\n',100*length(del_OM)*read_nr/testobj.sample_no);
75	75	end	end
76	76
77	77	%% Output textfile	%% Output textfile

File PXItraces/Tobias/sanity_check_header.m changed (mode: 100644) (index 722d3b3..425e9a2)
...	...	toc;
73	73	% 'samplingrate', 80E6,...	% 'samplingrate', 80E6,...
74	74	% 'DAQrange', 0.2,...	% 'DAQrange', 0.2,...
75	75	% 'amplification', sqrt(1E3));	% 'amplification', sqrt(1E3));
76		%
	76		%
77	77	% % cyc_end = 1+floor((testobj.sample_no)/(read_nr));	% % cyc_end = 1+floor((testobj.sample_no)/(read_nr));
78	78	% fprintf('\nOptomechanics:\n');	% fprintf('\nOptomechanics:\n');
79	79	% tic;	% tic;
...	...	fprintf('\nOptomechanics:\n');
100	100	if isempty(del_OM)	if isempty(del_OM)
101	101	fprintf('All data is usable!!!\n');	fprintf('All data is usable!!!\n');
102	102	else	else
103		fprintf('%.2f%% data not usable!!!\n',100*length(del_OM)*read_nr/testobj.sample_no);
	103		fprintf('%.2f%% data not usable!!!\n',100*length(del_OM)*read_nr/testobj.sample_no);
104	104	end	end
105	105
106	106	%% Output textfile	%% Output textfile
...	...	plot(x,upper_bound_sigma_OM,'color',[1,0,0]);
153	153	hold on;	hold on;
154	154	plot(x,lower_bound_sigma_OM,'color',[1,0,0]);	plot(x,lower_bound_sigma_OM,'color',[1,0,0]);
155	155
156		%% Sample length variance test
	156		%% Sample length variance test
157	157	% figure();	% figure();
158	158	% for j=1:30	% for j=1:30
159	159	% clear x && vd;	% clear x && vd;
...	...	plot(x,lower_bound_sigma_OM,'color',[1,0,0]);
208	208	% filepath = fullfile(basedir, 'testdata_mod_100mV_offset_+100mV_CH0_Range0200mV_Amplification0dB.dat');	% filepath = fullfile(basedir, 'testdata_mod_100mV_offset_+100mV_CH0_Range0200mV_Amplification0dB.dat');
209	209	% testobj = timetrace(filepath, 'samplingrate', 80E6,...	% testobj = timetrace(filepath, 'samplingrate', 80E6,...
210	210	% 'DAQrange', 0.2, 'amplification', sqrt(1E0));	% 'DAQrange', 0.2, 'amplification', sqrt(1E0));
211		%
	211		%
212	212	% [del_DN,median_variance_DN,upper_bound_sigma_DN,lower_bound_sigma_DN,variance_data_DN] = sanity_check_darknoise(testobj,cyc_end,read_nr);	% [del_DN,median_variance_DN,upper_bound_sigma_DN,lower_bound_sigma_DN,variance_data_DN] = sanity_check_darknoise(testobj,cyc_end,read_nr);
213	213	% [del_SN,median_variance_SN,upper_bound_sigma_SN,lower_bound_sigma_SN,variance_data_SN] = sanity_check_shotnoise(testobj,upper_bound_sigma_DN,cyc_end,read_nr);	% [del_SN,median_variance_SN,upper_bound_sigma_SN,lower_bound_sigma_SN,variance_data_SN] = sanity_check_shotnoise(testobj,upper_bound_sigma_DN,cyc_end,read_nr);
214	214	% sanity_check_mechanical(testobj,upper_bound_sigma_DN,upper_bound_sigma_SN,cyc_end,read_nr);	% sanity_check_mechanical(testobj,upper_bound_sigma_DN,upper_bound_sigma_SN,cyc_end,read_nr);

File PXItraces/Tobias/sanity_check_header_v2.m changed (mode: 100644) (index f4790b8..5224ead)
...	...	fprintf('\nOptomechanics:\n');
71	71	if isempty(del_OM)	if isempty(del_OM)
72	72	fprintf('All data is usable!!!\n');	fprintf('All data is usable!!!\n');
73	73	else	else
74		fprintf('%.2f%% of data not usable!!!\n',100*length(del_OM)*read_nr/testobj.sample_no);
	74		fprintf('%.2f%% of data not usable!!!\n',100*length(del_OM)*read_nr/testobj.sample_no);
75	75	end	end
76	76
77	77	%% Output textfile	%% Output textfile
78	78	% if ~isempty(del_DN)	% if ~isempty(del_DN)
79	79	% dlmwrite('C:\Users\aspel\Desktop\Test\DN.txt',del_DN);	% dlmwrite('C:\Users\aspel\Desktop\Test\DN.txt',del_DN);
80	80	% end	% end
81		%
	81		%
82	82	% if ~isempty(del_SN)	% if ~isempty(del_SN)
83	83	% dlmwrite('C:\Users\aspel\Desktop\Test\SN.txt',del_SN);	% dlmwrite('C:\Users\aspel\Desktop\Test\SN.txt',del_SN);
84	84	% end	% end
85		%
	85		%
86	86	% if ~isempty(del_OM)	% if ~isempty(del_OM)
87	87	% dlmwrite('C:\Users\aspel\Desktop\Test\OM.txt',del_OM);	% dlmwrite('C:\Users\aspel\Desktop\Test\OM.txt',del_OM);
88	88	% end	% end

File data_analysis/@entanglement_evaluation/calculateModeVectors.m changed (mode: 100644) (index 5c2e565..4492231)
...	...	function obj = calculateModeVectors(obj, varargin)
18	18
19	19	if ~dummy_mode	if ~dummy_mode
20	20	assert(obj.samplingrate > 0,...	assert(obj.samplingrate > 0,...
21		'Sampling rate must be positive.')
	21		'Sampling rate must be positive.')
22	22	assert(~isempty(obj.opt_pulse_widths),...	assert(~isempty(obj.opt_pulse_widths),...
23	23	'opt_pulse_widths must be provided or calculated using getOptPulseWidths');	'opt_pulse_widths must be provided or calculated using getOptPulseWidths');
24	24	end	end
25
	25
26	26	obj.readout_modes = [];	obj.readout_modes = [];
27	27	obj.entangling_modes = [];	obj.entangling_modes = [];
28	28
...	...	function obj = calculateModeVectors(obj, varargin)
59	59	[readout_mode_ON, R] = qr(readout_mode,0);	[readout_mode_ON, R] = qr(readout_mode,0);
60	60	readout_mode_ON=readout_mode_ON(:,1:obj.mech_frequencies_no);	readout_mode_ON=readout_mode_ON(:,1:obj.mech_frequencies_no);
61	61	entangling_mode_ON = flipud(readout_mode_ON);	entangling_mode_ON = flipud(readout_mode_ON);
62
	62
63	63	% test orthonormalization	% test orthonormalization
64		% (the following tolerances were found to yield reasonable constraints
	64		% (the following tolerances were found to yield reasonable constraints
65	65	% in a 47-mode evaluation of experimental data)	% in a 47-mode evaluation of experimental data)
66	66	norm_tol = 1E-12;	norm_tol = 1E-12;
67	67	orthog_tol = 1E-8;	orthog_tol = 1E-8;
68
	68
69	69	[orthonorm, orthog, normal, info] = orthonormality(readout_mode_ON',...	[orthonorm, orthog, normal, info] = orthonormality(readout_mode_ON',...
70	70	norm_tol, orthog_tol);	norm_tol, orthog_tol);
71
	71
72	72	if ~orthonorm	if ~orthonorm
73	73	message=sprintf('\nlight qr failed: launching hard qr...\n');	message=sprintf('\nlight qr failed: launching hard qr...\n');
74	74	disp(message)	disp(message)
...	...	function obj = calculateModeVectors(obj, varargin)
80	80	entangling_mode_ON = flipud(readout_mode_ON);	entangling_mode_ON = flipud(readout_mode_ON);
81	81	toc	toc
82	82	end	end
83
	83
84	84	assert_orthonormality(readout_mode_ON', norm_tol, orthog_tol);	assert_orthonormality(readout_mode_ON', norm_tol, orthog_tol);
85
	85
86	86	obj.readout_modes = [obj.readout_modes;...	obj.readout_modes = [obj.readout_modes;...
87	87	transpose(readout_mode_ON)];	transpose(readout_mode_ON)];
88	88	obj.entangling_modes = [obj.entangling_modes;...	obj.entangling_modes = [obj.entangling_modes;...

File data_analysis/@entanglement_evaluation/calculatePulsedCMs.m changed (mode: 100644) (index a112a62..a0a0fe0)
...	...	function obj = calculatePulsedCMs(obj, varargin)
4	4	for pow_ind=1:obj.tracepairs_no	for pow_ind=1:obj.tracepairs_no
5	5	obj = calculatePulsedCMsSingle(obj, pow_ind);	obj = calculatePulsedCMsSingle(obj, pow_ind);
6	6	end	end
7		case 2
	7		case 2
8	8	pow_ind = varargin{1};	pow_ind = varargin{1};
9	9	obj = calculatePulsedCMsSingle(obj, pow_ind);	obj = calculatePulsedCMsSingle(obj, pow_ind);
10	10	otherwise	otherwise
11	11	error('Too many varargin-arguments.')	error('Too many varargin-arguments.')
12		end
	12		end
13	13	end	end
14	14
15	15	function obj = calculatePulsedCMsSingle(obj, pow_ind)	function obj = calculatePulsedCMsSingle(obj, pow_ind)

File data_analysis/@entanglement_evaluation/calculatePulsedQuadratures.m changed (mode: 100644) (index 8b1cf78..5d56627)
...	...	function obj = calculatePulsedQuadratures(obj, tracepair_ind, varargin)
22	22	P = zeros(pulsepairs_no, modes_no);	P = zeros(pulsepairs_no, modes_no);
23	23	X = zeros(pulsepairs_no, modes_no);	X = zeros(pulsepairs_no, modes_no);
24	24	Y = zeros(pulsepairs_no, modes_no);	Y = zeros(pulsepairs_no, modes_no);
25
	25
26	26	[raw_data, close_raw_data] = get_raw_data_fct(obj, tracepair_ind);	[raw_data, close_raw_data] = get_raw_data_fct(obj, tracepair_ind);
27	27
28	28	print_eta = estimated_time_of_arrival;	print_eta = estimated_time_of_arrival;
29	29	percent_done=0;	percent_done=0;
30
	30
31	31	for pp=1:pulsepairs_no	for pp=1:pulsepairs_no
32	32	[q,p,x,y] = getQuadsForNextPulse(obj, raw_data);	[q,p,x,y] = getQuadsForNextPulse(obj, raw_data);
33	33	Q(pp,:) = q; P(pp,:) = p; X(pp,:) = x; Y(pp,:) = y;	Q(pp,:) = q; P(pp,:) = p; X(pp,:) = x; Y(pp,:) = y;
34
	34
35	35	if pp==20, print_eta(pp, pulsepairs_no);	if pp==20, print_eta(pp, pulsepairs_no);
36	36	elseif floor(pp/pulsepairs_no*100)>(percent_done+5)	elseif floor(pp/pulsepairs_no*100)>(percent_done+5)
37	37	percent_done = percent_done + 5	percent_done = percent_done + 5
...	...	function obj = calculatePulsedQuadratures(obj, tracepair_ind, varargin)
40	40
41	41	close_raw_data();	close_raw_data();
42	42
43
	43
44	44	% reorder into pulse_trace{tracepair_ind}{gam_ind}	% reorder into pulse_trace{tracepair_ind}{gam_ind}
45	45	for g=1:obj.pulse_gammas_no	for g=1:obj.pulse_gammas_no
46	46	%reorder into Q1, Q2,.., P1, P2,.., X1, X2,.., Y1, Y2,...	%reorder into Q1, Q2,.., P1, P2,.., X1, X2,.., Y1, Y2,...
47	47	% for each pulse_gamma	% for each pulse_gamma
48	48	first_mode_ind = (g-1)*obj.mech_frequencies_no + 1;	first_mode_ind = (g-1)*obj.mech_frequencies_no + 1;
49	49	last_mode_ind = g*obj.mech_frequencies_no;	last_mode_ind = g*obj.mech_frequencies_no;
50
	50
51	51	q = Q(:, first_mode_ind:last_mode_ind);	q = Q(:, first_mode_ind:last_mode_ind);
52	52	p = P(:, first_mode_ind:last_mode_ind);	p = P(:, first_mode_ind:last_mode_ind);
53	53	x = X(:, first_mode_ind:last_mode_ind);	x = X(:, first_mode_ind:last_mode_ind);
54	54	y = Y(:, first_mode_ind:last_mode_ind);	y = Y(:, first_mode_ind:last_mode_ind);
55
	55
56	56	% reorder into Q1, P1, Q2, P2,..., X1, Y1, X2, Y2,...	% reorder into Q1, P1, Q2, P2,..., X1, Y1, X2, Y2,...
57	57	pulse_trace = permute_data([q, p, x, y]);	pulse_trace = permute_data([q, p, x, y]);
58	58	obj.pulse_trace{tracepair_ind}{g} = pulse_trace;	obj.pulse_trace{tracepair_ind}{g} = pulse_trace;
...	...	end
63	63	function [q,p,x,y] = getQuadsForNextPulse(obj, raw_data_fct)	function [q,p,x,y] = getQuadsForNextPulse(obj, raw_data_fct)
64	64	entangling_data = raw_data_fct();	entangling_data = raw_data_fct();
65	65	readout_data = raw_data_fct();	readout_data = raw_data_fct();
66
	66
67	67	if obj.inverted_evaluation	if obj.inverted_evaluation
68	68	entangling_data = conj(entangling_data);	entangling_data = conj(entangling_data);
69	69	readout_data = conj(readout_data);	readout_data = conj(readout_data);
70	70	end	end
71
	71
72	72	entangling = obj.entangling_modes*entangling_data;	entangling = obj.entangling_modes*entangling_data;
73	73	readout = obj.readout_modes*readout_data;	readout = obj.readout_modes*readout_data;
74
	74
75	75	q = real(entangling); p = imag(entangling);	q = real(entangling); p = imag(entangling);
76	76	x = real(readout); y = imag(readout);	x = real(readout); y = imag(readout);
77	77	end	end
...	...	function [get_raw_data, close_raw_data] = get_raw_data_fct(obj, tracepair_ind)
99	99	end	end
100	100	end	end
101	101	get_raw_data = @fcthandle_get;	get_raw_data = @fcthandle_get;
102
	102
103	103	function fcthandle_close()	function fcthandle_close()
104	104	ampli_tr.fclose;	ampli_tr.fclose;
105	105	phase_tr.fclose;	phase_tr.fclose;

File data_analysis/@entanglement_evaluation/calculate_log_neg.m changed (mode: 100644) (index 8d8da18..44a32f8)
...	...	function calculate_log_neg(obj, shotnoise_ind)
14	14	end	end
15	15
16	16	% after (re)calculation of log.neg.	% after (re)calculation of log.neg.
17		% wipe clean any potential previous calculation of optimal log.neg. or pulse width
	17		% wipe clean any potential previous calculation of optimal log.neg. or pulse width
18	18	opt_logneg_corrected_private = [];	opt_logneg_corrected_private = [];
19	19	opt_pulse_gammas_private = [];	opt_pulse_gammas_private = [];

File data_analysis/@entanglement_evaluation/calculate_mode_vectors_gammalist.m changed (mode: 100644) (index 8db3411..8478e7b)
...	...	function ev = calculate_mode_vectors_gammalist(ev)
41	41	[readout_mode_ON, R] = qr(readout_mode,0);	[readout_mode_ON, R] = qr(readout_mode,0);
42	42	readout_mode_ON=readout_mode_ON(:,1:ev.mech_frequencies_no);	readout_mode_ON=readout_mode_ON(:,1:ev.mech_frequencies_no);
43	43	entangling_mode_ON = flipud(readout_mode_ON);	entangling_mode_ON = flipud(readout_mode_ON);
44
	44
45	45	% test orthonormalization	% test orthonormalization
46		% (the following tolerances were found to yield reasonable constraints
	46		% (the following tolerances were found to yield reasonable constraints
47	47	% in a 47-mode evaluation of experimental data)	% in a 47-mode evaluation of experimental data)
48	48	norm_tol = 1E-12;	norm_tol = 1E-12;
49	49	orthog_tol = 1E-8;	orthog_tol = 1E-8;
50
	50
51	51	[orthonorm, orthog, normal, info] = orthonormality(readout_mode_ON',...	[orthonorm, orthog, normal, info] = orthonormality(readout_mode_ON',...
52	52	norm_tol, orthog_tol);	norm_tol, orthog_tol);
53
	53
54	54	if ~orthonorm	if ~orthonorm
55	55	message=sprintf('\nlight qr failed: launching hard qr...\n');	message=sprintf('\nlight qr failed: launching hard qr...\n');
56	56	disp(message)	disp(message)
...	...	function ev = calculate_mode_vectors_gammalist(ev)
58	58	readout_mode_ON=readout_mode_ON(:,1:ev.mech_frequencies_no);	readout_mode_ON=readout_mode_ON(:,1:ev.mech_frequencies_no);
59	59	entangling_mode_ON = flipud(readout_mode_ON);	entangling_mode_ON = flipud(readout_mode_ON);
60	60	end	end
61
	61
62	62	assert_orthonormality(readout_mode_ON', norm_tol, orthog_tol);	assert_orthonormality(readout_mode_ON', norm_tol, orthog_tol);
63	63
64	64	ev.readout_modes = [ev.readout_modes;...	ev.readout_modes = [ev.readout_modes;...

File data_analysis/@entanglement_evaluation/calibratePulsedCMs.m changed (mode: 100644) (index b71e416..46c8c79)
1	1	function obj = calibratePulsedCMs(obj, varargin)	function obj = calibratePulsedCMs(obj, varargin)
2	2	shotnoise_ind = get_shotnoise_ind(obj, varargin{:});	shotnoise_ind = get_shotnoise_ind(obj, varargin{:});
3
	3
4	4	if obj.homodyne_visibility==1 && obj.detection_efficiency==1	if obj.homodyne_visibility==1 && obj.detection_efficiency==1
5	5	pulse_CM_corrected = obj.pulse_CM;	pulse_CM_corrected = obj.pulse_CM;
6	6	else	else
7	7	pulse_CM_corrected = correct_for_detection_imperfections(obj, shotnoise_ind);	pulse_CM_corrected = correct_for_detection_imperfections(obj, shotnoise_ind);
8	8	end	end
9	9	calibrate_to_shotnoise(obj, pulse_CM_corrected, shotnoise_ind);	calibrate_to_shotnoise(obj, pulse_CM_corrected, shotnoise_ind);
10
	10
11	11	calculate_log_neg(obj, shotnoise_ind);	calculate_log_neg(obj, shotnoise_ind);
12	12	calculate_physicality(obj);	calculate_physicality(obj);
13	13
...	...	function pulse_CM_corr = correct_for_detection_imperfections(obj, shotnoise_ind)
19	19	cm_shot_measured = obj.pulse_CM{shotnoise_ind}{g};	cm_shot_measured = obj.pulse_CM{shotnoise_ind}{g};
20	20	pulse_CM_corr{shotnoise_ind}{g} = cm_shot_measured/eta;	pulse_CM_corr{shotnoise_ind}{g} = cm_shot_measured/eta;
21	21	for signal_ind=1:obj.tracepairs_no	for signal_ind=1:obj.tracepairs_no
22		if signal_ind==shotnoise_ind, continue
	22		if signal_ind==shotnoise_ind, continue
23	23	else	else
24	24	cm_signal_measured = obj.pulse_CM{signal_ind}{g};	cm_signal_measured = obj.pulse_CM{signal_ind}{g};
25	25	pulse_CM_corr{signal_ind}{g} = (cm_signal_measured - (1 - (eta*(nu^2)))*cm_shot_measured)/((eta*nu)^2);	pulse_CM_corr{signal_ind}{g} = (cm_signal_measured - (1 - (eta*(nu^2)))*cm_shot_measured)/((eta*nu)^2);

File data_analysis/@entanglement_evaluation/calulate_optimal_entanglement.m changed (mode: 100644) (index 80da6a6..5bd0899)
1	1	function ev = calulate_optimal_entanglement(ev)	function ev = calulate_optimal_entanglement(ev)
2		% resample pulse_gammas to yield better resolution in pulse-width-space
3		resampling_factor = 100;
4		pulse_gammas_refined = interp(ev.pulse_gammas, resampling_factor);
5		pulse_gammas_refined = pulse_gammas_refined(...
6		pulse_gammas_refined<max(ev.pulse_gammas));
7		pulse_gammas_refined = [pulse_gammas_refined, max(ev.pulse_gammas)];
	2		% resample pulse_gammas to yield better resolution in pulse-width-space
	3		resampling_factor = 100;
	4		pulse_gammas_refined = interp(ev.pulse_gammas, resampling_factor);
	5		pulse_gammas_refined = pulse_gammas_refined(...
	6		pulse_gammas_refined<max(ev.pulse_gammas));
	7		pulse_gammas_refined = [pulse_gammas_refined, max(ev.pulse_gammas)];
8	8
9		K = length(ev.logneg_corrected);
10		opt_logneg_corrected_private = zeros(K,1);
11		opt_pulse_gammas_private = zeros(K,1);
12		for k=1:K
13		log_neg = [ev.logneg_corrected{k}{:}];
14
15		% interpolate evaluated pulse widths and log.neg.values
16		logneg_corrected_refined = interp1(ev.pulse_gammas, log_neg,...
17		pulse_gammas_refined, 'splines');
18
19		% optimize over interpolated values
20		[lnopt, optind] = max(logneg_corrected_refined);
21		opt_logneg_corrected_private(k) = lnopt;
22		opt_pulse_gammas_private(k) = pulse_gammas_refined(optind);
	9		K = length(ev.logneg_corrected);
	10		opt_logneg_corrected_private = zeros(K,1);
	11		opt_pulse_gammas_private = zeros(K,1);
	12		for k=1:K
	13		log_neg = [ev.logneg_corrected{k}{:}];
23	14
24		% save interpolated arrays for inspection
25		ev.pulse_gammas_interpolated{k} = pulse_gammas_refined;
26		ev.logneg_corrected_interpolated{k} = logneg_corrected_refined;
27		end
	15		% interpolate evaluated pulse widths and log.neg.values
	16		logneg_corrected_refined = interp1(ev.pulse_gammas, log_neg,...
	17		pulse_gammas_refined, 'splines');
28	18
29		ev.opt_logneg_corrected_private = opt_logneg_corrected_private;
30		ev.opt_pulse_gammas_private = opt_pulse_gammas_private;
	19		% optimize over interpolated values
	20		[lnopt, optind] = max(logneg_corrected_refined);
	21		opt_logneg_corrected_private(k) = lnopt;
	22		opt_pulse_gammas_private(k) = pulse_gammas_refined(optind);
	23
	24		% save interpolated arrays for inspection
	25		ev.pulse_gammas_interpolated{k} = pulse_gammas_refined;
	26		ev.logneg_corrected_interpolated{k} = logneg_corrected_refined;
	27		end
	28
	29		ev.opt_logneg_corrected_private = opt_logneg_corrected_private;
	30		ev.opt_pulse_gammas_private = opt_pulse_gammas_private;

File data_analysis/@entanglement_evaluation/darknoisesubtractPulsedCMs.m changed (mode: 100644) (index e171620..66cbd74)
...	...	function obj = darknoisesubtractPulsedCMs(obj, varargin)
2	2	darknoise_ind = get_darknoise_ind(obj, varargin{:})	darknoise_ind = get_darknoise_ind(obj, varargin{:})
3	3	for g=1:obj.pulse_gammas_no	for g=1:obj.pulse_gammas_no
4	4	cm_dark = obj.pulse_CM{darknoise_ind}{g};	cm_dark = obj.pulse_CM{darknoise_ind}{g};
5
	5
6	6	for p=1:obj.tracepairs_no	for p=1:obj.tracepairs_no
7	7	cm = obj.pulse_CM{p}{g};	cm = obj.pulse_CM{p}{g};
8	8	obj.pulse_CM{p}{g} = cm - cm_dark;	obj.pulse_CM{p}{g} = cm - cm_dark;
...	...	function darknoise_ind = get_darknoise_ind(obj, varargin)
19	19	error(['Unrecognized varargin-argument ', varargin{k}]);	error(['Unrecognized varargin-argument ', varargin{k}]);
20	20	end	end
21	21	end	end
22
	22
23	23	assert(length(darknoise_ind)==1,...	assert(length(darknoise_ind)==1,...
24	24	'No unique index for the darknoise traces was provided')	'No unique index for the darknoise traces was provided')

File data_analysis/@entanglement_evaluation/entanglement_evaluation.m changed (mode: 100644) (index 4fac9bf..15b2f73)
...	...	classdef entanglement_evaluation < class_conveniences & entanglement_evaluation_
53	53
54	54
55	55	%%% DEPENDENT PROPERTIES AND THEIR GETTERS %%%	%%% DEPENDENT PROPERTIES AND THEIR GETTERS %%%
56		properties(Dependent)
	56		properties(Dependent)
57	57	%% FROM PROPERTIES OF THE EVALUATION	%% FROM PROPERTIES OF THE EVALUATION
58	58	mech_frequencies_no@double scalar	mech_frequencies_no@double scalar
59	59	pulse_gammas_no@double scalar	pulse_gammas_no@double scalar

File data_analysis/@entanglement_evaluation/example_entanglement_evaluation.m changed (mode: 100644) (index 426bbed..867ab5e)
...	...	meas.add_trace_pair(...
14	14	'ts_07142015-185030_200muW_xQ_int16_sf368.000_Nts1.0e+06_MCr1.dat',...	'ts_07142015-185030_200muW_xQ_int16_sf368.000_Nts1.0e+06_MCr1.dat',...
15	15	'ts_07142015-185030_200muW_yQ_int16_sf368.000_Nts1.0e+06_MCr1.dat',...	'ts_07142015-185030_200muW_yQ_int16_sf368.000_Nts1.0e+06_MCr1.dat',...
16	16	'samplingrate', 70, 'amplification', 368, 'signal_power_muW', 200);	'samplingrate', 70, 'amplification', 368, 'signal_power_muW', 200);
17
18
	17
	18
19	19	%% create entanglement_evaluation object	%% create entanglement_evaluation object
20	20	clear('ev')	clear('ev')
21	21	ev = entanglement_evaluation();	ev = entanglement_evaluation();

File data_analysis/@entanglement_evaluation/exportResults.m changed (mode: 100644) (index 7eeadc3..2b94c44)
...	...	function export_data_and_CM(obj)
25	25	filename = ['.short_', filename];	filename = ['.short_', filename];
26	26	dlmwrite(fullfile(obj.evaluation_folder,filename),...	dlmwrite(fullfile(obj.evaluation_folder,filename),...
27	27	data, 'delimiter', '\t','precision',6);	data, 'delimiter', '\t','precision',6);
28
29
	28
	29
30	30	pulsed_CMs_calibrated = obj.pulse_CM_calibrated;	pulsed_CMs_calibrated = obj.pulse_CM_calibrated;
31	31	if ~isempty(pulsed_CMs_calibrated)	if ~isempty(pulsed_CMs_calibrated)
32	32	filename = 'pulsed_CMs_calibrated.mat';	filename = 'pulsed_CMs_calibrated.mat';
33	33	save(fullfile(obj.evaluation_folder, filename), 'pulsed_CMs_calibrated');	save(fullfile(obj.evaluation_folder, filename), 'pulsed_CMs_calibrated');
34	34	end	end
35
	35
36	36	lognegs = obj.logneg_corrected;	lognegs = obj.logneg_corrected;
37	37	if ~isempty(lognegs)	if ~isempty(lognegs)
38	38	filename = 'lognegs.mat';	filename = 'lognegs.mat';
39	39	save(fullfile(obj.evaluation_folder, filename), 'lognegs');	save(fullfile(obj.evaluation_folder, filename), 'lognegs');
40	40	end	end
41
	41
42	42	physicality = obj.pulse_CM_is_physical;	physicality = obj.pulse_CM_is_physical;
43	43	if ~isempty(physicality)	if ~isempty(physicality)
44	44	filename = 'physicality.mat';	filename = 'physicality.mat';

File data_analysis/@entanglement_evaluation/plotLogneg.m changed (mode: 100644) (index aba17ac..14abdf7)
...	...	function fig = plotLogneg(obj, varargin)
48	48	error(['Unrecognized varargin-argument ', varargin{k},'.']);	error(['Unrecognized varargin-argument ', varargin{k},'.']);
49	49	end	end
50	50	end	end
51
	51
52	52	if new_fig	if new_fig
53	53	fig = figure; hold all;	fig = figure; hold all;
54	54	else	else
...	...	function fig = plotLogneg(obj, varargin)
85	85	labels('Pulse width $$\Gamma \, [\mathrm{Hz}]$$',...	labels('Pulse width $$\Gamma \, [\mathrm{Hz}]$$',...
86	86	'Logarithmic negativity $$E_\mathcal{N}$$');	'Logarithmic negativity $$E_\mathcal{N}$$');
87	87	title(tex_escape(titlestr));	title(tex_escape(titlestr));
88
	88
89	89
90	90	% add info regarding physicality to plot	% add info regarding physicality to plot
91	91	if show_physicality	if show_physicality

File data_analysis/@entanglement_evaluation/plot_logneg_gammalist.m changed (mode: 100644) (index f6698e3..37913fc)
1	1	function plot_logneg_gammalist(ev, power_ind, varargin)	function plot_logneg_gammalist(ev, power_ind, varargin)
2		symmetric = false;
3		divergent = false;
4		invert = false;
5		export = true;
6		minvalue = -inf;
7		maxvalue = +inf;
8		titlestr = [];
9		export_suffix ='';
10		for k=1:2:length(varargin)
11		switch varargin{k}
12		case {'symmetric', 'Symmetric'}
13		symmetric = varargin{k+1};
14		case {'invert', 'Invert'}
15		invert = varargin{k+1};
16		case {'divergent', 'Divergent'}
17		divergent = varargin{k+1};
18		case {'minvalue', 'min'}
19		minvalue = varargin{k+1};
20		case {'maxvalue', 'max'}
21		maxvalue = varargin{k+1};
22		case {'title', 'Title'}
23		titlestr = varargin{k+1};
24		case {'export', 'Export', 'save', 'Save'}
25		export = varargin{k+1};
26		case {'export_suffix', 'exportsuffix', 'suffix', 'Suffix'}
27		export_suffix = ['.',varargin{k+1}];
28		otherwise
29		error(['Unrecognized varargin-argument ', varargin{k}])
30		end
31		end
	2		symmetric = false;
	3		divergent = false;
	4		invert = false;
	5		export = true;
	6		minvalue = -inf;
	7		maxvalue = +inf;
	8		titlestr = [];
	9		export_suffix ='';
	10		for k=1:2:length(varargin)
	11		switch varargin{k}
	12		case {'symmetric', 'Symmetric'}
	13		symmetric = varargin{k+1};
	14		case {'invert', 'Invert'}
	15		invert = varargin{k+1};
	16		case {'divergent', 'Divergent'}
	17		divergent = varargin{k+1};
	18		case {'minvalue', 'min'}
	19		minvalue = varargin{k+1};
	20		case {'maxvalue', 'max'}
	21		maxvalue = varargin{k+1};
	22		case {'title', 'Title'}
	23		titlestr = varargin{k+1};
	24		case {'export', 'Export', 'save', 'Save'}
	25		export = varargin{k+1};
	26		case {'export_suffix', 'exportsuffix', 'suffix', 'Suffix'}
	27		export_suffix = ['.',varargin{k+1}];
	28		otherwise
	29		error(['Unrecognized varargin-argument ', varargin{k}])
	30		end
	31		end
	32
	33		pulse_widths_factorized = allcomb_invert(ev.pulse_widths);
	34
	35		% right now, we only support plotting mode 1 on x and mode 2 on y
	36		x_mode_no = 1;
	37		y_mode_no = 2;
	38
	39		% get axis labels
	40		x_labels = pulse_widths_factorized{x_mode_no};
	41		x_labels = cellfun(@num2eng, num2cell(x_labels(:)), 'UniformOutput', false);
32	42
33		pulse_widths_factorized = allcomb_invert(ev.pulse_widths);
	43		y_labels = pulse_widths_factorized{y_mode_no};
	44		y_labels = flipud(y_labels);
	45		y_labels = cellfun(@num2eng, num2cell(y_labels(:)), 'UniformOutput', false);
34	46
35		% right now, we only support plotting mode 1 on x and mode 2 on y
36		x_mode_no = 1;
37		y_mode_no = 2;
	47		% reshape data to be consistent with axis labelling
	48		data = reshape(cell2mat(ev.logneg_corrected{power_ind})',...
	49		length(x_labels), length(y_labels));
	50		data = flipud(data);
38	51
39		% get axis labels
40		x_labels = pulse_widths_factorized{x_mode_no};
41		x_labels = cellfun(@num2eng, num2cell(x_labels(:)), 'UniformOutput', false);
42
43		y_labels = pulse_widths_factorized{y_mode_no};
44		y_labels = flipud(y_labels);
45		y_labels = cellfun(@num2eng, num2cell(y_labels(:)), 'UniformOutput', false);
46
47		% reshape data to be consistent with axis labelling
48		data = reshape(cell2mat(ev.logneg_corrected{power_ind})',...
49		length(x_labels), length(y_labels));
50		data = flipud(data);
	52		% calculate parameters
	53		ncolors = 1024;
	54		data_max = max(max(data));
	55		data_min = min(min(data));
51	56
52		% calculate parameters
53		ncolors = 1024;
54		data_max = max(max(data));
55		data_min = min(min(data));
56
57		if symmetric
58		cmap = cbrewer('div', 'RdBu', ncolors); %Divergent tables:'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn'
	57		if symmetric
	58		cmap = cbrewer('div', 'RdBu', ncolors); %Divergent tables:'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn'
	59
	60		extremum = max([abs(data_min), data_max]);
	61		if minvalue==-inf
	62		minvalue = -extremum;
	63		end
	64		maxvalue = -minvalue;
	65		else
	66		if minvalue==-inf
	67		minvalue = data_min;
	68		end
	69		if maxvalue==+inf
	70		maxvalue = data_max;
	71		end
	72		cmap = cbrewer('seq', 'Blues', ncolors); % Sequential tables:'Blues','BuGn','BuPu','GnBu','Greens','Greys','Oranges','OrRd','PuBu','PuBuGn','PuRd','Purples','RdPu','Reds','YlGn','YlGnBu','YlOrBr','YlOrRd','Spectral'
	73		end
	74		if divergent
	75		datarange = maxvalue-minvalue;
	76		maxcolno = round(ncolors*abs(maxvalue)/datarange);
	77		mincolno = round(ncolors*abs(minvalue)/datarange);
	78		maxmap = cbrewer('seq', 'Reds', maxcolno);
	79		minmap = flipud(cbrewer('seq', 'Blues', mincolno));
	80		cmap = [minmap; maxmap];
	81		end
59	82
60		extremum = max([abs(data_min), data_max]);
61		if minvalue==-inf
62		minvalue = -extremum;
63		end
64		maxvalue = -minvalue;
65		else
66		if minvalue==-inf
67		minvalue = data_min;
68		end
69		if maxvalue==+inf
70		maxvalue = data_max;
71		end
72		cmap = cbrewer('seq', 'Blues', ncolors); % Sequential tables:'Blues','BuGn','BuPu','GnBu','Greens','Greys','Oranges','OrRd','PuBu','PuBuGn','PuRd','Purples','RdPu','Reds','YlGn','YlGnBu','YlOrBr','YlOrRd','Spectral'
73		end
74		if divergent
75		datarange = maxvalue-minvalue;
76		maxcolno = round(ncolors*abs(maxvalue)/datarange);
77		mincolno = round(ncolors*abs(minvalue)/datarange);
78		maxmap = cbrewer('seq', 'Reds', maxcolno);
79		minmap = flipud(cbrewer('seq', 'Blues', mincolno));
80		cmap = [minmap; maxmap];
81		end
	83		if invert
	84		cmap = flipud(cmap);
	85		end
82	86
83		if invert
84		cmap = flipud(cmap);
85		end
	87		% plot
	88		heatmap(data, x_labels, y_labels, '%0.3f',...
	89		'Colormap', cmap, 'Colorbar', true,...
	90		'MinColorValue', minvalue, 'MaxColorValue', maxvalue,...
	91		'GridLines', ':', 'TextColor', 'k',...
	92		'ColorLevels', ncolors);
	93		xlabel(['Width [Hz] for mode with central frequency ', num2eng(ev.mech_frequencies(x_mode_no)),'Hz']);
	94		ylabel(['Width [Hz] for mode with central frequency ', num2eng(ev.mech_frequencies(y_mode_no)),'Hz']);
	95		title(titlestr);
86	96
87		% plot
88		heatmap(data, x_labels, y_labels, '%0.3f',...
89		'Colormap', cmap, 'Colorbar', true,...
90		'MinColorValue', minvalue, 'MaxColorValue', maxvalue,...
91		'GridLines', ':', 'TextColor', 'k',...
92		'ColorLevels', ncolors);
93		xlabel(['Width [Hz] for mode with central frequency ', num2eng(ev.mech_frequencies(x_mode_no)),'Hz']);
94		ylabel(['Width [Hz] for mode with central frequency ', num2eng(ev.mech_frequencies(y_mode_no)),'Hz']);
95		title(titlestr);
96
97		% export to ev.evaluation_folder
98		eval_folder_exists = isequal(exist(ev.evaluation_folder),7);
99		if export
100		if eval_folder_exists
101		wherewasi = cd;
102		cd(ev.evaluation_folder);
103		export_fig(['logneg_corrected_', num2str(power_ind), export_suffix],...
104		'-pdf', '-transparent')
105		cd(wherewasi);
106		else
	97		% export to ev.evaluation_folder
	98		eval_folder_exists = isequal(exist(ev.evaluation_folder),7);
	99		if export
	100		if eval_folder_exists
	101		wherewasi = cd;
	102		cd(ev.evaluation_folder);
	103		export_fig(['logneg_corrected_', num2str(power_ind), export_suffix],...
	104		'-pdf', '-transparent')
	105		cd(wherewasi);
	106		else
107	107	warning(['Cannot find the evaluation folder ',...	warning(['Cannot find the evaluation folder ',...
108	108	ev.evaluation_folder])	ev.evaluation_folder])
109	109	end	end
110		end
	110		end

File data_analysis/@entanglement_evaluation/plot_logneg_gammalist_split.m changed (mode: 100644) (index 031b925..f3acacf)
1	1	function fig = plot_logneg_gammalist_split(ev, power_ind, varargin)	function fig = plot_logneg_gammalist_split(ev, power_ind, varargin)
2		invert = false;
3		export = true;
4		titlestr = [];
5		ylabel_str = '';
6		export_suffix ='';
7		minvalue = [];
8		maxvalue = [];
9		for k=1:2:length(varargin)
10		switch varargin{k}
11		case {'invert', 'Invert'}
12		invert = varargin{k+1};
13		case {'title', 'Title'}
14		titlestr = varargin{k+1};
15		case {'export', 'Export', 'save', 'Save'}
16		export = varargin{k+1};
17		case {'ylabel', 'Ylabel', 'YLabel'}
18		ylabel_str = varargin{k+1};
19		case {'minvalue', 'min'}
20		minvalue = varargin{k+1};
21		case {'maxvalue', 'max'}
22		maxvalue = varargin{k+1};
23		case {'export_suffix', 'exportsuffix', 'suffix', 'Suffix'}
24		export_suffix = ['.',varargin{k+1}];
25		otherwise
26		error(['Unrecognized varargin-argument ', varargin{k}])
27		end
28		end
	2		invert = false;
	3		export = true;
	4		titlestr = [];
	5		ylabel_str = '';
	6		export_suffix ='';
	7		minvalue = [];
	8		maxvalue = [];
	9		for k=1:2:length(varargin)
	10		switch varargin{k}
	11		case {'invert', 'Invert'}
	12		invert = varargin{k+1};
	13		case {'title', 'Title'}
	14		titlestr = varargin{k+1};
	15		case {'export', 'Export', 'save', 'Save'}
	16		export = varargin{k+1};
	17		case {'ylabel', 'Ylabel', 'YLabel'}
	18		ylabel_str = varargin{k+1};
	19		case {'minvalue', 'min'}
	20		minvalue = varargin{k+1};
	21		case {'maxvalue', 'max'}
	22		maxvalue = varargin{k+1};
	23		case {'export_suffix', 'exportsuffix', 'suffix', 'Suffix'}
	24		export_suffix = ['.',varargin{k+1}];
	25		otherwise
	26		error(['Unrecognized varargin-argument ', varargin{k}])
	27		end
	28		end
29	29
30		lognegs = [ev.logneg_corrected{power_ind}{:}]';
31		fig = figure; hold all;
32
33		function mysubplot(subplotind, xind, lind, cmap)
34		cmap = flipud(cbrewer('seq', cmap, 9));
35		if invert
36		cmap = flipud(cmap);
37		end
38		pwx = ev.pulse_widths(:,xind);
39		pwl = ev.pulse_widths(:,lind);
	30		lognegs = [ev.logneg_corrected{power_ind}{:}]';
	31		fig = figure; hold all;
40	32
41		colind=0;
42		for w=unique(pwl)'
43		ax = subplot(subplotind); hold all
44		colind = colind+1;
45		indices = find(pwl==w);
46		x_data = pwx(indices);
47		y_data = lognegs(indices);
48		plot(x_data, y_data, 'o-',...
49		'Color', cmap(colind,:),...
50		'LineWidth',2,...
51		'MarkerFaceColor', cmap(colind,:),...
52		'DisplayName', num2eng(w))
53		xlim([0.9*min(x_data), 1.1*max(x_data)])
54		if (~isempty(minvalue))&&(~isempty(maxvalue))
55		ylim([minvalue, maxvalue])
56		end
57		end
58		title(['Log. negativity vs pulse width [Hz] of mode ', num2str(xind)])
59		postprocess_fig(xind, lind);
60		set(ax, 'box', 'on', 'Visible', 'on')
61		end
	33		function mysubplot(subplotind, xind, lind, cmap)
	34		cmap = flipud(cbrewer('seq', cmap, 9));
	35		if invert
	36		cmap = flipud(cmap);
	37		end
	38		pwx = ev.pulse_widths(:,xind);
	39		pwl = ev.pulse_widths(:,lind);
62	40
63		function postprocess_fig(xind, lind)
64		set(gca, 'xscale', 'log');
65		%xlabel(['Pulse Width ', num2str(xind), ' [Hz]']);
66		ylabel(ylabel_str);
	41		colind=0;
	42		for w=unique(pwl)'
	43		ax = subplot(subplotind); hold all
	44		colind = colind+1;
	45		indices = find(pwl==w);
	46		x_data = pwx(indices);
	47		y_data = lognegs(indices);
	48		plot(x_data, y_data, 'o-',...
	49		'Color', cmap(colind,:),...
	50		'LineWidth',2,...
	51		'MarkerFaceColor', cmap(colind,:),...
	52		'DisplayName', num2eng(w))
	53		xlim([0.9*min(x_data), 1.1*max(x_data)])
	54		if (~isempty(minvalue))&&(~isempty(maxvalue))
	55		ylim([minvalue, maxvalue])
	56		end
	57		end
	58		title(['Log. negativity vs pulse width [Hz] of mode ', num2str(xind)])
	59		postprocess_fig(xind, lind);
	60		set(ax, 'box', 'on', 'Visible', 'on')
	61		end
67	62
68		mylegend = legend('Location','EastOutside');
69		mytitle = get(mylegend,'title');
70		set(mytitle,'string',['Pulse Width ', num2str(lind), ' [Hz]']);
71
72		hline=refline(0,0); set(hline, 'Color', 'k');
73		grid on
74		end
	63		function postprocess_fig(xind, lind)
	64		set(gca, 'xscale', 'log');
	65		%xlabel(['Pulse Width ', num2str(xind), ' [Hz]']);
	66		ylabel(ylabel_str);
75	67
76		mysubplot(211,1,2, 'GnBu');
77		mysubplot(212,2,1, 'OrRd');
78		suptitle(titlestr);
	68		mylegend = legend('Location','EastOutside');
	69		mytitle = get(mylegend,'title');
	70		set(mytitle,'string',['Pulse Width ', num2str(lind), ' [Hz]']);
79	71
80		% export to ev.evaluation_folder
81		eval_folder_exists = isequal(exist(ev.evaluation_folder),7);
82		if export
83		if eval_folder_exists
84		wherewasi = cd;
85		cd(ev.evaluation_folder);
86		export_fig(['logneg_corrected_', num2str(power_ind), '.split',export_suffix],...
87		'-pdf', '-nocrop', '-transparent')
88		cd(wherewasi);
89		else
90		warning(['Cannot find the evaluation folder ',...
91		ev.evaluation_folder])
92		end
93		end
	72		hline=refline(0,0); set(hline, 'Color', 'k');
	73		grid on
	74		end
	75
	76		mysubplot(211,1,2, 'GnBu');
	77		mysubplot(212,2,1, 'OrRd');
	78		suptitle(titlestr);
	79
	80		% export to ev.evaluation_folder
	81		eval_folder_exists = isequal(exist(ev.evaluation_folder),7);
	82		if export
	83		if eval_folder_exists
	84		wherewasi = cd;
	85		cd(ev.evaluation_folder);
	86		export_fig(['logneg_corrected_', num2str(power_ind), '.split',export_suffix],...
	87		'-pdf', '-nocrop', '-transparent')
	88		cd(wherewasi);
	89		else
	90		warning(['Cannot find the evaluation folder ',...
	91		ev.evaluation_folder])
	92		end
	93		end

File data_analysis/@entanglement_evaluation/recalibrate.m changed (mode: 100644) (index 57096ff..4ed1de8)
1	1	function obj = recalibrate(obj, varargin)	function obj = recalibrate(obj, varargin)
2	2	[shotnoise_ind, eta, nu] = get_opt_args(obj, varargin{:});	[shotnoise_ind, eta, nu] = get_opt_args(obj, varargin{:});
3		obj.detection_efficiency = eta;
	3		obj.detection_efficiency = eta;
4	4	obj.homodyne_visibility = nu;	obj.homodyne_visibility = nu;
5		obj.calibratePulsedCMs('shotnoise_ind', shotnoise_ind);
	5		obj.calibratePulsedCMs('shotnoise_ind', shotnoise_ind);
6	6
7	7	function [shotnoise_ind, eta, nu] = get_opt_args(obj, varargin)	function [shotnoise_ind, eta, nu] = get_opt_args(obj, varargin)
8	8	shotnoise_ind = [];	shotnoise_ind = [];
...	...	function [shotnoise_ind, eta, nu] = get_opt_args(obj, varargin)
12	12	switch varargin{k}	switch varargin{k}
13	13	case 'shotnoise_ind'	case 'shotnoise_ind'
14	14	shotnoise_ind = varargin{k+1};	shotnoise_ind = varargin{k+1};
15		case 'eta'
	15		case 'eta'
16	16	eta = varargin{k+1};	eta = varargin{k+1};
17		case 'nu'
	17		case 'nu'
18	18	nu = varargin{k+1};	nu = varargin{k+1};
19	19	otherwise	otherwise
20	20	error(['Unrecognized varargin-argument ', varargin{k}]);	error(['Unrecognized varargin-argument ', varargin{k}]);

File data_analysis/@entanglement_evaluation/trace_out.m changed (mode: 100644) (index 7f1d004..7affa72)
1	1	function ev_traced = trace_out(ev, modes_to_trace, varargin)	function ev_traced = trace_out(ev, modes_to_trace, varargin)
2		% return a version of ev in which all light modes
3		% which correspond to the mechanical modes specified in modes_to_trace
4		% have been traced out. That is for which the interesting quantities
5		% logneg, physicality, covariance matrices have been recalculated appropriately
6		ev_traced = ev.copy();
	2		% return a version of ev in which all light modes
	3		% which correspond to the mechanical modes specified in modes_to_trace
	4		% have been traced out. That is for which the interesting quantities
	5		% logneg, physicality, covariance matrices have been recalculated appropriately
	6		ev_traced = ev.copy();
7	7
8		% throw away stuff which isn't strictly needed
9		ev_traced.entangling_modes = [];
10		ev_traced.readout_modes = [];
	8		% throw away stuff which isn't strictly needed
	9		ev_traced.entangling_modes = [];
	10		ev_traced.readout_modes = [];
11	11
12	12	% throw away stuff which needs to be recomputed after tracing out	% throw away stuff which needs to be recomputed after tracing out
13	13	ev_traced.opt_logneg_corrected_private = [];	ev_traced.opt_logneg_corrected_private = [];
14	14	ev_traced.opt_pulse_gammas_private = [];	ev_traced.opt_pulse_gammas_private = [];
15	15
16	16	%	%
17		mech_frequencies_old = ev.mech_frequencies;
18		mode_indices_old = 1:ev.mech_frequencies_no;
	17		mech_frequencies_old = ev.mech_frequencies;
	18		mode_indices_old = 1:ev.mech_frequencies_no;
19	19
20		assert(all(ismember(modes_to_trace, mode_indices_old)),...
21		'entanglement_evaluation:trace_out:modes_illegit',...
22		'The modes you specified for tracing out do not seem to be legit.');
	20		assert(all(ismember(modes_to_trace, mode_indices_old)),...
	21		'entanglement_evaluation:trace_out:modes_illegit',...
	22		'The modes you specified for tracing out do not seem to be legit.');
23	23
24		mode_indices_new = setdiff(mode_indices_old, modes_to_trace);
25		mech_frequencies_new = mech_frequencies_old(mode_indices_new);
	24		mode_indices_new = setdiff(mode_indices_old, modes_to_trace);
	25		mech_frequencies_new = mech_frequencies_old(mode_indices_new);
26	26
27		ev_traced.mech_frequencies = mech_frequencies_new;
	27		ev_traced.mech_frequencies = mech_frequencies_new;
28	28
29	29	light_mode_inds_new = [mode_indices_new, ev.mech_frequencies_no + mode_indices_new];	light_mode_inds_new = [mode_indices_new, ev.mech_frequencies_no + mode_indices_new];
30	30	light_quad_inds_new = quadIndicesFromModeIndices(light_mode_inds_new);	light_quad_inds_new = quadIndicesFromModeIndices(light_mode_inds_new);
31	31
32		%% trace out the pulsed traces if possible
	32		%% trace out the pulsed traces if possible
33	33	if ~isempty(ev.pulse_trace)	if ~isempty(ev.pulse_trace)
34		trace_out_traces = @(mat)mat(:,light_quad_inds_new);
35		for k=1:ev.tracepairs_no
36		ev_traced.pulse_trace{k} = cellfun(trace_out_traces,...
37		ev.pulse_trace{k}, 'UniformOutput', false);
38		end
	34		trace_out_traces = @(mat)mat(:,light_quad_inds_new);
	35		for k=1:ev.tracepairs_no
	36		ev_traced.pulse_trace{k} = cellfun(trace_out_traces,...
	37		ev.pulse_trace{k}, 'UniformOutput', false);
	38		end
39	39	end	end
40	40
41	41
42		%% trace out pulsed CMs
43		trace_out_CMs = @(mat) mat(light_quad_inds_new,light_quad_inds_new);
44		for k=1:ev.tracepairs_no
45		ev_traced.pulse_CM{k} = cellfun(trace_out_CMs,...
46		ev.pulse_CM{k}, 'UniformOutput', false);
47		ev_traced.pulse_CM_calibrated{k} = cellfun(trace_out_CMs,...
48		ev.pulse_CM_calibrated{k}, 'UniformOutput', false);
49		end
	42		%% trace out pulsed CMs
	43		trace_out_CMs = @(mat) mat(light_quad_inds_new,light_quad_inds_new);
	44		for k=1:ev.tracepairs_no
	45		ev_traced.pulse_CM{k} = cellfun(trace_out_CMs,...
	46		ev.pulse_CM{k}, 'UniformOutput', false);
	47		ev_traced.pulse_CM_calibrated{k} = cellfun(trace_out_CMs,...
	48		ev.pulse_CM_calibrated{k}, 'UniformOutput', false);
	49		end
50	50
51		%% recalculate shotnoise and physicality
52		shotnoise_ind = get_shotnoise_ind(ev_traced, varargin{:});
53		calculate_log_neg(ev_traced, shotnoise_ind);
	51		%% recalculate shotnoise and physicality
	52		shotnoise_ind = get_shotnoise_ind(ev_traced, varargin{:});
	53		calculate_log_neg(ev_traced, shotnoise_ind);
54	54	calculate_physicality(ev_traced);	calculate_physicality(ev_traced);

File data_analysis/@meta_ev/contour_plot.m changed (mode: 100644) (index f4abace..cf1d9f8)
1	1	function contour_plot(meta, plot_type, tracepairs_ind, varargin)	function contour_plot(meta, plot_type, tracepairs_ind, varargin)
2		assertEqual(meta.gridtype,'matrix',...
3		'only implemented for gridtype "matrix"')
	2		assertEqual(meta.gridtype,'matrix',...
	3		'only implemented for gridtype "matrix"')
4	4
5		switch lower(plot_type)
6		case {'logneg', 'opt_log_neg', 'entanglement'}
7		display_name = 'Max. log. negativity';
8		fig_handle = 'ln_cont_fig';
9		z_data = meta.opt_logneg_corrected_interp{tracepairs_ind};
10		abs_max = ceil(10*max(max(abs(z_data))))/10;
11		c_axis_lims = [-abs_max,abs_max];
12		if abs_max > 5
13		c_levels = [[-abs_max:0.5:0],[0.1:0.1:abs_max]];
14		elseif abs_max > 1
15		c_levels = [[-abs_max:0.1:0],[0.1:0.1:abs_max]];
16		elseif abs_max > 0.5
17		c_levels = [-abs_max:0.1:abs_max];
18		else
19		c_levels = [-abs_max:0.05:abs_max];
20		end
21		cmap = flipud(cbrewer('div','RdBu',512));
22		case {'pulsewidth', 'opt_pulse_gamma', 'opt_pulsewidth'}
23		display_name = 'Optimal pulse width [kHz]';
24		fig_handle = 'pw_cont_fig';
25		z_data = meta.opt_pulse_gammas_interp{tracepairs_ind}/1E3;
26		abs_max = ceil(10*max(max(abs(z_data))))/10;
27		abs_min = floor(10*min(min(abs(z_data))))/10;
28		c_axis_lims = [abs_min,abs_max];
29		c_levels = logspace(log10(abs_min), log10(abs_max), 10);
30		cmap = cbrewer('seq','Blues',512);
31		otherwise
32		error(['Unsupported plottype: ', plot_type]);
33		end
	5		switch lower(plot_type)
	6		case {'logneg', 'opt_log_neg', 'entanglement'}
	7		display_name = 'Max. log. negativity';
	8		fig_handle = 'ln_cont_fig';
	9		z_data = meta.opt_logneg_corrected_interp{tracepairs_ind};
	10		abs_max = ceil(10*max(max(abs(z_data))))/10;
	11		c_axis_lims = [-abs_max,abs_max];
	12		if abs_max > 5
	13		c_levels = [[-abs_max:0.5:0],[0.1:0.1:abs_max]];
	14		elseif abs_max > 1
	15		c_levels = [[-abs_max:0.1:0],[0.1:0.1:abs_max]];
	16		elseif abs_max > 0.5
	17		c_levels = [-abs_max:0.1:abs_max];
	18		else
	19		c_levels = [-abs_max:0.05:abs_max];
	20		end
	21		cmap = flipud(cbrewer('div','RdBu',512));
	22		case {'pulsewidth', 'opt_pulse_gamma', 'opt_pulsewidth'}
	23		display_name = 'Optimal pulse width [kHz]';
	24		fig_handle = 'pw_cont_fig';
	25		z_data = meta.opt_pulse_gammas_interp{tracepairs_ind}/1E3;
	26		abs_max = ceil(10*max(max(abs(z_data))))/10;
	27		abs_min = floor(10*min(min(abs(z_data))))/10;
	28		c_axis_lims = [abs_min,abs_max];
	29		c_levels = logspace(log10(abs_min), log10(abs_max), 10);
	30		cmap = cbrewer('seq','Blues',512);
	31		otherwise
	32		error(['Unsupported plottype: ', plot_type]);
	33		end
34	34
35		x_scale = 'lin'; y_scale = 'lin';
36		x_lims = []; y_lims = [];
37		fig = [];
38		for k=1:2:length(varargin)
39		switch lower(varargin{k})
40		case {'x_scale','xscale'}
41		x_scale = varargin{k+1};
42		case {'x_lims','xlims','x_lim','xlim'}
43		x_lims = varargin{k+1};
44		case {'y_scale','yscale'}
45		y_scale = varargin{k+1};
46		case {'y_lims','ylims','y_lim','ylim'}
47		y_lims = varargin{k+1};
48		case {'caxis','coloraxis','c_axis_lims','color_axis_lims', 'coloraxis_lims'}
49		c_axis_lims = varargin{k+1};
50		case {'label','fig_label','figlabel'}
51		meta.fig_label = varargin{k+1};
52		case {'fig', 'figure'}
53		fig = varargin{k+1};
54		otherwise
55		error(['unrecognized varargin-argument: ', varargin{k}]);
56		end
57		end
	35		x_scale = 'lin'; y_scale = 'lin';
	36		x_lims = []; y_lims = [];
	37		fig = [];
	38		for k=1:2:length(varargin)
	39		switch lower(varargin{k})
	40		case {'x_scale','xscale'}
	41		x_scale = varargin{k+1};
	42		case {'x_lims','xlims','x_lim','xlim'}
	43		x_lims = varargin{k+1};
	44		case {'y_scale','yscale'}
	45		y_scale = varargin{k+1};
	46		case {'y_lims','ylims','y_lim','ylim'}
	47		y_lims = varargin{k+1};
	48		case {'caxis','coloraxis','c_axis_lims','color_axis_lims', 'coloraxis_lims'}
	49		c_axis_lims = varargin{k+1};
	50		case {'label','fig_label','figlabel'}
	51		meta.fig_label = varargin{k+1};
	52		case {'fig', 'figure'}
	53		fig = varargin{k+1};
	54		otherwise
	55		error(['unrecognized varargin-argument: ', varargin{k}]);
	56		end
	57		end
58	58
59		if isempty(fig)
60		fig = figure; hold all;
61		else
	59		if isempty(fig)
	60		fig = figure; hold all;
	61		else
62	62	visibility = get(fig,'visible');	visibility = get(fig,'visible');
63	63	figure(fig); hold all; set(fig, 'visible', visibility);	figure(fig); hold all; set(fig, 'visible', visibility);
64	64	end	end
65	65
66		meta.(fig_handle) = my_contour_plot(meta, fig, z_data,...
67		c_levels, display_name, cmap, c_axis_lims,...
68		x_scale, x_lims, y_scale, y_lims);
	66		meta.(fig_handle) = my_contour_plot(meta, fig, z_data,...
	67		c_levels, display_name, cmap, c_axis_lims,...
	68		x_scale, x_lims, y_scale, y_lims);
69	69
70	70
71	71	function fig = my_contour_plot(meta, fig, z_data, c_levels, display_name, cmap, c_axis_lims, x_scale, x_lims, y_scale, y_lims)	function fig = my_contour_plot(meta, fig, z_data, c_levels, display_name, cmap, c_axis_lims, x_scale, x_lims, y_scale, y_lims)
72		%%% contour plot
73		[x_data, y_data] = meta.params_interp{:};
74		[x_data, y_data, z_data] = meta_ev.limit_x_and_y(x_data, y_data, z_data, x_lims, y_lims);
75		[c_data, c_handles] = contourf(x_data, y_data, z_data,...
76		c_levels, 'DisplayName', display_name);
77		colormap(cmap); caxis(c_axis_lims);
78		set(gca, 'xscale', x_scale, 'yscale', y_scale)
79		labels(meta.param_names{:});
	72		%%% contour plot
	73		[x_data, y_data] = meta.params_interp{:};
	74		[x_data, y_data, z_data] = meta_ev.limit_x_and_y(x_data, y_data, z_data, x_lims, y_lims);
	75		[c_data, c_handles] = contourf(x_data, y_data, z_data,...
	76		c_levels, 'DisplayName', display_name);
	77		colormap(cmap); caxis(c_axis_lims);
	78		set(gca, 'xscale', x_scale, 'yscale', y_scale)
	79		labels(meta.param_names{:});
	80
	81		%%% scatter plot of simulated data points
	82		set(gca, 'layer', 'top')
	83		scatter(meta.params{1}(:), meta.params{2}(:), 'xk',...
	84		'MarkerFaceColor','none',...
	85		'LineWidth',1,...
	86		'DisplayName', 'Simulated data points')
	87		leg = legend('Location', 'Best');
	88		set(leg, 'Color', 'w', 'FontWeight', 'normal', 'LineWidth', 1, 'Interpreter', 'latex')
80	89
81		%%% scatter plot of simulated data points
82		set(gca, 'layer', 'top')
83		scatter(meta.params{1}(:), meta.params{2}(:), 'xk',...
84		'MarkerFaceColor','none',...
85		'LineWidth',1,...
86		'DisplayName', 'Simulated data points')
87		leg = legend('Location', 'Best');
88		set(leg, 'Color', 'w', 'FontWeight', 'normal', 'LineWidth', 1, 'Interpreter', 'latex')
	90		%%% contour labels
	91		text_handle = clabel(c_data, 'FontSize', 13);
	92		for k=1:length(text_handle)/2
	93		z_value = get(text_handle(2*k), 'UserData');
	94		if z_value == 0
	95		c_color = [1,0,0];
	96		elseif z_value < 0
	97		c_color = cmap(1,:);
	98		elseif z_value > 0
	99		c_color = cmap(end,:);
	100		end
89	101
90		%%% contour labels
91		text_handle = clabel(c_data, 'FontSize', 13);
92		for k=1:length(text_handle)/2
93		z_value = get(text_handle(2*k), 'UserData');
94		if z_value == 0
95		c_color = [1,0,0];
96		elseif z_value < 0
97		c_color = cmap(1,:);
98		elseif z_value > 0
99		c_color = cmap(end,:);
100		end
101
102		set(text_handle(2*k), 'BackgroundColor','none', 'EdgeColor', 'none',...%c_colors(k,:),...
103		'Color', c_color, 'FontWeight', 'normal', 'LineWidth', 0.7);% text
104		set(text_handle(2*k-1),'Marker','o', 'MarkerFaceColor', c_color,...
105		'MarkerSize', 6, 'MarkerEdgeColor','none');% marker
106
107		if z_value < -0.1 && ismember(z_value, c_levels(1:2:end)) || z_value <-1
108		set(text_handle(2*k),'String','')
109		set(text_handle(2*k-1),'Marker','none')
110		end
111		end
112		meta.my_postprocess_plot(fig);
	102		set(text_handle(2*k), 'BackgroundColor','none', 'EdgeColor', 'none',...%c_colors(k,:),...
	103		'Color', c_color, 'FontWeight', 'normal', 'LineWidth', 0.7);% text
	104		set(text_handle(2*k-1),'Marker','o', 'MarkerFaceColor', c_color,...
	105		'MarkerSize', 6, 'MarkerEdgeColor','none');% marker
	106
	107		if z_value < -0.1 && ismember(z_value, c_levels(1:2:end)) || z_value <-1
	108		set(text_handle(2*k),'String','')
	109		set(text_handle(2*k-1),'Marker','none')
	110		end
	111		end
	112		meta.my_postprocess_plot(fig);

File data_analysis/@meta_ev/get_meta_data.m changed (mode: 100644) (index f0d5725..9b8f1f1)
...	...	function get_meta_data(meta, meta_data_file)
3	3	%	%
4	4	% meta.get_meta_data(meta_data_file) adds to the meta_ev-object meta	% meta.get_meta_data(meta_data_file) adds to the meta_ev-object meta
5	5	% all information contained in the textfile meta_data_file.	% all information contained in the textfile meta_data_file.
6		%
	6		%
7	7	% The metadata-file should be tab-separated. There should be one header line.	% The metadata-file should be tab-separated. There should be one header line.
8	8	% The header should look as follows: ev_file \t name_of_param1 \t name_of_param2 \t name_of_param3 ...	% The header should look as follows: ev_file \t name_of_param1 \t name_of_param2 \t name_of_param3 ...
9	9	headerlines = 1;	headerlines = 1;
10
	10
11	11	% clear any old postprocessing results	% clear any old postprocessing results
12	12	meta.reset;	meta.reset;
13	13

File data_analysis/@meta_ev/meta_ev.m changed (mode: 100644) (index 076695f..d26d142)
1	1	classdef meta_ev < class_conveniences	classdef meta_ev < class_conveniences
2		% convenience class for postprocessing entanglement_evaluations
3		% collects many ev-objects in a 2D-grid and
4		% - provides methods for batch loading and saving them
5		% - organizes them suitably
6		% - provides methods for plotting from those
7
8		properties
9		evaluations@cell % cell array of entanglment_evaluation-objects
10		ev_files@cell % cell array with paths to the ev.mat-files for individual evaluations
11		param_names@cell % cell array of parameter names (read from meta_data_file)
12		params@cell % cell array of parameter arrays (params{1} corresponds to param_names{1})
13		meta_label = '' % string describing the evaluation (could be 'traced_out_2' e.g.)
14		meta_title = '' % optional: string used as title string for plots
15		models@cell
16		end
17		properties(Dependent)
18		dimensions@double vector % dimensions of evaluations-vector or -matrix
19		gridtype % string: 'vector' or 'matrix' describing type of grid
20		evaluations_no@double % number of evaluations
21		opt_logneg_corrected@cell % cell array (index corresponding to tracepair) of optimal entanglement
22		opt_pulse_gammas@cell % cell array (index corresponding to tracepair) of optimal pulse gammas
23		tracepairs_no@double % number of tracepairs used for each evaluation (enforced to be the same)
24		common@struct % struct which contains all those evaluation_properties which are shared by all evaluations
25		common_path % longest path which is a parentdir to all ev_files
26		end
27		properties(Dependent, Hidden)
28		opt_logneg_corrected_interp@cell % cell array of interpolated optimal entanglement
29		opt_pulse_gammas_interp@cell % cell array of interpolated optimal pulse gammas
30		ev_dirs@cell = {} % cell array with directories containing the ev.mat-files for individual evaluations
31		end
32		properties(Hidden)
33		params_interp@cell % cell array of interpolated parameter values
34		opt_logneg_corrected_interp_private@cell = {} % ... to avoid unnecessary recomputation ...
35		opt_pulse_gammas_interp_private@cell = {} % ... to avoid unnecessary recomputation ...
36		common_private@struct = struct() % ... to avoid unnecessary recomputation ...
37		ln_fig = [] % figure handle for internally generated plot logneg vs gamma
38		ln_cont_fig = [] % figure handle for internally generated contour plot of opt. logneg
39		ln_cut_fig = [] % figure handle for internally generated cut plot of opt. logneg
40		pw_cont_fig = [] % figure handle for internally generated contour plot of opt. pulse width
41		pw_cut_fig = [] % figure handle for internally generated cut plot of opt. pulse width
42		fig_label = '' % additional label to distinguish plots when saving
43		end
44		methods
45		%%% CONSTRUCTOR %%%
	2		% convenience class for postprocessing entanglement_evaluations
	3		% collects many ev-objects in a 2D-grid and
	4		% - provides methods for batch loading and saving them
	5		% - organizes them suitably
	6		% - provides methods for plotting from those
	7
	8		properties
	9		evaluations@cell % cell array of entanglment_evaluation-objects
	10		ev_files@cell % cell array with paths to the ev.mat-files for individual evaluations
	11		param_names@cell % cell array of parameter names (read from meta_data_file)
	12		params@cell % cell array of parameter arrays (params{1} corresponds to param_names{1})
	13		meta_label = '' % string describing the evaluation (could be 'traced_out_2' e.g.)
	14		meta_title = '' % optional: string used as title string for plots
	15		models@cell
	16		end
	17		properties(Dependent)
	18		dimensions@double vector % dimensions of evaluations-vector or -matrix
	19		gridtype % string: 'vector' or 'matrix' describing type of grid
	20		evaluations_no@double % number of evaluations
	21		opt_logneg_corrected@cell % cell array (index corresponding to tracepair) of optimal entanglement
	22		opt_pulse_gammas@cell % cell array (index corresponding to tracepair) of optimal pulse gammas
	23		tracepairs_no@double % number of tracepairs used for each evaluation (enforced to be the same)
	24		common@struct % struct which contains all those evaluation_properties which are shared by all evaluations
	25		common_path % longest path which is a parentdir to all ev_files
	26		end
	27		properties(Dependent, Hidden)
	28		opt_logneg_corrected_interp@cell % cell array of interpolated optimal entanglement
	29		opt_pulse_gammas_interp@cell % cell array of interpolated optimal pulse gammas
	30		ev_dirs@cell = {} % cell array with directories containing the ev.mat-files for individual evaluations
	31		end
	32		properties(Hidden)
	33		params_interp@cell % cell array of interpolated parameter values
	34		opt_logneg_corrected_interp_private@cell = {} % ... to avoid unnecessary recomputation ...
	35		opt_pulse_gammas_interp_private@cell = {} % ... to avoid unnecessary recomputation ...
	36		common_private@struct = struct() % ... to avoid unnecessary recomputation ...
	37		ln_fig = [] % figure handle for internally generated plot logneg vs gamma
	38		ln_cont_fig = [] % figure handle for internally generated contour plot of opt. logneg
	39		ln_cut_fig = [] % figure handle for internally generated cut plot of opt. logneg
	40		pw_cont_fig = [] % figure handle for internally generated contour plot of opt. pulse width
	41		pw_cut_fig = [] % figure handle for internally generated cut plot of opt. pulse width
	42		fig_label = '' % additional label to distinguish plots when saving
	43		end
	44		methods
	45		%%% CONSTRUCTOR %%%
46	46	function meta = meta_ev(varargin)	function meta = meta_ev(varargin)
47		if length(varargin)==1
48		meta_data_file = varargin{1};
49		meta.get_meta_data(meta_data_file);
50		meta.load_evaluations;
51		elseif length(varargin)>1
52		error('Expect at most one varargin-argument in constructor of meta-ev-class.')
53		end
	47		if length(varargin)==1
	48		meta_data_file = varargin{1};
	49		meta.get_meta_data(meta_data_file);
	50		meta.load_evaluations;
	51		elseif length(varargin)>1
	52		error('Expect at most one varargin-argument in constructor of meta-ev-class.')
	53		end
54	54	end	end
55	55	%%%	%%%
56	56
57		function load_evaluations(meta)
58		meta.gridtype;
59		% call meta.gridtype to guarantee error is thrown if gridtype is unsupported
60
61		meta.reset; % clear previous postprocessing results
62		evaluations = cell(meta.dimensions);
63		for k=1:meta.evaluations_no
64		ev = load(meta.ev_files{k}); ev = ev.ev;
65		evaluations{k} = ev;
66		end
67		meta.evaluations = evaluations;
	57		function load_evaluations(meta)
	58		meta.gridtype;
	59		% call meta.gridtype to guarantee error is thrown if gridtype is unsupported
	60
	61		meta.reset; % clear previous postprocessing results
	62		evaluations = cell(meta.dimensions);
	63		for k=1:meta.evaluations_no
	64		ev = load(meta.ev_files{k}); ev = ev.ev;
	65		evaluations{k} = ev;
	66		end
	67		meta.evaluations = evaluations;
	68		end
	69		function load_models(meta)
	70		meta.models = cell(meta.dimensions);
	71		for k=1:meta.evaluations_no
	72		try
	73		model = load(fullfile(meta.ev_dirs{k}, 'model.mat')); model = model.model;
	74		meta.models{k} = model;
	75		end
	76		end
68	77	end	end
69		function load_models(meta)
70		meta.models = cell(meta.dimensions);
71		for k=1:meta.evaluations_no
72		try
73		model = load(fullfile(meta.ev_dirs{k}, 'model.mat')); model = model.model;
74		meta.models{k} = model;
75		end
76		end
77		end
78	78	function meta_traced = trace_out(meta, mode_indices)	function meta_traced = trace_out(meta, mode_indices)
79		% return a version of meta-ev in which all light modes
80		% which correspond to the mechanical modes specified in modes_to_trace
81		% have been traced out. That is for which the interesting quantities
82		% logneg, physicality, covariance matrices have been recalculated appropriately
83		meta_traced = meta.copy;
84		meta_traced.reset;
85		for k=1:meta.evaluations_no
86		meta_traced.evaluations{k} = meta.evaluations{k}.trace_out(mode_indices);
87		end
	79		% return a version of meta-ev in which all light modes
	80		% which correspond to the mechanical modes specified in modes_to_trace
	81		% have been traced out. That is for which the interesting quantities
	82		% logneg, physicality, covariance matrices have been recalculated appropriately
	83		meta_traced = meta.copy;
	84		meta_traced.reset;
	85		for k=1:meta.evaluations_no
	86		meta_traced.evaluations{k} = meta.evaluations{k}.trace_out(mode_indices);
	87		end
88	88	end	end
89	89	function restrict_pulse_gammas(meta,gamma_min, gamma_max)	function restrict_pulse_gammas(meta,gamma_min, gamma_max)
90		for k=1:meta.evaluations_no
91		ev = meta.evaluations{k};
92		pulse_gammas = ev.pulse_gammas;
93		allowed_indices = intersect(find(pulse_gammas >= gamma_min),...
94		find(pulse_gammas <= gamma_max));
95		ev.pulse_gammas = pulse_gammas(allowed_indices);
96		for l=1:numel(ev.logneg_corrected)
97		ln = ev.logneg_corrected{l};
98		ev.logneg_corrected{l} = {ln{allowed_indices}};
99		end
100		ev.calulate_optimal_entanglement;
101		end
102		meta.reset;
	90		for k=1:meta.evaluations_no
	91		ev = meta.evaluations{k};
	92		pulse_gammas = ev.pulse_gammas;
	93		allowed_indices = intersect(find(pulse_gammas >= gamma_min),...
	94		find(pulse_gammas <= gamma_max));
	95		ev.pulse_gammas = pulse_gammas(allowed_indices);
	96		for l=1:numel(ev.logneg_corrected)
	97		ln = ev.logneg_corrected{l};
	98		ev.logneg_corrected{l} = {ln{allowed_indices}};
	99		end
	100		ev.calulate_optimal_entanglement;
	101		end
	102		meta.reset;
103	103	end	end
104	104	function reset(meta)	function reset(meta)
105		meta.reset_hidden_props;
	105		meta.reset_hidden_props;
106	106	end	end
107	107	function meta_filtered = filter_params(meta, param_ind, param_values)	function meta_filtered = filter_params(meta, param_ind, param_values)
108		% param_ind determines the parameter to keep fixed
109		params_uniqe = unique(meta.params{param_ind});
110		indices = [];
111		for val = param_values(:)'
112		[~,ind] = min(abs(params_uniqe - val));
113		params_uniqe(ind) = NaN;
114		indices = [indices, ind];
115		end
116		if param_ind==1
117		myfilter = @(array) array(:,indices);
118		elseif param_ind==2
119		myfilter = @(array) array(indices,:);
120		end
	108		% param_ind determines the parameter to keep fixed
	109		params_uniqe = unique(meta.params{param_ind});
	110		indices = [];
	111		for val = param_values(:)'
	112		[~,ind] = min(abs(params_uniqe - val));
	113		params_uniqe(ind) = NaN;
	114		indices = [indices, ind];
	115		end
	116		if param_ind==1
	117		myfilter = @(array) array(:,indices);
	118		elseif param_ind==2
	119		myfilter = @(array) array(indices,:);
	120		end
121	121
122		meta_filtered = meta.copy;
123		meta_filtered.reset;
124		for field={'evaluations', 'ev_files', 'models'};
125		if ~isempty(meta.(field{:}))
126		meta_filtered.(field{:}) = myfilter(meta.(field{:}));
127		end
128		end
129		for k=1:length(meta.params)
130		meta_filtered.params{k} = myfilter(meta.params{k});
131		end
132		end
	122		meta_filtered = meta.copy;
	123		meta_filtered.reset;
	124		for field={'evaluations', 'ev_files', 'models'};
	125		if ~isempty(meta.(field{:}))
	126		meta_filtered.(field{:}) = myfilter(meta.(field{:}));
	127		end
	128		end
	129		for k=1:length(meta.params)
	130		meta_filtered.params{k} = myfilter(meta.params{k});
	131		end
	132		end
133	133	function cellarray = for_each_evaluation(meta, fieldname, varargin)	function cellarray = for_each_evaluation(meta, fieldname, varargin)
134		if length(varargin) > 0
135		assert(length(varargin)==1);
136		index = varargin{1};
137		get_element = @(array)array(index);
138		else
139		get_element = @(x) x;
140		end
141		get_field = @(ev)get_element(getfield(ev, fieldname));
	134		if length(varargin) > 0
	135		assert(length(varargin)==1);
	136		index = varargin{1};
	137		get_element = @(array)array(index);
	138		else
	139		get_element = @(x) x;
	140		end
	141		get_field = @(ev)get_element(getfield(ev, fieldname));
142	142
143		cellarray = cell(meta.dimensions);
144		for k=1:meta.evaluations_no
145		cellarray{k} = get_field(meta.evaluations{k});
146		end
	143		cellarray = cell(meta.dimensions);
	144		for k=1:meta.evaluations_no
	145		cellarray{k} = get_field(meta.evaluations{k});
	146		end
	147		end
	148		function set_default_sample_no(meta, default_sample_no)
	149		for k=1:meta.evaluations_no
	150		for l=1:meta.evaluations{k}.tracepairs_no
	151		meta.evaluations{k}.measurement.homotraces(l,1).default_sample_no = default_sample_no;
	152		meta.evaluations{k}.measurement.homotraces(l,2).default_sample_no = default_sample_no;
	153		end
	154		end
	155		end
	156		function save_figs(meta)
	157		meta.my_save_fig(meta.ln_fig, 'ln_versus_gamma'); meta.ln_fig = [];
	158		meta.my_save_fig(meta.ln_cont_fig, 'lnopt'); meta.ln_cont_fig = [];
	159		meta.my_save_fig(meta.pw_cont_fig, 'pwopt'); meta.pw_cont_fig = [];
	160		meta.my_save_fig(meta.ln_cut_fig, 'lnopt_cut'); meta.ln_cut_fig = [];
	161		meta.my_save_fig(meta.pw_cut_fig, 'pwopt_cut'); meta.pw_cut_fig = [];
147	162	end	end
148		function set_default_sample_no(meta, default_sample_no)
149		for k=1:meta.evaluations_no
150		for l=1:meta.evaluations{k}.tracepairs_no
151		meta.evaluations{k}.measurement.homotraces(l,1).default_sample_no = default_sample_no;
152		meta.evaluations{k}.measurement.homotraces(l,2).default_sample_no = default_sample_no;
153		end
154		end
155		end
156		function save_figs(meta)
157		meta.my_save_fig(meta.ln_fig, 'ln_versus_gamma'); meta.ln_fig = [];
158		meta.my_save_fig(meta.ln_cont_fig, 'lnopt'); meta.ln_cont_fig = [];
159		meta.my_save_fig(meta.pw_cont_fig, 'pwopt'); meta.pw_cont_fig = [];
160		meta.my_save_fig(meta.ln_cut_fig, 'lnopt_cut'); meta.ln_cut_fig = [];
161		meta.my_save_fig(meta.pw_cut_fig, 'pwopt_cut'); meta.pw_cut_fig = [];
162		end
163	163
164		%% GETTERS
165		function dimensions = get.dimensions(meta)
	164		%% GETTERS
	165		function dimensions = get.dimensions(meta)
166	166	dimensions = size(meta.ev_files);	dimensions = size(meta.ev_files);
167	167	end	end
168	168	function gridtype = get.gridtype(meta)	function gridtype = get.gridtype(meta)
169		switch sum(meta.dimensions>1)
170		case 1
171		gridtype = 'vector';
172		case 2
173		gridtype = 'matrix';
174		otherwise
175		error('meta_ev:gridtype:unsupported','Unsupported grid-type.')
176		end
177		end
178		function evaluations_no = get.evaluations_no(meta)
	169		switch sum(meta.dimensions>1)
	170		case 1
	171		gridtype = 'vector';
	172		case 2
	173		gridtype = 'matrix';
	174		otherwise
	175		error('meta_ev:gridtype:unsupported','Unsupported grid-type.')
	176		end
	177		end
	178		function evaluations_no = get.evaluations_no(meta)
179	179	evaluations_no = numel(meta.ev_files);	evaluations_no = numel(meta.ev_files);
180	180	end	end
181		function tracepairs_no = get.tracepairs_no(meta)
	181		function tracepairs_no = get.tracepairs_no(meta)
182	182	tracepairs_no = meta.common.tracepairs_no;	tracepairs_no = meta.common.tracepairs_no;
183	183	end	end
184		function opt_logneg_corrected = get.opt_logneg_corrected(meta)
185		opt_logneg_corrected = cell([1, meta.tracepairs_no]);
186		for k=1:meta.tracepairs_no
187		opt_logneg_corrected{k} = cell2mat(meta.for_each_evaluation('opt_logneg_corrected', k));
188		end
	184		function opt_logneg_corrected = get.opt_logneg_corrected(meta)
	185		opt_logneg_corrected = cell([1, meta.tracepairs_no]);
	186		for k=1:meta.tracepairs_no
	187		opt_logneg_corrected{k} = cell2mat(meta.for_each_evaluation('opt_logneg_corrected', k));
	188		end
	189		end
	190		function opt_pulse_gammas = get.opt_pulse_gammas(meta)
	191		opt_pulse_gammas = cell([1, meta.tracepairs_no]);
	192		for k=1:meta.tracepairs_no
	193		opt_pulse_gammas{k} = cell2mat(meta.for_each_evaluation('opt_pulse_gammas', k));
	194		end
189	195	end	end
190		function opt_pulse_gammas = get.opt_pulse_gammas(meta)
191		opt_pulse_gammas = cell([1, meta.tracepairs_no]);
192		for k=1:meta.tracepairs_no
193		opt_pulse_gammas{k} = cell2mat(meta.for_each_evaluation('opt_pulse_gammas', k));
194		end
195		end
196		function opt_logneg_corrected_interp = get.opt_logneg_corrected_interp(meta)
197		if isempty(meta.opt_logneg_corrected_interp_private)
198		opt_logneg_corrected_interp = cell([1, meta.tracepairs_no]);
199		for k=1:meta.tracepairs_no
200		opt_logneg_corrected_interp{k} = meta.interpolate_ln_pw(meta.opt_logneg_corrected{k});
201		end
202		meta.opt_logneg_corrected_interp_private = opt_logneg_corrected_interp;
203		else
204		opt_logneg_corrected_interp = meta.opt_logneg_corrected_interp_private;
205		end
	196		function opt_logneg_corrected_interp = get.opt_logneg_corrected_interp(meta)
	197		if isempty(meta.opt_logneg_corrected_interp_private)
	198		opt_logneg_corrected_interp = cell([1, meta.tracepairs_no]);
	199		for k=1:meta.tracepairs_no
	200		opt_logneg_corrected_interp{k} = meta.interpolate_ln_pw(meta.opt_logneg_corrected{k});
	201		end
	202		meta.opt_logneg_corrected_interp_private = opt_logneg_corrected_interp;
	203		else
	204		opt_logneg_corrected_interp = meta.opt_logneg_corrected_interp_private;
	205		end
206	206	end	end
207		function opt_pulse_gammas_interp = get.opt_pulse_gammas_interp(meta)
208		if isempty(meta.opt_pulse_gammas_interp_private)
209		opt_pulse_gammas_interp = cell([1, meta.tracepairs_no]);
210		for k=1:meta.tracepairs_no
211		opt_pulse_gammas_interp{k} = meta.interpolate_ln_pw(meta.opt_pulse_gammas{k});
212		end
213		meta.opt_pulse_gammas_interp_private = opt_pulse_gammas_interp;
214		else
215		opt_pulse_gammas_interp = meta.opt_pulse_gammas_interp_private;
216		end
	207		function opt_pulse_gammas_interp = get.opt_pulse_gammas_interp(meta)
	208		if isempty(meta.opt_pulse_gammas_interp_private)
	209		opt_pulse_gammas_interp = cell([1, meta.tracepairs_no]);
	210		for k=1:meta.tracepairs_no
	211		opt_pulse_gammas_interp{k} = meta.interpolate_ln_pw(meta.opt_pulse_gammas{k});
	212		end
	213		meta.opt_pulse_gammas_interp_private = opt_pulse_gammas_interp;
	214		else
	215		opt_pulse_gammas_interp = meta.opt_pulse_gammas_interp_private;
	216		end
	217		end
	218		function common = get.common(meta)
	219		if isempty(fieldnames(meta.common_private))
	220		fields = {'inverted_evaluation';...
	221		'mech_frequencies'; 'mech_frequencies_no';...
	222		'pulse_gammas'; 'pulse_gammas_no'; 'pulse_widths';...
	223		'detection_efficiency'; 'homodyne_visibility';...
	224		'pulse_pairs_coverage'; 'pulse_pairs_per_trace'; 'pulse_duration_seconds';...
	225		'samplingrate'; 'samples_per_trace'; 'samples_per_pulse';...
	226		'tracepairs_no'; 'signal_power_muW'};
	227		for k=1:numel(fields)
	228		field = fields{k};
	229		try
	230		values = meta.for_each_evaluation(field);
	231		equal_to_first = @(value)isequal(value, values{1});
	232		all_equal = all(cellfun(equal_to_first, values));
	233		if all_equal
	234		common.(field) = values{1};
	235		else
	236		common.(field) = 'values differ';
	237		end
	238		catch
	239		common.(field) = NaN;
	240		end
	241		end
	242		meta.common_private = common;
	243		else
	244		common = meta.common_private;
	245		end
	246		end
	247		function common_path = get.common_path(meta)
	248		ev_files = {meta.ev_files{:}};
	249		test_path = ev_files{1};
	250		splitindices = strfind(test_path,'/');
	251		for k=1:length(splitindices)
	252		inds = 1:splitindices(k);
	253		testfun = @(str)isequal(str(inds), test_path(inds));
	254		if all(cellfun(testfun, ev_files))
	255		common_path = test_path(inds);
	256		end
	257		end
	258		end
	259		function ev_dirs = get.ev_dirs(meta)
	260		ev_dirs = cell(meta.dimensions);
	261		for k=1:meta.evaluations_no
	262		ev_dirs{k} = fileparts(meta.ev_files{k});
	263		end
	264		end
	265		end
	266		methods(Hidden)
	267		function reset_hidden_props(meta)
	268		meta.params_interp = {};
	269		meta.opt_logneg_corrected_interp_private = {};
	270		meta.opt_pulse_gammas_interp_private = {};
	271		meta.common_private = struct();
	272		end
	273		function ln_or_pw_interp = interpolate_ln_pw(meta, ln_or_pw, varargin)
	274		refinement_factor = 5;
	275		if length(varargin)>0
	276		refinement_factor = varargin{1};
	277		end
	278		switch meta.gridtype
	279		case 'vector'
	280		error('not implemented')
	281		case 'matrix'
	282		X = meta.params{1};
	283		Xq = interp2(X,refinement_factor);
	284		meta.params_interp{1} = Xq;
	285		Y = meta.params{2};
	286		Yq = interp2(Y,refinement_factor);
	287		meta.params_interp{2} = Yq;
	288		ln_or_pw_interp = interp2(X,Y,ln_or_pw,Xq,Yq,'linear'); % other options: 'splines', 'linear'
	289		end
	290		end
	291		function fig_path = my_save_fig(meta, fig_handle, fig_name)
	292		if ~isempty(fig_handle)
	293		label = '';
	294		if ~isempty(meta.fig_label)
	295		label = [label,'__', meta.fig_label];
	296		end
	297		if ~isempty(meta.meta_label)
	298		label = [label,'__', meta.meta_label];
	299		end
	300		meta.fig_label = ''; % reset fig_label after usage
	301		fig_path = fullfile(meta.common_path, [fig_name, label]);
	302		disp(['saving figures to ', fig_path, ' .fig / .pdf']);
	303		saveas(fig_handle, fig_path, 'fig');
	304		saveas(fig_handle, fig_path, 'pdf');
	305		close(fig_handle);
	306		try
	307		[stat,stdout] = system(['pdfcrop ', fig_path,'.pdf ', fig_path,'.pdf']);
	308		end
	309		end
	310		end
	311		function my_postprocess_plot(meta, fig_handle)
	312		figure(fig_handle);
	313		title(tex_escape(sentence_case(meta.meta_title)));
	314		grid
217	315	end	end
218		function common = get.common(meta)
219		if isempty(fieldnames(meta.common_private))
220		fields = {'inverted_evaluation';...
221		'mech_frequencies'; 'mech_frequencies_no';...
222		'pulse_gammas'; 'pulse_gammas_no'; 'pulse_widths';...
223		'detection_efficiency'; 'homodyne_visibility';...
224		'pulse_pairs_coverage'; 'pulse_pairs_per_trace'; 'pulse_duration_seconds';...
225		'samplingrate'; 'samples_per_trace'; 'samples_per_pulse';...
226		'tracepairs_no'; 'signal_power_muW'};
227		for k=1:numel(fields)
228		field = fields{k};
229		try
230		values = meta.for_each_evaluation(field);
231		equal_to_first = @(value)isequal(value, values{1});
232		all_equal = all(cellfun(equal_to_first, values));
233		if all_equal
234		common.(field) = values{1};
235		else
236		common.(field) = 'values differ';
237		end
238		catch
239		common.(field) = NaN;
240		end
241		end
242		meta.common_private = common;
243		else
244		common = meta.common_private;
245		end
246		end
247		function common_path = get.common_path(meta)
248		ev_files = {meta.ev_files{:}};
249		test_path = ev_files{1};
250		splitindices = strfind(test_path,'/');
251		for k=1:length(splitindices)
252		inds = 1:splitindices(k);
253		testfun = @(str)isequal(str(inds), test_path(inds));
254		if all(cellfun(testfun, ev_files))
255		common_path = test_path(inds);
256		end
257		end
258		end
259		function ev_dirs = get.ev_dirs(meta)
260		ev_dirs = cell(meta.dimensions);
261		for k=1:meta.evaluations_no
262		ev_dirs{k} = fileparts(meta.ev_files{k});
263		end
264		end
265		end
266		methods(Hidden)
267		function reset_hidden_props(meta)
268		meta.params_interp = {};
269		meta.opt_logneg_corrected_interp_private = {};
270		meta.opt_pulse_gammas_interp_private = {};
271		meta.common_private = struct();
272		end
273		function ln_or_pw_interp = interpolate_ln_pw(meta, ln_or_pw, varargin)
274		refinement_factor = 5;
275		if length(varargin)>0
276		refinement_factor = varargin{1};
277		end
278		switch meta.gridtype
279		case 'vector'
280		error('not implemented')
281		case 'matrix'
282		X = meta.params{1};
283		Xq = interp2(X,refinement_factor);
284		meta.params_interp{1} = Xq;
285		Y = meta.params{2};
286		Yq = interp2(Y,refinement_factor);
287		meta.params_interp{2} = Yq;
288		ln_or_pw_interp = interp2(X,Y,ln_or_pw,Xq,Yq,'linear'); % other options: 'splines', 'linear'
289		end
290		end
291		function fig_path = my_save_fig(meta, fig_handle, fig_name)
292		if ~isempty(fig_handle)
293		label = '';
294		if ~isempty(meta.fig_label)
295		label = [label,'__', meta.fig_label];
296		end
297		if ~isempty(meta.meta_label)
298		label = [label,'__', meta.meta_label];
299		end
300		meta.fig_label = ''; % reset fig_label after usage
301		fig_path = fullfile(meta.common_path, [fig_name, label]);
302		disp(['saving figures to ', fig_path, ' .fig / .pdf']);
303		saveas(fig_handle, fig_path, 'fig');
304		saveas(fig_handle, fig_path, 'pdf');
305		close(fig_handle);
306		try
307		[stat,stdout] = system(['pdfcrop ', fig_path,'.pdf ', fig_path,'.pdf']);
308		end
309		end
310		end
311		function my_postprocess_plot(meta, fig_handle)
312		figure(fig_handle);
313		title(tex_escape(sentence_case(meta.meta_title)));
314		grid
315		end
316	316	end	end
317	317	methods(Hidden, Static)	methods(Hidden, Static)
318		function [x_data, y_data, z_data] = limit_x_and_y(x_data_in, y_data_in, z_data_in, x_lims, y_lims)
319		if isempty(x_lims); x_lims = [-inf, inf]; end
320		if isempty(y_lims); y_lims = [-inf, inf]; end
	318		function [x_data, y_data, z_data] = limit_x_and_y(x_data_in, y_data_in, z_data_in, x_lims, y_lims)
	319		if isempty(x_lims); x_lims = [-inf, inf]; end
	320		if isempty(y_lims); y_lims = [-inf, inf]; end
321	321
322		x_allowed = intersect(find(x_data_in>=min(x_lims)), find(x_data_in<=max(x_lims)));
323		y_allowed = intersect(find(y_data_in>=min(y_lims)), find(y_data_in<=max(y_lims)));
324		allowed = intersect(x_allowed, y_allowed);
325		[allowed_x, allowed_y] = ind2sub(size(x_data_in), allowed);
326		x_min_ind = min(allowed_x);
327		x_max_ind = max(allowed_x);
328		y_min_ind = min(allowed_y);
329		y_max_ind = max(allowed_y);
330		x_data = x_data_in(x_min_ind:x_max_ind, y_min_ind:y_max_ind);
331		y_data = y_data_in(x_min_ind:x_max_ind, y_min_ind:y_max_ind);
332		z_data = z_data_in(x_min_ind:x_max_ind, y_min_ind:y_max_ind);
333		end
334		function xylims(x_lims, y_lims)
335		if ~isempty(x_lims); xlim(x_lims); end
336		if ~isempty(y_lims); ylim(y_lims); end
337		end
338		end
	322		x_allowed = intersect(find(x_data_in>=min(x_lims)), find(x_data_in<=max(x_lims)));
	323		y_allowed = intersect(find(y_data_in>=min(y_lims)), find(y_data_in<=max(y_lims)));
	324		allowed = intersect(x_allowed, y_allowed);
	325		[allowed_x, allowed_y] = ind2sub(size(x_data_in), allowed);
	326		x_min_ind = min(allowed_x);
	327		x_max_ind = max(allowed_x);
	328		y_min_ind = min(allowed_y);
	329		y_max_ind = max(allowed_y);
	330		x_data = x_data_in(x_min_ind:x_max_ind, y_min_ind:y_max_ind);
	331		y_data = y_data_in(x_min_ind:x_max_ind, y_min_ind:y_max_ind);
	332		z_data = z_data_in(x_min_ind:x_max_ind, y_min_ind:y_max_ind);
	333		end
	334		function xylims(x_lims, y_lims)
	335		if ~isempty(x_lims); xlim(x_lims); end
	336		if ~isempty(y_lims); ylim(y_lims); end
	337		end
	338		end
339	339	end	end

File data_analysis/@meta_ev/plot_cuts.m changed (mode: 100644) (index 9dcb9bd..30cd24e)
1	1	function plot_cuts(meta, plot_type, tracepairs_ind, par_ind, par_vals, varargin)	function plot_cuts(meta, plot_type, tracepairs_ind, par_ind, par_vals, varargin)
2		assertEqual(meta.gridtype,'matrix',...
3		'only implemented for gridtype "matrix"')
4		if isequal(par_vals, 'all')
5		meta_filtered = meta.copy;
6		else
7		meta_filtered = meta.filter_params(par_ind, par_vals);
8		end
	2		assertEqual(meta.gridtype,'matrix',...
	3		'only implemented for gridtype "matrix"')
	4		if isequal(par_vals, 'all')
	5		meta_filtered = meta.copy;
	6		else
	7		meta_filtered = meta.filter_params(par_ind, par_vals);
	8		end
9	9
10		switch lower(plot_type)
11		case {'logneg', 'opt_log_neg', 'entanglement'}
12		display_name = 'Max. log. negativity';
13		fig_handle = 'ln_cut_fig';
14		y_data = meta_filtered.opt_logneg_corrected{tracepairs_ind};
15		cmap = flipud(cbrewer('div','RdBu',512));
16		case {'pulsewidth', 'opt_pulse_gamma', 'opt_pulsewidth'}
17		display_name = 'Optimal pulse width [kHz]';
18		fig_handle = 'pw_cut_fig';
19		y_data = meta_filtered.opt_pulse_gammas{tracepairs_ind}/1E3;
20		cmap = cbrewer('seq','Blues',512);
21		otherwise
22		error(['Unsupported plottype: ', plot_type]);
	10		switch lower(plot_type)
	11		case {'logneg', 'opt_log_neg', 'entanglement'}
	12		display_name = 'Max. log. negativity';
	13		fig_handle = 'ln_cut_fig';
	14		y_data = meta_filtered.opt_logneg_corrected{tracepairs_ind};
	15		cmap = flipud(cbrewer('div','RdBu',512));
	16		case {'pulsewidth', 'opt_pulse_gamma', 'opt_pulsewidth'}
	17		display_name = 'Optimal pulse width [kHz]';
	18		fig_handle = 'pw_cut_fig';
	19		y_data = meta_filtered.opt_pulse_gammas{tracepairs_ind}/1E3;
	20		cmap = cbrewer('seq','Blues',512);
	21		otherwise
	22		error(['Unsupported plottype: ', plot_type]);
23	23	end	end
24	24
25		x_scale = 'lin'; y_scale = 'lin';
26		x_lims = []; y_lims = [];
27		fig = [];
28		linestyle = 'o:';
29		colororder = [];
30		legend_postfix = '';
31		legend_location = 'Best';
32		for k=1:2:length(varargin)
33		switch lower(varargin{k})
34		case {'x_scale','xscale'}
35		x_scale = varargin{k+1};
36		case {'style','linestyle'}
37		linestyle = varargin{k+1};
38		case {'x_lims','xlims','x_lim','xlim'}
39		x_lims = varargin{k+1};
40		case {'y_scale','yscale'}
41		y_scale = varargin{k+1};
42		case {'y_lims','ylims','y_lim','ylim'}
43		y_lims = varargin{k+1};
44		case {'figure','fig'}
45		fig = varargin{k+1};
46		case {'label','fig_label','figlabel'}
47		meta.fig_label = varargin{k+1};
48		case {'colororder', 'color_order', 'colors'}
49		colororder = varargin{k+1};
50		case {'legend_postfix','legendpostfix'}
51		legend_postfix = varargin{k+1};
52		case {'legend_position','legendposition','legend_location','legendlocation'}
53		legend_location = varargin{k+1};
54		otherwise
55		error(['unrecognized varargin-argument: ', varargin{k}]);
56		end
57		end
58		if isempty(fig)
59		meta.(fig_handle) = figure; hold all;
60		else
61		figure(fig); hold all;
62		meta.(fig_handle) = fig;
63		end
64		if ~isempty(colororder)
65		set(gca, 'ColorOrder', colororder);
66		end
	25		x_scale = 'lin'; y_scale = 'lin';
	26		x_lims = []; y_lims = [];
	27		fig = [];
	28		linestyle = 'o:';
	29		colororder = [];
	30		legend_postfix = '';
	31		legend_location = 'Best';
	32		for k=1:2:length(varargin)
	33		switch lower(varargin{k})
	34		case {'x_scale','xscale'}
	35		x_scale = varargin{k+1};
	36		case {'style','linestyle'}
	37		linestyle = varargin{k+1};
	38		case {'x_lims','xlims','x_lim','xlim'}
	39		x_lims = varargin{k+1};
	40		case {'y_scale','yscale'}
	41		y_scale = varargin{k+1};
	42		case {'y_lims','ylims','y_lim','ylim'}
	43		y_lims = varargin{k+1};
	44		case {'figure','fig'}
	45		fig = varargin{k+1};
	46		case {'label','fig_label','figlabel'}
	47		meta.fig_label = varargin{k+1};
	48		case {'colororder', 'color_order', 'colors'}
	49		colororder = varargin{k+1};
	50		case {'legend_postfix','legendpostfix'}
	51		legend_postfix = varargin{k+1};
	52		case {'legend_position','legendposition','legend_location','legendlocation'}
	53		legend_location = varargin{k+1};
	54		otherwise
	55		error(['unrecognized varargin-argument: ', varargin{k}]);
	56		end
	57		end
	58		if isempty(fig)
	59		meta.(fig_handle) = figure; hold all;
	60		else
	61		figure(fig); hold all;
	62		meta.(fig_handle) = fig;
	63		end
	64		if ~isempty(colororder)
	65		set(gca, 'ColorOrder', colororder);
	66		end
67	67
68		if par_ind==1
69		x_ind = 2;
70		x_data = meta_filtered.params{x_ind};
71		elseif par_ind==2
72		x_ind = 1;
73		x_data = meta_filtered.params{x_ind}';
74		y_data = y_data';
	68		if par_ind==1
	69		x_ind = 2;
	70		x_data = meta_filtered.params{x_ind};
	71		elseif par_ind==2
	72		x_ind = 1;
	73		x_data = meta_filtered.params{x_ind}';
	74		y_data = y_data';
75	75
76		end
77		if isempty(legend_postfix)
78		legend_parser = @(num) num2eng(num);
79		else
80		legend_parser = @(num) [num2eng(num), ' (', legend_postfix,')'];
81		end
82		cut_labels = arrayfun(legend_parser, unique(meta_filtered.params{par_ind}),...
83		'UniformOutput', false);
84		plot(x_data, y_data, linestyle, 'LineWidth', 1.5, 'DisplayName', cut_labels);
85		labels(meta_filtered.param_names{x_ind},display_name);
86		set(gca, 'xscale', x_scale, 'yscale', y_scale)
87		meta_ev.xylims(x_lims, y_lims);
88		leg = legend('Location', legend_location);
89		legtitle = get(leg,'title');
90		set(legtitle,'string', sentence_case(meta_filtered.param_names{par_ind}),...
91		'interpreter', 'latex','FontSize',11);
92		set(leg, 'Color', 'w', 'FontWeight', 'normal',...
93		'LineWidth', 1, 'Interpreter', 'latex','FontSize',11);
	76		end
	77		if isempty(legend_postfix)
	78		legend_parser = @(num) num2eng(num);
	79		else
	80		legend_parser = @(num) [num2eng(num), ' (', legend_postfix,')'];
	81		end
	82		cut_labels = arrayfun(legend_parser, unique(meta_filtered.params{par_ind}),...
	83		'UniformOutput', false);
	84		plot(x_data, y_data, linestyle, 'LineWidth', 1.5, 'DisplayName', cut_labels);
	85		labels(meta_filtered.param_names{x_ind},display_name);
	86		set(gca, 'xscale', x_scale, 'yscale', y_scale)
	87		meta_ev.xylims(x_lims, y_lims);
	88		leg = legend('Location', legend_location);
	89		legtitle = get(leg,'title');
	90		set(legtitle,'string', sentence_case(meta_filtered.param_names{par_ind}),...
	91		'interpreter', 'latex','FontSize',11);
	92		set(leg, 'Color', 'w', 'FontWeight', 'normal',...
	93		'LineWidth', 1, 'Interpreter', 'latex','FontSize',11);
94	94
95		meta.my_postprocess_plot(meta.(fig_handle));
96		if isequal(display_name, 'Max. log. negativity')
97		hline=refline(0,0); set(hline, 'Color', 'k', 'DisplayName',' ');
98		end
	95		meta.my_postprocess_plot(meta.(fig_handle));
	96		if isequal(display_name, 'Max. log. negativity')
	97		hline=refline(0,0); set(hline, 'Color', 'k', 'DisplayName',' ');
	98		end

File data_analysis/@meta_ev/plot_logneg_versus_gamma.m changed (mode: 100644) (index 8627db9..d129631)
1	1	function plot_logneg_versus_gamma(meta, tracepairs_ind, varargin)	function plot_logneg_versus_gamma(meta, tracepairs_ind, varargin)
2		cmap = cbrewer('qual', 'Set1', meta.evaluations_no);
3		x_scale = 'log'; y_scale = 'lin';
4		x_lims = []; y_lims = [];
5		fig = [];
6		for k=1:2:length(varargin)
7		switch lower(varargin{k})
8		case {'x_scale','xscale'}
9		x_scale = varargin{k+1};
10		case {'x_lims','xlims','x_lim','xlim'}
11		x_lims = varargin{k+1};
12		case {'y_scale','yscale'}
13		y_scale = varargin{k+1};
14		case {'y_lims','ylims','y_lim','ylim'}
15		y_lims = varargin{k+1};
16		case {'label','fig_label','figlabel'}
17		meta.fig_label = varargin{k+1};
18		case {'colororder', 'color_order', 'colors'}
19		cmap = varargin{k+1};
20		case {'fig', 'figure'}
21		fig = varargin{k+1};
22		otherwise
23		error(['unrecognized varargin-argument: ', varargin{k}]);
24		end
25		end
26		if isempty(fig)
27		fig = figure;
28		else
	2		cmap = cbrewer('qual', 'Set1', meta.evaluations_no);
	3		x_scale = 'log'; y_scale = 'lin';
	4		x_lims = []; y_lims = [];
	5		fig = [];
	6		for k=1:2:length(varargin)
	7		switch lower(varargin{k})
	8		case {'x_scale','xscale'}
	9		x_scale = varargin{k+1};
	10		case {'x_lims','xlims','x_lim','xlim'}
	11		x_lims = varargin{k+1};
	12		case {'y_scale','yscale'}
	13		y_scale = varargin{k+1};
	14		case {'y_lims','ylims','y_lim','ylim'}
	15		y_lims = varargin{k+1};
	16		case {'label','fig_label','figlabel'}
	17		meta.fig_label = varargin{k+1};
	18		case {'colororder', 'color_order', 'colors'}
	19		cmap = varargin{k+1};
	20		case {'fig', 'figure'}
	21		fig = varargin{k+1};
	22		otherwise
	23		error(['unrecognized varargin-argument: ', varargin{k}]);
	24		end
	25		end
	26		if isempty(fig)
	27		fig = figure;
	28		else
29	29	visibility = get(fig,'visible');	visibility = get(fig,'visible');
30	30	figure(fig); hold all; set(fig, 'visible', visibility);	figure(fig); hold all; set(fig, 'visible', visibility);
31	31	end	end
32		myplotlogneg = @(ev, color_ind) ev.plotLogneg('figure', fig,...
33		'tracepairs', tracepairs_ind, 'save_fig', false, ...
34		'color', cmap(color_ind,:),...
35		'title', '', 'legend', false,...
36		'show_physicality', false);
37		for k=1:meta.evaluations_no
38		myplotlogneg(meta.evaluations{k}, k);
39		end
40		set(gca, 'xscale', x_scale, 'yscale', y_scale)
41		meta.xylims(x_lims, y_lims);
42		meta.my_postprocess_plot(fig);
43		meta.ln_fig = fig;
	32		myplotlogneg = @(ev, color_ind) ev.plotLogneg('figure', fig,...
	33		'tracepairs', tracepairs_ind, 'save_fig', false, ...
	34		'color', cmap(color_ind,:),...
	35		'title', '', 'legend', false,...
	36		'show_physicality', false);
	37		for k=1:meta.evaluations_no
	38		myplotlogneg(meta.evaluations{k}, k);
	39		end
	40		set(gca, 'xscale', x_scale, 'yscale', y_scale)
	41		meta.xylims(x_lims, y_lims);
	42		meta.my_postprocess_plot(fig);
	43		meta.ln_fig = fig;

File data_analysis/auxfunctions/OOP/assert_inherits_from.m changed (mode: 100644) (index 878e47e..f54da51)
1	1	function assert_inherits_from(obj, target_class)	function assert_inherits_from(obj, target_class)
2		myclass = class(obj);
3		supclasses = superclasses(myclass);
	2		myclass = class(obj);
	3		supclasses = superclasses(myclass);
4	4
5		is_target_class = isequal(myclass, target_class);
6		inherits_from_target_class = any(ismember(supclasses, target_class));
	5		is_target_class = isequal(myclass, target_class);
	6		inherits_from_target_class = any(ismember(supclasses, target_class));
7	7
8		assert(is_target_class || inherits_from_target_class,...
9		['Class of input is ', myclass, ' which does not inherit from ', target_class])
	8		assert(is_target_class || inherits_from_target_class,...
	9		['Class of input is ', myclass, ' which does not inherit from ', target_class])

File data_analysis/auxfunctions/files/append_string_to_txtfile.m changed (mode: 100644) (index cfe9f42..e0a6eb2)
1	1	%% APPEND_STRING_TO_TXTFILE(string, file): takes a string and a file(name)	%% APPEND_STRING_TO_TXTFILE(string, file): takes a string and a file(name)
2	2	% and appends the string to the file (on a new line)	% and appends the string to the file (on a new line)
3	3	function append_string_to_txtfile(string, file)	function append_string_to_txtfile(string, file)
4		fid = fopen(file, 'a+');
5		fprintf(fid, '%s\n', string);
6		fclose(fid);
	4		fid = fopen(file, 'a+');
	5		fprintf(fid, '%s\n', string);
	6		fclose(fid);
7	7	end	end

File data_analysis/auxfunctions/files/isafile.m changed (mode: 100644) (index d012988..cfd6d2c)
1	1	function [ boolean ] = isafile( filePath, varargin)	function [ boolean ] = isafile( filePath, varargin)
2		%ISAFILE takes a file path and returns true if the path exists and points to a file and false otherwise
3		% Detailed explanation goes here
4		% if called with several input arguments it returns true if ALL inputs are filepaths
5		boolean = isafile_aux(filePath);
6		for k=1:length(varargin)
7		boolean = boolean && isafile_aux(varargin{k});
8		end
	2		%ISAFILE takes a file path and returns true if the path exists and points to a file and false otherwise
	3		% Detailed explanation goes here
	4		% if called with several input arguments it returns true if ALL inputs are filepaths
	5		boolean = isafile_aux(filePath);
	6		for k=1:length(varargin)
	7		boolean = boolean && isafile_aux(varargin{k});
	8		end
9	9
10	10	function boolean = isafile_aux(filePath)	function boolean = isafile_aux(filePath)
11		boolean = isequal(exist(filePath,'file'),2);
	11		boolean = isequal(exist(filePath,'file'),2);

File data_analysis/auxfunctions/files/pseudo_hash.m changed (mode: 100644) (index bc5bfc3..d885418)
1	1	function hashsum = pseudo_hash(filepath, varargin)	function hashsum = pseudo_hash(filepath, varargin)
2		% takes path to a file which may contain large amounts of binary data
3		% and returns a quick "pseudo-hashsum" by inspecting the beginning and end of the file
4		%
5		% details:
6		% reads (up to) first 1e6 and last 1e6 bytes plus the filesize in bytes into an array
7		% returns SHA1-hashsum of the array
8		%
9		% TODO: opening a file for writing (but not writing anything) should not change the pseudo-hash
10		assert(exist(filepath)==2,...
11		['File ', filepath, ' not found.']);
	2		% takes path to a file which may contain large amounts of binary data
	3		% and returns a quick "pseudo-hashsum" by inspecting the beginning and end of the file
	4		%
	5		% details:
	6		% reads (up to) first 1e6 and last 1e6 bytes plus the filesize in bytes into an array
	7		% returns SHA1-hashsum of the array
	8		%
	9		% TODO: opening a file for writing (but not writing anything) should not change the pseudo-hash
	10		assert(exist(filepath)==2,...
	11		['File ', filepath, ' not found.']);
12	12
13		fileinfo = dir(filepath);
14		byte_no = fileinfo.bytes;
	13		fileinfo = dir(filepath);
	14		byte_no = fileinfo.bytes;
15	15
16		bytes = 1e6;
17		short = false;
18		for k=1:2:length(varargin)
19		switch varargin{k}
20		case 'bytes'
21		bytes = varargin{k+1};
22		case 'short'
23		short = varargin{k+1};
24		otherwise
25		error(['Unrecognized varargin-argument:', varargin{k}]);
26		end
27		end
	16		bytes = 1e6;
	17		short = false;
	18		for k=1:2:length(varargin)
	19		switch varargin{k}
	20		case 'bytes'
	21		bytes = varargin{k+1};
	22		case 'short'
	23		short = varargin{k+1};
	24		otherwise
	25		error(['Unrecognized varargin-argument:', varargin{k}]);
	26		end
	27		end
28	28
29		fid = fopen(filepath,'r');
30		data_start = fread(fid, bytes);
31		fseek(fid, bytes, 'eof');
32		data_end = fread(fid, bytes);
33		fclose(fid);
	29		fid = fopen(filepath,'r');
	30		data_start = fread(fid, bytes);
	31		fseek(fid, bytes, 'eof');
	32		data_end = fread(fid, bytes);
	33		fclose(fid);
34	34
35		data = [data_start; data_end; byte_no];
	35		data = [data_start; data_end; byte_no];
36	36
37		opt.Method = 'SHA-1';
38		hashsum = DataHash(data, opt);
39		if short
40		hashsum = hashsum(1:8);
	37		opt.Method = 'SHA-1';
	38		hashsum = DataHash(data, opt);
	39		if short
	40		hashsum = hashsum(1:8);
41	41	end	end

File data_analysis/auxfunctions/gitrepos/get_git_status.m changed (mode: 100644) (index 72dec79..922c8c3)
...	...	function status = get_git_status(git_repo)
3	3	cd(git_repo);	cd(git_repo);
4	4	status = git('status');	status = git('status');
5	5	cd(working_dir)	cd(working_dir)
6
	6
7	7	clean_status_string = 'nothing to commit, working directory clean';	clean_status_string = 'nothing to commit, working directory clean';
8	8	if isempty(strfind(status, clean_status_string))	if isempty(strfind(status, clean_status_string))
9		cd(git_repo);
10		gitdiff = git('diff --name-status');
11		cd(working_dir)
12
13		status = ['DIRTY',10, gitdiff];
	9		cd(git_repo);
	10		gitdiff = git('diff --name-status');
	11		cd(working_dir)
	12
	13		status = ['DIRTY',10, gitdiff];
14	14	else	else
15		status = 'CLEAN';
	15		status = 'CLEAN';
16	16	end	end

File data_analysis/auxfunctions/gitrepos/status_git_repos_on_path.m changed (mode: 100644) (index 7446e4a..8c7a3bc)
1	1	function gitstring = status_git_repos_on_path	function gitstring = status_git_repos_on_path
2		gitrepos_array = git_repos_on_path;
3		horiz_line = [10,repmat('_',1,72),10,10];
4		gitstring = horiz_line;
5		for k=1:length(gitrepos_array)
6		git_repo = gitrepos_array{k};
7		git_commit = get_git_commit_hash(git_repo);
8		git_status = get_git_status(git_repo);
	2		gitrepos_array = git_repos_on_path;
	3		horiz_line = [10,repmat('_',1,72),10,10];
	4		gitstring = horiz_line;
	5		for k=1:length(gitrepos_array)
	6		git_repo = gitrepos_array{k};
	7		git_commit = get_git_commit_hash(git_repo);
	8		git_status = get_git_status(git_repo);
9	9
10		gitstring = [gitstring, git_repo, 9, git_commit, 9, git_status, horiz_line];
11		end
	10		gitstring = [gitstring, git_repo, 9, git_commit, 9, git_status, horiz_line];
	11		end

File data_analysis/auxfunctions/gitrepos/test_git_conveniences.m changed (mode: 100644) (index 71bec51..7b31008)
1	1	function test_suite = get_git_commit_hash_test %#ok<STOUT>	function test_suite = get_git_commit_hash_test %#ok<STOUT>
2		clc; initTestSuite;
	2		clc; initTestSuite;
3	3
4	4	function shared = setup	function shared = setup
5		shared.working_dir = pwd;
6		shared.testrepo = '__testrepo__';
7		shared.testrepo2 = '__testrepo2__';
8		shared.testfile = 'test.txt';
	5		shared.working_dir = pwd;
	6		shared.testrepo = '__testrepo__';
	7		shared.testrepo2 = '__testrepo2__';
	8		shared.testfile = 'test.txt';
9	9
10	10	% create test repository	% create test repository
11	11	git('init',shared.testrepo);	git('init',shared.testrepo);
...	...	function shared = setup
13	13	dlmwrite(shared.testfile, [1,2,3], 'delimiter', 't');	dlmwrite(shared.testfile, [1,2,3], 'delimiter', 't');
14	14	git('add', shared.testfile);	git('add', shared.testfile);
15	15	git commit -m "Initialize repo"	git commit -m "Initialize repo"
16		cd(shared.working_dir);
	16		cd(shared.working_dir);
17	17
18		% create test repository 2
19		git('init',shared.testrepo2);
20		cd(shared.testrepo2);
21		dlmwrite(shared.testfile, [1,2,3], 'delimiter', 't');
22		git('add', shared.testfile);
23		git commit -m "Initialize repo 2"
24		cd(shared.working_dir);
	18		% create test repository 2
	19		git('init',shared.testrepo2);
	20		cd(shared.testrepo2);
	21		dlmwrite(shared.testfile, [1,2,3], 'delimiter', 't');
	22		git('add', shared.testfile);
	23		git commit -m "Initialize repo 2"
	24		cd(shared.working_dir);
25	25
26	26	function teardown(shared)	function teardown(shared)
27	27	rmdir(shared.testrepo, 's');	rmdir(shared.testrepo, 's');
28		rmdir(shared.testrepo2, 's');
29		cd(shared.working_dir)
30		clear('shared');
	28		rmdir(shared.testrepo2, 's');
	29		cd(shared.working_dir)
	30		clear('shared');
31	31
32	32	function ht = test_identical_repos(shared)	function ht = test_identical_repos(shared)
33		hash = get_git_commit_hash(shared.testrepo);
34		hash_ = get_git_commit_hash(shared.testrepo);
35		hash__ = get_git_commit_hash(GetFullPath(shared.testrepo));
36		assertEqual(hash, hash_)
37		assertEqual(hash, hash__)
	33		hash = get_git_commit_hash(shared.testrepo);
	34		hash_ = get_git_commit_hash(shared.testrepo);
	35		hash__ = get_git_commit_hash(GetFullPath(shared.testrepo));
	36		assertEqual(hash, hash_)
	37		assertEqual(hash, hash__)
38	38
39	39	function ht = test_identical_remote_repos(shared)	function ht = test_identical_remote_repos(shared)
40		hash = get_git_commit_hash(shared.testrepo);
41		cloned_repo = '__anothertestrepo__'
42		git('clone', shared.testrepo, cloned_repo)
43		hash = get_git_commit_hash(shared.testrepo);
44		hash_ = get_git_commit_hash(cloned_repo);
45		assertEqual(hash, hash_)
46		rmdir(cloned_repo, 's')
	40		hash = get_git_commit_hash(shared.testrepo);
	41		cloned_repo = '__anothertestrepo__'
	42		git('clone', shared.testrepo, cloned_repo)
	43		hash = get_git_commit_hash(shared.testrepo);
	44		hash_ = get_git_commit_hash(cloned_repo);
	45		assertEqual(hash, hash_)
	46		rmdir(cloned_repo, 's')
47	47
48	48	function ht = test_distinct_remote_repos(shared)	function ht = test_distinct_remote_repos(shared)
49		hash = get_git_commit_hash(shared.testrepo);
50		cloned_repo = '__anothertestrepo__'
51		git('clone', shared.testrepo, cloned_repo)
52		cd(cloned_repo)
53		dlmwrite('test2.txt', [1,2,3], 'delimiter', 't');
54		git('add', 'test2.txt');
55		git commit -m "Modify repo"
56		cd(shared.working_dir)
57		hash = get_git_commit_hash(shared.testrepo);
58		hash_ = get_git_commit_hash(cloned_repo);
59		assertFalse(isequal(hash, hash_))
60		rmdir(cloned_repo, 's')
	49		hash = get_git_commit_hash(shared.testrepo);
	50		cloned_repo = '__anothertestrepo__'
	51		git('clone', shared.testrepo, cloned_repo)
	52		cd(cloned_repo)
	53		dlmwrite('test2.txt', [1,2,3], 'delimiter', 't');
	54		git('add', 'test2.txt');
	55		git commit -m "Modify repo"
	56		cd(shared.working_dir)
	57		hash = get_git_commit_hash(shared.testrepo);
	58		hash_ = get_git_commit_hash(cloned_repo);
	59		assertFalse(isequal(hash, hash_))
	60		rmdir(cloned_repo, 's')
61	61
62	62	function ht = test_distinct_repos(shared)	function ht = test_distinct_repos(shared)
63		hash = get_git_commit_hash(shared.testrepo);
64		hash2 = get_git_commit_hash(shared.testrepo2);
65		assertFalse(isequal(hash, hash2))
	63		hash = get_git_commit_hash(shared.testrepo);
	64		hash2 = get_git_commit_hash(shared.testrepo2);
	65		assertFalse(isequal(hash, hash2))
66	66
67	67	function ht = test_status(shared)	function ht = test_status(shared)
68		status = get_git_status(shared.testrepo);
69		assertEqual(status, 'CLEAN');
	68		status = get_git_status(shared.testrepo);
	69		assertEqual(status, 'CLEAN');
70	70
71		% locally modify testrepo
72		cd(shared.testrepo);
73		dlmwrite(shared.testfile, [1,2,3,4,5], 'delimiter', 't');
74		cd(shared.working_dir);
75		status = get_git_status(shared.testrepo);
76		assertEqual(status(1:5), 'DIRTY');
	71		% locally modify testrepo
	72		cd(shared.testrepo);
	73		dlmwrite(shared.testfile, [1,2,3,4,5], 'delimiter', 't');
	74		cd(shared.working_dir);
	75		status = get_git_status(shared.testrepo);
	76		assertEqual(status(1:5), 'DIRTY');

File data_analysis/auxfunctions/matrixcomp/householder_ON.m changed (mode: 100644 -> 100755) (index 11302df..45fe333)
...	...	function Q = householder_ON(A)
3	3	% produces an m-by-m othogonal matrix Q out of the n-columns of A (m-by-n),	% produces an m-by-m othogonal matrix Q out of the n-columns of A (m-by-n),
4	4	% s.t. if the columns of A are independent the first n columns of Q span	% s.t. if the columns of A are independent the first n columns of Q span
5	5	% the same sub vector space.	% the same sub vector space.
6		% This algorithm is based on Housholder relfections
	6		% This algorithm is based on Housholder relfections
7	7	% (see https://en.wikipedia.org/wiki/QR_decomposition#Using_Householder_reflections)	% (see https://en.wikipedia.org/wiki/QR_decomposition#Using_Householder_reflections)
8	8	% and provide more robust othogonality than a modified Gram-Schmidt	% and provide more robust othogonality than a modified Gram-Schmidt
9	9	% algorithm; in the sense that the deviation of Q*Q' from identity marix is	% algorithm; in the sense that the deviation of Q*Q' from identity marix is
...	...	for k=1:min(m-1,n)
19	19	end	end
20	20
21	21	for k=min(m-1,n):-1:1	for k=min(m-1,n):-1:1
22		Q = Q - 2*v(:,k)*(v(:,k)'*Q);
	22		Q = Q - 2*v(:,k)*(v(:,k)'*Q);
23	23	end	end

File data_analysis/auxfunctions/matrixcomp/rootm.m changed (mode: 100644) (index d74f274..c420eee)
...	...	for c = 1:n-1
41	41	for i = 1:n-c	for i = 1:n-c
42	42	sum1 = 0;	sum1 = 0;
43	43	for d = 1:p-2	for d = 1:p-2
44		sum2 = 0;
45		for k = i+1:i+c-1
	44		sum2 = 0;
	45		for k = i+1:i+c-1
46	46	sum2 = sum2 + U(i,k)*R(k,(d-1)*n + i+c);	sum2 = sum2 + U(i,k)*R(k,(d-1)*n + i+c);
47	47	end	end
48	48	sum1 = sum1 + U(i,i)^(p-2-d)*sum2;	sum1 = sum1 + U(i,i)^(p-2-d)*sum2;
49		end
50		sum3 = 0;
	49		end
	50		sum3 = 0;
51	51	for k = i+1:i+c-1	for k = i+1:i+c-1
52	52	sum3 = sum3 + U(i,k)*U(k,i+c);	sum3 = sum3 + U(i,k)*U(k,i+c);
53	53	end	end
...	...	for c = 1:n-1
55	55	sum4 = 0;	sum4 = 0;
56	56	for j = 0:p-1	for j = 0:p-1
57	57	sum4 = sum4 + U(i,i)^j*U(i+c,i+c)^(p-1-j);	sum4 = sum4 + U(i,i)^j*U(i+c,i+c)^(p-1-j);
58		end
	58		end
59	59
60	60	U(i,i+c) = (T(i,i+c) - sum1)/(sum4);	U(i,i+c) = (T(i,i+c) - sum1)/(sum4);
61	61
...	...	for c = 1:n-1
67	67	sum6 = 0;	sum6 = 0;
68	68	for h = 1:q-1	for h = 1:q-1
69	69	sum7 = 0;	sum7 = 0;
70		for w=i+1:i+c-1
71		sum7 = sum7 + U(i,w)*R(w,(h-1)*n +i+c);
	70		for w=i+1:i+c-1
	71		sum7 = sum7 + U(i,w)*R(w,(h-1)*n +i+c);
72	72	end	end
73	73	sum6 = sum6 + U(i,i)^(q-1-h)*sum7;	sum6 = sum6 + U(i,i)^(q-1-h)*sum7;
74	74	end	end

File data_analysis/auxfunctions/monitor/estimated_time_of_arrival.m changed (mode: 100644) (index f0b4f37..7a6396f)
1	1	function print_eta = estimated_time_of_arrival()	function print_eta = estimated_time_of_arrival()
2		start_time = clock;
3
	2		start_time = clock;
	3
4	4	function print_eta_fct(iterations_passed, iteration_no)	function print_eta_fct(iterations_passed, iteration_no)
5		time_diff = etime(clock, start_time);
6		est_duration_min = (time_diff/60)*iteration_no/iterations_passed;
	5		time_diff = etime(clock, start_time);
	6		est_duration_min = (time_diff/60)*iteration_no/iterations_passed;
7	7	precision = 3;	precision = 3;
8		if est_duration_min < 1
9		return
	8		if est_duration_min < 1
	9		return
10	10	%disp(['estimated duration [seconds]: ',...	%disp(['estimated duration [seconds]: ',...
11	11	%num2str(60*est_duration_min, precision)]);	%num2str(60*est_duration_min, precision)]);
12	12
13		elseif est_duration_min < 60
14		disp(['estimated duration [minutes]: ',...
	13		elseif est_duration_min < 60
	14		disp(['estimated duration [minutes]: ',...
15	15	num2str(est_duration_min, precision)]);	num2str(est_duration_min, precision)]);
16		elseif est_duration_min < 60*24
17		disp(['estimated duration [hours]: ',...
	16		elseif est_duration_min < 60*24
	17		disp(['estimated duration [hours]: ',...
18	18	num2str(est_duration_min/60, precision)]);	num2str(est_duration_min/60, precision)]);
19		else
20		disp(['estimated duration [days]: ',...
	19		else
	20		disp(['estimated duration [days]: ',...
21	21	num2str(est_duration_min/(24*60), precision)]);	num2str(est_duration_min/(24*60), precision)]);
22		end
23		est_arrival = datestr(addtodate(now, round(est_duration_min*60),'second'));
24		disp(['estimated time of arrival: ',...
25		est_arrival]);
	22		end
	23		est_arrival = datestr(addtodate(now, round(est_duration_min*60),'second'));
	24		disp(['estimated time of arrival: ',...
	25		est_arrival]);
26	26	end	end
27	27
28	28	print_eta = @print_eta_fct;	print_eta = @print_eta_fct;

File data_analysis/auxfunctions/orthonormality/assert_orthonormality.m changed (mode: 100644) (index 5316acf..252d674)
1	1	function on_info = assert_orthonormality(Q, norm_tol, orthog_tol)	function on_info = assert_orthonormality(Q, norm_tol, orthog_tol)
2		% Asserts that the rows of matrix Q are mutually orthonormal
3		% within numerical tolerances specified by 'norm_tol' and 'orthog_tol'
4		% If the assertion fails, additional information is displayed
5		% about the cause of the failure and the size of the deviation
6
7		failure = false;
8		[~, orthog, normal, on_info] = orthonormality(Q, norm_tol, orthog_tol);
9
10		if ~orthog
11		failure = true;
12
13		% display info about the cause and size of failure
14		[deviations, sortorder] = sort(on_info.orthog.deviations, 'descend');
15		off_cols = on_info.orthog.offenders.cols(sortorder);
16		off_rows = on_info.orthog.offenders.rows(sortorder);
17
18		disp(['\nPairs of rows violating orthogonality & amount of violation:',10,...
19		'(sorted in order of decreasing violation)'])
20		disp(arrayfun(@num2str, [off_cols, off_rows, deviations],...
21		'UniformOutput', false))
22		disp('Orthogonality test failed!');
23		% disp(arrayfun(@num2str, Q*Q', 'UniformOutput', false))
24		end
25
26		if ~normal
27		failure = true;
28
29		% display info about the cause and size of failure
30		[deviations, sortorder] = sort(on_info.norm.deviations, 'descend');
31		offenders = on_info.norm.offenders(sortorder);
32
33		disp(['Rows violating normality and amount of violation:',10,...
34		'(sorted in order of decreasing violation)'])
35		disp(arrayfun(@num2str, [offenders, deviations],...
36		'UniformOutput', false))
37		disp('Normality test failed!');
38		end
39
40		if failure
41		error('Orthonormality test failed!')
42		end
	2		% Asserts that the rows of matrix Q are mutually orthonormal
	3		% within numerical tolerances specified by 'norm_tol' and 'orthog_tol'
	4		% If the assertion fails, additional information is displayed
	5		% about the cause of the failure and the size of the deviation
	6
	7		failure = false;
	8		[~, orthog, normal, on_info] = orthonormality(Q, norm_tol, orthog_tol);
	9
	10		if ~orthog
	11		failure = true;
	12
	13		% display info about the cause and size of failure
	14		[deviations, sortorder] = sort(on_info.orthog.deviations, 'descend');
	15		off_cols = on_info.orthog.offenders.cols(sortorder);
	16		off_rows = on_info.orthog.offenders.rows(sortorder);
	17
	18		disp(['\nPairs of rows violating orthogonality & amount of violation:',10,...
	19		'(sorted in order of decreasing violation)'])
	20		disp(arrayfun(@num2str, [off_cols, off_rows, deviations],...
	21		'UniformOutput', false))
	22		disp('Orthogonality test failed!');
	23		% disp(arrayfun(@num2str, Q*Q', 'UniformOutput', false))
	24		end
	25
	26		if ~normal
	27		failure = true;
	28
	29		% display info about the cause and size of failure
	30		[deviations, sortorder] = sort(on_info.norm.deviations, 'descend');
	31		offenders = on_info.norm.offenders(sortorder);
	32
	33		disp(['Rows violating normality and amount of violation:',10,...
	34		'(sorted in order of decreasing violation)'])
	35		disp(arrayfun(@num2str, [offenders, deviations],...
	36		'UniformOutput', false))
	37		disp('Normality test failed!');
	38		end
	39
	40		if failure
	41		error('Orthonormality test failed!')
	42		end

File data_analysis/auxfunctions/orthonormality/orthonormality.m changed (mode: 100644) (index d05e4db..f55635c)
...	...	function [orthonorm, orthog, normal, on_info] = orthonormality(Q, norm_tol, orth
2	2	% [orthonorm, orthog, normal, info] = orthonormality(Q, norm_tol, orthog_tol)	% [orthonorm, orthog, normal, info] = orthonormality(Q, norm_tol, orthog_tol)
3	3	% returns a boolean 'orthonorm' indicating whether the rows of matrix Q	% returns a boolean 'orthonorm' indicating whether the rows of matrix Q
4	4	% are orthonormal within numerical tolerances norn_tol and orthog_tol.	% are orthonormal within numerical tolerances norn_tol and orthog_tol.
5		%
	5		%
6	6	% The booleans 'orthog' and 'normal' indicate orthogonality and normality separately.	% The booleans 'orthog' and 'normal' indicate orthogonality and normality separately.
7	7	%	%
8	8	% orthonorm (orthog, normal) = True ==> All rows are orthonormal (orthogonal, normal).	% orthonorm (orthog, normal) = True ==> All rows are orthonormal (orthogonal, normal).
...	...	function [orthonorm, orthog, normal, on_info] = orthonormality(Q, norm_tol, orth
12	12	%	%
13	13	% Basic Idea:	% Basic Idea:
14	14	% for QQ^T, check if the diagonal elements are within a tolerence 'norm_tol'	% for QQ^T, check if the diagonal elements are within a tolerence 'norm_tol'
15		% from 1 (assesses the normalisation) and if the non-diagonal elements are
16		% within a tolerence 'orthog_tol' from 0 (assesses orthogonality).
	15		% from 1 (assesses the normalisation) and if the non-diagonal elements are
	16		% within a tolerence 'orthog_tol' from 0 (assesses orthogonality).
17	17
18	18	on_info = {};	on_info = {};
19	19
...	...	orthog = isempty(orthog_viol_rows);
43	43	if ~orthog	if ~orthog
44	44	on_info.orthog.offenders.cols = orthog_viol_cols;	on_info.orthog.offenders.cols = orthog_viol_cols;
45	45	on_info.orthog.offenders.rows = orthog_viol_rows;	on_info.orthog.offenders.rows = orthog_viol_rows;
46
	46
47	47	orthog_critical_devs = zeros(size(orthog_viol_rows));	orthog_critical_devs = zeros(size(orthog_viol_rows));
48	48	for k=1:length(orthog_viol_rows)	for k=1:length(orthog_viol_rows)
49	49	row = orthog_viol_rows(k);	row = orthog_viol_rows(k);

File data_analysis/auxfunctions/permute_data/permute_data.m changed (mode: 100644) (index 27b5818..a787395)
1	1	function data_permuted = permute_data(data_original, varargin)	function data_permuted = permute_data(data_original, varargin)
2		% permute_data takes traces with quadratures ordered as 'q1', 'q2',... 'p1',
3		% 'p2',... 'x1', 'x2',... 'y1', 'y2' and returns the the same traces
4		% reordered with quadratures ordered as 'q1', 'p1', 'q2',
5		%'p2',... 'x1', 'y1', 'x2', 'y2',...
6		%
7		% permute_data(traces, 'inverse', true) performs the inverse reordering.
	2		% permute_data takes traces with quadratures ordered as 'q1', 'q2',... 'p1',
	3		% 'p2',... 'x1', 'x2',... 'y1', 'y2' and returns the the same traces
	4		% reordered with quadratures ordered as 'q1', 'p1', 'q2',
	5		%'p2',... 'x1', 'y1', 'x2', 'y2',...
	6		%
	7		% permute_data(traces, 'inverse', true) performs the inverse reordering.
8	8
9		inverse = false;
10		for k=1:2:length(varargin)
11		switch varargin{k}
12		case {'inverse', 'Inverse'}
13		inverse = varargin{k+1};
14		otherwise
15		error(['Unrecognized varargin-argument ', varargin{k}]);
16		end
17		end
	9		inverse = false;
	10		for k=1:2:length(varargin)
	11		switch varargin{k}
	12		case {'inverse', 'Inverse'}
	13		inverse = varargin{k+1};
	14		otherwise
	15		error(['Unrecognized varargin-argument ', varargin{k}]);
	16		end
	17		end
18	18
19		col_no = size(data_original, 2);
20		mode_no = col_no/4;
21
22		perm_vec = [];
23		perm_vec_stub = [1:mode_no:col_no/2];
24		for k=1:mode_no
25		perm_vec = [perm_vec, perm_vec_stub + (k -1)];
26		end
	19		col_no = size(data_original, 2);
	20		mode_no = col_no/4;
27	21
28		perm_vec_stub = perm_vec_stub + 2*mode_no;
29		for k=1:mode_no
30		perm_vec = [perm_vec, perm_vec_stub + (k -1)];
31		end
	22		perm_vec = [];
	23		perm_vec_stub = [1:mode_no:col_no/2];
	24		for k=1:mode_no
	25		perm_vec = [perm_vec, perm_vec_stub + (k -1)];
	26		end
32	27
33		if inverse
34		perm_vec_inv(perm_vec) = 1:length(perm_vec);
35		perm_vec = perm_vec_inv;
36		end
37		%disp(perm_vec)
38		data_permuted = data_original(:,perm_vec);
	28		perm_vec_stub = perm_vec_stub + 2*mode_no;
	29		for k=1:mode_no
	30		perm_vec = [perm_vec, perm_vec_stub + (k -1)];
	31		end
	32
	33		if inverse
	34		perm_vec_inv(perm_vec) = 1:length(perm_vec);
	35		perm_vec = perm_vec_inv;
	36		end
	37		%disp(perm_vec)
	38		data_permuted = data_original(:,perm_vec);

File data_analysis/auxfunctions/permute_data/read_permute_write_data.m changed (mode: 100644) (index e04d610..24d232d)
1	1	function [permuted_data_file, permuted_data_CM_file] = read_permute_write_data(fullpath, varargin)	function [permuted_data_file, permuted_data_CM_file] = read_permute_write_data(fullpath, varargin)
2		data_original = dlmread(fullpath, '\t');
3		data_permuted = permute_data(data_original, varargin{:});
4		dlmwrite(fullpath, data_permuted, '\t');
5		permuted_data_file = fullpath;
	2		data_original = dlmread(fullpath, '\t');
	3		data_permuted = permute_data(data_original, varargin{:});
	4		dlmwrite(fullpath, data_permuted, '\t');
	5		permuted_data_file = fullpath;
6	6
7		CM = cov(data_permuted);
8		[folder, filename, fileext] = fileparts(fullpath);
9		CM_filepath = fullfile(folder, ['CM_', filename, fileext]);
10		dlmwrite(CM_filepath, CM, '\t');
11		permuted_data_CM_file = CM_filepath;
	7		CM = cov(data_permuted);
	8		[folder, filename, fileext] = fileparts(fullpath);
	9		CM_filepath = fullfile(folder, ['CM_', filename, fileext]);
	10		dlmwrite(CM_filepath, CM, '\t');
	11		permuted_data_CM_file = CM_filepath;

File data_analysis/auxfunctions/permute_data/read_permute_write_data__folder.m changed (mode: 100644) (index 30ba997..60a02d7)
1	1	function read_permute_write_data__folder()	function read_permute_write_data__folder()
2		inverse_permutation = false;
3		for k=1:30
4		filename = sprintf('%02d.tsv', k)
	2		inverse_permutation = false;
	3		for k=1:30
	4		filename = sprintf('%02d.tsv', k)
5	5
6		%% check:
7		if k==30
8		CM_filename = ['CM_', filename];
9		CM = dlmread(CM_filename, '\t');
10		figure; heatmap(CM)
11		title(['CM before; p1p1: ',num2str(CM(4,4)),'; x1x2: ', num2str(CM(7,8))]);
12		end
13
14		read_permute_write_data(filename, 'inverse', inverse_permutation)
15		end
16
17		%% check:
18		CM_filename = ['CM_', filename];
19		CM = dlmread(CM_filename, '\t');
20		figure; heatmap(CM)
21		title(['CM after; p1p1: ',num2str(CM(2,2)),'; x1x2: ', num2str(CM(7,9))]);
	6		%% check:
	7		if k==30
	8		CM_filename = ['CM_', filename];
	9		CM = dlmread(CM_filename, '\t');
	10		figure; heatmap(CM)
	11		title(['CM before; p1p1: ',num2str(CM(4,4)),'; x1x2: ', num2str(CM(7,8))]);
	12		end
	13
	14		read_permute_write_data(filename, 'inverse', inverse_permutation)
	15		end
	16
	17		%% check:
	18		CM_filename = ['CM_', filename];
	19		CM = dlmread(CM_filename, '\t');
	20		figure; heatmap(CM)
	21		title(['CM after; p1p1: ',num2str(CM(2,2)),'; x1x2: ', num2str(CM(7,9))]);

File data_analysis/auxfunctions/plots/gridxy.m changed (mode: 100644) (index cedcbe6..82eb533)
...	...	function hh = gridxy(x,varargin)
7	7	% or horizontal lines are plotted.	% or horizontal lines are plotted.
8	8	%	%
9	9	% The lines are plotted as a single graphics object. H = GRIDXY(..) returns	% The lines are plotted as a single graphics object. H = GRIDXY(..) returns
10		% a graphics handle to that line object.
	10		% a graphics handle to that line object.
11	11	%	%
12	12	% GRIDXY(..., 'Prop1','Val1','Prop2','Val2', ...) uses the properties	% GRIDXY(..., 'Prop1','Val1','Prop2','Val2', ...) uses the properties
13	13	% and values specified for color, linestyle, etc. Execute GET(H), where H is	% and values specified for color, linestyle, etc. Execute GET(H), where H is
...	...	function hh = gridxy(x,varargin)
16	16	%	%
17	17	% Examples	% Examples
18	18	% % some random plot	% % some random plot
19		% plot(10*rand(100,1), 10*rand(100,1),'bo') ;
	19		% plot(10*rand(100,1), 10*rand(100,1),'bo') ;
20	20	% % horizontal red dashed grid	% % horizontal red dashed grid
21	21	% gridxy([1.1 3.2 4.5],'Color','r','Linestyle',':') ;	% gridxy([1.1 3.2 4.5],'Color','r','Linestyle',':') ;
22	22	% % vertical solid thicker yellowish grid, and store the handle	% % vertical solid thicker yellowish grid, and store the handle
...	...	else
56	56	if ischar(va{1}),	if ischar(va{1}),
57	57	% optional arguments are	% optional arguments are
58	58	y = [] ;	y = [] ;
59		elseif isnumeric(va{1})
	59		elseif isnumeric(va{1})
60	60	y = va{1} ;	y = va{1} ;
61	61	va = va(2:end) ;	va = va(2:end) ;
62	62	else	else
...	...	yy2 = repmat(y(:).',3,1) ;
90	90
91	91	% create data for a single line object	% create data for a single line object
92	92	xx1 = [xx1 xx2] ;	xx1 = [xx1 xx2] ;
93		if ~isempty(xx1),
	93		if ~isempty(xx1),
94	94	yy1 = [yy1 yy2] ;	yy1 = [yy1 yy2] ;
95	95	% add the line to the current axes	% add the line to the current axes
96	96	np = get(hca,'nextplot') ;	np = get(hca,'nextplot') ;
97	97	set(hca,'nextplot','add') ;	set(hca,'nextplot','add') ;
98		h = line('xdata',xx1(:),'ydata',yy1(:)) ;
	98		h = line('xdata',xx1(:),'ydata',yy1(:)) ;
99	99	set(hca,'ylim',ylim,'xlim',xlim) ; % reset the limits	set(hca,'ylim',ylim,'xlim',xlim) ; % reset the limits
100
	100
101	101	uistack(h,'bottom') ; % push lines to the bottom of the graph	uistack(h,'bottom') ; % push lines to the bottom of the graph
102	102	set(hca,'nextplot',np,'Layer','top') ; % reset the nextplot state	set(hca,'nextplot',np,'Layer','top') ; % reset the nextplot state
103	103
104	104	if ~isempty(va),	if ~isempty(va),
105	105	try	try
106		set(h,va{:}) ; % set line properties
	106		set(h,va{:}) ; % set line properties
107	107	catch	catch
108	108	msg = lasterror ;	msg = lasterror ;
109	109	error(msg.message(21:end)) ;	error(msg.message(21:end)) ;
...	...	end
117	117	if nargout==1, % if requested return handle	if nargout==1, % if requested return handle
118	118	hh = h ;	hh = h ;
119	119	end	end
120
121
122
123
124
125
126
127
128

File data_analysis/auxfunctions/plots/linspecer.m changed (mode: 100644) (index bf242c3..faeaada)
2	2	% This function creates an Nx3 array of N [R B G] colors	% This function creates an Nx3 array of N [R B G] colors
3	3	% These can be used to plot lots of lines with distinguishable and nice	% These can be used to plot lots of lines with distinguishable and nice
4	4	% looking colors.	% looking colors.
5		%
	5		%
6	6	% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)	% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)
7		%
8		% colormap(linspecer); set your colormap to have easily distinguishable
	7		%
	8		% colormap(linspecer); set your colormap to have easily distinguishable
9	9	% colors and a pleasing aesthetic	% colors and a pleasing aesthetic
10		%
	10		%
11	11	% lineStyles = linspecer(N,'qualitative'); forces the colors to all be distinguishable (up to 12)	% lineStyles = linspecer(N,'qualitative'); forces the colors to all be distinguishable (up to 12)
12		% lineStyles = linspecer(N,'sequential'); forces the colors to vary along a spectrum
13		%
	12		% lineStyles = linspecer(N,'sequential'); forces the colors to vary along a spectrum
	13		%
14	14	% % Examples demonstrating the colors.	% % Examples demonstrating the colors.
15		%
	15		%
16	16	% LINE COLORS	% LINE COLORS
17	17	% N=6;	% N=6;
18		% X = linspace(0,pi*3,1000);
19		% Y = bsxfun(@(x,n)sin(x+2*n*pi/N), X.', 1:N);
	18		% X = linspace(0,pi*3,1000);
	19		% Y = bsxfun(@(x,n)sin(x+2*n*pi/N), X.', 1:N);
20	20	% C = linspecer(N);	% C = linspecer(N);
21	21	% axes('NextPlot','replacechildren', 'ColorOrder',C);	% axes('NextPlot','replacechildren', 'ColorOrder',C);
22	22	% plot(X,Y,'linewidth',5)	% plot(X,Y,'linewidth',5)
23	23	% ylim([-1.1 1.1]);	% ylim([-1.1 1.1]);
24		%
	24		%
25	25	% SIMPLER LINE COLOR EXAMPLE	% SIMPLER LINE COLOR EXAMPLE
26	26	% N = 6; X = linspace(0,pi*3,1000);	% N = 6; X = linspace(0,pi*3,1000);
27	27	% C = linspecer(N)	% C = linspecer(N)
31	31	% plot(X,Y,'color',C(ii,:),'linewidth',3);	% plot(X,Y,'color',C(ii,:),'linewidth',3);
32	32	% hold on;	% hold on;
33	33	% end	% end
34		%
	34		%
35	35	% COLORMAP EXAMPLE	% COLORMAP EXAMPLE
36	36	% A = rand(15);	% A = rand(15);
37	37	% figure; imagesc(A); % default colormap	% figure; imagesc(A); % default colormap
38	38	% figure; imagesc(A); colormap(linspecer); % linspecer colormap	% figure; imagesc(A); colormap(linspecer); % linspecer colormap
39		%
	39		%
40	40	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
41	41	% by Jonathan Lansey, March 2009-2013 � Lansey at gmail.com %	% by Jonathan Lansey, March 2009-2013 � Lansey at gmail.com %
42	42	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
43		%
	43		%
44	44	%% credits and where the function came from	%% credits and where the function came from
45	45	% The colors are largely taken from:	% The colors are largely taken from:
46	46	% http://colorbrewer2.org and Cynthia Brewer, Mark Harrower and The Pennsylvania State University	% http://colorbrewer2.org and Cynthia Brewer, Mark Harrower and The Pennsylvania State University
47		%
48		%
	47		%
	48		%
49	49	% She studied this from a phsychometric perspective and crafted the colors	% She studied this from a phsychometric perspective and crafted the colors
50	50	% beautifully.	% beautifully.
51		%
	51		%
52	52	% I made choices from the many there to decide the nicest once for plotting	% I made choices from the many there to decide the nicest once for plotting
53	53	% lines in Matlab. I also made a small change to one of the colors I	% lines in Matlab. I also made a small change to one of the colors I
54	54	% thought was a bit too bright. In addition some interpolation is going on	% thought was a bit too bright. In addition some interpolation is going on
55	55	% for the sequential line styles.	% for the sequential line styles.
56		%
57		%
	56		%
	57		%
58	58	%%	%%
59	59
60	60	function lineStyles=linspecer(N,varargin)	function lineStyles=linspecer(N,varargin)
...	...	if ~isempty(varargin)>0 % you set a parameter?
91	91	otherwise	otherwise
92	92	warning(['parameter ''' varargin{1} ''' not recognized']);	warning(['parameter ''' varargin{1} ''' not recognized']);
93	93	end	end
94		end
95
	94		end
	95
96	96	% predefine some colormaps	% predefine some colormaps
97	97	set3 = colorBrew2mat({[141, 211, 199];[ 255, 237, 111];[ 190, 186, 218];[ 251, 128, 114];[ 128, 177, 211];[ 253, 180, 98];[ 179, 222, 105];[ 188, 128, 189];[ 217, 217, 217];[ 204, 235, 197];[ 252, 205, 229];[ 255, 255, 179]}');	set3 = colorBrew2mat({[141, 211, 199];[ 255, 237, 111];[ 190, 186, 218];[ 251, 128, 114];[ 128, 177, 211];[ 253, 180, 98];[ 179, 222, 105];[ 188, 128, 189];[ 217, 217, 217];[ 204, 235, 197];[ 252, 205, 229];[ 255, 255, 179]}');
98	98	set1JL = brighten(colorBrew2mat({[228, 26, 28];[ 55, 126, 184];[ 77, 175, 74];[ 255, 127, 0];[ 255, 237, 111]*.95;[ 166, 86, 40];[ 247, 129, 191];[ 153, 153, 153];[ 152, 78, 163]}'));	set1JL = brighten(colorBrew2mat({[228, 26, 28];[ 55, 126, 184];[ 77, 175, 74];[ 255, 127, 0];[ 255, 237, 111]*.95;[ 166, 86, 40];[ 247, 129, 191];[ 153, 153, 153];[ 152, 78, 163]}'));
...	...	end
123	123	function varIn = colorBrew2mat(varIn)	function varIn = colorBrew2mat(varIn)
124	124	for ii=1:length(varIn) % just divide by 255	for ii=1:length(varIn) % just divide by 255
125	125	varIn{ii}=varIn{ii}/255;	varIn{ii}=varIn{ii}/255;
126		end
	126		end
127	127	end	end
128	128
129	129	function varIn = brighten(varIn,varargin) % increase the brightness	function varIn = brighten(varIn,varargin) % increase the brightness
130	130
131	131	if isempty(varargin),	if isempty(varargin),
132		frac = .9;
	132		frac = .9;
133	133	else	else
134		frac = varargin{1};
	134		frac = varargin{1};
135	135	end	end
136	136
137	137	for ii=1:length(varIn)	for ii=1:length(varIn)
138	138	varIn{ii}=varIn{ii}*frac+(1-frac);	varIn{ii}=varIn{ii}*frac+(1-frac);
139		end
	139		end
140	140	end	end
141	141
142	142	function varIn = dim(varIn,f)	function varIn = dim(varIn,f)

File data_analysis/auxfunctions/plots/plotCorrelationMatrix.m changed (mode: 100644) (index b7bd875..4b8c1a8)
1	1	function plotCorrelationMatrix(cm, varargin)	function plotCorrelationMatrix(cm, varargin)
2	2	% plotCorrelationMatrix(cm, varargin) takes a covariance matrix cm and plots the entries of cm using a color encoding	% plotCorrelationMatrix(cm, varargin) takes a covariance matrix cm and plots the entries of cm using a color encoding
3		%
	3		%
4	4	% customization via key-value-pairs:	% customization via key-value-pairs:
5	5	% - 'log', true (default: false): use logscale for the color scale	% - 'log', true (default: false): use logscale for the color scale
6	6	% - 'abs', true (default: false): plot the absolute values of the covariances	% - 'abs', true (default: false): plot the absolute values of the covariances
...	...	format = '%0.1e';
22	22	muted = false;	muted = false;
23	23
24	24	for k=1:2:length(varargin)	for k=1:2:length(varargin)
25		switch varargin{k}
	25		switch varargin{k}
26	26	case {'abs', 'Abs'}	case {'abs', 'Abs'}
27		absvalues = varargin{k+1};
28		case {'logscale', 'log', 'Log', 'Logscale', 'Log_scale'}
29		logscale = varargin{k+1};
	27		absvalues = varargin{k+1};
	28		case {'logscale', 'log', 'Log', 'Logscale', 'Log_scale'}
	29		logscale = varargin{k+1};
30	30
31		case {'title', 'title_string', 'Title', 'titlestring', 'Titlestring'}
32		title_string = varargin{k+1};
	31		case {'title', 'title_string', 'Title', 'titlestring', 'Titlestring'}
	32		title_string = varargin{k+1};
33	33	case {'show_labels', 'labels', 'Labels', 'Show_labels'}	case {'show_labels', 'labels', 'Labels', 'Show_labels'}
34	34	show_labels = varargin{k+1};	show_labels = varargin{k+1};
35	35	case {'colormap', 'Colormap'}	case {'colormap', 'Colormap'}
36		colormap = varargin{k+1};
37		case {'fontsize', 'Fontsize'}
38		fontsize = varargin{k+1};
	36		colormap = varargin{k+1};
	37		case {'fontsize', 'Fontsize'}
	38		fontsize = varargin{k+1};
39	39	case {'format', 'Format'}	case {'format', 'Format'}
40		format = varargin{k+1};
	40		format = varargin{k+1};
41	41	case {'mute', 'Mute'}	case {'mute', 'Mute'}
42	42	if varargin{k+1}	if varargin{k+1}
43	43	muted = true	muted = true
44	44	elseif ismember(varargin{k+1}, {'true', 'True'})	elseif ismember(varargin{k+1}, {'true', 'True'})
45	45	muted = true;	muted = true;
46	46	end	end
47
48		otherwise
49		error(['Unrecognized varargin-argument: ', varargin{k}]);
50		end
	47
	48		otherwise
	49		error(['Unrecognized varargin-argument: ', varargin{k}]);
	50		end
51	51	end	end
52	52
53	53	N = length(cm);	N = length(cm);
...	...	if absvalues
75	75	colormap = 'summer';	colormap = 'summer';
76	76	end	end
77	77	% don't change colormap if a colormap was chose	% don't change colormap if a colormap was chose
78		cm = abs(cm);
	78		cm = abs(cm);
79	79	end	end
80	80
81	81
82	82	if logscale	if logscale
83		heatmap(cm, labels, labels,...
84		format,'Colormap', colormap,...
85		'UseLogColormap', true,...
86		'FontSize', fontsize,...
	83		heatmap(cm, labels, labels,...
	84		format,'Colormap', colormap,...
	85		'UseLogColormap', true,...
	86		'FontSize', fontsize,...
87	87	'Colorbar', {'SouthOutside'}, 'GridLines', ':');	'Colorbar', {'SouthOutside'}, 'GridLines', ':');
88	88	else	else
89		heatmap(cm, labels, labels,...
90		format,'Colormap', colormap,...
91		'FontSize', fontsize,...
	89		heatmap(cm, labels, labels,...
	90		format,'Colormap', colormap,...
	91		'FontSize', fontsize,...
92	92	'Colorbar', {'SouthOutside'}, 'GridLines', ':');	'Colorbar', {'SouthOutside'}, 'GridLines', ':');
93	93	end	end
94	94
95	95	title(title_string)	title(title_string)
96

File data_analysis/auxfunctions/plots/quickCreateSaveNPS.m changed (mode: 100644) (index 17ba264..e14ace8)
...	...	titlestr = varargin{end-1};
12	12	filepath = varargin{end};	filepath = varargin{end};
13	13
14	14	for k = 1:2:length(varargin)-2	for k = 1:2:length(varargin)-2
15		assert(ischar(varargin{k}),['Legend string expected at position ', num2str(k)]);
16		assert(isnumeric(varargin{k+1}),['Numeric data expected at position ', num2str(k+1)]);
	15		assert(ischar(varargin{k}),['Legend string expected at position ', num2str(k)]);
	16		assert(isnumeric(varargin{k+1}),['Numeric data expected at position ', num2str(k+1)]);
17	17
18		datasetno = datasetno + 1;
	18		datasetno = datasetno + 1;
19	19
20		legendstr = varargin{k};
21		data = varargin{k+1};
	20		legendstr = varargin{k};
	21		data = varargin{k+1};
22	22
23		[nps, freq] = pwelch(data, [], [], [], 70);
	23		[nps, freq] = pwelch(data, [], [], [], 70);
24	24
25		plot(freq, pow2db(nps), 'o-.','color',cc(datasetno,:), 'MarkerFaceColor', cc(datasetno,:),...
26		'LineWidth',1,'MarkerSize',2, 'DisplayName', legendstr);
	25		plot(freq, pow2db(nps), 'o-.','color',cc(datasetno,:), 'MarkerFaceColor', cc(datasetno,:),...
	26		'LineWidth',1,'MarkerSize',2, 'DisplayName', legendstr);
27	27	end	end
28	28	title(titlestr, 'interpreter', 'none');	title(titlestr, 'interpreter', 'none');
29	29	xlabel('Frequency [in units of mechanical frequency]');	xlabel('Frequency [in units of mechanical frequency]');

File data_analysis/auxfunctions/plots/sampleNPSfromMeasurementData.m changed (mode: 100644) (index d409a4d..20c2edb)
...	...	fid = fopen( filepath, 'r');
15	15	[],[],[], samplingRate);	[],[],[], samplingRate);
16	16	fclose(fid);	fclose(fid);
17	17	end	end
18

File data_analysis/auxfunctions/postprocess/calibrate_cm.m changed (mode: 100644) (index 5907696..47d11c8)
1	1	function [cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm)	function [cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm)
2		assert(isequal(size(cm), size(shotnoise_cm)),...
3		'Matrices must have the same shape!');
4		assert(size(cm,1)==size(cm,1),...
5		'Matrices must be quadratic.')
6		assert(mod(size(cm,1),2)==0,...
7		'Matrices must have even number of rows and columns.')
	2		assert(isequal(size(cm), size(shotnoise_cm)),...
	3		'Matrices must have the same shape!');
	4		assert(size(cm,1)==size(cm,1),...
	5		'Matrices must be quadratic.')
	6		assert(mod(size(cm,1),2)==0,...
	7		'Matrices must have even number of rows and columns.')
8	8
9		mode_no = size(cm, 1)/2;
10		shotnoise_vars = diag(shotnoise_cm);
11		shotnoise_vars_means = mean(reshape(shotnoise_vars, 2, mode_no));
12		shotnoise_std_devs = reshape(...
13		repmat(sqrt(shotnoise_vars_means),2,1),...
14		2*mode_no,1);
15		calibration_mat = diag(1./shotnoise_std_devs)/sqrt(2);
16
17		cm_cal = calibration_mat*cm*calibration_mat;
18		shotnoise_cm_cal = calibration_mat*shotnoise_cm*calibration_mat;
	9		mode_no = size(cm, 1)/2;
	10		shotnoise_vars = diag(shotnoise_cm);
	11		shotnoise_vars_means = mean(reshape(shotnoise_vars, 2, mode_no));
	12		shotnoise_std_devs = reshape(...
	13		repmat(sqrt(shotnoise_vars_means),2,1),...
	14		2*mode_no,1);
	15		calibration_mat = diag(1./shotnoise_std_devs)/sqrt(2);
	16
	17		cm_cal = calibration_mat*cm*calibration_mat;
	18		shotnoise_cm_cal = calibration_mat*shotnoise_cm*calibration_mat;

File data_analysis/auxfunctions/postprocess/calibrate_cm_test.m changed (mode: 100644) (index cde8426..6110da0)
1	1	function test_suite = calibrate_cm_test %#ok<STOUT>	function test_suite = calibrate_cm_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function shotnoise_cm_test	function shotnoise_cm_test
5		mode_no = 10;
6		% expected mode order: q1, p1, q2, p2, x1, y1, x2, y2
7		shotnoise_cm = diag([0.4, 0.6, 0.9, 1.1, 2.1, 1.9, 2, 3]);
8		shotnoise_cm_cal_exp = diag([0.4, 0.6, 0.45, 0.55, 0.525, 0.475, 0.4, 0.6]);
9		cm = randn(size(shotnoise_cm));
10
11		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
12		assertAlmostEqual(shotnoise_cm_cal, shotnoise_cm_cal_exp, 10*eps)
	5		mode_no = 10;
	6		% expected mode order: q1, p1, q2, p2, x1, y1, x2, y2
	7		shotnoise_cm = diag([0.4, 0.6, 0.9, 1.1, 2.1, 1.9, 2, 3]);
	8		shotnoise_cm_cal_exp = diag([0.4, 0.6, 0.45, 0.55, 0.525, 0.475, 0.4, 0.6]);
	9		cm = randn(size(shotnoise_cm));
	10
	11		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
	12		assertAlmostEqual(shotnoise_cm_cal, shotnoise_cm_cal_exp, 10*eps)
13	13
14	14	function cm_multimode_trivial_test	function cm_multimode_trivial_test
15		mode_no = 10;
16		shotnoise_cm = diag(0.5*ones(2*mode_no,1));
17		cm = randn(size(shotnoise_cm));
18
19		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
20		assertAlmostEqual(cm, cm_cal, 10*eps)
	15		mode_no = 10;
	16		shotnoise_cm = diag(0.5*ones(2*mode_no,1));
	17		cm = randn(size(shotnoise_cm));
	18
	19		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
	20		assertAlmostEqual(cm, cm_cal, 10*eps)
21	21
22	22	function cm_multimode_almost_trivial_test	function cm_multimode_almost_trivial_test
23		mode_no = 10;
24		shotnoise_cm = diag(ones(2*mode_no,1));
25		cm = randn(size(shotnoise_cm));
26
27		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
28		assertAlmostEqual(cm/2, cm_cal, 10*eps)
	23		mode_no = 10;
	24		shotnoise_cm = diag(ones(2*mode_no,1));
	25		cm = randn(size(shotnoise_cm));
	26
	27		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
	28		assertAlmostEqual(cm/2, cm_cal, 10*eps)
29	29
30	30	function cm_singlemode_test	function cm_singlemode_test
31		shotnoise_cm = diag([0.9, 1.1, 1.9, 2.1]);
32		cm = randn(size(shotnoise_cm));
33		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
34		shotnoise_vars_check(shotnoise_cm_cal);
	31		shotnoise_cm = diag([0.9, 1.1, 1.9, 2.1]);
	32		cm = randn(size(shotnoise_cm));
	33		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
	34		shotnoise_vars_check(shotnoise_cm_cal);
35	35
36	36	function cm_twomode_test	function cm_twomode_test
37		shotnoise_cm = diag([0.9, 1.1, 1.9, 2.1, 3.9, 4.1, 5.9, 6.1]);
38		cm = randn(size(shotnoise_cm));
39		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
40		shotnoise_vars_check(shotnoise_cm_cal);
	37		shotnoise_cm = diag([0.9, 1.1, 1.9, 2.1, 3.9, 4.1, 5.9, 6.1]);
	38		cm = randn(size(shotnoise_cm));
	39		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
	40		shotnoise_vars_check(shotnoise_cm_cal);
41	41
42	42	function cm_twomode_invariance_test	function cm_twomode_invariance_test
43		shotnoise_cm = diag([0.9, 1.1, 1.9, 2.1, 3.9, 4.1, 5.9, 6.1]);
44		cm = randn(size(shotnoise_cm));
45		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
46		[cm_cal2, shotnoise_cm_cal2] = calibrate_cm(cm_cal, shotnoise_cm_cal);
47
48		assertElementsAlmostEqual(cm_cal, cm_cal2);
49		assertElementsAlmostEqual(shotnoise_cm_cal, shotnoise_cm_cal2);
	43		shotnoise_cm = diag([0.9, 1.1, 1.9, 2.1, 3.9, 4.1, 5.9, 6.1]);
	44		cm = randn(size(shotnoise_cm));
	45		[cm_cal, shotnoise_cm_cal] = calibrate_cm(cm, shotnoise_cm);
	46		[cm_cal2, shotnoise_cm_cal2] = calibrate_cm(cm_cal, shotnoise_cm_cal);
	47
	48		assertElementsAlmostEqual(cm_cal, cm_cal2);
	49		assertElementsAlmostEqual(shotnoise_cm_cal, shotnoise_cm_cal2);
50	50
51	51	function shotnoise_vars_check(cm)	function shotnoise_vars_check(cm)
52		mode_no = size(cm,1)/2;
53		means = mean(repmat(diag(cm),2,mode_no));
54		assertElementsAlmostEqual(means, 0.5*ones(size(means)));
	52		mode_no = size(cm,1)/2;
	53		means = mean(repmat(diag(cm),2,mode_no));
	54		assertElementsAlmostEqual(means, 0.5*ones(size(means)));

File data_analysis/auxfunctions/structs/export_fields.m changed (mode: 100644) (index 451e6fc..b3f2a44)
8	8	% export_fields(struc, fields, 'save', filename, 'force', BOOL)	% export_fields(struc, fields, 'save', filename, 'force', BOOL)
9	9	% overwrite the existing file	% overwrite the existing file
10	10	function [outstruct] = export_fields(struc, fields, varargin)	function [outstruct] = export_fields(struc, fields, varargin)
11		for i=1:length(fields)
12		field = fields{i};
13		disp(field)
14		outstruct.(field) = struc.(field);
15		end
	11		for i=1:length(fields)
	12		field = fields{i};
	13		disp(field)
	14		outstruct.(field) = struc.(field);
	15		end
16	16
17		filename = '';
18		force = false;
19		for k=1:2:length(varargin)
20		switch varargin{k}
21		case 'save'
22		filename = varargin{k+1};
23		case 'force'
24		force = varargin{k+1};
25		otherwise
26		error(['unrecognized varargin_argument: ',varargin{k}]);
27		end
28		end
	17		filename = '';
	18		force = false;
	19		for k=1:2:length(varargin)
	20		switch varargin{k}
	21		case 'save'
	22		filename = varargin{k+1};
	23		case 'force'
	24		force = varargin{k+1};
	25		otherwise
	26		error(['unrecognized varargin_argument: ',varargin{k}]);
	27		end
	28		end
29	29
30		if filename
31		if ~force
32		assert(~isafile(filename), [filename, ' already exists!']);
33		end
34		save(filename, '-struct', 'outstruct')
35		end
	30		if filename
	31		if ~force
	32		assert(~isafile(filename), [filename, ' already exists!']);
	33		end
	34		save(filename, '-struct', 'outstruct')
	35		end
36	36	end	end
37	37	%target_fields = {'pulse_gammas', 'mech_frequencies', 'samples_per_pulse', 'logneg_corrected', 'tracepairs_no', 'samples_per_trace', 'pulse_pairs_per_trace', 'pulse_duration_periods', 'mech_frequencies_no', 'pulse_gammas_no', 'samplingrate', 'signal_power_muW'}	%target_fields = {'pulse_gammas', 'mech_frequencies', 'samples_per_pulse', 'logneg_corrected', 'tracepairs_no', 'samples_per_trace', 'pulse_pairs_per_trace', 'pulse_duration_periods', 'mech_frequencies_no', 'pulse_gammas_no', 'samplingrate', 'signal_power_muW'}

File data_analysis/auxfunctions/structs/getPropertyQueryFunction.m changed (mode: 100644) (index 7f9e332..57eb43c)
1	1	function getPropFunction = getPropertyQueryFunction( property)	function getPropFunction = getPropertyQueryFunction( property)
2		getPropFunction = @(object)(object.(property));
	2		getPropFunction = @(object)(object.(property));
3	3	end	end

File data_analysis/entanglement/calculate_pulsed_logneg_theory.m changed (mode: 100644) (index d84e5eb..0dd7ab0)
...	...	function [model] = calculate_pulsed_logneg_theory(varargin)
4	4	% and returns model augmented by the fields pulse_gammas and log_neg	% and returns model augmented by the fields pulse_gammas and log_neg
5	5	%	%
6	6	% model = calculate_pulsed_logneg_theory() does the same using	% model = calculate_pulsed_logneg_theory() does the same using
7		% model.mat in the current directory
	7		% model.mat in the current directory
8	8	if nargin==0	if nargin==0
9	9	disp(['using model.mat in ',10, pwd]);	disp(['using model.mat in ',10, pwd]);
10	10	load(fullfile(pwd,'model.mat'));	load(fullfile(pwd,'model.mat'));

File data_analysis/entanglement/combine_evaluations.m changed (mode: 100644) (index 82cf05e..1d4f092)
...	...	end
27	27	% second index l .... pulse width	% second index l .... pulse width
28	28	% %%	% %%
29	29	% for k=1:5	% for k=1:5
30		% figure;
	30		% figure;
31	31	% plotCorrelationMatrix(cov(combine_traces(k,5)))	% plotCorrelationMatrix(cov(combine_traces(k,5)))
32	32	% end	% end
33		%
	33		%
34	34	% %%	% %%
35	35	% for l=3:6	% for l=3:6
36		% figure;
	36		% figure;
37	37	% plotCorrelationMatrix(cov(combine_traces(3,l)))	% plotCorrelationMatrix(cov(combine_traces(3,l)))
38	38	% end	% end
39	39

File data_analysis/entanglement/running_logneg_CM.m changed (mode: 100644) (index 67bb9fe..f43c86d)
...	...	dttcm = zeros(107,1); dttln = zeros(107,1);
8	8	start=5; stop=12;	start=5; stop=12;
9	9	for gamind = start:stop	for gamind = start:stop
10	10	gamma = pulse_gammas(gamind);	gamma = pulse_gammas(gamind);
11
12		%% calculate theory CM and logneg
	11
	12		%% calculate theory CM and logneg
13	13	if true%set theory parameters	if true%set theory parameters
14	14	omm = model.om.omm;	omm = model.om.omm;
15	15	omd = omm; % demodulation frequency	omd = omm; % demodulation frequency
...	...	for gamind = start:stop
26	26	end	end
27	27	fref = model.om.exp_omm/(2*pi);	fref = model.om.exp_omm/(2*pi);
28	28	pulsegamma = gamma/fref;	pulsegamma = gamma/fref;
29
	29
30	30
31	31	theorycm = pccovbm(cp*nmech*ga, ga, 2*pi*pulsegamma,nmech,1-eta,omd,omm);	theorycm = pccovbm(cp*nmech*ga, ga, 2*pi*pulsegamma,nmech,1-eta,omd,omm);
32		theoryln = real(logneg(theorycm));
33
	32		theoryln = real(logneg(theorycm));
	33
34	34	%% data	%% data
35	35	snvar = mean(diag(pulse_CM{1}{gamind}));	snvar = mean(diag(pulse_CM{1}{gamind}));
36	36	array = [...	array = [...
...	...	for gamind = start:stop
42	42	%% calculate running logneg and CM	%% calculate running logneg and CM
43	43	interval = 100;	interval = 100;
44	44	[covs, means] = running_stats(array, interval);	[covs, means] = running_stats(array, interval);
45
	45
46	46	%dual homo correction	%dual homo correction
47	47	covs = cellfun(@(cm) (2*cm-eye(4)/2), covs, 'UniformOutput',false);	covs = cellfun(@(cm) (2*cm-eye(4)/2), covs, 'UniformOutput',false);
48	48	lognegs = cellfun(@logneg, covs);	lognegs = cellfun(@logneg, covs);
49
	49
50	50	stepno = length(covs);	stepno = length(covs);
51	51
52	52	%% distances to theory	%% distances to theory
...	...	for gamind = start:stop
56	56
57	57	%% distances to final	%% distances to final
58	58	finalcm = covs{stepno};	finalcm = covs{stepno};
59		finalln = lognegs(stepno);
	59		finalln = lognegs(stepno);
60	60	distances_to_final = cellfun(@(cm) norm(cm-finalcm)/norm(finalcm), covs);	distances_to_final = cellfun(@(cm) norm(cm-finalcm)/norm(finalcm), covs);
61	61	ln_distances_to_final = abs((lognegs-finalln)/finalln);	ln_distances_to_final = abs((lognegs-finalln)/finalln);
62
	62
63	63	%% add to average	%% add to average
64	64	dtfcm = dtfcm + distances_to_final;	dtfcm = dtfcm + distances_to_final;
65	65	dtfln = dtfln + ln_distances_to_final;	dtfln = dtfln + ln_distances_to_final;

File data_analysis/gaussianity/gaussianity_plots.m changed (mode: 100644) (index 73ffa2b..ea05ffa)
1	1	function gaussianity_plots(trace, varargin)	function gaussianity_plots(trace, varargin)
2	2	% don't show figures while batch processing	% don't show figures while batch processing
3	3	set(0,'DefaultFigureVisible','off')	set(0,'DefaultFigureVisible','off')
4
	4
5	5	quadno = size(trace,2);	quadno = size(trace,2);
6	6	modeno = quadno/4;	modeno = quadno/4;
7	7	assert((modeno==round(modeno))&&(modeno>0));	assert((modeno==round(modeno))&&(modeno>0));
8
	8
9	9	figwidth = 560;	figwidth = 560;
10	10	figheight = 420;	figheight = 420;
11
12		modes = 1:modeno;
	11
	12		modes = 1:modeno;
13	13	subplotwidth = min(2,modeno);	subplotwidth = min(2,modeno);
14	14	subplotheight = ceil(modeno/subplotwidth);	subplotheight = ceil(modeno/subplotwidth);
15
	15
16	16	normalize = true;	normalize = true;
17	17	export = false;	export = false;
18	18	mute = false;	mute = false;
...	...	function gaussianity_plots(trace, varargin)
47	47	if subplotwidth > 3	if subplotwidth > 3
48	48	figwidth = (subplotwidth/3)*figwidth;	figwidth = (subplotwidth/3)*figwidth;
49	49	end	end
50
	50
51	51	stddevs = std(trace);	stddevs = std(trace);
52	52	inset = ' ';	inset = ' ';
53		getstddevstr = @(modeind)[inset, 'q: ',num2str(stddevs(2*modeind-1)),10,...
	53		getstddevstr = @(modeind)[inset, 'q: ',num2str(stddevs(2*modeind-1)),10,...
54	54	inset, 'p: ',num2str(stddevs(2*modeind)),10,...	inset, 'p: ',num2str(stddevs(2*modeind)),10,...
55	55	inset, 'x: ',num2str(stddevs(2*modeno + 2*modeind-1)),10,...	inset, 'x: ',num2str(stddevs(2*modeno + 2*modeind-1)),10,...
56	56	inset, 'y: ',num2str(stddevs(2*modeno + 2*modeind))];	inset, 'y: ',num2str(stddevs(2*modeno + 2*modeind))];
...	...	function gaussianity_plots(trace, varargin)
59	59	trace(:,k) = trace(:,k)/stddevs(k);	trace(:,k) = trace(:,k)/stddevs(k);
60	60	end	end
61	61	end	end
62
	62
63	63	getinds = @(modeind)[2*modeind-1, 2*modeind,...	getinds = @(modeind)[2*modeind-1, 2*modeind,...
64	64	2*modeno + 2*modeind-1, 2*modeno + 2*modeind];	2*modeno + 2*modeind-1, 2*modeno + 2*modeind];
65	65	getsubtrace = @(m)trace(:,getinds(m));	getsubtrace = @(m)trace(:,getinds(m));
66
	66
67	67	if normalize	if normalize
68	68	normalizestr = [10, ' (data has been divided by its std. deviation)'];	normalizestr = [10, ' (data has been divided by its std. deviation)'];
69	69	else	else
...	...	function gaussianity_plots(trace, varargin)
73	73	cdffig = figure('visible',visible,'position', [0, 0, figwidth, figheight]);	cdffig = figure('visible',visible,'position', [0, 0, figwidth, figheight]);
74	74	qqfig = figure('visible',visible,'position', [0, 0, figwidth, figheight]);	qqfig = figure('visible',visible,'position', [0, 0, figwidth, figheight]);
75	75	kdensfig = figure('visible',visible,'position', [0, 0, figwidth, figheight]);	kdensfig = figure('visible',visible,'position', [0, 0, figwidth, figheight]);
76
	76
77	77	if mute	if mute
78	78	postprocess = @(m){ title('');...	postprocess = @(m){ title('');...
79	79	ntitle([inset, 'Mode ',num2str(m)],...	ntitle([inset, 'Mode ',num2str(m)],...
...	...	function gaussianity_plots(trace, varargin)
88	88	'location','northwest');...	'location','northwest');...
89	89	legend({'q','p','x','y'}, 'Location', 'SouthEast')};	legend({'q','p','x','y'}, 'Location', 'SouthEast')};
90	90	end	end
91		s = 0;
	91		s = 0;
92	92	for m=modes	for m=modes
93	93	s = s + 1;	s = s + 1;
94	94	change_current_figure(histfig); subplot(subplotheight, subplotwidth, s); hold all	change_current_figure(histfig); subplot(subplotheight, subplotwidth, s); hold all
...	...	function gaussianity_plots(trace, varargin)
98	98	if mute && s==numel(modes)	if mute && s==numel(modes)
99	99	legend({'q','p','x','y'}, 'Location', 'SouthEastOutside');	legend({'q','p','x','y'}, 'Location', 'SouthEastOutside');
100	100	end	end
101
	101
102	102	change_current_figure(cdffig); subplot(subplotheight, subplotwidth, s); hold all	change_current_figure(cdffig); subplot(subplotheight, subplotwidth, s); hold all
103	103	arrayfun(@(n)cdfplot(trace(:,n)), getinds(m));	arrayfun(@(n)cdfplot(trace(:,n)), getinds(m));
104	104	postprocess(m);	postprocess(m);
...	...	function gaussianity_plots(trace, varargin)
106	106	if mute && s==numel(modes)	if mute && s==numel(modes)
107	107	legend({'q','p','x','y'}, 'Location', 'SouthEastOutside');	legend({'q','p','x','y'}, 'Location', 'SouthEastOutside');
108	108	end	end
109
	109
110	110	change_current_figure(kdensfig); subplot(subplotheight, subplotwidth, s); hold all	change_current_figure(kdensfig); subplot(subplotheight, subplotwidth, s); hold all
111	111	arrayfun(@(n)ksdensity(trace(:,n)), getinds(m));	arrayfun(@(n)ksdensity(trace(:,n)), getinds(m));
112	112	postprocess(m);	postprocess(m);
...	...	function gaussianity_plots(trace, varargin)
114	114	if mute && s==numel(modes)	if mute && s==numel(modes)
115	115	legend({'q','p','x','y'}, 'Location', 'SouthEastOutside');	legend({'q','p','x','y'}, 'Location', 'SouthEastOutside');
116	116	end	end
117
	117
118	118	change_current_figure(qqfig); subplot(subplotheight, subplotwidth, s); hold all	change_current_figure(qqfig); subplot(subplotheight, subplotwidth, s); hold all
119	119	qqplot(getsubtrace(m));	qqplot(getsubtrace(m));
120	120	postprocess(m);	postprocess(m);
...	...	function gaussianity_plots(trace, varargin)
131	131	change_current_figure(cdffig); mysuptitle('Empirical CDF'); set(cdffig, 'visible', visible)	change_current_figure(cdffig); mysuptitle('Empirical CDF'); set(cdffig, 'visible', visible)
132	132	change_current_figure(kdensfig); mysuptitle('Kernel density estimate'); set(kdensfig, 'visible', visible)	change_current_figure(kdensfig); mysuptitle('Kernel density estimate'); set(kdensfig, 'visible', visible)
133	133	change_current_figure(qqfig); mysuptitle('Quantile-quantile plot'); set(qqfig, 'visible', visible)	change_current_figure(qqfig); mysuptitle('Quantile-quantile plot'); set(qqfig, 'visible', visible)
134
	134
135	135	if export	if export
136	136	% note: hist does not work with pdf export.	% note: hist does not work with pdf export.
137	137	export_fig(histfig, [exportstr, '_', 'hist'],...	export_fig(histfig, [exportstr, '_', 'hist'],...
...	...	function gaussianity_plots(trace, varargin)
144	144	'-pdf','-transparent')	'-pdf','-transparent')
145	145	end	end
146	146	end	end
147
148
	147
	148
149	149
150	150
151	151

File data_analysis/gaussianity/gaussianity_plots_2d.m changed (mode: 100644) (index 5144ccc..3b48a27)
...	...	function gaussianity_plots_2d(trace, varargin)
2	2	quadno = size(trace,2);	quadno = size(trace,2);
3	3	modeno = quadno/4;	modeno = quadno/4;
4	4	assert((modeno==round(modeno))&&(modeno>0));	assert((modeno==round(modeno))&&(modeno>0));
5
6		modes = 1:modeno;
	5
	6		modes = 1:modeno;
7	7	quadlabels = cell(1,4*modeno);	quadlabels = cell(1,4*modeno);
8	8	for m=modes	for m=modes
9	9	quadlabels{2*m-1} = ['q',num2str(m)];	quadlabels{2*m-1} = ['q',num2str(m)];
...	...	function gaussianity_plots_2d(trace, varargin)
11	11	quadlabels{2*modeno+2*m-1} = ['x',num2str(m)];	quadlabels{2*modeno+2*m-1} = ['x',num2str(m)];
12	12	quadlabels{2*modeno+2*m} = ['y',num2str(m)];	quadlabels{2*modeno+2*m} = ['y',num2str(m)];
13	13	end	end
14
	14
15	15	normalize = true;	normalize = true;
16	16	export = false;	export = false;
17	17	visible = 'on';	visible = 'on';
...	...	function gaussianity_plots_2d(trace, varargin)
30	30	case {'export','Export','save','Save'}	case {'export','Export','save','Save'}
31	31	if ~(varargin{k+1}==false)	if ~(varargin{k+1}==false)
32	32	export = true;	export = true;
33		visible = 'off';
	33		visible = 'off';
34	34	exportstr = varargin{k+1};	exportstr = varargin{k+1};
35	35	end	end
36	36	otherwise	otherwise
37	37	error(['Unrecognized varargin-argument: ', varargin{k}])	error(['Unrecognized varargin-argument: ', varargin{k}])
38	38	end	end
39	39	end	end
40
	40
41	41	stddevs = std(trace);	stddevs = std(trace);
42	42	getstddevstr = @(m,n)[' std. deviations of raw data: ',10,...	getstddevstr = @(m,n)[' std. deviations of raw data: ',10,...
43		' ', quadlabels{m},': ', num2str(stddevs(m)),10,...
	43		' ', quadlabels{m},': ', num2str(stddevs(m)),10,...
44	44	' ', quadlabels{n},': ', num2str(stddevs(n))];	' ', quadlabels{n},': ', num2str(stddevs(n))];
45	45	if normalize	if normalize
46	46	for k=1:quadno	for k=1:quadno
...	...	function gaussianity_plots_2d(trace, varargin)
50	50
51	51	getsubtrace = @(m)trace(:,m);	getsubtrace = @(m)trace(:,m);
52	52	gettracepair = @(m,n)(getsubtrace([m,n]));	gettracepair = @(m,n)(getsubtrace([m,n]));
53
	53
54	54	fig = figure('visible', visible); hold all	fig = figure('visible', visible); hold all
55	55	kde_contour_2 = @(m,n) kde_contour(gettracepair(m,n),...	kde_contour_2 = @(m,n) kde_contour(gettracepair(m,n),...
56	56	quadlabels{m}, quadlabels{n},...	quadlabels{m}, quadlabels{n},...
57	57	[titlestr,10,getstddevstr(m,n)]);	[titlestr,10,getstddevstr(m,n)]);
58	58	for k=1:numel(modeorder)	for k=1:numel(modeorder)
59	59	quads = modeorder{k};	quads = modeorder{k};
60		subplot(subplotheight, subplotwidth,k);
	60		subplot(subplotheight, subplotwidth,k);
61	61	kde_contour_2(quads(1), quads(2))	kde_contour_2(quads(1), quads(2))
62	62	if normalize	if normalize
63	63	mylim = 3;	mylim = 3;
...	...	function gaussianity_plots_2d(trace, varargin)
67	67	end	end
68	68	suptitle(['Bivariate distribution for ',...	suptitle(['Bivariate distribution for ',...
69	69	titlestr],'plotregion',0.95)	titlestr],'plotregion',0.95)
70
	70
71	71	if export	if export
72	72	filename = [exportstr, '_', 'hist2d.pdf'];	filename = [exportstr, '_', 'hist2d.pdf'];
73	73	saveas(fig,filename)	saveas(fig,filename)
...	...	function [] = kde_contour(data, xlab, ylab, titlestr)
79	79	cmap = cbrewer('seq','Blues',50);	cmap = cbrewer('seq','Blues',50);
80	80	contourf(X,Y,density); colorbar; colormap(cmap);	contourf(X,Y,density); colorbar; colormap(cmap);
81	81	xlabel(xlab); ylabel(ylab);	xlabel(xlab); ylabel(ylab);
82
83

File data_analysis/obsolete/@entanglement_evaluation_obsolete/entanglement_evaluation_obsolete.m changed (mode: 100644) (index 8ba1cfb..cbf4731)
1	1	classdef entanglement_evaluation_obsolete < class_conveniences	classdef entanglement_evaluation_obsolete < class_conveniences
2		% entanglement_evaluation_obsolete is a handle class
3		% which collects deprecated methods and properties of the
4		% entanglement_evaluation class
5		properties(Hidden)
6		quality_factors@double vector % for each mode
	2		% entanglement_evaluation_obsolete is a handle class
	3		% which collects deprecated methods and properties of the
	4		% entanglement_evaluation class
	5		properties(Hidden)
	6		quality_factors@double vector % for each mode
7	7	coupling_rates@double vector % linearized coupling for each mode	coupling_rates@double vector % linearized coupling for each mode
8	8	bare_couplings@double vector % single-photon coupling for each mode	bare_couplings@double vector % single-photon coupling for each mode
9	9
...	...	classdef entanglement_evaluation_obsolete < class_conveniences
14	14
15	15	opt_pulse_widths@double vector % optimal pulsewidth acc. to singlemode theory	opt_pulse_widths@double vector % optimal pulsewidth acc. to singlemode theory
16	16	opt_log_negs@double vector % optimal logneg acc. to singlemode theory	opt_log_negs@double vector % optimal logneg acc. to singlemode theory
17		end
	17		end
18	18
19		properties(Hidden, Transient, Dependent)
20		pulse_Q@cell matrix
	19		properties(Hidden, Transient, Dependent)
	20		pulse_Q@cell matrix
21	21	pulse_P@cell matrix	pulse_P@cell matrix
22	22	pulse_X@cell matrix	pulse_X@cell matrix
23	23	pulse_Y@cell matrix	pulse_Y@cell matrix
24	24	end	end
25	25
26	26	properties(Hidden, Dependent)	properties(Hidden, Dependent)
27		cooperativities@double vector % cooperativity from g, kappa, gamma, T for each mode
	27		cooperativities@double vector % cooperativity from g, kappa, gamma, T for each mode
28	28	nbars@double vector %thermal occupation from T and omega for each mode	nbars@double vector %thermal occupation from T and omega for each mode
29	29	omegas@double vector %mechanical frequencies for each mode	omegas@double vector %mechanical frequencies for each mode
30	30	gammas@double vector %mechanical linewidths from Q and omega for each mode	gammas@double vector %mechanical linewidths from Q and omega for each mode
31		end
	31		end
32	32
33	33	properties(Hidden, Transient)	properties(Hidden, Transient)
34	34	correlation_functions@cell matrix	correlation_functions@cell matrix
35	35	end	end
36	36
37		methods(Static, Hidden)
	37		methods(Static, Hidden)
38	38	[optWidth, optLogNeg] = findOptimalPulseWidth(g,kappa,gamma_m, n_bar)	[optWidth, optLogNeg] = findOptimalPulseWidth(g,kappa,gamma_m, n_bar)
39	39	end	end
40	40
...	...	classdef entanglement_evaluation_obsolete < class_conveniences
114	114	gammas(k) = omega_m/Q;	gammas(k) = omega_m/Q;
115	115	end	end
116	116	end	end
117		end
	117		end
118	118	end	end

File data_analysis/obsolete/@entanglement_evaluation_obsolete/exportCrossCorrelationFunctions.m changed (mode: 100644) (index 6582a36..43a319e)
...	...	function obj = exportCrossCorrelationFunctions(obj)
2	2	% saves the correlation fcts to suitably named files in obj.evaluation_folder	% saves the correlation fcts to suitably named files in obj.evaluation_folder
3	3	correlation_functions = {};	correlation_functions = {};
4	4	for tp=1:obj.tracepairs_no	for tp=1:obj.tracepairs_no
5		correlation_functions{tp} = obj.correlation_functions{tp};
	5		correlation_functions{tp} = obj.correlation_functions{tp};
6	6	end	end
7	7	save(fullfile(obj.evaluation_folder, 'correlation_functions.mat'),...	save(fullfile(obj.evaluation_folder, 'correlation_functions.mat'),...
8	8	'correlation_functions');	'correlation_functions');

File data_analysis/obsolete/@entanglement_evaluation_obsolete/findOptimalPulseWidth.m changed (mode: 100644) (index 2fd7a81..a998f56)
...	...	function [optWidth, optLogNeg] = findOptimalPulseWidth(g,kappa,gamma_m, n_bar)
5	5	losses = 0; %losses do not change the optimal pulse width!	losses = 0; %losses do not change the optimal pulse width!
6	6	om_demod = 1;	om_demod = 1;
7	7	om_mech = 1;	om_mech = 1;
8
	8
9	9	cm = pccovbm(g^2/kappa,gamma_m,pulse_width,n_bar,...	cm = pccovbm(g^2/kappa,gamma_m,pulse_width,n_bar,...
10	10	losses,om_demod, om_mech);	losses,om_demod, om_mech);
11	11	ln = logneg(cm);	ln = logneg(cm);

File data_analysis/obsolete/@entanglement_evaluation_obsolete/getOptPulseWidths.m changed (mode: 100644) (index 9f8b0b4..0ee628b)
...	...	function obj = getOptPulseWidths(obj)
7	7	gamma_m = obj.gammas(f);	gamma_m = obj.gammas(f);
8	8	kappa = obj.kappa;	kappa = obj.kappa;
9	9
10		%find the optimal pulse width for the above parameters
	10		%find the optimal pulse width for the above parameters
11	11	[opt_pw, opt_ln] = obj.findOptimalPulseWidth(...	[opt_pw, opt_ln] = obj.findOptimalPulseWidth(...
12	12	g/omega_m, kappa/omega_m, gamma_m/omega_m, nbar);	g/omega_m, kappa/omega_m, gamma_m/omega_m, nbar);
13	13	obj.opt_pulse_widths(f) = opt_pw*omega_m;	obj.opt_pulse_widths(f) = opt_pw*omega_m;

File data_analysis/obsolete/@entanglement_evaluation_obsolete/importCrossCorrelationFunctions.m changed (mode: 100644) (index 3776e25..6c1d672)
1	1	function obj = importCrossCorrelationFunctions(obj, folder)	function obj = importCrossCorrelationFunctions(obj, folder)
2		% reads the correlation fcts from suitably named files in folder
	2		% reads the correlation fcts from suitably named files in folder
3	3	correlation_functions = load(fullfile(folder, 'correlation_functions.mat'));	correlation_functions = load(fullfile(folder, 'correlation_functions.mat'));
4	4	obj.correlation_functions = correlation_functions.correlation_functions;	obj.correlation_functions = correlation_functions.correlation_functions;
5	5	end	end

File data_analysis/obsolete/plottable_property/noise_power_spectrum.m changed (mode: 100644) (index b059e31..bb0e31d)
1	1	classdef noise_power_spectrum < plottable_property	classdef noise_power_spectrum < plottable_property
2		%Uvarvstime = varianceVersusTime(timetrace) creates an object with
	2		%Uvarvstime = varianceVersusTime(timetrace) creates an object with
3	3	% PROPERTIES:	% PROPERTIES:
4	4	% - x : time	% - x : time
5	5	% - y : noisepower integrated from HF_cutoff to LF_cutoff	% - y : noisepower integrated from HF_cutoff to LF_cutoff
6	6	% - x_label, y_label	% - x_label, y_label
7	7	% - property_description	% - property_description
8		%
	8		%
9	9	% varvstime is a plottable_property object	% varvstime is a plottable_property object
10	10	properties	properties
11	11	x	x
...	...	classdef noise_power_spectrum < plottable_property
14	14	properties(Constant)	properties(Constant)
15	15	x_label = 'Frequency [Hz]';	x_label = 'Frequency [Hz]';
16	16	y_label = 'Noise power spectral density [V^2/Hz]'	y_label = 'Noise power spectral density [V^2/Hz]'
17
	17
18	18	start_time = 0;	start_time = 0;
19	19	end_time = 0.1;	end_time = 0.1;
20	20	end	end
21	21	properties(Dependent)	properties(Dependent)
22	22	property_description	property_description
23	23	end	end
24
	24
25	25	methods	methods
26	26	%% constructor	%% constructor
27	27	function obj = noise_power_spectrum(timetrace)	function obj = noise_power_spectrum(timetrace)
28	28	obj = obj@plottable_property(timetrace);	obj = obj@plottable_property(timetrace);
29
	29
30	30	start_sample = timetrace.sample_from_time(obj.start_time);	start_sample = timetrace.sample_from_time(obj.start_time);
31	31	end_sample = timetrace.sample_from_time(obj.end_time);	end_sample = timetrace.sample_from_time(obj.end_time);
32	32	sample_no = end_sample - start_sample;	sample_no = end_sample - start_sample;
33
	33
34	34	timetrace.fopen;	timetrace.fopen;
35	35	timetrace.fseek(start_sample);	timetrace.fseek(start_sample);
36	36	data = timetrace.fread(sample_no);	data = timetrace.fread(sample_no);
37	37	timetrace.fclose	timetrace.fclose
38
	38
39	39	[obj.y,obj.x] = pwelch(data,[],[],[],timetrace.samplingrate);	[obj.y,obj.x] = pwelch(data,[],[],[],timetrace.samplingrate);
40	40	end	end
41
	41
42	42	%% other	%% other
43	43	function desc =get.property_description(obj)	function desc =get.property_description(obj)
44	44	desc = ['Noise power spectral density for t=',...	desc = ['Noise power spectral density for t=',...
45	45	num2str(obj.start_time),' to t=',...	num2str(obj.start_time),' to t=',...
46	46	num2str(obj.end_time)];	num2str(obj.end_time)];
47	47	end	end
48		end
	48		end
49	49	end	end

File data_analysis/obsolete/plottable_property/plottable_property.m changed (mode: 100644) (index 439d2a1..55bc2d2)
...	...	classdef (Abstract) plottable_property
9	9	% PROPERTIES:	% PROPERTIES:
10	10	% - x, y: x- and y-data	% - x, y: x- and y-data
11	11	% - x_label, y_label (Constant across all instances)	% - x_label, y_label (Constant across all instances)
12		%
	12		%
13	13	% in addition plottable_property provides	% in addition plottable_property provides
14	14	% PROPERTIES	% PROPERTIES
15		% - dataset_label: dataset_label
	15		% - dataset_label: dataset_label
16	16	% - some selfexplenatory prop.s controlling the plot	% - some selfexplenatory prop.s controlling the plot
17	17	% METHODS	% METHODS
18	18	% - plot: plot y vs x with suitable labels	% - plot: plot y vs x with suitable labels
...	...	classdef (Abstract) plottable_property
27	27	properties(Abstract, Dependent)	properties(Abstract, Dependent)
28	28	property_description	property_description
29	29	end	end
30		properties
	30		properties
31	31	dataset_label	dataset_label
32	32	end	end
33	33	properties(Constant)	properties(Constant)
34	34	LineWidth = 1.4;	LineWidth = 1.4;
35	35	MarkerSize = 3;	MarkerSize = 3;
36	36	end	end
37
	37
38	38	methods	methods
39	39	function obj = plottable_property(timetrace)	function obj = plottable_property(timetrace)
40	40	assert(isa(timetrace, 'timetrace'),...	assert(isa(timetrace, 'timetrace'),...
41	41	'Expecting a timetrace object');	'Expecting a timetrace object');
42	42	obj.dataset_label = timetrace.dataset_label;	obj.dataset_label = timetrace.dataset_label;
43	43	end	end
44
	44
45	45	function plot(obj, varargin)	function plot(obj, varargin)
46	46	plot(obj.x, obj.y, 'c-',...	plot(obj.x, obj.y, 'c-',...
47	47	'DisplayName',obj.dataset_label,...	'DisplayName',obj.dataset_label,...
48	48	'LineWidth', obj.LineWidth,...	'LineWidth', obj.LineWidth,...
49	49	'MarkerSize',obj.MarkerSize,...	'MarkerSize',obj.MarkerSize,...
50	50	varargin{:});	varargin{:});
51
	51
52	52	xlabel(obj.x_label);	xlabel(obj.x_label);
53	53	ylabel(obj.y_label);	ylabel(obj.y_label);
54	54	t = title(obj.property_description);	t = title(obj.property_description);
...	...	classdef (Abstract) plottable_property
56	56	set(t, 'Interpreter', 'none');	set(t, 'Interpreter', 'none');
57	57	set(l, 'Interpreter', 'none');	set(l, 'Interpreter', 'none');
58	58	end	end
59
	59
60	60	end	end
61	61	end	end

File data_analysis/obsolete/plottable_property/variance_versus_time.m changed (mode: 100644) (index 4788812..137bd32)
1	1	classdef variance_versus_time < plottable_property	classdef variance_versus_time < plottable_property
2		%Uvarvstime = variance_versus_time(timetrace) creates an object with
	2		%Uvarvstime = variance_versus_time(timetrace) creates an object with
3	3	% PROPERTIES:	% PROPERTIES:
4	4	% - x : time	% - x : time
5	5	% - y : noisepower integrated from HF_cutoff to LF_cutoff	% - y : noisepower integrated from HF_cutoff to LF_cutoff
6	6	% - x_label, y_label	% - x_label, y_label
7	7	% - property_description	% - property_description
8		%
	8		%
9	9	% varvstime is a plottable_property object	% varvstime is a plottable_property object
10	10	properties	properties
11	11	x	x
...	...	classdef variance_versus_time < plottable_property
14	14	properties(Constant)	properties(Constant)
15	15	x_label = 'Time [s]';	x_label = 'Time [s]';
16	16	y_label = 'Integrated Noise power [V^2]'	y_label = 'Integrated Noise power [V^2]'
17
	17
18	18	HF_cutoff = 2E6;	HF_cutoff = 2E6;
19	19	LF_cutoff = 1E3;	LF_cutoff = 1E3;
20	20	end	end
21	21	properties(Dependent)	properties(Dependent)
22	22	property_description	property_description
23	23	end	end
24
	24
25	25	methods	methods
26	26	%% constructor	%% constructor
27	27	function obj = variance_versus_time(timetrace)	function obj = variance_versus_time(timetrace)
28	28	obj = obj@plottable_property(timetrace);	obj = obj@plottable_property(timetrace);
29	29	samplingrate = timetrace.samplingrate;	samplingrate = timetrace.samplingrate;
30
	30
31	31	samples_to_skip = samplingrate/(2*obj.HF_cutoff);	samples_to_skip = samplingrate/(2*obj.HF_cutoff);
32	32	block_size = samplingrate/obj.LF_cutoff;	block_size = samplingrate/obj.LF_cutoff;
33	33	nBlocks = floor(timetrace.sample_no/...	nBlocks = floor(timetrace.sample_no/...
...	...	classdef variance_versus_time < plottable_property
37	37	%%%%	%%%%
38	38	disp([10,'calculating variance vs time for '...	disp([10,'calculating variance vs time for '...
39	39	timetrace.filename,'...',10])	timetrace.filename,'...',10])
40
	40
41	41	fopen(timetrace);	fopen(timetrace);
42	42	for iBlock=1:nBlocks	for iBlock=1:nBlocks
43	43	data = fread(timetrace, block_size, samples_to_skip);	data = fread(timetrace, block_size, samples_to_skip);
...	...	classdef variance_versus_time < plottable_property
49	49	fclose(timetrace);	fclose(timetrace);
50	50	obj.dataset_label = timetrace.dataset_label;	obj.dataset_label = timetrace.dataset_label;
51	51	end	end
52
	52
53	53	%% other	%% other
54	54	function desc =get.property_description(obj)	function desc =get.property_description(obj)
55	55	desc = ['Noise power integrated from ',...	desc = ['Noise power integrated from ',...
56	56	num2str(obj.LF_cutoff), '[Hz] to ', ...	num2str(obj.LF_cutoff), '[Hz] to ', ...
57	57	num2str(obj.HF_cutoff), '[Hz].'];	num2str(obj.HF_cutoff), '[Hz].'];
58	58	end	end
59		end
	59		end
60	60	end	end

File data_analysis/unittests/PXItrace_test.m changed (mode: 100644) (index 6cdf1d1..63e28ea)
1	1	function test_suite = PXItrace_test %#ok<STOUT>	function test_suite = PXItrace_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	5
6	6	function shared = setup	function shared = setup
7		shared.DAQrange = 0;
8		shared.amplification = 10;
9
10		shared.sample_no = 1e4;
11		shared.data = randn(shared.sample_no,1);
12
13		shared.precision = 'double';
14		shared.folder = tempname;
15		assert(~(exist(shared.folder)==7),...
16		[shared.folder, ' exists already.']);
17		mkdir(shared.folder)
18		shared.file = fullfile(shared.folder, '.PXItrace.dat');
19
20		fid = fopen(shared.file,'w');
21		fwrite(fid, shared.data, shared.precision);
22		fclose(fid);
	7		shared.DAQrange = 0;
	8		shared.amplification = 10;
	9
	10		shared.sample_no = 1e4;
	11		shared.data = randn(shared.sample_no,1);
	12
	13		shared.precision = 'double';
	14		shared.folder = tempname;
	15		assert(~(exist(shared.folder)==7),...
	16		[shared.folder, ' exists already.']);
	17		mkdir(shared.folder)
	18		shared.file = fullfile(shared.folder, '.PXItrace.dat');
	19
	20		fid = fopen(shared.file,'w');
	21		fwrite(fid, shared.data, shared.precision);
	22		fclose(fid);
23	23
24	24	function teardown(shared)	function teardown(shared)
25		delete(shared.file);
26		rmdir(shared.folder);
	25		delete(shared.file);
	26		rmdir(shared.folder);
27	27
28	28	function pt = pt_setup_test(shared)	function pt = pt_setup_test(shared)
29		pt = PXItrace(shared.file,...
30		'precision', shared.precision,...
31		'DAQrange', shared.DAQrange,...
32		'amplification', shared.amplification);
	29		pt = PXItrace(shared.file,...
	30		'precision', shared.precision,...
	31		'DAQrange', shared.DAQrange,...
	32		'amplification', shared.amplification);
33	33
34	34	function pt = read_data_test(shared)	function pt = read_data_test(shared)
35		pt = pt_setup_test(shared);
36		pt.fopen();
37		assertEqual(pt.fread(shared.sample_no), shared.data/shared.amplification);
	35		pt = pt_setup_test(shared);
	36		pt.fopen();
	37		assertEqual(pt.fread(shared.sample_no), shared.data/shared.amplification);
38	38
39		startingsample = 10;
40		sample_no = 2;
41		pt.fseek(startingsample);
42		assertEqual(...
43		pt.fread(sample_no),...
44		shared.data(startingsample:startingsample+sample_no-1)/shared.amplification);
	39		startingsample = 10;
	40		sample_no = 2;
	41		pt.fseek(startingsample);
	42		assertEqual(...
	43		pt.fread(sample_no),...
	44		shared.data(startingsample:startingsample+sample_no-1)/shared.amplification);
45	45
46		pt.fseek(1);
47		assertEqual(...
48		pt.fread(shared.sample_no),...
49		shared.data/shared.amplification);
	46		pt.fseek(1);
	47		assertEqual(...
	48		pt.fread(shared.sample_no),...
	49		shared.data/shared.amplification);
50	50
51		pt.fclose();
	51		pt.fclose();
52	52
53	53	function pt = properties_test(shared)	function pt = properties_test(shared)
54		pt = pt_setup_test(shared);
55		assertEqual(pt.sample_no, shared.sample_no)
56		assertEqual(pt.precision, shared.precision)
	54		pt = pt_setup_test(shared);
	55		assertEqual(pt.sample_no, shared.sample_no)
	56		assertEqual(pt.precision, shared.precision)
57	57
58		assertEqual(pt.DAQrange, shared.DAQrange)
59		assertEqual(pt.amplification, shared.amplification)
	58		assertEqual(pt.DAQrange, shared.DAQrange)
	59		assertEqual(pt.amplification, shared.amplification)
60	60
61		[folder, filename, fileext] = fileparts(shared.file);
62		assertEqual(pt.filepath, shared.file)
63		assertEqual(pt.directory, folder)
64		assertEqual(pt.filename, [filename, fileext])
	61		[folder, filename, fileext] = fileparts(shared.file);
	62		assertEqual(pt.filepath, shared.file)
	63		assertEqual(pt.directory, folder)
	64		assertEqual(pt.filename, [filename, fileext])
65	65
66		if pt.precision=='double'
67		assertEqual(pt.bytes_per_sample, 8)
68		end
	66		if pt.precision=='double'
	67		assertEqual(pt.bytes_per_sample, 8)
	68		end
69	69
70	70	function pt = pseudohash_test(shared)	function pt = pseudohash_test(shared)
71		pt = pt_setup_test(shared);
72		assertEqual(...
73		pseudo_hash(shared.file),...
74		pt.pseudohash)
	71		pt = pt_setup_test(shared);
	72		assertEqual(...
	73		pseudo_hash(shared.file),...
	74		pt.pseudohash)
75	75
76	76	function pt = pseudohash_modified_test(shared)	function pt = pseudohash_modified_test(shared)
77		pt = pt_setup_test(shared);
78		% fid = fopen(shared.file, 'w');
79		% %fwrite(fid, randn(1));
80		% fclose(fid);
81		assertEqual(...
82		pseudo_hash(shared.file),...
83		pt.pseudohash)
	77		pt = pt_setup_test(shared);
	78		% fid = fopen(shared.file, 'w');
	79		% %fwrite(fid, randn(1));
	80		% fclose(fid);
	81		assertEqual(...
	82		pseudo_hash(shared.file),...
	83		pt.pseudohash)
84	84
85	85	function pt = dummy_test(shared)	function pt = dummy_test(shared)
86		constructor_test = @()PXItrace(shared.file, 'dummy', true);
87		assertExceptionThrown(constructor_test, 'PXItrace:FileExists')
88
89		file = '.PXItrace_dummy.dat';
90		pt = PXItrace(file, 'dummy', true);
91		assert(isafile(file));
92		%delete(file); not necesary, since the dummy file is deleted upon destruction anyways..
	86		constructor_test = @()PXItrace(shared.file, 'dummy', true);
	87		assertExceptionThrown(constructor_test, 'PXItrace:FileExists')
	88
	89		file = '.PXItrace_dummy.dat';
	90		pt = PXItrace(file, 'dummy', true);
	91		assert(isafile(file));
	92		%delete(file); not necesary, since the dummy file is deleted upon destruction anyways..
93	93
94	94	function pt = delete_dummy_test(shared)	function pt = delete_dummy_test(shared)
95		file = '.PXItrace_dummy.dat';
96		pt = PXItrace(file, 'dummy', true);
97		% deleting the object should call the destructor which should delete the dummy file
98		clear('pt')
99		assert(~isafile(file))
	95		file = '.PXItrace_dummy.dat';
	96		pt = PXItrace(file, 'dummy', true);
	97		% deleting the object should call the destructor which should delete the dummy file
	98		clear('pt')
	99		assert(~isafile(file))

File data_analysis/unittests/blocked_cross_correlation_test.m changed (mode: 100644) (index eb65bab..fa75520)
1	1	function test_suite = blocked_cross_correlation_test %#ok<STOUT>	function test_suite = blocked_cross_correlation_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function shared = setup	function shared = setup
7		shared.sample_no = 1e2;
8		% NOTE: only get same results if blocked_cross_correlation can process all data!
9		% --> need to have shared.sample_no a multiple of block_size for the tests to pass..
10		shared.x.data = randn(shared.sample_no, 1);
11		shared.p.data = randn(shared.sample_no, 1);
12
13		shared.precision = 'double';
14		shared.DAQrange = 0;
15		shared.amplification = 1;
16
17		shared.folder = tempname;
18		assert(~(exist(shared.folder)==7),...
19		[shared.folder, ' exists already.']);
20		mkdir(shared.folder)
21		shared.x.file = fullfile(shared.folder, '.x.dat');
22		shared.p.file = fullfile(shared.folder, '.p.dat');
23
24		for mytrace = {shared.x, shared.p}
25		mytrace = mytrace{:};
26		fid = fopen(mytrace.file,'w');
27		fwrite(fid, mytrace.data, shared.precision);
28		fclose(fid);
29		end
30		myPXItrace = @(file)PXItrace(file,...
31		'precision', shared.precision,...
32		'DAQrange',shared.DAQrange,...
33		'amplification', shared.amplification);
34		shared.x.PXItrace = myPXItrace(shared.x.file);
35		shared.p.PXItrace = myPXItrace(shared.p.file);
	7		shared.sample_no = 1e2;
	8		% NOTE: only get same results if blocked_cross_correlation can process all data!
	9		% --> need to have shared.sample_no a multiple of block_size for the tests to pass..
	10		shared.x.data = randn(shared.sample_no, 1);
	11		shared.p.data = randn(shared.sample_no, 1);
	12
	13		shared.precision = 'double';
	14		shared.DAQrange = 0;
	15		shared.amplification = 1;
	16
	17		shared.folder = tempname;
	18		assert(~(exist(shared.folder)==7),...
	19		[shared.folder, ' exists already.']);
	20		mkdir(shared.folder)
	21		shared.x.file = fullfile(shared.folder, '.x.dat');
	22		shared.p.file = fullfile(shared.folder, '.p.dat');
	23
	24		for mytrace = {shared.x, shared.p}
	25		mytrace = mytrace{:};
	26		fid = fopen(mytrace.file,'w');
	27		fwrite(fid, mytrace.data, shared.precision);
	28		fclose(fid);
	29		end
	30		myPXItrace = @(file)PXItrace(file,...
	31		'precision', shared.precision,...
	32		'DAQrange',shared.DAQrange,...
	33		'amplification', shared.amplification);
	34		shared.x.PXItrace = myPXItrace(shared.x.file);
	35		shared.p.PXItrace = myPXItrace(shared.p.file);
36	36
37	37	function teardown(shared)	function teardown(shared)
38		for mytrace = {shared.x, shared.p}
39		mytrace = mytrace{:};
40		delete(mytrace.file);
41		end
42		rmdir(shared.folder)
	38		for mytrace = {shared.x, shared.p}
	39		mytrace = mytrace{:};
	40		delete(mytrace.file);
	41		end
	42		rmdir(shared.folder)
43	43
44	44	function read_data_test(shared)	function read_data_test(shared)
45		for mytrace = {shared.x, shared.p}
46		mytrace = mytrace{:};
47		mytrace.PXItrace.fopen();
48		mydata = mytrace.PXItrace.fread(mytrace.PXItrace.sample_no);
49		mytrace.PXItrace.fclose();
	45		for mytrace = {shared.x, shared.p}
	46		mytrace = mytrace{:};
	47		mytrace.PXItrace.fopen();
	48		mydata = mytrace.PXItrace.fread(mytrace.PXItrace.sample_no);
	49		mytrace.PXItrace.fclose();
50	50
51		assertEqual(mytrace.data, mydata);
	51		assertEqual(mytrace.data, mydata);
52	52	end	end
53	53
54	54	function blocked_cross_correlation_regular_test(shared)	function blocked_cross_correlation_regular_test(shared)
55		max_lags = 10;
	55		max_lags = 10;
56	56	xtrace = shared.x.PXItrace;	xtrace = shared.x.PXItrace;
57	57	ptrace = shared.p.PXItrace;	ptrace = shared.p.PXItrace;
58	58
59		[T, xp, xx, pp] = evalc('blocked_cross_correlation(xtrace, ptrace, max_lags);');
60		% suppress std out from call to blocked_cross_correlation
	59		[T, xp, xx, pp] = evalc('blocked_cross_correlation(xtrace, ptrace, max_lags);');
	60		% suppress std out from call to blocked_cross_correlation
61	61
62	62	rel_tol = 1e-13;	rel_tol = 1e-13;
63		assertElementsAlmostEqual(xx, flipud(xx), 'relative', rel_tol);
64		assertElementsAlmostEqual(pp, flipud(pp), 'relative', rel_tol);
	63		assertElementsAlmostEqual(xx, flipud(xx), 'relative', rel_tol);
	64		assertElementsAlmostEqual(pp, flipud(pp), 'relative', rel_tol);
65	65
66		xx_ = xcorr(shared.x.data, shared.x.data, max_lags);
67		xp_ = xcorr(shared.x.data, shared.p.data, max_lags);
68		assertElementsAlmostEqual(xx, xx_, 'relative', rel_tol);
69		assertElementsAlmostEqual(xp, xp_, 'relative', rel_tol);
	66		xx_ = xcorr(shared.x.data, shared.x.data, max_lags);
	67		xp_ = xcorr(shared.x.data, shared.p.data, max_lags);
	68		assertElementsAlmostEqual(xx, xx_, 'relative', rel_tol);
	69		assertElementsAlmostEqual(xp, xp_, 'relative', rel_tol);
70	70
71	71	function blocked_cross_correlation_methods_test(shared)	function blocked_cross_correlation_methods_test(shared)
72		max_lags = 2;
	72		max_lags = 2;
73	73
74	74	xtrace = shared.x.PXItrace;	xtrace = shared.x.PXItrace;
75	75	ptrace = shared.p.PXItrace;	ptrace = shared.p.PXItrace;
76	76
77	77	for scaleopt = {'none', 'biased', 'unbiased', 'coeff'}	for scaleopt = {'none', 'biased', 'unbiased', 'coeff'}
78		scaleopt = scaleopt{:};
	78		scaleopt = scaleopt{:};
79	79
80		[T, xp, xx, pp] = evalc('blocked_cross_correlation(xtrace, ptrace, max_lags, scaleopt);');
81		% suppress std out from call to blocked_cross_correlation
	80		[T, xp, xx, pp] = evalc('blocked_cross_correlation(xtrace, ptrace, max_lags, scaleopt);');
	81		% suppress std out from call to blocked_cross_correlation
82	82
83		rel_tol = 1e-13;
84		assertElementsAlmostEqual(xx, flipud(xx), 'relative', rel_tol);
85		assertElementsAlmostEqual(pp, flipud(pp), 'relative', rel_tol);
	83		rel_tol = 1e-13;
	84		assertElementsAlmostEqual(xx, flipud(xx), 'relative', rel_tol);
	85		assertElementsAlmostEqual(pp, flipud(pp), 'relative', rel_tol);
86	86
87		xx_ = xcorr(shared.x.data, shared.x.data, max_lags, scaleopt);
88		xp_ = xcorr(shared.x.data, shared.p.data, max_lags, scaleopt);
	87		xx_ = xcorr(shared.x.data, shared.x.data, max_lags, scaleopt);
	88		xp_ = xcorr(shared.x.data, shared.p.data, max_lags, scaleopt);
89	89
90		assertElementsAlmostEqual(xx, xx_, 'relative', rel_tol);
91		assertElementsAlmostEqual(xp, xp_, 'relative', rel_tol);
92		end
	90		assertElementsAlmostEqual(xx, xx_, 'relative', rel_tol);
	91		assertElementsAlmostEqual(xp, xp_, 'relative', rel_tol);
	92		end

File data_analysis/unittests/entanglement_evaluation_test.m changed (mode: 100644) (index 9cfe5be..8f959b1)
1	1	function test_suite = entanglement_evaluation_test %#ok<STOUT>	function test_suite = entanglement_evaluation_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function shared = setup	function shared = setup
7		shared.evaluation_folder = tempname;
8		assert(~(exist(shared.evaluation_folder)==7),...
9		[shared.evaluation_folder, ' exists already.']);
10		mkdir(shared.evaluation_folder)
	7		shared.evaluation_folder = tempname;
	8		assert(~(exist(shared.evaluation_folder)==7),...
	9		[shared.evaluation_folder, ' exists already.']);
	10		mkdir(shared.evaluation_folder)
11	11
12		shared.samplingrate = 100;
13		shared.sampleno = 1e4;
	12		shared.samplingrate = 100;
	13		shared.sampleno = 1e4;
14	14
15		shared.mech_freqs = [1, 1.345];
16		shared.timevec = (0:shared.sampleno-1)'/shared.samplingrate;
	15		shared.mech_freqs = [1, 1.345];
	16		shared.timevec = (0:shared.sampleno-1)'/shared.samplingrate;
17	17
18		shared.x.data = sin(2*pi*shared.mech_freqs(1)*shared.timevec) + sin(2*pi*shared.mech_freqs(2)*shared.timevec);
19		shared.p.data = cos(2*pi*shared.mech_freqs(1)*shared.timevec) + cos(2*pi*shared.mech_freqs(1)*shared.timevec);
	18		shared.x.data = sin(2*pi*shared.mech_freqs(1)*shared.timevec) + sin(2*pi*shared.mech_freqs(2)*shared.timevec);
	19		shared.p.data = cos(2*pi*shared.mech_freqs(1)*shared.timevec) + cos(2*pi*shared.mech_freqs(1)*shared.timevec);
20	20
21		shared.precision = 'double';
	21		shared.precision = 'double';
22	22
23		shared.x.file = [tempname, '.dat'];
24		shared.p.file = [tempname, '.dat'];
	23		shared.x.file = [tempname, '.dat'];
	24		shared.p.file = [tempname, '.dat'];
25	25
26		for mytrace = {shared.x, shared.p}
27		mytrace = mytrace{:};
28		fid = fopen(mytrace.file,'w');
29		fwrite(fid, mytrace.data, shared.precision);
30		fclose(fid);
31		end
	26		for mytrace = {shared.x, shared.p}
	27		mytrace = mytrace{:};
	28		fid = fopen(mytrace.file,'w');
	29		fwrite(fid, mytrace.data, shared.precision);
	30		fclose(fid);
	31		end
32	32
33	33	function teardown(shared)	function teardown(shared)
34		for mytrace = {shared.x, shared.p}
35		mytrace = mytrace{:};
36		delete(mytrace.file);
37		end
38		rmdir(shared.evaluation_folder, 's');
39		clear('shared');
	34		for mytrace = {shared.x, shared.p}
	35		mytrace = mytrace{:};
	36		delete(mytrace.file);
	37		end
	38		rmdir(shared.evaluation_folder, 's');
	39		clear('shared');
40	40
41	41	%% OTHER AUX FUNCTIONS %%	%% OTHER AUX FUNCTIONS %%
42	42	function meas = dual_homo_measurement_setup(shared)	function meas = dual_homo_measurement_setup(shared)
43		meas = dual_homo_measurement('samplingrate', shared.samplingrate);
44		meas.add_trace_pair(...
45		shared.x.file,...
46		shared.p.file,...
47		'precision',shared.precision,...
48		'DAQrange', 0,...
49		'amplification', 1,...
50		'signal_power_muW', 0);
51		meas.add_trace_pair(...
52		shared.x.file,...
53		shared.p.file,...
54		'precision',shared.precision,...
55		'DAQrange', 0,...
56		'amplification', 1,...
57		'signal_power_muW', 100);
	43		meas = dual_homo_measurement('samplingrate', shared.samplingrate);
	44		meas.add_trace_pair(...
	45		shared.x.file,...
	46		shared.p.file,...
	47		'precision',shared.precision,...
	48		'DAQrange', 0,...
	49		'amplification', 1,...
	50		'signal_power_muW', 0);
	51		meas.add_trace_pair(...
	52		shared.x.file,...
	53		shared.p.file,...
	54		'precision',shared.precision,...
	55		'DAQrange', 0,...
	56		'amplification', 1,...
	57		'signal_power_muW', 100);
58	58	%%%%%%%%%%%%%%%%%%%%%%%%%%%%	%%%%%%%%%%%%%%%%%%%%%%%%%%%%
59	59
60	60	function ev = ev_setup_test(shared)	function ev = ev_setup_test(shared)
61		meas = dual_homo_measurement_setup(shared);
62		ev = entanglement_evaluation('measurement', meas);
63		ev.evaluation_folder = shared.evaluation_folder;
	61		meas = dual_homo_measurement_setup(shared);
	62		ev = entanglement_evaluation('measurement', meas);
	63		ev.evaluation_folder = shared.evaluation_folder;
64	64
65	65	function properties_test(shared)	function properties_test(shared)
66		ev = ev_setup_test(shared);
67		assertEqual(ev.samplingrate, shared.samplingrate);
	66		ev = ev_setup_test(shared);
	67		assertEqual(ev.samplingrate, shared.samplingrate);
68	68
69	69	function software_version_test(shared)	function software_version_test(shared)
70		ev = ev_setup_test(shared);
71		swv = ev.software_version;
72		assert(~isempty(swv) && isequal(class(swv), 'char'));
73		swv2 = ev.software_version;
74		assertEqual(swv, swv2);
	70		ev = ev_setup_test(shared);
	71		swv = ev.software_version;
	72		assert(~isempty(swv) && isequal(class(swv), 'char'));
	73		swv2 = ev.software_version;
	74		assertEqual(swv, swv2);
75	75
76	76	function modefunctions_dummy_test(shared)	function modefunctions_dummy_test(shared)
77		ev = ev_setup_test(shared);
78		ev.mech_frequencies = [1, 2, 3];
79		ev.pulse_gammas = logspace(-1,1,2);
80		[T,ev] = evalc('ev.calculateModeVectors(''dummy'',true)'); % supress std out from fct call
	77		ev = ev_setup_test(shared);
	78		ev.mech_frequencies = [1, 2, 3];
	79		ev.pulse_gammas = logspace(-1,1,2);
	80		[T,ev] = evalc('ev.calculateModeVectors(''dummy'',true)'); % supress std out from fct call
81	81
82	82	function modefunctions_gammalist_test(shared)	function modefunctions_gammalist_test(shared)
83		ev = ev_setup_test(shared);
84		ev.mech_frequencies = [1, 3];
85		gamma1_list = logspace(-3,-1,3);
86		gamma2_list = logspace(-3,-2,2);
87		gamma_lists = {gamma1_list, gamma2_list};
88		ev.pulse_widths = allcomb(gamma_lists{:});
89		ev.samples_per_pulse = round(ev.samples_per_pulse/100);
90
91		[T,ev] = evalc('ev.calculate_mode_vectors_gammalist()');
92
93		% pulse_gammas is simply an index running over all possible combinations of pulse widths
94		% (in this version of the mode vector calculation)
95		assertEqual(ev.pulse_gammas, 1:length(gamma1_list)*length(gamma2_list));
96
97		% estimate decay constant
98		testgammas_nonangular = zeros(size(ev.readout_modes,1),1);
99		for g=1:size(ev.readout_modes,1)
100		testmode = abs(ev.readout_modes(g,1:1000))';
101		testgammas_nonangular(g) = ev.samplingrate*mean(-diff(testmode)./testmode(1:end-1))/(2*pi);
102		end
103		ratios = testgammas_nonangular./reshape(ev.pulse_widths',[],1);
104		% ratios
105		assert(all((0.8<ratios) & (ratios<1.2)))
106		% figure; bar(ratios)
107
108		% quick evaluation run
109		gamma1_list = logspace(1,3,3);
110		gamma2_list = logspace(1,2,2);
111		gamma_lists = {gamma1_list, gamma2_list};
112		ev.pulse_widths = allcomb(gamma_lists{:});
113		ev.samples_per_pulse = 100;
114
115		[T,ev] = evalc('ev.calculate_mode_vectors_gammalist()');
116		pulsepairs_no = 2;
117		[T,ev] = evalc('ev.calculatePulsedQuadratures(1, ''pulsepairs'', pulsepairs_no);');
118		[T,ev] = evalc('ev.calculatePulsedQuadratures(2, ''pulsepairs'', pulsepairs_no);');
119
120		ev.calculatePulsedCMs;
121		ev.calibratePulsedCMs('shotnoise_ind', 1);
122		ev.exportResults;
	83		ev = ev_setup_test(shared);
	84		ev.mech_frequencies = [1, 3];
	85		gamma1_list = logspace(-3,-1,3);
	86		gamma2_list = logspace(-3,-2,2);
	87		gamma_lists = {gamma1_list, gamma2_list};
	88		ev.pulse_widths = allcomb(gamma_lists{:});
	89		ev.samples_per_pulse = round(ev.samples_per_pulse/100);
	90
	91		[T,ev] = evalc('ev.calculate_mode_vectors_gammalist()');
	92
	93		% pulse_gammas is simply an index running over all possible combinations of pulse widths
	94		% (in this version of the mode vector calculation)
	95		assertEqual(ev.pulse_gammas, 1:length(gamma1_list)*length(gamma2_list));
	96
	97		% estimate decay constant
	98		testgammas_nonangular = zeros(size(ev.readout_modes,1),1);
	99		for g=1:size(ev.readout_modes,1)
	100		testmode = abs(ev.readout_modes(g,1:1000))';
	101		testgammas_nonangular(g) = ev.samplingrate*mean(-diff(testmode)./testmode(1:end-1))/(2*pi);
	102		end
	103		ratios = testgammas_nonangular./reshape(ev.pulse_widths',[],1);
	104		% ratios
	105		assert(all((0.8<ratios) & (ratios<1.2)))
	106		% figure; bar(ratios)
	107
	108		% quick evaluation run
	109		gamma1_list = logspace(1,3,3);
	110		gamma2_list = logspace(1,2,2);
	111		gamma_lists = {gamma1_list, gamma2_list};
	112		ev.pulse_widths = allcomb(gamma_lists{:});
	113		ev.samples_per_pulse = 100;
	114
	115		[T,ev] = evalc('ev.calculate_mode_vectors_gammalist()');
	116		pulsepairs_no = 2;
	117		[T,ev] = evalc('ev.calculatePulsedQuadratures(1, ''pulsepairs'', pulsepairs_no);');
	118		[T,ev] = evalc('ev.calculatePulsedQuadratures(2, ''pulsepairs'', pulsepairs_no);');
	119
	120		ev.calculatePulsedCMs;
	121		ev.calibratePulsedCMs('shotnoise_ind', 1);
	122		ev.exportResults;
123	123
124	124	function modefunctions_test(shared)	function modefunctions_test(shared)
125		ev = ev_setup_test(shared);
126		ev.mech_frequencies = [1, 2, 3];
127		ev.opt_pulse_widths = ones(size(ev.mech_frequencies));
128		ev.pulse_gammas = logspace(-1,1,2);
129		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
130
131		% check approximate equality with mode functions as of commit e3e72443e0acce38cf9de712f32dd57dde7f069a
132		% (up to different relative signs for different mode functions)
133		tol = 1e-2;
134		relsign = assertRowsAlmostEqualUptoSign(ev.readout_modes(:,1:3),...
135		[0.1115 + 0.0070i, 0.1102 + 0.0139i, 0.1084 + 0.0207i;...
136		0.1108 - 0.0080i, 0.1088 + 0.0060i, 0.1051 + 0.0194i;...
137		0.1097 - 0.0119i, 0.1065 + 0.0088i, 0.0996 + 0.0282i;...
138		0.8441 + 0.0531i, 0.4477 + 0.0566i, 0.2365 + 0.0451i;...
139		0.0817 - 0.4438i, -0.0738 + 0.3576i, -0.1607 + 0.4920i;...
140		-0.2255 - 0.0842i, 0.4708 + 0.2149i, 0.1491 + 0.1134i],...
141		'relative', tol);
142		relsign = assertRowsAlmostEqualUptoSign(ev.entangling_modes(:,end-2:end),...
143		[0.1084 + 0.0207i, 0.1102 + 0.0139i, 0.1115 + 0.0070i;...
144		0.1051 + 0.0194i, 0.1088 + 0.0060i, 0.1108 - 0.0080i;...
145		0.0996 + 0.0282i, 0.1065 + 0.0088i, 0.1097 - 0.0119i;...
146		0.2365 + 0.0451i, 0.4477 + 0.0566i, 0.8441 + 0.0531i;...
147		-0.1607 + 0.4920i, -0.0738 + 0.3576i, 0.0817 - 0.4438i;...
148		0.1491 + 0.1134i, 0.4708 + 0.2149i, -0.2255 - 0.0842i],...
149		'relative', tol);
150
151		%figure; plot(real(ev.readout_modes'))
152
153		%%% check that the orthonormality test does the right thing
154		on_matrix_entangling = ev.entangling_modes*(ev.entangling_modes)';
155		[modefct_no, samples_per_pulse] = size(ev.entangling_modes);
156		assertEqual(size(on_matrix_entangling), modefct_no*[1,1])
157		norm_tol = inf; orthog_tol = inf; % don't care about precision here, only about checking rows vs checking columns
158		on_info = assert_orthonormality(ev.readout_modes,norm_tol, orthog_tol);
159		assertEqual(on_info.matrix_size, modefct_no*[1,1])
160		%%%%
161
162		% check orthonormality
163		on_matrix_readout = ev.readout_modes*(ev.readout_modes)';
164		for k=1:ev.pulse_gammas_no
165		start_mode = (k-1)*ev.mech_frequencies_no + 1;
166		stop_mode = k*ev.mech_frequencies_no;
167		assertElementsAlmostEqual(...
168		on_matrix_entangling(start_mode:stop_mode,start_mode:stop_mode),...
169		eye(ev.mech_frequencies_no), 'absolute', 1e-12)
170		assertElementsAlmostEqual(...
171		on_matrix_readout(start_mode:stop_mode,start_mode:stop_mode),...
172		eye(ev.mech_frequencies_no), 'absolute', 1e-12)
173		end
174
175		% check correct sidebands are addressed
176		tol = 1e-12;
177		mean_rotation_freq = @(vector)mean(diff(phase(vector))*shared.samplingrate)/(2*pi);
178		for f=1:ev.mech_frequencies_no
179		% entangling_modes should pick out down-scattered light --> should be prop. to exp(+i om t)
180		assertElementsAlmostEqual(...
181		mean_rotation_freq(ev.entangling_modes(f,:)),...
182		-ev.mech_frequencies(f), 'absolute', tol)
183		% readout modes should pick out up-scattered light --> should be prop. to exp(-i om t)
184		assertElementsAlmostEqual(...
185		mean_rotation_freq(ev.readout_modes(f,:)),...
186		+ev.mech_frequencies(f), 'absolute', tol)
187		% for some reasons it's exactly the other way round...
188		end
189		%figure; plot(phase(ev.readout_modes(1,1:100)))
	125		ev = ev_setup_test(shared);
	126		ev.mech_frequencies = [1, 2, 3];
	127		ev.opt_pulse_widths = ones(size(ev.mech_frequencies));
	128		ev.pulse_gammas = logspace(-1,1,2);
	129		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
	130
	131		% check approximate equality with mode functions as of commit e3e72443e0acce38cf9de712f32dd57dde7f069a
	132		% (up to different relative signs for different mode functions)
	133		tol = 1e-2;
	134		relsign = assertRowsAlmostEqualUptoSign(ev.readout_modes(:,1:3),...
	135		[0.1115 + 0.0070i, 0.1102 + 0.0139i, 0.1084 + 0.0207i;...
	136		0.1108 - 0.0080i, 0.1088 + 0.0060i, 0.1051 + 0.0194i;...
	137		0.1097 - 0.0119i, 0.1065 + 0.0088i, 0.0996 + 0.0282i;...
	138		0.8441 + 0.0531i, 0.4477 + 0.0566i, 0.2365 + 0.0451i;...
	139		0.0817 - 0.4438i, -0.0738 + 0.3576i, -0.1607 + 0.4920i;...
	140		-0.2255 - 0.0842i, 0.4708 + 0.2149i, 0.1491 + 0.1134i],...
	141		'relative', tol);
	142		relsign = assertRowsAlmostEqualUptoSign(ev.entangling_modes(:,end-2:end),...
	143		[0.1084 + 0.0207i, 0.1102 + 0.0139i, 0.1115 + 0.0070i;...
	144		0.1051 + 0.0194i, 0.1088 + 0.0060i, 0.1108 - 0.0080i;...
	145		0.0996 + 0.0282i, 0.1065 + 0.0088i, 0.1097 - 0.0119i;...
	146		0.2365 + 0.0451i, 0.4477 + 0.0566i, 0.8441 + 0.0531i;...
	147		-0.1607 + 0.4920i, -0.0738 + 0.3576i, 0.0817 - 0.4438i;...
	148		0.1491 + 0.1134i, 0.4708 + 0.2149i, -0.2255 - 0.0842i],...
	149		'relative', tol);
	150
	151		%figure; plot(real(ev.readout_modes'))
	152
	153		%%% check that the orthonormality test does the right thing
	154		on_matrix_entangling = ev.entangling_modes*(ev.entangling_modes)';
	155		[modefct_no, samples_per_pulse] = size(ev.entangling_modes);
	156		assertEqual(size(on_matrix_entangling), modefct_no*[1,1])
	157		norm_tol = inf; orthog_tol = inf; % don't care about precision here, only about checking rows vs checking columns
	158		on_info = assert_orthonormality(ev.readout_modes,norm_tol, orthog_tol);
	159		assertEqual(on_info.matrix_size, modefct_no*[1,1])
	160		%%%%
	161
	162		% check orthonormality
	163		on_matrix_readout = ev.readout_modes*(ev.readout_modes)';
	164		for k=1:ev.pulse_gammas_no
	165		start_mode = (k-1)*ev.mech_frequencies_no + 1;
	166		stop_mode = k*ev.mech_frequencies_no;
	167		assertElementsAlmostEqual(...
	168		on_matrix_entangling(start_mode:stop_mode,start_mode:stop_mode),...
	169		eye(ev.mech_frequencies_no), 'absolute', 1e-12)
	170		assertElementsAlmostEqual(...
	171		on_matrix_readout(start_mode:stop_mode,start_mode:stop_mode),...
	172		eye(ev.mech_frequencies_no), 'absolute', 1e-12)
	173		end
	174
	175		% check correct sidebands are addressed
	176		tol = 1e-12;
	177		mean_rotation_freq = @(vector)mean(diff(phase(vector))*shared.samplingrate)/(2*pi);
	178		for f=1:ev.mech_frequencies_no
	179		% entangling_modes should pick out down-scattered light --> should be prop. to exp(+i om t)
	180		assertElementsAlmostEqual(...
	181		mean_rotation_freq(ev.entangling_modes(f,:)),...
	182		-ev.mech_frequencies(f), 'absolute', tol)
	183		% readout modes should pick out up-scattered light --> should be prop. to exp(-i om t)
	184		assertElementsAlmostEqual(...
	185		mean_rotation_freq(ev.readout_modes(f,:)),...
	186		+ev.mech_frequencies(f), 'absolute', tol)
	187		% for some reasons it's exactly the other way round...
	188		end
	189		%figure; plot(phase(ev.readout_modes(1,1:100)))
190	190
191	191	function run_test(shared)	function run_test(shared)
192		ev = ev_setup_test(shared);
193		ev.mech_frequencies = shared.mech_freqs;
194		ev.opt_pulse_widths = 1e2*[1, 1];
195		ev.pulse_gammas = logspace(-1,1,10);
196		ev.samples_per_pulse = 4*ev.samples_per_pulse; % adjust samples_per_pulse, since in commit 6dcad7dc617c9bed38f45823b049e556264b694f it has been lower by a factor of 4;
197		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
198		[T,ev] = evalc('ev.calculatePulsedQuadratures(1);'); % supress std out from fct call
199		assertEqual(size(ev.pulse_Y{1}),...
200		[ev.pulse_pairs_per_trace, ev.mech_frequencies_no*ev.pulse_gammas_no]);
201
202		% compare to results as of datoolbox-commit 754e283187e9e9a6385b0f9a8a26f5fd2807ccd0
203		% (after modifying mode-fct calculation to avoid numerical instabilities during orthonormalization)
204		tolerance = 1e-2;
205		relsign = assertColsAlmostEqualUptoSign(...
206		[ev.pulse_Q{1}(1:2,:); ev.pulse_P{1}(1:2,:);...
207		ev.pulse_X{1}(1:2,:); ev.pulse_Y{1}(1:2,:)],...
208		[-2.2115, -2.4141, -1.6618, -2.3063, -1.3238, -2.0629, -1.1502, -1.8850, -1.0911, -1.8164, -1.0820, -1.8055, -1.0816, -1.8051, -1.0816, -1.8051, -1.0816, -1.8051, -1.0816, -1.8051;...
209		-0.2433, -2.8269, 0.0246, -2.6458, 0.1230, -2.3094, 0.1520, -2.0811, 0.1583, -1.9958, 0.1592, -1.9824, 0.1592, -1.9819, 0.1592, -1.9819, 0.1592, -1.9819, 0.1592, -1.9819;...
210		3.2915, -2.9633, 2.7127, -2.1359, 2.2809, -1.6272, 2.0343, -1.3764, 1.9462, -1.2934, 1.9325, -1.2808, 1.9320, -1.2803, 1.9320, -1.2803, 1.9320, -1.2803, 1.9320, -1.2803;...
211		3.5199, -1.8524, 2.8666, -0.8733, 2.3888, -0.4121, 2.1204, -0.2318, 2.0254, -0.1800, 2.0106, -0.1725, 2.0100, -0.1722, 2.0100, -0.1722, 2.0100, -0.1722, 2.0100, -0.1722;...
212		-2.0535, -2.6901, -1.5796, -2.4139, -1.2766, -2.1129, -1.1172, -1.9160, -1.0623, -1.8423, -1.0539, -1.8307, -1.0535, -1.8302, -1.0535, -1.8302, -1.0535, -1.8302, -1.0535, -1.8302;...
213		0.5499, -3.9050, 0.4369, -3.0405, 0.3590, -2.4789, 0.3166, -2.1801, 0.3017, -2.0765, 0.2995, -2.0605, 0.2994, -2.0599, 0.2994, -2.0599, 0.2994, -2.0599, 0.2994, -2.0599;...
214		3.3518, -2.5439, 2.7367, -1.9035, 2.2923, -1.4906, 2.0413, -1.2802, 1.9521, -1.2093, 1.9382, -1.1984, 1.9377, -1.1980, 1.9377, -1.1980, 1.9377, -1.1980, 1.9377, -1.1980;...
215		3.7251, 0.2762, 2.9392, 0.3040, 2.4188, 0.2777, 2.1371, 0.2534, 2.0387, 0.2436, 2.0234, 0.2420, 2.0228, 0.2420, 2.0228, 0.2420, 2.0228, 0.2420, 2.0228, 0.2420],...
216		'relative', tolerance, 'Pulsed traces differ for first pulse width!');
217
218		relsign = assertColsAlmostEqualUptoSign(...
219		[ev.pulse_Q{1}(end-1:end,:); ev.pulse_P{1}(end-1:end,:);...
220		ev.pulse_X{1}(end-1:end,:); ev.pulse_Y{1}(end-1:end,:)],...
221		[0.3231, -3.4203, 0.3579, -2.8694, 0.3420, -2.4087, 0.3219, -2.1409, 0.3131, -2.0453, 0.3117, -2.0304, 0.3116, -2.0298, 0.3116, -2.0298, 0.3116, -2.0298, 0.3116, -2.0298;...
222		-2.4419, -2.1727, -1.7974, -2.2153, -1.4129, -2.0225, -1.2193, -1.8607, -1.1541, -1.7963, -1.1440, -1.7860, -1.1436, -1.7856, -1.1436, -1.7856, -1.1436, -1.7856, -1.1436, -1.7856;...
223		3.6361, -0.5743, 2.9101, -0.1286, 2.4083, 0.0519, 2.1323, 0.1114, 2.0353, 0.1263, 2.0202, 0.1284, 2.0196, 0.1285, 2.0196, 0.1285, 2.0196, 0.1285, 2.0196, 0.1285;...
224		3.2442, -3.4832, 2.6950, -2.4389, 2.2729, -1.8160, 2.0294, -1.5161, 1.9422, -1.4180, 1.9286, -1.4032, 1.9281, -1.4026, 1.9280, -1.4026, 1.9280, -1.4026, 1.9280, -1.4026;...
225		0.2522, -3.4073, 0.3212, -2.8737, 0.3212, -2.4156, 0.3074, -2.1473, 0.3006, -2.0514, 0.2994, -2.0364, 0.2994, -2.0358, 0.2994, -2.0358, 0.2994, -2.0358, 0.2994, -2.0358;...
226		-1.9324, -2.8926, -1.5325, -2.4818, -1.2612, -2.1386, -1.1135, -1.9293, -1.0619, -1.8525, -1.0538, -1.8405, -1.0535, -1.8400, -1.0535, -1.8400, -1.0535, -1.8400, -1.0535, -1.8400;...
227		3.6442, -0.7709, 2.9166, -0.2367, 2.4130, -0.0109, 2.1359, 0.0676, 2.0385, 0.0881, 2.0234, 0.0910, 2.0228, 0.0911, 2.0228, 0.0911, 2.0228, 0.0911, 2.0228, 0.0911;...
228		3.3847, -2.1179, 2.7459, -1.6835, 2.2946, -1.3732, 2.0418, -1.2046, 1.9522, -1.1460, 1.9382, -1.1370, 1.9377, -1.1366, 1.9377, -1.1366, 1.9377, -1.1366, 1.9377, -1.1366],...
229		'relative', tolerance, 'Pulsed traces differ for last pulse width!');
230
231		ev.calculatePulsedCMs(1);
232		[T,ev] = evalc('ev.calculatePulsedQuadratures(2);'); % supress std out from fct call
233		ev.calculatePulsedCMs(2);
234		ev.calibratePulsedCMs;
235
236		% check shotnoise entanglement values
237		assertElementsAlmostEqual([ev.logneg_corrected{1}{:}],...
238		[109.0853, 111.0687, 112.5557, 113.4168, 114.8301, 112.9490, 113.0331, 112.9725, 113.3471, 113.5596],...
239		'absolute', 1e-3, 'Log. neg. values for shotnoise not as expected!');
240		% before excluding shotnoise log.neg. from the dual-rail-homo-correction the values were
241		% [0.4272, 0.4583, 0.1683, 0.0973, 0.0795, 0.0771, 0.0770, 0.0770, 0.0770, 0.0770]
242		% cmnew = ev.pulse_CM_calibrated{1}{1}
243		% logneg(cmnew)
244		% cmold = 2*ev.pulse_CM_calibrated{1}{1}-eye(8)/2
245		% logneg(cmold)
246
247		% check save and reload of ev: transient properties should not be stored
248		tmpfile = [tempname,'.mat'];
249		save(tmpfile, 'ev');
250		assert(~isempty(ev.pulse_trace));
251		clear('ev');
252
253		load(tmpfile);
254		assert(isempty(ev.pulse_trace));
255		delete(tmpfile);
256
257		% compare to results as of datoolbox-commit 997cd1260b8b222bd0b6ebf3f8567db04ace24fd
258		assertCMsEqualUptoSign(ev.pulse_CM_calibrated{2}{1},...
259		[[0.982359896462452, 0.131028168234013, -0.367189369366558, 0.880348741612935, 0.946576778024290, 0.122862699486762, -0.321838837984714, 0.783722707358365];...
260		[0.131028168234013, 0.017640103537548, -0.050517532058795, 0.120534462978138, 0.122866665973445, 0.015778083289827, -0.040175061783999, 0.098497215865329];...
261		[-0.367189369366558, -0.050517532058795, 0.151787439279659, -0.358416760477882, -0.321853066589406, -0.040175675239897, 0.094340702342072, -0.236007779549885];...
262		[0.880348741612935, 0.120534462978138, -0.358416760477882, 0.848212560720341, 0.783741799699913, 0.098496642634894, -0.236002490360790, 0.587370230126137];...
263		[0.946576778024290, 0.122866665973445, -0.321853066589406, 0.783741799699913, 0.982360871144707, 0.131024476233387, -0.367180133703403, 0.880340233793472];...
264		[0.122862699486762, 0.015778083289827, -0.040175675239897, 0.098496642634894, 0.131024476233387, 0.017639128855292, -0.050515242330064, 0.120530651668499];...
265		[-0.321838837984714, -0.040175061783999, 0.094340702342072, -0.236002490360790, -0.367180133703403, -0.050515242330064, 0.151784942728085, -0.358414152751479];...
266		[0.783722707358365, 0.098497215865329, -0.236007779549885, 0.587370230126137, 0.880340233793472, 0.120530651668499, -0.358414152751479, 0.848215057271915]]);
	192		ev = ev_setup_test(shared);
	193		ev.mech_frequencies = shared.mech_freqs;
	194		ev.opt_pulse_widths = 1e2*[1, 1];
	195		ev.pulse_gammas = logspace(-1,1,10);
	196		ev.samples_per_pulse = 4*ev.samples_per_pulse; % adjust samples_per_pulse, since in commit 6dcad7dc617c9bed38f45823b049e556264b694f it has been lower by a factor of 4;
	197		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
	198		[T,ev] = evalc('ev.calculatePulsedQuadratures(1);'); % supress std out from fct call
	199		assertEqual(size(ev.pulse_Y{1}),...
	200		[ev.pulse_pairs_per_trace, ev.mech_frequencies_no*ev.pulse_gammas_no]);
	201
	202		% compare to results as of datoolbox-commit 754e283187e9e9a6385b0f9a8a26f5fd2807ccd0
	203		% (after modifying mode-fct calculation to avoid numerical instabilities during orthonormalization)
	204		tolerance = 1e-2;
	205		relsign = assertColsAlmostEqualUptoSign(...
	206		[ev.pulse_Q{1}(1:2,:); ev.pulse_P{1}(1:2,:);...
	207		ev.pulse_X{1}(1:2,:); ev.pulse_Y{1}(1:2,:)],...
	208		[-2.2115, -2.4141, -1.6618, -2.3063, -1.3238, -2.0629, -1.1502, -1.8850, -1.0911, -1.8164, -1.0820, -1.8055, -1.0816, -1.8051, -1.0816, -1.8051, -1.0816, -1.8051, -1.0816, -1.8051;...
	209		-0.2433, -2.8269, 0.0246, -2.6458, 0.1230, -2.3094, 0.1520, -2.0811, 0.1583, -1.9958, 0.1592, -1.9824, 0.1592, -1.9819, 0.1592, -1.9819, 0.1592, -1.9819, 0.1592, -1.9819;...
	210		3.2915, -2.9633, 2.7127, -2.1359, 2.2809, -1.6272, 2.0343, -1.3764, 1.9462, -1.2934, 1.9325, -1.2808, 1.9320, -1.2803, 1.9320, -1.2803, 1.9320, -1.2803, 1.9320, -1.2803;...
	211		3.5199, -1.8524, 2.8666, -0.8733, 2.3888, -0.4121, 2.1204, -0.2318, 2.0254, -0.1800, 2.0106, -0.1725, 2.0100, -0.1722, 2.0100, -0.1722, 2.0100, -0.1722, 2.0100, -0.1722;...
	212		-2.0535, -2.6901, -1.5796, -2.4139, -1.2766, -2.1129, -1.1172, -1.9160, -1.0623, -1.8423, -1.0539, -1.8307, -1.0535, -1.8302, -1.0535, -1.8302, -1.0535, -1.8302, -1.0535, -1.8302;...
	213		0.5499, -3.9050, 0.4369, -3.0405, 0.3590, -2.4789, 0.3166, -2.1801, 0.3017, -2.0765, 0.2995, -2.0605, 0.2994, -2.0599, 0.2994, -2.0599, 0.2994, -2.0599, 0.2994, -2.0599;...
	214		3.3518, -2.5439, 2.7367, -1.9035, 2.2923, -1.4906, 2.0413, -1.2802, 1.9521, -1.2093, 1.9382, -1.1984, 1.9377, -1.1980, 1.9377, -1.1980, 1.9377, -1.1980, 1.9377, -1.1980;...
	215		3.7251, 0.2762, 2.9392, 0.3040, 2.4188, 0.2777, 2.1371, 0.2534, 2.0387, 0.2436, 2.0234, 0.2420, 2.0228, 0.2420, 2.0228, 0.2420, 2.0228, 0.2420, 2.0228, 0.2420],...
	216		'relative', tolerance, 'Pulsed traces differ for first pulse width!');
	217
	218		relsign = assertColsAlmostEqualUptoSign(...
	219		[ev.pulse_Q{1}(end-1:end,:); ev.pulse_P{1}(end-1:end,:);...
	220		ev.pulse_X{1}(end-1:end,:); ev.pulse_Y{1}(end-1:end,:)],...
	221		[0.3231, -3.4203, 0.3579, -2.8694, 0.3420, -2.4087, 0.3219, -2.1409, 0.3131, -2.0453, 0.3117, -2.0304, 0.3116, -2.0298, 0.3116, -2.0298, 0.3116, -2.0298, 0.3116, -2.0298;...
	222		-2.4419, -2.1727, -1.7974, -2.2153, -1.4129, -2.0225, -1.2193, -1.8607, -1.1541, -1.7963, -1.1440, -1.7860, -1.1436, -1.7856, -1.1436, -1.7856, -1.1436, -1.7856, -1.1436, -1.7856;...
	223		3.6361, -0.5743, 2.9101, -0.1286, 2.4083, 0.0519, 2.1323, 0.1114, 2.0353, 0.1263, 2.0202, 0.1284, 2.0196, 0.1285, 2.0196, 0.1285, 2.0196, 0.1285, 2.0196, 0.1285;...
	224		3.2442, -3.4832, 2.6950, -2.4389, 2.2729, -1.8160, 2.0294, -1.5161, 1.9422, -1.4180, 1.9286, -1.4032, 1.9281, -1.4026, 1.9280, -1.4026, 1.9280, -1.4026, 1.9280, -1.4026;...
	225		0.2522, -3.4073, 0.3212, -2.8737, 0.3212, -2.4156, 0.3074, -2.1473, 0.3006, -2.0514, 0.2994, -2.0364, 0.2994, -2.0358, 0.2994, -2.0358, 0.2994, -2.0358, 0.2994, -2.0358;...
	226		-1.9324, -2.8926, -1.5325, -2.4818, -1.2612, -2.1386, -1.1135, -1.9293, -1.0619, -1.8525, -1.0538, -1.8405, -1.0535, -1.8400, -1.0535, -1.8400, -1.0535, -1.8400, -1.0535, -1.8400;...
	227		3.6442, -0.7709, 2.9166, -0.2367, 2.4130, -0.0109, 2.1359, 0.0676, 2.0385, 0.0881, 2.0234, 0.0910, 2.0228, 0.0911, 2.0228, 0.0911, 2.0228, 0.0911, 2.0228, 0.0911;...
	228		3.3847, -2.1179, 2.7459, -1.6835, 2.2946, -1.3732, 2.0418, -1.2046, 1.9522, -1.1460, 1.9382, -1.1370, 1.9377, -1.1366, 1.9377, -1.1366, 1.9377, -1.1366, 1.9377, -1.1366],...
	229		'relative', tolerance, 'Pulsed traces differ for last pulse width!');
	230
	231		ev.calculatePulsedCMs(1);
	232		[T,ev] = evalc('ev.calculatePulsedQuadratures(2);'); % supress std out from fct call
	233		ev.calculatePulsedCMs(2);
	234		ev.calibratePulsedCMs;
	235
	236		% check shotnoise entanglement values
	237		assertElementsAlmostEqual([ev.logneg_corrected{1}{:}],...
	238		[109.0853, 111.0687, 112.5557, 113.4168, 114.8301, 112.9490, 113.0331, 112.9725, 113.3471, 113.5596],...
	239		'absolute', 1e-3, 'Log. neg. values for shotnoise not as expected!');
	240		% before excluding shotnoise log.neg. from the dual-rail-homo-correction the values were
	241		% [0.4272, 0.4583, 0.1683, 0.0973, 0.0795, 0.0771, 0.0770, 0.0770, 0.0770, 0.0770]
	242		% cmnew = ev.pulse_CM_calibrated{1}{1}
	243		% logneg(cmnew)
	244		% cmold = 2*ev.pulse_CM_calibrated{1}{1}-eye(8)/2
	245		% logneg(cmold)
	246
	247		% check save and reload of ev: transient properties should not be stored
	248		tmpfile = [tempname,'.mat'];
	249		save(tmpfile, 'ev');
	250		assert(~isempty(ev.pulse_trace));
	251		clear('ev');
	252
	253		load(tmpfile);
	254		assert(isempty(ev.pulse_trace));
	255		delete(tmpfile);
	256
	257		% compare to results as of datoolbox-commit 997cd1260b8b222bd0b6ebf3f8567db04ace24fd
	258		assertCMsEqualUptoSign(ev.pulse_CM_calibrated{2}{1},...
	259		[[0.982359896462452, 0.131028168234013, -0.367189369366558, 0.880348741612935, 0.946576778024290, 0.122862699486762, -0.321838837984714, 0.783722707358365];...
	260		[0.131028168234013, 0.017640103537548, -0.050517532058795, 0.120534462978138, 0.122866665973445, 0.015778083289827, -0.040175061783999, 0.098497215865329];...
	261		[-0.367189369366558, -0.050517532058795, 0.151787439279659, -0.358416760477882, -0.321853066589406, -0.040175675239897, 0.094340702342072, -0.236007779549885];...
	262		[0.880348741612935, 0.120534462978138, -0.358416760477882, 0.848212560720341, 0.783741799699913, 0.098496642634894, -0.236002490360790, 0.587370230126137];...
	263		[0.946576778024290, 0.122866665973445, -0.321853066589406, 0.783741799699913, 0.982360871144707, 0.131024476233387, -0.367180133703403, 0.880340233793472];...
	264		[0.122862699486762, 0.015778083289827, -0.040175675239897, 0.098496642634894, 0.131024476233387, 0.017639128855292, -0.050515242330064, 0.120530651668499];...
	265		[-0.321838837984714, -0.040175061783999, 0.094340702342072, -0.236002490360790, -0.367180133703403, -0.050515242330064, 0.151784942728085, -0.358414152751479];...
	266		[0.783722707358365, 0.098497215865329, -0.236007779549885, 0.587370230126137, 0.880340233793472, 0.120530651668499, -0.358414152751479, 0.848215057271915]]);
267	267
268	268	function run_inverted_test(shared)	function run_inverted_test(shared)
269		% passes as of commit 486cf46dec3f7f6cf493f947709dc605ae44930f and its child
270		ev = ev_setup_test(shared);
271		ev.inverted_evaluation = true;
272		ev.mech_frequencies = shared.mech_freqs;
273		ev.opt_pulse_widths = 1e2*[1, 1];
274		ev.pulse_gammas = logspace(-1,1,10);
275		ev.samples_per_pulse = 4*ev.samples_per_pulse; % adjust samples_per_pulse, since in commit 6dcad7dc617c9bed38f45823b049e556264b694f it has been lower by a factor of 4;
276		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
277		[T,ev] = evalc('ev.calculatePulsedQuadratures(1);'); % supress std out from fct call
278		assertEqual(size(ev.pulse_Y{1}),...
279		[ev.pulse_pairs_per_trace, ev.mech_frequencies_no*ev.pulse_gammas_no]);
280
281		% compare to results as of datoolbox-commit 754e283187e9e9a6385b0f9a8a26f5fd2807ccd0
282		% (after modifying mode-fct calculation to avoid numerical instabilities during orthonormalization)
283		tolerance = 1e-2;
284		assertColsAlmostEqualUptoSign(...
285		[ev.pulse_Q{1}(1:2,:); ev.pulse_P{1}(1:2,:);...
286		ev.pulse_X{1}(1:2,:); ev.pulse_Y{1}(1:2,:)],...
287		[-1.2735, 3.8137, -1.1123, 3.0828, -0.9634, 2.5385, -0.8707, 2.2366, -0.8365, 2.1309, -0.8312, 2.1145, -0.8310, 2.1138, -0.8310, 2.1138, -0.8310, 2.1138, -0.8310, 2.1138;...
288		0.6946, 3.4010, 0.5740, 2.7433, 0.4834, 2.2920, 0.4315, 2.0406, 0.4129, 1.9515, 0.4100, 1.9376, 0.4099, 1.9371, 0.4099, 1.9371, 0.4099, 1.9371, 0.4099, 1.9371;...
289		-3.7667, -0.3446, -2.9840, -0.6290, -2.4559, -0.6815, -2.1689, -0.6681, -2.0685, -0.6572, -2.0529, -0.6552, -2.0523, -0.6551, -2.0523, -0.6551, -2.0523, -0.6551, -2.0523, -0.6551;...
290		-3.5383, 0.7663, -2.8301, 0.6336, -2.3480, 0.5337, -2.0827, 0.4765, -1.9893, 0.4562, -1.9748, 0.4531, -1.9742, 0.4530, -1.9742, 0.4530, -1.9742, 0.4530, -1.9742, 0.4530;...
291		-1.1052, 3.6599, -1.0267, 3.0421, -0.9149, 2.5275, -0.8371, 2.2330, -0.8073, 2.1289, -0.8026, 2.1127, -0.8024, 2.1121, -0.8024, 2.1121, -0.8024, 2.1121, -0.8024, 2.1121;...
292		1.4981, 2.4450, 0.9898, 2.4155, 0.7207, 2.1615, 0.5968, 1.9689, 0.5568, 1.8947, 0.5508, 1.8829, 0.5505, 1.8824, 0.5505, 1.8824, 0.5505, 1.8824, 0.5505, 1.8824;...
293		-3.7514, 0.1327, -2.9832, -0.3762, -2.4578, -0.5365, -2.1710, -0.5671, -2.0706, -0.5692, -2.0551, -0.5692, -2.0544, -0.5692, -2.0544, -0.5692, -2.0544, -0.5692, -2.0544, -0.5692;...
294		-3.3781, 2.9527, -2.7808, 1.8313, -2.3313, 1.2319, -2.0753, 0.9665, -1.9841, 0.8837, -1.9699, 0.8713, -1.9693, 0.8708, -1.9693, 0.8708, -1.9693, 0.8708, -1.9693, 0.8708],...
295		'relative', tolerance, 'Pulsed traces differ for first pulse width!');
296
297		assertColsAlmostEqualUptoSign(...
298		[ev.pulse_Q{1}(end-1:end,:); ev.pulse_P{1}(end-1:end,:);...
299		ev.pulse_X{1}(end-1:end,:); ev.pulse_Y{1}(end-1:end,:)],...
300		[1.2610, 2.8076, 0.9074, 2.5197, 0.7024, 2.1927, 0.6014, 1.9808, 0.5677, 1.9020, 0.5625, 1.8896, 0.5623, 1.8891, 0.5623, 1.8891, 0.5623, 1.8891, 0.5623, 1.8891;...
301		-1.5040, 4.0552, -1.2480, 3.1738, -1.0525, 2.5789, -0.9398, 2.2610, -0.8995, 2.1510, -0.8932, 2.1340, -0.8930, 2.1333, -0.8930, 2.1333, -0.8930, 2.1333, -0.8930, 2.1333;...
302		-3.4221, 2.0444, -2.7866, 1.3783, -2.3285, 0.9976, -2.0708, 0.8197, -1.9794, 0.7626, -1.9652, 0.7539, -1.9646, 0.7536, -1.9646, 0.7536, -1.9646, 0.7536, -1.9646, 0.7536;...
303		-3.8140, -0.8646, -3.0017, -0.9320, -2.4639, -0.8703, -2.1737, -0.8078, -2.0725, -0.7818, -2.0568, -0.7776, -2.0562, -0.7774, -2.0562, -0.7774, -2.0562, -0.7774, -2.0562, -0.7774;...
304		1.2005, 2.9428, 0.8741, 2.5823, 0.6829, 2.2248, 0.5876, 2.0017, 0.5556, 1.9198, 0.5507, 1.9070, 0.5505, 1.9064, 0.5505, 1.9064, 0.5505, 1.9064, 0.5505, 1.9064;...
305		-0.9841, 3.4574, -0.9796, 2.9742, -0.8995, 2.5017, -0.8333, 2.2197, -0.8068, 2.1187, -0.8025, 2.1029, -0.8024, 2.1023, -0.8024, 2.1023, -0.8024, 2.1023, -0.8024, 2.1023;...
306		-3.4590, 1.9057, -2.8033, 1.2906, -2.3371, 0.9433, -2.0765, 0.7807, -1.9842, 0.7282, -1.9699, 0.7203, -1.9693, 0.7200, -1.9693, 0.7200, -1.9693, 0.7200, -1.9693, 0.7200;...
307		-3.7185, 0.5587, -2.9740, -0.1562, -2.4555, -0.4191, -2.1705, -0.4915, -2.0706, -0.5060, -2.0551, -0.5077, -2.0544, -0.5078, -2.0544, -0.5078, -2.0544, -0.5078, -2.0544, -0.5078],...
308		'relative', tolerance, 'Pulsed traces differ for last pulse width!');
309
310		ev.calculatePulsedCMs(1);
311		[T,ev] = evalc('ev.calculatePulsedQuadratures(2);'); % supress std out from fct call
312		ev.calculatePulsedCMs(2);
313		ev.calibratePulsedCMs;
314
315		% check save and reload of ev: transient properties should not be stored
316		tmpfile = [tempname,'.mat'];
317		save(tmpfile, 'ev');
318		assert(~isempty(ev.pulse_trace));
319		clear('ev');
320
321		load(tmpfile);
322		assert(isempty(ev.pulse_trace));
323		delete(tmpfile);
324
325		% compare to results as of datoolbox-commit 997cd1260b8b222bd0b6ebf3f8567db04ace24fd
326		assertCMsEqualUptoSign(ev.pulse_CM_calibrated{2}{1},...
327		[[0.982359896462452, 0.131028168234013, -0.367189369366558, 0.880348741612935, 0.946576778024290, 0.122862699486762, -0.321838837984714, 0.783722707358365];...
328		[0.131028168234013, 0.017640103537548, -0.050517532058795, 0.120534462978138, 0.122866665973445, 0.015778083289827, -0.040175061783999, 0.098497215865329];...
329		[-0.367189369366558, -0.050517532058795, 0.151787439279659, -0.358416760477882, -0.321853066589406, -0.040175675239897, 0.094340702342072, -0.236007779549885];...
330		[0.880348741612935, 0.120534462978138, -0.358416760477882, 0.848212560720341, 0.783741799699913, 0.098496642634894, -0.236002490360790, 0.587370230126137];...
331		[0.946576778024290, 0.122866665973445, -0.321853066589406, 0.783741799699913, 0.982360871144707, 0.131024476233387, -0.367180133703403, 0.880340233793472];...
332		[0.122862699486762, 0.015778083289827, -0.040175675239897, 0.098496642634894, 0.131024476233387, 0.017639128855292, -0.050515242330064, 0.120530651668499];...
333		[-0.321838837984714, -0.040175061783999, 0.094340702342072, -0.236002490360790, -0.367180133703403, -0.050515242330064, 0.151784942728085, -0.358414152751479];...
334		[0.783722707358365, 0.098497215865329, -0.236007779549885, 0.587370230126137, 0.880340233793472, 0.120530651668499, -0.358414152751479, 0.848215057271915]]);
	269		% passes as of commit 486cf46dec3f7f6cf493f947709dc605ae44930f and its child
	270		ev = ev_setup_test(shared);
	271		ev.inverted_evaluation = true;
	272		ev.mech_frequencies = shared.mech_freqs;
	273		ev.opt_pulse_widths = 1e2*[1, 1];
	274		ev.pulse_gammas = logspace(-1,1,10);
	275		ev.samples_per_pulse = 4*ev.samples_per_pulse; % adjust samples_per_pulse, since in commit 6dcad7dc617c9bed38f45823b049e556264b694f it has been lower by a factor of 4;
	276		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
	277		[T,ev] = evalc('ev.calculatePulsedQuadratures(1);'); % supress std out from fct call
	278		assertEqual(size(ev.pulse_Y{1}),...
	279		[ev.pulse_pairs_per_trace, ev.mech_frequencies_no*ev.pulse_gammas_no]);
	280
	281		% compare to results as of datoolbox-commit 754e283187e9e9a6385b0f9a8a26f5fd2807ccd0
	282		% (after modifying mode-fct calculation to avoid numerical instabilities during orthonormalization)
	283		tolerance = 1e-2;
	284		assertColsAlmostEqualUptoSign(...
	285		[ev.pulse_Q{1}(1:2,:); ev.pulse_P{1}(1:2,:);...
	286		ev.pulse_X{1}(1:2,:); ev.pulse_Y{1}(1:2,:)],...
	287		[-1.2735, 3.8137, -1.1123, 3.0828, -0.9634, 2.5385, -0.8707, 2.2366, -0.8365, 2.1309, -0.8312, 2.1145, -0.8310, 2.1138, -0.8310, 2.1138, -0.8310, 2.1138, -0.8310, 2.1138;...
	288		0.6946, 3.4010, 0.5740, 2.7433, 0.4834, 2.2920, 0.4315, 2.0406, 0.4129, 1.9515, 0.4100, 1.9376, 0.4099, 1.9371, 0.4099, 1.9371, 0.4099, 1.9371, 0.4099, 1.9371;...
	289		-3.7667, -0.3446, -2.9840, -0.6290, -2.4559, -0.6815, -2.1689, -0.6681, -2.0685, -0.6572, -2.0529, -0.6552, -2.0523, -0.6551, -2.0523, -0.6551, -2.0523, -0.6551, -2.0523, -0.6551;...
	290		-3.5383, 0.7663, -2.8301, 0.6336, -2.3480, 0.5337, -2.0827, 0.4765, -1.9893, 0.4562, -1.9748, 0.4531, -1.9742, 0.4530, -1.9742, 0.4530, -1.9742, 0.4530, -1.9742, 0.4530;...
	291		-1.1052, 3.6599, -1.0267, 3.0421, -0.9149, 2.5275, -0.8371, 2.2330, -0.8073, 2.1289, -0.8026, 2.1127, -0.8024, 2.1121, -0.8024, 2.1121, -0.8024, 2.1121, -0.8024, 2.1121;...
	292		1.4981, 2.4450, 0.9898, 2.4155, 0.7207, 2.1615, 0.5968, 1.9689, 0.5568, 1.8947, 0.5508, 1.8829, 0.5505, 1.8824, 0.5505, 1.8824, 0.5505, 1.8824, 0.5505, 1.8824;...
	293		-3.7514, 0.1327, -2.9832, -0.3762, -2.4578, -0.5365, -2.1710, -0.5671, -2.0706, -0.5692, -2.0551, -0.5692, -2.0544, -0.5692, -2.0544, -0.5692, -2.0544, -0.5692, -2.0544, -0.5692;...
	294		-3.3781, 2.9527, -2.7808, 1.8313, -2.3313, 1.2319, -2.0753, 0.9665, -1.9841, 0.8837, -1.9699, 0.8713, -1.9693, 0.8708, -1.9693, 0.8708, -1.9693, 0.8708, -1.9693, 0.8708],...
	295		'relative', tolerance, 'Pulsed traces differ for first pulse width!');
	296
	297		assertColsAlmostEqualUptoSign(...
	298		[ev.pulse_Q{1}(end-1:end,:); ev.pulse_P{1}(end-1:end,:);...
	299		ev.pulse_X{1}(end-1:end,:); ev.pulse_Y{1}(end-1:end,:)],...
	300		[1.2610, 2.8076, 0.9074, 2.5197, 0.7024, 2.1927, 0.6014, 1.9808, 0.5677, 1.9020, 0.5625, 1.8896, 0.5623, 1.8891, 0.5623, 1.8891, 0.5623, 1.8891, 0.5623, 1.8891;...
	301		-1.5040, 4.0552, -1.2480, 3.1738, -1.0525, 2.5789, -0.9398, 2.2610, -0.8995, 2.1510, -0.8932, 2.1340, -0.8930, 2.1333, -0.8930, 2.1333, -0.8930, 2.1333, -0.8930, 2.1333;...
	302		-3.4221, 2.0444, -2.7866, 1.3783, -2.3285, 0.9976, -2.0708, 0.8197, -1.9794, 0.7626, -1.9652, 0.7539, -1.9646, 0.7536, -1.9646, 0.7536, -1.9646, 0.7536, -1.9646, 0.7536;...
	303		-3.8140, -0.8646, -3.0017, -0.9320, -2.4639, -0.8703, -2.1737, -0.8078, -2.0725, -0.7818, -2.0568, -0.7776, -2.0562, -0.7774, -2.0562, -0.7774, -2.0562, -0.7774, -2.0562, -0.7774;...
	304		1.2005, 2.9428, 0.8741, 2.5823, 0.6829, 2.2248, 0.5876, 2.0017, 0.5556, 1.9198, 0.5507, 1.9070, 0.5505, 1.9064, 0.5505, 1.9064, 0.5505, 1.9064, 0.5505, 1.9064;...
	305		-0.9841, 3.4574, -0.9796, 2.9742, -0.8995, 2.5017, -0.8333, 2.2197, -0.8068, 2.1187, -0.8025, 2.1029, -0.8024, 2.1023, -0.8024, 2.1023, -0.8024, 2.1023, -0.8024, 2.1023;...
	306		-3.4590, 1.9057, -2.8033, 1.2906, -2.3371, 0.9433, -2.0765, 0.7807, -1.9842, 0.7282, -1.9699, 0.7203, -1.9693, 0.7200, -1.9693, 0.7200, -1.9693, 0.7200, -1.9693, 0.7200;...
	307		-3.7185, 0.5587, -2.9740, -0.1562, -2.4555, -0.4191, -2.1705, -0.4915, -2.0706, -0.5060, -2.0551, -0.5077, -2.0544, -0.5078, -2.0544, -0.5078, -2.0544, -0.5078, -2.0544, -0.5078],...
	308		'relative', tolerance, 'Pulsed traces differ for last pulse width!');
	309
	310		ev.calculatePulsedCMs(1);
	311		[T,ev] = evalc('ev.calculatePulsedQuadratures(2);'); % supress std out from fct call
	312		ev.calculatePulsedCMs(2);
	313		ev.calibratePulsedCMs;
	314
	315		% check save and reload of ev: transient properties should not be stored
	316		tmpfile = [tempname,'.mat'];
	317		save(tmpfile, 'ev');
	318		assert(~isempty(ev.pulse_trace));
	319		clear('ev');
	320
	321		load(tmpfile);
	322		assert(isempty(ev.pulse_trace));
	323		delete(tmpfile);
	324
	325		% compare to results as of datoolbox-commit 997cd1260b8b222bd0b6ebf3f8567db04ace24fd
	326		assertCMsEqualUptoSign(ev.pulse_CM_calibrated{2}{1},...
	327		[[0.982359896462452, 0.131028168234013, -0.367189369366558, 0.880348741612935, 0.946576778024290, 0.122862699486762, -0.321838837984714, 0.783722707358365];...
	328		[0.131028168234013, 0.017640103537548, -0.050517532058795, 0.120534462978138, 0.122866665973445, 0.015778083289827, -0.040175061783999, 0.098497215865329];...
	329		[-0.367189369366558, -0.050517532058795, 0.151787439279659, -0.358416760477882, -0.321853066589406, -0.040175675239897, 0.094340702342072, -0.236007779549885];...
	330		[0.880348741612935, 0.120534462978138, -0.358416760477882, 0.848212560720341, 0.783741799699913, 0.098496642634894, -0.236002490360790, 0.587370230126137];...
	331		[0.946576778024290, 0.122866665973445, -0.321853066589406, 0.783741799699913, 0.982360871144707, 0.131024476233387, -0.367180133703403, 0.880340233793472];...
	332		[0.122862699486762, 0.015778083289827, -0.040175675239897, 0.098496642634894, 0.131024476233387, 0.017639128855292, -0.050515242330064, 0.120530651668499];...
	333		[-0.321838837984714, -0.040175061783999, 0.094340702342072, -0.236002490360790, -0.367180133703403, -0.050515242330064, 0.151784942728085, -0.358414152751479];...
	334		[0.783722707358365, 0.098497215865329, -0.236007779549885, 0.587370230126137, 0.880340233793472, 0.120530651668499, -0.358414152751479, 0.848215057271915]]);
335	335
336	336	function pulse_pairs_coverage_test(shared)	function pulse_pairs_coverage_test(shared)
337		ev = ev_setup_test(shared);
338		ev.mech_frequencies = shared.mech_freqs;
339		ev.opt_pulse_widths = 1e2*[1, 1];
340		ev.pulse_gammas = logspace(-1,1,10);
341		ev.samples_per_pulse = 4*ev.samples_per_pulse; % adjust samples_per_pulse, since in commit 6dcad7dc617c9bed38f45823b049e556264b694f it has been lower by a factor of 4;
342
343		ev.pulse_pairs_coverage = 0.1;
344
345		% check that pulse_pairs_per_trace depends on pulse_pairs_coverage
346		assertEqual(ev.pulse_pairs_per_trace,2)
347		ev.pulse_pairs_coverage = 0.2;
348		assertEqual(ev.pulse_pairs_per_trace,5)
349
350
351		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
352		[T,ev] = evalc('ev.calculatePulsedQuadratures(1);'); % supress std out from fct call
353		assertEqual(size(ev.pulse_Y{1}),...
354		[ev.pulse_pairs_per_trace, ev.mech_frequencies_no*ev.pulse_gammas_no]);
355
356		% first pulse is the same as in run_test (as expected)
357		tolerance = 1e-2;
358		% assertElementsAlmostEqual(...
359		% [ev.pulse_Q{1}(1,:); ev.pulse_P{1}(1,:);...
360		% ev.pulse_X{1}(1,:); ev.pulse_Y{1}(1,:)],...
361		% [-2.2115, -2.4141, -1.6618, -2.3063, -1.3238, -2.0629, -1.1502, -1.8850, -1.0911, -1.8164, -1.0820, -1.8055, -1.0816, -1.8051, -1.0816, -1.8051, -1.0816, -1.8051, -1.0816, -1.8051;...
362		% 3.2915, -2.9633, 2.7127, -2.1359, 2.2809, -1.6272, 2.0343, -1.3764, 1.9462, -1.2934, 1.9325, -1.2808, 1.9320, -1.2803, 1.9320, -1.2803, 1.9320, -1.2803, 1.9320, -1.2803;...
363		% -2.0535, -2.6901, -1.5796, -2.4139, -1.2766, -2.1129, -1.1172, -1.9160, -1.0623, -1.8423, -1.0539, -1.8307, -1.0535, -1.8302, -1.0535, -1.8302, -1.0535, -1.8302, -1.0535, -1.8302;...
364		% 3.3518, -2.5439, 2.7367, -1.9035, 2.2923, -1.4906, 2.0413, -1.2802, 1.9521, -1.2093, 1.9382, -1.1984, 1.9377, -1.1980, 1.9377, -1.1980, 1.9377, -1.1980, 1.9377, -1.1980],...
365		% 'relative', tolerance, 'Pulsed traces differ for first pulse width!');
366
367		% last pulse is different from run_test (as expected) and equals:
368		assertColsAlmostEqualUptoSign(...
369		[ev.pulse_Q{1}(end,:); ev.pulse_P{1}(end,:);...
370		ev.pulse_X{1}(end,:); ev.pulse_Y{1}(end,:)],...
371		[-0.2815, -3.2285, -0.1393, -2.7479, -0.0754, -2.3291, -0.0500, -2.0802, -0.0426, -1.9905, -0.0415, -1.9765 -0.0415, -1.9759, -0.0415, -1.9759, -0.0415, -1.9759, -0.0415, -1.9759;...
372		3.5591, -1.0467, 2.8629, -0.5686, 2.3768, -0.3370, 2.1076, -0.2420, 2.0127, -0.2138, 1.9980, -0.2096 1.9974, -0.2094, 1.9974, -0.2094, 1.9974, -0.2094, 1.9974, -0.2094;...
373		-0.4042, -3.1501, -0.2029, -2.7287, -0.1116, -2.3262, -0.0753, -2.0811, -0.0645, -1.9921, -0.0630, -1.9782 -0.0629, -1.9776, -0.0629, -1.9776, -0.0629, -1.9776, -0.0629, -1.9776;...
374		3.5553, -1.3827, 2.8655, -0.7538, 2.3799, -0.4449, 2.1104, -0.3176, 2.0154, -0.2796, 2.0006, -0.2740 2.0000, -0.2738, 2.0000, -0.2738, 2.0000, -0.2738, 2.0000, -0.2738],...
375		'relative', tolerance, 'Pulsed traces differ for last pulse width!');
	337		ev = ev_setup_test(shared);
	338		ev.mech_frequencies = shared.mech_freqs;
	339		ev.opt_pulse_widths = 1e2*[1, 1];
	340		ev.pulse_gammas = logspace(-1,1,10);
	341		ev.samples_per_pulse = 4*ev.samples_per_pulse; % adjust samples_per_pulse, since in commit 6dcad7dc617c9bed38f45823b049e556264b694f it has been lower by a factor of 4;
	342
	343		ev.pulse_pairs_coverage = 0.1;
	344
	345		% check that pulse_pairs_per_trace depends on pulse_pairs_coverage
	346		assertEqual(ev.pulse_pairs_per_trace,2)
	347		ev.pulse_pairs_coverage = 0.2;
	348		assertEqual(ev.pulse_pairs_per_trace,5)
	349
	350
	351		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
	352		[T,ev] = evalc('ev.calculatePulsedQuadratures(1);'); % supress std out from fct call
	353		assertEqual(size(ev.pulse_Y{1}),...
	354		[ev.pulse_pairs_per_trace, ev.mech_frequencies_no*ev.pulse_gammas_no]);
	355
	356		% first pulse is the same as in run_test (as expected)
	357		tolerance = 1e-2;
	358		% assertElementsAlmostEqual(...
	359		% [ev.pulse_Q{1}(1,:); ev.pulse_P{1}(1,:);...
	360		% ev.pulse_X{1}(1,:); ev.pulse_Y{1}(1,:)],...
	361		% [-2.2115, -2.4141, -1.6618, -2.3063, -1.3238, -2.0629, -1.1502, -1.8850, -1.0911, -1.8164, -1.0820, -1.8055, -1.0816, -1.8051, -1.0816, -1.8051, -1.0816, -1.8051, -1.0816, -1.8051;...
	362		% 3.2915, -2.9633, 2.7127, -2.1359, 2.2809, -1.6272, 2.0343, -1.3764, 1.9462, -1.2934, 1.9325, -1.2808, 1.9320, -1.2803, 1.9320, -1.2803, 1.9320, -1.2803, 1.9320, -1.2803;...
	363		% -2.0535, -2.6901, -1.5796, -2.4139, -1.2766, -2.1129, -1.1172, -1.9160, -1.0623, -1.8423, -1.0539, -1.8307, -1.0535, -1.8302, -1.0535, -1.8302, -1.0535, -1.8302, -1.0535, -1.8302;...
	364		% 3.3518, -2.5439, 2.7367, -1.9035, 2.2923, -1.4906, 2.0413, -1.2802, 1.9521, -1.2093, 1.9382, -1.1984, 1.9377, -1.1980, 1.9377, -1.1980, 1.9377, -1.1980, 1.9377, -1.1980],...
	365		% 'relative', tolerance, 'Pulsed traces differ for first pulse width!');
	366
	367		% last pulse is different from run_test (as expected) and equals:
	368		assertColsAlmostEqualUptoSign(...
	369		[ev.pulse_Q{1}(end,:); ev.pulse_P{1}(end,:);...
	370		ev.pulse_X{1}(end,:); ev.pulse_Y{1}(end,:)],...
	371		[-0.2815, -3.2285, -0.1393, -2.7479, -0.0754, -2.3291, -0.0500, -2.0802, -0.0426, -1.9905, -0.0415, -1.9765 -0.0415, -1.9759, -0.0415, -1.9759, -0.0415, -1.9759, -0.0415, -1.9759;...
	372		3.5591, -1.0467, 2.8629, -0.5686, 2.3768, -0.3370, 2.1076, -0.2420, 2.0127, -0.2138, 1.9980, -0.2096 1.9974, -0.2094, 1.9974, -0.2094, 1.9974, -0.2094, 1.9974, -0.2094;...
	373		-0.4042, -3.1501, -0.2029, -2.7287, -0.1116, -2.3262, -0.0753, -2.0811, -0.0645, -1.9921, -0.0630, -1.9782 -0.0629, -1.9776, -0.0629, -1.9776, -0.0629, -1.9776, -0.0629, -1.9776;...
	374		3.5553, -1.3827, 2.8655, -0.7538, 2.3799, -0.4449, 2.1104, -0.3176, 2.0154, -0.2796, 2.0006, -0.2740 2.0000, -0.2738, 2.0000, -0.2738, 2.0000, -0.2738, 2.0000, -0.2738],...
	375		'relative', tolerance, 'Pulsed traces differ for last pulse width!');
376	376
377	377	function set_samples_per_pulse_test(shared)	function set_samples_per_pulse_test(shared)
378		ev = ev_setup_test(shared);
	378		ev = ev_setup_test(shared);
379	379
380		% information is insufficient ==> samples_per_pulse should default to 0
381		assertEqual(ev.samples_per_pulse, 0)
	380		% information is insufficient ==> samples_per_pulse should default to 0
	381		assertEqual(ev.samples_per_pulse, 0)
382	382
383		% check default setting with adequate information
384		ev.mech_frequencies = shared.mech_freqs;
385		ev.opt_pulse_widths = 1e2*[1, 1];
386		ev.pulse_gammas = logspace(-1,1,10);
	383		% check default setting with adequate information
	384		ev.mech_frequencies = shared.mech_freqs;
	385		ev.opt_pulse_widths = 1e2*[1, 1];
	386		ev.pulse_gammas = logspace(-1,1,10);
387	387
388		samples_per_pulse = 50; % default for evaluation specifed above
389		assertEqual(ev.samples_per_pulse, samples_per_pulse)
390		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
391		assertEqual(size(ev.entangling_modes,2), samples_per_pulse)
	388		samples_per_pulse = 50; % default for evaluation specifed above
	389		assertEqual(ev.samples_per_pulse, samples_per_pulse)
	390		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
	391		assertEqual(size(ev.entangling_modes,2), samples_per_pulse)
392	392
393		% check explicit setting of samples_per_pulse
394		samples_per_pulse = 30;
395		ev.samples_per_pulse = samples_per_pulse;
396		assertEqual(ev.samples_per_pulse, samples_per_pulse)
397		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
398		assertEqual(size(ev.entangling_modes,2), samples_per_pulse)
	393		% check explicit setting of samples_per_pulse
	394		samples_per_pulse = 30;
	395		ev.samples_per_pulse = samples_per_pulse;
	396		assertEqual(ev.samples_per_pulse, samples_per_pulse)
	397		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
	398		assertEqual(size(ev.entangling_modes,2), samples_per_pulse)
399	399
400	400	function ev = run_quick_test(shared)	function ev = run_quick_test(shared)
401		ev = ev_setup_test(shared);
402		ev.mech_frequencies = [1, 2];
403		ev.opt_pulse_widths = 1e2*[1, 1];
404		ev.pulse_gammas = logspace(-1,1,10);
405
406		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
407		pulsepairs_no = 100;
408		[T,ev] = evalc('ev.calculatePulsedQuadratures(1, ''pulsepairs'', pulsepairs_no);');
409		[T,ev] = evalc('ev.calculatePulsedQuadratures(2, ''pulsepairs'', pulsepairs_no);');
410		assertEqual(size(ev.pulse_Q{1}),...
411		[pulsepairs_no, ev.mech_frequencies_no*ev.pulse_gammas_no]);
412		ev.exportResults('software_version', false);
413		%type(fullfile(ev.evaluation_folder, 'evaluation_parameters.txt'))
414
415		ev.calculatePulsedCMs;
416
417		% test calibratePulsedCMs with wrong shotnoise_ind
418		% manipulate data to create a difference between the two sets of pulsed CMs
419		ev.pulse_CM{2}{1}=eye(size(ev.pulse_CM{2}{1}));
420		% calibrate with the wrong data --> non-shotnoise-data should now look like shotnoise
421		ev.calibratePulsedCMs('shotnoise_ind', 2);
422		assertElementsAlmostEqual(ev.pulse_CM_calibrated{2}{1}, eye(size(ev.pulse_CM{2}{1}))/2)
423		ev.calibratePulsedCMs;
424		assertElementsAlmostEqual(ev.pulse_CM_calibrated{1}{1}, ...
425		calibrate_cm(ev.pulse_CM{1}{1}, ev.pulse_CM{1}{1}))
	401		ev = ev_setup_test(shared);
	402		ev.mech_frequencies = [1, 2];
	403		ev.opt_pulse_widths = 1e2*[1, 1];
	404		ev.pulse_gammas = logspace(-1,1,10);
	405
	406		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
	407		pulsepairs_no = 100;
	408		[T,ev] = evalc('ev.calculatePulsedQuadratures(1, ''pulsepairs'', pulsepairs_no);');
	409		[T,ev] = evalc('ev.calculatePulsedQuadratures(2, ''pulsepairs'', pulsepairs_no);');
	410		assertEqual(size(ev.pulse_Q{1}),...
	411		[pulsepairs_no, ev.mech_frequencies_no*ev.pulse_gammas_no]);
	412		ev.exportResults('software_version', false);
	413		%type(fullfile(ev.evaluation_folder, 'evaluation_parameters.txt'))
	414
	415		ev.calculatePulsedCMs;
	416
	417		% test calibratePulsedCMs with wrong shotnoise_ind
	418		% manipulate data to create a difference between the two sets of pulsed CMs
	419		ev.pulse_CM{2}{1}=eye(size(ev.pulse_CM{2}{1}));
	420		% calibrate with the wrong data --> non-shotnoise-data should now look like shotnoise
	421		ev.calibratePulsedCMs('shotnoise_ind', 2);
	422		assertElementsAlmostEqual(ev.pulse_CM_calibrated{2}{1}, eye(size(ev.pulse_CM{2}{1}))/2)
	423		ev.calibratePulsedCMs;
	424		assertElementsAlmostEqual(ev.pulse_CM_calibrated{1}{1}, ...
	425		calibrate_cm(ev.pulse_CM{1}{1}, ev.pulse_CM{1}{1}))
426	426
427	427	function ev = run_quicker_test(shared)	function ev = run_quicker_test(shared)
428		ev = ev_setup_test(shared);
429		ev.mech_frequencies = [1.2, 2.3, 4.1,5.7];
430		ev.opt_pulse_widths = 1e2*ones(size(ev.mech_frequencies));
431		ev.pulse_gammas = logspace(-1,1,10);
	428		ev = ev_setup_test(shared);
	429		ev.mech_frequencies = [1.2, 2.3, 4.1,5.7];
	430		ev.opt_pulse_widths = 1e2*ones(size(ev.mech_frequencies));
	431		ev.pulse_gammas = logspace(-1,1,10);
432	432
433		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
434		pulsepairs_no = 100;
435		[T,ev] = evalc('ev.calculatePulsedQuadratures(1, ''pulsepairs'', pulsepairs_no);');
436		[T,ev] = evalc('ev.calculatePulsedQuadratures(2, ''pulsepairs'', pulsepairs_no);');
437		[T,ev] = evalc('ev.calibratePulsedCMs;');
	433		[T,ev] = evalc('ev.calculateModeVectors;'); % supress std out from fct call
	434		pulsepairs_no = 100;
	435		[T,ev] = evalc('ev.calculatePulsedQuadratures(1, ''pulsepairs'', pulsepairs_no);');
	436		[T,ev] = evalc('ev.calculatePulsedQuadratures(2, ''pulsepairs'', pulsepairs_no);');
	437		[T,ev] = evalc('ev.calibratePulsedCMs;');
438	438
439	439	function getters_with_missing_properties_test(shared)	function getters_with_missing_properties_test(shared)
440		%% check that the fallback options provided for the getters for
441		%% mech_frequencies_no, pulse_gammas_no, and tracepairs_no
442		%% are defined correctly
443		%% (fallback means: if mech_frequencies, pulse_gammas, or tracepairs are undefined)
444
445		% do quick eval to generate data
446		ev = run_quick_test(shared);
447		ev.exportResults;
448
449		% reimport traces to a blank eval-object to get
450		% values with measurement-object, mech_frequencies, pulse_gammas removed
451		ev_traced = entanglement_evaluation;
452		ev_traced.evaluation_folder = ev.evaluation_folder;
453		ev_traced.import_traces;
454
455		% values without measurement-object
456		assertEqual(ev_traced.tracepairs_no, ev.tracepairs_no,...
457		['tracepairs_no does not match']);
458		assertEqual(ev_traced.mech_frequencies_no, ev.mech_frequencies_no,...
459		['mech_frequencies_no does not match']);
460		assertEqual(ev_traced.pulse_gammas_no, ev.pulse_gammas_no,...
461		['pulse_gammas_no does not match']);
462
463		% reimport CMs to a blank eval-object to get
464		% values with measurement-object, mech_frequencies, pulse_gammas removed
465		ev3 = entanglement_evaluation;
466		ev3.evaluation_folder = ev.evaluation_folder;
467		ev3.import_CMs;
468
469		% values without measurement-object
470		assertEqual(ev3.tracepairs_no, ev.tracepairs_no,...
471		['tracepairs_no does not match']);
472		assertEqual(ev3.mech_frequencies_no, ev.mech_frequencies_no,...
473		['mech_frequencies_no does not match']);
474		assertEqual(ev3.pulse_gammas_no, ev.pulse_gammas_no,...
475		['pulse_gammas_no does not match']);
	440		%% check that the fallback options provided for the getters for
	441		%% mech_frequencies_no, pulse_gammas_no, and tracepairs_no
	442		%% are defined correctly
	443		%% (fallback means: if mech_frequencies, pulse_gammas, or tracepairs are undefined)
	444
	445		% do quick eval to generate data
	446		ev = run_quick_test(shared);
	447		ev.exportResults;
	448
	449		% reimport traces to a blank eval-object to get
	450		% values with measurement-object, mech_frequencies, pulse_gammas removed
	451		ev_traced = entanglement_evaluation;
	452		ev_traced.evaluation_folder = ev.evaluation_folder;
	453		ev_traced.import_traces;
	454
	455		% values without measurement-object
	456		assertEqual(ev_traced.tracepairs_no, ev.tracepairs_no,...
	457		['tracepairs_no does not match']);
	458		assertEqual(ev_traced.mech_frequencies_no, ev.mech_frequencies_no,...
	459		['mech_frequencies_no does not match']);
	460		assertEqual(ev_traced.pulse_gammas_no, ev.pulse_gammas_no,...
	461		['pulse_gammas_no does not match']);
	462
	463		% reimport CMs to a blank eval-object to get
	464		% values with measurement-object, mech_frequencies, pulse_gammas removed
	465		ev3 = entanglement_evaluation;
	466		ev3.evaluation_folder = ev.evaluation_folder;
	467		ev3.import_CMs;
	468
	469		% values without measurement-object
	470		assertEqual(ev3.tracepairs_no, ev.tracepairs_no,...
	471		['tracepairs_no does not match']);
	472		assertEqual(ev3.mech_frequencies_no, ev.mech_frequencies_no,...
	473		['mech_frequencies_no does not match']);
	474		assertEqual(ev3.pulse_gammas_no, ev.pulse_gammas_no,...
	475		['pulse_gammas_no does not match']);
476	476
477	477	function export_import_test(shared)	function export_import_test(shared)
478		ev = ev_setup_test(shared);
479
480		mech_freqs_no = 2;
481		pulse_gammas_no = 3;
482		pulse_pairs_no = 100;
483
484		ev.mech_frequencies = ones(mech_freqs_no, 1);
485		ev.opt_pulse_widths = ones(mech_freqs_no, 1);
486		ev.pulse_gammas = ones(pulse_gammas_no, 1);
487
488		%%%% NEW EXPORT/IMPORT TEST
489		% mock data
490		Q_before = repmat([11, 12, 21, 22, 31, 32],pulse_pairs_no,1);
491		Q = {}; P = {}; X = {}; Y = {};
492		Q{1} = Q_before; P{1} = 10*Q{1}; X{1} = 100*Q{1}; Y{1} = 1000*Q{1};
493		Q{2} = Q_before+1; P{2} = 10*Q{2}; X{2} = 100*Q{2}; Y{2} = 1000*Q{2};
494
495		% reorder mock data into pulse_trace{tracepair_ind}{gam_ind}
496		for pow_ind=1:ev.tracepairs_no
497		for gam_ind=1:ev.pulse_gammas_no
498		%reorder into Q1, Q2,.., P1, P2,.., X1, X2,.., Y1, Y2,...
499		first_mode_ind = (gam_ind-1)*ev.mech_frequencies_no + 1;
500		last_mode_ind = gam_ind*ev.mech_frequencies_no;
501
502		q = Q{pow_ind}(:, first_mode_ind:last_mode_ind);
503		p = P{pow_ind}(:, first_mode_ind:last_mode_ind);
504		x = X{pow_ind}(:, first_mode_ind:last_mode_ind);
505		y = Y{pow_ind}(:, first_mode_ind:last_mode_ind);
506
507		% reorder into Q1, P1, Q2, P2,..., X1, Y1, X2, Y2,...
508		pulse_trace = permute_data([q, p, x, y]);
509		ev.pulse_trace{pow_ind}{gam_ind} = pulse_trace;
510		ev.pulse_CM{pow_ind}{gam_ind} = cov(pulse_trace);
511		end
512		end
513
514		% check export from ev-object
515		ev.exportResults('software_version', false);
516
517		import = load(fullfile(ev.evaluation_folder, 'pulsed_traces.mat'));
518		assertEqual(import.pulsed_traces, ev.pulse_trace)
519		ev.pulse_trace{1}{1}=2;
520		assert(~isequal(import.pulsed_traces, ev.pulse_trace))
521
522		% check (re-)import into ev-object
523		ev.pulse_trace = {};
524		assert(~isequal(import.pulsed_traces, ev.pulse_trace))
525		ev.import_traces();
526		assertEqual(import.pulsed_traces, ev.pulse_trace)
527
528		% import via import_data_and_CM
529		% CAUTION: DON'T USE just ev_original = ev;
530		% since entanglement_evaluation is a handle object, we need a proper copy-method
531		% (as provided by the abstract handle superclass Copyable) to make the following tests run
532		ev_original = copy(ev);
533		testval_original = ev.pulse_Q{2}(1,2);
534		ev.pulse_trace = {[]}; % empty the pulse_trace property
535		% ---> also gets rid of the dependent pulse_Q, pulse_P,... properties
536		assert(~isequal(ev_original.pulse_Q, ev.pulse_Q), 'They should not be equal!')
537
538		ev.import_traces();
539		assertEqual(testval_original, ev.pulse_Q{2}(1,2))
540		assertEqual(ev_original.pulse_Q{1}, ev.pulse_Q{1})
541		assertEqual(ev_original.pulse_P{1}, ev.pulse_P{1})
542		assertEqual(ev_original.pulse_X{1}, ev.pulse_X{1})
543		assertEqual(ev_original.pulse_Y{1}, ev.pulse_Y{1})
544
545		assertEqual(ev_original.pulse_Q{2}, ev.pulse_Q{2})
546		assertEqual(ev_original.pulse_P{2}, ev.pulse_P{2})
547		assertEqual(ev_original.pulse_X{2}, ev.pulse_X{2})
548		assertEqual(ev_original.pulse_Y{2}, ev.pulse_Y{2})
549
550		% check that saving the evaluation-object via method exportResults works as expected
551		ev.exportResults('software_version', false);
552
553		assert(~isempty(ev.pulse_trace));
554		clear('ev')
555		load(fullfile(shared.evaluation_folder, 'ev.mat'))
556		assert(isempty(ev.pulse_trace));
	478		ev = ev_setup_test(shared);
	479
	480		mech_freqs_no = 2;
	481		pulse_gammas_no = 3;
	482		pulse_pairs_no = 100;
	483
	484		ev.mech_frequencies = ones(mech_freqs_no, 1);
	485		ev.opt_pulse_widths = ones(mech_freqs_no, 1);
	486		ev.pulse_gammas = ones(pulse_gammas_no, 1);
	487
	488		%%%% NEW EXPORT/IMPORT TEST
	489		% mock data
	490		Q_before = repmat([11, 12, 21, 22, 31, 32],pulse_pairs_no,1);
	491		Q = {}; P = {}; X = {}; Y = {};
	492		Q{1} = Q_before; P{1} = 10*Q{1}; X{1} = 100*Q{1}; Y{1} = 1000*Q{1};
	493		Q{2} = Q_before+1; P{2} = 10*Q{2}; X{2} = 100*Q{2}; Y{2} = 1000*Q{2};
	494
	495		% reorder mock data into pulse_trace{tracepair_ind}{gam_ind}
	496		for pow_ind=1:ev.tracepairs_no
	497		for gam_ind=1:ev.pulse_gammas_no
	498		%reorder into Q1, Q2,.., P1, P2,.., X1, X2,.., Y1, Y2,...
	499		first_mode_ind = (gam_ind-1)*ev.mech_frequencies_no + 1;
	500		last_mode_ind = gam_ind*ev.mech_frequencies_no;
	501
	502		q = Q{pow_ind}(:, first_mode_ind:last_mode_ind);
	503		p = P{pow_ind}(:, first_mode_ind:last_mode_ind);
	504		x = X{pow_ind}(:, first_mode_ind:last_mode_ind);
	505		y = Y{pow_ind}(:, first_mode_ind:last_mode_ind);
	506
	507		% reorder into Q1, P1, Q2, P2,..., X1, Y1, X2, Y2,...
	508		pulse_trace = permute_data([q, p, x, y]);
	509		ev.pulse_trace{pow_ind}{gam_ind} = pulse_trace;
	510		ev.pulse_CM{pow_ind}{gam_ind} = cov(pulse_trace);
	511		end
	512		end
	513
	514		% check export from ev-object
	515		ev.exportResults('software_version', false);
	516
	517		import = load(fullfile(ev.evaluation_folder, 'pulsed_traces.mat'));
	518		assertEqual(import.pulsed_traces, ev.pulse_trace)
	519		ev.pulse_trace{1}{1}=2;
	520		assert(~isequal(import.pulsed_traces, ev.pulse_trace))
	521
	522		% check (re-)import into ev-object
	523		ev.pulse_trace = {};
	524		assert(~isequal(import.pulsed_traces, ev.pulse_trace))
	525		ev.import_traces();
	526		assertEqual(import.pulsed_traces, ev.pulse_trace)
	527
	528		% import via import_data_and_CM
	529		% CAUTION: DON'T USE just ev_original = ev;
	530		% since entanglement_evaluation is a handle object, we need a proper copy-method
	531		% (as provided by the abstract handle superclass Copyable) to make the following tests run
	532		ev_original = copy(ev);
	533		testval_original = ev.pulse_Q{2}(1,2);
	534		ev.pulse_trace = {[]}; % empty the pulse_trace property
	535		% ---> also gets rid of the dependent pulse_Q, pulse_P,... properties
	536		assert(~isequal(ev_original.pulse_Q, ev.pulse_Q), 'They should not be equal!')
	537
	538		ev.import_traces();
	539		assertEqual(testval_original, ev.pulse_Q{2}(1,2))
	540		assertEqual(ev_original.pulse_Q{1}, ev.pulse_Q{1})
	541		assertEqual(ev_original.pulse_P{1}, ev.pulse_P{1})
	542		assertEqual(ev_original.pulse_X{1}, ev.pulse_X{1})
	543		assertEqual(ev_original.pulse_Y{1}, ev.pulse_Y{1})
	544
	545		assertEqual(ev_original.pulse_Q{2}, ev.pulse_Q{2})
	546		assertEqual(ev_original.pulse_P{2}, ev.pulse_P{2})
	547		assertEqual(ev_original.pulse_X{2}, ev.pulse_X{2})
	548		assertEqual(ev_original.pulse_Y{2}, ev.pulse_Y{2})
	549
	550		% check that saving the evaluation-object via method exportResults works as expected
	551		ev.exportResults('software_version', false);
	552
	553		assert(~isempty(ev.pulse_trace));
	554		clear('ev')
	555		load(fullfile(shared.evaluation_folder, 'ev.mat'))
	556		assert(isempty(ev.pulse_trace));
557	557
558	558	function corrfct_test(shared)	function corrfct_test(shared)
559		ev = ev_setup_test(shared);
560
561		max_lags = 10;
562		[T,ev] = evalc('ev.blocked_cross_correlation(''blocksize'', max_lags);'); % supress std out from fct call
563		xx = ev.correlation_functions{1}{1,1};
564		pp = ev.correlation_functions{1}{2,2};
565		xp = ev.correlation_functions{1}{1,2};
566		px = ev.correlation_functions{1}{2,1};
567		assertElementsAlmostEqual(xx, flipud(xx));
568		assertElementsAlmostEqual(pp, flipud(pp));
569		assertElementsAlmostEqual(xp, flipud(px));
570
571		xx_ = xcorr(shared.x.data, shared.x.data, max_lags);
572		xp_ = xcorr(shared.x.data, shared.p.data, max_lags);
573		assertElementsAlmostEqual(xx, xx_);
574
575		rel_tol = 1e-12;
576		assertElementsAlmostEqual(xp, xp_, 'relative', rel_tol);
577
578		% test export and import
579		corrfct_old = ev.correlation_functions;
580		ev.exportCrossCorrelationFunctions;
581		ev.correlation_functions = {[]};
582		ev.importCrossCorrelationFunctions(ev.evaluation_folder);
583		for k=1:2
584		for l=1:2
585		for m=1:2
586		assertEqual(ev.correlation_functions{k}{l,m},...
587		corrfct_old{k}{l,m})
588		end
589		end
590		end
	559		ev = ev_setup_test(shared);
	560
	561		max_lags = 10;
	562		[T,ev] = evalc('ev.blocked_cross_correlation(''blocksize'', max_lags);'); % supress std out from fct call
	563		xx = ev.correlation_functions{1}{1,1};
	564		pp = ev.correlation_functions{1}{2,2};
	565		xp = ev.correlation_functions{1}{1,2};
	566		px = ev.correlation_functions{1}{2,1};
	567		assertElementsAlmostEqual(xx, flipud(xx));
	568		assertElementsAlmostEqual(pp, flipud(pp));
	569		assertElementsAlmostEqual(xp, flipud(px));
	570
	571		xx_ = xcorr(shared.x.data, shared.x.data, max_lags);
	572		xp_ = xcorr(shared.x.data, shared.p.data, max_lags);
	573		assertElementsAlmostEqual(xx, xx_);
	574
	575		rel_tol = 1e-12;
	576		assertElementsAlmostEqual(xp, xp_, 'relative', rel_tol);
	577
	578		% test export and import
	579		corrfct_old = ev.correlation_functions;
	580		ev.exportCrossCorrelationFunctions;
	581		ev.correlation_functions = {[]};
	582		ev.importCrossCorrelationFunctions(ev.evaluation_folder);
	583		for k=1:2
	584		for l=1:2
	585		for m=1:2
	586		assertEqual(ev.correlation_functions{k}{l,m},...
	587		corrfct_old{k}{l,m})
	588		end
	589		end
	590		end
591	591
592	592	function efficiency_visibility_simple_test(shared)	function efficiency_visibility_simple_test(shared)
593		ev = ev_setup_test(shared);
594		ev.mech_frequencies = shared.mech_freqs;
595
596		%% check for correct default values
597		assertEqual(ev.homodyne_visibility, 1)
598		assertEqual(ev.detection_efficiency, 1)
599
600		%% checK: calibration works for trivial case
601		quad_no = 8;
602		shot_CM = eye(quad_no)/2;
603		sig_CM = 2*shot_CM;
604		ev.pulse_CM{1}{1} = shot_CM;
605		ev.pulse_CM{2}{1} = sig_CM;
606		ev.calibratePulsedCMs('shotnoise_ind', 1);
607		tolerance = 1E-6;
608		assertElementsAlmostEqual(ev.pulse_CM_calibrated{1}{1}, shot_CM, 'absolute', tolerance);
609		assertElementsAlmostEqual(ev.pulse_CM_calibrated{2}{1}, sig_CM, 'absolute', tolerance);
610
611		%% check: calibration works for (almost) trivial case
612		ev.detection_efficiency = 0.999999;
613		ev.homodyne_visibility = 0.99999999;
614		quad_no = 8;
615		shot_CM = eye(quad_no)/2;
616		sig_CM = 2*shot_CM;
617		ev.pulse_CM{1}{1} = shot_CM;
618		ev.pulse_CM{2}{1} = sig_CM;
619		ev.calibratePulsedCMs('shotnoise_ind', 1);
620		tolerance = 1E-6;
621		assertElementsAlmostEqual(ev.pulse_CM_calibrated{1}{1}, shot_CM, 'absolute', tolerance);
622		assertElementsAlmostEqual(ev.pulse_CM_calibrated{2}{1}, sig_CM, 'absolute', tolerance);
	593		ev = ev_setup_test(shared);
	594		ev.mech_frequencies = shared.mech_freqs;
	595
	596		%% check for correct default values
	597		assertEqual(ev.homodyne_visibility, 1)
	598		assertEqual(ev.detection_efficiency, 1)
	599
	600		%% checK: calibration works for trivial case
	601		quad_no = 8;
	602		shot_CM = eye(quad_no)/2;
	603		sig_CM = 2*shot_CM;
	604		ev.pulse_CM{1}{1} = shot_CM;
	605		ev.pulse_CM{2}{1} = sig_CM;
	606		ev.calibratePulsedCMs('shotnoise_ind', 1);
	607		tolerance = 1E-6;
	608		assertElementsAlmostEqual(ev.pulse_CM_calibrated{1}{1}, shot_CM, 'absolute', tolerance);
	609		assertElementsAlmostEqual(ev.pulse_CM_calibrated{2}{1}, sig_CM, 'absolute', tolerance);
	610
	611		%% check: calibration works for (almost) trivial case
	612		ev.detection_efficiency = 0.999999;
	613		ev.homodyne_visibility = 0.99999999;
	614		quad_no = 8;
	615		shot_CM = eye(quad_no)/2;
	616		sig_CM = 2*shot_CM;
	617		ev.pulse_CM{1}{1} = shot_CM;
	618		ev.pulse_CM{2}{1} = sig_CM;
	619		ev.calibratePulsedCMs('shotnoise_ind', 1);
	620		tolerance = 1E-6;
	621		assertElementsAlmostEqual(ev.pulse_CM_calibrated{1}{1}, shot_CM, 'absolute', tolerance);
	622		assertElementsAlmostEqual(ev.pulse_CM_calibrated{2}{1}, sig_CM, 'absolute', tolerance);
623	623
624	624	function efficiency_visibility_test(shared)	function efficiency_visibility_test(shared)
625		ev = ev_setup_test(shared);
626		ev.mech_frequencies = shared.mech_freqs;
627
628		%% check calibration with visibility and efficiency = 1; passes
629		nu = 1; % homodyne visibility
630		eta = 1; % detector efficiency
631		efficiency_visibility_test_sub(ev, nu, eta)
632
633		%% check calibration with suboptimal efficiency; shotnoise passes
634		nu = 1; % homodyne visibility
635		eta = rand(1); % detector efficiency
636		efficiency_visibility_test_sub(ev, nu, eta)
637
638		%% check calibration with suboptimal visibility; shotnoise passes
639		nu = rand(1); % homodyne visibility
640		eta = 1; % detector efficiency
641		efficiency_visibility_test_sub(ev, nu, eta)
642
643		%% check calibration with suboptimal visibility and efficiency; shotnoise passes
644		nu = rand(1); % homodyne visibility
645		eta = rand(1); % detector efficiency
646		efficiency_visibility_test_sub(ev, nu, eta)
	625		ev = ev_setup_test(shared);
	626		ev.mech_frequencies = shared.mech_freqs;
	627
	628		%% check calibration with visibility and efficiency = 1; passes
	629		nu = 1; % homodyne visibility
	630		eta = 1; % detector efficiency
	631		efficiency_visibility_test_sub(ev, nu, eta)
	632
	633		%% check calibration with suboptimal efficiency; shotnoise passes
	634		nu = 1; % homodyne visibility
	635		eta = rand(1); % detector efficiency
	636		efficiency_visibility_test_sub(ev, nu, eta)
	637
	638		%% check calibration with suboptimal visibility; shotnoise passes
	639		nu = rand(1); % homodyne visibility
	640		eta = 1; % detector efficiency
	641		efficiency_visibility_test_sub(ev, nu, eta)
	642
	643		%% check calibration with suboptimal visibility and efficiency; shotnoise passes
	644		nu = rand(1); % homodyne visibility
	645		eta = rand(1); % detector efficiency
	646		efficiency_visibility_test_sub(ev, nu, eta)
647	647
648	648	function efficiency_visibility_test_sub(ev, nu, eta)	function efficiency_visibility_test_sub(ev, nu, eta)
649		amp = 0.1; % fake conversion factor from units of vacua to Volt
650		prefix = ['nu=',num2str(nu),'; eta=', num2str(eta), '. ', 10];
651
652		%% create fake idealized data
653		sample_no = 1E5; quad_no = 8;
654		shot_CM_ideal = eye(quad_no)/2;
655		sig_CM_ideal = 3*2*shot_CM_ideal + 0.1*ones(quad_no);
656		[shot_ideal, sig_ideal] = get_uncorr_rand_nums_from_sample_covs(...
657		sample_no, shot_CM_ideal, sig_CM_ideal);
658
659		%% check: correct CMs?
660		assertElementsAlmostEqual(cov(shot_ideal), shot_CM_ideal,...
661		[prefix, 'CM for fake ideal shotnoise not as expected.'])
662		assertElementsAlmostEqual(cov(sig_ideal), sig_CM_ideal,...
663		[prefix, 'CM for fake ideal signal not as expected.'])
664
665		%% check: shotnoise and signal uncorrelated?
666		assertElementsAlmostEqual(corr(shot_ideal, sig_ideal), zeros(quad_no),...
667		[prefix, 'Correlations between fake signal and shotnoise not as expected.'])
668
669		% disp([10, 10, '***************',10, prefix, 'fake ideal data as expected!',10])
670
671		%% convert to fake measured data
672		shot_measured = sqrt(eta)*shot_ideal;
673		sig_measured = eta*nu*sig_ideal + sqrt(eta*(1 - eta*(nu^2)))*shot_ideal;
674
675		shot_measured = amp*shot_measured;
676		sig_measured = amp*sig_measured;
677
678		%% check that fake measured data satisfies expected relations
679		tolerance = 1E-2;
680		CM_shot_expected = (amp^2)*eta*shot_CM_ideal;
681		% reldiff (CM_shot_expected, cov(shot_measured))
682		assertElementsAlmostEqual(cov(shot_measured), CM_shot_expected, 'absolute', tolerance,...
683		[prefix, 'CM for fake measured shotnoise not as expected!'])
684
685		CM_sig_expected = (amp^2)*((eta^2)*(nu^2)*sig_CM_ideal + eta*(1 - eta*(nu^2))*shot_CM_ideal);
686		assertElementsAlmostEqual(cov(sig_measured), CM_sig_expected, 'absolute', tolerance,...
687		[prefix, 'CM for fake measured signal not as expected!'])
688
689		% disp([10, 10, '***************',10, prefix, 'fake measurement data as expected!',10])
690
691		%% evaluate fake data
692		ev.homodyne_visibility = nu;
693		ev.detection_efficiency = eta;
694
695		ev.pulse_trace{1}{1} = shot_measured;
696		ev.pulse_trace{2}{1} = sig_measured;
697
698		ev.calculatePulsedCMs;
699		assertEqual(cov(shot_measured), ev.pulse_CM{1}{1})
700		assertEqual(cov(sig_measured), ev.pulse_CM{2}{1})
701		ev.calibratePulsedCMs('shotnoise_ind', 1);
702
703		% expect to recover specified sample CMs
704		tolerance = 1E-2;
705		assertElementsAlmostEqual(ev.pulse_CM_calibrated{1}{1}, shot_CM_ideal, 'absolute', tolerance,...
706		[prefix, 'Efficiency- and visibility-corrected CM for shotnoise not as expected!']);
707		assertElementsAlmostEqual(ev.pulse_CM_calibrated{2}{1}, sig_CM_ideal, 'absolute', tolerance,...
708		[prefix, 'Efficiency- and visibility-corrected CM for signal not as expected!']);
709		% disp([10, 10, '***************',10, prefix, 'correction performed as expected!',10])
	649		amp = 0.1; % fake conversion factor from units of vacua to Volt
	650		prefix = ['nu=',num2str(nu),'; eta=', num2str(eta), '. ', 10];
	651
	652		%% create fake idealized data
	653		sample_no = 1E5; quad_no = 8;
	654		shot_CM_ideal = eye(quad_no)/2;
	655		sig_CM_ideal = 3*2*shot_CM_ideal + 0.1*ones(quad_no);
	656		[shot_ideal, sig_ideal] = get_uncorr_rand_nums_from_sample_covs(...
	657		sample_no, shot_CM_ideal, sig_CM_ideal);
	658
	659		%% check: correct CMs?
	660		assertElementsAlmostEqual(cov(shot_ideal), shot_CM_ideal,...
	661		[prefix, 'CM for fake ideal shotnoise not as expected.'])
	662		assertElementsAlmostEqual(cov(sig_ideal), sig_CM_ideal,...
	663		[prefix, 'CM for fake ideal signal not as expected.'])
	664
	665		%% check: shotnoise and signal uncorrelated?
	666		assertElementsAlmostEqual(corr(shot_ideal, sig_ideal), zeros(quad_no),...
	667		[prefix, 'Correlations between fake signal and shotnoise not as expected.'])
	668
	669		% disp([10, 10, '***************',10, prefix, 'fake ideal data as expected!',10])
	670
	671		%% convert to fake measured data
	672		shot_measured = sqrt(eta)*shot_ideal;
	673		sig_measured = eta*nu*sig_ideal + sqrt(eta*(1 - eta*(nu^2)))*shot_ideal;
	674
	675		shot_measured = amp*shot_measured;
	676		sig_measured = amp*sig_measured;
	677
	678		%% check that fake measured data satisfies expected relations
	679		tolerance = 1E-2;
	680		CM_shot_expected = (amp^2)*eta*shot_CM_ideal;
	681		% reldiff (CM_shot_expected, cov(shot_measured))
	682		assertElementsAlmostEqual(cov(shot_measured), CM_shot_expected, 'absolute', tolerance,...
	683		[prefix, 'CM for fake measured shotnoise not as expected!'])
	684
	685		CM_sig_expected = (amp^2)*((eta^2)*(nu^2)*sig_CM_ideal + eta*(1 - eta*(nu^2))*shot_CM_ideal);
	686		assertElementsAlmostEqual(cov(sig_measured), CM_sig_expected, 'absolute', tolerance,...
	687		[prefix, 'CM for fake measured signal not as expected!'])
	688
	689		% disp([10, 10, '***************',10, prefix, 'fake measurement data as expected!',10])
	690
	691		%% evaluate fake data
	692		ev.homodyne_visibility = nu;
	693		ev.detection_efficiency = eta;
	694
	695		ev.pulse_trace{1}{1} = shot_measured;
	696		ev.pulse_trace{2}{1} = sig_measured;
	697
	698		ev.calculatePulsedCMs;
	699		assertEqual(cov(shot_measured), ev.pulse_CM{1}{1})
	700		assertEqual(cov(sig_measured), ev.pulse_CM{2}{1})
	701		ev.calibratePulsedCMs('shotnoise_ind', 1);
	702
	703		% expect to recover specified sample CMs
	704		tolerance = 1E-2;
	705		assertElementsAlmostEqual(ev.pulse_CM_calibrated{1}{1}, shot_CM_ideal, 'absolute', tolerance,...
	706		[prefix, 'Efficiency- and visibility-corrected CM for shotnoise not as expected!']);
	707		assertElementsAlmostEqual(ev.pulse_CM_calibrated{2}{1}, sig_CM_ideal, 'absolute', tolerance,...
	708		[prefix, 'Efficiency- and visibility-corrected CM for signal not as expected!']);
	709		% disp([10, 10, '***************',10, prefix, 'correction performed as expected!',10])
710	710
711	711	function traceout_test(shared)	function traceout_test(shared)
712		ev = run_quicker_test(shared);
713		% ev.mech_frequencies = [1.2, 2.3, 4.1,5.7];
	712		ev = run_quicker_test(shared);
	713		% ev.mech_frequencies = [1.2, 2.3, 4.1,5.7];
714	714
715		%% check: an error is thrown if modes to trace out are not legit
716		modes_to_trace = [2,3,5];
717		test_call = @()ev.trace_out(modes_to_trace);
718		assertExceptionThrown(test_call,...
719		'entanglement_evaluation:trace_out:modes_illegit')
	715		%% check: an error is thrown if modes to trace out are not legit
	716		modes_to_trace = [2,3,5];
	717		test_call = @()ev.trace_out(modes_to_trace);
	718		assertExceptionThrown(test_call,...
	719		'entanglement_evaluation:trace_out:modes_illegit')
720	720
721		%% now the actual tests
722		modes_to_trace = [1,3];
723		ev_traced = ev.trace_out(modes_to_trace);
724		mech_frequencies_old = ev.mech_frequencies;
	721		%% now the actual tests
	722		modes_to_trace = [1,3];
	723		ev_traced = ev.trace_out(modes_to_trace);
	724		mech_frequencies_old = ev.mech_frequencies;
725	725
726		assertEqual(ev_traced.mech_frequencies_no,...
727		length(mech_frequencies_old)-length(modes_to_trace),...
728		'traced out evaluation does not have expected number of mechanical modes')
729
730		assertEqual(ev_traced.mech_frequencies,[2.3,5.7],...
731		'traced out evaluation does not have expected mechanical modes')
	726		assertEqual(ev_traced.mech_frequencies_no,...
	727		length(mech_frequencies_old)-length(modes_to_trace),...
	728		'traced out evaluation does not have expected number of mechanical modes')
732	729
733		assertEqual(size(ev_traced.pulse_trace{2}{2}),...
734		[size(ev.pulse_trace{2}{2},1), 4*ev_traced.mech_frequencies_no],...
735		'pulsed traces of traced out evaluation does not have the expected form')
	730		assertEqual(ev_traced.mech_frequencies,[2.3,5.7],...
	731		'traced out evaluation does not have expected mechanical modes')
736	732
737		assertEqual(size(ev_traced.pulse_CM_calibrated{2}{2}),...
738		4*ev_traced.mech_frequencies_no*[1,1],...
739		'calibrated CMs of the traced out evaluation do not have the expected form')
	733		assertEqual(size(ev_traced.pulse_trace{2}{2}),...
	734		[size(ev.pulse_trace{2}{2},1), 4*ev_traced.mech_frequencies_no],...
	735		'pulsed traces of traced out evaluation does not have the expected form')
740	736
741		assert(~isequal(...
742		[ev_traced.logneg_corrected{2}{:}],...
743		[ev.logneg_corrected{2}{:}]),...
744		'log.neg. values should have changed due to the tracing out but they have not')
	737		assertEqual(size(ev_traced.pulse_CM_calibrated{2}{2}),...
	738		4*ev_traced.mech_frequencies_no*[1,1],...
	739		'calibrated CMs of the traced out evaluation do not have the expected form')
745	740
	741		assert(~isequal(...
	742		[ev_traced.logneg_corrected{2}{:}],...
	743		[ev.logneg_corrected{2}{:}]),...
	744		'log.neg. values should have changed due to the tracing out but they have not')
746	745
747		% %% test different edge cases regarding the modes to trace out
748		% disp('light_quad_inds_new expected to be [1, 2, 9, 10]')
749		% ev_traced = ev.trace_out([2:4]);
750
751		% disp('light_quad_inds_new expected to be [3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16]')
752	746
753		% ev_traced = ev.trace_out([1]);
	747		% %% test different edge cases regarding the modes to trace out
	748		% disp('light_quad_inds_new expected to be [1, 2, 9, 10]')
	749		% ev_traced = ev.trace_out([2:4]);
	750
	751		% disp('light_quad_inds_new expected to be [3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16]')
	752
	753		% ev_traced = ev.trace_out([1]);
754	754
755	755
756	756	%%% OTHER AUX FUNCTIONS %%%	%%% OTHER AUX FUNCTIONS %%%
757	757	function data = get_rand_nums_from_sample_cov(sample_no, sample_CM)	function data = get_rand_nums_from_sample_cov(sample_no, sample_CM)
758		dim = size(sample_CM,1);
759		assertEqual(dim, size(sample_CM,2));
760		% see https://stats.stackexchange.com/questions/120179/generating-data-with-a-given-sample-covariance-matrix
761		data = randn(sample_no, dim);
762		data = bsxfun(@minus, data, mean(data)); % ensure zero mean
763		data = data * inv(chol(cov(data))); % remove existing correlations and standardize
764		data = data * chol(sample_CM); % transform to match given sample CM
	758		dim = size(sample_CM,1);
	759		assertEqual(dim, size(sample_CM,2));
	760		% see https://stats.stackexchange.com/questions/120179/generating-data-with-a-given-sample-covariance-matrix
	761		data = randn(sample_no, dim);
	762		data = bsxfun(@minus, data, mean(data)); % ensure zero mean
	763		data = data * inv(chol(cov(data))); % remove existing correlations and standardize
	764		data = data * chol(sample_CM); % transform to match given sample CM
765	765
766	766	function [data1, data2] = get_uncorr_rand_nums_from_sample_covs(sample_no, sampleCM1, sampleCM2)	function [data1, data2] = get_uncorr_rand_nums_from_sample_covs(sample_no, sampleCM1, sampleCM2)
767		dim1 = size(sampleCM1,1);
768		assertEqual(dim1, size(sampleCM1,2));
769
770		sample_CM = blkdiag(sampleCM1, sampleCM2);
771		data = get_rand_nums_from_sample_cov(sample_no, sample_CM);
	767		dim1 = size(sampleCM1,1);
	768		assertEqual(dim1, size(sampleCM1,2));
	769
	770		sample_CM = blkdiag(sampleCM1, sampleCM2);
	771		data = get_rand_nums_from_sample_cov(sample_no, sample_CM);
772	772
773		data1 = data(:,1:dim1);
774		data2 = data(:,dim1+1:end);
	773		data1 = data(:,1:dim1);
	774		data2 = data(:,dim1+1:end);
775	775
776	776	function reldiff_mat = reldiff(mat1, mat2) % can be useful for spotting mistakes	function reldiff_mat = reldiff(mat1, mat2) % can be useful for spotting mistakes
777		reldiff_mat = (mat1-mat2)./(abs(mat1)+abs(mat2));
	777		reldiff_mat = (mat1-mat2)./(abs(mat1)+abs(mat2));
778	778
779	779
780	780	function relsign = assertRowsAlmostEqualUptoSign(mat1, mat2, varargin)	function relsign = assertRowsAlmostEqualUptoSign(mat1, mat2, varargin)
781		assertEqual(size(mat1), size(mat2))
782		rows_no = size(mat2,1);
783		relsign = zeros(1,rows_no);
784		for row_no=1:rows_no
785		relsign(row_no) = sign(real(mat1(row_no,1)))*sign(real(mat2(row_no,1)));
786		end
787		assertElementsAlmostEqual(mat1, diag(relsign)*mat2,varargin{:})
	781		assertEqual(size(mat1), size(mat2))
	782		rows_no = size(mat2,1);
	783		relsign = zeros(1,rows_no);
	784		for row_no=1:rows_no
	785		relsign(row_no) = sign(real(mat1(row_no,1)))*sign(real(mat2(row_no,1)));
	786		end
	787		assertElementsAlmostEqual(mat1, diag(relsign)*mat2,varargin{:})
788	788
789	789	function relsign = assertColsAlmostEqualUptoSign(mat1, mat2, varargin)	function relsign = assertColsAlmostEqualUptoSign(mat1, mat2, varargin)
790		assertEqual(size(mat1), size(mat2))
791		cols_no = size(mat2,2);
792		relsign = zeros(1,cols_no);
793		for col_no=1:cols_no
794		relsign(col_no) = sign(real(mat1(1,col_no)))*sign(real(mat2(1,col_no)));
795		end
796		assertElementsAlmostEqual(mat1, mat2*diag(relsign),varargin{:})
	790		assertEqual(size(mat1), size(mat2))
	791		cols_no = size(mat2,2);
	792		relsign = zeros(1,cols_no);
	793		for col_no=1:cols_no
	794		relsign(col_no) = sign(real(mat1(1,col_no)))*sign(real(mat2(1,col_no)));
	795		end
	796		assertElementsAlmostEqual(mat1, mat2*diag(relsign),varargin{:})
797	797
798	798	function relsign = assertCMsEqualUptoSign(mat1,mat2, varargin)	function relsign = assertCMsEqualUptoSign(mat1,mat2, varargin)
799		assertEqual(size(mat1), size(mat2))
800		cols_no = size(mat2,2);
801		relsign = zeros(1,cols_no);
802		for col_no=1:cols_no
803		relsign(col_no) = sign(real(mat1(1,col_no)))*sign(real(mat2(1,col_no)));
804		end
805		assertElementsAlmostEqual(mat1, diag(relsign)*mat2*diag(relsign),varargin{:})
	799		assertEqual(size(mat1), size(mat2))
	800		cols_no = size(mat2,2);
	801		relsign = zeros(1,cols_no);
	802		for col_no=1:cols_no
	803		relsign(col_no) = sign(real(mat1(1,col_no)))*sign(real(mat2(1,col_no)));
	804		end
	805		assertElementsAlmostEqual(mat1, diag(relsign)*mat2*diag(relsign),varargin{:})

File data_analysis/unittests/homo_measurement_test.m changed (mode: 100644) (index 7d788da..54187e1)
1	1	function test_suite = homo_measurement_test %#ok<STOUT>	function test_suite = homo_measurement_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function shared = setup	function shared = setup
7		shared.evaluation_folder = tempname;
8		assert(~(exist(shared.evaluation_folder)==7),...
9		[shared.evaluation_folder, ' exists already.']);
10		mkdir(shared.evaluation_folder)
	7		shared.evaluation_folder = tempname;
	8		assert(~(exist(shared.evaluation_folder)==7),...
	9		[shared.evaluation_folder, ' exists already.']);
	10		mkdir(shared.evaluation_folder)
11	11
12		shared.samplingrate = 100;
13		shared.sampleno = 1e4;
	12		shared.samplingrate = 100;
	13		shared.sampleno = 1e4;
14	14
15		shared.mech_freqs = [1, 1.345];
16		shared.timevec = (0:shared.sampleno-1)'/shared.samplingrate;
	15		shared.mech_freqs = [1, 1.345];
	16		shared.timevec = (0:shared.sampleno-1)'/shared.samplingrate;
17	17
18		shared.x.data = sin(2*pi*shared.mech_freqs(1)*shared.timevec) + sin(2*pi*shared.mech_freqs(2)*shared.timevec);
19		shared.p.data = cos(2*pi*shared.mech_freqs(1)*shared.timevec) + cos(2*pi*shared.mech_freqs(1)*shared.timevec);
	18		shared.x.data = sin(2*pi*shared.mech_freqs(1)*shared.timevec) + sin(2*pi*shared.mech_freqs(2)*shared.timevec);
	19		shared.p.data = cos(2*pi*shared.mech_freqs(1)*shared.timevec) + cos(2*pi*shared.mech_freqs(1)*shared.timevec);
20	20
21		shared.precision = 'double';
	21		shared.precision = 'double';
22	22
23		shared.x.file = fullfile(shared.evaluation_folder, '.x.dat');
24		shared.p.file = fullfile(shared.evaluation_folder, '.p.dat');
	23		shared.x.file = fullfile(shared.evaluation_folder, '.x.dat');
	24		shared.p.file = fullfile(shared.evaluation_folder, '.p.dat');
25	25
26		for mytrace = {shared.x, shared.p}
27		mytrace = mytrace{:};
28		fid = fopen(mytrace.file,'w');
29		fwrite(fid, mytrace.data, shared.precision);
30		fclose(fid);
31		end
	26		for mytrace = {shared.x, shared.p}
	27		mytrace = mytrace{:};
	28		fid = fopen(mytrace.file,'w');
	29		fwrite(fid, mytrace.data, shared.precision);
	30		fclose(fid);
	31		end
32	32
33	33	function teardown(shared)	function teardown(shared)
34		for mytrace = {shared.x, shared.p}
35		mytrace = mytrace{:};
36		delete(mytrace.file);
37		end
38		rmdir(shared.evaluation_folder, 's');
39		clear('shared');
	34		for mytrace = {shared.x, shared.p}
	35		mytrace = mytrace{:};
	36		delete(mytrace.file);
	37		end
	38		rmdir(shared.evaluation_folder, 's');
	39		clear('shared');
40	40
41	41	function meas = dhm_setup_test(shared)	function meas = dhm_setup_test(shared)
42		meas = dual_homo_measurement('samplingrate', shared.samplingrate);
43		meas.add_trace_pair(...
44		shared.x.file,...
45		shared.p.file,...
46		'precision',shared.precision,...
47		'DAQrange', 0,...
48		'amplification', 1,...
49		'signal_power_muW', 0);
50		meas.add_trace_pair(...
51		shared.x.file,...
52		shared.p.file,...
53		'precision',shared.precision,...
54		'DAQrange', 0,...
55		'amplification', 1,...
56		'signal_power_muW', 100);
	42		meas = dual_homo_measurement('samplingrate', shared.samplingrate);
	43		meas.add_trace_pair(...
	44		shared.x.file,...
	45		shared.p.file,...
	46		'precision',shared.precision,...
	47		'DAQrange', 0,...
	48		'amplification', 1,...
	49		'signal_power_muW', 0);
	50		meas.add_trace_pair(...
	51		shared.x.file,...
	52		shared.p.file,...
	53		'precision',shared.precision,...
	54		'DAQrange', 0,...
	55		'amplification', 1,...
	56		'signal_power_muW', 100);
57	57
58	58	function meas = properties_test(shared)	function meas = properties_test(shared)
59		meas = dhm_setup_test(shared);
60		for k=1:2
61		mytrace = meas.homotraces(1,k);
62		mytrace.fopen();
63		if k==1
64		data = shared.x.data;
65		elseif k==2
66		data = shared.p.data;
67		end
68		assertEqual(data, mytrace.fread(shared.sampleno));
69		mytrace.fclose();
70		end
71		assertEqual(meas.samplingrate, shared.samplingrate);
	59		meas = dhm_setup_test(shared);
	60		for k=1:2
	61		mytrace = meas.homotraces(1,k);
	62		mytrace.fopen();
	63		if k==1
	64		data = shared.x.data;
	65		elseif k==2
	66		data = shared.p.data;
	67		end
	68		assertEqual(data, mytrace.fread(shared.sampleno));
	69		mytrace.fclose();
	70		end
	71		assertEqual(meas.samplingrate, shared.samplingrate);
72	72
73	73	function meas = dummy_test(shared)	function meas = dummy_test(shared)
74		meas = dual_homo_measurement('samplingrate', 10, 'dummy', true);
	74		meas = dual_homo_measurement('samplingrate', 10, 'dummy', true);
75	75	assertEqual(meas.dummy, true)	assertEqual(meas.dummy, true)
76		add_trace_pair_test = @()meas.add_trace_pair(...
77		shared.x.file,...
78		shared.p.file,...
79		'precision',shared.precision,...
80		'DAQrange', 0,...
81		'amplification', 1,...
82		'signal_power_muW', 0);
83		assertExceptionThrown(add_trace_pair_test, 'PXItrace:FileExists')
84
85		meas = dual_homo_measurement('samplingrate', 10, 'dummy', true);
86		meas.add_trace_pair(...
87		'.x_dummy.dat',...
88		'.p_dummy.dat',...
89		'precision',shared.precision,...
90		'DAQrange', 0,...
91		'amplification', 1,...
92		'signal_power_muW', 0);
	76		add_trace_pair_test = @()meas.add_trace_pair(...
	77		shared.x.file,...
	78		shared.p.file,...
	79		'precision',shared.precision,...
	80		'DAQrange', 0,...
	81		'amplification', 1,...
	82		'signal_power_muW', 0);
	83		assertExceptionThrown(add_trace_pair_test, 'PXItrace:FileExists')
	84
	85		meas = dual_homo_measurement('samplingrate', 10, 'dummy', true);
	86		meas.add_trace_pair(...
	87		'.x_dummy.dat',...
	88		'.p_dummy.dat',...
	89		'precision',shared.precision,...
	90		'DAQrange', 0,...
	91		'amplification', 1,...
	92		'signal_power_muW', 0);
93	93	assertEqual(all([meas.homotraces.dummy]), true)	assertEqual(all([meas.homotraces.dummy]), true)
94		% dummy-files are automatically deleted upon destruction of the corresponding PXItrace-object
	94		% dummy-files are automatically deleted upon destruction of the corresponding PXItrace-object

File data_analysis/unittests/homotrace_test.m changed (mode: 100644) (index 2c294e2..f09843e)
1	1	function test_suite = homotrace_test %#ok<STOUT>	function test_suite = homotrace_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function shared = setup	function shared = setup
7		shared.samplingrate = 100;
8		shared.sample_no = 1e4;
	7		shared.samplingrate = 100;
	8		shared.sample_no = 1e4;
9	9
10		shared.mech_freq = 1;
11		shared.timevec = (0:shared.sample_no-1)'/shared.samplingrate;
12		shared.signal_power_muW = 10;
13		shared.quadrature = 'amplitude';
14		shared.DAQrange = 0;
15		shared.amplification = 10;
	10		shared.mech_freq = 1;
	11		shared.timevec = (0:shared.sample_no-1)'/shared.samplingrate;
	12		shared.signal_power_muW = 10;
	13		shared.quadrature = 'amplitude';
	14		shared.DAQrange = 0;
	15		shared.amplification = 10;
16	16
17		shared.data = sin(2*pi*shared.mech_freq*shared.timevec);
18		shared.precision = 'double';
	17		shared.data = sin(2*pi*shared.mech_freq*shared.timevec);
	18		shared.precision = 'double';
19	19
20		shared.folder = tempname;
21		assert(~(exist(shared.folder)==7),...
22		[shared.folder, ' exists already.']);
23		mkdir(shared.folder)
24		shared.file = fullfile(shared.folder, '.x.dat');
	20		shared.folder = tempname;
	21		assert(~(exist(shared.folder)==7),...
	22		[shared.folder, ' exists already.']);
	23		mkdir(shared.folder)
	24		shared.file = fullfile(shared.folder, '.x.dat');
25	25
26		fid = fopen(shared.file,'w');
27		fwrite(fid, shared.data, shared.precision);
28		fclose(fid);
	26		fid = fopen(shared.file,'w');
	27		fwrite(fid, shared.data, shared.precision);
	28		fclose(fid);
29	29
30	30	function teardown(shared)	function teardown(shared)
31		delete(shared.file);
32		rmdir(shared.folder);
	31		delete(shared.file);
	32		rmdir(shared.folder);
33	33
34	34	function ht = ht_setup_test(shared)	function ht = ht_setup_test(shared)
35		ht = homotrace(shared.file,...
36		'samplingrate', shared.samplingrate,...
37		'signal_power_muW', shared.signal_power_muW,...
38		'quadrature', shared.quadrature,...
39		'precision', shared.precision,...
40		'DAQrange', shared.DAQrange,...
41		'amplification', shared.amplification);
	35		ht = homotrace(shared.file,...
	36		'samplingrate', shared.samplingrate,...
	37		'signal_power_muW', shared.signal_power_muW,...
	38		'quadrature', shared.quadrature,...
	39		'precision', shared.precision,...
	40		'DAQrange', shared.DAQrange,...
	41		'amplification', shared.amplification);
42	42
43	43	function ht = read_data_test(shared)	function ht = read_data_test(shared)
44		ht = ht_setup_test(shared);
45		ht.fopen();
46		assertEqual(ht.fread(shared.sample_no), shared.data/shared.amplification);
47		ht.fclose();
	44		ht = ht_setup_test(shared);
	45		ht.fopen();
	46		assertEqual(ht.fread(shared.sample_no), shared.data/shared.amplification);
	47		ht.fclose();
48	48
49	49	function ht = properties_test(shared)	function ht = properties_test(shared)
50		ht = ht_setup_test(shared);
51		assertEqual(ht.samplingrate, shared.samplingrate)
52		assertEqual(ht.sample_no, shared.sample_no)
53		assertEqual(ht.precision, shared.precision)
54
55		assertEqual(ht.quadrature, shared.quadrature)
56		assertEqual(ht.signal_power_muW, shared.signal_power_muW)
57		assertEqual(ht.dataset_label,...
58		[shared.quadrature, ' quad., ',...
59		num2str(shared.signal_power_muW),'muW signal.'])
60
61		assertEqual(ht.DAQrange, shared.DAQrange)
62		assertEqual(ht.amplification, shared.amplification)
	50		ht = ht_setup_test(shared);
	51		assertEqual(ht.samplingrate, shared.samplingrate)
	52		assertEqual(ht.sample_no, shared.sample_no)
	53		assertEqual(ht.precision, shared.precision)
	54
	55		assertEqual(ht.quadrature, shared.quadrature)
	56		assertEqual(ht.signal_power_muW, shared.signal_power_muW)
	57		assertEqual(ht.dataset_label,...
	58		[shared.quadrature, ' quad., ',...
	59		num2str(shared.signal_power_muW),'muW signal.'])
	60
	61		assertEqual(ht.DAQrange, shared.DAQrange)
	62		assertEqual(ht.amplification, shared.amplification)

File data_analysis/unittests/meta_ev_test.m changed (mode: 100644) (index b04401b..3861a83)
1	1	function test_suite = meta_ev_test %#ok<STOUT>	function test_suite = meta_ev_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function shared = setup	function shared = setup
7		% shared.evaluation_folder = tempname;
8		% assert(~(exist(shared.evaluation_folder)==7),...
9		% [shared.evaluation_folder, ' exists already.']);
10		% mkdir(shared.evaluation_folder)
11		test_dir = fileparts(mfilename('fullpath'));
12		get_meta_data_file = @(ind)fullfile(test_dir, 'test_meta_ev/',['metadata', num2str(ind),'.tsv.test']);
13		shared.meta_data_files = arrayfun(get_meta_data_file, 1:5, 'UniformOutput', false);
	7		% shared.evaluation_folder = tempname;
	8		% assert(~(exist(shared.evaluation_folder)==7),...
	9		% [shared.evaluation_folder, ' exists already.']);
	10		% mkdir(shared.evaluation_folder)
	11		test_dir = fileparts(mfilename('fullpath'));
	12		get_meta_data_file = @(ind)fullfile(test_dir, 'test_meta_ev/',['metadata', num2str(ind),'.tsv.test']);
	13		shared.meta_data_files = arrayfun(get_meta_data_file, 1:5, 'UniformOutput', false);
14	14
15	15	function teardown(shared)	function teardown(shared)
16		% rmdir(shared.evaluation_folder, 's');
17		clear('shared');
	16		% rmdir(shared.evaluation_folder, 's');
	17		clear('shared');
18	18
19	19	function get_meta_data1_test(shared)	function get_meta_data1_test(shared)
20		meta = meta_ev();
21		meta.get_meta_data(shared.meta_data_files{1});
	20		meta = meta_ev();
	21		meta.get_meta_data(shared.meta_data_files{1});
22	22
23	23	function get_meta_data2_test(shared)	function get_meta_data2_test(shared)
24		meta = meta_ev();
25		meta.get_meta_data(shared.meta_data_files{2});
	24		meta = meta_ev();
	25		meta.get_meta_data(shared.meta_data_files{2});
26	26
27	27	function get_meta_data3_test(shared)	function get_meta_data3_test(shared)
28		meta = meta_ev();
29		meta.get_meta_data(shared.meta_data_files{3});
	28		meta = meta_ev();
	29		meta.get_meta_data(shared.meta_data_files{3});
30	30
31	31	function get_meta_data4_test(shared)	function get_meta_data4_test(shared)
32		meta = meta_ev();
33		meta.get_meta_data(shared.meta_data_files{4});
	32		meta = meta_ev();
	33		meta.get_meta_data(shared.meta_data_files{4});
34	34
35	35	function get_meta_data5_test(shared)	function get_meta_data5_test(shared)
36		meta = meta_ev();
37		load_error_test = @() meta.get_meta_data(shared.meta_data_files{5});
38		assertExceptionThrown(load_error_test, 'meta_ev:get_meta_data:empty_params')
	36		meta = meta_ev();
	37		load_error_test = @() meta.get_meta_data(shared.meta_data_files{5});
	38		assertExceptionThrown(load_error_test, 'meta_ev:get_meta_data:empty_params')
39	39
40	40	function load_meta1_test(shared)	function load_meta1_test(shared)
41		meta = meta_ev(shared.meta_data_files{1});
	41		meta = meta_ev(shared.meta_data_files{1});
42	42
43	43	function load_meta2_test(shared)	function load_meta2_test(shared)
44		meta = meta_ev(shared.meta_data_files{2});
	44		meta = meta_ev(shared.meta_data_files{2});
45	45
46	46	function load_meta3_test(shared)	function load_meta3_test(shared)
47		unsupported_dims_test = @()meta_ev(shared.meta_data_files{3});
48		assertExceptionThrown(unsupported_dims_test, 'meta_ev:gridtype:unsupported')
	47		unsupported_dims_test = @()meta_ev(shared.meta_data_files{3});
	48		assertExceptionThrown(unsupported_dims_test, 'meta_ev:gridtype:unsupported')
49	49
50	50	function load_meta4_test(shared)	function load_meta4_test(shared)
51		meta = meta_ev(shared.meta_data_files{4});
	51		meta = meta_ev(shared.meta_data_files{4});
52	52
53	53	function load_meta5_test(shared)	function load_meta5_test(shared)
54		load_error_test = @() meta_ev(shared.meta_data_files{5});
55		assertExceptionThrown(load_error_test, 'meta_ev:get_meta_data:empty_params')
	54		load_error_test = @() meta_ev(shared.meta_data_files{5});
	55		assertExceptionThrown(load_error_test, 'meta_ev:get_meta_data:empty_params')
56	56
57	57	function cut_plot_test(shared)	function cut_plot_test(shared)
58		fig = figure; set(fig,'visible','off')
59		meta = meta_ev(shared.meta_data_files{1});
60		meta.meta_label = 'test';
61		param_ind = 2;
62		% meta.plot_cuts('logneg', 2, param_ind, unique(meta.params{param_ind}));
63		[T] = evalc(...
64		'meta.plot_cuts(''logneg'',2, param_ind, [2.5,10,50], ''x_scale'',''log'', ''fig'', fig);');
65		[T] = evalc('meta.save_figs;');
66		% meta.plot_cuts('logneg', 2, param_ind, 'all');
67		% try
68		% system(['ls ', meta.common_path]);
69		% end
70		% try
71		% system(['mupdf ' fullfile(meta.common_path,'lnopt_cut__test.pdf')]);
72		% end
	58		fig = figure; set(fig,'visible','off')
	59		meta = meta_ev(shared.meta_data_files{1});
	60		meta.meta_label = 'test';
	61		param_ind = 2;
	62		% meta.plot_cuts('logneg', 2, param_ind, unique(meta.params{param_ind}));
	63		[T] = evalc(...
	64		'meta.plot_cuts(''logneg'',2, param_ind, [2.5,10,50], ''x_scale'',''log'', ''fig'', fig);');
	65		[T] = evalc('meta.save_figs;');
	66		% meta.plot_cuts('logneg', 2, param_ind, 'all');
	67		% try
	68		% system(['ls ', meta.common_path]);
	69		% end
	70		% try
	71		% system(['mupdf ' fullfile(meta.common_path,'lnopt_cut__test.pdf')]);
	72		% end
73	73
74	74	function contour_plot_test(shared)	function contour_plot_test(shared)
75		fig = figure; set(fig,'visible','off')
76		meta = meta_ev(shared.meta_data_files{1});
77		meta.meta_label = 'test';
78		[T] = evalc(...
79		'meta.contour_plot(''logneg'',2, ''x_scale'',''log'', ''fig'', fig);');
80		[T] = evalc('meta.save_figs;');
81		% try
82		% system(['ls ', meta.common_path]);
83		% end
84		% try
85		% system(['mupdf ', fullfile(meta.common_path, 'lnopt__test.pdf')]);
86		% end
	75		fig = figure; set(fig,'visible','off')
	76		meta = meta_ev(shared.meta_data_files{1});
	77		meta.meta_label = 'test';
	78		[T] = evalc(...
	79		'meta.contour_plot(''logneg'',2, ''x_scale'',''log'', ''fig'', fig);');
	80		[T] = evalc('meta.save_figs;');
	81		% try
	82		% system(['ls ', meta.common_path]);
	83		% end
	84		% try
	85		% system(['mupdf ', fullfile(meta.common_path, 'lnopt__test.pdf')]);
	86		% end
87	87
88	88	function plot_logneg_versus_gamma_test(shared)	function plot_logneg_versus_gamma_test(shared)
89		fig = figure; set(fig,'visible','off')
90		meta = meta_ev(shared.meta_data_files{1});
91		meta.meta_label = 'test';
92		meta = meta.filter_params(1, [5E5,5E7]);
93		cmap = [cbrewer('seq','RdPu',9);...
94		cbrewer('seq','GnBu',9)];
95		[T] = evalc(...
96		'meta.plot_logneg_versus_gamma(2, ''colors'', cmap, ''x_scale'',''log'',''y_lims'', [-1,0.5],''fig'', fig);');
97		[T] = evalc('meta.save_figs;');
98		% try
99		% system(['ls ', meta.common_path]);
100		% end
101		% try
102		% system(['mupdf ', fullfile(meta.common_path, 'ln_versus_gamma__test.pdf')]);
103		% end
	89		fig = figure; set(fig,'visible','off')
	90		meta = meta_ev(shared.meta_data_files{1});
	91		meta.meta_label = 'test';
	92		meta = meta.filter_params(1, [5E5,5E7]);
	93		cmap = [cbrewer('seq','RdPu',9);...
	94		cbrewer('seq','GnBu',9)];
	95		[T] = evalc(...
	96		'meta.plot_logneg_versus_gamma(2, ''colors'', cmap, ''x_scale'',''log'',''y_lims'', [-1,0.5],''fig'', fig);');
	97		[T] = evalc('meta.save_figs;');
	98		% try
	99		% system(['ls ', meta.common_path]);
	100		% end
	101		% try
	102		% system(['mupdf ', fullfile(meta.common_path, 'ln_versus_gamma__test.pdf')]);
	103		% end
104	104
105	105
106	106	function filter_params_test(shared)	function filter_params_test(shared)
107		meta = meta_ev(shared.meta_data_files{1});
108		meta.load_models;
109
110		%% if only one parameter is selected expect ...
111		param_ind = 1;
112		target = meta.params{param_ind}(1,1);
113		meta_filtered = meta.filter_params(param_ind, target);
114		% ... new dimensions 1xN
115		assertEqual(meta_filtered.dimensions(2),1,...
116		'expected single entry for parameter 1')
117		assertEqual(meta_filtered.dimensions(1),meta.dimensions(1),...
118		'expected unchanged dimensions of parameter 2')
119		% ... one-dim. evaluation
120		assertEqual(meta_filtered.gridtype, 'vector',...
121		'expected gridtype "vector"')
122		% ... only selected param survives
123		assertEqual(unique(meta_filtered.params{1}), target,...
124		'expected only value of parameter 1 equal to input')
125		% ... along other dimension all params survive
126		assertEqual(unique(meta_filtered.params{2}),...
127		unique(meta.params{2}),...
128		'expected values of parameter 2 to remain unchanged')
129		% ... old evaluation to remain unchanged
130		meta2 = meta_ev(shared.meta_data_files{1});
131		meta2.load_models;
132		assertEqual(meta, meta2);
133		% assert(isequal(meta, meta2))
134		meta2.ev_files{1,1} = 'somechangedpath';
135		assert(~isequal(meta, meta2));
136
137		%% if only one parameter is selected INEXACTLY expect ...
138		param_ind = 1;
139		target = meta.params{param_ind}(1,1) + 1;
140		meta_filtered = meta.filter_params(param_ind, target);
141		% ... new dimensions 1xN
142		assertEqual(meta_filtered.dimensions(2),1,...
143		'expected single entry for parameter 1')
144		assertEqual(meta_filtered.dimensions(1),meta.dimensions(1),...
145		'expected unchanged dimensions of parameter 2')
146		% ... one-dim. evaluation
147		assertEqual(meta_filtered.gridtype, 'vector',...
148		'expected gridtype "vector"')
149		% ... only selected param survives
150		assertEqual(unique(meta_filtered.params{1}), target -1,...
151		'expected only value of parameter 1 almost equal to input')
152		% ... along other dimension all params survive
153		assertEqual(unique(meta_filtered.params{2}),...
154		unique(meta.params{2}),...
155		'expected values of parameter 2 to remain unchanged')
156		% ... old evaluation to remain unchanged
157		meta2 = meta_ev(shared.meta_data_files{1});
158		meta2.load_models;
159		assertEqual(meta, meta2);
160		% assert(isequal(meta, meta2))
161		meta2.ev_files{1,1} = 'somechangedpath';
162		assert(~isequal(meta, meta2));
163
164		%% if two parameter are selected INEXACTLY expect ...
165		param_ind = 1;
166		target = meta.params{param_ind}(1,[1,4]) + 1;
167		meta_filtered = meta.filter_params(param_ind, target);
168		% ... new dimensions 2xN
169		assertEqual(meta_filtered.dimensions(2),2,...
170		'expected two entries for parameter 1')
171		assertEqual(meta_filtered.dimensions(1),meta.dimensions(1),...
172		'expected unchanged dimensions of parameter 2')
173		% ... one-dim. evaluation
174		assertEqual(meta_filtered.gridtype, 'matrix',...
175		'expected gridtype "matrix"')
176		% ... only selected param survives
177		assertEqual(unique(meta_filtered.params{1})', target -1,...
178		'expected two values of parameter 1 almost equal to input')
179		% ... along other dimension all params survive
180		assertEqual(unique(meta_filtered.params{2}),...
181		unique(meta.params{2}),...
182		'expected values of parameter 2 to remain unchanged')
183		% ... old evaluation to remain unchanged
184		meta2 = meta_ev(shared.meta_data_files{1});
185		meta2.load_models;
186		assertEqual(meta, meta2);
187		% assert(isequal(meta, meta2))
188		meta2.ev_files{1,1} = 'somechangedpath';
189		assert(~isequal(meta, meta2));
190
191		%% if two parameters of param2 are selected INEXACTLY expect ...
192		param_ind = 2;
193		target = meta.params{param_ind}([1,4],1) + 0.01;
194		meta_filtered = meta.filter_params(param_ind, target);
195		% ... new dimensions 2xN
196		assertEqual(meta_filtered.dimensions(1),2,...
197		'expected two entries for parameter 2')
198		assertEqual(meta_filtered.dimensions(2),meta.dimensions(2),...
199		'expected unchanged dimensions of parameter 1')
200		% ... one-dim. evaluation
201		assertEqual(meta_filtered.gridtype, 'matrix',...
202		'expected gridtype "matrix"')
203		% ... only selected param survives
204		assertEqual(unique(meta_filtered.params{2}), target - 0.01,...
205		'expected two values of parameter 2 almost equal to input')
206		% ... along other dimension all params survive
207		assertEqual(unique(meta_filtered.params{1}),...
208		unique(meta.params{1}),...
209		'expected values of parameter 1 to remain unchanged')
210		% ... old evaluation to remain unchanged
211		meta2 = meta_ev(shared.meta_data_files{1});
212		meta2.load_models;
213		assertEqual(meta, meta2);
214		% assert(isequal(meta, meta2))
215		meta2.ev_files{ 1 ,1}='somechangedpath';
216		assert(~isequal(meta, meta2));
217
218		%% REPEATED FILTERING
219		%% if two parameters of param2 and 3 params of param1are selected INEXACTLY expect ...
220		param_ind = 2;
221		target2 = meta.params{param_ind}([1,4],1) + 0.01;
222		meta_filtered = meta.filter_params(param_ind, target2);
223		param_ind = 1;
224		target1 = meta_filtered.params{param_ind}(1,[1,3,5]) + 0.01;
225		meta_filtered = meta_filtered.filter_params(param_ind, target1);
226		% ... new dimensions 2xN
227		assertEqual(meta_filtered.dimensions(1),2,...
228		'expected two entries for parameter 2')
229		assertEqual(meta_filtered.dimensions(2),3,...
230		'expected three entries for parameter 1')
231		% ... one-dim. evaluation
232		assertEqual(meta_filtered.gridtype, 'matrix',...
233		'expected gridtype "matrix"')
234		% ... only selected params survive for param2
235		assertEqual(unique(meta_filtered.params{2}), target2 - 0.01,...
236		'expected two values of parameter 2 almost equal to input')
237		% ... only selected params survive for param1
238		assertEqual(unique(meta_filtered.params{1})', target1 - 0.01,...
239		'expected three values of parameter 3 almost equal to input')
240
241		% ... old evaluation to remain unchanged
242		meta2 = meta_ev(shared.meta_data_files{1});
243		meta2.load_models;
244		assertEqual(meta, meta2);
245		% assert(isequal(meta, meta2))
246		meta2.ev_files{ 1 ,1}='somechangedpath';
247		assert(~isequal(meta, meta2));
	107		meta = meta_ev(shared.meta_data_files{1});
	108		meta.load_models;
	109
	110		%% if only one parameter is selected expect ...
	111		param_ind = 1;
	112		target = meta.params{param_ind}(1,1);
	113		meta_filtered = meta.filter_params(param_ind, target);
	114		% ... new dimensions 1xN
	115		assertEqual(meta_filtered.dimensions(2),1,...
	116		'expected single entry for parameter 1')
	117		assertEqual(meta_filtered.dimensions(1),meta.dimensions(1),...
	118		'expected unchanged dimensions of parameter 2')
	119		% ... one-dim. evaluation
	120		assertEqual(meta_filtered.gridtype, 'vector',...
	121		'expected gridtype "vector"')
	122		% ... only selected param survives
	123		assertEqual(unique(meta_filtered.params{1}), target,...
	124		'expected only value of parameter 1 equal to input')
	125		% ... along other dimension all params survive
	126		assertEqual(unique(meta_filtered.params{2}),...
	127		unique(meta.params{2}),...
	128		'expected values of parameter 2 to remain unchanged')
	129		% ... old evaluation to remain unchanged
	130		meta2 = meta_ev(shared.meta_data_files{1});
	131		meta2.load_models;
	132		assertEqual(meta, meta2);
	133		% assert(isequal(meta, meta2))
	134		meta2.ev_files{1,1} = 'somechangedpath';
	135		assert(~isequal(meta, meta2));
	136
	137		%% if only one parameter is selected INEXACTLY expect ...
	138		param_ind = 1;
	139		target = meta.params{param_ind}(1,1) + 1;
	140		meta_filtered = meta.filter_params(param_ind, target);
	141		% ... new dimensions 1xN
	142		assertEqual(meta_filtered.dimensions(2),1,...
	143		'expected single entry for parameter 1')
	144		assertEqual(meta_filtered.dimensions(1),meta.dimensions(1),...
	145		'expected unchanged dimensions of parameter 2')
	146		% ... one-dim. evaluation
	147		assertEqual(meta_filtered.gridtype, 'vector',...
	148		'expected gridtype "vector"')
	149		% ... only selected param survives
	150		assertEqual(unique(meta_filtered.params{1}), target -1,...
	151		'expected only value of parameter 1 almost equal to input')
	152		% ... along other dimension all params survive
	153		assertEqual(unique(meta_filtered.params{2}),...
	154		unique(meta.params{2}),...
	155		'expected values of parameter 2 to remain unchanged')
	156		% ... old evaluation to remain unchanged
	157		meta2 = meta_ev(shared.meta_data_files{1});
	158		meta2.load_models;
	159		assertEqual(meta, meta2);
	160		% assert(isequal(meta, meta2))
	161		meta2.ev_files{1,1} = 'somechangedpath';
	162		assert(~isequal(meta, meta2));
	163
	164		%% if two parameter are selected INEXACTLY expect ...
	165		param_ind = 1;
	166		target = meta.params{param_ind}(1,[1,4]) + 1;
	167		meta_filtered = meta.filter_params(param_ind, target);
	168		% ... new dimensions 2xN
	169		assertEqual(meta_filtered.dimensions(2),2,...
	170		'expected two entries for parameter 1')
	171		assertEqual(meta_filtered.dimensions(1),meta.dimensions(1),...
	172		'expected unchanged dimensions of parameter 2')
	173		% ... one-dim. evaluation
	174		assertEqual(meta_filtered.gridtype, 'matrix',...
	175		'expected gridtype "matrix"')
	176		% ... only selected param survives
	177		assertEqual(unique(meta_filtered.params{1})', target -1,...
	178		'expected two values of parameter 1 almost equal to input')
	179		% ... along other dimension all params survive
	180		assertEqual(unique(meta_filtered.params{2}),...
	181		unique(meta.params{2}),...
	182		'expected values of parameter 2 to remain unchanged')
	183		% ... old evaluation to remain unchanged
	184		meta2 = meta_ev(shared.meta_data_files{1});
	185		meta2.load_models;
	186		assertEqual(meta, meta2);
	187		% assert(isequal(meta, meta2))
	188		meta2.ev_files{1,1} = 'somechangedpath';
	189		assert(~isequal(meta, meta2));
	190
	191		%% if two parameters of param2 are selected INEXACTLY expect ...
	192		param_ind = 2;
	193		target = meta.params{param_ind}([1,4],1) + 0.01;
	194		meta_filtered = meta.filter_params(param_ind, target);
	195		% ... new dimensions 2xN
	196		assertEqual(meta_filtered.dimensions(1),2,...
	197		'expected two entries for parameter 2')
	198		assertEqual(meta_filtered.dimensions(2),meta.dimensions(2),...
	199		'expected unchanged dimensions of parameter 1')
	200		% ... one-dim. evaluation
	201		assertEqual(meta_filtered.gridtype, 'matrix',...
	202		'expected gridtype "matrix"')
	203		% ... only selected param survives
	204		assertEqual(unique(meta_filtered.params{2}), target - 0.01,...
	205		'expected two values of parameter 2 almost equal to input')
	206		% ... along other dimension all params survive
	207		assertEqual(unique(meta_filtered.params{1}),...
	208		unique(meta.params{1}),...
	209		'expected values of parameter 1 to remain unchanged')
	210		% ... old evaluation to remain unchanged
	211		meta2 = meta_ev(shared.meta_data_files{1});
	212		meta2.load_models;
	213		assertEqual(meta, meta2);
	214		% assert(isequal(meta, meta2))
	215		meta2.ev_files{ 1 ,1}='somechangedpath';
	216		assert(~isequal(meta, meta2));
	217
	218		%% REPEATED FILTERING
	219		%% if two parameters of param2 and 3 params of param1are selected INEXACTLY expect ...
	220		param_ind = 2;
	221		target2 = meta.params{param_ind}([1,4],1) + 0.01;
	222		meta_filtered = meta.filter_params(param_ind, target2);
	223		param_ind = 1;
	224		target1 = meta_filtered.params{param_ind}(1,[1,3,5]) + 0.01;
	225		meta_filtered = meta_filtered.filter_params(param_ind, target1);
	226		% ... new dimensions 2xN
	227		assertEqual(meta_filtered.dimensions(1),2,...
	228		'expected two entries for parameter 2')
	229		assertEqual(meta_filtered.dimensions(2),3,...
	230		'expected three entries for parameter 1')
	231		% ... one-dim. evaluation
	232		assertEqual(meta_filtered.gridtype, 'matrix',...
	233		'expected gridtype "matrix"')
	234		% ... only selected params survive for param2
	235		assertEqual(unique(meta_filtered.params{2}), target2 - 0.01,...
	236		'expected two values of parameter 2 almost equal to input')
	237		% ... only selected params survive for param1
	238		assertEqual(unique(meta_filtered.params{1})', target1 - 0.01,...
	239		'expected three values of parameter 3 almost equal to input')
	240
	241		% ... old evaluation to remain unchanged
	242		meta2 = meta_ev(shared.meta_data_files{1});
	243		meta2.load_models;
	244		assertEqual(meta, meta2);
	245		% assert(isequal(meta, meta2))
	246		meta2.ev_files{ 1 ,1}='somechangedpath';
	247		assert(~isequal(meta, meta2));

File data_analysis/unittests/old/gausseof_test.m changed (mode: 100644) (index f396afd..cf79a37)
...	...	for r = 0:0.01:1
11	11
12	12	% teststate:	% teststate:
13	13	symtmgs = tmss(r)+n*eye(4); %thermal two-mode squeezed state	symtmgs = tmss(r)+n*eye(4); %thermal two-mode squeezed state
14
	14
15	15	%%% 'brute force' (source: 10.1103/PhysRevLett.101.220403)...	%%% 'brute force' (source: 10.1103/PhysRevLett.101.220403)...
16	16	sfparams = gausssfparams(symtmgs);	sfparams = gausssfparams(symtmgs);
17	17	b = sfparams(1);	b = sfparams(1);
...	...	for r = 0:0.01:1
20	20
21	21	sympeval = sqrt((b - c)*(b-abs(d)));	sympeval = sqrt((b - c)*(b-abs(d)));
22	22	xm = (sympeval^2 + 1/4)/(2*sympeval);	xm = (sympeval^2 + 1/4)/(2*sympeval);
23
	23
24	24	rarr(i,j)=r;	rarr(i,j)=r;
25	25	narr(i,j)=n;	narr(i,j)=n;
26	26
27	27	eof_test(i,j) = (xm+1/2)*log(xm+1/2) - (xm-1/2)*log(xm-1/2);	eof_test(i,j) = (xm+1/2)*log(xm+1/2) - (xm-1/2)*log(xm-1/2);
28
	28
29	29	%%% ... vs the general method implemented by gausseof.m (source: 10.1103/PhysRevLett.101.220403)	%%% ... vs the general method implemented by gausseof.m (source: 10.1103/PhysRevLett.101.220403)
30	30	eof(i,j) = gausseof(symtmgs);	eof(i,j) = gausseof(symtmgs);
31	31	end	end

File data_analysis/unittests/old/gausssfparams_test.m changed (mode: 100644) (index 059ec67..6a1f4ce)
...	...	testcm{4} = testcm{3} + 1/2*(noise + noise');
10	10
11	11	%% test the standard forms	%% test the standard forms
12	12	for i=1:4	for i=1:4
13		disp('***********************************************************');
	13		disp('***********************************************************');
14	14	disp([10,'Testing CM number ', num2str(i)]);	disp([10,'Testing CM number ', num2str(i)]);
15	15	disp([10,'Standard form 1 ']);	disp([10,'Standard form 1 ']);
16	16	testsfparams = gausssfparams(testcm{i})	testsfparams = gausssfparams(testcm{i})
17	17	gausssfcm(testsfparams)-testcm{i}	gausssfcm(testsfparams)-testcm{i}
18	18	disp([10,'Standard form 1 ']);	disp([10,'Standard form 1 ']);
19		testsfparams2 = gausssfparams2(testcm{i})
	19		testsfparams2 = gausssfparams2(testcm{i})
20	20	gausssfcm2(testsfparams2)-testcm{i}	gausssfcm2(testsfparams2)-testcm{i}
21	21	end	end

File data_analysis/unittests/other/estimated_time_of_arrival_test.m changed (mode: 100644) (index cd3e23f..f9307ae)
1	1	function test_suite = estimated_time_of_arrival_test%#ok<STOUT>	function test_suite = estimated_time_of_arrival_test%#ok<STOUT>
2		clc;
3		initTestSuite;
	2		clc;
	3		initTestSuite;
4	4
5	5	function shared = setup	function shared = setup
6		shared.pause_time = 0.2;
7		shared.print_output = false;
	6		shared.pause_time = 0.2;
	7		shared.print_output = false;
8	8
9	9	function teardown(shared)	function teardown(shared)
10		clear shared
	10		clear shared
11	11
12	12	function estimated_time_of_arrival_minutes_test(shared)	function estimated_time_of_arrival_minutes_test(shared)
13		print_eta = estimated_time_of_arrival;
14		pause(shared.pause_time);
15
16		%minutes, output expected
17		iteration = 10; iteration_no = 60*1e3;
18		expected_duration_mins = shared.pause_time*(iteration_no/iteration)/60;
19		output = evalc('print_eta(iteration, iteration_no)');
20		assertEqual(...
21		floor(find_time_number(output, '[minutes]: ')),...
22		expected_duration_mins)
23		if shared.print_output
24		disp(expected_duration_mins);
25		disp(output);
26		end
27
28		%seconds, no output expected
29		iteration = 10; iteration_no = 1e3;
30		expected_duration_sec = shared.pause_time*iteration_no/iteration;
31		output = evalc('print_eta(iteration, iteration_no)');
32		assertEqual(output,'');
33		if shared.print_output
34		disp(expected_duration_sec);
35		disp(output);
36		end
37
38		%days, output expected
39		iteration = 10; iteration_no = 60*60*24*1e3;
40		expected_duration_days = shared.pause_time*(iteration_no/iteration)/(60*60*24);
41		output = evalc('print_eta(iteration, iteration_no)');
42		assertEqual(...
43		floor(find_time_number(output, '[days]: ')),...
44		expected_duration_days);
45		if shared.print_output
46		disp(expected_duration_days);
47		disp(output);
48		end
49
	13		print_eta = estimated_time_of_arrival;
	14		pause(shared.pause_time);
	15
	16		%minutes, output expected
	17		iteration = 10; iteration_no = 60*1e3;
	18		expected_duration_mins = shared.pause_time*(iteration_no/iteration)/60;
	19		output = evalc('print_eta(iteration, iteration_no)');
	20		assertEqual(...
	21		floor(find_time_number(output, '[minutes]: ')),...
	22		expected_duration_mins)
	23		if shared.print_output
	24		disp(expected_duration_mins);
	25		disp(output);
	26		end
	27
	28		%seconds, no output expected
	29		iteration = 10; iteration_no = 1e3;
	30		expected_duration_sec = shared.pause_time*iteration_no/iteration;
	31		output = evalc('print_eta(iteration, iteration_no)');
	32		assertEqual(output,'');
	33		if shared.print_output
	34		disp(expected_duration_sec);
	35		disp(output);
	36		end
	37
	38		%days, output expected
	39		iteration = 10; iteration_no = 60*60*24*1e3;
	40		expected_duration_days = shared.pause_time*(iteration_no/iteration)/(60*60*24);
	41		output = evalc('print_eta(iteration, iteration_no)');
	42		assertEqual(...
	43		floor(find_time_number(output, '[days]: ')),...
	44		expected_duration_days);
	45		if shared.print_output
	46		disp(expected_duration_days);
	47		disp(output);
	48		end
50	49
51		function time_num = find_time_number(string, pattern)
52		startind = strfind(string, pattern) + length(pattern);
53		time_num = str2num(string(startind:startind+3));
54	50
	51		function time_num = find_time_number(string, pattern)
	52		startind = strfind(string, pattern) + length(pattern);
	53		time_num = str2num(string(startind:startind+3));

File data_analysis/unittests/permute_data_test.m changed (mode: 100644) (index e50ef92..0ef4b81)
1	1	function test_suite = permute_data_test %#ok<STOUT>	function test_suite = permute_data_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function shared = setup	function shared = setup
7		sample_no = 2;
8		mode_no = 3;
9		qdata = repmat([1:mode_no], sample_no, 1);
10		pdata = repmat([1:mode_no]*10, sample_no, 1);
11		xdata = repmat([1:mode_no]*100, sample_no, 1);
12		ydata = repmat([1:mode_no]*1000, sample_no, 1);
13
14		shared.data = [qdata, pdata, xdata, ydata];
15		shared.data_ordered = [ qdata(:,1), pdata(:,1),...
16		qdata(:,2), pdata(:,2),...
17		qdata(:,3), pdata(:,3),...
18		xdata(:,1), ydata(:,1),...
19		xdata(:,2), ydata(:,2),...
20		xdata(:,3), ydata(:,3) ];
21
22		shared.data_singlemode = repmat([1,2,3,4], sample_no, 1);
	7		sample_no = 2;
	8		mode_no = 3;
	9		qdata = repmat([1:mode_no], sample_no, 1);
	10		pdata = repmat([1:mode_no]*10, sample_no, 1);
	11		xdata = repmat([1:mode_no]*100, sample_no, 1);
	12		ydata = repmat([1:mode_no]*1000, sample_no, 1);
	13
	14		shared.data = [qdata, pdata, xdata, ydata];
	15		shared.data_ordered = [ qdata(:,1), pdata(:,1),...
	16		qdata(:,2), pdata(:,2),...
	17		qdata(:,3), pdata(:,3),...
	18		xdata(:,1), ydata(:,1),...
	19		xdata(:,2), ydata(:,2),...
	20		xdata(:,3), ydata(:,3) ];
	21
	22		shared.data_singlemode = repmat([1,2,3,4], sample_no, 1);
23	23
24	24	function teardown(shared)	function teardown(shared)
25	25
26	26	function permute_data_labels_test(shared)	function permute_data_labels_test(shared)
27		labels_before = {'q1', 'q2', 'p1', 'p2', 'x1', 'x2', 'y1', 'y2'};
28		labels_after = {'q1', 'p1', 'q2', 'p2', 'x1', 'y1', 'x2', 'y2'};
29		assertEqual(labels_after, permute_data(labels_before));
	27		labels_before = {'q1', 'q2', 'p1', 'p2', 'x1', 'x2', 'y1', 'y2'};
	28		labels_after = {'q1', 'p1', 'q2', 'p2', 'x1', 'y1', 'x2', 'y2'};
	29		assertEqual(labels_after, permute_data(labels_before));
30	30
31	31	function permute_data_2modes_test(shared)	function permute_data_2modes_test(shared)
32		%q->1, p->2, x->3, y->4; hence: q1->11, q2->12,...
33		%[q1, q2, p1, p2, x1, x2, y1, y2]
34		modes_before = [11, 12, 21, 22, 31, 32, 41, 42];
35		%[q1, p1, q2, p2, x1, y1, x2, y2]
36		modes_after = [11, 21, 12, 22, 31, 41, 32, 42];
37		assertEqual(modes_after, permute_data(modes_before));
	32		%q->1, p->2, x->3, y->4; hence: q1->11, q2->12,...
	33		%[q1, q2, p1, p2, x1, x2, y1, y2]
	34		modes_before = [11, 12, 21, 22, 31, 32, 41, 42];
	35		%[q1, p1, q2, p2, x1, y1, x2, y2]
	36		modes_after = [11, 21, 12, 22, 31, 41, 32, 42];
	37		assertEqual(modes_after, permute_data(modes_before));
38	38
39	39	function permute_data_3modes_test(shared)	function permute_data_3modes_test(shared)
40		data_ordered = permute_data(shared.data);
41		assertEqual(data_ordered, shared.data_ordered);
	40		data_ordered = permute_data(shared.data);
	41		assertEqual(data_ordered, shared.data_ordered);
42	42
43	43	function permute_data_twice_test(shared)	function permute_data_twice_test(shared)
44		data = permute_data(...
45		permute_data(shared.data),...
46		'inverse', true);
47		assertEqual(data, shared.data);
	44		data = permute_data(...
	45		permute_data(shared.data),...
	46		'inverse', true);
	47		assertEqual(data, shared.data);
48	48
49	49	function permute_data_twice_random_test(shared)	function permute_data_twice_random_test(shared)
50		randdata = randn(48,48);
51		data = permute_data(...
52		permute_data(randdata),...
53		'inverse', true);
54		assertEqual(data, randdata);
	50		randdata = randn(48,48);
	51		data = permute_data(...
	52		permute_data(randdata),...
	53		'inverse', true);
	54		assertEqual(data, randdata);
55	55
56	56	function permute_data_1mode_test(shared)	function permute_data_1mode_test(shared)
57		invariant_data = shared.data_singlemode(1,:);
58		assertEqual(permute_data(invariant_data), invariant_data);
	57		invariant_data = shared.data_singlemode(1,:);
	58		assertEqual(permute_data(invariant_data), invariant_data);
59	59
60		invariant_data = shared.data_singlemode;
61		assertEqual(permute_data(invariant_data), invariant_data);
	60		invariant_data = shared.data_singlemode;
	61		assertEqual(permute_data(invariant_data), invariant_data);

File data_analysis/unittests/pseudo_hash_test.m changed (mode: 100644) (index de5e52f..06608d3)
1	1	function test_suite = pseudo_hash_test %#ok<STOUT>	function test_suite = pseudo_hash_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	5
6	6	function shared = setup	function shared = setup
7		shared.sample_no = 1e3;
8		shared.data = randn(shared.sample_no, 1);
9		shared.precision = 'double';
10		shared.DAQrange = 0;
11		shared.amplification = 1;
12
13		shared.bytes_for_test = 1e2;
14		shared.shafunction = @(filename) pseudo_hash(filename, 'bytes', shared.bytes_for_test);
15
16		shared.folder = tempname;
17		assert(~(exist(shared.folder)==7),...
18		[shared.folder, ' exists already.']);
19		mkdir(shared.folder)
20
21		shared.file = fullfile(shared.folder, '.test.dat');
22		assert_file_nonexistent(shared.file);
23		fid = fopen(shared.file,'w+');
24		fwrite(fid, shared.data, shared.precision);
25		fclose(fid);
26
27		shared.copy.file = fullfile(shared.folder, '.test_copy.dat');
28		assert_file_nonexistent(shared.copy.file);
29		copyfile(shared.file, shared.copy.file);
30
31		shared.empty.file = fullfile(shared.folder, '.test_empty.dat');
32		assert_file_nonexistent(shared.empty.file);
33
34		exist_not_empty = @(folder)(exist(folder)==7)&&(~isempty(ls(folder)));
35		if exist_not_empty('/media/schatzkammer/RAID1')
36		shared.ext.folder = '/media/schatzkammer/RAID1';
37		elseif exist_not_empty('~/Downloads')
38		shared.ext.folder = '~/Downloads';
39		else
40		error(['No suitable folder for tests involving copying files found.',...
41		'Modify test accordingly!']);
42		end
43		shared.ext.file = fullfile(shared.ext.folder, '.test_copy.dat');
44		assert_file_nonexistent(shared.ext.file);
45		copyfile(shared.file, shared.ext.file);
46
47		shared.minifile1 = fullfile(shared.folder, '.minitest.dat');
48		shared.minifile2 = fullfile(shared.folder, '.minitest2.dat');
49		assert_file_nonexistent(shared.minifile1);
50		assert_file_nonexistent(shared.minifile2);
51		dlmwrite(shared.minifile1, [1, 2, 3], 'delimiter', '\t');
52		dlmwrite(shared.minifile2, [1, 2, 4], 'delimiter', '\t');
	7		shared.sample_no = 1e3;
	8		shared.data = randn(shared.sample_no, 1);
	9		shared.precision = 'double';
	10		shared.DAQrange = 0;
	11		shared.amplification = 1;
	12
	13		shared.bytes_for_test = 1e2;
	14		shared.shafunction = @(filename) pseudo_hash(filename, 'bytes', shared.bytes_for_test);
	15
	16		shared.folder = tempname;
	17		assert(~(exist(shared.folder)==7),...
	18		[shared.folder, ' exists already.']);
	19		mkdir(shared.folder)
	20
	21		shared.file = fullfile(shared.folder, '.test.dat');
	22		assert_file_nonexistent(shared.file);
	23		fid = fopen(shared.file,'w+');
	24		fwrite(fid, shared.data, shared.precision);
	25		fclose(fid);
	26
	27		shared.copy.file = fullfile(shared.folder, '.test_copy.dat');
	28		assert_file_nonexistent(shared.copy.file);
	29		copyfile(shared.file, shared.copy.file);
	30
	31		shared.empty.file = fullfile(shared.folder, '.test_empty.dat');
	32		assert_file_nonexistent(shared.empty.file);
	33
	34		exist_not_empty = @(folder)(exist(folder)==7)&&(~isempty(ls(folder)));
	35		if exist_not_empty('/media/schatzkammer/RAID1')
	36		shared.ext.folder = '/media/schatzkammer/RAID1';
	37		elseif exist_not_empty('~/Downloads')
	38		shared.ext.folder = '~/Downloads';
	39		else
	40		error(['No suitable folder for tests involving copying files found.',...
	41		'Modify test accordingly!']);
	42		end
	43		shared.ext.file = fullfile(shared.ext.folder, '.test_copy.dat');
	44		assert_file_nonexistent(shared.ext.file);
	45		copyfile(shared.file, shared.ext.file);
	46
	47		shared.minifile1 = fullfile(shared.folder, '.minitest.dat');
	48		shared.minifile2 = fullfile(shared.folder, '.minitest2.dat');
	49		assert_file_nonexistent(shared.minifile1);
	50		assert_file_nonexistent(shared.minifile2);
	51		dlmwrite(shared.minifile1, [1, 2, 3], 'delimiter', '\t');
	52		dlmwrite(shared.minifile2, [1, 2, 4], 'delimiter', '\t');
53	53
54	54	function teardown(shared)	function teardown(shared)
55		delete(shared.file);
56		delete(shared.copy.file);
57		delete(shared.ext.file);
58		delete(shared.minifile1);
59		delete(shared.minifile2);
60		rmdir(shared.folder);
	55		delete(shared.file);
	56		delete(shared.copy.file);
	57		delete(shared.ext.file);
	58		delete(shared.minifile1);
	59		delete(shared.minifile2);
	60		rmdir(shared.folder);
61	61
62	62	function function_handle_test(shared)	function function_handle_test(shared)
63		assertEqual(shared.shafunction(shared.file),...
64		pseudo_hash(shared.file, 'bytes', shared.bytes_for_test));
	63		assertEqual(shared.shafunction(shared.file),...
	64		pseudo_hash(shared.file, 'bytes', shared.bytes_for_test));
65	65
66	66	function bytes_sensitive_test(shared)	function bytes_sensitive_test(shared)
67		assertFalse(...
68		isequal(...
69		shared.shafunction(shared.file),...
70		pseudo_hash(shared.file, 'bytes', 1+shared.bytes_for_test)));
	67		assertFalse(...
	68		isequal(...
	69		shared.shafunction(shared.file),...
	70		pseudo_hash(shared.file, 'bytes', 1+shared.bytes_for_test)));
71	71
72	72	function default_bytes_test(shared)	function default_bytes_test(shared)
73		assertEqual(pseudo_hash(shared.file),...
74		pseudo_hash(shared.file, 'bytes', 1e6));
	73		assertEqual(pseudo_hash(shared.file),...
	74		pseudo_hash(shared.file, 'bytes', 1e6));
75	75
76	76	function filename_filepath_test(shared)	function filename_filepath_test(shared)
77		assertEqual(shared.shafunction(shared.file),...
78		shared.shafunction(GetFullPath(shared.file)));
	77		assertEqual(shared.shafunction(shared.file),...
	78		shared.shafunction(GetFullPath(shared.file)));
79	79
80	80	function open_close_forread_test(shared)	function open_close_forread_test(shared)
81		before = shared.shafunction(shared.file);
82		fid = fopen(shared.file, 'r');
83		fclose(fid);
84		after = shared.shafunction(shared.file);
85		assertEqual(before, after);
	81		before = shared.shafunction(shared.file);
	82		fid = fopen(shared.file, 'r');
	83		fclose(fid);
	84		after = shared.shafunction(shared.file);
	85		assertEqual(before, after);
86	86
87	87	% function open_close_forwrite_test(shared)	% function open_close_forwrite_test(shared)
88	88	% before = shared.shafunction(shared.file);	% before = shared.shafunction(shared.file);
...	...	function open_close_forread_test(shared)
92	92	% assertEqual(before, after);	% assertEqual(before, after);
93	93
94	94	function copy_test(shared)	function copy_test(shared)
95		assertEqual(shared.shafunction(shared.file),...
96		shared.shafunction(shared.copy.file));
	95		assertEqual(shared.shafunction(shared.file),...
	96		shared.shafunction(shared.copy.file));
97	97
98	98	function modify_begin_test(shared)	function modify_begin_test(shared)
99		fid = fopen(shared.copy.file, 'w');
100		fseek(fid, 10, 'bof');
101		fwrite(fid, randn(1));
102		fclose(fid);
103		assertFalse(...
104		isequal(shared.shafunction(shared.file),...
105		shared.shafunction(shared.copy.file)));
	99		fid = fopen(shared.copy.file, 'w');
	100		fseek(fid, 10, 'bof');
	101		fwrite(fid, randn(1));
	102		fclose(fid);
	103		assertFalse(...
	104		isequal(shared.shafunction(shared.file),...
	105		shared.shafunction(shared.copy.file)));
106	106
107	107	function modify_end_test(shared)	function modify_end_test(shared)
108		assertEqual(shared.shafunction(shared.file),...
109		shared.shafunction(shared.copy.file));
110		fid = fopen(shared.copy.file, 'w');
111		fseek(fid, -10, 'eof');
112		fwrite(fid, randn(1));
113		fclose(fid);
114		assertFalse(...
115		isequal(shared.shafunction(shared.file),...
116		shared.shafunction(shared.copy.file)));
	108		assertEqual(shared.shafunction(shared.file),...
	109		shared.shafunction(shared.copy.file));
	110		fid = fopen(shared.copy.file, 'w');
	111		fseek(fid, -10, 'eof');
	112		fwrite(fid, randn(1));
	113		fclose(fid);
	114		assertFalse(...
	115		isequal(shared.shafunction(shared.file),...
	116		shared.shafunction(shared.copy.file)));
117	117
118	118	function modify_length_test(shared)	function modify_length_test(shared)
119		fid = fopen(shared.empty.file, 'w+');
120		fwrite(fid, shared.data, shared.precision);
121		%write same data twice to get file with same beginning and end, but different length
122		fwrite(fid, shared.data, shared.precision);
123		fclose(fid);
	119		fid = fopen(shared.empty.file, 'w+');
	120		fwrite(fid, shared.data, shared.precision);
	121		%write same data twice to get file with same beginning and end, but different length
	122		fwrite(fid, shared.data, shared.precision);
	123		fclose(fid);
124	124
125		hash_original = shared.shafunction(shared.file);
126		hash_doublesize = shared.shafunction(shared.empty.file);
127		delete(shared.empty.file)
128		assertFalse( isequal( hash_original, hash_doublesize));
	125		hash_original = shared.shafunction(shared.file);
	126		hash_doublesize = shared.shafunction(shared.empty.file);
	127		delete(shared.empty.file)
	128		assertFalse( isequal( hash_original, hash_doublesize));
129	129
130	130	function minifile_test(shared)	function minifile_test(shared)
131		assertFalse(...
132		isequal(...
133		shared.shafunction(shared.minifile1),...
134		shared.shafunction(shared.minifile2)));
	131		assertFalse(...
	132		isequal(...
	133		shared.shafunction(shared.minifile1),...
	134		shared.shafunction(shared.minifile2)));
135	135
136	136	function external_filesystem_test(shared)	function external_filesystem_test(shared)
137		assertEqual(...
138		shared.shafunction(shared.file),...
139		shared.shafunction(shared.ext.file));
	137		assertEqual(...
	138		shared.shafunction(shared.file),...
	139		shared.shafunction(shared.ext.file));
140	140
141	141	%% aux functions	%% aux functions
142	142	function assert_file_nonexistent(filename)	function assert_file_nonexistent(filename)
143		assertFalse(exist(filename)==2,...
144		[GetFullPath(filename), ' already exists!']);
	143		assertFalse(exist(filename)==2,...
	144		[GetFullPath(filename), ' already exists!']);

File data_analysis/unittests/read_permute_write_data_test.m changed (mode: 100644) (index 5322ed6..e4937e5)
1	1	function test_suite = read_permute_write_data_test %#ok<STOUT>	function test_suite = read_permute_write_data_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function shared = setup	function shared = setup
7		shared.folder = tempname;
8		assert(~(exist(shared.folder)==7),...
9		[shared.folder, ' exists already.']);
10		mkdir(shared.folder)
11		shared.file = fullfile(shared.folder, 'read_permute_write_data_test.tsv');
12		assert(~(exist(shared.file)==2),...
13		[shared.file, ' exists already.']);
14
15		sample_no = 2;
16		mode_no = 3;
17
18		shared.precision = 1e4;
19		myround = @(float_num) round(float_num*shared.precision)/shared.precision;
20
21		qdata = myround(randn(sample_no, mode_no));
22		pdata = myround(randn(sample_no, mode_no));
23		xdata = myround(randn(sample_no, mode_no));
24		ydata = myround(randn(sample_no, mode_no));
25
26		shared.data = [qdata, pdata, xdata, ydata];
27		shared.data_ordered = [ qdata(:,1), pdata(:,1),...
28		qdata(:,2), pdata(:,2),...
29		qdata(:,3), pdata(:,3),...
30		xdata(:,1), ydata(:,1),...
31		xdata(:,2), ydata(:,2),...
32		xdata(:,3), ydata(:,3) ];
33		dlmwrite(shared.file, shared.data, '\t');
	7		shared.folder = tempname;
	8		assert(~(exist(shared.folder)==7),...
	9		[shared.folder, ' exists already.']);
	10		mkdir(shared.folder)
	11		shared.file = fullfile(shared.folder, 'read_permute_write_data_test.tsv');
	12		assert(~(exist(shared.file)==2),...
	13		[shared.file, ' exists already.']);
	14
	15		sample_no = 2;
	16		mode_no = 3;
	17
	18		shared.precision = 1e4;
	19		myround = @(float_num) round(float_num*shared.precision)/shared.precision;
	20
	21		qdata = myround(randn(sample_no, mode_no));
	22		pdata = myround(randn(sample_no, mode_no));
	23		xdata = myround(randn(sample_no, mode_no));
	24		ydata = myround(randn(sample_no, mode_no));
	25
	26		shared.data = [qdata, pdata, xdata, ydata];
	27		shared.data_ordered = [ qdata(:,1), pdata(:,1),...
	28		qdata(:,2), pdata(:,2),...
	29		qdata(:,3), pdata(:,3),...
	30		xdata(:,1), ydata(:,1),...
	31		xdata(:,2), ydata(:,2),...
	32		xdata(:,3), ydata(:,3) ];
	33		dlmwrite(shared.file, shared.data, '\t');
34	34
35	35	function teardown(shared)	function teardown(shared)
36		delete(shared.file);
37		rmdir(shared.folder);
	36		delete(shared.file);
	37		rmdir(shared.folder);
38	38
39	39	function read_permute_write_data_3modes_test(shared)	function read_permute_write_data_3modes_test(shared)
40		[permuted_data_file, permuted_data_CM_file] = read_permute_write_data(shared.file);
41		assertEqual(permuted_data_file, shared.file)
42		data_ordered = dlmread(shared.file, '\t');
43		assertEqual(data_ordered, shared.data_ordered);
44		delete(permuted_data_CM_file);
	40		[permuted_data_file, permuted_data_CM_file] = read_permute_write_data(shared.file);
	41		assertEqual(permuted_data_file, shared.file)
	42		data_ordered = dlmread(shared.file, '\t');
	43		assertEqual(data_ordered, shared.data_ordered);
	44		delete(permuted_data_CM_file);
45	45
46	46	function read_permute_write_data_3modes_CM_test(shared)	function read_permute_write_data_3modes_CM_test(shared)
47		[permuted_data_file, permuted_data_CM_file] = read_permute_write_data(shared.file);
48		CM_ordered = dlmread(permuted_data_CM_file, '\t');
49		% CM_ordered(1:2,1:2)
50		% cm2 = cov(shared.data_ordered);
51		% cm2(1:2,1:2)
52		assertElementsAlmostEqual(CM_ordered, cov(shared.data_ordered), 'absolute', shared.precision);
53		delete(permuted_data_CM_file)
	47		[permuted_data_file, permuted_data_CM_file] = read_permute_write_data(shared.file);
	48		CM_ordered = dlmread(permuted_data_CM_file, '\t');
	49		% CM_ordered(1:2,1:2)
	50		% cm2 = cov(shared.data_ordered);
	51		% cm2(1:2,1:2)
	52		assertElementsAlmostEqual(CM_ordered, cov(shared.data_ordered), 'absolute', shared.precision);
	53		delete(permuted_data_CM_file)

File data_analysis/unittests/test_cf-based_evaluation/calculate_cf_test_data.m changed (mode: 100644) (index 8bac799..a82e3b3)
1	1	%% calculate blocked cross-correlation %%%	%% calculate blocked cross-correlation %%%
2		%% setup
	2		%% setup
3	3	clear all; rehash; clc	clear all; rehash; clc
4	4	addpath(genpath('/home/membrane/Documents/MATLAB'))	addpath(genpath('/home/membrane/Documents/MATLAB'))
5	5	cd('/home/membrane/Documents/MATLAB/datoolbox/data_analysis/unittests/test_cf-based_evaluation')	cd('/home/membrane/Documents/MATLAB/datoolbox/data_analysis/unittests/test_cf-based_evaluation')
...	...	title('covariances')
154	154	%%	%%
155	155	k=1;	k=1;
156	156	l=4;	l=4;
157		figure;
	157		figure;
158	158	lower = myCIs(:,1,k,l);	lower = myCIs(:,1,k,l);
159	159	upper = myCIs(:,2,k,l);	upper = myCIs(:,2,k,l);
160	160	lower = lower./(lower+upper);	lower = lower./(lower+upper);
...	...	refline(0,cf_CM_signal(k,l))
165	165	% set(gca, 'yscale','log')	% set(gca, 'yscale','log')
166	166	title(['covariances: ',num2str(k),num2str(l)])	title(['covariances: ',num2str(k),num2str(l)])
167	167
168		%% plot
	168		%% plot
169	169	close all	close all
170	170	figure;	figure;
171	171	subplot(1,2,1)	subplot(1,2,1)
...	...	end
180	180	set(gca, 'yscale', 'log')	set(gca, 'yscale', 'log')
181	181	title('Diagonal elements')	title('Diagonal elements')
182	182	legend({'pulse based'; 'corr. fct. based'}, 'Location', 'SouthOutside')	legend({'pulse based'; 'corr. fct. based'}, 'Location', 'SouthOutside')
183		set(gca,'xticklabel',[])
	183		set(gca,'xticklabel',[])
184	184
185	185	subplot(1,2,2)	subplot(1,2,2)
186	186	hold all	hold all
...	...	for k=1:4
197	197	end	end
198	198	title('Offdiagonal elements')	title('Offdiagonal elements')
199	199	legend({'pulse based'; 'corr. fct. based'}, 'Location', 'SouthOutside')	legend({'pulse based'; 'corr. fct. based'}, 'Location', 'SouthOutside')
200		set(gca,'xticklabel',[])
	200		set(gca,'xticklabel',[])
201	201
202	202
203	203
...	...	cf_yy = ev.correlation_functions{2,2}./myvec;
281	281	cf_xy = ev.correlation_functions{1,2}./myvec;	cf_xy = ev.correlation_functions{1,2}./myvec;
282	282	cf_yx = ev.correlation_functions{2,1}./myvec;	cf_yx = ev.correlation_functions{2,1}./myvec;
283	283
284		%%
	284		%%
285	285	pulseCM = ev.pulse_CM{1}{1};	pulseCM = ev.pulse_CM{1}{1};
286	286
287	287	%% entangling block	%% entangling block
...	...	for kk=[1, 3]
382	382	for ll=[1, 3]	for ll=[1, 3]
383	383	mat{1} = cf_CM_signal(kk:kk+1,ll:ll+1);	mat{1} = cf_CM_signal(kk:kk+1,ll:ll+1);
384	384	mat{2} = pulseCM(kk:kk+1,ll:ll+1);	mat{2} = pulseCM(kk:kk+1,ll:ll+1);
385		figure;
	385		figure;
386	386	for k=1:2	for k=1:2
387	387	subplot(3,1,k);	subplot(3,1,k);
388	388	myimagesc(mypreproc(mat{k}))	myimagesc(mypreproc(mat{k}))
...	...	end
408	408	%%	%%
409	409	diag(reldiff(pulseCM, cf_CM_signal))	diag(reldiff(pulseCM, cf_CM_signal))
410	410	figure; imagesc(triu(reldiff(pulseCM, cf_CM_signal),1)); colorbar()	figure; imagesc(triu(reldiff(pulseCM, cf_CM_signal),1)); colorbar()
411

File data_analysis/unittests/test_cf-based_evaluation/cf_based_CM.m changed (mode: 100644) (index b630a9d..4f25320)
...	...	function cm = cf_based_CM_subblock(cfp,cf,part)
9	9	cf_21 = part(cf{2,1});	cf_21 = part(cf{2,1});
10	10	cf_22 = part(cf{2,2});	cf_22 = part(cf{2,2});
11	11
12		cfp_11 = cfp{1,1};
	12		cfp_11 = cfp{1,1};
13	13	cfp_12 = cfp{1,2};	cfp_12 = cfp{1,2};
14	14	cfp_21 = cfp{2,1};	cfp_21 = cfp{2,1};
15	15	cfp_22 = cfp{2,2};	cfp_22 = cfp{2,2};
16
	16
17	17	cm = zeros(2,2);	cm = zeros(2,2);
18	18	cm(1,1) = cfp_11*cf_11 + cfp_22*cf_22 - cfp_12*cf_12 - cfp_21*cf_21;	cm(1,1) = cfp_11*cf_11 + cfp_22*cf_22 - cfp_12*cf_12 - cfp_21*cf_21;
19	19	cm(1,2) = cfp_12*cf_11 - cfp_21*cf_22 + cfp_11*cf_12 - cfp_22*cf_21;	cm(1,2) = cfp_12*cf_11 - cfp_21*cf_22 + cfp_11*cf_12 - cfp_22*cf_21;
20		cm(2,1) = cfp_21*cf_11 - cfp_12*cf_22 - cfp_22*cf_12 + cfp_11*cf_21;
21		cm(2,2) = cfp_22*cf_11 + cfp_11*cf_22 + cfp_21*cf_12 + cfp_12*cf_21;
	20		cm(2,1) = cfp_21*cf_11 - cfp_12*cf_22 - cfp_22*cf_12 + cfp_11*cf_21;
	21		cm(2,2) = cfp_22*cf_11 + cfp_11*cf_22 + cfp_21*cf_12 + cfp_12*cf_21;
22	22	end	end

File data_analysis/unittests/test_cf-based_evaluation/quick_comparison_CMs.m changed (mode: 100644) (index c5fdedb..d3e02a0)
1	1	function quick_comparison_CMs(pulsedCM, pulsedCMci, rows, cols, cfCM)	function quick_comparison_CMs(pulsedCM, pulsedCMci, rows, cols, cfCM)
2		mytriu = @(mat)mat(:)';%[mat(1,1), mat(1,2), mat(2,1), mat(2,2)];
3
	2		mytriu = @(mat)mat(:)';%[mat(1,1), mat(1,2), mat(2,1), mat(2,2)];
	3
4	4	disp('** pulse-based result with lower and upper CI-bounds **')	disp('** pulse-based result with lower and upper CI-bounds **')
5	5	disp(mytriu(squeeze(pulsedCMci(1,rows,cols)))) %lower CI-bound	disp(mytriu(squeeze(pulsedCMci(1,rows,cols)))) %lower CI-bound
6	6	disp(mytriu(pulsedCM(rows,cols))) %actually measured value	disp(mytriu(pulsedCM(rows,cols))) %actually measured value
...	...	function quick_comparison_CMs(pulsedCM, pulsedCMci, rows, cols, cfCM)
13	13
14	14	disp('** cf-based result **')	disp('** cf-based result **')
15	15	disp(mytriu(cfCM))	disp(mytriu(cfCM))
16
	16
17	17	disp('** relative deviation in percent **')	disp('** relative deviation in percent **')
18	18	myreldiff = @(mat1,mat2)100*abs(2*(mat1-mat2)./(mat1+mat2));	myreldiff = @(mat1,mat2)100*abs(2*(mat1-mat2)./(mat1+mat2));
19	19	myreldiff2 = @(mat1, mat2)myreldiff(mytriu(mat1),mytriu(mat2));	myreldiff2 = @(mat1, mat2)myreldiff(mytriu(mat1),mytriu(mat2));

File data_analysis/unittests/test_cf-based_evaluation/simulate_test_data.m changed (mode: 100644) (index bec943c..f1f9bca)
...	...	fidp = fopen(fullfile(datadir, 'p.dat'),'w');
22	22
23	23	for k=1:block_no	for k=1:block_no
24	24	k	k
25		start_sample = (k-1)*N+1;
26		stop_sample = k*N;
27		if k==1
28		x0 = simgauss(zeros(length(ss_cov),1), ss_cov,1);
29		else
30		x0 = states_full(end,:);
31		end
32		[~, ~, states_full] = lsim(sys, u, t, x0);
	25		start_sample = (k-1)*N+1;
	26		stop_sample = k*N;
	27		if k==1
	28		x0 = simgauss(zeros(length(ss_cov),1), ss_cov,1);
	29		else
	30		x0 = states_full(end,:);
	31		end
	32		[~, ~, states_full] = lsim(sys, u, t, x0);
33	33	x = states_full(2:end,1);	x = states_full(2:end,1);
34	34	p = states_full(2:end,2);	p = states_full(2:end,2);
35
	35
36	36	% write to binary files	% write to binary files
37	37	fwrite(fidx,x, precision);	fwrite(fidx,x, precision);
38	38	fwrite(fidp,p, precision);	fwrite(fidp,p, precision);
...	...	for k=1:reps
51	51	k	k
52	52	x0 = simgauss(zeros(length(ss_cov),1), ss_cov,1);	x0 = simgauss(zeros(length(ss_cov),1), ss_cov,1);
53	53	[~,~, states_full] = lsim(sys, u, t, x0);	[~,~, states_full] = lsim(sys, u, t, x0);
54		for l=1:6
	54		for l=1:6
55	55	energies{l}(k) = sum(diag(cov(states_full(1:10^l,:))));	energies{l}(k) = sum(diag(cov(states_full(1:10^l,:))));
56	56	end	end
57	57	% for visual inspection...	% for visual inspection...
...	...	end
62	62
63	63	%% plot spread of energies vs stretch length	%% plot spread of energies vs stretch length
64	64	figure; hold all	figure; hold all
65		for l=1:6
	65		for l=1:6
66	66	scatter([10^l],[std(energies{l})/mean(energies{l})],'b','filled')	scatter([10^l],[std(energies{l})/mean(energies{l})],'b','filled')
67	67	end	end
68	68	plot([10,1e6],[1e3,1]*std(energies{6})/mean(energies{6}))	plot([10,1e6],[1e3,1]*std(energies{6})/mean(energies{6}))
...	...	set(gca, 'xscale','log')
111	111	%% autocorrelation function	%% autocorrelation function
112	112	figure; hold all	figure; hold all
113	113	plot(xcorr(data(1:1e7,:),'biased'))	plot(xcorr(data(1:1e7,:),'biased'))
114
	114
115	115	%% ETC	%% ETC
116	116	w = warning('query','last')	w = warning('query','last')
117	117	warning('off',w.identifier)	warning('off',w.identifier)

File data_analysis/unittests/test_cf-based_evaluation/test_cf_based_evaluation.m changed (mode: 100644) (index 15a4a41..bde236b)
...	...	delta = imag(readout_mode);
78	78
79	79	%% plots	%% plots
80	80	figure; hold all;	figure; hold all;
81		subplot(2,1,1);
	81		subplot(2,1,1);
82	82	plot(ev.pulse_timevector, alpha, ...	plot(ev.pulse_timevector, alpha, ...
83	83	ev.pulse_timevector, beta)	ev.pulse_timevector, beta)
84	84	legend({'\alpha';'\beta'})	legend({'\alpha';'\beta'})
85	85	xlabel('time [sec]')	xlabel('time [sec]')
86	86
87		subplot(2,1,2);
	87		subplot(2,1,2);
88	88	plot(ev.pulse_timevector, gamma, ...	plot(ev.pulse_timevector, gamma, ...
89	89	ev.pulse_timevector, delta)	ev.pulse_timevector, delta)
90	90	legend({'\gamma';'\delta'})	legend({'\gamma';'\delta'})
...	...	ratio_shot = cf_CM_shot./pulsedCM_shot
200	200
201	201	%%	%%
202	202	ratio_signal	ratio_signal
203		ratio_shot
	203		ratio_shot
204	204
205	205	%%	%%
206	206
...	...	plot(real(cf{1,5}))
251	251	testmat = zeros(2,50);	testmat = zeros(2,50);
252	252	testmat(1,:) = ones(50,1);	testmat(1,:) = ones(50,1);
253	253	testmat*randn(50,1)	testmat*randn(50,1)
254

File data_analysis/unittests/testevaluation/setup_eval.m changed (mode: 100644) (index 223a5b2..5550ae8)
...	...	f_ref = 1.2E6;
11	11	ev.mech_frequencies = f_ref;	ev.mech_frequencies = f_ref;
12	12	ev.opt_pulse_widths = 1;	ev.opt_pulse_widths = 1;
13	13	ev.pulse_gammas = f_ref*logspace(-2,0,20);	ev.pulse_gammas = f_ref*logspace(-2,0,20);
14

File data_analysis/unittests/testevaluation/setup_meas.m changed (mode: 100644) (index 888573f..1167af3)
...	...	meas.add_trace_pair(...
19	19	'200muW_yQ_int16_sf40.000_Nts1.0e+05_MCr1.dat',...	'200muW_yQ_int16_sf40.000_Nts1.0e+05_MCr1.dat',...
20	20	'amplification', 40,...	'amplification', 40,...
21	21	'signal_power_muW', 200);	'signal_power_muW', 200);
22

File data_analysis/unittests/timetrace_test.m changed (mode: 100644) (index bf09b37..fff7be0)
1	1	function test_suite = timetrace_test %#ok<STOUT>	function test_suite = timetrace_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function shared = setup	function shared = setup
7		shared.show_plots = false;
	7		shared.show_plots = false;
8	8
9		shared.DAQrange = 0;
10		shared.amplification = 10;
	9		shared.DAQrange = 0;
	10		shared.amplification = 10;
11	11
12		shared.sample_no = 1e5 + 1;
13		shared.samplingrate = 1e2;
14		shared.timevec = (0:shared.sample_no-1)'/shared.samplingrate;
15		shared.mech_freq = 3.0626;
16		shared.data = sin(2*pi*shared.mech_freq*shared.timevec);
	12		shared.sample_no = 1e5 + 1;
	13		shared.samplingrate = 1e2;
	14		shared.timevec = (0:shared.sample_no-1)'/shared.samplingrate;
	15		shared.mech_freq = 3.0626;
	16		shared.data = sin(2*pi*shared.mech_freq*shared.timevec);
17	17
18		shared.precision = 'double';
19		shared.folder = tempname;
20		assert(~(exist(shared.folder)==7),...
21		[shared.folder, ' exists already.']);
22		mkdir(shared.folder)
23		shared.file = fullfile(shared.folder, '.timetrace.dat');
	18		shared.precision = 'double';
	19		shared.folder = tempname;
	20		assert(~(exist(shared.folder)==7),...
	21		[shared.folder, ' exists already.']);
	22		mkdir(shared.folder)
	23		shared.file = fullfile(shared.folder, '.timetrace.dat');
24	24
25		fid = fopen(shared.file,'w');
26		fwrite(fid, shared.data, shared.precision);
27		fclose(fid);
	25		fid = fopen(shared.file,'w');
	26		fwrite(fid, shared.data, shared.precision);
	27		fclose(fid);
28	28
29	29	function teardown(shared)	function teardown(shared)
30		delete(shared.file);
31		rmdir(shared.folder);
	30		delete(shared.file);
	31		rmdir(shared.folder);
32	32
33	33	function ht = timetrace_setup_test(shared)	function ht = timetrace_setup_test(shared)
34		ht = timetrace(shared.file,...
35		'samplingrate', shared.samplingrate,...
36		'precision', shared.precision,...
37		'DAQrange', shared.DAQrange,...
38		'amplification', shared.amplification);
	34		ht = timetrace(shared.file,...
	35		'samplingrate', shared.samplingrate,...
	36		'precision', shared.precision,...
	37		'DAQrange', shared.DAQrange,...
	38		'amplification', shared.amplification);
39	39
40	40	function ht = timetrace_read_data_test(shared)	function ht = timetrace_read_data_test(shared)
41		ht = timetrace_setup_test(shared);
42		ht.fopen();
43		assertEqual(ht.fread(shared.sample_no), shared.data/shared.amplification);
44		ht.fclose();
45
46		assertEqual(ht.read_from_t1_to_t2(0,ht.duration), shared.data/shared.amplification);
47		assertEqual(...
48		ht.read_from_t1_to_t2(0,ht.duration/2),...
49		shared.data(1:floor(shared.sample_no/2))/shared.amplification);
50
51		if shared.show_plots
52		div = 1e3;
53		figure;
54		plot(shared.timevec(1:floor(shared.sample_no/div)), ht.read_from_t1_to_t2(0,ht.duration/div))
55		end
	41		ht = timetrace_setup_test(shared);
	42		ht.fopen();
	43		assertEqual(ht.fread(shared.sample_no), shared.data/shared.amplification);
	44		ht.fclose();
	45
	46		assertEqual(ht.read_from_t1_to_t2(0,ht.duration), shared.data/shared.amplification);
	47		assertEqual(...
	48		ht.read_from_t1_to_t2(0,ht.duration/2),...
	49		shared.data(1:floor(shared.sample_no/2))/shared.amplification);
	50
	51		if shared.show_plots
	52		div = 1e3;
	53		figure;
	54		plot(shared.timevec(1:floor(shared.sample_no/div)), ht.read_from_t1_to_t2(0,ht.duration/div))
	55		end
56	56
57	57	function ht = timetrace_NPS_test(shared)	function ht = timetrace_NPS_test(shared)
58		ht = timetrace_setup_test(shared);
59		ht.get_NPS;
60		ht.get_peaks;
61		if shared.show_plots
62		figure; hold all
63		plot(ht.spectrum.freq, ht.spectrum.noisepower)
64		plot(ht.spectrum.peaks.freq, ht.spectrum.peaks.power,'o')
65		set(gca, 'yscale','log')
66		set(gca, 'xscale','log')
67		end
68		relTol = 1e-2;
69		assertElementsAlmostEqual(ht.spectrum.peaks.freq, shared.mech_freq, 'relative', relTol);
70
	58		ht = timetrace_setup_test(shared);
	59		ht.get_NPS;
	60		ht.get_peaks;
	61		if shared.show_plots
	62		figure; hold all
	63		plot(ht.spectrum.freq, ht.spectrum.noisepower)
	64		plot(ht.spectrum.peaks.freq, ht.spectrum.peaks.power,'o')
	65		set(gca, 'yscale','log')
	66		set(gca, 'xscale','log')
	67		end
	68		relTol = 1e-2;
	69		assertElementsAlmostEqual(ht.spectrum.peaks.freq, shared.mech_freq, 'relative', relTol);
	70
71	71
72	72	function ht = timetrace_properties_test(shared)	function ht = timetrace_properties_test(shared)
73		ht = timetrace_setup_test(shared);
74		assertEqual(ht.sample_no, shared.sample_no)
75		assertEqual(ht.samplingrate, shared.samplingrate)
76		assertEqual(ht.precision, shared.precision)
	73		ht = timetrace_setup_test(shared);
	74		assertEqual(ht.sample_no, shared.sample_no)
	75		assertEqual(ht.samplingrate, shared.samplingrate)
	76		assertEqual(ht.precision, shared.precision)
77	77
78		assertEqual(ht.DAQrange, shared.DAQrange)
79		assertEqual(ht.amplification, shared.amplification)
	78		assertEqual(ht.DAQrange, shared.DAQrange)
	79		assertEqual(ht.amplification, shared.amplification)
80	80
81	81
82		assertEqual(ht.filepath, shared.file)
83		[folder, filename, fileext] = fileparts(shared.file);
84		assertEqual(ht.directory, folder)
85		assertEqual(ht.filename, [filename, fileext])
	82		assertEqual(ht.filepath, shared.file)
	83		[folder, filename, fileext] = fileparts(shared.file);
	84		assertEqual(ht.directory, folder)
	85		assertEqual(ht.filename, [filename, fileext])
86	86
87		assertEqual(ht.sample_from_time(0), 1)
88		assertEqual(ht.duration, ht.sample_no/ht.samplingrate)
89		assertEqual(ht.sample_from_time(ht.duration),ht.sample_no+1)
	87		assertEqual(ht.sample_from_time(0), 1)
	88		assertEqual(ht.duration, ht.sample_no/ht.samplingrate)
	89		assertEqual(ht.sample_from_time(ht.duration),ht.sample_no+1)

File homo_measurements/@dual_homo_measurement/test_dual_homo_measurement.m changed (mode: 100644) (index 85d9ef6..6e1c6b8)
...	...	function meas = test_dual_homo_measurement(meas_file)
12	12	cd(meas_folder);	cd(meas_folder);
13	13	run(meas_script)	run(meas_script)
14	14	disp(meas)	disp(meas)
15
	15
16	16	%% create subfolder for test output	%% create subfolder for test output
17	17	mkdir(meas_folder, 'test')	mkdir(meas_folder, 'test')
18	18	test_folder = fullfile(meas_folder, 'test');	test_folder = fullfile(meas_folder, 'test');
...	...	function [npsfig] = create_save_sample_NPS(meas, test_folder)
43	43	fclose(outid);	fclose(outid);
44	44
45	45	dlmwrite(outfile, data, 'delimiter', '\t', '-append');	dlmwrite(outfile, data, 'delimiter', '\t', '-append');
46
	46
47	47	%% print shotnoise PSD and non-shotnoise PSD	%% print shotnoise PSD and non-shotnoise PSD
48		ii = 1;
	48		ii = 1;
49	49	for kk=1:2	for kk=1:2
50	50	disp(['mean noise power of ', meas.homotraces(ii,kk).dataset_label])	disp(['mean noise power of ', meas.homotraces(ii,kk).dataset_label])
51	51	disp(mean(meas.homotraces(ii,kk).spectrum.noisepower))	disp(mean(meas.homotraces(ii,kk).spectrum.noisepower))
52	52	disp('')	disp('')
53	53	end	end
54
	54
55	55	ii = 2;	ii = 2;
56	56	for kk=1:2	for kk=1:2
57	57	disp(['mean noise power of ', meas.homotraces(ii,kk).dataset_label, ' for high freqs.'])	disp(['mean noise power of ', meas.homotraces(ii,kk).dataset_label, ' for high freqs.'])

File homo_measurements/@homo_measurement/homo_measurement.m changed (mode: 100644) (index f6fa907..b77cf1b)
...	...	classdef homo_measurement < handle
25	25
26	26	%scalar properties: the same for all homotraces	%scalar properties: the same for all homotraces
27	27	samplingrate@double scalar	samplingrate@double scalar
28
	28
29	29	%dummy = true creates a short dummy file with random numbers to play around with	%dummy = true creates a short dummy file with random numbers to play around with
30	30	% for more details see PXItrace-code	% for more details see PXItrace-code
31		dummy = false;
	31		dummy = false;
32	32	end	end
33	33	properties(Dependent)	properties(Dependent)
34	34	% scalar	% scalar

File homo_measurements/quickcheck_dualhomo.m changed (mode: 100644) (index 67a09e6..7653daf)
...	...	DAQrange_mV = 200;
7	7	signalpower_muW = 588;	signalpower_muW = 588;
8	8
9	9	filename1 = ['P', num2str(signalpower_muW),'muW_'];	filename1 = ['P', num2str(signalpower_muW),'muW_'];
10		% or
	10		% or
11	11	%filename1 = 'Pdarknoise_';	%filename1 = 'Pdarknoise_';
12	12	filename1 = 'Pshotnoise_';	filename1 = 'Pshotnoise_';
13	13	amplification_dB = 40;	amplification_dB = 40;
...	...	meas.add_trace_pair(...
29	29
30	30	%% spectra	%% spectra
31	31	meas.get_NPS	meas.get_NPS
32		fig = figure; meas.show_NPS;
	32		fig = figure; meas.show_NPS;
33	33	title([filename1, '...', filename2], 'Interpreter', 'none');	title([filename1, '...', filename2], 'Interpreter', 'none');
34	34	saveas(fig, ['spectra_', filename1, filename2, '.fig'])	saveas(fig, ['spectra_', filename1, filename2, '.fig'])
35	35	saveas(fig, ['spectra_', filename1, filename2, '.jpg'])	saveas(fig, ['spectra_', filename1, filename2, '.jpg'])
36	36
37	37	%% snapshot of timetrace	%% snapshot of timetrace
38		fig = figure; meas.show_snapshot;
	38		fig = figure; meas.show_snapshot;
39	39	title([filename1, '...', filename2], 'Interpreter', 'none');	title([filename1, '...', filename2], 'Interpreter', 'none');
40	40	saveas(fig, ['snapshot_', filename1, filename2, '.fig'])	saveas(fig, ['snapshot_', filename1, filename2, '.fig'])
41	41	saveas(fig, ['snapshot_', filename1, filename2, '.jpg'])	saveas(fig, ['snapshot_', filename1, filename2, '.jpg'])
42	42
43	43	%% histogram of timetrace	%% histogram of timetrace
44		fig = figure; meas.show_hist;
	44		fig = figure; meas.show_hist;
45	45	title([filename1, '...', filename2], 'Interpreter', 'none');	title([filename1, '...', filename2], 'Interpreter', 'none');
46	46	saveas(fig, ['hist_', filename1, filename2, '.fig'])	saveas(fig, ['hist_', filename1, filename2, '.fig'])
47	47	saveas(fig, ['hist_', filename1, filename2, '.jpg'])	saveas(fig, ['hist_', filename1, filename2, '.jpg'])

File homo_measurements/quickcheck_dualhomo_long.m changed (mode: 100644) (index b442278..3cffe48)
...	...	end
40	40	tic;	tic;
41	41	meas.get_NPS('duration', 0.01, 'averages', 20)	meas.get_NPS('duration', 0.01, 'averages', 20)
42	42	toc;	toc;
43		fig = figure; meas.show_NPS;
	43		fig = figure; meas.show_NPS;
44	44	title([filename1, '...', filename2], 'Interpreter', 'none');	title([filename1, '...', filename2], 'Interpreter', 'none');
45	45	saveas(fig, ['spectra_high_res_', filename1, filename2, '.fig'])	saveas(fig, ['spectra_high_res_', filename1, filename2, '.fig'])
46	46	saveas(fig, ['spectra_high_res_', filename1, filename2, '.jpg'])	saveas(fig, ['spectra_high_res_', filename1, filename2, '.jpg'])
...	...	saveas(fig, ['spectra_high_res_', filename1, filename2, '.jpg'])
48	48	%% get peaks	%% get peaks
49	49	meas.get_peaks('minheight', 1E-14, 'minfreq', 500E3, 'maxfreq', 5E6,...	meas.get_peaks('minheight', 1E-14, 'minfreq', 500E3, 'maxfreq', 5E6,...
50	50	'mindistance', 1)	'mindistance', 1)
51		%
	51		%
52	52	% %% plot peaks for different chunks	% %% plot peaks for different chunks
53	53	% figure; hold all	% figure; hold all
54	54	% chunk_no = 10;	% chunk_no = 10;
...	...	for l=all_peaks
78	78	peak_ind = peak_ind+1;	peak_ind = peak_ind+1;
79	79	end	end
80	80
81		%% plot
	81		%% plot
82	82	figure; hold all	figure; hold all
83	83	for k=1:chunk_no	for k=1:chunk_no
84	84	plot(all_peaks, k*occurences(:,k), 'o')	plot(all_peaks, k*occurences(:,k), 'o')
...	...	dlmwrite(['robust_peaks_', filename1, filename2, '.csv'], robust_peaks')
112	112	tic;	tic;
113	113	meas.get_NPS('duration', 0.0005, 'averages', 100)	meas.get_NPS('duration', 0.0005, 'averages', 100)
114	114	toc;	toc;
115		fig = figure; meas.show_NPS;
	115		fig = figure; meas.show_NPS;
116	116	title([filename1, '...', filename2], 'Interpreter', 'none');	title([filename1, '...', filename2], 'Interpreter', 'none');
117	117	saveas(fig, ['spectra_many_avgs_', filename1, filename2, '.fig'])	saveas(fig, ['spectra_many_avgs_', filename1, filename2, '.fig'])
118	118	saveas(fig, ['spectra_many_avgs_', filename1, filename2, '.jpg'])	saveas(fig, ['spectra_many_avgs_', filename1, filename2, '.jpg'])
119	119
120	120
121	121	%% snapshot of timetrace	%% snapshot of timetrace
122		fig = figure; meas.show_snapshot;
	122		fig = figure; meas.show_snapshot;
123	123	title([filename1, '...', filename2], 'Interpreter', 'none');	title([filename1, '...', filename2], 'Interpreter', 'none');
124	124	saveas(fig, ['snapshot_', filename1, filename2, '.fig'])	saveas(fig, ['snapshot_', filename1, filename2, '.fig'])
125	125	saveas(fig, ['snapshot_', filename1, filename2, '.jpg'])	saveas(fig, ['snapshot_', filename1, filename2, '.jpg'])
126	126
127	127
128	128	%% histogram of timetrace	%% histogram of timetrace
129		fig = figure; meas.show_hist;
	129		fig = figure; meas.show_hist;
130	130	title([filename1, '...', filename2], 'Interpreter', 'none');	title([filename1, '...', filename2], 'Interpreter', 'none');
131	131	saveas(fig, ['hist_', filename1, filename2, '.fig'])	saveas(fig, ['hist_', filename1, filename2, '.fig'])
132	132	saveas(fig, ['hist_', filename1, filename2, '.jpg'])	saveas(fig, ['hist_', filename1, filename2, '.jpg'])

File kalmanfilter/kalmanfilter.m changed (mode: 100644) (index 4af2b17..46a68e0)
...	...	function [xest,pest,varargout] = kalmanfilter(F,G,H,D,L,Q,R,M,z,u,xinit,pinit,va
4	4	%	%
5	5	% Kalman filter for a linear discrete system with a time-dependent	% Kalman filter for a linear discrete system with a time-dependent
6	6	% state-space model given by F(k), G(k), H(k), D(k)	% state-space model given by F(k), G(k), H(k), D(k)
7		%
	7		%
8	8	% x(k+1) = F(k) x(k) + G(k) u(k) + L w(k)	% x(k+1) = F(k) x(k) + G(k) u(k) + L w(k)
9	9	% z(k) = H(k) x(k) + D(k) u(k) + n(k)	% z(k) = H(k) x(k) + D(k) u(k) + n(k)
10	10	%	%
...	...	function [xest,pest,varargout] = kalmanfilter(F,G,H,D,L,Q,R,M,z,u,xinit,pinit,va
18	18	% measurement basis in a homodyne-detection measurement.)	% measurement basis in a homodyne-detection measurement.)
19	19	%	%
20	20	% xinit and pinit are initial estimates. xinit can be replaced by [].	% xinit and pinit are initial estimates. xinit can be replaced by [].
21		%
	21		%
22	22	% The optional output arguments are the predicted measurement output	% The optional output arguments are the predicted measurement output
23	23	% (which can be used to find the innovation sequence), the statistics	% (which can be used to find the innovation sequence), the statistics
24	24	% of the NIS test and the NIS sequence.	% of the NIS test and the NIS sequence.
...	...	end
127	127
128	128	% check if inputs are time-dependent	% check if inputs are time-dependent
129	129	% x_TD=true <> x is time dependent!	% x_TD=true <> x is time dependent!
130		if size(F,3)==1, F_TD=false; f=F; else F_TD=true; fprintf(fID,'Kalmanfilter: Matrix F is time dependent.\n'); end
	130		if size(F,3)==1, F_TD=false; f=F; else F_TD=true; fprintf(fID,'Kalmanfilter: Matrix F is time dependent.\n'); end
131	131	if size(G,3)==1, G_TD=false; g=G; else G_TD=true; fprintf(fID,'Kalmanfilter: Matrix G is time dependent.\n'); end	if size(G,3)==1, G_TD=false; g=G; else G_TD=true; fprintf(fID,'Kalmanfilter: Matrix G is time dependent.\n'); end
132	132	if size(H,3)==1, H_TD=false; h=H; else H_TD=true; fprintf(fID,'Kalmanfilter: Matrix H is time dependent.\n'); end	if size(H,3)==1, H_TD=false; h=H; else H_TD=true; fprintf(fID,'Kalmanfilter: Matrix H is time dependent.\n'); end
133	133	if size(D,3)==1, D_TD=false; d=D; else D_TD=true; fprintf(fID,'Kalmanfilter: Matrix D is time dependent.\n'); end	if size(D,3)==1, D_TD=false; d=D; else D_TD=true; fprintf(fID,'Kalmanfilter: Matrix D is time dependent.\n'); end
...	...	for mm = 1:NT
182	182
183	183	% update state	% update state
184	184	% alternatively:	% alternatively:
185		% xest(:,mm)=(id-K*h)*xm+K*z(:,mm);
	185		% xest(:,mm)=(id-K*h)*xm+K*z(:,mm);
186	186	xest(:,mm) = xm+K*innov;	xest(:,mm) = xm+K*innov;
187	187
188	188	% update error covariance	% update error covariance
...	...	zmvec = zmvec.';
213	213	%% setting output arguments	%% setting output arguments
214	214	varargout = cell(1,nargout);	varargout = cell(1,nargout);
215	215	if PREDICT_OUTPUT	if PREDICT_OUTPUT
216		varargout{1} = zmvec;
	216		varargout{1} = zmvec;
217	217	end	end
218		if NIS_TEST
	218		if NIS_TEST
219	219	[varargout{2:3}] = nistest(z-zmvec,pmvec,H,R,args{:});	[varargout{2:3}] = nistest(z-zmvec,pmvec,H,R,args{:});
220	220	end	end
221	221

File misc/3rdparty/AnDarksamtest.m changed (mode: 100644) (index 04e1c33..5bf7c04)
1	1	function [AnDarksamtest] = AnDarksamtest(X,alpha)	function [AnDarksamtest] = AnDarksamtest(X,alpha)
2	2	%ANDARKSAMTEST Anderson-Darling k-sample procedure to test whether	%ANDARKSAMTEST Anderson-Darling k-sample procedure to test whether
3	3	% k sampled populations are identical	% k sampled populations are identical
4		%
	4		%
5	5	% Anderson and Darling (1952,1954) introduced a goodness-of-fit statistic	% Anderson and Darling (1952,1954) introduced a goodness-of-fit statistic
6	6	%	%
7	7	% _inf 2	% _inf 2
8	8	% / {Fm(x) - Fo(x)}	% / {Fm(x) - Fo(x)}
9	9	% ADm = m / ----------------- dFo(x)	% ADm = m / ----------------- dFo(x)
10	10	% -inf_/ Fo(x){1 - Fo(x)}	% -inf_/ Fo(x){1 - Fo(x)}
11		%
	11		%
12	12	% to test the hypothesis that a random sample Fm(x) comes from a continuous	% to test the hypothesis that a random sample Fm(x) comes from a continuous
13		% population with a specified distribution function Fo(x). It is a
	13		% population with a specified distribution function Fo(x). It is a
14	14	% modification of the Kolmogorov-Smirnov (K-S) test and gives more weight	% modification of the Kolmogorov-Smirnov (K-S) test and gives more weight
15	15	% to the tails than the K-S test.	% to the tails than the K-S test.
16	16	%	%
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
21	21	% ADmn = ---- / ----------------- dHn(x)	% ADmn = ---- / ----------------- dHn(x)
22	22	% N -inf_/ Hn(x){1 - Hn(x)}	% N -inf_/ Hn(x){1 - Hn(x)}
23	23	%	%
24		% was proposed by Darling (1957) and studied in detail by Pettitt (1976).
25		% Where Gn(x) is the empirical distribution function of the second
	24		% was proposed by Darling (1957) and studied in detail by Pettitt (1976).
	25		% Where Gn(x) is the empirical distribution function of the second
26	26	% independent sample G(x) and Hn(x) = {mFm(x) + nGn(x)}/N, with N = m+n, is	% independent sample G(x) and Hn(x) = {mFm(x) + nGn(x)}/N, with N = m+n, is
27	27	% the empirical distribution function of the pooled sample. It is used to	% the empirical distribution function of the pooled sample. It is used to
28	28	% test the hypothesis that F = G.	% test the hypothesis that F = G.
29	29	%	%
30	30	% The Anderson-Darling k-sample test was introduced by Scholz and Stephens	% The Anderson-Darling k-sample test was introduced by Scholz and Stephens
31	31	% (1987) as a generalization of the two-sample Anderson-Darling test. It is	% (1987) as a generalization of the two-sample Anderson-Darling test. It is
32		% a nonparametric statistical procedure, i.e., a rank test, and, thus,
33		% requires no assumptions other than that the samples are true independent
34		% random samples from their respective continuous populations (although
	32		% a nonparametric statistical procedure, i.e., a rank test, and, thus,
	33		% requires no assumptions other than that the samples are true independent
	34		% random samples from their respective continuous populations (although
35	35	% provisions for tied observations are made). It tests the hypothesis that	% provisions for tied observations are made). It tests the hypothesis that
36	36	% the populations from which two or more independent samples of data were	% the populations from which two or more independent samples of data were
37	37	% drawn are identical. This test can be used to decide whether data from	% drawn are identical. This test can be used to decide whether data from
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
46	46	% it is effective for changes in scale while locations are matched, which is	% it is effective for changes in scale while locations are matched, which is
47	47	% a weakness of the Kruskal-Wallis test.	% a weakness of the Kruskal-Wallis test.
48	48	%	%
49		% The Anderson-Darling k-sample procedure assumes that i-th sample has a
	49		% The Anderson-Darling k-sample procedure assumes that i-th sample has a
50	50	% continuous distribution function and we are interested in testing the null	% continuous distribution function and we are interested in testing the null
51	51	% hypothesis that all sampled populations have the same distribution, without	% hypothesis that all sampled populations have the same distribution, without
52	52	% specifying the nature of that common distribution,	% specifying the nature of that common distribution,
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
69	69	% n-1 \ 1 \ {nFij - niHj}	% n-1 \ 1 \ {nFij - niHj}
70	70	% ADK = ---- /_ _ [ ---- /_ _ ---------------- ]	% ADK = ---- /_ _ [ ---- /_ _ ---------------- ]
71	71	% n^2 i=1 ni j=1 Hj(n-Hj) - nhj/4	% n^2 i=1 ni j=1 Hj(n-Hj) - nhj/4
72		%
	72		%
73	73	%	%
74	74	% and the corresponding not adjusted for ties is	% and the corresponding not adjusted for ties is
75	75	%	%
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
91	91	% the number of values in the combined samples equal to zj	% the number of values in the combined samples equal to zj
92	92	% Fij = number of values in the ith group which are less than zj plus one	% Fij = number of values in the ith group which are less than zj plus one
93	93	% half the number of values in this group which are equal to zj	% half the number of values in this group which are equal to zj
94		%
	94		%
95	95	% Under Ho and assuming continuity of the common F distribution the mean of	% Under Ho and assuming continuity of the common F distribution the mean of
96	96	% ADK is k-1 and the variance* is,	% ADK is k-1 and the variance* is,
97		%
98		% an^3 + bn^2 + cn + d
99		% var(ADK) = ---------------------
	97		%
	98		% an^3 + bn^2 + cn + d
	99		% var(ADK) = ---------------------
100	100	% (n-1)(n-2)(n-3)	% (n-1)(n-2)(n-3)
101	101	%	%
102	102	% where,	% where,
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
104	104	% b = (2g -4)k^2 + 8Tk + (2g - 14T -4)S - 8T + 4g -6	% b = (2g -4)k^2 + 8Tk + (2g - 14T -4)S - 8T + 4g -6
105	105	% c = (6T + 2g -2)k^2 + (4T - 4g + 6)k + (2T -6)S + 4T	% c = (6T + 2g -2)k^2 + (4T - 4g + 6)k + (2T -6)S + 4T
106	106	% d = (2T +6)k^2 - 4Tk	% d = (2T +6)k^2 - 4Tk
107		%
	107		%
108	108	% with,	% with,
109	109	% _k _ _n-1_	% _k _ _n-1_
110	110	% \ 1 \ 1	% \ 1 \ 1
111		% S = /_ _ ---- , T = /_ _ ----
	111		% S = /_ _ ---- , T = /_ _ ----
112	112	% ni i=1 i	% ni i=1 i
113	113	%	%
114	114	% and	% and
115	115	% _n-2_ _n-1_	% _n-2_ _n-1_
116	116	% \ \ 1	% \ \ 1
117		% g = /_ _ /_ _ ------
	117		% g = /_ _ /_ _ ------
118	118	% i=1 j=i+1 (n-i)j	% i=1 j=i+1 (n-i)j
119	119	%	%
120	120	% The observed k-sample Anderson-Darling statistic (ADK) is standardized	% The observed k-sample Anderson-Darling statistic (ADK) is standardized
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
136	136	% n^2(k-1) i=1 ni j=1 Hj(n-Hj) - nhj/4	% n^2(k-1) i=1 ni j=1 Hj(n-Hj) - nhj/4
137	137	%	%
138	138	%	%
139		% an^3 + bn^2 + cn + d
	139		% an^3 + bn^2 + cn + d
140	140	% var(ADK) = ------------------------ ,	% var(ADK) = ------------------------ ,
141	141	% (n-1)(n-2)(n-3)(k-1)^2	% (n-1)(n-2)(n-3)(k-1)^2
142	142	%	%
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
149	149	%	%
150	150	% The approximate P-value of the observed ADK statistic can be calculated	% The approximate P-value of the observed ADK statistic can be calculated
151	151	% using a spline interpolation method. For the interested users, we are	% using a spline interpolation method. For the interested users, we are
152		% including, as a comment, the mathematical procedure to get the ADK
	152		% including, as a comment, the mathematical procedure to get the ADK
153	153	% critical value.	% critical value.
154	154	%	%
155	155	% The interpolation coefficients for each alpa-value,	% The interpolation coefficients for each alpa-value,
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
164	164	%	%
165	165	% qnt = bo + b1/sqrt(k-1) + b2/(k-1)	% qnt = bo + b1/sqrt(k-1) + b2/(k-1)
166	166	%	%
167		% We give the Anderson-Darling k-sample procedure with and without
	167		% We give the Anderson-Darling k-sample procedure with and without
168	168	% adjustment for ties.	% adjustment for ties.
169	169	%	%
170	170	% Finally, we compare the P-value with the desired significance level alpha	% Finally, we compare the P-value with the desired significance level alpha
171		% to facilitate a decision about the null hypothesis Ho.
	171		% to facilitate a decision about the null hypothesis Ho.
	172		%
	173		% Syntax: function AnDarksamtest(X,alpha)
172	174	%	%
173		% Syntax: function AnDarksamtest(X,alpha)
174		%
175	175	% Inputs:	% Inputs:
176	176	% X - data matrix (Size of matrix must be n-by-2; data=column 1,	% X - data matrix (Size of matrix must be n-by-2; data=column 1,
177		% sample=column 2)
	177		% sample=column 2)
178	178	% alpha - significance level (default = 0.05)	% alpha - significance level (default = 0.05)
179	179	%	%
180	180	% Output:	% Output:
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
182	182	%	%
183	183	% Example: From the example given by Scholz and Stephens (1987, p.922). It	% Example: From the example given by Scholz and Stephens (1987, p.922). It
184	184	% is interested to test if the four sets of eight measurements each of the	% is interested to test if the four sets of eight measurements each of the
185		% smoothness of a certain type of paper obtained by four independent
	185		% smoothness of a certain type of paper obtained by four independent
186	186	% laboratories are homogeneous or identical to consider them	% laboratories are homogeneous or identical to consider them
187	187	% unstructurated. We use an alpha-value = 0.05. Data are,	% unstructurated. We use an alpha-value = 0.05. Data are,
188	188	%	%
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
198	198	% Data vector is:	% Data vector is:
199	199	% X=[38.7 1;41.5 1;43.8 1;44.5 1;45.5 1;46.0 1;47.7 1;58.0 1;	% X=[38.7 1;41.5 1;43.8 1;44.5 1;45.5 1;46.0 1;47.7 1;58.0 1;
200	200	% 39.2 2;39.3 2;39.7 2;41.4 2;41.8 2;42.9 2;43.3 2;45.8 2;	% 39.2 2;39.3 2;39.7 2;41.4 2;41.8 2;42.9 2;43.3 2;45.8 2;
201		% 34.0 3;35.0 3;39.0 3;40.0 3;43.0 3;43.0 3;44.0 3;45.0 3;
	201		% 34.0 3;35.0 3;39.0 3;40.0 3;43.0 3;43.0 3;44.0 3;45.0 3;
202	202	% 34.0 4;34.8 4;34.8 4;35.4 4;37.2 4;37.8 4;41.2 4;42.8 4];	% 34.0 4;34.8 4;34.8 4;35.4 4;37.2 4;37.8 4;41.2 4;42.8 4];
203	203	%	%
204		% Calling on Matlab the function:
	204		% Calling on Matlab the function:
205	205	% AnDarksamtest(X)	% AnDarksamtest(X)
206	206	%	%
207	207	% Answer is:	% Answer is:
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
220	220	% Anderson-Darling rank statistic: 8.3558723	% Anderson-Darling rank statistic: 8.3558723
221	221	% Standardized Anderson-Darling rank statistic: 4.4492622	% Standardized Anderson-Darling rank statistic: 4.4492622
222	222	% Probability associated to the Anderson-Darling rank statistic = 0.0022547	% Probability associated to the Anderson-Darling rank statistic = 0.0022547
223		%
	223		%
224	224	% With a given significance = 0.050	% With a given significance = 0.050
225	225	% The samples were drawn from different populations: data may be considered	% The samples were drawn from different populations: data may be considered
226	226	% structurated with respect to the random of fixed effect in question.	% structurated with respect to the random of fixed effect in question.
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
230	230	% Anderson-Darling rank statistic: 8.3926093	% Anderson-Darling rank statistic: 8.3926093
231	231	% Standardized Anderson-Darling rank statistic: 4.4797806	% Standardized Anderson-Darling rank statistic: 4.4797806
232	232	% Probability associated to the Anderson-Darling rank statistic = 0.0021700	% Probability associated to the Anderson-Darling rank statistic = 0.0021700
233		%
	233		%
234	234	% With a given significance = 0.050	% With a given significance = 0.050
235	235	% The samples were drawn from different populations: data may be considered	% The samples were drawn from different populations: data may be considered
236	236	% structurated with respect to the random of fixed effect in question.	% structurated with respect to the random of fixed effect in question.
237	237	% ----------------------------------------------------------------------------	% ----------------------------------------------------------------------------
238	238	%	%
239		% Created by A. Trujillo-Ortiz, R. Hernandez-Walls, K. Barba-Rojo,
	239		% Created by A. Trujillo-Ortiz, R. Hernandez-Walls, K. Barba-Rojo,
240	240	% L. Cupul-Magana and R.C. Zavala-Garcia	% L. Cupul-Magana and R.C. Zavala-Garcia
241	241	% Facultad de Ciencias Marinas	% Facultad de Ciencias Marinas
242	242	% Universidad Autonoma de Baja California	% Universidad Autonoma de Baja California
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
247	247	%	%
248	248	% Copyright. November 1, 2007.	% Copyright. November 1, 2007.
249	249	%	%
250		% ---Special thanks are given to Michael Singer, Department of Earth &
	250		% ---Special thanks are given to Michael Singer, Department of Earth &
251	251	% Planetary Science, Institute for Computational Earth System Science,	% Planetary Science, Institute for Computational Earth System Science,
252	252	% University of California at Berkely, for encouraging us to create	% University of California at Berkely, for encouraging us to create
253	253	% this m-file--	% this m-file--
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
266	266	% Anderson, T.W. and Darling, D.A. (1952), Asymptotic Theory of Certain	% Anderson, T.W. and Darling, D.A. (1952), Asymptotic Theory of Certain
267	267	% Goodness of Fit Criteria Based on Stochastic Processes. Annals of	% Goodness of Fit Criteria Based on Stochastic Processes. Annals of
268	268	% Mathematical Statistics, 23:193-212.	% Mathematical Statistics, 23:193-212.
269		% Anderson, T.W. and Darling, D.A. (1954), A Test of Goodness of Fit.
	269		% Anderson, T.W. and Darling, D.A. (1954), A Test of Goodness of Fit.
270	270	% Journal of the American Statistical Association, 49:765-769.	% Journal of the American Statistical Association, 49:765-769.
271	271	% Darling, D.A. (1957), The Kolmogorov-Smirnov, Cramer-von Mises	% Darling, D.A. (1957), The Kolmogorov-Smirnov, Cramer-von Mises
272	272	% Tests. Annals of Mathematical Statistics, 28:823-838.	% Tests. Annals of Mathematical Statistics, 28:823-838.
...	...	function [AnDarksamtest] = AnDarksamtest(X,alpha)
274	274	% variance analysis. Journal of the American Statistical Association,	% variance analysis. Journal of the American Statistical Association,
275	275	% 47(260):583�621.	% 47(260):583�621.
276	276	% Kruskal, W.H. and Wallis, W.A. (1953), Errata to Use of ranks in one-	% Kruskal, W.H. and Wallis, W.A. (1953), Errata to Use of ranks in one-
277		% criterion variance analysis. Journal of the American Statistical
	277		% criterion variance analysis. Journal of the American Statistical
278	278	% Association, 48:907-911.	% Association, 48:907-911.
279	279	% MIL-HDBK-17-1E (1997), The Composite Materials Handbook, Volume 1.	% MIL-HDBK-17-1E (1997), The Composite Materials Handbook, Volume 1.
280	280	% Polymer Matrix Composites: Guidelines for Characterization of	% Polymer Matrix Composites: Guidelines for Characterization of
281		% Structural Materials. Chapter 8, Department of Defense,
	281		% Structural Materials. Chapter 8, Department of Defense,
282	282	% Washington, D.C.	% Washington, D.C.
283	283	% Pettitt, A.N. (1976), A Two-Sample Anderson-Darling Rank Statistic.	% Pettitt, A.N. (1976), A Two-Sample Anderson-Darling Rank Statistic.
284	284	% Biometrika, 63:161-168.	% Biometrika, 63:161-168.
...	...	switch nargin
296	296	end	end
297	297	case{1}	case{1}
298	298	alpha = 0.05;	alpha = 0.05;
299		otherwise
	299		otherwise
300	300	error('Requires at least one input argument.');	error('Requires at least one input argument.');
301	301	end	end
302	302
...	...	hj = c(:,2); %number of values in combined samples equal to zj
316	316	hjn = c(1:end-1,2);	hjn = c(1:end-1,2);
317	317	O = sort(X1);	O = sort(X1);
318	318	zj = unique(O); %distinct values in the combined data set ordered	zj = unique(O); %distinct values in the combined data set ordered
319		L = length(zj);
320		zjn = zj(1:end-1); %distinct values in the combined data set ordered
	319		L = length(zj);
	320		zjn = zj(1:end-1); %distinct values in the combined data set ordered
321	321	%for adjust for ties	%for adjust for ties
322	322	Ln = length(zjn);	Ln = length(zjn);
323	323
324	324	n=[];fij=0;Fij=0;Fijn=0;NN=0;DD=0;ADK1=0;ADK1n=0;	n=[];fij=0;Fij=0;Fijn=0;NN=0;DD=0;ADK1=0;ADK1n=0;
325	325	for i=1:k,	for i=1:k,
326		r = find(X2 == i);
	326		r = find(X2 == i);
327	327	ni = length(r); %number of observations in sample i	ni = length(r); %number of observations in sample i
328	328	n = [n ni];	n = [n ni];
329	329	if (any(ni==0)),	if (any(ni==0)),
...	...	for i=1:k,
339	339	%half the number of values in this	%half the number of values in this
340	340	%group which are equal to zj. It is	%group which are equal to zj. It is
341	341	%adjusted for ties.	%adjusted for ties.
342
	342
343	343	Fijn = [cumsum(fijn)]'; %number of values in the ith group	Fijn = [cumsum(fijn)]'; %number of values in the ith group
344	344	%which are less than zj plus one	%which are less than zj plus one
345	345	%half the number of values in this	%half the number of values in this
346	346	%group which are equal to zj. It is	%group which are equal to zj. It is
347	347	%not adjusted for ties.	%not adjusted for ties.
348	348
349		Hj = [cumsum(hj)-(hj*.5)]; %number of values in the combined samples less
	349		Hj = [cumsum(hj)-(hj*.5)]; %number of values in the combined samples less
350	350	%than zj plus one half the number of values in	%than zj plus one half the number of values in
351		%the combined samples equal to zj. It is
	351		%the combined samples equal to zj. It is
352	352	%adjusted for ties.	%adjusted for ties.
353	353
354		Hjn = [cumsum(hjn)]; %number of values in the combined samples less
	354		Hjn = [cumsum(hjn)]; %number of values in the combined samples less
355	355	%than zj plus one half the number of values in	%than zj plus one half the number of values in
356	356	%the combined samples equal to zj. It is not	%the combined samples equal to zj. It is not
357	357	%adjusted for ties.	%adjusted for ties.
...	...	for i=1:k,
359	359	NN = (N*Fij-ni*Hj).^2;	NN = (N*Fij-ni*Hj).^2;
360	360	DD = (Hj.*(N-Hj))-(N*hj./4);	DD = (Hj.*(N-Hj))-(N*hj./4);
361	361	ADK1 = ADK1 + (1/ni*sum(hj.*(NN./DD)));	ADK1 = ADK1 + (1/ni*sum(hj.*(NN./DD)));
362
	362
363	363	NNn = (N*Fijn-ni*Hjn).^2;	NNn = (N*Fijn-ni*Hjn).^2;
364	364	DDn = Hjn.*(N-Hjn);	DDn = Hjn.*(N-Hjn);
365	365	ADK1n = ADK1n + (1/ni*sum(hjn.*(NNn./DDn)));	ADK1n = ADK1n + (1/ni*sum(hjn.*(NNn./DDn)));
...	...	d = (2*T+6)*k^2-4*T*k;
388	388
389	389	vADK = ((a*N^3)+(b*N^2)+(c*N)+d)/((N-1)*(N-2)*(N-3));	vADK = ((a*N^3)+(b*N^2)+(c*N)+d)/((N-1)*(N-2)*(N-3));
390	390
391		ADKs = (ADK-(k-1))/sqrt(vADK); %standardized k-sample Anderson-Darling
	391		ADKs = (ADK-(k-1))/sqrt(vADK); %standardized k-sample Anderson-Darling
392	392	%statistic	%statistic
393	393
394		ADKsn = (ADKn-(k-1))/sqrt(vADK); %standardized k-sample Anderson-Darling
	394		ADKsn = (ADKn-(k-1))/sqrt(vADK); %standardized k-sample Anderson-Darling
395	395	%statistic not adjusted for ties	%statistic not adjusted for ties
396	396
397	397	%k-sample Anderson-Darling P-value calculation by an extrapolate-interpolate	%k-sample Anderson-Darling P-value calculation by an extrapolate-interpolate
...	...	Pn = 1/(1+exp(Y));
451	451	% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%	% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
452	452	%Optional k-sample Anderson-Darling critical value calculation procedure	%Optional k-sample Anderson-Darling critical value calculation procedure
453	453	% zc=norminv(1-alpha);	% zc=norminv(1-alpha);
454		%
	454		%
455	455	% if (alpha == 0.25),	% if (alpha == 0.25),
456	456	% ADKc = 1 + sqrt(vADK)*(zc+(0.25/sqrt(k-1)) - (-0.105/(k-1)));	% ADKc = 1 + sqrt(vADK)*(zc+(0.25/sqrt(k-1)) - (-0.105/(k-1)));
457	457	% elseif (alpha == 0.10),	% elseif (alpha == 0.10),
...	...	if Pn >= alpha;
487	487	disp('The populations from which the k-samples of data were drawn are identical:');	disp('The populations from which the k-samples of data were drawn are identical:');
488	488	disp('natural groupings have no significant effect (unstructurated).');	disp('natural groupings have no significant effect (unstructurated).');
489	489	else	else
490		disp('The samples were drawn from different populations: data may be considered');
	490		disp('The samples were drawn from different populations: data may be considered');
491	491	disp('structurated with respect to the random of fixed effect in question.');	disp('structurated with respect to the random of fixed effect in question.');
492	492	end	end
493	493	disp('----------------------------------------------------------------------------')	disp('----------------------------------------------------------------------------')
...	...	if P >= alpha;
502	502	disp('The populations from which the k-samples of data were drawn are identical:');	disp('The populations from which the k-samples of data were drawn are identical:');
503	503	disp('natural groupings have no significant effect (unstructurated).');	disp('natural groupings have no significant effect (unstructurated).');
504	504	else	else
505		disp('The samples were drawn from different populations: data may be considered');
	505		disp('The samples were drawn from different populations: data may be considered');
506	506	disp('structurated with respect to the random of fixed effect in question.');	disp('structurated with respect to the random of fixed effect in question.');
507	507	end	end
508	508	disp('----------------------------------------------------------------------------')	disp('----------------------------------------------------------------------------')

File misc/3rdparty/DataHash.m changed (mode: 100644) (index a5dde25..ae29a53)
...	...	if nArg == 2
121	121	error(['JSimon:', mfilename, ':BadInput2'], ...	error(['JSimon:', mfilename, ':BadInput2'], ...
122	122	'*** %s: 2nd input [Opt] must be a struct.', mfilename);	'*** %s: 2nd input [Opt] must be a struct.', mfilename);
123	123	end	end
124
	124
125	125	% Specify hash algorithm:	% Specify hash algorithm:
126	126	if isfield(Opt, 'Method')	if isfield(Opt, 'Method')
127	127	Method = upper(Opt.Method);	Method = upper(Opt.Method);
128	128	end	end
129
	129
130	130	% Specify output format:	% Specify output format:
131	131	if isfield(Opt, 'Format')	if isfield(Opt, 'Format')
132	132	OutFormat = Opt.Format;	OutFormat = Opt.Format;
133	133	end	end
134
	134
135	135	% Check if the Input type is specified - default: 'array':	% Check if the Input type is specified - default: 'array':
136	136	if isfield(Opt, 'Input')	if isfield(Opt, 'Input')
137	137	if strcmpi(Opt.Input, 'File')	if strcmpi(Opt.Input, 'File')
...	...	if nArg == 2
140	140	error(['JSimon:', mfilename, ':CannotOpen'], ...	error(['JSimon:', mfilename, ':CannotOpen'], ...
141	141	'*** %s: 1st input is not a file name', mfilename);	'*** %s: 1st input is not a file name', mfilename);
142	142	end	end
143
	143
144	144	if exist(Data, 'file') ~= 2	if exist(Data, 'file') ~= 2
145	145	error(['JSimon:', mfilename, ':FileNotFound'], ...	error(['JSimon:', mfilename, ':FileNotFound'], ...
146	146	'*** %s: File not found: %s.', mfilename, Data);	'*** %s: File not found: %s.', mfilename, Data);
147	147	end	end
148
	148
149	149	elseif strncmpi(Opt.Input, 'bin', 3) % Accept 'binary'	elseif strncmpi(Opt.Input, 'bin', 3) % Accept 'binary'
150	150	isBin = true;	isBin = true;
151	151	if (isnumeric(Data) || ischar(Data) || islogical(Data)) == 0	if (isnumeric(Data) || ischar(Data) || islogical(Data)) == 0
...	...	if nArg == 2
154	154	end	end
155	155	end	end
156	156	end	end
157
	157
158	158	elseif nArg ~= 1 % Bad number of arguments:	elseif nArg ~= 1 % Bad number of arguments:
159	159	error(['JSimon:', mfilename, ':BadNInput'], ...	error(['JSimon:', mfilename, ':BadNInput'], ...
160	160	'*** %s: 1 or 2 inputs required.', mfilename);	'*** %s: 1 or 2 inputs required.', mfilename);
...	...	if isFile
178	178	end	end
179	179	Data = fread(FID, Inf, '*uint8');	Data = fread(FID, Inf, '*uint8');
180	180	fclose(FID);	fclose(FID);
181
	181
182	182	Engine.update(Data);	Engine.update(Data);
183	183	if usetypecastx	if usetypecastx
184	184	Hash = typecastx(Engine.digest, 'uint8');	Hash = typecastx(Engine.digest, 'uint8');
...	...	elseif isBin % Contents of an elementary array:
197	197	Engine.update(typecastx(imag(Data(:)), 'uint8'));	Engine.update(typecastx(imag(Data(:)), 'uint8'));
198	198	end	end
199	199	Hash = typecastx(Engine.digest, 'uint8');	Hash = typecastx(Engine.digest, 'uint8');
200
	200
201	201	else % Matlab's TYPECAST is less elegant:	else % Matlab's TYPECAST is less elegant:
202	202	if isnumeric(Data)	if isnumeric(Data)
203	203	if isreal(Data)	if isreal(Data)
...	...	elseif isBin % Contents of an elementary array:
214	214	end	end
215	215	Hash = typecast(Engine.digest, 'uint8');	Hash = typecast(Engine.digest, 'uint8');
216	216	end	end
217
	217
218	218	elseif usetypecastx % Faster typecastx:	elseif usetypecastx % Faster typecastx:
219	219	Engine = CoreHash_(Data, Engine);	Engine = CoreHash_(Data, Engine);
220	220	Hash = typecastx(Engine.digest, 'uint8');	Hash = typecastx(Engine.digest, 'uint8');
221
	221
222	222	else % Slower built-in TYPECAST:	else % Slower built-in TYPECAST:
223	223	Engine = CoreHash(Data, Engine);	Engine = CoreHash(Data, Engine);
224	224	Hash = typecast(Engine.digest, 'uint8');	Hash = typecast(Engine.digest, 'uint8');
...	...	Engine.update([uint8(class(Data)), typecastx(size(Data), 'uint8')]);
256	256	if isstruct(Data) % Hash for all array elements and fields:	if isstruct(Data) % Hash for all array elements and fields:
257	257	F = sort(fieldnames(Data)); % Ignore order of fields	F = sort(fieldnames(Data)); % Ignore order of fields
258	258	Engine = CoreHash_(F, Engine); % Catch the fieldnames	Engine = CoreHash_(F, Engine); % Catch the fieldnames
259
	259
260	260	for iS = 1:numel(Data) % Loop over elements of struct array	for iS = 1:numel(Data) % Loop over elements of struct array
261	261	for iField = 1:length(F) % Loop over fields	for iField = 1:length(F) % Loop over fields
262	262	Engine = CoreHash_(Data(iS).(F{iField}), Engine);	Engine = CoreHash_(Data(iS).(F{iField}), Engine);
263	263	end	end
264	264	end	end
265
	265
266	266	elseif iscell(Data) % Get hash for all cell elements:	elseif iscell(Data) % Get hash for all cell elements:
267	267	for iS = 1:numel(Data)	for iS = 1:numel(Data)
268	268	Engine = CoreHash_(Data{iS}, Engine);	Engine = CoreHash_(Data{iS}, Engine);
269	269	end	end
270
	270
271	271	elseif isnumeric(Data) || islogical(Data) || ischar(Data)	elseif isnumeric(Data) || islogical(Data) || ischar(Data)
272	272	if isempty(Data) == 0	if isempty(Data) == 0
273	273	if isreal(Data) % TRUE for LOGICAL and CHAR also:	if isreal(Data) % TRUE for LOGICAL and CHAR also:
...	...	elseif isnumeric(Data) || islogical(Data) || ischar(Data)
277	277	Engine.update(typecastx(imag(Data(:)), 'uint8'));	Engine.update(typecastx(imag(Data(:)), 'uint8'));
278	278	end	end
279	279	end	end
280
	280
281	281	elseif isa(Data, 'function_handle')	elseif isa(Data, 'function_handle')
282	282	Engine = CoreHash(ConvertFuncHandle(Data), Engine);	Engine = CoreHash(ConvertFuncHandle(Data), Engine);
283
	283
284	284	else % Most likely this is a user-defined object:	else % Most likely this is a user-defined object:
285	285	try	try
286	286	Engine = CoreHash(ConvertObject(Data), Engine);	Engine = CoreHash(ConvertObject(Data), Engine);

File misc/3rdparty/GetFullPath/GetFullPath.m changed (mode: 100644 -> 100755) (index 79788af..8820f3b)
...	...	if isempty(hasDataRead)
125	125	% ['GetFullPath: Using slow Matlab-version instead of fast Mex.', ...	% ['GetFullPath: Using slow Matlab-version instead of fast Mex.', ...
126	126	% char(10), 'Compile: InstallMex GetFullPath.c']);	% char(10), 'Compile: InstallMex GetFullPath.c']);
127	127	%end	%end
128
	128
129	129	% DATAREAD is deprecated in 2011b, but still available. In Matlab 6.5, REGEXP	% DATAREAD is deprecated in 2011b, but still available. In Matlab 6.5, REGEXP
130	130	% does not know the 'split' command, therefore DATAREAD is preferred:	% does not know the 'split' command, therefore DATAREAD is preferred:
131	131	hasDataRead = ~isempty(which('dataread'));	hasDataRead = ~isempty(which('dataread'));
...	...	end
149	149	if isWIN % Windows: --------------------------------------------------------	if isWIN % Windows: --------------------------------------------------------
150	150	FSep = '\';	FSep = '\';
151	151	File = strrep(File, '/', FSep);	File = strrep(File, '/', FSep);
152
	152
153	153	% Remove the magic key on demand, it is appended finally again:	% Remove the magic key on demand, it is appended finally again:
154	154	if strncmp(File, '\\?\', 4)	if strncmp(File, '\\?\', 4)
155	155	if strncmpi(File, '\\?\UNC\', 8)	if strncmpi(File, '\\?\UNC\', 8)
...	...	if isWIN % Windows: --------------------------------------------------------
158	158	File = File(5:length(File));	File = File(5:length(File));
159	159	end	end
160	160	end	end
161
	161
162	162	isUNC = strncmp(File, '\\', 2);	isUNC = strncmp(File, '\\', 2);
163	163	FileLen = length(File);	FileLen = length(File);
164	164	if isUNC == 0 % File is not a UNC path	if isUNC == 0 % File is not a UNC path
...	...	if isWIN % Windows: --------------------------------------------------------
172	172	ThePath = ThePath(1:3); % Drive letter only	ThePath = ThePath(1:3); % Drive letter only
173	173	end	end
174	174	end	end
175
	175
176	176	if FileLen < 2 || File(2) ~= ':' % Does not start with drive letter	if FileLen < 2 || File(2) ~= ':' % Does not start with drive letter
177	177	if ThePath(length(ThePath)) ~= FSep	if ThePath(length(ThePath)) ~= FSep
178	178	if File(1) ~= FSep	if File(1) ~= FSep
...	...	if isWIN % Windows: --------------------------------------------------------
188	188	File = [ThePath, File];	File = [ThePath, File];
189	189	end	end
190	190	end	end
191
	191
192	192	elseif FileLen == 2 && File(2) == ':' % "C:" current directory on C!	elseif FileLen == 2 && File(2) == ':' % "C:" current directory on C!
193	193	% "C:" is the current directory on the C-disk, even if the current	% "C:" is the current directory on the C-disk, even if the current
194	194	% directory is on another disk! This was ignored in Matlab 6.5, but	% directory is on another disk! This was ignored in Matlab 6.5, but
...	...	if isWIN % Windows: --------------------------------------------------------
208	208	end	end
209	209	end	end
210	210	end	end
211
	211
212	212	else % Linux, MacOS: ---------------------------------------------------	else % Linux, MacOS: ---------------------------------------------------
213	213	FSep = '/';	FSep = '/';
214	214	File = strrep(File, '\', FSep);	File = strrep(File, '\', FSep);
215
	215
216	216	if strcmp(File, '~') || strncmp(File, '~/', 2) % Home directory:	if strcmp(File, '~') || strncmp(File, '~/', 2) % Home directory:
217	217	HomeDir = getenv('HOME');	HomeDir = getenv('HOME');
218	218	if ~isempty(HomeDir)	if ~isempty(HomeDir)
219	219	File(1) = [];	File(1) = [];
220	220	File = [HomeDir, File];	File = [HomeDir, File];
221	221	end	end
222
	222
223	223	elseif strncmpi(File, FSep, 1) == 0	elseif strncmpi(File, FSep, 1) == 0
224	224	% Append relative path to current folder:	% Append relative path to current folder:
225	225	ThePath = cd;	ThePath = cd;
...	...	if ~isempty(strfind(File, [FSep, '.']))
251	251	Drive = FSep;	Drive = FSep;
252	252	File(1) = [];	File(1) = [];
253	253	end	end
254
	254
255	255	hasTrailFSep = (File(length(File)) == FSep);	hasTrailFSep = (File(length(File)) == FSep);
256	256	if hasTrailFSep	if hasTrailFSep
257	257	File(length(File)) = [];	File(length(File)) = [];
258	258	end	end
259
	259
260	260	if hasDataRead	if hasDataRead
261	261	if isWIN % Need "\\" as separator:	if isWIN % Need "\\" as separator:
262	262	C = dataread('string', File, '%s', 'delimiter', '\\'); %#ok<REMFF1>	C = dataread('string', File, '%s', 'delimiter', '\\'); %#ok<REMFF1>
...	...	if ~isempty(strfind(File, [FSep, '.']))
266	266	else % Use the slower REGEXP, when DATAREAD is not available anymore:	else % Use the slower REGEXP, when DATAREAD is not available anymore:
267	267	C = regexp(File, FSep, 'split');	C = regexp(File, FSep, 'split');
268	268	end	end
269
	269
270	270	% Remove '\.\' directly without side effects:	% Remove '\.\' directly without side effects:
271	271	C(strcmp(C, '.')) = [];	C(strcmp(C, '.')) = [];
272
	272
273	273	% Remove '\..' with the parent recursively:	% Remove '\..' with the parent recursively:
274	274	R = 1:length(C);	R = 1:length(C);
275	275	for dd = reshape(find(strcmp(C, '..')), 1, [])	for dd = reshape(find(strcmp(C, '..')), 1, [])
...	...	if ~isempty(strfind(File, [FSep, '.']))
279	279	R(index - 1) = [];	R(index - 1) = [];
280	280	end	end
281	281	end	end
282
	282
283	283	if isempty(R)	if isempty(R)
284	284	File = Drive;	File = Drive;
285	285	if isUNC && ~hasTrailFSep	if isUNC && ~hasTrailFSep
286	286	File(length(File)) = [];	File(length(File)) = [];
287	287	end	end
288
	288
289	289	elseif isWIN	elseif isWIN
290	290	% If you have CStr2String, use the faster:	% If you have CStr2String, use the faster:
291	291	% File = CStr2String(C(R), FSep, hasTrailFSep);	% File = CStr2String(C(R), FSep, hasTrailFSep);
...	...	if ~isempty(strfind(File, [FSep, '.']))
295	295	else	else
296	296	File = [Drive, File(1:length(File) - 1)];	File = [Drive, File(1:length(File) - 1)];
297	297	end	end
298
	298
299	299	else % Unix:	else % Unix:
300	300	File = [Drive, sprintf('%s/', C{R})];	File = [Drive, sprintf('%s/', C{R})];
301	301	if ~hasTrailFSep	if ~hasTrailFSep
...	...	if isWIN
310	310	error(['JSimon:', mfilename, ':BadTypeInput2'], ...	error(['JSimon:', mfilename, ':BadTypeInput2'], ...
311	311	['*** ', mfilename, ': Input must be a string or cell string']);	['*** ', mfilename, ': Input must be a string or cell string']);
312	312	end	end
313
	313
314	314	if (strncmpi(Style, 'a', 1) && length(File) >= MAX_PATH) || ...	if (strncmpi(Style, 'a', 1) && length(File) >= MAX_PATH) || ...
315	315	strncmpi(Style, 'f', 1)	strncmpi(Style, 'f', 1)
316	316	% Do not use [isUNC] here, because this concerns the input, which can	% Do not use [isUNC] here, because this concerns the input, which can

File misc/3rdparty/GetFullPath/InstallMex.m changed (mode: 100644 -> 100755) (index c3fc72f..89f88f1)
...	...	end
148	148	Flags = cat(1, Flags(:), Param(:));	Flags = cat(1, Flags(:), Param(:));
149	149	cmd = ['mex', sprintf(' %s', Flags{:})];	cmd = ['mex', sprintf(' %s', Flags{:})];
150	150	fprintf(' %s\n', cmd);	fprintf(' %s\n', cmd);
151
	151
152	152	try	try
153	153	% Start the compilation:	% Start the compilation:
154	154	cd(SourcePath);	cd(SourcePath);
155	155	mex(Flags{:});	mex(Flags{:});
156	156	fprintf('Success:\n %s\n', which([SourceName, '.', mexext]));	fprintf('Success:\n %s\n', which([SourceName, '.', mexext]));
157	157	compiled = true;	compiled = true;
158
	158
159	159	catch % No MException to support Matlab 6.5	catch % No MException to support Matlab 6.5
160	160	compiled = false;	compiled = false;
161	161	err = lasterror;	err = lasterror;

File misc/3rdparty/GetFullPath/uTest_GetFullPath.m changed (mode: 100644 -> 100755) (index 8003311..f428e99)
...	...	isWindows = ispc;
34	34
35	35	[dum, fName, fExt] = fileparts(whichFunc); %#ok<ASGLU>	[dum, fName, fExt] = fileparts(whichFunc); %#ok<ASGLU>
36	36	examined = [fName, fExt];	examined = [fName, fExt];
37
	37
38	38	if isWindows	if isWindows
39	39	magicLong = '\\?\';	magicLong = '\\?\';
40	40	magicUNC = '\\?\UNC\';	magicUNC = '\\?\UNC\';
...	...	if isWindows
134	134	fullfile(UNCPath, longName, longName), ... % Input	fullfile(UNCPath, longName, longName), ... % Input
135	135	fullfile(magicUNC, UNCBare, longName, longName); ... % Want	fullfile(magicUNC, UNCBare, longName, longName); ... % Want
136	136	});	});
137
	137
138	138	else	else
139	139	TestSet = cat(1, TestSet, { ...	TestSet = cat(1, TestSet, { ...
140	140	fullfile(Base, '..', '..'), '/'; ...	fullfile(Base, '..', '..'), '/'; ...
...	...	for iTest = 1:size(TestSet, 1)
157	157	In_ = strrep(In, longName, longReplace);	In_ = strrep(In, longName, longReplace);
158	158	Got_ = strrep(Got, longName, longReplace);	Got_ = strrep(Got, longName, longReplace);
159	159	Want_ = strrep(Want, longName, longReplace);	Want_ = strrep(Want, longName, longReplace);
160
	160
161	161	if strcmpi(Got, Want)	if strcmpi(Got, Want)
162	162	fprintf(' ok: [%s]\n => [%s]\n', In_, Want_);	fprintf(' ok: [%s]\n => [%s]\n', In_, Want_);
163	163	else	else
...	...	for i = depth:-1:2
206	206	try	try
207	207	cf = GetFullPath(testStr);	cf = GetFullPath(testStr);
208	208	xfl = fl(:, 1:i - 1);	xfl = fl(:, 1:i - 1);
209
	209
210	210	% Construct the reply by hand:	% Construct the reply by hand:
211	211	cf2 = cat(2, xfl{:}, testFolder);	cf2 = cat(2, xfl{:}, testFolder);
212	212	if strcmpi(cf, cf2)	if strcmpi(cf, cf2)
...	...	for i = depth:-1:2
234	234	try	try
235	235	cf = GetFullPath(testStr);	cf = GetFullPath(testStr);
236	236	xfl = fl(:, 1:i - 1);	xfl = fl(:, 1:i - 1);
237
	237
238	238	% Construct the reply by hand:	% Construct the reply by hand:
239	239	cf2 = [cat(2, xfl{:}), testFile];	cf2 = [cat(2, xfl{:}), testFile];
240	240	if strcmpi(cf, cf2)	if strcmpi(cf, cf2)
...	...	for i = depth:-1:2
263	263	try	try
264	264	cf = GetFullPath(testStr);	cf = GetFullPath(testStr);
265	265	xfl = fl(:, 1:i - 1);	xfl = fl(:, 1:i - 1);
266
	266
267	267	% Construct the reply by hand:	% Construct the reply by hand:
268	268	cf2 = [cat(2, xfl{:}), testFolder];	cf2 = [cat(2, xfl{:}), testFolder];
269	269	if strcmpi(cf, cf2)	if strcmpi(cf, cf2)
...	...	for i = depth:-1:2
292	292	try	try
293	293	cf = GetFullPath(testStr);	cf = GetFullPath(testStr);
294	294	xfl = fl(:, 1:i - 1);	xfl = fl(:, 1:i - 1);
295
	295
296	296	% Construct the reply by hand:	% Construct the reply by hand:
297	297	cf2 = [cat(2, xfl{:}), testFile];	cf2 = [cat(2, xfl{:}), testFile];
298	298	if strcmpi(cf, cf2)	if strcmpi(cf, cf2)
...	...	for i = depth:-1:1
318	318	testStr = [testStr, FSep, '..']; %#ok<AGROW>	testStr = [testStr, FSep, '..']; %#ok<AGROW>
319	319	try	try
320	320	cf = GetFullPath(testStr);	cf = GetFullPath(testStr);
321
	321
322	322	% Construct the reply by hand:	% Construct the reply by hand:
323	323	cf2 = CleanReply(testStr);	cf2 = CleanReply(testStr);
324	324	if strcmpi(cf, cf2)	if strcmpi(cf, cf2)
...	...	end
342	342	if isWindows	if isWindows
343	343	fprintf('\n== Test Magic prefix under Windows:\n');	fprintf('\n== Test Magic prefix under Windows:\n');
344	344	MAX_PATH = 260;	MAX_PATH = 260;
345
	345
346	346	% Test data folders do not need to exist:	% Test data folders do not need to exist:
347	347	longName = repmat('a', 1, 250);	longName = repmat('a', 1, 250);
348	348	testData = {'C:\Temp', fullfile('C:\Temp', longName, longName), ...	testData = {'C:\Temp', fullfile('C:\Temp', longName, longName), ...
...	...	if isWindows
352	352	'\\Server\Share', ...	'\\Server\Share', ...
353	353	fullfile('\\Server\Share', longName, longName), ...	fullfile('\\Server\Share', longName, longName), ...
354	354	'short', fullfile(longName, longName)};	'short', fullfile(longName, longName)};
355
	355
356	356	cd(tempdir);	cd(tempdir);
357
	357
358	358	for iTestData = 1:numel(testData)	for iTestData = 1:numel(testData)
359	359	folder = testData{iTestData}; % Need not exist	folder = testData{iTestData}; % Need not exist
360	360	leanPath = GetFullPath(folder, 'lean');	leanPath = GetFullPath(folder, 'lean');
361	361	fatPath = GetFullPath(folder, 'fat');	fatPath = GetFullPath(folder, 'fat');
362	362	autoPath = GetFullPath(folder, 'auto');	autoPath = GetFullPath(folder, 'auto');
363
	363
364	364	fullWant = GetFullPath(folder);	fullWant = GetFullPath(folder);
365	365	leanWant = strrep(strrep(fullWant, '\\?\UNC', '\'), '\\?\', '');	leanWant = strrep(strrep(fullWant, '\\?\UNC', '\'), '\\?\', '');
366	366	uncIndex = strfind(fullWant, '\Server\Share');	uncIndex = strfind(fullWant, '\Server\Share');
...	...	if isWindows
374	374	else	else
375	375	autoWant = leanWant;	autoWant = leanWant;
376	376	end	end
377
	377
378	378	if strcmp(leanPath, leanWant) == 0	if strcmp(leanPath, leanWant) == 0
379	379	error(ErrID, '%s: Unexpected name for lean style?! [%s]', ...	error(ErrID, '%s: Unexpected name for lean style?! [%s]', ...
380	380	mfilename, leanPath);	mfilename, leanPath);

File misc/3rdparty/Singleton.m changed (mode: 100644) (index 34e24ec..4aa6a56)
...	...	classdef Singleton < handle
24	24	% 1. Constructor must be hidden from the user (Access=private).	% 1. Constructor must be hidden from the user (Access=private).
25	25	% 2. The concrete implementation of instance() must use a persistent	% 2. The concrete implementation of instance() must use a persistent
26	26	% variable to store the unique instance of the subclass.	% variable to store the unique instance of the subclass.
27		%
	27		%
28	28	% Refer to pp.127-134 Gamma et al.[1] for more information on the	% Refer to pp.127-134 Gamma et al.[1] for more information on the
29	29	% Singleton Design Pattern.	% Singleton Design Pattern.
30		%
	30		%
31	31	% Written by Bobby Nedelkovski	% Written by Bobby Nedelkovski
32	32	% The MathWorks Australia Pty Ltd	% The MathWorks Australia Pty Ltd
33	33	% Copyright 2009, The MathWorks, Inc.	% Copyright 2009, The MathWorks, Inc.
...	...	classdef Singleton < handle
42	42	properties(Access=private)	properties(Access=private)
43	43	singletonData = [];	singletonData = [];
44	44	end	end
45
	45
46	46	methods(Abstract, Static)	methods(Abstract, Static)
47	47	% This method serves as the global point of access in creating a	% This method serves as the global point of access in creating a
48	48	% single instance *or* acquiring a reference to the singleton.	% single instance *or* acquiring a reference to the singleton.
...	...	classdef Singleton < handle
54	54	% obj = reference to a persistent instance of the class	% obj = reference to a persistent instance of the class
55	55	obj = instance();	obj = instance();
56	56	end	end
57
	57
58	58	methods % Public Access	methods % Public Access
59	59	% Accessor method for querying the singleton data.	% Accessor method for querying the singleton data.
60	60	% Input:	% Input:
...	...	classdef Singleton < handle
64	64	function singletonData = getSingletonData(obj)	function singletonData = getSingletonData(obj)
65	65	singletonData = obj.singletonData;	singletonData = obj.singletonData;
66	66	end	end
67
	67
68	68	% Accessor method for modifying the singleton data.	% Accessor method for modifying the singleton data.
69	69	% Input:	% Input:
70	70	% obj = reference to the singleton instance of the subclass	% obj = reference to the singleton instance of the subclass
...	...	classdef Singleton < handle
74	74	function setSingletonData(obj, singletonData)	function setSingletonData(obj, singletonData)
75	75	obj.singletonData = singletonData;	obj.singletonData = singletonData;
76	76	end	end
77
	77
78	78	end	end
79	79
80	80	end	end

File misc/3rdparty/allcomb.m changed (mode: 100644) (index a200cc3..5e5ea44)
...	...	function A = allcomb(varargin)
33	33	% % 'Bye' [1x3 double] [] } ; % a 8-by-3 cell array	% % 'Bye' [1x3 double] [] } ; % a 8-by-3 cell array
34	34	%	%
35	35	% ALLCOMB(..., 'matlab') causes the first column to change fastest which	% ALLCOMB(..., 'matlab') causes the first column to change fastest which
36		% is consistent with matlab indexing. Example:
37		% allcomb(1:2,3:4,5:6,'matlab')
	36		% is consistent with matlab indexing. Example:
	37		% allcomb(1:2,3:4,5:6,'matlab')
38	38	% % -> [ 1 3 5 ; 1 4 5 ; 1 3 6 ; ... ; 2 4 6 ]	% % -> [ 1 3 5 ; 1 4 5 ; 1 3 6 ; ... ; 2 4 6 ]
39	39	%	%
40	40	% If one of the arguments is empty, ALLCOMB returns a 0-by-N empty array.	% If one of the arguments is empty, ALLCOMB returns a 0-by-N empty array.
41		%
	41		%
42	42	% See also NCHOOSEK, PERMS, NDGRID	% See also NCHOOSEK, PERMS, NDGRID
43	43	% and NCHOOSE, COMBN, KTHCOMBN (Matlab Central FEX)	% and NCHOOSE, COMBN, KTHCOMBN (Matlab Central FEX)
44	44
...	...	elseif NC > 1
90	90	end	end
91	91	% for cell input, we use to indices to get all combinations	% for cell input, we use to indices to get all combinations
92	92	ix = cellfun(@(c) 1:numel(c), args,'un',0) ;	ix = cellfun(@(c) 1:numel(c), args,'un',0) ;
93
	93
94	94	% flip using ii if last column is changing fastest	% flip using ii if last column is changing fastest
95	95	[ix{ii}] = ndgrid(ix{ii}) ;	[ix{ii}] = ndgrid(ix{ii}) ;
96
	96
97	97	A = cell(numel(ix{1}),NC) ; % pre-allocate the output	A = cell(numel(ix{1}),NC) ; % pre-allocate the output
98	98	for k=1:NC,	for k=1:NC,
99	99	% combine	% combine

File misc/3rdparty/allcomb_invert.m changed (mode: 100644) (index d18d9c8..47866d8)
1	1	function factorlist = allcomb_invert(array)	function factorlist = allcomb_invert(array)
2		% invert the Cartesian product:
3		% if array = allcomb(list_1, ..., list_n), return list_1, ..., list_n (column vectors)
4		[elems, list_no] = size(array);
5		factorlist = {};
6		if isequal(class(array),'double')
7		for k=1:list_no
8		factorlist{k} = unique(array(:,k), 'stable');
9		end
10		% functionality for handling nonnumeric input does not work yet
11		% elseif isequal(class(array),'cell')
12		% for k=1:list_no
13		% factorlist{k} = unique({array{:,k}}, 'stable');
14		% end
15		else
16		error('Input to allcomb_invert must be cell array or double array');
17		end
	2		% invert the Cartesian product:
	3		% if array = allcomb(list_1, ..., list_n), return list_1, ..., list_n (column vectors)
	4		[elems, list_no] = size(array);
	5		factorlist = {};
	6		if isequal(class(array),'double')
	7		for k=1:list_no
	8		factorlist{k} = unique(array(:,k), 'stable');
	9		end
	10		% functionality for handling nonnumeric input does not work yet
	11		% elseif isequal(class(array),'cell')
	12		% for k=1:list_no
	13		% factorlist{k} = unique({array{:,k}}, 'stable');
	14		% end
	15		else
	16		error('Input to allcomb_invert must be cell array or double array');
	17		end

File misc/3rdparty/allcomb_invert_test.m changed (mode: 100644) (index 7c517ab..df8d045)
1	1	function test_suite = allcomb_invert_test %#ok<STOUT>	function test_suite = allcomb_invert_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function two_dimensional_numeric_test	function two_dimensional_numeric_test
5		list_1 = [1:2]';
6		list_2 = [10:12]';
7		lists = {list_1, list_2};
	5		list_1 = [1:2]';
	6		list_2 = [10:12]';
	7		lists = {list_1, list_2};
8	8
9		cartprod = allcomb(lists{:});
10		factorlist = allcomb_invert(cartprod);
11		assertEqual(factorlist, lists)
	9		cartprod = allcomb(lists{:});
	10		factorlist = allcomb_invert(cartprod);
	11		assertEqual(factorlist, lists)
12	12
13	13	function two_dimensional_numeric_unsorted_test	function two_dimensional_numeric_unsorted_test
14		list_1 = [1; 2];
15		list_2 = [11; 10; 12];
16		lists = {list_1, list_2};
	14		list_1 = [1; 2];
	15		list_2 = [11; 10; 12];
	16		lists = {list_1, list_2};
17	17
18		cartprod = allcomb(lists{:});
19		factorlist = allcomb_invert(cartprod);
20		assertEqual(factorlist, lists)
	18		cartprod = allcomb(lists{:});
	19		factorlist = allcomb_invert(cartprod);
	20		assertEqual(factorlist, lists)
21	21
22	22	function two_dimensional_singleton_test	function two_dimensional_singleton_test
23		list_1 = 1;
24		list_2 = [10:12]';
25		lists = {list_1, list_2};
	23		list_1 = 1;
	24		list_2 = [10:12]';
	25		lists = {list_1, list_2};
26	26
27		cartprod = allcomb(lists{:});
28		factorlist = allcomb_invert(cartprod);
29		assertEqual(factorlist, lists)
	27		cartprod = allcomb(lists{:});
	28		factorlist = allcomb_invert(cartprod);
	29		assertEqual(factorlist, lists)
30	30
31	31	function three_dimensional_numeric_test	function three_dimensional_numeric_test
32		list_1 = [1; 2];
33		list_2 = [11; 10; 12];
34		list_3 = [11; 100; -12; 1];
35		lists = {list_1, list_2, list_3};
	32		list_1 = [1; 2];
	33		list_2 = [11; 10; 12];
	34		list_3 = [11; 100; -12; 1];
	35		lists = {list_1, list_2, list_3};
36	36
37		cartprod = allcomb(lists{:});
38		factorlist = allcomb_invert(cartprod);
39		assertEqual(factorlist, lists)
	37		cartprod = allcomb(lists{:});
	38		factorlist = allcomb_invert(cartprod);
	39		assertEqual(factorlist, lists)
40	40
41	41	function random_numeric_test	function random_numeric_test
42		list_1 = randn(10,1);
43		list_2 = randn(10,1);
44		list_3 = randn(10,1);
45		lists = {list_1, list_2, list_3};
46
47		cartprod = allcomb(lists{:});
48		factorlist = allcomb_invert(cartprod);
49		assertEqual(factorlist, lists)
	42		list_1 = randn(10,1);
	43		list_2 = randn(10,1);
	44		list_3 = randn(10,1);
	45		lists = {list_1, list_2, list_3};
	46
	47		cartprod = allcomb(lists{:});
	48		factorlist = allcomb_invert(cartprod);
	49		assertEqual(factorlist, lists)
50	50
51	51	% % functionality for handling nonnumeric input does not work yet	% % functionality for handling nonnumeric input does not work yet
52	52	% function two_dimensional_nonnumeric_test	% function two_dimensional_nonnumeric_test

File misc/3rdparty/bplot.m changed (mode: 100644) (index 24b1c7b..cedb79a)
1	1	% function forLegend = bplot(X,varargin)	% function forLegend = bplot(X,varargin)
2	2	% This function will create a nice boxplot from a set of data. You don't	% This function will create a nice boxplot from a set of data. You don't
3	3	% need any toolboxes.	% need any toolboxes.
4		%
	4		%
5	5	% bplot(D) will create a boxplot of data D, no fuss.	% bplot(D) will create a boxplot of data D, no fuss.
6		%
	6		%
7	7	% T = bplot(D) If X is a matrix, there is one box per column; if X is a	% T = bplot(D) If X is a matrix, there is one box per column; if X is a
8	8	% vector, there is just one box. On each box, the central	% vector, there is just one box. On each box, the central
9	9	% mark is the median, the edges of the box are the 25th and	% mark is the median, the edges of the box are the 25th and
10	10	% 75th percentiles	% 75th percentiles
11	11	% array 'T' for a legend. You can add the legend as legend(T)	% array 'T' for a legend. You can add the legend as legend(T)
12		%
	12		%
13	13	% T = bplot(D,x) will plot the boxplot of data D above the 'x' value x	% T = bplot(D,x) will plot the boxplot of data D above the 'x' value x
14		%
	14		%
15	15	% T = bplot(D,y,'horiz') will plot a horizontal boxplot at the 'y' value of y	% T = bplot(D,y,'horiz') will plot a horizontal boxplot at the 'y' value of y
16		%
	16		%
17	17	% T = bplot(...,'Property', . . . )	% T = bplot(...,'Property', . . . )
18	18	% T = bplot(...,'PropertyName',PropertyValue, . . . )	% T = bplot(...,'PropertyName',PropertyValue, . . . )
19		%
	19		%
20	20	% SINGLE PARAMETERS:	% SINGLE PARAMETERS:
21		%
	21		%
22	22	% 'horizontal': Display the boxplot along the horizontal axis. The	% 'horizontal': Display the boxplot along the horizontal axis. The
23	23	% default is to display the boxplot vertically.	% default is to display the boxplot vertically.
24	24	% 'horiz'	% 'horiz'
45	45	% 'boxes','boxedge'	% 'boxes','boxedge'
46	46	% 'whisker': Set the percentage of points that the whiskers	% 'whisker': Set the percentage of points that the whiskers
47	47	% span. Default is the 9% and 91%. Choose only the	% span. Default is the 9% and 91%. Choose only the
48		% lower number in %, for example: 9.
	48		% lower number in %, for example: 9.
49	49	% 'whiskers','whiskeredge'	% 'whiskers','whiskeredge'
50	50	% 'linewidth': Set the width of all the lines.	% 'linewidth': Set the width of all the lines.
51	51	% 'color': Change the color of all the lines. If you use this	% 'color': Change the color of all the lines. If you use this
57	57	% horizontal plots the height is 1.5/20 of the y axis	% horizontal plots the height is 1.5/20 of the y axis
58	58	% the bars.	% the bars.
59	59	% 'barwidth'	% 'barwidth'
60		%
61		%% Jitter feature
	60		%
	61		%% Jitter feature
62	62	% The boxplot has a cool jitter feature which will help you view each	% The boxplot has a cool jitter feature which will help you view each
63	63	% outlier separately even if two have identical values. It jitters the	% outlier separately even if two have identical values. It jitters the
64	64	% points around the other axis so that you can see exactly how many are	% points around the other axis so that you can see exactly how many are
65	65	% there.	% there.
66		%
67		% % Examples:
	66		%
	67		% % Examples:
68	68	% bplot(randn(30,3),'outliers')	% bplot(randn(30,3),'outliers')
69	69	% bplot(randn(30,3),'color','black');	% bplot(randn(30,3),'color','black');
70	70	% ----	% ----
71	71	% X = round(randn(30,4)*5)/5; % random, with some duplicates	% X = round(randn(30,4)*5)/5; % random, with some duplicates
72	72	% T = bplot(X,'points');	% T = bplot(X,'points');
73	73	% legend(T,'location','eastoutside');	% legend(T,'location','eastoutside');
74		%
	74		%
75	75	%% development notes:	%% development notes:
76	76	% This function was developed to be part of a larger histogram function	% This function was developed to be part of a larger histogram function
77	77	% which can be found at this location:	% which can be found at this location:
78	78	% http://www.mathworks.com/matlabcentral/fileexchange/27388-plot-and-compare-histograms-pretty-by-default	% http://www.mathworks.com/matlabcentral/fileexchange/27388-plot-and-compare-histograms-pretty-by-default
79		%
	79		%
80	80	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
81	81	% Jonathan Lansey 2013, %	% Jonathan Lansey 2013, %
82	82	% questions to Lansey at gmail.com %	% questions to Lansey at gmail.com %
83		%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
	83		%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
84	84	%%	%%
85	85	function [forLegend, boxEdge, wisEdge] = bplot(x,varargin)	function [forLegend, boxEdge, wisEdge] = bplot(x,varargin)
86	86	%% save the initial hold state of the figure.	%% save the initial hold state of the figure.
...	...	function [forLegend, boxEdge, wisEdge] = bplot(x,varargin)
145	145
146	146	%% Initialize some things before accepting user parameters	%% Initialize some things before accepting user parameters
147	147	horizontalFlag=0;	horizontalFlag=0;
148		barFactor=1; %
	148		barFactor=1; %
149	149	linewidth=2;	linewidth=2;
150	150	forceNoLegend=0; % will any legend items be allowed in.	forceNoLegend=0; % will any legend items be allowed in.
151	151	stdFlag = 0;	stdFlag = 0;
152	152	meanFlag = 1;	meanFlag = 1;
153		specialWidthFlag = 0; % this flag will determine whether the bar width is
	153		specialWidthFlag = 0; % this flag will determine whether the bar width is
154	154	% automatically set as a proportion of the axis width	% automatically set as a proportion of the axis width
155	155
156		toScale = 0; % this flag is to scale the jitter function in case the
	156		toScale = 0; % this flag is to scale the jitter function in case the
157	157	% histogram function is calling it	% histogram function is calling it
158	158	xoffset = 0;	xoffset = 0;
159	159	if justOneInputFlag	if justOneInputFlag
...	...	function [forLegend, boxEdge, wisEdge] = bplot(x,varargin)
273	273	hReg2(end+1) = rectangle('Position',[y-barWidth/2,boxEdge(1),barWidth,IQR],'linewidth',linewidth,...	hReg2(end+1) = rectangle('Position',[y-barWidth/2,boxEdge(1),barWidth,IQR],'linewidth',linewidth,...
274	274	'EdgeColor',boxColor, 'facecolor',[1 1 1]);	'EdgeColor',boxColor, 'facecolor',[1 1 1]);
275	275	hold on;	hold on;
276
	276
277	277	hReg2(end+1) = plot([y-barWidth/2 y+barWidth/2],[medianX medianX],'color',meanColor,'linewidth',linewidth);	hReg2(end+1) = plot([y-barWidth/2 y+barWidth/2],[medianX medianX],'color',meanColor,'linewidth',linewidth);
278	278	if meanFlag	if meanFlag
279	279	hReg2(end+1) = plot(y,meanX,'+','linewidth',linewidth,'color',meanColor,'markersize',10);	hReg2(end+1) = plot(y,meanX,'+','linewidth',linewidth,'color',meanColor,'markersize',10);
...	...	function [forLegend, boxEdge, wisEdge] = bplot(x,varargin)
314	314	end	end
315	315	end	end
316	316	end	end
317		%% Remove the legend entries
	317		%% Remove the legend entries
318	318	% remove extras for all the items.	% remove extras for all the items.
319	319	for ii=1:length(hReg)	for ii=1:length(hReg)
320	320	set(get(get(hReg(ii),'Annotation'),'LegendInformation'),'IconDisplayStyle','off'); % Exclude line from legend	set(get(get(hReg(ii),'Annotation'),'LegendInformation'),'IconDisplayStyle','off'); % Exclude line from legend
...	...	function [forLegend, boxEdge, wisEdge] = bplot(x,varargin)
338	338	padxfac = 2;	padxfac = 2;
339	339	padyfac = .1;	padyfac = .1;
340	340	end	end
341
	341
342	342	axis tight;	axis tight;
343	343	p = axis;	p = axis;
344	344	padx = (p(2)-p(1))*padxfac; pady = (p(4)-p(3))*padyfac;	padx = (p(2)-p(1))*padxfac; pady = (p(4)-p(3))*padyfac;

File misc/3rdparty/brewermap.m changed (mode: 100644) (index 98eacf9..0494e1b)
...	...	persistent dcs
135	135	str = 'A colorscheme must be preselected before calling without a scheme token.';	str = 'A colorscheme must be preselected before calling without a scheme token.';
136	136	%	%
137	137	if nargin==0 % Current figure's colormap length and the preselected colorscheme.	if nargin==0 % Current figure's colormap length and the preselected colorscheme.
138		assert(~isempty(dcs),str)
139		[map,num,typ] = cbSample([],dcs);
	138		assert(~isempty(dcs),str)
	139		[map,num,typ] = cbSample([],dcs);
140	140	elseif nargin==2 % Input colormap length and colorscheme.	elseif nargin==2 % Input colormap length and colorscheme.
141		assert(isnumeric(N),'The first argument must be a numeric scalar, or empty.')
142		assert(ischar(scheme)&&isrow(scheme),'The second argument must be a string.')
143		[map,num,typ] = cbSample(N,scheme);
	141		assert(isnumeric(N),'The first argument must be a numeric scalar, or empty.')
	142		assert(ischar(scheme)&&isrow(scheme),'The second argument must be a string.')
	143		[map,num,typ] = cbSample(N,scheme);
144	144	elseif isnumeric(N) % Input colormap length and the preselected colorscheme.	elseif isnumeric(N) % Input colormap length and the preselected colorscheme.
145		assert(~isempty(dcs),str)
146		[map,num,typ] = cbSample(N,dcs);
	145		assert(~isempty(dcs),str)
	146		[map,num,typ] = cbSample(N,dcs);
147	147	else% String	else% String
148		assert(ischar(N)&&isrow(N),'The first argument must be a string or numeric.')
149		% The order of <vec>: case-insensitive sort by type and then name.
150		vec = {'BrBG';'PiYG';'PRGn';'PuOr';'RdBu';'RdGy';'RdYlBu';'RdYlGn';'Spectral';'Accent';'Dark2';'Paired';'Pastel1';'Pastel2';'Set1';'Set2';'Set3';'Blues';'BuGn';'BuPu';'GnBu';'Greens';'Greys';'OrRd';'Oranges';'PuBu';'PuBuGn';'PuRd';'Purples';'RdPu';'Reds';'YlGn';'YlGnBu';'YlOrBr';'YlOrRd'};
151		switch lower(N)
152		case 'demo' % Plot all colorschemes in a figure.
153		cbDemoFig(vec)
154		case 'list' % Return a list of all colorschemes.
155		[num,typ] = cellfun(@cbSelect,vec,'UniformOutput',false);
156		num = cat(1,num{:});
157		map = vec;
158		otherwise % Store the preselected colorscheme token.
159		map = dcs;
160		isr = strncmp('*',N,1);
161		scm = N(1+isr:end);
162		[num,typ] = cbSelect(scm);
163		dcs = [N(1:+isr),vec{strcmpi(vec,scm)}];
164		end
	148		assert(ischar(N)&&isrow(N),'The first argument must be a string or numeric.')
	149		% The order of <vec>: case-insensitive sort by type and then name.
	150		vec = {'BrBG';'PiYG';'PRGn';'PuOr';'RdBu';'RdGy';'RdYlBu';'RdYlGn';'Spectral';'Accent';'Dark2';'Paired';'Pastel1';'Pastel2';'Set1';'Set2';'Set3';'Blues';'BuGn';'BuPu';'GnBu';'Greens';'Greys';'OrRd';'Oranges';'PuBu';'PuBuGn';'PuRd';'Purples';'RdPu';'Reds';'YlGn';'YlGnBu';'YlOrBr';'YlOrRd'};
	151		switch lower(N)
	152		case 'demo' % Plot all colorschemes in a figure.
	153		cbDemoFig(vec)
	154		case 'list' % Return a list of all colorschemes.
	155		[num,typ] = cellfun(@cbSelect,vec,'UniformOutput',false);
	156		num = cat(1,num{:});
	157		map = vec;
	158		otherwise % Store the preselected colorscheme token.
	159		map = dcs;
	160		isr = strncmp('*',N,1);
	161		scm = N(1+isr:end);
	162		[num,typ] = cbSelect(scm);
	163		dcs = [N(1:+isr),vec{strcmpi(vec,scm)}];
	164		end
165	165	end	end
166	166	%	%
167	167	end	end
...	...	function [map,num,typ] = cbSample(N,tok)
170	170	% Pick a colorscheme, downsample/interpolate to the requested colormap length.	% Pick a colorscheme, downsample/interpolate to the requested colormap length.
171	171	%	%
172	172	if isempty(N)	if isempty(N)
173		N = size(get(gcf,'colormap'),1);
	173		N = size(get(gcf,'colormap'),1);
174	174	else	else
175		assert(isscalar(N)&&isreal(N),'First argument must be a real numeric scalar, or empty.')
	175		assert(isscalar(N)&&isreal(N),'First argument must be a real numeric scalar, or empty.')
176	176	end	end
177	177	%	%
178	178	% obtain nodes:	% obtain nodes:
...	...	tok = tok(1+isr:end);
183	183	map = rgb(cbIndex(N,num,typ,tok,isr),:);	map = rgb(cbIndex(N,num,typ,tok,isr),:);
184	184	% interpolate:	% interpolate:
185	185	if N>num	if N>num
186		map = interp1(1:num,map,linspace(1,num,N),'pchip');
	186		map = interp1(1:num,map,linspace(1,num,N),'pchip');
187	187	end	end
188	188	%	%
189	189	end	end
...	...	xmx = max(cellfun(@cbSelect,seq));
197	197	ymx = numel(seq);	ymx = numel(seq);
198	198	%	%
199	199	if ishghandle(cbh)	if ishghandle(cbh)
200		figure(cbh);
201		delete(axh);
	200		figure(cbh);
	201		delete(axh);
202	202	else	else
203		cbh = figure('HandleVisibility','callback', 'IntegerHandle','off',...
204		'NumberTitle','off', 'Name',[mfilename,' Demo'],'Color','white');
	203		cbh = figure('HandleVisibility','callback', 'IntegerHandle','off',...
	204		'NumberTitle','off', 'Name',[mfilename,' Demo'],'Color','white');
205	205	end	end
206	206	%	%
207	207	axh = axes('Parent',cbh, 'Color','none',...	axh = axes('Parent',cbh, 'Color','none',...
208		'XTick',0:xmx, 'YTick',0.5:ymx, 'YTickLabel',seq, 'YDir','reverse');
	208		'XTick',0:xmx, 'YTick',0.5:ymx, 'YTickLabel',seq, 'YDir','reverse');
209	209	title(axh,['ColorBrewer Color Schemes (',mfilename,'.m)'], 'Interpreter','none')	title(axh,['ColorBrewer Color Schemes (',mfilename,'.m)'], 'Interpreter','none')
210	210	xlabel(axh,'Scheme Nodes')	xlabel(axh,'Scheme Nodes')
211	211	ylabel(axh,'Scheme Name')	ylabel(axh,'Scheme Name')
212	212	axf = get(axh,'FontName');	axf = get(axh,'FontName');
213	213	%	%
214	214	for y = 1:ymx	for y = 1:ymx
215		[num,typ,rgb] = cbSelect(seq{y});
216		rgb = rgb(cbIndex(num,num,typ,seq{y},false),:); % downsample
217		for x = 1:num
218		patch([x-1,x-1,x,x],[y-1,y,y,y-1],1, 'FaceColor',rgb(x,:), 'Parent',axh)
219		end
220		text(xmx+0.1,y-0.5,typ, 'Parent',axh, 'FontName',axf)
	215		[num,typ,rgb] = cbSelect(seq{y});
	216		rgb = rgb(cbIndex(num,num,typ,seq{y},false),:); % downsample
	217		for x = 1:num
	218		patch([x-1,x-1,x,x],[y-1,y,y,y-1],1, 'FaceColor',rgb(x,:), 'Parent',axh)
	219		end
	220		text(xmx+0.1,y-0.5,typ, 'Parent',axh, 'FontName',axf)
221	221	end	end
222	222	%	%
223	223	end	end
...	...	function idx = cbIndex(N,num,typ,tok,isr)
226	226	% Ensure exactly the same colors as in the online ColorBrewer schemes.	% Ensure exactly the same colors as in the online ColorBrewer schemes.
227	227	%	%
228	228	if strcmp(typ,'Qualitative')	if strcmp(typ,'Qualitative')
229		assert(N<=num,'Colorscheme "%s" maximum colormap length: %d. Requested: %d.',tok,num,N)
230		idx = 1:N;
	229		assert(N<=num,'Colorscheme "%s" maximum colormap length: %d. Requested: %d.',tok,num,N)
	230		idx = 1:N;
231	231	elseif strcmp(typ,'Diverging')	elseif strcmp(typ,'Diverging')
232		switch min(N,num)
233		case 1 % extrapolated
234		idx = 8;
235		case 2 % extrapolated
236		idx = [4,12];
237		case 3
238		idx = [5,8,11];
239		case 4
240		idx = [3,6,10,13];
241		case 5
242		idx = [3,6,8,10,13];
243		case 6
244		idx = [2,5,7,9,11,14];
245		case 7
246		idx = [2,5,7,8,9,11,14];
247		case 8
248		idx = [2,4,6,7,9,10,12,14];
249		case 9
250		idx = [2,4,6,7,8,9,10,12,14];
251		case 10
252		idx = [1,2,4,6,7,9,10,12,14,15];
253		case 11
254		idx = [1,2,4,6,7,8,9,10,12,14,15];
255		end
	232		switch min(N,num)
	233		case 1 % extrapolated
	234		idx = 8;
	235		case 2 % extrapolated
	236		idx = [4,12];
	237		case 3
	238		idx = [5,8,11];
	239		case 4
	240		idx = [3,6,10,13];
	241		case 5
	242		idx = [3,6,8,10,13];
	243		case 6
	244		idx = [2,5,7,9,11,14];
	245		case 7
	246		idx = [2,5,7,8,9,11,14];
	247		case 8
	248		idx = [2,4,6,7,9,10,12,14];
	249		case 9
	250		idx = [2,4,6,7,8,9,10,12,14];
	251		case 10
	252		idx = [1,2,4,6,7,9,10,12,14,15];
	253		case 11
	254		idx = [1,2,4,6,7,8,9,10,12,14,15];
	255		end
256	256	elseif strcmp(typ,'Sequential')	elseif strcmp(typ,'Sequential')
257		switch min(N,num)
258		case 1 % extrapolated
259		idx = 6;
260		case 2 % extrapolated
261		idx = [4,8];
262		case 3
263		idx = [3,6,9];
264		case 4
265		idx = [2,5,7,10];
266		case 5
267		idx = [2,5,7,9,11];
268		case 6
269		idx = [2,4,6,7,9,11];
270		case 7
271		idx = [2,4,6,7,8,10,12];
272		case 8
273		idx = [1,3,4,6,7,8,10,12];
274		case 9
275		idx = [1,3,4,6,7,8,10,11,13];
276		end
	257		switch min(N,num)
	258		case 1 % extrapolated
	259		idx = 6;
	260		case 2 % extrapolated
	261		idx = [4,8];
	262		case 3
	263		idx = [3,6,9];
	264		case 4
	265		idx = [2,5,7,10];
	266		case 5
	267		idx = [2,5,7,9,11];
	268		case 6
	269		idx = [2,4,6,7,9,11];
	270		case 7
	271		idx = [2,4,6,7,8,10,12];
	272		case 8
	273		idx = [1,3,4,6,7,8,10,12];
	274		case 9
	275		idx = [1,3,4,6,7,8,10,11,13];
	276		end
277	277	else	else
278		error('The colorscheme type "%s" is not recognized',typ)
	278		error('The colorscheme type "%s" is not recognized',typ)
279	279	end	end
280	280	%	%
281	281	if isr	if isr
282		idx = idx(end:-1:1);
	282		idx = idx(end:-1:1);
283	283	end	end
284	284	%	%
285	285	end	end
...	...	function [num,typ,rgb] = cbSelect(tok)
289	289	%	%
290	290	switch lower(tok) % ColorName	switch lower(tok) % ColorName
291	291	case 'brbg' % BrBG	case 'brbg' % BrBG
292		rgb = [84,48,5;140,81,10;166,97,26;191,129,45;216,179,101;223,194,125;246,232,195;245,245,245;199,234,229;128,205,193;90,180,172;53,151,143;1,133,113;1,102,94;0,60,48];
293		typ = 'Diverging';
	292		rgb = [84,48,5;140,81,10;166,97,26;191,129,45;216,179,101;223,194,125;246,232,195;245,245,245;199,234,229;128,205,193;90,180,172;53,151,143;1,133,113;1,102,94;0,60,48];
	293		typ = 'Diverging';
294	294	case 'piyg' % PiYG	case 'piyg' % PiYG
295		rgb = [142,1,82;197,27,125;208,28,139;222,119,174;233,163,201;241,182,218;253,224,239;247,247,247;230,245,208;184,225,134;161,215,106;127,188,65;77,172,38;77,146,33;39,100,25];
296		typ = 'Diverging';
	295		rgb = [142,1,82;197,27,125;208,28,139;222,119,174;233,163,201;241,182,218;253,224,239;247,247,247;230,245,208;184,225,134;161,215,106;127,188,65;77,172,38;77,146,33;39,100,25];
	296		typ = 'Diverging';
297	297	case 'prgn' % PRGn	case 'prgn' % PRGn
298		rgb = [64,0,75;118,42,131;123,50,148;153,112,171;175,141,195;194,165,207;231,212,232;247,247,247;217,240,211;166,219,160;127,191,123;90,174,97;0,136,55;27,120,55;0,68,27];
299		typ = 'Diverging';
	298		rgb = [64,0,75;118,42,131;123,50,148;153,112,171;175,141,195;194,165,207;231,212,232;247,247,247;217,240,211;166,219,160;127,191,123;90,174,97;0,136,55;27,120,55;0,68,27];
	299		typ = 'Diverging';
300	300	case 'puor' % PuOr	case 'puor' % PuOr
301		rgb = [127,59,8;179,88,6;230,97,1;224,130,20;241,163,64;253,184,99;254,224,182;247,247,247;216,218,235;178,171,210;153,142,195;128,115,172;94,60,153;84,39,136;45,0,75];
302		typ = 'Diverging';
	301		rgb = [127,59,8;179,88,6;230,97,1;224,130,20;241,163,64;253,184,99;254,224,182;247,247,247;216,218,235;178,171,210;153,142,195;128,115,172;94,60,153;84,39,136;45,0,75];
	302		typ = 'Diverging';
303	303	case 'rdbu' % RdBu	case 'rdbu' % RdBu
304		rgb = [103,0,31;178,24,43;202,0,32;214,96,77;239,138,98;244,165,130;253,219,199;247,247,247;209,229,240;146,197,222;103,169,207;67,147,195;5,113,176;33,102,172;5,48,97];
305		typ = 'Diverging';
	304		rgb = [103,0,31;178,24,43;202,0,32;214,96,77;239,138,98;244,165,130;253,219,199;247,247,247;209,229,240;146,197,222;103,169,207;67,147,195;5,113,176;33,102,172;5,48,97];
	305		typ = 'Diverging';
306	306	case 'rdgy' % RdGy	case 'rdgy' % RdGy
307		rgb = [103,0,31;178,24,43;202,0,32;214,96,77;239,138,98;244,165,130;253,219,199;255,255,255;224,224,224;186,186,186;153,153,153;135,135,135;64,64,64;77,77,77;26,26,26];
308		typ = 'Diverging';
	307		rgb = [103,0,31;178,24,43;202,0,32;214,96,77;239,138,98;244,165,130;253,219,199;255,255,255;224,224,224;186,186,186;153,153,153;135,135,135;64,64,64;77,77,77;26,26,26];
	308		typ = 'Diverging';
309	309	case 'rdylbu' % RdYlBu	case 'rdylbu' % RdYlBu
310		rgb = [165,0,38;215,48,39;215,25,28;244,109,67;252,141,89;253,174,97;254,224,144;255,255,191;224,243,248;171,217,233;145,191,219;116,173,209;44,123,182;69,117,180;49,54,149];
311		typ = 'Diverging';
	310		rgb = [165,0,38;215,48,39;215,25,28;244,109,67;252,141,89;253,174,97;254,224,144;255,255,191;224,243,248;171,217,233;145,191,219;116,173,209;44,123,182;69,117,180;49,54,149];
	311		typ = 'Diverging';
312	312	case 'rdylgn' % RdYlGn	case 'rdylgn' % RdYlGn
313		rgb = [165,0,38;215,48,39;215,25,28;244,109,67;252,141,89;253,174,97;254,224,139;255,255,191;217,239,139;166,217,106;145,207,96;102,189,99;26,150,65;26,152,80;0,104,55];
314		typ = 'Diverging';
	313		rgb = [165,0,38;215,48,39;215,25,28;244,109,67;252,141,89;253,174,97;254,224,139;255,255,191;217,239,139;166,217,106;145,207,96;102,189,99;26,150,65;26,152,80;0,104,55];
	314		typ = 'Diverging';
315	315	case 'spectral' % Spectral	case 'spectral' % Spectral
316		rgb = [158,1,66;213,62,79;215,25,28;244,109,67;252,141,89;253,174,97;254,224,139;255,255,191;230,245,152;171,221,164;153,213,148;102,194,165;43,131,186;50,136,189;94,79,162];
317		typ = 'Diverging';
	316		rgb = [158,1,66;213,62,79;215,25,28;244,109,67;252,141,89;253,174,97;254,224,139;255,255,191;230,245,152;171,221,164;153,213,148;102,194,165;43,131,186;50,136,189;94,79,162];
	317		typ = 'Diverging';
318	318	case 'accent' % Accent	case 'accent' % Accent
319		rgb = [127,201,127;190,174,212;253,192,134;255,255,153;56,108,176;240,2,127;191,91,23;102,102,102];
320		typ = 'Qualitative';
	319		rgb = [127,201,127;190,174,212;253,192,134;255,255,153;56,108,176;240,2,127;191,91,23;102,102,102];
	320		typ = 'Qualitative';
321	321	case 'dark2' % Dark2	case 'dark2' % Dark2
322		rgb = [27,158,119;217,95,2;117,112,179;231,41,138;102,166,30;230,171,2;166,118,29;102,102,102];
323		typ = 'Qualitative';
	322		rgb = [27,158,119;217,95,2;117,112,179;231,41,138;102,166,30;230,171,2;166,118,29;102,102,102];
	323		typ = 'Qualitative';
324	324	case 'paired' % Paired	case 'paired' % Paired
325		rgb = [166,206,227;31,120,180;178,223,138;51,160,44;251,154,153;227,26,28;253,191,111;255,127,0;202,178,214;106,61,154;255,255,153;177,89,40];
326		typ = 'Qualitative';
	325		rgb = [166,206,227;31,120,180;178,223,138;51,160,44;251,154,153;227,26,28;253,191,111;255,127,0;202,178,214;106,61,154;255,255,153;177,89,40];
	326		typ = 'Qualitative';
327	327	case 'pastel1' % Pastel1	case 'pastel1' % Pastel1
328		rgb = [251,180,174;179,205,227;204,235,197;222,203,228;254,217,166;255,255,204;229,216,189;253,218,236;242,242,242];
329		typ = 'Qualitative';
	328		rgb = [251,180,174;179,205,227;204,235,197;222,203,228;254,217,166;255,255,204;229,216,189;253,218,236;242,242,242];
	329		typ = 'Qualitative';
330	330	case 'pastel2' % Pastel2	case 'pastel2' % Pastel2
331		rgb = [179,226,205;253,205,172;203,213,232;244,202,228;230,245,201;255,242,174;241,226,204;204,204,204];
332		typ = 'Qualitative';
	331		rgb = [179,226,205;253,205,172;203,213,232;244,202,228;230,245,201;255,242,174;241,226,204;204,204,204];
	332		typ = 'Qualitative';
333	333	case 'set1' % Set1	case 'set1' % Set1
334		rgb = [228,26,28;55,126,184;77,175,74;152,78,163;255,127,0;255,255,51;166,86,40;247,129,191;153,153,153];
335		typ = 'Qualitative';
	334		rgb = [228,26,28;55,126,184;77,175,74;152,78,163;255,127,0;255,255,51;166,86,40;247,129,191;153,153,153];
	335		typ = 'Qualitative';
336	336	case 'set2' % Set2	case 'set2' % Set2
337		rgb = [102,194,165;252,141,98;141,160,203;231,138,195;166,216,84;255,217,47;229,196,148;179,179,179];
338		typ = 'Qualitative';
	337		rgb = [102,194,165;252,141,98;141,160,203;231,138,195;166,216,84;255,217,47;229,196,148;179,179,179];
	338		typ = 'Qualitative';
339	339	case 'set3' % Set3	case 'set3' % Set3
340		rgb = [141,211,199;255,255,179;190,186,218;251,128,114;128,177,211;253,180,98;179,222,105;252,205,229;217,217,217;188,128,189;204,235,197;255,237,111];
341		typ = 'Qualitative';
	340		rgb = [141,211,199;255,255,179;190,186,218;251,128,114;128,177,211;253,180,98;179,222,105;252,205,229;217,217,217;188,128,189;204,235,197;255,237,111];
	341		typ = 'Qualitative';
342	342	case 'blues' % Blues	case 'blues' % Blues
343		rgb = [247,251,255;239,243,255;222,235,247;198,219,239;189,215,231;158,202,225;107,174,214;66,146,198;49,130,189;33,113,181;8,81,156;8,69,148;8,48,107];
344		typ = 'Sequential';
	343		rgb = [247,251,255;239,243,255;222,235,247;198,219,239;189,215,231;158,202,225;107,174,214;66,146,198;49,130,189;33,113,181;8,81,156;8,69,148;8,48,107];
	344		typ = 'Sequential';
345	345	case 'bugn' % BuGn	case 'bugn' % BuGn
346		rgb = [247,252,253;237,248,251;229,245,249;204,236,230;178,226,226;153,216,201;102,194,164;65,174,118;44,162,95;35,139,69;0,109,44;0,88,36;0,68,27];
347		typ = 'Sequential';
	346		rgb = [247,252,253;237,248,251;229,245,249;204,236,230;178,226,226;153,216,201;102,194,164;65,174,118;44,162,95;35,139,69;0,109,44;0,88,36;0,68,27];
	347		typ = 'Sequential';
348	348	case 'bupu' % BuPu	case 'bupu' % BuPu
349		rgb = [247,252,253;237,248,251;224,236,244;191,211,230;179,205,227;158,188,218;140,150,198;140,107,177;136,86,167;136,65,157;129,15,124;110,1,107;77,0,75];
350		typ = 'Sequential';
	349		rgb = [247,252,253;237,248,251;224,236,244;191,211,230;179,205,227;158,188,218;140,150,198;140,107,177;136,86,167;136,65,157;129,15,124;110,1,107;77,0,75];
	350		typ = 'Sequential';
351	351	case 'gnbu' % GnBu	case 'gnbu' % GnBu
352		rgb = [247,252,240;240,249,232;224,243,219;204,235,197;186,228,188;168,221,181;123,204,196;78,179,211;67,162,202;43,140,190;8,104,172;8,88,158;8,64,129];
353		typ = 'Sequential';
	352		rgb = [247,252,240;240,249,232;224,243,219;204,235,197;186,228,188;168,221,181;123,204,196;78,179,211;67,162,202;43,140,190;8,104,172;8,88,158;8,64,129];
	353		typ = 'Sequential';
354	354	case 'greens' % Greens	case 'greens' % Greens
355		rgb = [247,252,245;237,248,233;229,245,224;199,233,192;186,228,179;161,217,155;116,196,118;65,171,93;49,163,84;35,139,69;0,109,44;0,90,50;0,68,27];
356		typ = 'Sequential';
	355		rgb = [247,252,245;237,248,233;229,245,224;199,233,192;186,228,179;161,217,155;116,196,118;65,171,93;49,163,84;35,139,69;0,109,44;0,90,50;0,68,27];
	356		typ = 'Sequential';
357	357	case 'greys' % Greys	case 'greys' % Greys
358		rgb = [255,255,255;247,247,247;240,240,240;217,217,217;204,204,204;189,189,189;150,150,150;115,115,115;99,99,99;82,82,82;37,37,37;37,37,37;0,0,0];
359		typ = 'Sequential';
	358		rgb = [255,255,255;247,247,247;240,240,240;217,217,217;204,204,204;189,189,189;150,150,150;115,115,115;99,99,99;82,82,82;37,37,37;37,37,37;0,0,0];
	359		typ = 'Sequential';
360	360	case 'orrd' % OrRd	case 'orrd' % OrRd
361		rgb = [255,247,236;254,240,217;254,232,200;253,212,158;253,204,138;253,187,132;252,141,89;239,101,72;227,74,51;215,48,31;179,0,0;153,0,0;127,0,0];
362		typ = 'Sequential';
	361		rgb = [255,247,236;254,240,217;254,232,200;253,212,158;253,204,138;253,187,132;252,141,89;239,101,72;227,74,51;215,48,31;179,0,0;153,0,0;127,0,0];
	362		typ = 'Sequential';
363	363	case 'oranges' % Oranges	case 'oranges' % Oranges
364		rgb = [255,245,235;254,237,222;254,230,206;253,208,162;253,190,133;253,174,107;253,141,60;241,105,19;230,85,13;217,72,1;166,54,3;140,45,4;127,39,4];
365		typ = 'Sequential';
	364		rgb = [255,245,235;254,237,222;254,230,206;253,208,162;253,190,133;253,174,107;253,141,60;241,105,19;230,85,13;217,72,1;166,54,3;140,45,4;127,39,4];
	365		typ = 'Sequential';
366	366	case 'pubu' % PuBu	case 'pubu' % PuBu
367		rgb = [255,247,251;241,238,246;236,231,242;208,209,230;189,201,225;166,189,219;116,169,207;54,144,192;43,140,190;5,112,176;4,90,141;3,78,123;2,56,88];
368		typ = 'Sequential';
	367		rgb = [255,247,251;241,238,246;236,231,242;208,209,230;189,201,225;166,189,219;116,169,207;54,144,192;43,140,190;5,112,176;4,90,141;3,78,123;2,56,88];
	368		typ = 'Sequential';
369	369	case 'pubugn' % PuBuGn	case 'pubugn' % PuBuGn
370		rgb = [255,247,251;246,239,247;236,226,240;208,209,230;189,201,225;166,189,219;103,169,207;54,144,192;28,144,153;2,129,138;1,108,89;1,100,80;1,70,54];
371		typ = 'Sequential';
	370		rgb = [255,247,251;246,239,247;236,226,240;208,209,230;189,201,225;166,189,219;103,169,207;54,144,192;28,144,153;2,129,138;1,108,89;1,100,80;1,70,54];
	371		typ = 'Sequential';
372	372	case 'purd' % PuRd	case 'purd' % PuRd
373		rgb = [247,244,249;241,238,246;231,225,239;212,185,218;215,181,216;201,148,199;223,101,176;231,41,138;221,28,119;206,18,86;152,0,67;145,0,63;103,0,31];
374		typ = 'Sequential';
	373		rgb = [247,244,249;241,238,246;231,225,239;212,185,218;215,181,216;201,148,199;223,101,176;231,41,138;221,28,119;206,18,86;152,0,67;145,0,63;103,0,31];
	374		typ = 'Sequential';
375	375	case 'purples' % Purples	case 'purples' % Purples
376		rgb = [252,251,253;242,240,247;239,237,245;218,218,235;203,201,226;188,189,220;158,154,200;128,125,186;117,107,177;106,81,163;84,39,143;74,20,134;63,0,125];
377		typ = 'Sequential';
	376		rgb = [252,251,253;242,240,247;239,237,245;218,218,235;203,201,226;188,189,220;158,154,200;128,125,186;117,107,177;106,81,163;84,39,143;74,20,134;63,0,125];
	377		typ = 'Sequential';
378	378	case 'rdpu' % RdPu	case 'rdpu' % RdPu
379		rgb = [255,247,243;254,235,226;253,224,221;252,197,192;251,180,185;250,159,181;247,104,161;221,52,151;197,27,138;174,1,126;122,1,119;122,1,119;73,0,106];
380		typ = 'Sequential';
	379		rgb = [255,247,243;254,235,226;253,224,221;252,197,192;251,180,185;250,159,181;247,104,161;221,52,151;197,27,138;174,1,126;122,1,119;122,1,119;73,0,106];
	380		typ = 'Sequential';
381	381	case 'reds' % Reds	case 'reds' % Reds
382		rgb = [255,245,240;254,229,217;254,224,210;252,187,161;252,174,145;252,146,114;251,106,74;239,59,44;222,45,38;203,24,29;165,15,21;153,0,13;103,0,13];
383		typ = 'Sequential';
	382		rgb = [255,245,240;254,229,217;254,224,210;252,187,161;252,174,145;252,146,114;251,106,74;239,59,44;222,45,38;203,24,29;165,15,21;153,0,13;103,0,13];
	383		typ = 'Sequential';
384	384	case 'ylgn' % YlGn	case 'ylgn' % YlGn
385		rgb = [255,255,229;255,255,204;247,252,185;217,240,163;194,230,153;173,221,142;120,198,121;65,171,93;49,163,84;35,132,67;0,104,55;0,90,50;0,69,41];
386		typ = 'Sequential';
	385		rgb = [255,255,229;255,255,204;247,252,185;217,240,163;194,230,153;173,221,142;120,198,121;65,171,93;49,163,84;35,132,67;0,104,55;0,90,50;0,69,41];
	386		typ = 'Sequential';
387	387	case 'ylgnbu' % YlGnBu	case 'ylgnbu' % YlGnBu
388		rgb = [255,255,217;255,255,204;237,248,177;199,233,180;161,218,180;127,205,187;65,182,196;29,145,192;44,127,184;34,94,168;37,52,148;12,44,132;8,29,88];
389		typ = 'Sequential';
	388		rgb = [255,255,217;255,255,204;237,248,177;199,233,180;161,218,180;127,205,187;65,182,196;29,145,192;44,127,184;34,94,168;37,52,148;12,44,132;8,29,88];
	389		typ = 'Sequential';
390	390	case 'ylorbr' % YlOrBr	case 'ylorbr' % YlOrBr
391		rgb = [255,255,229;255,255,212;255,247,188;254,227,145;254,217,142;254,196,79;254,153,41;236,112,20;217,95,14;204,76,2;153,52,4;140,45,4;102,37,6];
392		typ = 'Sequential';
	391		rgb = [255,255,229;255,255,212;255,247,188;254,227,145;254,217,142;254,196,79;254,153,41;236,112,20;217,95,14;204,76,2;153,52,4;140,45,4;102,37,6];
	392		typ = 'Sequential';
393	393	case 'ylorrd' % YlOrRd	case 'ylorrd' % YlOrRd
394		rgb = [255,255,204;255,255,178;255,237,160;254,217,118;254,204,92;254,178,76;253,141,60;252,78,42;240,59,32;227,26,28;189,0,38;177,0,38;128,0,38];
395		typ = 'Sequential';
	394		rgb = [255,255,204;255,255,178;255,237,160;254,217,118;254,204,92;254,178,76;253,141,60;252,78,42;240,59,32;227,26,28;189,0,38;177,0,38;128,0,38];
	395		typ = 'Sequential';
396	396	otherwise	otherwise
397		error('Colorscheme "%s" is not supported. Check the token tables.',tok)
	397		error('Colorscheme "%s" is not supported. Check the token tables.',tok)
398	398	end	end
399	399	%	%
400	400	rgb = rgb./255;	rgb = rgb./255;
401	401	%	%
402	402	switch typ	switch typ
403	403	case 'Diverging'	case 'Diverging'
404		num = 11;
	404		num = 11;
405	405	case 'Qualitative'	case 'Qualitative'
406		num = size(rgb,1);
	406		num = size(rgb,1);
407	407	case 'Sequential'	case 'Sequential'
408		num = 9;
	408		num = 9;
409	409	otherwise	otherwise
410		error('The colorscheme type "%s" is not recognized',typ)
	410		error('The colorscheme type "%s" is not recognized',typ)
411	411	end	end
412	412	%	%
413	413	end	end

File misc/3rdparty/brewermap_view.m changed (mode: 100644) (index d1c29b2..ea439de)
...	...	function [map,scheme] = brewermap_view(N,scheme)
54	54	% ### Input Wrangling ###	% ### Input Wrangling ###
55	55	%	%
56	56	if nargin<1 || isnumeric(N)&&isempty(N)	if nargin<1 || isnumeric(N)&&isempty(N)
57		N = 128;
	57		N = 128;
58	58	elseif iscell(N)&&numel(N)	elseif iscell(N)&&numel(N)
59		tmp = all(1==cellfun('prodofsize',N)&cellfun(@ishghandle,N));
60		assert(tmp,'Input <N> may be a cell array of axes or figure handles.')
	59		tmp = all(1==cellfun('prodofsize',N)&cellfun(@ishghandle,N));
	60		assert(tmp,'Input <N> may be a cell array of axes or figure handles.')
61	61	else	else
62		assert(isnumeric(N)&&isscalar(N),'Input <N> must be a scalar numeric.')
63		assert(isreal(N)&&fix(N)==N&&N>0,'Input <N> must be positive real integer: %g+%gi',N,imag(N))
64		N = double(N);
	62		assert(isnumeric(N)&&isscalar(N),'Input <N> must be a scalar numeric.')
	63		assert(isreal(N)&&fix(N)==N&&N>0,'Input <N> must be positive real integer: %g+%gi',N,imag(N))
	64		N = double(N);
65	65	end	end
66	66	%	%
67	67	if nargin<2	if nargin<2
68		L = brewermap('list');
69		scheme = L{1+rem(round(now*1e7),numel(L))};
	68		L = brewermap('list');
	69		scheme = L{1+rem(round(now*1e7),numel(L))};
70	70	else	else
71		assert(ischar(scheme)&&isrow(scheme),'Second input <scheme> must be a string.')
	71		assert(ischar(scheme)&&isrow(scheme),'Second input <scheme> must be a string.')
72	72	end	end
73	73	%	%
74	74	[~,~,h] = bmvUpDt(N, scheme);	[~,~,h] = bmvUpDt(N, scheme);
75	75	%	%
76	76	if nargout	if nargout
77		waitfor(h);
78		[~,scheme,map] = bmvUpDt();
	77		waitfor(h);
	78		[~,scheme,map] = bmvUpDt();
79	79	end	end
80	80	%	%
81	81	end	end
...	...	stp = [1,10];
93	93	[L,V,T] = brewermap('list');	[L,V,T] = brewermap('list');
94	94	%	%
95	95	switch nargin	switch nargin
96		case 0 % Demo initialize OR return colormap
97		N = prvN;
98		S = prvS;
99		if nargout==3 % Return colormap
100		ghnd = brewermap(N,H.fRev(S));
101		return
102		end
103		case 1 % Button/Slider callback
104		if get(H.demo,'Value') % Demo
105		return
106		elseif ischar(N) % scheme name
107		S = N;
108		N = prvN;
109		else % parameter N only
110		S = prvS;
111		N = round(get(H.vSld(1),'Value'));
112		end
113		case 2 % Demo update OR function call
114		if iscell(N) % External axes/figure
115		extH = N;
116		N = size(colormap(extH{1}),1);
117		end
118		if isempty(H) || ~ishghandle(H.fig)
119		if nargout<3 % Demo
120		return
121		end
122		% Check brewermap version:
123		str = 'The function "brewermap" returned an unusual %s.';
124		assert(all(35==[numel(L),numel(V),numel(T)]),str,'array size')
125		tmp = find(any(diff(+char(T)),2));
126		assert(numel(tmp)==2&&all(tmp==[9;17]),str,'scheme name sequence')
127		% Create a new figure:
128		H = bmvPlot(lbd,rbd,stp,N,S,L);
129		else % Update slider position:
130		set(H.vSld, 'Value',max(lbd,min(rbd,N)));
131		if nargout<3 % Demo
132		S = L{1+mod(find(strcmpi(S,L)),numel(L))};
133		set(H.bGrp,'SelectedObject',H.bSep(strcmpi(S,L)));
134		end
135		end
136		%
137		otherwise
138		error('How did this happen? This should not be possible.')
	96		case 0 % Demo initialize OR return colormap
	97		N = prvN;
	98		S = prvS;
	99		if nargout==3 % Return colormap
	100		ghnd = brewermap(N,H.fRev(S));
	101		return
	102		end
	103		case 1 % Button/Slider callback
	104		if get(H.demo,'Value') % Demo
	105		return
	106		elseif ischar(N) % scheme name
	107		S = N;
	108		N = prvN;
	109		else % parameter N only
	110		S = prvS;
	111		N = round(get(H.vSld(1),'Value'));
	112		end
	113		case 2 % Demo update OR function call
	114		if iscell(N) % External axes/figure
	115		extH = N;
	116		N = size(colormap(extH{1}),1);
	117		end
	118		if isempty(H) || ~ishghandle(H.fig)
	119		if nargout<3 % Demo
	120		return
	121		end
	122		% Check brewermap version:
	123		str = 'The function "brewermap" returned an unusual %s.';
	124		assert(all(35==[numel(L),numel(V),numel(T)]),str,'array size')
	125		tmp = find(any(diff(+char(T)),2));
	126		assert(numel(tmp)==2&&all(tmp==[9;17]),str,'scheme name sequence')
	127		% Create a new figure:
	128		H = bmvPlot(lbd,rbd,stp,N,S,L);
	129		else % Update slider position:
	130		set(H.vSld, 'Value',max(lbd,min(rbd,N)));
	131		if nargout<3 % Demo
	132		S = L{1+mod(find(strcmpi(S,L)),numel(L))};
	133		set(H.bGrp,'SelectedObject',H.bSep(strcmpi(S,L)));
	134		end
	135		end
	136		%
	137		otherwise
	138		error('How did this happen? This should not be possible.')
139	139	end	end
140	140	%	%
141	141	prvN = N;	prvN = N;
...	...	ghnd = H.fig;
146	146	idx = strcmpi(S,L);	idx = strcmpi(S,L);
147	147	idy = strcmp('Qualitative',T{idx}) && V(idx)<N;	idy = strcmp('Qualitative',T{idx}) && V(idx)<N;
148	148	if idy	if idy
149		N = V(idx);
150		set(H.vSld, 'Value',max(lbd,min(rbd,N)));
	149		N = V(idx);
	150		set(H.vSld, 'Value',max(lbd,min(rbd,N)));
151	151	end	end
152	152	%	%
153	153	% Update parameter value text:	% Update parameter value text:
...	...	set(H.cbIm(2), 'CData',repmat(mag,[1,1,3]))
164	164	%	%
165	165	% Update 2D line / 3D patch values:	% Update 2D line / 3D patch values:
166	166	if get(H.D2D3,'Value')	if get(H.D2D3,'Value')
167		set(H.ln2D, 'XData',linspace(0,1,abs(N)));
168		set(H.ln2D, {'YData'},num2cell([map,mag],1).');
	167		set(H.ln2D, 'XData',linspace(0,1,abs(N)));
	168		set(H.ln2D, {'YData'},num2cell([map,mag],1).');
169	169	else	else
170		set(H.pt3D, 'XData',map(:,1), 'YData',map(:,2), 'ZData',map(:,3), 'FaceVertexCData',map)
	170		set(H.pt3D, 'XData',map(:,1), 'YData',map(:,2), 'ZData',map(:,3), 'FaceVertexCData',map)
171	171	end	end
172	172	%	%
173	173	% Update warning text:	% Update warning text:
...	...	set(H.warn,'String',str);
176	176	%	%
177	177	% Update external axes/figure:	% Update external axes/figure:
178	178	if ~isempty(extH)	if ~isempty(extH)
179		for k = find(cellfun(@ishghandle,extH))
180		colormap(extH{k},map);
181		end
	179		for k = find(cellfun(@ishghandle,extH))
	180		colormap(extH{k},map);
	181		end
182	182	end	end
183	183	%	%
184	184	end	end
...	...	axh = 1-uih-2*gap; % axes height
196	196	wdt = 1-cbw-2*gap; % axes width	wdt = 1-cbw-2*gap; % axes width
197	197	%	%
198	198	H.fig = figure('HandleVisibility','callback', 'Color','white',...	H.fig = figure('HandleVisibility','callback', 'Color','white',...
199		'IntegerHandle','off', 'NumberTitle','off',...
200		'Name','ColorBrewer Interactive Scheme Selector');
	199		'IntegerHandle','off', 'NumberTitle','off',...
	200		'Name','ColorBrewer Interactive Scheme Selector');
201	201	%	%
202	202	% Add 2D lineplot:	% Add 2D lineplot:
203	203	H.ax2D = axes('Parent',H.fig, 'Position',[gap, uih+gap, wdt, axh],...	H.ax2D = axes('Parent',H.fig, 'Position',[gap, uih+gap, wdt, axh],...
204		'ColorOrder',[1,0,0; 0,1,0; 0,0,1; 0.6,0.6,0.6], 'HitTest','off',...
205		'Visible','off', 'XLim',[0,1], 'YLim',[0,1], 'XTick',[], 'YTick',[]);
	204		'ColorOrder',[1,0,0; 0,1,0; 0,0,1; 0.6,0.6,0.6], 'HitTest','off',...
	205		'Visible','off', 'XLim',[0,1], 'YLim',[0,1], 'XTick',[], 'YTick',[]);
206	206	H.ln2D = line([0,0,0,0;1,1,1,1],[0,0,0,0;1,1,1,1], 'Parent',H.ax2D, 'Visible','off');	H.ln2D = line([0,0,0,0;1,1,1,1],[0,0,0,0;1,1,1,1], 'Parent',H.ax2D, 'Visible','off');
207	207	%	%
208	208	% Add 3D scatterplot:	% Add 3D scatterplot:
209	209	H.ax3D = axes('Parent',H.fig, 'OuterPosition',[0, uih, wdt+2*gap, 1-uih],...	H.ax3D = axes('Parent',H.fig, 'OuterPosition',[0, uih, wdt+2*gap, 1-uih],...
210		'Visible','on', 'XLim',[0,1], 'YLim',[0,1], 'ZLim',[0,1], 'HitTest','on');
	210		'Visible','on', 'XLim',[0,1], 'YLim',[0,1], 'ZLim',[0,1], 'HitTest','on');
211	211	H.pt3D = patch('Parent',H.ax3D, 'XData',[0;1], 'YData',[0;1], 'ZData',[0;1],...	H.pt3D = patch('Parent',H.ax3D, 'XData',[0;1], 'YData',[0;1], 'ZData',[0;1],...
212		'Visible','on', 'LineStyle','none', 'FaceColor','none', 'MarkerEdgeColor','none',...
213		'Marker','o', 'MarkerFaceColor','flat', 'MarkerSize',10, 'FaceVertexCData',[1,1,0;1,0,1]);
	212		'Visible','on', 'LineStyle','none', 'FaceColor','none', 'MarkerEdgeColor','none',...
	213		'Marker','o', 'MarkerFaceColor','flat', 'MarkerSize',10, 'FaceVertexCData',[1,1,0;1,0,1]);
214	214	view(H.ax3D,3);	view(H.ax3D,3);
215	215	grid(H.ax3D,'on')	grid(H.ax3D,'on')
216	216	xlabel(H.ax3D,'Red')	xlabel(H.ax3D,'Red')
...	...	zlabel(H.ax3D,'Blue')
219	219	%	%
220	220	% Add warning text:	% Add warning text:
221	221	H.warn = text('Parent',H.ax2D, 'Units','normalized', 'Position',[1,1],...	H.warn = text('Parent',H.ax2D, 'Units','normalized', 'Position',[1,1],...
222		'HorizontalAlignment','right', 'VerticalAlignment','top', 'Color','k');
	222		'HorizontalAlignment','right', 'VerticalAlignment','top', 'Color','k');
223	223	%	%
224	224	% Add demo button:	% Add demo button:
225	225	H.demo = uicontrol(H.fig, 'Style','togglebutton', 'Units','normalized',...	H.demo = uicontrol(H.fig, 'Style','togglebutton', 'Units','normalized',...
226		'Position',[gap,uih+gap+0*bth,btw,bth], 'String','Demo',...
227		'Max',1, 'Min',0, 'Callback',@bmvDemo);
	226		'Position',[gap,uih+gap+0*bth,btw,bth], 'String','Demo',...
	227		'Max',1, 'Min',0, 'Callback',@bmvDemo);
228	228	% Add 2D/3D button:	% Add 2D/3D button:
229	229	H.D2D3 = uicontrol(H.fig, 'Style','togglebutton', 'Units','normalized',...	H.D2D3 = uicontrol(H.fig, 'Style','togglebutton', 'Units','normalized',...
230		'Position',[gap,uih+gap+1*bth,btw,bth], 'String','2D / 3D',...
231		'Max',1, 'Min',0, 'Callback',@(h,~)bmv2D3D(H,h));
	230		'Position',[gap,uih+gap+1*bth,btw,bth], 'String','2D / 3D',...
	231		'Max',1, 'Min',0, 'Callback',@(h,~)bmv2D3D(H,h));
232	232	% Add reverse button:	% Add reverse button:
233	233	H.bRev = uicontrol(H.fig, 'Style','togglebutton', 'Units','normalized',...	H.bRev = uicontrol(H.fig, 'Style','togglebutton', 'Units','normalized',...
234		'Position',[gap,uih+gap+2*bth,btw,bth], 'String','Reverse',...
235		'Max',1, 'Min',0, 'Callback',@(~,~)bmvUpDt());
	234		'Position',[gap,uih+gap+2*bth,btw,bth], 'String','Reverse',...
	235		'Max',1, 'Min',0, 'Callback',@(~,~)bmvUpDt());
236	236	H.fRev = @(s)[char(42*ones(1,get(H.bRev,'Value'))),s];	H.fRev = @(s)[char(42*ones(1,get(H.bRev,'Value'))),s];
237	237	%	%
238	238	% Add colorbars:	% Add colorbars:
239	239	C = reshape([1,1,1],1,[],3);	C = reshape([1,1,1],1,[],3);
240	240	H.cbAx(1) = axes('Parent',H.fig, 'Visible','off', 'Units','normalized',...	H.cbAx(1) = axes('Parent',H.fig, 'Visible','off', 'Units','normalized',...
241		'Position',[1-cbw/1,gap,cbw/2-gap,1-2*gap], 'YLim',[0.5,1.5], 'HitTest','off');
	241		'Position',[1-cbw/1,gap,cbw/2-gap,1-2*gap], 'YLim',[0.5,1.5], 'HitTest','off');
242	242	H.cbAx(2) = axes('Parent',H.fig, 'Visible','off', 'Units','normalized',...	H.cbAx(2) = axes('Parent',H.fig, 'Visible','off', 'Units','normalized',...
243		'Position',[1-cbw/2,gap,cbw/2-gap,1-2*gap], 'YLim',[0.5,1.5], 'HitTest','off');
	243		'Position',[1-cbw/2,gap,cbw/2-gap,1-2*gap], 'YLim',[0.5,1.5], 'HitTest','off');
244	244	H.cbIm(1) = image('Parent',H.cbAx(1), 'CData',C);	H.cbIm(1) = image('Parent',H.cbAx(1), 'CData',C);
245	245	H.cbIm(2) = image('Parent',H.cbAx(2), 'CData',C);	H.cbIm(2) = image('Parent',H.cbAx(2), 'CData',C);
246	246	%	%
247	247	% Add parameter slider, listener, and corresponding text:	% Add parameter slider, listener, and corresponding text:
248	248	V = max(lbd,min(rbd,V));	V = max(lbd,min(rbd,V));
249	249	H.vTxt = uicontrol(H.fig,'Style','text', 'Units','normalized',...	H.vTxt = uicontrol(H.fig,'Style','text', 'Units','normalized',...
250		'Position',[gap,uih-bth,btw,bth], 'String','X');
	250		'Position',[gap,uih-bth,btw,bth], 'String','X');
251	251	H.vSld = uicontrol(H.fig,'Style','slider', 'Units','normalized',...	H.vSld = uicontrol(H.fig,'Style','slider', 'Units','normalized',...
252		'Position',[gap,gap,btw,uih-bth], 'Min',lbd(1), 'Max',rbd(1),...
253		'SliderStep',stp(1,:)/(rbd(1)-lbd(1)), 'Value',V(1));
	252		'Position',[gap,gap,btw,uih-bth], 'Min',lbd(1), 'Max',rbd(1),...
	253		'SliderStep',stp(1,:)/(rbd(1)-lbd(1)), 'Value',V(1));
254	254	addlistener(H.vSld, 'Value', 'PostSet',@(a,b)bmvUpDt(0));	addlistener(H.vSld, 'Value', 'PostSet',@(a,b)bmvUpDt(0));
255	255	%	%
256	256	% Add scheme button group:	% Add scheme button group:
257	257	H.bGrp = uibuttongroup('Parent',H.fig, 'BorderType','none', 'Units','normalized',...	H.bGrp = uibuttongroup('Parent',H.fig, 'BorderType','none', 'Units','normalized',...
258		'BackgroundColor','white', 'Position',[2*gap+btw,gap,wdt-btw-gap,uih-gap]);
	258		'BackgroundColor','white', 'Position',[2*gap+btw,gap,wdt-btw-gap,uih-gap]);
259	259	% Determine button locations:	% Determine button locations:
260	260	Z = 1:numel(L);	Z = 1:numel(L);
261	261	Z = Z+(Z>17);	Z = Z+(Z>17);
...	...	C = (ceil(Z/M)-1)/4;
263	263	R = (M-1-mod(Z-1,M))/M;	R = (M-1-mod(Z-1,M))/M;
264	264	% Add scheme buttons to group:	% Add scheme buttons to group:
265	265	for k = numel(L):-1:1	for k = numel(L):-1:1
266		H.bSep(k) = uicontrol('Style','Toggle', 'String',L{k}, 'Parent',H.bGrp,...
267		'Unit','normalized', 'Position',[C(k),R(k),1/4,1/M]);
	266		H.bSep(k) = uicontrol('Style','Toggle', 'String',L{k}, 'Parent',H.bGrp,...
	267		'Unit','normalized', 'Position',[C(k),R(k),1/4,1/M]);
268	268	end	end
269	269	set(H.bGrp,'SelectedObject',H.bSep(strcmpi(S,L)));	set(H.bGrp,'SelectedObject',H.bSep(strcmpi(S,L)));
270	270	set(H.bGrp,'SelectionChangeFcn',@(~,e)bmvUpDt(get(e.NewValue,'String')));	set(H.bGrp,'SelectionChangeFcn',@(~,e)bmvUpDt(get(e.NewValue,'String')));
...	...	function bmv2D3D(H,tgh)
275	275	% Switch between 2D-line and 3D-cube: swap visibility and hittest, then update.	% Switch between 2D-line and 3D-cube: swap visibility and hittest, then update.
276	276	%	%
277	277	if get(tgh,'Value')% 2D	if get(tgh,'Value')% 2D
278		set(H.ax3D, 'HitTest','off', 'Visible','off')
279		set(H.ax2D, 'HitTest','on')
280		set(H.pt3D, 'Visible','off')
281		set(H.ln2D, 'Visible','on')
	278		set(H.ax3D, 'HitTest','off', 'Visible','off')
	279		set(H.ax2D, 'HitTest','on')
	280		set(H.pt3D, 'Visible','off')
	281		set(H.ln2D, 'Visible','on')
282	282	else % 3D	else % 3D
283		set(H.ax2D, 'HitTest','off')
284		set(H.ax3D, 'HitTest','on', 'Visible','on')
285		set(H.ln2D, 'Visible','off')
286		set(H.pt3D, 'Visible','on')
	283		set(H.ax2D, 'HitTest','off')
	284		set(H.ax3D, 'HitTest','on', 'Visible','on')
	285		set(H.ln2D, 'Visible','off')
	286		set(H.pt3D, 'Visible','on')
287	287	end	end
288	288	bmvUpDt();	bmvUpDt();
289	289	%	%
...	...	function bmvDemo(tgh,~)
297	297	%	%
298	298	% While the toggle button is down, step schemes:	% While the toggle button is down, step schemes:
299	299	while ishghandle(tgh)&&get(tgh,'Value')	while ishghandle(tgh)&&get(tgh,'Value')
300		%
301		% Update figure:
302		[~,S] = bmvUpDt(round(N),S);
303		%
304		% Frame-rate faster/slower:
305		pause(1.1);
	300		%
	301		% Update figure:
	302		[~,S] = bmvUpDt(round(N),S);
	303		%
	304		% Frame-rate faster/slower:
	305		pause(1.1);
306	306	end	end
307	307	%	%
308	308	end	end

File misc/3rdparty/cbrewer/cbrewer.m changed (mode: 100644) (index 26be891..4d9cfd6)
1	1	function [colormap]=cbrewer(ctype, cname, ncol, interp_method)	function [colormap]=cbrewer(ctype, cname, ncol, interp_method)
2	2	%	%
3		% CBREWER - This function produces a colorbrewer table (rgb data) for a
4		% given type, name and number of colors of the colorbrewer tables.
	3		% CBREWER - This function produces a colorbrewer table (rgb data) for a
	4		% given type, name and number of colors of the colorbrewer tables.
5	5	% For more information on 'colorbrewer', please visit	% For more information on 'colorbrewer', please visit
6	6	% http://colorbrewer2.org/	% http://colorbrewer2.org/
7		%
	7		%
8	8	% The tables were generated from an MS-Excel file provided on the website	% The tables were generated from an MS-Excel file provided on the website
9	9	% http://www.personal.psu.edu/cab38/ColorBrewer/ColorBrewer_updates.html	% http://www.personal.psu.edu/cab38/ColorBrewer/ColorBrewer_updates.html
10	10	%	%
11		%
	11		%
12	12	% [colormap]=cbrewer(ctype, cname, ncol, interp_method)	% [colormap]=cbrewer(ctype, cname, ncol, interp_method)
13	13	%	%
14	14	% INPUT:	% INPUT:
...	...	function [colormap]=cbrewer(ctype, cname, ncol, interp_method)
17	17	% - ncol: number of color in the table. It changes according to ctype and	% - ncol: number of color in the table. It changes according to ctype and
18	18	% cname	% cname
19	19	% - interp_method: interpolation method (see interp1.m). Default is "cubic" )	% - interp_method: interpolation method (see interp1.m). Default is "cubic" )
20		%
	20		%
21	21	% A note on the number of colors: Based on the original data, there is	% A note on the number of colors: Based on the original data, there is
22	22	% only a certain number of colors available for each type and name of	% only a certain number of colors available for each type and name of
23	23	% colortable. When 'ncol' is larger then the maximum number of colors	% colortable. When 'ncol' is larger then the maximum number of colors
24		% originally given, an interpolation routine is called (interp1) to produce
	24		% originally given, an interpolation routine is called (interp1) to produce
25	25	% the "extended" colormaps.	% the "extended" colormaps.
26	26	%	%
27		% Example: To produce a colortable CT of ncol X 3 entries (RGB) of
	27		% Example: To produce a colortable CT of ncol X 3 entries (RGB) of
28	28	% sequential type and named 'Blues' with 8 colors:	% sequential type and named 'Blues' with 8 colors:
29	29	% CT=cbrewer('seq', 'Blues', 8);	% CT=cbrewer('seq', 'Blues', 8);
30	30	% To use this colortable as colormap, simply call:	% To use this colortable as colormap, simply call:
31	31	% colormap(CT)	% colormap(CT)
32		%
	32		%
33	33	% To see the various colormaps available according to their types and	% To see the various colormaps available according to their types and
34	34	% names, simply call: cbrewer()	% names, simply call: cbrewer()
35	35	%	%
...	...	if (~exist('ctype', 'var') | ~exist('cname', 'var') | ~exist('ncol', 'var'))
61	61	disp(' - cname: name of colortable. It changes depending on ctype.')	disp(' - cname: name of colortable. It changes depending on ctype.')
62	62	disp(' - ncol: number of color in the table. It changes according to ctype and cname')	disp(' - ncol: number of color in the table. It changes according to ctype and cname')
63	63	disp(' - interp_method: interpolation method (see interp1.m). Default is "cubic" )')	disp(' - interp_method: interpolation method (see interp1.m). Default is "cubic" )')
64
	64
65	65	disp(' ')	disp(' ')
66	66	disp('Sequential tables:')	disp('Sequential tables:')
67	67	z={'Blues','BuGn','BuPu','GnBu','Greens','Greys','Oranges','OrRd','PuBu','PuBuGn','PuRd',...	z={'Blues','BuGn','BuPu','GnBu','Greens','Greys','Oranges','OrRd','PuBu','PuBuGn','PuRd',...
68	68	'Purples','RdPu', 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'Spectral'};	'Purples','RdPu', 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'Spectral'};
69		disp(z')
70
	69		disp(z')
	70
71	71	disp('Divergent tables:')	disp('Divergent tables:')
72	72	z={'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn'};	z={'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn'};
73	73	disp(z')	disp(z')
74
	74
75	75	disp(' ')	disp(' ')
76	76	disp('Qualitative tables:')	disp('Qualitative tables:')
77	77	%getfield(colorbrewer, 'qual')	%getfield(colorbrewer, 'qual')
...	...	if (ncol>length(colorbrewer.(ctype).(cname)))
111	111	end	end
112	112
113	113	if (isempty(colorbrewer.(ctype).(cname){ncol}))	if (isempty(colorbrewer.(ctype).(cname){ncol}))
114
	114
115	115	while(isempty(colorbrewer.(ctype).(cname){ncol}))	while(isempty(colorbrewer.(ctype).(cname){ncol}))
116	116	ncol=ncol+1;	ncol=ncol+1;
117		end
	117		end
118	118	disp(' ')	disp(' ')
119	119	disp('----------------------------------------------------------------------')	disp('----------------------------------------------------------------------')
120	120	disp(['The minimum number of colors for table *' cname '* is ' num2str(ncol)])	disp(['The minimum number of colors for table *' cname '* is ' num2str(ncol)])

File misc/3rdparty/cbrewer/change_jet.m changed (mode: 100644) (index b8d4ecb..7ba58fa)
1	1	% This script help produce a new 'jet'-like colormap based on other RGB reference colors	% This script help produce a new 'jet'-like colormap based on other RGB reference colors
2	2
3	3	% ------- I WAS ASKED ---------------	% ------- I WAS ASKED ---------------
4		% "is there a chance that you could add a diverging map going from blue to green to red as in jet,
	4		% "is there a chance that you could add a diverging map going from blue to green to red as in jet,
5	5	% but using the red and blue from your RdBu map and the third darkest green from your RdYlGn map?""	% but using the red and blue from your RdBu map and the third darkest green from your RdYlGn map?""
6	6	%	%
7	7	% ANSWER:	% ANSWER:
8	8	% You should construct the new colormap based on the existing RGB values of 'jet'	% You should construct the new colormap based on the existing RGB values of 'jet'
9		% but projecting these RGB values on your new RGB basis.
	9		% but projecting these RGB values on your new RGB basis.
10	10	% -----------------------------------	% -----------------------------------
11	11
12	12	% load colormaps	% load colormaps
...	...	for iname=1:length(cnames)
61	61	set(gcf, 'color', [1 1 1])	set(gcf, 'color', [1 1 1])
62	62	ylim([0.1 1.05.*max(Y)]);	ylim([0.1 1.05.*max(Y)]);
63	63	end % iname	end % iname
64

File misc/3rdparty/cbrewer/interpolate_cbrewer.m changed (mode: 100644) (index e8b5e21..b108cf1)
1	1	function [interp_cmap]=interpolate_cbrewer(cbrew_init, interp_method, ncolors)	function [interp_cmap]=interpolate_cbrewer(cbrew_init, interp_method, ncolors)
2		%
	2		%
3	3	% INTERPOLATE_CBREWER - interpolate a colorbrewer map to ncolors levels	% INTERPOLATE_CBREWER - interpolate a colorbrewer map to ncolors levels
4	4	%	%
5	5	% INPUT:	% INPUT:
...	...	function [interp_cmap]=interpolate_cbrewer(cbrew_init, interp_method, ncolors)
10	10	% 'spline' - spline interpolation	% 'spline' - spline interpolation
11	11	% 'cubic' - bicubic interpolation as long as the data is	% 'cubic' - bicubic interpolation as long as the data is
12	12	% uniformly spaced, otherwise the same as 'spline'	% uniformly spaced, otherwise the same as 'spline'
13		% - ncolors=desired number of colors
	13		% - ncolors=desired number of colors
14	14	%	%
15	15	% Author: Charles Robert	% Author: Charles Robert
16	16	% email: tannoudji@hotmail.com	% email: tannoudji@hotmail.com

File misc/3rdparty/cbrewer/plot_brewer_cmap.m changed (mode: 100644) (index a5cab9e..ce0748b)
...	...	cnames{3,:}={'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2',
18	18
19	19	figure('position', [314 327 807 420])	figure('position', [314 327 807 420])
20	20	for itype=1:3	for itype=1:3
21
	21
22	22	%fh(itype)=figure();	%fh(itype)=figure();
23	23	subplot(1,3,itype)	subplot(1,3,itype)
24
	24
25	25	for iname=1:length(cnames{itype,:})	for iname=1:length(cnames{itype,:})
26
	26
27	27	ncol=length(colorbrewer.(ctypes{itype}).(cnames{itype}{iname}));	ncol=length(colorbrewer.(ctypes{itype}).(cnames{itype}{iname}));
28	28	fg=1./ncol; % geometrical factor	fg=1./ncol; % geometrical factor
29	29

File misc/3rdparty/comp_struct/comp_struct.m changed (mode: 100644) (index 7d022db..e6d6113)
...	...	function [common, d1, d2] = comp_struct(s1,s2,prt,pse,tol,n1,n2)
36	36
37	37	%% default arguent check	%% default arguent check
38	38	if nargin < 2	if nargin < 2
39		help comp_struct; error('I / O error');
	39		help comp_struct; error('I / O error');
40	40	end	end
41	41
42	42	if nargin < 3 || isempty(prt); prt = 0; end	if nargin < 3 || isempty(prt); prt = 0; end
...	...	d1 = s1; d2 = s2; common = s1;
56	56	flag = [0 0];	flag = [0 0];
57	57	% test entire structure	% test entire structure
58	58	if ~isequal(s1,s2)	if ~isequal(s1,s2)
59		% differances noted - parse
60		if isstruct(s1) && isstruct(s2)
61		% both structures - once sub structures are tested, do not
62		% modify the parrent
63		flag(2) = 1;
64		% both structures - get the field names for each structure
65		fn1 = fieldnames(s1);
66		fn2 = fieldnames(s2);
67		% missing fields? (common was a copy of s1 - so only s1 needs checking)
68		temp = find(~ismember(fn1,fn2));
69		for ii = 1:numel(temp)
70		% drop unique fields
71		common = rmfield(common,fn1{temp(ii)});
72		end
73		% get the common fields
74		fn = fieldnames(common);
75		% missing fields in set 1
76		for ii = 1:numel(fn)
77		% common field - recurse and test
78		for jj = 1:min([numel(d1) numel(d2)])
79		[common(jj).(fn{ii}) d1(jj).(fn{ii}) d2(jj).(fn{ii})] = ...
80		comp_struct(s1(jj).(fn{ii}),s2(jj).(fn{ii}), ...
81		prt,pse,tol, ...
82		[n1 '(' num2str(jj) ').' fn{ii}], ...
83		[n2 '(' num2str(jj) ').' fn{ii}]);
84		end
85		end
86		% one or both not structures evaluate
87		elseif isstruct(s1)
88		% first variable is structure - second is not
89		if prt
90		fprintf('Error: %s is a structure and %s is not a structure\n',n1,n2);
91		end
92		if pse
93		uiwait(msgbox(sprintf('Error: %s is a structure and %s is not a structure\n',n1,n2)))
94		end
95		% flag purge
96		flag(1) = 1;
97
98		elseif isstruct(s2)
99		% second variable is structure - first is not
100		if prt
101		fprintf('Error: %s is not a structure and %s is a structure\n',n1,n2);
102		end
103		if pse; pause; end
104		% flag purge
105		flag(1) = 1;
106		else
107		% the same?
108		if ~isequal(s1,s2)
109		flag(1) = 1;
110		% not the same - differance?
111		% class error
112		if ~strcmp(class(s1),class(s2))
113		% different classes
114		if prt
115		fprintf('Error: %s is class %s and %s class %s\n',n1,class(s1),n2,class(s2));
116		end
117		if pse; pause; end
118		flag(1) = 1;
119		else
120		% tolerance error?
121		if min(size(s1) == size(s2)) && ...
122		(isa(s1,'single') || isa(s1,'double'))
123		% tolerance match?
124		if numel(find(abs(s1-s2) < tol)) == numel(s1)
125		flag(1) = 0;
126		end
127		else
128		% print and pause?
129		if prt > 1
130		fprintf('%s is ',n1); disp(s1);
131		fprintf('\b, %s is ',n2); disp(s2);
132		end
133		if pse > 1; pause; end
134		end
135		end
136		end
137		end
	59		% differances noted - parse
	60		if isstruct(s1) && isstruct(s2)
	61		% both structures - once sub structures are tested, do not
	62		% modify the parrent
	63		flag(2) = 1;
	64		% both structures - get the field names for each structure
	65		fn1 = fieldnames(s1);
	66		fn2 = fieldnames(s2);
	67		% missing fields? (common was a copy of s1 - so only s1 needs checking)
	68		temp = find(~ismember(fn1,fn2));
	69		for ii = 1:numel(temp)
	70		% drop unique fields
	71		common = rmfield(common,fn1{temp(ii)});
	72		end
	73		% get the common fields
	74		fn = fieldnames(common);
	75		% missing fields in set 1
	76		for ii = 1:numel(fn)
	77		% common field - recurse and test
	78		for jj = 1:min([numel(d1) numel(d2)])
	79		[common(jj).(fn{ii}) d1(jj).(fn{ii}) d2(jj).(fn{ii})] = ...
	80		comp_struct(s1(jj).(fn{ii}),s2(jj).(fn{ii}), ...
	81		prt,pse,tol, ...
	82		[n1 '(' num2str(jj) ').' fn{ii}], ...
	83		[n2 '(' num2str(jj) ').' fn{ii}]);
	84		end
	85		end
	86		% one or both not structures evaluate
	87		elseif isstruct(s1)
	88		% first variable is structure - second is not
	89		if prt
	90		fprintf('Error: %s is a structure and %s is not a structure\n',n1,n2);
	91		end
	92		if pse
	93		uiwait(msgbox(sprintf('Error: %s is a structure and %s is not a structure\n',n1,n2)))
	94		end
	95		% flag purge
	96		flag(1) = 1;
	97
	98		elseif isstruct(s2)
	99		% second variable is structure - first is not
	100		if prt
	101		fprintf('Error: %s is not a structure and %s is a structure\n',n1,n2);
	102		end
	103		if pse; pause; end
	104		% flag purge
	105		flag(1) = 1;
	106		else
	107		% the same?
	108		if ~isequal(s1,s2)
	109		flag(1) = 1;
	110		% not the same - differance?
	111		% class error
	112		if ~strcmp(class(s1),class(s2))
	113		% different classes
	114		if prt
	115		fprintf('Error: %s is class %s and %s class %s\n',n1,class(s1),n2,class(s2));
	116		end
	117		if pse; pause; end
	118		flag(1) = 1;
	119		else
	120		% tolerance error?
	121		if min(size(s1) == size(s2)) && ...
	122		(isa(s1,'single') || isa(s1,'double'))
	123		% tolerance match?
	124		if numel(find(abs(s1-s2) < tol)) == numel(s1)
	125		flag(1) = 0;
	126		end
	127		else
	128		% print and pause?
	129		if prt > 1
	130		fprintf('%s is ',n1); disp(s1);
	131		fprintf('\b, %s is ',n2); disp(s2);
	132		end
	133		if pse > 1; pause; end
	134		end
	135		end
	136		end
	137		end
138	138	end	end
139	139
140	140
141	141	%% keep or delete	%% keep or delete
142	142	if flag(1) && ~flag(2)	if flag(1) && ~flag(2)
143		% denote error
144		common = [];
	143		% denote error
	144		common = [];
145	145	elseif ~flag(2)	elseif ~flag(2)
146		% remove from error structures
147		d1 = [];
148		d2 = [];
	146		% remove from error structures
	147		d1 = [];
	148		d2 = [];
149	149	end	end
150	150
151	151
152	152	%% purge empty fields	%% purge empty fields
153	153	% test common	% test common
154	154	if isstruct(common)	if isstruct(common)
155		% fieldnames
156		fn = fieldnames(common);
157		for ii = 1:numel(fn)
158		temp = 1;
159		% test for empty field
160		for jj = 1:numel(common)
161		if ~isempty(common(jj).(fn{ii})); temp = 0; end
162		end
163		% purge if field was empty
164		if temp
165		common = rmfield(common,fn{ii});
166		end
167		end
	155		% fieldnames
	156		fn = fieldnames(common);
	157		for ii = 1:numel(fn)
	158		temp = 1;
	159		% test for empty field
	160		for jj = 1:numel(common)
	161		if ~isempty(common(jj).(fn{ii})); temp = 0; end
	162		end
	163		% purge if field was empty
	164		if temp
	165		common = rmfield(common,fn{ii});
	166		end
	167		end
168	168	end	end
169	169	% test d1	% test d1
170	170	if isstruct(d1)	if isstruct(d1)
171		% fieldnames
172		fn = fieldnames(d1);
173		for ii = 1:numel(fn)
174		temp = 1;
175		% test for empty field
176		for jj = 1:numel(d1)
177		if ~isempty(d1(jj).(fn{ii})); temp = 0; end
178		end
179		% purge if field was empty
180		if temp
181		d1 = rmfield(d1,fn{ii});
182		end
183		end
	171		% fieldnames
	172		fn = fieldnames(d1);
	173		for ii = 1:numel(fn)
	174		temp = 1;
	175		% test for empty field
	176		for jj = 1:numel(d1)
	177		if ~isempty(d1(jj).(fn{ii})); temp = 0; end
	178		end
	179		% purge if field was empty
	180		if temp
	181		d1 = rmfield(d1,fn{ii});
	182		end
	183		end
184	184	end	end
185	185	% test d2	% test d2
186	186	if isstruct(d2)	if isstruct(d2)
187		% fieldnames
188		fn = fieldnames(d2);
189		for ii = 1:numel(fn)
190		temp = 1;
191		% test for empty field
192		for jj = 1:numel(d2)
193		if ~isempty(d2(jj).(fn{ii})); temp = 0; end
194		end
195		% purge if field was empty
196		if temp
197		d2 = rmfield(d2,fn{ii});
198		end
199		end
	187		% fieldnames
	188		fn = fieldnames(d2);
	189		for ii = 1:numel(fn)
	190		temp = 1;
	191		% test for empty field
	192		for jj = 1:numel(d2)
	193		if ~isempty(d2(jj).(fn{ii})); temp = 0; end
	194		end
	195		% purge if field was empty
	196		if temp
	197		d2 = rmfield(d2,fn{ii});
	198		end
	199		end
200	200	end	end
201	201
202	202
203	203	%% test for null fields	%% test for null fields
204	204	if isstruct(common)	if isstruct(common)
205		% fieldnames
206		if isempty(fieldnames(common)); common = []; end
	205		% fieldnames
	206		if isempty(fieldnames(common)); common = []; end
207	207	end	end
208	208	if isstruct(d1)	if isstruct(d1)
209		% fieldnames
210		if isempty(fieldnames(d1)); d1 = []; end
	209		% fieldnames
	210		if isempty(fieldnames(d1)); d1 = []; end
211	211	end	end
212	212	if isstruct(d2)	if isstruct(d2)
213		% fieldnames
214		if isempty(fieldnames(d2)); d2 = []; end
	213		% fieldnames
	214		if isempty(fieldnames(d2)); d2 = []; end
215	215	end	end

File misc/3rdparty/comp_struct/comp_struct_old.m changed (mode: 100644) (index 34d9522..42d3967)
...	...	eval([char(n2) ' = s2;']);
57	57	ner1 = numel(er1); ner2 = numel(er2);	ner1 = numel(er1); ner2 = numel(er2);
58	58	% check that same number of errors were listed in each cell	% check that same number of errors were listed in each cell
59	59	if ner1 ~= ner2	if ner1 ~= ner2
60		error(char('Something went very wrong in capturing errors.', ...
61		'If possible, please email the two structures to moarant@gmail.com'));
	60		error(char('Something went very wrong in capturing errors.', ...
	61		'If possible, please email the two structures to moarant@gmail.com'));
62	62	else	else
63		n = ner1;
	63		n = ner1;
64	64	end	end
65	65
66	66	% populate the error list	% populate the error list
67	67	df = cell(n,3);	df = cell(n,3);
68	68	% loop the error lists	% loop the error lists
69	69	for ii = 1:n	for ii = 1:n
70		% capture the error text list
71		temp1 = er1{ii}; temp2 = er2{ii};
72
73		% see if the second structure exists
74		if isempty(regexp(temp2,'is missing', 'once'))
75		% record text error
76		df{ii,1} = temp2;
77		% see if matching structure 1 is missing (struture 2 listed as unique)
78		if isempty(regexp(temp2,'is unique', 'once'))
79		% unique to structure 2 - record value
80		junk = regexp(temp2,' ','once');
81		df{ii,3} = eval(temp2(1:junk-1));
82		else
83		% exists in 1 and 2 - evaluate types
84		junk = regexp(temp2,' ','once'); temp2(junk:end) = [];
85		junk = strfind(temp2,'.'); trash = temp2(junk+1:end); temp2(junk:end) = [];
86		% if trash is empty, the field is a sub structure - list sub fields
87		if isempty(trash)
88		df{ii,3} = eval(['fieldnames(' temp2 ')'])';
89		else
90		% if numel(temp2) is > 1, then this is an indexed field
91		% list the number if indexes and the type
92		if (numel(eval(temp2)) - 1)
93		df{ii,3} = sprintf('%s(#%g).%s is class %s', ...
94		temp2,numel(eval(temp2)),trash,class(eval([temp2 '.' trash])));
95		else
96		% list the contents of the field
97		df{ii,3} = eval([temp2 '.' trash]);
98		end
99		end
100		end
101		end
102		if isempty(regexp(temp1,'is missing', 'once'))
103		% record text error
104		df{ii,1} = temp1;
105		% see if matching structure 1 is missing (struture 2 listed as unique)
106		if isempty(regexp(temp1,'is unique', 'once'))
107		% unique to structure 2 - record value
108		junk = regexp(temp1,' ','once');
109		df{ii,2} = eval([temp1(1:junk-1)]);
110		else
111		% exists in 1 and 2 - evaluate types
112		junk = regexp(temp1,' ','once'); temp1(junk:end) = [];
113		junk = strfind(temp1,'.'); trash = temp1(junk+1:end); temp1(junk:end) = [];
114		% if trash is empty, the field is a sub structure - list sub fields
115		if isempty(trash)
116		df{ii,2} = eval(['fieldnames(' temp1 ')'])';
117		else
118		% if numel(temp2) is > 1, then this is an indexed field
119		% list the number if indexes and the type
120		if (numel(eval(temp1)) - 1)
121		df{ii,2} = sprintf('%s(#%g).%s is class %s', ...
122		temp1,numel(eval(temp1)),trash,class(eval([temp1 '.' trash])));
123		else
124		% list the contents of the field
125		df{ii,2} = eval([temp1 '.' trash]);
126		end
127		end
128		end
129		end
	70		% capture the error text list
	71		temp1 = er1{ii}; temp2 = er2{ii};
	72
	73		% see if the second structure exists
	74		if isempty(regexp(temp2,'is missing', 'once'))
	75		% record text error
	76		df{ii,1} = temp2;
	77		% see if matching structure 1 is missing (struture 2 listed as unique)
	78		if isempty(regexp(temp2,'is unique', 'once'))
	79		% unique to structure 2 - record value
	80		junk = regexp(temp2,' ','once');
	81		df{ii,3} = eval(temp2(1:junk-1));
	82		else
	83		% exists in 1 and 2 - evaluate types
	84		junk = regexp(temp2,' ','once'); temp2(junk:end) = [];
	85		junk = strfind(temp2,'.'); trash = temp2(junk+1:end); temp2(junk:end) = [];
	86		% if trash is empty, the field is a sub structure - list sub fields
	87		if isempty(trash)
	88		df{ii,3} = eval(['fieldnames(' temp2 ')'])';
	89		else
	90		% if numel(temp2) is > 1, then this is an indexed field
	91		% list the number if indexes and the type
	92		if (numel(eval(temp2)) - 1)
	93		df{ii,3} = sprintf('%s(#%g).%s is class %s', ...
	94		temp2,numel(eval(temp2)),trash,class(eval([temp2 '.' trash])));
	95		else
	96		% list the contents of the field
	97		df{ii,3} = eval([temp2 '.' trash]);
	98		end
	99		end
	100		end
	101		end
	102		if isempty(regexp(temp1,'is missing', 'once'))
	103		% record text error
	104		df{ii,1} = temp1;
	105		% see if matching structure 1 is missing (struture 2 listed as unique)
	106		if isempty(regexp(temp1,'is unique', 'once'))
	107		% unique to structure 2 - record value
	108		junk = regexp(temp1,' ','once');
	109		df{ii,2} = eval([temp1(1:junk-1)]);
	110		else
	111		% exists in 1 and 2 - evaluate types
	112		junk = regexp(temp1,' ','once'); temp1(junk:end) = [];
	113		junk = strfind(temp1,'.'); trash = temp1(junk+1:end); temp1(junk:end) = [];
	114		% if trash is empty, the field is a sub structure - list sub fields
	115		if isempty(trash)
	116		df{ii,2} = eval(['fieldnames(' temp1 ')'])';
	117		else
	118		% if numel(temp2) is > 1, then this is an indexed field
	119		% list the number if indexes and the type
	120		if (numel(eval(temp1)) - 1)
	121		df{ii,2} = sprintf('%s(#%g).%s is class %s', ...
	122		temp1,numel(eval(temp1)),trash,class(eval([temp1 '.' trash])));
	123		else
	124		% list the contents of the field
	125		df{ii,2} = eval([temp1 '.' trash]);
	126		end
	127		end
	128		end
	129		end
130	130	end	end
131	131
132	132	% optional text output	% optional text output
133	133	if prt	if prt
134		fprintf('\n Error table\n');
135		for ii = 1:n
136		fprintf('\n%s \n',df{ii,1});
137		fprintf('Structure 1: ');
138		if isempty(df{ii,2}); fprintf('\n'); else; disp(df{ii,2}); end
139		fprintf('Structure 2: ');
140		if isempty(df{ii,3}); fprintf('\n'); else; disp(df{ii,3}); end
141		end
142		fprintf('\n\n\n\n\n');
	134		fprintf('\n Error table\n');
	135		for ii = 1:n
	136		fprintf('\n%s \n',df{ii,1});
	137		fprintf('Structure 1: ');
	138		if isempty(df{ii,2}); fprintf('\n'); else; disp(df{ii,2}); end
	139		fprintf('Structure 2: ');
	140		if isempty(df{ii,3}); fprintf('\n'); else; disp(df{ii,3}); end
	141		end
	142		fprintf('\n\n\n\n\n');
143	143	end	end
144	144
145	145
...	...	end
148	148	function [match, er1, er2] = comp_struct_loop(s1,s2,prt,pse,tol,n1,n2,wbf)	function [match, er1, er2] = comp_struct_loop(s1,s2,prt,pse,tol,n1,n2,wbf)
149	149
150	150	% init outputs	% init outputs
151		match = {}; er1 = {}; er2 = {};
	151		match = {}; er1 = {}; er2 = {};
152	152
153	153	% test to see if both are structures	% test to see if both are structures
154	154	if isstruct(s1) && isstruct(s2)	if isstruct(s1) && isstruct(s2)
155		% both structures - get the field names for each structure
156		fn1 = fieldnames(s1);
157		fn2 = fieldnames(s2);
158		% missing fields? get the common fields
159		temp1 = ismember(fn1,fn2);
160		temp2 = ismember(fn2,fn1);
161		% missing fields in set 1
162		for ii = find(~temp2)'
163		er1{end+1} = sprintf('%s is missing field %s',n1,fn2{ii});
164		er2{end+1} = sprintf('%s.%s is unique',n2,fn2{ii});
	155		% both structures - get the field names for each structure
	156		fn1 = fieldnames(s1);
	157		fn2 = fieldnames(s2);
	158		% missing fields? get the common fields
	159		temp1 = ismember(fn1,fn2);
	160		temp2 = ismember(fn2,fn1);
	161		% missing fields in set 1
	162		for ii = find(~temp2)'
	163		er1{end+1} = sprintf('%s is missing field %s',n1,fn2{ii});
	164		er2{end+1} = sprintf('%s.%s is unique',n2,fn2{ii});
165	165	% er2{end+1} = s2.(fn2{ii});	% er2{end+1} = s2.(fn2{ii});
166		if prt > 1; fprintf('%s\n',er1{end}); end; if pse; pause; end
167		end
168		% missing fields in set 2
169		for ii = find(~temp1)'
170		er2{end+1} = sprintf('%s is missing field %s',n2,fn1{ii});
171		er1{end+1} = sprintf('%s.%s is unique',n1,fn1{ii});
	166		if prt > 1; fprintf('%s\n',er1{end}); end; if pse; pause; end
	167		end
	168		% missing fields in set 2
	169		for ii = find(~temp1)'
	170		er2{end+1} = sprintf('%s is missing field %s',n2,fn1{ii});
	171		er1{end+1} = sprintf('%s.%s is unique',n1,fn1{ii});
172	172	% er1{end+1} = s1.(fn1{ii});	% er1{end+1} = s1.(fn1{ii});
173		if prt > 1; fprintf('%s\n',er2{end}); end; if pse; pause; end
174		end
175		% index sizes match? i.e. do both structures have the same # of indexes?
176		inda = numel(s1); indb = numel(s2); inder = inda-indb;
177		if inder < 0
178		% struct 1 is smaller
179		for ii = inda+1:indb
180		er1{end+1} = sprintf('%s(%g) is missing',n1,ii);
181		er2{end+1} = sprintf('%s(%g) is unique',n2,ii);
182		if prt > 1; fprintf('%s\n',er1{end}); end; if pse; pause; end
183		end
184		elseif inder > 0
185		% index 2 is smaller
186		for ii = indb+1:inda
187		er2{end+1} = sprintf('%s(%g) is missing',n2,ii);
188		er1{end+1} = sprintf('%s(%g) is unique',n1,ii);
189		if prt > 1; fprintf('%s\n',er2{end}); end; if pse; pause; end
190		end
191		end
192		% get common fields
193		fn = fn1(temp1); fnn = numel(fn);
194		% loop through structure 1 and match to structure 2
195		ind = min([inda indb]); cnt = 0;
196		if wbf; wb = waitbar(0,'Comparing ....'); end
197		for ii = 1:ind
198		% loop each index
199		for jj = 1:fnn
200		% loop common field names
201		if wbf; cnt = cnt + 1; waitbar(cnt/(ind*fnn),wb); drawnow; end
202		% add index and field name to the structure name
203		n1p = sprintf('%s(%g).%s',n1,ii,fn{jj});
204		n2p = sprintf('%s(%g).%s',n2,ii,fn{jj});
205		% recurse - run the program again on the sub-set of the structure
206		[m e1 e2] = comp_struct_loop(s1(ii).(fn{jj}),s2(ii).(fn{jj}),prt,pse, ...
207		tol,n1p,n2p,wbf);
208		% add the sub-set (field name) results to the total results
209		match = [match m'];
210		if ~isempty(e1) || ~isempty(e2)
211		er1 = [er1 e1']; er2 = [er2 e2'];
212		end
213		end
214		end
215		if wbf; close(wb); end
	173		if prt > 1; fprintf('%s\n',er2{end}); end; if pse; pause; end
	174		end
	175		% index sizes match? i.e. do both structures have the same # of indexes?
	176		inda = numel(s1); indb = numel(s2); inder = inda-indb;
	177		if inder < 0
	178		% struct 1 is smaller
	179		for ii = inda+1:indb
	180		er1{end+1} = sprintf('%s(%g) is missing',n1,ii);
	181		er2{end+1} = sprintf('%s(%g) is unique',n2,ii);
	182		if prt > 1; fprintf('%s\n',er1{end}); end; if pse; pause; end
	183		end
	184		elseif inder > 0
	185		% index 2 is smaller
	186		for ii = indb+1:inda
	187		er2{end+1} = sprintf('%s(%g) is missing',n2,ii);
	188		er1{end+1} = sprintf('%s(%g) is unique',n1,ii);
	189		if prt > 1; fprintf('%s\n',er2{end}); end; if pse; pause; end
	190		end
	191		end
	192		% get common fields
	193		fn = fn1(temp1); fnn = numel(fn);
	194		% loop through structure 1 and match to structure 2
	195		ind = min([inda indb]); cnt = 0;
	196		if wbf; wb = waitbar(0,'Comparing ....'); end
	197		for ii = 1:ind
	198		% loop each index
	199		for jj = 1:fnn
	200		% loop common field names
	201		if wbf; cnt = cnt + 1; waitbar(cnt/(ind*fnn),wb); drawnow; end
	202		% add index and field name to the structure name
	203		n1p = sprintf('%s(%g).%s',n1,ii,fn{jj});
	204		n2p = sprintf('%s(%g).%s',n2,ii,fn{jj});
	205		% recurse - run the program again on the sub-set of the structure
	206		[m e1 e2] = comp_struct_loop(s1(ii).(fn{jj}),s2(ii).(fn{jj}),prt,pse, ...
	207		tol,n1p,n2p,wbf);
	208		% add the sub-set (field name) results to the total results
	209		match = [match m'];
	210		if ~isempty(e1) || ~isempty(e2)
	211		er1 = [er1 e1']; er2 = [er2 e2'];
	212		end
	213		end
	214		end
	215		if wbf; close(wb); end
216	216	else	else
217		% both are non-structures - compare
218		% get the varable class and test
219		c1 = class(s1); c2 = class(s2);
220		if strcmp(c1,c2);
221		% both are the same class
222		if isequal(s1,s2)
223		% results are equal
224		match{end+1} = sprintf('%s and %s match',n1,n2);
225		if prt == 3; fprintf('%s\n',match{end}); end
226		else
227		% same class but not equal
228		% calculate error if type is single or double
229		% test for function type match if function handle
230		switch c1
231		case {'single', 'double'},
232		if numel(s1) ~= numel(s2) || size(s1,1) ~= size(s2,1)
233		er = 1;
234		else
235		er = norm(s1-s2);
236		end
237		case {'function_handle'},
238		s1f = functions(s1); s2f = functions(s2);
239		if strcmp(s1f.function,s2f.function)
240		% same function with different values - record deviation and exit
241		er = 0;
242		er1{end+1} = sprintf('%s and %s are both %s but have different values', ...
243		n1,n2,char(s1));
244		er2{end+1} = er1{end};
245		if prt > 1; fprintf('%s\n',er1{end}); end;
246		if pse > 1; pause; end
247		else
248		er = 1;
249		end
250		otherwise, er = 1;
251		end
252		% test error - error will be 0 (no error) or 1 (error) for all
253		% classes except double and single. double and single are the
254		% actual error which is tested against the tolerance
255		% this was done for cases where structures are run on different
256		% platforms and numerical precision errors are observed
257		if er > tol
258		% sets do not match
259		er1{end+1} = sprintf('%s and %s do not match',n1,n2);
260		er2{end+1} = sprintf('%s and %s do not match',n2,n1);
261		if prt > 1; fprintf('%s\n',er1{end}); end;
262		if pse > 1; pause; end
263		else
264		% sets are a tolerance match
265		match{end+1} = sprintf('%s and %s are tolerance match',n1,n2);
266		if prt > 2; fprintf('%s\n',match{end}); end
267		end
268		end
269		else
270		% fields are different classes
271		er1{end+1} = sprintf('%s is class %s, %s is class %s',n1,c1,n2,c2);
272		er2{end+1} = sprintf('%s is class %s, %s is class %s',n2,c2,n1,c1);
273		if prt > 1; fprintf('%s\n',er1{end}); end
274		if pse; pause; end
275		end
	217		% both are non-structures - compare
	218		% get the varable class and test
	219		c1 = class(s1); c2 = class(s2);
	220		if strcmp(c1,c2);
	221		% both are the same class
	222		if isequal(s1,s2)
	223		% results are equal
	224		match{end+1} = sprintf('%s and %s match',n1,n2);
	225		if prt == 3; fprintf('%s\n',match{end}); end
	226		else
	227		% same class but not equal
	228		% calculate error if type is single or double
	229		% test for function type match if function handle
	230		switch c1
	231		case {'single', 'double'},
	232		if numel(s1) ~= numel(s2) || size(s1,1) ~= size(s2,1)
	233		er = 1;
	234		else
	235		er = norm(s1-s2);
	236		end
	237		case {'function_handle'},
	238		s1f = functions(s1); s2f = functions(s2);
	239		if strcmp(s1f.function,s2f.function)
	240		% same function with different values - record deviation and exit
	241		er = 0;
	242		er1{end+1} = sprintf('%s and %s are both %s but have different values', ...
	243		n1,n2,char(s1));
	244		er2{end+1} = er1{end};
	245		if prt > 1; fprintf('%s\n',er1{end}); end;
	246		if pse > 1; pause; end
	247		else
	248		er = 1;
	249		end
	250		otherwise, er = 1;
	251		end
	252		% test error - error will be 0 (no error) or 1 (error) for all
	253		% classes except double and single. double and single are the
	254		% actual error which is tested against the tolerance
	255		% this was done for cases where structures are run on different
	256		% platforms and numerical precision errors are observed
	257		if er > tol
	258		% sets do not match
	259		er1{end+1} = sprintf('%s and %s do not match',n1,n2);
	260		er2{end+1} = sprintf('%s and %s do not match',n2,n1);
	261		if prt > 1; fprintf('%s\n',er1{end}); end;
	262		if pse > 1; pause; end
	263		else
	264		% sets are a tolerance match
	265		match{end+1} = sprintf('%s and %s are tolerance match',n1,n2);
	266		if prt > 2; fprintf('%s\n',match{end}); end
	267		end
	268		end
	269		else
	270		% fields are different classes
	271		er1{end+1} = sprintf('%s is class %s, %s is class %s',n1,c1,n2,c2);
	272		er2{end+1} = sprintf('%s is class %s, %s is class %s',n2,c2,n1,c1);
	273		if prt > 1; fprintf('%s\n',er1{end}); end
	274		if pse; pause; end
	275		end
276	276	end	end
277	277
278	278	% transpose outputs	% transpose outputs

File misc/3rdparty/comp_struct/comp_struct_test.m changed (mode: 100644) (index 86bfac4..a8f0b83)
...	...	fprintf('\n\n\nCase 3\n')
119	119	a.a = 1; b.a = 1;	a.a = 1; b.a = 1;
120	120	a.b = 2; b.b = 1;	a.b = 2; b.b = 1;
121	121	a.c.a = 1; a.c.b = 2; b.c = 1;	a.c.a = 1; a.c.b = 2; b.c = 1;
122		a.d = cellstr('test')'; b.d = cellstr('test')';
123		a.e = cellstr('test')'; b.e = cellstr('a')';
	122		a.d = cellstr('test')'; b.d = cellstr('test')';
	123		a.e = cellstr('test')'; b.e = cellstr('a')';
124	124	a.f = 'tt'; b.f = 'tt';	a.f = 'tt'; b.f = 'tt';
125	125	a.g = 'tt'; b.g = 't';	a.g = 'tt'; b.g = 't';
126	126	a.h.a = 1; b.h.b = 1;	a.h.a = 1; b.h.b = 1;
127	127	a.i.a = 1;	a.i.a = 1;
128	128	a.j.a = @sin; b.j.a = @sin;	a.j.a = @sin; b.j.a = @sin;
129	129	a.j.b = @sin; b.j.b = @cos;	a.j.b = @sin; b.j.b = @cos;
130		x = .1; a.j.c = @(x) sin(x); x = .5; b.j.c = @(x) sin(x);
	130		x = .1; a.j.c = @(x) sin(x); x = .5; b.j.c = @(x) sin(x);
131	131	a(2) = a(1); b(2) = b(1); a(3) = a(1);	a(2) = a(1); b(2) = b(1); a(3) = a(1);
132	132
133	133
...	...	str = char(97:122);
151	151
152	152	% build structure	% build structure
153	153	for ii = 1:26	for ii = 1:26
154		stu1.(str(ii)) = rand(ceil(5*rand(1)),ceil(5*rand(1)));
	154		stu1.(str(ii)) = rand(ceil(5*rand(1)),ceil(5*rand(1)));
155	155	end	end
156	156	stu2 = stu1;	stu2 = stu1;
157	157	for ii = 1:26	for ii = 1:26
158		if round(rand(1))
159		if rand(1) > 0.3
160		if round(rand)
161		stu2.(str(ii)) = rand(size(stu2.(str(ii))));
162		else
163		stu2.(str(ii)) = stu2.(str(ii)) + 1e-8;
164		end
165		else
166		stu2.(str(ii)) = rand(ceil(5*rand(1)),ceil(5*rand(1)));
167		end
168		end
	158		if round(rand(1))
	159		if rand(1) > 0.3
	160		if round(rand)
	161		stu2.(str(ii)) = rand(size(stu2.(str(ii))));
	162		else
	163		stu2.(str(ii)) = stu2.(str(ii)) + 1e-8;
	164		end
	165		else
	166		stu2.(str(ii)) = rand(ceil(5*rand(1)),ceil(5*rand(1)));
	167		end
	168		end
169	169	end	end
170	170
171	171	[match, er1, er2] = comp_struct(stu1,stu2);	[match, er1, er2] = comp_struct(stu1,stu2);

File misc/3rdparty/comp_struct/list_struct.m changed (mode: 100644) (index cb708fe..8fa354d)
...	...	if nargin < 3; n1 = inputname(1); end
19	19	%% is the variable a structures	%% is the variable a structures
20	20	if isstruct(s1)	if isstruct(s1)
21	21	% structure - get the field names	% structure - get the field names
22		fn1 = fieldnames(s1);
	22		fn1 = fieldnames(s1);
23	23	% added loop for indexed structured variables	% added loop for indexed structured variables
24		for jj = 1:size(s1,2)
	24		for jj = 1:size(s1,2)
25	25	% loop through structure 1	% loop through structure 1
26		for ii = 1:length(fn1)
	26		for ii = 1:length(fn1)
27	27	% clean display - add index if needed	% clean display - add index if needed
28		if size(s1,2) == 1;
29		n1p = [n1 '.' char(fn1(ii))];
30		else
31		n1p = [n1 '(' num2str(jj) ').' char(fn1(ii))];
32		end
33		list_struct(getfield(s1(jj),char(fn1(ii))),v,n1p);
34		end
35		end
	28		if size(s1,2) == 1;
	29		n1p = [n1 '.' char(fn1(ii))];
	30		else
	31		n1p = [n1 '(' num2str(jj) ').' char(fn1(ii))];
	32		end
	33		list_struct(getfield(s1(jj),char(fn1(ii))),v,n1p);
	34		end
	35		end
36	36	else	else
37	37	% not structure - display	% not structure - display
38		if v
39		if ischar(s1);
40		fprintf('Field: %s = %s\n',n1,s1);
41		elseif iscell(s1)
42		if isstruct(s1{1})
43		for ii = 1:numel(s1)
44		temp = s1{ii};
45		fn1 = fieldnames(temp);
46		for jj = 1:numel(fn1)
47		list_struct(getfield(temp,char(fn1(jj))),v,[char(n1) '{' num2str(ii) '}.' char(fn1(jj))]);
48		end
49		end
50		else
51		%disp(sprintf('Field: %s = %s',n1,s1{1:end}))
52		fprintf('Field: %s = %s\n',n1,s1{1});
53		fprintf(' %s\n',s1{2:end});
54		end
55		elseif isnumeric(s1)
56		fprintf('Field: %s = \n',n1); disp(s1)
57		else
58		fprintf('Field: %s = [%s]\n',n1,num2str(s1));
59		end
60		else
61		fprintf('Field: %s\n',n1);
62		end
	38		if v
	39		if ischar(s1);
	40		fprintf('Field: %s = %s\n',n1,s1);
	41		elseif iscell(s1)
	42		if isstruct(s1{1})
	43		for ii = 1:numel(s1)
	44		temp = s1{ii};
	45		fn1 = fieldnames(temp);
	46		for jj = 1:numel(fn1)
	47		list_struct(getfield(temp,char(fn1(jj))),v,[char(n1) '{' num2str(ii) '}.' char(fn1(jj))]);
	48		end
	49		end
	50		else
	51		%disp(sprintf('Field: %s = %s',n1,s1{1:end}))
	52		fprintf('Field: %s = %s\n',n1,s1{1});
	53		fprintf(' %s\n',s1{2:end});
	54		end
	55		elseif isnumeric(s1)
	56		fprintf('Field: %s = \n',n1); disp(s1)
	57		else
	58		fprintf('Field: %s = [%s]\n',n1,num2str(s1));
	59		end
	60		else
	61		fprintf('Field: %s\n',n1);
	62		end
63	63	end	end

File misc/3rdparty/heatmap.m changed (mode: 100644) (index af7812d..ef15089)
...	...	function [hImage, hText, hXText] = heatmap(mat, xlab, ylab, textmat, varargin)
6	6	%	%
7	7	% INPUTS:	% INPUTS:
8	8	% * HEATMAP displays "matrix" as an image whose color intensities reflect	% * HEATMAP displays "matrix" as an image whose color intensities reflect
9		% the magnitude of the values in "matrix".
	9		% the magnitude of the values in "matrix".
10	10	%	%
11	11	% * "xlabels" (and "ylabels") can be either a numeric vector or cell array	% * "xlabels" (and "ylabels") can be either a numeric vector or cell array
12	12	% of strings that represent the columns (or rows) of the matrix. If either	% of strings that represent the columns (or rows) of the matrix. If either
13		% is not specified or empty, no labels will be drawn.
	13		% is not specified or empty, no labels will be drawn.
14	14	%	%
15	15	% * "textmat" can either be: 1 (or true), in which case the "matrix" values will be	% * "textmat" can either be: 1 (or true), in which case the "matrix" values will be
16	16	% displayed in each square, a format string, in which case the matrix	% displayed in each square, a format string, in which case the matrix
...	...	function [hImage, hText, hXText] = heatmap(mat, xlab, ylab, textmat, varargin)
48	48	% default MATLAB displays NaN values using the color assigned to the	% default MATLAB displays NaN values using the color assigned to the
49	49	% lowest value in the colormap. Specifying this option automatically sets	% lowest value in the colormap. Specifying this option automatically sets
50	50	% the 'UseFigureColormap' option to false because the color mapping must	% the 'UseFigureColormap' option to false because the color mapping must
51		% be computed prior to setting the nan color.
	51		% be computed prior to setting the nan color.
52	52	%	%
53	53	% * 'MinColorValue': A scalar number corresponding to the value of the data	% * 'MinColorValue': A scalar number corresponding to the value of the data
54		% that is mapped to the lowest color of the colormap. By default this is
55		% the minimum value of the matrix input.
	54		% that is mapped to the lowest color of the colormap. By default this is
	55		% the minimum value of the matrix input.
56	56	%	%
57	57	% * 'MaxColorValue': A scalar number corresponding to the value of the data	% * 'MaxColorValue': A scalar number corresponding to the value of the data
58		% that is mapped to the highest color of the colormap. By default this is
59		% the maximum value of the matrix input.
60		%
	58		% that is mapped to the highest color of the colormap. By default this is
	59		% the maximum value of the matrix input.
	60		%
61	61	% * 'Parent': Handle to an axes object	% * 'Parent': Handle to an axes object
62	62	%	%
63	63	% * 'TextColor': Either a color specification of all the text displayed on	% * 'TextColor': Either a color specification of all the text displayed on
...	...	end
172	172	% ---------------------- Heatmap Creation Functions ----------------------	% ---------------------- Heatmap Creation Functions ----------------------
173	173
174	174	% Parse PV inputs & return structure of parameters	% Parse PV inputs & return structure of parameters
175		function param = parseInputs(mat, varargin)
	175		function param = parseInputs(mat, varargin)
176	176
177	177	p = inputParser;	p = inputParser;
178	178	p.addParamValue('Colormap',[]); %#ok<*NVREPL>	p.addParamValue('Colormap',[]); %#ok<*NVREPL>
...	...	param.ExplicitlyComputeImage = ~all(isnan(param.NaNColor)) ... NaNColor is speci
218	218	% if param.IsGraphics2 && ~param.UseFigureColormap && ~isempty(param.ColorBar) % graphics v2	% if param.IsGraphics2 && ~param.UseFigureColormap && ~isempty(param.ColorBar) % graphics v2
219	219	% warning('heatmap:graphics2figurecolormap', 'The UseFigureColormap false option with colorbar is not supported in versions R2014b and above. In most such cases UseFigureColormap false is unnecessary');	% warning('heatmap:graphics2figurecolormap', 'The UseFigureColormap false option with colorbar is not supported in versions R2014b and above. In most such cases UseFigureColormap false is unnecessary');
220	220	% end	% end
221
	221
222	222
223	223	end	end
224	224
...	...	else
312	312	tickAxis = 'X';	tickAxis = 'X';
313	313	end	end
314	314	end	end
315
	315
316	316	if ~isempty(ticks)	if ~isempty(ticks)
317
	317
318	318	if ischar(textmat) % If format string, format colorbar ticks in the same way	if ischar(textmat) % If format string, format colorbar ticks in the same way
319	319	ticklabels = arrayfun(@(x){sprintf(textmat,x)},ticks);	ticklabels = arrayfun(@(x){sprintf(textmat,x)},ticks);
320	320	else	else
...	...	if ~isempty(ticks)
325	325	else	else
326	326	set(c, [tickAxis 'TickLabel'], ticklabels);	set(c, [tickAxis 'TickLabel'], ticklabels);
327	327	end	end
328
	328
329	329	end	end
330	330
331	331
...	...	else
360	360	if ischar(ylab)	if ischar(ylab)
361	361	ylab = cellstr(ylab);	ylab = cellstr(ylab);
362	362	end	end
363		ytick = get(p.hAxes, 'YTick');
	363		ytick = get(p.hAxes, 'YTick');
364	364	ytick(ytick<1|ytick>length(ylab)) = [];	ytick(ytick<1|ytick>length(ylab)) = [];
365	365	if p.ShowAllTicks || length(ytick) > length(ylab)	if p.ShowAllTicks || length(ytick) > length(ylab)
366	366	ytick = 1:length(ylab);	ytick = 1:length(ylab);
...	...	function hXText = createXTicks(hAxes, tickAngle, xticks, xticklabels, texInterpr
411	411
412	412	axXLim = get(hAxes, 'XLim');	axXLim = get(hAxes, 'XLim');
413	413	[xPos, yPos] = calculateTextTickPositions(hAxes, axXLim, xticks);	[xPos, yPos] = calculateTextTickPositions(hAxes, axXLim, xticks);
414
	414
415	415	if texInterpreter	if texInterpreter
416	416	interpreter = 'tex';	interpreter = 'tex';
417	417	else	else
...	...	function hXText = createXTicks(hAxes, tickAngle, xticks, xticklabels, texInterpr
421	421	'Parent', hAxes, 'FontSize', get(hAxes,'FontSize'), ...	'Parent', hAxes, 'FontSize', get(hAxes,'FontSize'), ...
422	422	'HorizontalAlignment', 'right', 'Rotation', tickAngle,...	'HorizontalAlignment', 'right', 'Rotation', tickAngle,...
423	423	'Interpreter', interpreter);	'Interpreter', interpreter);
424
	424
425	425	set(hAxes, 'XTick', xticks, 'XTickLabel', '');	set(hAxes, 'XTick', xticks, 'XTickLabel', '');
426	426
427	427	end	end
...	...	function adjustAxesToAccommodateTickLabels(hAxes, hXText)
449	449	[axPosP, axPosN, axOPP, axOPN, coPosP, textPosP] = ...	[axPosP, axPosN, axOPP, axOPN, coPosP, textPosP] = ...
450	450	getGraphicsObjectsPositions(hAxes, hXText); %#ok<ASGLU>	getGraphicsObjectsPositions(hAxes, hXText); %#ok<ASGLU>
451	451
452		header = axOPP(4) + axOPP(2) - axPosP(4) - axPosP(2); % Distance between top of axes and container in pixels;
	452		header = axOPP(4) + axOPP(2) - axPosP(4) - axPosP(2); % Distance between top of axes and container in pixels;
453	453	delta = 5; % To adjust for overlap between area designated for regular ticks and area occupied by rotated ticks	delta = 5; % To adjust for overlap between area designated for regular ticks and area occupied by rotated ticks
454	454	axHeightP = axOPP(4) - header - delta - textPosP(4);	axHeightP = axOPP(4) - header - delta - textPosP(4);
455	455
456	456	% Fudge axis position if labels are taking up too much room	% Fudge axis position if labels are taking up too much room
457	457	if textPosP(4)/(textPosP(4)+axHeightP) > .7 % It's taking up more than 70% of total height	if textPosP(4)/(textPosP(4)+axHeightP) > .7 % It's taking up more than 70% of total height
458		axHeightP = (1/.7-1) * textPosP(4); % Minimum axis
	458		axHeightP = (1/.7-1) * textPosP(4); % Minimum axis
459	459	end	end
460	460	axHeightN = max(0.0001, axHeightP / coPosP(4));	axHeightN = max(0.0001, axHeightP / coPosP(4));
461	461
...	...	if axesLimitsChanged && ~isempty(axInfo.displayText) % If limits change & text l
527	527	set(p.hText(ind), 'Visible', 'on');	set(p.hText(ind), 'Visible', 'on');
528	528	set(p.hText(~ind), 'Visible', 'off');	set(p.hText(~ind), 'Visible', 'off');
529	529	end	end
530
	530
531	531	% Modify Y Tick Labels	% Modify Y Tick Labels
532	532
533	533	if ~isempty(axInfo.ylab)	if ~isempty(axInfo.ylab)
...	...	if ~isempty(axInfo.ylab)
544	544	ylabels(1:length(axInfo.ylab)) = axInfo.ylab;	ylabels(1:length(axInfo.ylab)) = axInfo.ylab;
545	545	set(hAxes, 'YTick', yticks, 'YTickLabel', ylabels(yticks));	set(hAxes, 'YTick', yticks, 'YTickLabel', ylabels(yticks));
546	546	end	end
547
548		if ~isempty(axInfo.xlab)
549
	547
	548		if ~isempty(axInfo.xlab)
	549
550	550	axXLim = get(hAxes, 'XLim');	axXLim = get(hAxes, 'XLim');
551	551
552	552	if p.ShowAllTicks	if p.ShowAllTicks
...	...	if ~isempty(axInfo.xlab)
559	559	end	end
560	560	xlabels = repmat({''},1,max(xticks));	xlabels = repmat({''},1,max(xticks));
561	561	xlabels(1:length(axInfo.xlab)) = axInfo.xlab;	xlabels(1:length(axInfo.xlab)) = axInfo.xlab;
562
	562
563	563	if ~isempty(axInfo.hXText) % Rotated X tick labels exist	if ~isempty(axInfo.hXText) % Rotated X tick labels exist
564	564	try delete(axInfo.hXText); end %#ok<TRYNC>	try delete(axInfo.hXText); end %#ok<TRYNC>
565	565	axInfo.hXText = createXTicks(hAxes, p.TickAngle, xticks, xlabels(xticks), p.TickTexInterpreter);	axInfo.hXText = createXTicks(hAxes, p.TickAngle, xticks, xlabels(xticks), p.TickTexInterpreter);
...	...	if ~isempty(axInfo.xlab)
567	567	else	else
568	568	set(hAxes, 'XTick', xticks, 'XTickLabel', xlabels(xticks));	set(hAxes, 'XTick', xticks, 'XTickLabel', xlabels(xticks));
569	569	end	end
570
	570
571	571	%adjustAxesToAccommodateTickLabels(hAxes, axInfo.hXText)	%adjustAxesToAccommodateTickLabels(hAxes, axInfo.hXText)
572	572	end	end
573	573
...	...	axInfo = getHeatmapAxesInfo(hAxes);
581	581	pos = eventdata.Position;	pos = eventdata.Position;
582	582
583	583	if ~isempty(axInfo)	if ~isempty(axInfo)
584
	584
585	585	try	try
586	586	val = axInfo.displayText{pos(2), pos(1)};	val = axInfo.displayText{pos(2), pos(1)};
587	587	catch %#ok<CTCH>	catch %#ok<CTCH>
...	...	if ~isempty(axInfo)
590	590	if isempty(axInfo.xlab), i = int2str(pos(1)); else i = axInfo.xlab{pos(1)}; end	if isempty(axInfo.xlab), i = int2str(pos(1)); else i = axInfo.xlab{pos(1)}; end
591	591	if isempty(axInfo.ylab), j = int2str(pos(2)); else j = axInfo.ylab{pos(2)}; end	if isempty(axInfo.ylab), j = int2str(pos(2)); else j = axInfo.ylab{pos(2)}; end
592	592	output_txt = sprintf('X: %s\nY: %s\nVal: %s', i, j, val);	output_txt = sprintf('X: %s\nY: %s\nVal: %s', i, j, val);
593
	593
594	594	else	else
595	595	if length(pos) == 2	if length(pos) == 2
596	596	output_txt = sprintf('X: %0.4g\nY: %0.4g', pos(1), pos(2));	output_txt = sprintf('X: %0.4g\nY: %0.4g', pos(1), pos(2));
...	...	end
604	604	function resize(obj, evd)	function resize(obj, evd)
605	605	hAxes = findobj(obj, 'type', 'axes');	hAxes = findobj(obj, 'type', 'axes');
606	606	for i = 1:length(hAxes)	for i = 1:length(hAxes)
607		updateLabels(hAxes(i), false);
	607		updateLabels(hAxes(i), false);
608	608	end	end
609	609	end	end
610	610
...	...	if isempty(textmat)
632	632	factor = 0;	factor = 0;
633	633	return	return
634	634	end	end
635
	635
636	636	if isscalar(textmat) && textmat % If true convert mat to text	if isscalar(textmat) && textmat % If true convert mat to text
637	637	displaytext = arrayfun(@(x){num2str(x)},mat);	displaytext = arrayfun(@(x){num2str(x)},mat);
638	638	elseif ischar(textmat) % If a format string, convert mat to text with specific format	elseif ischar(textmat) % If a format string, convert mat to text with specific format
...	...	function cmap = resamplecmap(cmap, clevels, xi)
754	754
755	755	t = cmap;	t = cmap;
756	756	if nargin < 3	if nargin < 3
757		xi = linspace(1,clevels,size(t,1));
	757		xi = linspace(1,clevels,size(t,1));
758	758	end	end
759		xi([1 end]) = [1 clevels]; % These need to be exact for the interpolation to
	759		xi([1 end]) = [1 clevels]; % These need to be exact for the interpolation to
760	760	% work and we don't want machine precision messing it up	% work and we don't want machine precision messing it up
761	761	cmap = [interp1(xi, t(:,1), 1:clevels);...	cmap = [interp1(xi, t(:,1), 1:clevels);...
762	762	interp1(xi, t(:,2), 1:clevels);...	interp1(xi, t(:,2), 1:clevels);...
...	...	elseif zeroInd >= clevels, % Just red
779	779	g = interp1([1 clevels], [0 1], 1:clevels);	g = interp1([1 clevels], [0 1], 1:clevels);
780	780	r = interp1([1 clevels], [1 1], 1:clevels);	r = interp1([1 clevels], [1 1], 1:clevels);
781	781	else	else
782		b = interp1([1 zeroInd clevels], [0 1 0], 1:clevels);
	782		b = interp1([1 zeroInd clevels], [0 1 0], 1:clevels);
783	783	g = interp1([1 zeroInd clevels], [0 1 1], 1:clevels);	g = interp1([1 zeroInd clevels], [0 1 1], 1:clevels);
784	784	r = interp1([1 zeroInd clevels], [1 1 0], 1:clevels);	r = interp1([1 zeroInd clevels], [1 1 0], 1:clevels);
785	785	end	end
...	...	end
790	790	% Generate Red-White color map	% Generate Red-White color map
791	791	function cmap = red(levels)	function cmap = red(levels)
792	792	r = ones(levels, 1);	r = ones(levels, 1);
793		g = linspace(1, 0, levels)';
	793		g = linspace(1, 0, levels)';
794	794	cmap = [r g g];	cmap = [r g g];
795	795	end	end
796	796

File misc/3rdparty/isalmost.m changed (mode: 100644) (index 4b20ba3..fb9af0f)
...	...	function test = isalmost(a,b,tol)
2	2	%	%
3	3	% usage: test = isalmost(a,b,tol)	% usage: test = isalmost(a,b,tol)
4	4	%	%
5		% tests if matrix a is approximately equal to b within a specified
	5		% tests if matrix a is approximately equal to b within a specified
6	6	% tolerance interval (b-tol <= a <= b+tol)	% tolerance interval (b-tol <= a <= b+tol)
7	7	%	%
8	8	% note: if b is given as a scalar, all values in a are compared against	% note: if b is given as a scalar, all values in a are compared against
...	...	function test = isalmost(a,b,tol)
23	23	% test(i,j) = 0 -> a(i,j) is not equal to b(i,j) (or is NaN)	% test(i,j) = 0 -> a(i,j) is not equal to b(i,j) (or is NaN)
24	24	% test(i,j) = 1 -> a(i,j) is approximately equal to b(i,j)	% test(i,j) = 1 -> a(i,j) is approximately equal to b(i,j)
25	25	%	%
26		% author : James Crawford
	26		% author : James Crawford
27	27	% created 01/08/2007	% created 01/08/2007
28	28	%	%
29	29	% history: v1.0 (01/08/2007)	% history: v1.0 (01/08/2007)

File misc/3rdparty/kde2d.m changed (mode: 100644) (index 567ddc3..f700ea7)
...	...	p_02=func([0,2],t_star);p_20=func([2,0],t_star); p_11=func([1,1],t_star);
97	97	t_y=(p_02^(3/4)/(4*pi*N*p_20^(3/4)*(p_11+sqrt(p_20*p_02))))^(1/3);	t_y=(p_02^(3/4)/(4*pi*N*p_20^(3/4)*(p_11+sqrt(p_20*p_02))))^(1/3);
98	98	t_x=(p_20^(3/4)/(4*pi*N*p_02^(3/4)*(p_11+sqrt(p_20*p_02))))^(1/3);	t_x=(p_20^(3/4)/(4*pi*N*p_02^(3/4)*(p_11+sqrt(p_20*p_02))))^(1/3);
99	99	% smooth the discrete cosine transform of initial data using t_star	% smooth the discrete cosine transform of initial data using t_star
100		a_t=exp(-(0:n-1)'.^2*pi^2*t_x/2)*exp(-(0:n-1).^2*pi^2*t_y/2).*a;
	100		a_t=exp(-(0:n-1)'.^2*pi^2*t_x/2)*exp(-(0:n-1).^2*pi^2*t_y/2).*a;
101	101	% now apply the inverse discrete cosine transform	% now apply the inverse discrete cosine transform
102	102	if nargout>1	if nargout>1
103	103	density=idct2d(a_t)*(numel(a_t)/prod(scaling));	density=idct2d(a_t)*(numel(a_t)/prod(scaling));
104		density(density<0)=eps; % remove any negative density values
	104		density(density<0)=eps; % remove any negative density values
105	105	[X,Y]=meshgrid(MIN_XY(1):scaling(1)/(n-1):MAX_XY(1),MIN_XY(2):scaling(2)/(n-1):MAX_XY(2));	[X,Y]=meshgrid(MIN_XY(1):scaling(1)/(n-1):MAX_XY(1),MIN_XY(2):scaling(2)/(n-1):MAX_XY(2));
106	106	end	end
107		bandwidth=sqrt([t_x,t_y]).*scaling;
	107		bandwidth=sqrt([t_x,t_y]).*scaling;
108	108	end	end
109	109	%#######################################	%#######################################
110	110	function [out,time]=evolve(t)	function [out,time]=evolve(t)
...	...	while flag==0
210	210	end	end
211	211	end	end
212	212	end	end
213
214
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File misc/3rdparty/ndhist.m changed (mode: 100644) (index 32e2658..b9af632)
1	1	% Add logx,logy,loglog	% Add logx,logy,loglog
2		%
	2		%
3	3
4	4	% Displays a 2d histogram for your data, it will set the bins appropriately	% Displays a 2d histogram for your data, it will set the bins appropriately
5		%
	5		%
6	6	% NDHIST(x,y); where x and y are equal length vectors. It will choose	% NDHIST(x,y); where x and y are equal length vectors. It will choose
7	7	% reasonable axis bounds and bins to present your data. The	% reasonable axis bounds and bins to present your data. The
8	8	% default parameters may leave some data off the chart.	% default parameters may leave some data off the chart.
9		%
	9		%
10	10	% NDHIST(XY); where XY = [x y] and x,y are vectors	% NDHIST(XY); where XY = [x y] and x,y are vectors
11		%
	11		%
12	12	% NDHIST(z); where z is a vector of complex numbers, (x+1i*y) or amplitude*exp(1i*theta)	% NDHIST(z); where z is a vector of complex numbers, (x+1i*y) or amplitude*exp(1i*theta)
13		%
	13		%
14	14	% NDHIST(y); where y is a vector of real numbers will plot a 2d histogram	% NDHIST(y); where y is a vector of real numbers will plot a 2d histogram
15	15	% of the points, as if it were a line-chart. It is equivalent	% of the points, as if it were a line-chart. It is equivalent
16	16	% to calling ndhist(1:length(y),y);	% to calling ndhist(1:length(y),y);
17		%
	17		%
18	18	% N = NDHIST(x,y); returns a matrix N containing the counts for each bin	% N = NDHIST(x,y); returns a matrix N containing the counts for each bin
19	19	% determined by the histogram.	% determined by the histogram.
20		%
	20		%
21	21	% [edgesX2,edgesY2,N,h] = NDHIST(x,y); which returns a matrix N containing	% [edgesX2,edgesY2,N,h] = NDHIST(x,y); which returns a matrix N containing
22	22	% the counts for each bin determined by the histogram. You can	% the counts for each bin determined by the histogram. You can
23	23	% plot it with sanePColor(edgesX2,edgesY2,N); (from Matlabcentral)	% plot it with sanePColor(edgesX2,edgesY2,N); (from Matlabcentral)
24	24	% h is the plot handle.	% h is the plot handle.
25		%
	25		%
26	26	% NDHIST(...,'param','value','param','value', ... ); Run ndhist with specific	% NDHIST(...,'param','value','param','value', ... ); Run ndhist with specific
27	27	% parameters	% parameters
28		%
29		% List of special parameters:
30		%
	28		%
	29		% List of special parameters:
	30		%
31	31	% 'filter' : This will apply a gaussian filter to the final histogram data.	% 'filter' : This will apply a gaussian filter to the final histogram data.
32	32	% The default filter width is 5 bins wide. If you pass a number	% The default filter width is 5 bins wide. If you pass a number
33	33	% then that will be used. Even numbered filter parameters will be	% then that will be used. Even numbered filter parameters will be
34	34	% changed to odd numbers to keep the filter perfectly symetrical.	% changed to odd numbers to keep the filter perfectly symetrical.
35	35	% 'filt','filtering','smooth'	% 'filt','filtering','smooth'
36		%
	36		%
37	37	% 'log' : Change the colormap to be on a log scale to represent data	% 'log' : Change the colormap to be on a log scale to represent data
38		% over a large dynamic range.
	38		% over a large dynamic range.
39	39	% 'logplot'	% 'logplot'
40		%
	40		%
41	41	% 'bins' : Change the size of the bins. For example '2' will create a	% 'bins' : Change the size of the bins. For example '2' will create a
42	42	% plot with twice the default number of bins; 0.5 will have half	% plot with twice the default number of bins; 0.5 will have half
43	43	% the default number of bins. The default uses Scott's normal	% the default number of bins. The default uses Scott's normal
46	46	% subfunction hist3. Feel free to implement it as an additional	% subfunction hist3. Feel free to implement it as an additional
47	47	% parameter 'edgdes','edgesx' or 'edgesy'	% parameter 'edgdes','edgesx' or 'edgesy'
48	48	% 'f','numbins'	% 'f','numbins'
49		%
	49		%
50	50	% 'binsx' : Change the size of only the x bins. 'fx'	% 'binsx' : Change the size of only the x bins. 'fx'
51	51	% 'binsy' : Change the size of only the y bins. 'fy'	% 'binsy' : Change the size of only the y bins. 'fy'
52		%
	52		%
53	53	% axis : This is to set the range of the plot, [xmin xmax ymin ymax]	% axis : This is to set the range of the plot, [xmin xmax ymin ymax]
54	54	% The default range is set to 3*std(x) and 3*std(y) where the	% The default range is set to 3*std(x) and 3*std(y) where the
55	55	% parameter stdTimes=3 is hard-coded in this version and	% parameter stdTimes=3 is hard-coded in this version and
56	56	% potentially added as a parameter in a later version.	% potentially added as a parameter in a later version.
57		%
	57		%
58	58	% max : This is to set the range of the plot to be such that every	% max : This is to set the range of the plot to be such that every
59	59	% point will be contained within the plot.	% point will be contained within the plot.
60	60	% 'themax'	% 'themax'
61		%
	61		%
62	62	% intbins : Set the bins to be intiger widths. For both x and y	% intbins : Set the bins to be intiger widths. For both x and y
63	63	% 'int'	% 'int'
64		%
	64		%
65	65	% intbinsx : Set the x bins to be intiger widths. 'intx'	% intbinsx : Set the x bins to be intiger widths. 'intx'
66	66	% intbinsy : Set the y bins to be intiger widths. 'inty'	% intbinsy : Set the y bins to be intiger widths. 'inty'
67		%
	67		%
68	68	%normalizex: Normalize the plot so that the sum of all the y values in each	%normalizex: Normalize the plot so that the sum of all the y values in each
69	69	% x bin sum to one.	% x bin sum to one.
70		% 'normx','nx'
71		%
	70		% 'normx','nx'
	71		%
72	72	%normalizey: Normalize the plot so that the sum of all the x values in each	%normalizey: Normalize the plot so that the sum of all the x values in each
73	73	% y bin sum to one.	% y bin sum to one.
74	74	% 'normy','ny'	% 'normy','ny'
75		%
	75		%
76	76	%normalizeR: Normalize the plot so that the you can clearly see how the	%normalizeR: Normalize the plot so that the you can clearly see how the
77	77	% distribution vary's over angle. It weights points in the outer	% distribution vary's over angle. It weights points in the outer
78	78	% radius by the diameter at that radius.	% radius by the diameter at that radius.
79	79	% 'nr'	% 'nr'
80	80	% points: Plot the points on top of the colored histogram.	% points: Plot the points on top of the colored histogram.
81		%
	81		%
82	82	% 3D: Use a 3D column graph instead of a colored heatmap	% 3D: Use a 3D column graph instead of a colored heatmap
83	83	% 'threeD','3d','columns'	% 'threeD','3d','columns'
84		%
	84		%
85	85	% PARTIALLY IMPLEMENTED	% PARTIALLY IMPLEMENTED
86	86	% radial : Set the axis to be equal and add a polar grid 'r'	% radial : Set the axis to be equal and add a polar grid 'r'
87		%
	87		%
88	88	% NOT IMPLEMENTED YET	% NOT IMPLEMENTED YET
89	89	%'samebins': NOT IMPLEMENTED YET. Would set the width of the x and y bins	%'samebins': NOT IMPLEMENTED YET. Would set the width of the x and y bins
90	90	% to be equal to each other and the axis equal too.	% to be equal to each other and the axis equal too.
91	91
92		%
	92		%
93	93	% user parameters:	% user parameters:
94	94	% filter: This will filter the data, you may choose to follow it with a	% filter: This will filter the data, you may choose to follow it with a
95	95	% number. This number will represent the radius of the circular	% number. This number will represent the radius of the circular
96	96	% gaussian filter. Other ways to call it: 'filt','filtering','f'	% gaussian filter. Other ways to call it: 'filt','filtering','f'
97		%
98		%
	97		%
	98		%
99	99	% examples	% examples
100		%
	100		%
101	101	% To test the function you may use this example:	% To test the function you may use this example:
102	102	% z=2*randn(1,100000)+1i*(randn(1,100000));	% z=2*randn(1,100000)+1i*(randn(1,100000));
103		%
	103		%
104	104	% If you have amplitude and angle measures then pass this:	% If you have amplitude and angle measures then pass this:
105	105	% z = amp*exp(1i*ang);	% z = amp*exp(1i*ang);
106		%
	106		%
107	107	% NDHIST(z)	% NDHIST(z)
108	108	% NDHIST(z,'lansey')	% NDHIST(z,'lansey')
109	109	% NDHIST(z,'filter')	% NDHIST(z,'filter')
110		%
	110		%
111	111	% % Note	% % Note
112	112	% The name of this function comes because really its a 2d hist, but since I	% The name of this function comes because really its a 2d hist, but since I
113	113	% already have an 'nhist' I thought I might name it this.	% already have an 'nhist' I thought I might name it this.
114		%
	114		%
115	115	% SEE ALSO: HIST, HIST3	% SEE ALSO: HIST, HIST3
116	116	%%	%%
117	117	function [edgesX2,edgesY2,N,h] = ndhist(z,varargin)	function [edgesX2,edgesY2,N,h] = ndhist(z,varargin)
...	...	end
134	134	if isreal(z)	if isreal(z)
135	135	if size(z,2)==2 % we have a [x y] case	if size(z,2)==2 % we have a [x y] case
136	136	[edgesX2,edgesY2,N] = ndhist(z(:,1)+1i*z(:,2),varargin{:});	[edgesX2,edgesY2,N] = ndhist(z(:,1)+1i*z(:,2),varargin{:});
137		else % we've got either
	137		else % we've got either
138	138	if isempty(varargin) % just one value passed	if isempty(varargin) % just one value passed
139	139	display('your data are all ''real'' so we intepreted it as a timeseries');	display('your data are all ''real'' so we intepreted it as a timeseries');
140	140	idx = (1:length(z))';	idx = (1:length(z))';
...	...	if isreal(z)
161	161	if nargout==2	if nargout==2
162	162	warning('you are being passed out ''edgesX2,edgesY2'' is that really what you want from this function?');	warning('you are being passed out ''edgesX2,edgesY2'' is that really what you want from this function?');
163	163	end	end
164
	164
165	165	return;	return;
166	166	end	end
167	167	% great we can continue	% great we can continue
...	...	while k <= nargin-1
272	272	case {'bins','numbins','f'} % 'f' comes from the binfactor of nhist	case {'bins','numbins','f'} % 'f' comes from the binfactor of nhist
273	273	binFactorY=varargin{k+1};	binFactorY=varargin{k+1};
274	274	binFactorX=varargin{k+1};	binFactorX=varargin{k+1};
275		k = k + 1;
	275		k = k + 1;
276	276	case {'binsx','fx'} % 'f' comes from the binfactor of nhist	case {'binsx','fx'} % 'f' comes from the binfactor of nhist
277	277	binFactorX=varargin{k+1};	binFactorX=varargin{k+1};
278		k = k + 1;
	278		k = k + 1;
279	279	case {'binsy','fy'} % 'f' comes from the binfactor of nhist	case {'binsy','fy'} % 'f' comes from the binfactor of nhist
280	280	binFactorY=varargin{k+1};	binFactorY=varargin{k+1};
281		k = k + 1;
	281		k = k + 1;
282	282	case {'intbins','int'} % 'f' comes from the binfactor of nhist	case {'intbins','int'} % 'f' comes from the binfactor of nhist
283	283	intbinsFlagX = 1;	intbinsFlagX = 1;
284	284	intbinsFlagY = 1;	intbinsFlagY = 1;
...	...	while k <= nargin-1
309	309	end	end
310	310	end	end
311	311	case {'lansey','normalizer','nr'} % this guy weights the values based on radius	case {'lansey','normalizer','nr'} % this guy weights the values based on radius
312		userParam=1;
	312		userParam=1;
313	313	case {'log','logplot'}	case {'log','logplot'}
314	314	logColorFlag = 1;	logColorFlag = 1;
315	315	case {'normalizex','normx','nx'}	case {'normalizex','normx','nx'}
...	...	while k <= nargin-1
329	329	error('stdTimes must be a number')	error('stdTimes must be a number')
330	330	end	end
331	331
332		otherwise
	332		otherwise
333	333	warning(['you have passed a strange parameter: ''' varargin{k} ''' please roll again']);	warning(['you have passed a strange parameter: ''' varargin{k} ''' please roll again']);
334	334	end	end
335	335	else	else
...	...	if normalizeFlagX && normalizeFlagY
343	343	warning('Only normalize X was used');	warning('Only normalize X was used');
344	344	normalizeFlagY = 0;	normalizeFlagY = 0;
345	345	end	end
346
	346
347	347
348	348	%% set the bin widths	%% set the bin widths
349	349	% Using Scott's normal reference rule, unclear if it is ideal for 2D histograms ...	% Using Scott's normal reference rule, unclear if it is ideal for 2D histograms ...
...	...	if intbinsFlagX
379	379	axisXY(1)=round(axisXY(1))-.5; % subtract 1/2 to make the bin peaks appear on the numbers.	axisXY(1)=round(axisXY(1))-.5; % subtract 1/2 to make the bin peaks appear on the numbers.
380	380	axisXY(2)=round(axisXY(2))+.5;	axisXY(2)=round(axisXY(2))+.5;
381	381	binWidthX=max(round(binWidthX),1);	binWidthX=max(round(binWidthX),1);
382
	382
383	383	% numBinsX = rangeX/binWidthX; % Approx number of bins	% numBinsX = rangeX/binWidthX; % Approx number of bins
384
385
	384
	385
386	386	end	end
387	387	if intbinsFlagY	if intbinsFlagY
388	388	axisXY(3)=round(axisXY(3))-.5; % subtract 1/2 to make the bin peaks appear on the numbers.	axisXY(3)=round(axisXY(3))-.5; % subtract 1/2 to make the bin peaks appear on the numbers.
389	389	axisXY(4)=round(axisXY(4))+.5;	axisXY(4)=round(axisXY(4))+.5;
390	390	binWidthY=max(round(binWidthY),1);	binWidthY=max(round(binWidthY),1);
391
	391
392	392	% numBinsY = rangeY/binWidthY; % Approx number of bins	% numBinsY = rangeY/binWidthY; % Approx number of bins
393	393
394	394
395
	395
396	396	end	end
397	397
398	398	% finally set the bins	% finally set the bins
...	...	N = hist3(x,y,edgesX,edgesY);
409	409	if filtering	if filtering
410	410	N2 = smooth2(N,filtering);	N2 = smooth2(N,filtering);
411	411	N = N2;	N = N2;
412
	412
413	413	% % if you have the image processing toolbox then you could use this:	% % if you have the image processing toolbox then you could use this:
414	414	% H = fspecial('disk',filtering);	% H = fspecial('disk',filtering);
415	415	% filtN = imfilter(N,H,'replicate');	% filtN = imfilter(N,H,'replicate');
...	...	end
497	497
498	498	% if ~hold_state, hold off; end;	% if ~hold_state, hold off; end;
499	499
500		end % ndhist function over
	500		end % ndhist function over
501	501
502	502
503	503	%%	%%
...	...	minError=eps*100; % the minimum average difference from integers they can be.
545	545	L=0; % double until proven integer	L=0; % double until proven integer
546	546	if ~isempty(varargin)	if ~isempty(varargin)
547	547	minError=varargin{1};	minError=varargin{1};
548		end
	548		end
549	549	if isinteger(x)||islogical(x) % your done, duh, its an int.	if isinteger(x)||islogical(x) % your done, duh, its an int.
550	550	L=1;	L=1;
551		return;
	551		return;
552	552	else	else
553	553	if sum(abs(x-round(x)))/length(x)<minError	if sum(abs(x-round(x)))/length(x)<minError
554	554	L=1;	L=1;
...	...	end
562	562
563	563	%%	%%
564	564	% This version is for plotting a certain figure.	% This version is for plotting a certain figure.
565		%
	565		%
566	566	% myPolar: Polar coordinate plot, draws a polar grid onto the axis	% myPolar: Polar coordinate plot, draws a polar grid onto the axis
567		%
	567		%
568	568	% myPolar(); % just adds the grid to the axis	% myPolar(); % just adds the grid to the axis
569	569	% myPolar(theta,rho) % makes a plot using polar coordinates of	% myPolar(theta,rho) % makes a plot using polar coordinates of
570	570	% the angle THETA, in radians, versus the radius RHO	% the angle THETA, in radians, versus the radius RHO
...	...	end
572	572	% myPolar(THETA,RHO,S) uses the linestyle specified in string, like 'z'	% myPolar(THETA,RHO,S) uses the linestyle specified in string, like 'z'
573	573	% myPolar(THETA,RHO,'linespec','parameter',...) uses the linestyle	% myPolar(THETA,RHO,'linespec','parameter',...) uses the linestyle
574	574	% specified by the linspec functions of plot	% specified by the linspec functions of plot
575		%
576		%
	575		%
	576		%
577	577	% Here are the specific list of improvements from Matlab's function:	% Here are the specific list of improvements from Matlab's function:
578	578	% The ability to add a polar grid to any other plot you make	% The ability to add a polar grid to any other plot you make
579	579	% the ability to use all linspec parameters, like 'linewidth' and 'color'	% the ability to use all linspec parameters, like 'linewidth' and 'color'
...	...	end
581	581	% The plot is still square, it does not gray out anything.	% The plot is still square, it does not gray out anything.
582	582	% Grid axis ticks go to two decimal places	% Grid axis ticks go to two decimal places
583	583	%	%
584		%
	584		%
585	585	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
586	586	% Jonathan Lansey May 2009, questions to Lansey at gmail.com %	% Jonathan Lansey May 2009, questions to Lansey at gmail.com %
587	587	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
...	...	hold_state = ishold;
592	592
593	593	nCircles=3;	nCircles=3;
594	594	nDegrees=30;	nDegrees=30;
595
	595
596	596	if nargin==0 % plot the grid now!	if nargin==0 % plot the grid now!
597	597
598	598	addPolarGrid(nCircles,nDegrees);	addPolarGrid(nCircles,nDegrees);
599	599	addPolarText(nCircles,nDegrees);	addPolarText(nCircles,nDegrees);
600
	600
601	601	else	else
602	602	if ~isreal(varargin{1})% if its complex	if ~isreal(varargin{1})% if its complex
603	603	% we just get x and y from the complex data	% we just get x and y from the complex data
...	...	end % myPolar over
707	707	% This function creates an Nx3 array of N [R B G] colors	% This function creates an Nx3 array of N [R B G] colors
708	708	% These can be used to plot lots of lines with distinguishable and nice	% These can be used to plot lots of lines with distinguishable and nice
709	709	% looking colors.	% looking colors.
710		%
	710		%
711	711	% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)	% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)
712		%
713		% colormap(linspecer); set your colormap to have easily distinguishable
	712		%
	713		% colormap(linspecer); set your colormap to have easily distinguishable
714	714	% colors and a pleasing aesthetic	% colors and a pleasing aesthetic
715		%
	715		%
716	716	% lineStyles = linspecer(N,'qualitative'); forces the colors to all be distinguishable (up to 12)	% lineStyles = linspecer(N,'qualitative'); forces the colors to all be distinguishable (up to 12)
717		% lineStyles = linspecer(N,'sequential'); forces the colors to vary along a spectrum
718		%
	717		% lineStyles = linspecer(N,'sequential'); forces the colors to vary along a spectrum
	718		%
719	719	% % Examples demonstrating the colors.	% % Examples demonstrating the colors.
720		%
	720		%
721	721	% LINE COLORS	% LINE COLORS
722	722	% N=6;	% N=6;
723		% X = linspace(0,pi*3,1000);
724		% Y = bsxfun(@(x,n)sin(x+2*n*pi/N), X.', 1:N);
	723		% X = linspace(0,pi*3,1000);
	724		% Y = bsxfun(@(x,n)sin(x+2*n*pi/N), X.', 1:N);
725	725	% C = linspecer(N);	% C = linspecer(N);
726	726	% axes('NextPlot','replacechildren', 'ColorOrder',C);	% axes('NextPlot','replacechildren', 'ColorOrder',C);
727	727	% plot(X,Y,'linewidth',5)	% plot(X,Y,'linewidth',5)
728	728	% ylim([-1.1 1.1]);	% ylim([-1.1 1.1]);
729		%
	729		%
730	730	% SIMPLER LINE COLOR EXAMPLE	% SIMPLER LINE COLOR EXAMPLE
731	731	% N = 6; X = linspace(0,pi*3,1000);	% N = 6; X = linspace(0,pi*3,1000);
732	732	% C = linspecer(N)	% C = linspecer(N)
...	...	end % myPolar over
736	736	% plot(X,Y,'color',C(ii,:),'linewidth',3);	% plot(X,Y,'color',C(ii,:),'linewidth',3);
737	737	% hold on;	% hold on;
738	738	% end	% end
739		%
	739		%
740	740	% COLORMAP EXAMPLE	% COLORMAP EXAMPLE
741	741	% A = rand(15);	% A = rand(15);
742	742	% figure; imagesc(A); % default colormap	% figure; imagesc(A); % default colormap
743	743	% figure; imagesc(A); colormap(linspecer); % linspecer colormap	% figure; imagesc(A); colormap(linspecer); % linspecer colormap
744		%
	744		%
745	745	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
746	746	% by Jonathan Lansey, March 2009-2013 � Lansey at gmail.com %	% by Jonathan Lansey, March 2009-2013 � Lansey at gmail.com %
747	747	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
748		%
	748		%
749	749	%% credits and where the function came from	%% credits and where the function came from
750	750	% The colors are largely taken from:	% The colors are largely taken from:
751	751	% http://colorbrewer2.org and Cynthia Brewer, Mark Harrower and The Pennsylvania State University	% http://colorbrewer2.org and Cynthia Brewer, Mark Harrower and The Pennsylvania State University
752		%
753		%
	752		%
	753		%
754	754	% She studied this from a phsychometric perspective and crafted the colors	% She studied this from a phsychometric perspective and crafted the colors
755	755	% beautifully.	% beautifully.
756		%
	756		%
757	757	% I made choices from the many there to decide the nicest once for plotting	% I made choices from the many there to decide the nicest once for plotting
758	758	% lines in Matlab. I also made a small change to one of the colors I	% lines in Matlab. I also made a small change to one of the colors I
759	759	% thought was a bit too bright. In addition some interpolation is going on	% thought was a bit too bright. In addition some interpolation is going on
760	760	% for the sequential line styles.	% for the sequential line styles.
761		%
762		%
	761		%
	762		%
763	763	%%	%%
764	764
765	765	function lineStyles=linspecer(N,varargin)	function lineStyles=linspecer(N,varargin)
...	...	if ~isempty(varargin)>0 % you set a parameter?
809	809	otherwise	otherwise
810	810	warning(['parameter ''' varargin{1} ''' not recognized']);	warning(['parameter ''' varargin{1} ''' not recognized']);
811	811	end	end
812		end
813
	812		end
	813
814	814	% predefine some colormaps	% predefine some colormaps
815	815	set3 = colorBrew2mat({[141, 211, 199];[ 255, 237, 111];[ 190, 186, 218];[ 251, 128, 114];[ 128, 177, 211];[ 253, 180, 98];[ 179, 222, 105];[ 188, 128, 189];[ 217, 217, 217];[ 204, 235, 197];[ 252, 205, 229];[ 255, 255, 179]}');	set3 = colorBrew2mat({[141, 211, 199];[ 255, 237, 111];[ 190, 186, 218];[ 251, 128, 114];[ 128, 177, 211];[ 253, 180, 98];[ 179, 222, 105];[ 188, 128, 189];[ 217, 217, 217];[ 204, 235, 197];[ 252, 205, 229];[ 255, 255, 179]}');
816	816	set1JL = brighten(colorBrew2mat({[228, 26, 28];[ 55, 126, 184];[ 77, 175, 74];[ 255, 127, 0];[ 255, 237, 111]*.95;[ 166, 86, 40];[ 247, 129, 191];[ 153, 153, 153];[ 152, 78, 163]}'));	set1JL = brighten(colorBrew2mat({[228, 26, 28];[ 55, 126, 184];[ 77, 175, 74];[ 255, 127, 0];[ 255, 237, 111]*.95;[ 166, 86, 40];[ 247, 129, 191];[ 153, 153, 153];[ 152, 78, 163]}'));
...	...	end
841	841	function varIn = colorBrew2mat(varIn)	function varIn = colorBrew2mat(varIn)
842	842	for ii=1:length(varIn) % just divide by 255	for ii=1:length(varIn) % just divide by 255
843	843	varIn{ii}=varIn{ii}/255;	varIn{ii}=varIn{ii}/255;
844		end
	844		end
845	845	end	end
846	846
847	847	function varIn = brighten(varIn,varargin) % increase the brightness	function varIn = brighten(varIn,varargin) % increase the brightness
848	848
849	849	if isempty(varargin),	if isempty(varargin),
850		frac = .9;
	850		frac = .9;
851	851	else	else
852		frac = varargin{1};
	852		frac = varargin{1};
853	853	end	end
854	854
855	855	for ii=1:length(varIn)	for ii=1:length(varIn)
856	856	varIn{ii}=varIn{ii}*frac+(1-frac);	varIn{ii}=varIn{ii}*frac+(1-frac);
857		end
	857		end
858	858	end	end
859	859
860	860	function varIn = dim(varIn,f)	function varIn = dim(varIn,f)
...	...	end
947	947
948	948	% This is a fast smooth function that will return a smoothed	% This is a fast smooth function that will return a smoothed
949	949	% version of the original that you pass it.	% version of the original that you pass it.
950		%
	950		%
951	951	% Hey now the default is actually going to be a gaussian filter not a	% Hey now the default is actually going to be a gaussian filter not a
952	952	% moving average filter	% moving average filter
953		%
	953		%
954	954	% It will be the same length as the original, and it will be centered on	% It will be the same length as the original, and it will be centered on
955	955	% the original function. To padd the edges it extends the average of the	% the original function. To padd the edges it extends the average of the
956	956	% last 'n' values on the end out further.	% last 'n' values on the end out further.
957		%
	957		%
958	958	% Note that it uses a convolution which uses the	% Note that it uses a convolution which uses the
959	959	% cool fft trick to do it effeciently.	% cool fft trick to do it effeciently.
960		%% set things up if you want to test it as a script
	960		%% set things up if you want to test it as a script
961	961	% N=301;	% N=301;
962	962	% % y = rand(N,1);	% % y = rand(N,1);
963	963	% y=-1./[1:N];	% y=-1./[1:N];
...	...	if length(n)==1
978	978	yout = A;	yout = A;
979	979	return;	return;
980	980	end	end
981
	981
982	982	% forcing you to have an odd 'n'	% forcing you to have an odd 'n'
983	983	if double(~logical(round(n/2)-n/2));	if double(~logical(round(n/2)-n/2));
984	984	n = n+1;	n = n+1;
985	985	end	end
986
	986
987	987	bee = linspace(-1.96,1.96,n); % normal distribution with 95% confidence bounds	bee = linspace(-1.96,1.96,n); % normal distribution with 95% confidence bounds
988	988	[BX, BY] = meshgrid(bee);	[BX, BY] = meshgrid(bee);
989	989	R2 = BX.^2+BY.^2;	R2 = BX.^2+BY.^2;
990	990	toConvolve = exp(-R2)/sum(exp(-R2(:)));	toConvolve = exp(-R2)/sum(exp(-R2(:)));
991
	991
992	992	% toConvolve = exp(-bee.^2)/sum(exp(-bee.^2));	% toConvolve = exp(-bee.^2)/sum(exp(-bee.^2));
993		% old moving average computation:
	993		% old moving average computation:
994	994	% toConvolve = ones(n,1)/n;	% toConvolve = ones(n,1)/n;
995	995	else	else
996	996	toConvolve = n;	toConvolve = n;
...	...	A = [padTop; A; padBot];
1024	1024
1025	1025	% the main event	% the main event
1026	1026	As=conv2(A,toConvolve);	As=conv2(A,toConvolve);
1027		% % % % % % % % % % % % %
	1027		% % % % % % % % % % % % %
1028	1028	% clf;	% clf;
1029	1029
1030	1030	% sanePColor(As);	% sanePColor(As);
...	...	Y = [0 Y 100];
1053	1053	yi = interp1(Y,x,p);	yi = interp1(Y,x,p);
1054	1054
1055	1055	end	end
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065

File misc/3rdparty/ntitle.m changed (mode: 100644) (index 8e198b6..e4d1493)
1	1	function [ h ] = ntitle(titlestring,varargin)	function [ h ] = ntitle(titlestring,varargin)
2	2	% ntitle(titlestring,varargin) places a title within the plot instead of on	% ntitle(titlestring,varargin) places a title within the plot instead of on
3	3	% top. In the spirit of Edward Tufte, this is intended to keep the title	% top. In the spirit of Edward Tufte, this is intended to keep the title
4		% close to the data it describes and minimize wasted space. The ntitle function
5		% may prove particularly useful for figures with several subplots, where titles
6		% can sometimes become confused with xlabels above them.
7		%
	4		% close to the data it describes and minimize wasted space. The ntitle function
	5		% may prove particularly useful for figures with several subplots, where titles
	6		% can sometimes become confused with xlabels above them.
	7		%
8	8	% By default, ntitle centers the title at the top of the plot, but any of	% By default, ntitle centers the title at the top of the plot, but any of
9	9	% the location coordinates (e.g., north, southwest, etc.) can be used with	% the location coordinates (e.g., north, southwest, etc.) can be used with
10		% the argument 'location'. Output h provides the handle for the ntitle.
11		%
12		% Created by Chad A. Greene on the fifth of June, 2013.
	10		% the argument 'location'. Output h provides the handle for the ntitle.
	11		%
	12		% Created by Chad A. Greene on the fifth of June, 2013.
13	13	%	%
14		% % Example:
15		%
16		% % First make a plot:
17		% x = -50:.1:50;
18		% y = 10*sin(x)+.1*x.^2;
	14		% % Example:
	15		%
	16		% % First make a plot:
	17		% x = -50:.1:50;
	18		% y = 10*sin(x)+.1*x.^2;
19	19	% plot(x,y);	% plot(x,y);
20	20	% axis([-50 50 -50 300])	% axis([-50 50 -50 300])
21	21	% box off	% box off
22		%
23		% % Then here are 9 obnoxious examples of how to use ntitle:
	22		%
	23		% % Then here are 9 obnoxious examples of how to use ntitle:
24	24	% ntitle('Title 1');	% ntitle('Title 1');
25	25	% ntitle('{\it Title 2}','location','northwest');	% ntitle('{\it Title 2}','location','northwest');
26	26	% ntitle('Title 3 ','location','northeast','fontsize',10,'edgecolor','b');	% ntitle('Title 3 ','location','northeast','fontsize',10,'edgecolor','b');
...	...	for n=1:2:length(varargin)
42	42	if strcmpi(varargin{n},'location')	if strcmpi(varargin{n},'location')
43	43	if strcmpi(varargin{n+1},'northwest')	if strcmpi(varargin{n+1},'northwest')
44	44	set(h,'horizontalalignment','left','verticalalignment','top','position',[0 1 0])	set(h,'horizontalalignment','left','verticalalignment','top','position',[0 1 0])
45
	45
46	46	elseif strcmpi(varargin{n+1},'north')	elseif strcmpi(varargin{n+1},'north')
47	47	% This case is default and has already been set.	% This case is default and has already been set.
48
	48
49	49	elseif strcmpi(varargin{n+1},'northeast')	elseif strcmpi(varargin{n+1},'northeast')
50	50	set(h,'horizontalalignment','right','verticalalignment','top','position',[1 1 0])	set(h,'horizontalalignment','right','verticalalignment','top','position',[1 1 0])
51
	51
52	52	elseif strcmpi(varargin{n+1},'west')	elseif strcmpi(varargin{n+1},'west')
53	53	set(h,'horizontalalignment','left','verticalalignment','middle','position',[0 .5 0])	set(h,'horizontalalignment','left','verticalalignment','middle','position',[0 .5 0])
54
	54
55	55	elseif strcmpi(varargin{n+1},'center')	elseif strcmpi(varargin{n+1},'center')
56	56	set(h,'horizontalalignment','center','verticalalignment','middle','position',[.5 .5 0])	set(h,'horizontalalignment','center','verticalalignment','middle','position',[.5 .5 0])
57
	57
58	58	elseif strcmpi(varargin{n+1},'east')	elseif strcmpi(varargin{n+1},'east')
59	59	set(h,'horizontalalignment','right','verticalalignment','middle','position',[1 .5 0])	set(h,'horizontalalignment','right','verticalalignment','middle','position',[1 .5 0])
60
	60
61	61	elseif strcmpi(varargin{n+1},'southwest')	elseif strcmpi(varargin{n+1},'southwest')
62	62	set(h,'horizontalalignment','left','verticalalignment','bottom','position',[0 0 0])	set(h,'horizontalalignment','left','verticalalignment','bottom','position',[0 0 0])
63
	63
64	64	elseif strcmpi(varargin{n+1},'south')	elseif strcmpi(varargin{n+1},'south')
65	65	set(h,'horizontalalignment','center','verticalalignment','bottom','position',[.5 0 0])	set(h,'horizontalalignment','center','verticalalignment','bottom','position',[.5 0 0])
66
	66
67	67	elseif strcmpi(varargin{n+1},'southeast')	elseif strcmpi(varargin{n+1},'southeast')
68	68	set(h,'horizontalalignment','right','verticalalignment','bottom','position',[1 0 0])	set(h,'horizontalalignment','right','verticalalignment','bottom','position',[1 0 0])
69
	69
70	70	end	end
71
	71
72	72	else	else
73	73	set(h,varargin{n},varargin{n+1})	set(h,varargin{n},varargin{n+1})
74	74	end	end
75	75	end	end
76	76
77		% Return the title handle only if it is desired:
	77		% Return the title handle only if it is desired:
78	78	if nargout==0	if nargout==0
79		clear h;
	79		clear h;
80	80	end	end
81	81
82	82
83	83	end	end
84

File misc/3rdparty/num2eng.m changed (mode: 100644) (index 1b8aae6..ab351cd)
...	...	function str=num2eng(num);
4	4	% notation string using International System of Units (SI) prefixes.	% notation string using International System of Units (SI) prefixes.
5	5	%	%
6	6	% Examples:	% Examples:
7		% num2eng(23e8) produces the string
	7		% num2eng(23e8) produces the string
8	8	% '2.3G'	% '2.3G'
9	9	%	%
10		% num2eng(0.0000004) produces the string
	10		% num2eng(0.0000004) produces the string
11	11	% '400n'	% '400n'
12	12
13	13	% Copyright (c) by Federico Forte	% Copyright (c) by Federico Forte
14		% Date: 2006/10/20
	14		% Date: 2006/10/20
15	15
16	16	if abs(num)<=eps	if abs(num)<=eps
17		str = '0';
	17		str = '0';
18	18	elseif isequal(num,inf)	elseif isequal(num,inf)
19		str = 'inf';
	19		str = 'inf';
20	20	elseif isequal(num,-inf)	elseif isequal(num,-inf)
21		str = '-inf';
	21		str = '-inf';
22	22	else	else
23		suffix_str='yzafpnum kMGTPEZY';
24		k=1;
25		while abs(num)>=10^(3*(k-8)) && k<=16,
26		k=k+1;
27		end
28		if k~=9,
29		suff=suffix_str(k);
30		else
31		suff='';
32		end
	23		suffix_str='yzafpnum kMGTPEZY';
	24		k=1;
	25		while abs(num)>=10^(3*(k-8)) && k<=16,
	26		k=k+1;
	27		end
	28		if k~=9,
	29		suff=suffix_str(k);
	30		else
	31		suff='';
	32		end
33	33
34		str=[num2str(num/10^(3*(k-9))),suff];
	34		str=[num2str(num/10^(3*(k-9))),suff];
35	35	end	end

File misc/3rdparty/printstruct.m changed (mode: 100644) (index e729af0..f08f3bf)
1	1	function pS = printstruct(S, varargin)	function pS = printstruct(S, varargin)
2	2	% PRINTSTRUCT Recursively print hierarchical outline of structure contents	% PRINTSTRUCT Recursively print hierarchical outline of structure contents
3		%
	3		%
4	4	% This is a minor adaptation of the File Exchange contribution "Structure	% This is a minor adaptation of the File Exchange contribution "Structure
5	5	% outline" written by B. Roossien <roossien@ecn.nl> and available here:	% outline" written by B. Roossien <roossien@ecn.nl> and available here:
6	6	% http://mathworks.com/matlabcentral/fileexchange/13500-structure-outline	% http://mathworks.com/matlabcentral/fileexchange/13500-structure-outline
7	7	% __________________________________________________________________________	% __________________________________________________________________________
8	8	% USAGE: pS = printstruct(S, varargin)	% USAGE: pS = printstruct(S, varargin)
9		%
	9		%
10	10	% IN: S = structure variable to print	% IN: S = structure variable to print
11	11	% OUT*: pS = cell array containing printed structure	% OUT*: pS = cell array containing printed structure
12		%
	12		%
13	13	% *If defined, result will NOT display in command window	% *If defined, result will NOT display in command window
14	14	% __________________________________________________________________________	% __________________________________________________________________________
15	15	% OPTIONAL VARARGIN* [entered as 'name', value pairs]:	% OPTIONAL VARARGIN* [entered as 'name', value pairs]:
16	16	% *Run printstruct w/no arguments to see default values	% *Run printstruct w/no arguments to see default values
17		%
18		% NLEVELS: N levels to print. If negative, all levels printed.
	17		%
	18		% NLEVELS: N levels to print. If negative, all levels printed.
19	19	% NINDENT: number of tab indents for each line of printed struct	% NINDENT: number of tab indents for each line of printed struct
20	20	% STRUCTNAME: top level name (if empty, variable name will be used)	% STRUCTNAME: top level name (if empty, variable name will be used)
21	21	% PRINTCONTENTS: flag to print field values/contents as well	% PRINTCONTENTS: flag to print field values/contents as well
22	22	% MAXARRAYLENGTH: for fields with array data, max length of values to	% MAXARRAYLENGTH: for fields with array data, max length of values to
23	23	% print. Values of a 2-D (m,n) array are printed if the number of	% print. Values of a 2-D (m,n) array are printed if the number of
24	24	% elements (m x n) is smaller or equal to maxarraylength. This is ignored	% elements (m x n) is smaller or equal to maxarraylength. This is ignored
25		% if printvalues is 0.
	25		% if printvalues is 0.
26	26	% _______________________________	% _______________________________
27	27	% EXAMPLES	% EXAMPLES
28		%
29		% pS = printstruct(S, 'maxarray', 100);
	28		%
	29		% pS = printstruct(S, 'maxarray', 100);
30	30	% printstruct(S, 'nlevels', 2, 'printcontents', 0, 'nindent', 3)	% printstruct(S, 'nlevels', 2, 'printcontents', 0, 'nindent', 3)
31		%
	31		%
32	32
33	33	% ---------------------- Copyright (C) 2015 Bob Spunt ----------------------	% ---------------------- Copyright (C) 2015 Bob Spunt ----------------------
34	34	% Created: 2015-08-13	% Created: 2015-08-13
35	35	% Email: spunt@caltech.edu	% Email: spunt@caltech.edu
36	36	% __________________________________________________________________________	% __________________________________________________________________________
37		def = { ...
38		'nlevels', -1, ...
39		'printcontents', 1, ...
	37		def = { ...
	38		'nlevels', -1, ...
	39		'printcontents', 1, ...
40	40	'nindent', 0, ...	'nindent', 0, ...
41	41	'structname', '', ...	'structname', '', ...
42	42	'maxarraylength', 10 ...	'maxarraylength', 10 ...
43		};
	43		};
44	44	vals = setargs(def, varargin);	vals = setargs(def, varargin);
45	45	if nargin==0, mfile_showhelp; fprintf('\t| - VARARGIN DEFAULTS - |\n'); disp(vals); return; end	if nargin==0, mfile_showhelp; fprintf('\t| - VARARGIN DEFAULTS - |\n'); disp(vals); return; end
46	46	if nlevels==0, nlevels = -1; end;	if nlevels==0, nlevels = -1; end;
...	...	if length(S)==1
49	49	str = recFieldPrint(S, nindent, nlevels, printcontents, maxarraylength, structname);	str = recFieldPrint(S, nindent, nlevels, printcontents, maxarraylength, structname);
50	50	str = [cellstr(structname); str];	str = [cellstr(structname); str];
51	51	else	else
52		str = [];
	52		str = [];
53	53	for i = 1:length(S)	for i = 1:length(S)
54	54	tmpstr = recFieldPrint(S(i), nindent, nlevels, printcontents, maxarraylength, structname);	tmpstr = recFieldPrint(S(i), nindent, nlevels, printcontents, maxarraylength, structname);
55	55	str = [str; cellstr(sprintf('%s(%d)', structname, i)); tmpstr];	str = [str; cellstr(sprintf('%s(%d)', structname, i)); tmpstr];
...	...	end
59	59	if nargout==0	if nargout==0
60	60	for i = 1:length(str), disp(cell2mat(str(i, 1))); end	for i = 1:length(str), disp(cell2mat(str(i, 1))); end
61	61	else	else
62		pS = str;
	62		pS = str;
63	63	end	end
64	64	end	end
65	65	% ==========================================================================	% ==========================================================================
...	...	end
69	69	% ==========================================================================	% ==========================================================================
70	70	function argstruct = setargs(defaults, optargs)	function argstruct = setargs(defaults, optargs)
71	71	% SETARGS Name/value parsing and assignment of varargin with default values	% SETARGS Name/value parsing and assignment of varargin with default values
72		%
	72		%
73	73	% This is a utility for setting the value of optional arguments to a	% This is a utility for setting the value of optional arguments to a
74	74	% function. The first argument is required and should be a cell array of	% function. The first argument is required and should be a cell array of
75	75	% "name, default value" pairs for all optional arguments. The second	% "name, default value" pairs for all optional arguments. The second
76	76	% argument is optional and should be a cell array of "name, custom value"	% argument is optional and should be a cell array of "name, custom value"
77	77	% pairs for at least one of the optional arguments.	% pairs for at least one of the optional arguments.
78		%
79		% USAGE: argstruct = setargs(defaults, args)
	78		%
	79		% USAGE: argstruct = setargs(defaults, args)
80	80	% __________________________________________________________________________	% __________________________________________________________________________
81	81	% OUTPUT	% OUTPUT
82		%
	82		%
83	83	% argstruct: structure containing the final argument values	% argstruct: structure containing the final argument values
84	84	% __________________________________________________________________________	% __________________________________________________________________________
85	85	% INPUTS	% INPUTS
86		%
87		% defaults:
	86		%
	87		% defaults:
88	88	% cell array of "name, default value" pairs for all optional arguments	% cell array of "name, default value" pairs for all optional arguments
89		%
90		% optargs [optional]
	89		%
	90		% optargs [optional]
91	91	% cell array of "name, custom value" pairs for at least one of the	% cell array of "name, custom value" pairs for at least one of the
92		% optional arguments. this will typically be the "varargin" array.
	92		% optional arguments. this will typically be the "varargin" array.
93	93	% __________________________________________________________________________	% __________________________________________________________________________
94	94	% USAGE EXAMPLE (WITHIN FUNCTION)	% USAGE EXAMPLE (WITHIN FUNCTION)
95		%
96		% defaults = {'arg1', 0, 'arg2', 'words', 'arg3', rand};
	95		%
	96		% defaults = {'arg1', 0, 'arg2', 'words', 'arg3', rand};
97	97	% argstruct = setargs(defaults, varargin)	% argstruct = setargs(defaults, varargin)
98	98	%	%
99	99
...	...	function argstruct = setargs(defaults, optargs)
101	101	% ---------------------- Copyright (C) 2015 Bob Spunt ----------------------	% ---------------------- Copyright (C) 2015 Bob Spunt ----------------------
102	102	% Created: 2015-03-11	% Created: 2015-03-11
103	103	% Email: spunt@caltech.edu	% Email: spunt@caltech.edu
104		%
	104		%
105	105	% This program is free software: you can redistribute it and/or modify	% This program is free software: you can redistribute it and/or modify
106	106	% it under the terms of the GNU General Public License as published by	% it under the terms of the GNU General Public License as published by
107	107	% the Free Software Foundation, either version 3 of the License, or (at	% the Free Software Foundation, either version 3 of the License, or (at
...	...	function argstruct = setargs(defaults, optargs)
115	115	% __________________________________________________________________________	% __________________________________________________________________________
116	116	if nargin < 1, mfile_showhelp; return; end	if nargin < 1, mfile_showhelp; return; end
117	117	if nargin < 2, optargs = []; end	if nargin < 2, optargs = []; end
118		defaults = reshape(defaults, 2, length(defaults)/2)';
	118		defaults = reshape(defaults, 2, length(defaults)/2)';
119	119	if ~isempty(optargs)	if ~isempty(optargs)
120	120	if mod(length(optargs), 2)	if mod(length(optargs), 2)
121		error('Optional inputs must be entered as Name, Value pairs, e.g., myfunction(''name'', value)');
	121		error('Optional inputs must be entered as Name, Value pairs, e.g., myfunction(''name'', value)');
122	122	end	end
123	123	arg = reshape(optargs, 2, length(optargs)/2)';	arg = reshape(optargs, 2, length(optargs)/2)';
124	124	for i = 1:size(arg,1)	for i = 1:size(arg,1)
...	...	if ~isempty(optargs)
129	129	error('Input "%s" does not match a valid input.', arg{i,1});	error('Input "%s" does not match a valid input.', arg{i,1});
130	130	else	else
131	131	defaults{idx,2} = arg{i,2};	defaults{idx,2} = arg{i,2};
132		end
	132		end
133	133	end	end
134	134	end	end
135	135	for i = 1:size(defaults,1), assignin('caller', defaults{i,1}, defaults{i,2}); end	for i = 1:size(defaults,1), assignin('caller', defaults{i,1}, defaults{i,2}); end
...	...	function mfile_showhelp(varargin)
139	139	% MFILE_SHOWHELP	% MFILE_SHOWHELP
140	140	ST = dbstack('-completenames');	ST = dbstack('-completenames');
141	141	if isempty(ST), fprintf('\nYou must call this within a function\n\n'); return; end	if isempty(ST), fprintf('\nYou must call this within a function\n\n'); return; end
142		eval(sprintf('help %s', ST(2).file));
	142		eval(sprintf('help %s', ST(2).file));
143	143	end	end
144	144	% | ADAPTED FROM FROM STRUCDISP	% | ADAPTED FROM FROM STRUCDISP
145	145	function listStr = recFieldPrint(Structure, indent, depth, printValues, maxArrayLength, structname)	function listStr = recFieldPrint(Structure, indent, depth, printValues, maxArrayLength, structname)
146	146	% RECFIELDPRINT Recursive printing of a structure variable	% RECFIELDPRINT Recursive printing of a structure variable
147	147	% This function and its dependencies were taken from:	% This function and its dependencies were taken from:
148		% STRUCDISP.m by
	148		% STRUCDISP.m by
149	149	% Contact author: B. Roossien <roossien@ecn.nl>	% Contact author: B. Roossien <roossien@ecn.nl>
150	150	% (c) ECN 2007-2008	% (c) ECN 2007-2008
151	151	% Version 1.3.0	% Version 1.3.0
...	...	if length(Structure) > 1
157	157	varStr = createArraySize(Structure, structname);	varStr = createArraySize(Structure, structname);
158	158	listStr = [{' '}; {[structname, varStr]}];	listStr = [{' '}; {[structname, varStr]}];
159	159	body = recFieldPrint(Structure(1), indent, depth, ...	body = recFieldPrint(Structure(1), indent, depth, ...
160		printValues, maxArrayLength);
	160		printValues, maxArrayLength);
161	161	listStr = [listStr; body];	listStr = [listStr; body];
162	162	else	else
163	163	for iStruc = 1 : length(Structure)	for iStruc = 1 : length(Structure)
...	...	strIndent = getIndentation(indent + 1);
176	176	listStr = [listStr; {strIndent}];	listStr = [listStr; {strIndent}];
177	177	strIndent = getIndentation(indent);	strIndent = getIndentation(indent);
178	178	for iField = 1 : length(strucFields)	for iField = 1 : length(strucFields)
179
	179
180	180	fieldName = cell2mat(strucFields(iField));	fieldName = cell2mat(strucFields(iField));
181	181	Field = Structure.(fieldName);	Field = Structure.(fieldName);
182
	182
183	183	% Empty structure	% Empty structure
184	184	if isempty(Field)	if isempty(Field)
185	185	strSize = createArraySize(Field, 'Structure');	strSize = createArraySize(Field, 'Structure');
...	...	for iField = 1 : length(strucFields)
197	197	else	else
198	198	listStr = [listStr; {line}];	listStr = [listStr; {line}];
199	199	end	end
200		% Short vector structure of which the values should be printed
	200		% Short vector structure of which the values should be printed
201	201	elseif (isvector(Field)) && ...	elseif (isvector(Field)) && ...
202	202	(printValues > 0) && ...	(printValues > 0) && ...
203	203	(length(Field) < maxArrayLength) && ...	(length(Field) < maxArrayLength) && ...
...	...	for iField = 1 : length(strucFields)
224	224	listStr = [listStr; {line}; lines];	listStr = [listStr; {line}; lines];
225	225	end	end
226	226	% Some extra blank lines to increase readability	% Some extra blank lines to increase readability
227		listStr = [listStr; {[strIndent ' | ']}];
228
	227		listStr = [listStr; {[strIndent ' | ']}];
	228
229	229	end % End iField for-loop	end % End iField for-loop
230	230	%% Field Filler	%% Field Filler
231	231	% To properly align the field names, a filler is required. To know how long	% To properly align the field names, a filler is required. To know how long
...	...	otherFields = fields(isOther == 1);
291	291	% - Vectors are printed as long as they are shorter than maxArrayLength	% - Vectors are printed as long as they are shorter than maxArrayLength
292	292	% - Matrices are printed if they have less elements than maxArrayLength	% - Matrices are printed if they have less elements than maxArrayLength
293	293	% - The values of cells are not printed	% - The values of cells are not printed
294		% Start with printing strings and characters. To avoid the display screen
295		% becoming a mess, the part of the string that is printed is limited to 31
	294		% Start with printing strings and characters. To avoid the display screen
	295		% becoming a mess, the part of the string that is printed is limited to 31
296	296	% characters. In the future this might become an optional parameter in this	% characters. In the future this might become an optional parameter in this
297	297	% function, but for now, it is placed in the code itself.	% function, but for now, it is placed in the code itself.
298	298	% if the string is longer than 31 characters, only the first 31 characters	% if the string is longer than 31 characters, only the first 31 characters
...	...	for iField = 1 : length(matrixFields)
373	373	filler2 = char(ones(1, maxFieldLength + 6) * 32);	filler2 = char(ones(1, maxFieldLength + 6) * 32);
374	374	dashes = char(ones(1, 12 * matrixSize(2) + 1) * 45);	dashes = char(ones(1, 12 * matrixSize(2) + 1) * 45);
375	375	varCell = {[strIndent ' |' filler2 dashes]};	varCell = {[strIndent ' |' filler2 dashes]};
376
	376
377	377	% first line with field name	% first line with field name
378	378	varStr = sprintf('%#10.2e |', Structure.(Field)(1, :));	varStr = sprintf('%#10.2e |', Structure.(Field)(1, :));
379	379	varCell = [varCell; {[strIndent ' |' filler ' ' ...	varCell = [varCell; {[strIndent ' |' filler ' ' ...
...	...	for iField = 1 : length(matrixFields)
384	384	varCell = [varCell; {[strIndent ' |' filler2 '|' varStr]}];	varCell = [varCell; {[strIndent ' |' filler2 '|' varStr]}];
385	385	end	end
386	386	varCell = [varCell; {[strIndent ' |' filler2 dashes]}];	varCell = [varCell; {[strIndent ' |' filler2 dashes]}];
387
	387
388	388	end	end
389
	389
390	390	listStr = [listStr; varCell];	listStr = [listStr; varCell];
391	391	end	end
392	392	% Print cell array information, i.e. the size of the cell array. The	% Print cell array information, i.e. the size of the cell array. The
...	...	end
408	408	function str = getIndentation(indent)	function str = getIndentation(indent)
409	409	x = ' | ';	x = ' | ';
410	410	str = '';	str = '';
411
	411
412	412	for i = 1 : indent	for i = 1 : indent
413	413	str = cat(2, str, x);	str = cat(2, str, x);
414	414	end	end
...	...	function varStr = createArraySize(varName, type)
418	418
419	419	arraySizeStr = sprintf('%gx', varSize);	arraySizeStr = sprintf('%gx', varSize);
420	420	arraySizeStr(length(arraySizeStr)) = [];	arraySizeStr(length(arraySizeStr)) = [];
421
	421
422	422	varStr = [' [' arraySizeStr ' ' type ']'];	varStr = [' [' arraySizeStr ' ' type ']'];
423	423	end	end

File misc/3rdparty/ringdown.m changed (mode: 100644) (index b28883c..c26516d)
...	...	function res_rd=ringdown(res,noiseLevel,plots,verbose)
23	23	% NOISELEVEL sets a threshold level for the data analysis. Only data for	% NOISELEVEL sets a threshold level for the data analysis. Only data for
24	24	% which the amplitude is greater than NOISELEVEL*MAX(data) will be used.	% which the amplitude is greater than NOISELEVEL*MAX(data) will be used.
25	25	% Default is 0.1 (i.e., 10%).	% Default is 0.1 (i.e., 10%).
26		%
	26		%
27	27	% Use PLOTS (0|1|2) and VERBOSE (0|1|2) to set the number of plots and the	% Use PLOTS (0|1|2) and VERBOSE (0|1|2) to set the number of plots and the
28	28	% number of messages.	% number of messages.
29	29	%	%
...	...	function res_rd=ringdown(res,noiseLevel,plots,verbose)
43	43	% ringdown: Data after t=2.275 ignored (signal < 0.9)	% ringdown: Data after t=2.275 ignored (signal < 0.9)
44	44	% ringdown: Min. search function value: 0.001	% ringdown: Min. search function value: 0.001
45	45	%	%
46		% r =
47		%
	46		% r =
	47		%
48	48	% trace1: [1x1 struct]	% trace1: [1x1 struct]
49	49	% f0: 3.3241	% f0: 3.3241
50	50	% Q: 10.4441	% Q: 10.4441
...	...	if plots >= 1, datafig=figure; end
99	99	else	else
100	100	fprintf(1,'ringdown: Error: no data trace from oscilloscope.\n');	fprintf(1,'ringdown: Error: no data trace from oscilloscope.\n');
101	101	end	end
102
	102
103	103	% determine sample interval	% determine sample interval
104	104	time_length=t(end)-t(1);	time_length=t(end)-t(1);
105	105	sample_int=time_length/(length(t)-1);	sample_int=time_length/(length(t)-1);
...	...	Qest=mean([Qestu Qestl]);
243	243	% plot Q estimated results	% plot Q estimated results
244	244	if plots >= 2	if plots >= 2
245	245	figure(fftfig); subplot(1,2,2);	figure(fftfig); subplot(1,2,2);
246		plot(tuefit,real(log(Vuefit)),'.-r');
	246		plot(tuefit,real(log(Vuefit)),'.-r');
247	247	hold on;	hold on;
248	248	plot(tlefit,real(log(Vlefit)),'.-g');	plot(tlefit,real(log(Vlefit)),'.-g');
249	249	title('Envelope & Log Fit');	title('Envelope & Log Fit');

File misc/3rdparty/suptitle.m changed (mode: 100644) (index efce003..6cf01da)
...	...	axes(haold); %#ok<MAXES>
109	109	if nargout,	if nargout,
110	110	hout=ht;	hout=ht;
111	111	end	end
112
113
114

File misc/3rdparty/textprogressbar.m changed (mode: 100644) (index 87ba5ff..082b9d9)
1	1	function textprogressbar(c)	function textprogressbar(c)
2		% This function creates a text progress bar. It should be called with a
3		% STRING argument to initialize and terminate. Otherwise the number correspoding
	2		% This function creates a text progress bar. It should be called with a
	3		% STRING argument to initialize and terminate. Otherwise the number correspoding
4	4	% to progress in % should be supplied.	% to progress in % should be supplied.
5		% INPUTS: C Either: Text string to initialize or terminate
6		% Percentage number to show progress
	5		% INPUTS: C Either: Text string to initialize or terminate
	6		% Percentage number to show progress
7	7	% OUTPUTS: N/A	% OUTPUTS: N/A
8	8	% Example: Please refer to demo_textprogressbar.m	% Example: Please refer to demo_textprogressbar.m
9	9
...	...	persistent strCR; % Carriage return pesistent variable
20	20	strPercentageLength = 10; % Length of percentage string (must be >5)	strPercentageLength = 10; % Length of percentage string (must be >5)
21	21	strDotsMaximum = 10; % The total number of dots in a progress bar	strDotsMaximum = 10; % The total number of dots in a progress bar
22	22
23		%% Main
	23		%% Main
24	24
25	25	if isempty(strCR) && ~ischar(c),	if isempty(strCR) && ~ischar(c),
26	26	% Progress bar must be initialized with a string	% Progress bar must be initialized with a string
...	...	elseif isempty(strCR) && ischar(c),
31	31	strCR = -1;	strCR = -1;
32	32	elseif ~isempty(strCR) && ischar(c),	elseif ~isempty(strCR) && ischar(c),
33	33	% Progress bar - termination	% Progress bar - termination
34		strCR = [];
	34		strCR = [];
35	35	fprintf([c '\n']);	fprintf([c '\n']);
36	36	elseif isnumeric(c)	elseif isnumeric(c)
37	37	% Progress bar - normal progress	% Progress bar - normal progress
...	...	elseif isnumeric(c)
41	41	nDots = floor(c/100*strDotsMaximum);	nDots = floor(c/100*strDotsMaximum);
42	42	dotOut = ['[' repmat('.',1,nDots) repmat(' ',1,strDotsMaximum-nDots) ']'];	dotOut = ['[' repmat('.',1,nDots) repmat(' ',1,strDotsMaximum-nDots) ']'];
43	43	strOut = [percentageOut dotOut];	strOut = [percentageOut dotOut];
44
	44
45	45	% Print it on the screen	% Print it on the screen
46	46	if strCR == -1,	if strCR == -1,
47	47	% Don't do carriage return during first run	% Don't do carriage return during first run
...	...	elseif isnumeric(c)
50	50	% Do it during all the other runs	% Do it during all the other runs
51	51	fprintf([strCR strOut]);	fprintf([strCR strOut]);
52	52	end	end
53
	53
54	54	% Update carriage return	% Update carriage return
55	55	strCR = repmat('\b',1,length(strOut)-1);	strCR = repmat('\b',1,length(strOut)-1);
56
	56
57	57	else	else
58	58	% Any other unexpected input	% Any other unexpected input
59	59	error('Unsupported argument type');	error('Unsupported argument type');

File misc/checkdim.m changed (mode: 100644) (index aa8c3c8..06d4465)
...	...	if ~isempty(H)
35	35	if h1~=d1, warning('H and D must have same number of rows');h=0; end	if h1~=d1, warning('H and D must have same number of rows');h=0; end
36	36	if g2~=d2, warning('G and D must have same number of columns');h=0; end	if g2~=d2, warning('G and D must have same number of columns');h=0; end
37	37	end	end
38
	38
39	39	% check measurement vector	% check measurement vector
40	40	if ~isempty(z)	if ~isempty(z)
41	41	[z1,z2]=size(z);	[z1,z2]=size(z);

File misc/convenience/class_conveniences.m changed (mode: 100644) (index 818ace4..2dfa447)
1	1	classdef class_conveniences < matlab.mixin.Copyable	classdef class_conveniences < matlab.mixin.Copyable
2		%% class_conveniences is a convenience superclass which inherits from matlab.mixin.Copyable
3		%% it implements some methods and properties which are useful for several other classes
4		properties(Constant, Hidden)
5		hbar = 1.0545718e-34;
6		kB = 1.38064852e-23;
7		end
8		properties(Dependent, Hidden)
	2		%% class_conveniences is a convenience superclass which inherits from matlab.mixin.Copyable
	3		%% it implements some methods and properties which are useful for several other classes
	4		properties(Constant, Hidden)
	5		hbar = 1.0545718e-34;
	6		kB = 1.38064852e-23;
	7		end
	8		properties(Dependent, Hidden)
9	9	software_version % stores path and id of current commit for every git repository on MATLAB path at query time	software_version % stores path and id of current commit for every git repository on MATLAB path at query time
10	10	end	end
11
12		properties(Hidden)
	11
	12		properties(Hidden)
13	13	software_version_private	software_version_private
14		end
	14		end
15	15
16	16	methods	methods
17	17	function software_version = get.software_version(obj)	function software_version = get.software_version(obj)
...	...	classdef class_conveniences < matlab.mixin.Copyable
24	24	end	end
25	25
26	26	function obj = parseVararginForMissingFields(obj, varargin)	function obj = parseVararginForMissingFields(obj, varargin)
27		%obj = parseVararginForMissingFields(obj, varargin) attempts to fill empty
28		%properties of obj from the varargin-list...
29		% it checks varargin for for key-value-pairs of the form 'property', value
30		% where 'property' is the name of a property which is either empty or
31		% has a default value and then sets obj.('property') to value
	27		%obj = parseVararginForMissingFields(obj, varargin) attempts to fill empty
	28		%properties of obj from the varargin-list...
	29		% it checks varargin for for key-value-pairs of the form 'property', value
	30		% where 'property' is the name of a property which is either empty or
	31		% has a default value and then sets obj.('property') to value
32	32
33		assert(isa(obj, 'handle'),...
34		'Expecting handle object');
	33		assert(isa(obj, 'handle'),...
	34		'Expecting handle object');
35	35
36		%% find empty properties which are not dependent
37		props = properties(obj);
	36		%% find empty properties which are not dependent
	37		props = properties(obj);
38	38
39		empty_or_default_props = {};
40		empty_or_default_props_no = 0;
	39		empty_or_default_props = {};
	40		empty_or_default_props_no = 0;
41	41
42		for k=1:length(props)
43		prop = props{k};
44		prop_attributes = findprop(obj, prop);
45		if ~prop_attributes.Dependent
46		% don't mess with dependent properties
47		if isempty(obj.(prop)) || prop_attributes.HasDefault
48		% check empty properties or properties with default values
49		empty_or_default_props_no = empty_or_default_props_no +1;
50		empty_or_default_props{ empty_or_default_props_no } = prop; %#ok<*AGROW>
51		end
52		end
53		end
	42		for k=1:length(props)
	43		prop = props{k};
	44		prop_attributes = findprop(obj, prop);
	45		if ~prop_attributes.Dependent
	46		% don't mess with dependent properties
	47		if isempty(obj.(prop)) || prop_attributes.HasDefault
	48		% check empty properties or properties with default values
	49		empty_or_default_props_no = empty_or_default_props_no +1;
	50		empty_or_default_props{ empty_or_default_props_no } = prop; %#ok<*AGROW>
	51		end
	52		end
	53		end
54	54
55		%% parse varargin for the names of such properties
56		% and set property to the following argument
57		for k=1:2:length(varargin)
58		if any(ismember(empty_or_default_props,varargin{k}))
59		obj.(varargin{k}) = varargin{k+1};
60		end
61		end
62		end
	55		%% parse varargin for the names of such properties
	56		% and set property to the following argument
	57		for k=1:2:length(varargin)
	58		if any(ismember(empty_or_default_props,varargin{k}))
	59		obj.(varargin{k}) = varargin{k+1};
	60		end
	61		end
	62		end
63	63
64	64	end	end
65	65	end	end

File misc/covmats/beam_splitter.m changed (mode: 100644) (index 8f89a95..70b2487)
...	...	function bs_sympmat = beam_splitter( r)
7	7
8	8	id = eye(2);	id = eye(2);
9	9	bs_sympmat = [ r*id, sqrt(1-r^2)*id;...	bs_sympmat = [ r*id, sqrt(1-r^2)*id;...
10		sqrt(1-r^2)*id, -r*id];
	10		sqrt(1-r^2)*id, -r*id];

File misc/covmats/combineCMs_traceOutCM_test.m changed (mode: 100644) (index 75ad403..b04c16b)
1	1	function test_suite = combineCMs_traceOutCM_test %#ok<STOUT>	function test_suite = combineCMs_traceOutCM_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function combine_traceout_randommat_test	function combine_traceout_randommat_test
5	5	mat1 = randn(6,6);	mat1 = randn(6,6);

File misc/covmats/fidelity_test.m changed (mode: 100644) (index cb2f9cc..d28f710)
1	1	% fid = fidelity(X1,X2,cm1,cm2)	% fid = fidelity(X1,X2,cm1,cm2)
2	2	function test_suite = fidelity_test %#ok<STOUT>	function test_suite = fidelity_test %#ok<STOUT>
3		initTestSuite;
	3		initTestSuite;
4	4
5	5	function smss_test	function smss_test
6	6	cm = smss(1)+0.001*eye(2); %need some noise to pass physicality-test	cm = smss(1)+0.001*eye(2); %need some noise to pass physicality-test

File misc/covmats/gausseof.m changed (mode: 100644) (index 033a856..0e01bb8)
...	...	disp(sqrt((m2 - 1)/(n2 - 1)));
26	26
27	27
28	28	eof = 0;	eof = 0;
29		% %%
	29		% %%
30	30	% if ~isequal(size(sfparams), [1, 4])	% if ~isequal(size(sfparams), [1, 4])
31	31	% warning('Expecting a vector of 4 real parameters!');	% warning('Expecting a vector of 4 real parameters!');
32	32	% eof = 0;	% eof = 0;
...	...	eof = 0;
44	44	% b2 = sfparams(2);	% b2 = sfparams(2);
45	45	% c = sfparams(3);	% c = sfparams(3);
46	46	% d = abs(sfparams(4)); %caution: we take abs already here	% d = abs(sfparams(4)); %caution: we take abs already here
47		%
48		% %%
	47		%
	48		% %%
49	49	% % a0, a4	% % a0, a4
50	50	% tc = b1*b2 - c^2;	% tc = b1*b2 - c^2;
51	51	% td = b1*b2 - d^2;	% td = b1*b2 - d^2;
52		%
	52		%
53	53	% a0 = td*(b1*td - b2/4)*(b2*td - b1/4);	% a0 = td*(b1*td - b2/4)*(b2*td - b1/4);
54	54	% a4 = tc*(b1*tc - b2/4)*(b2*tc - b1/4);	% a4 = tc*(b1*tc - b2/4)*(b2*tc - b1/4);
55		%
	55		%
56	56	% % a1, a3	% % a1, a3
57	57	% oc = c*td + d/4;	% oc = c*td + d/4;
58	58	% od = d*tc + c/4;	% od = d*tc + c/4;
59		%
	59		%
60	60	% a1 = -oc*( oc*(b1 - b2)^2 + 2*b1*b2*(c-d)*(td - 1/4));	% a1 = -oc*( oc*(b1 - b2)^2 + 2*b1*b2*(c-d)*(td - 1/4));
61	61	% a3 = -od*( od*(b1 - b2)^2 + 2*b1*b2*(d-c)*(tc - 1/4));	% a3 = -od*( od*(b1 - b2)^2 + 2*b1*b2*(d-c)*(tc - 1/4));
62		%
	62		%
63	63	% % a2	% % a2
64	64	% detV = tc*td; %checked	% detV = tc*td; %checked
65	65	% cd = c*d;	% cd = c*d;
66		%
	66		%
67	67	% bigZ = b1^2 + b2^2 + 2*cd;	% bigZ = b1^2 + b2^2 + 2*cd;
68	68	% bigD = 1/16 - (b1^2)/4 - (b2^2)/4 + b1*b2*tc - cd/2 - b1*b2*d^2 + cd^2;	% bigD = 1/16 - (b1^2)/4 - (b2^2)/4 + b1*b2*tc - cd/2 - b1*b2*d^2 + cd^2;
69		%
	69		%
70	70	% a2 = ((b1*c - b2*d)*(b1*d - b2*c) + cd*bigZ)*(detV + 1/16) - cd*detV - 2*((b1^2)*(b2^2) - cd^2)*bigD;	% a2 = ((b1*c - b2*d)*(b1*d - b2*c) + cd*bigZ)*(detV + 1/16) - cd*detV - 2*((b1^2)*(b2^2) - cd^2)*bigD;
71		%
	71		%
72	72	% %%	% %%
73	73	% % scriptz = a^2+b^2+2*cp*cm;	% % scriptz = a^2+b^2+2*cp*cm;
74	74	% % detSF = (a^2)*(b^2)-a*b*cm^2-a*b*cp^2+(cm^2)*(cp^2);	% % detSF = (a^2)*(b^2)-a*b*cm^2-a*b*cp^2+(cm^2)*(cp^2);
75		% %
	75		% %
76	76	% % scriptd = a^2*(-(1/4)+b^2)-a*b*(cm^2+cp^2)+ (1/16)*(-4*b^2+(1 - 4*cm*cp)^2);	% % scriptd = a^2*(-(1/4)+b^2)-a*b*(cm^2+cp^2)+ (1/16)*(-4*b^2+(1 - 4*cm*cp)^2);
77	77	% % scriptdtilda = detSF - (1/4)*(a^2 + b^2 +2*cp*abs(cm)) + 1/16;	% % scriptdtilda = detSF - (1/4)*(a^2 + b^2 +2*cp*abs(cm)) + 1/16;
78		%
	78		%
79	79	% % a1 = -(cp*(a*b -cm^2) + abs(cm)/4)*((a-b)^2*(cp*(a*b -cm^2)+ abs(cm)/4)+2*a*b*(cp - abs(cm))*(a*b -cm^2 -1/4));	% % a1 = -(cp*(a*b -cm^2) + abs(cm)/4)*((a-b)^2*(cp*(a*b -cm^2)+ abs(cm)/4)+2*a*b*(cp - abs(cm))*(a*b -cm^2 -1/4));
80	80	% % a2 = ((a*cp - b*abs(cm))*(a*abs(cm) - b*cp) + cp*abs(cm)*scriptz)*(detSF+1/16)-2*((a^2)*(b^2) - (cp^2)*(cm^2))*scriptz - cp*abs(cm)*detSF;	% % a2 = ((a*cp - b*abs(cm))*(a*abs(cm) - b*cp) + cp*abs(cm)*scriptz)*(detSF+1/16)-2*((a^2)*(b^2) - (cp^2)*(cm^2))*scriptz - cp*abs(cm)*detSF;
81	81	% % a3 = (-(cp/4)-(a*b-cp^2)*abs(cm))*(2*a*b*(-(1/4)+a*b-cp^2)*(-cp+abs(cm))+((a-b)^2)*(cp/4+(a*b-cp^2)*abs(cm)));	% % a3 = (-(cp/4)-(a*b-cp^2)*abs(cm))*(2*a*b*(-(1/4)+a*b-cp^2)*(-cp+abs(cm))+((a-b)^2)*(cp/4+(a*b-cp^2)*abs(cm)));
82	82	% % a4 = (a*b-cp^2)*(-(b/4)+a*(a*b-cp^2))*(-(a/4)+b*(a*b-cp^2));	% % a4 = (a*b-cp^2)*(-(b/4)+a*(a*b-cp^2))*(-(a/4)+b*(a*b-cp^2));
83		%
	83		%
84	84	% %%	% %%
85	85	% aCoeffs = [a4 a3 a2 a1 a0];	% aCoeffs = [a4 a3 a2 a1 a0];
86	86	% polyval(aCoeffs,1) %MUST BE NEGATIVE ACCORDING TO MARIAN AND MARIAN!!!	% polyval(aCoeffs,1) %MUST BE NEGATIVE ACCORDING TO MARIAN AND MARIAN!!!
87		%
	87		%
88	88	% pCandidates = roots(aCoeffs);	% pCandidates = roots(aCoeffs);
89	89	% pm = max(pCandidates(~imag(pCandidates))); %choose maximum within real roots	% pm = max(pCandidates(~imag(pCandidates))); %choose maximum within real roots
90		%
	90		%
91	91	% % If[pm<1,(*Print["No pm bigger than one.	% % If[pm<1,(*Print["No pm bigger than one.
92	92	% % Is the CM inseparable? Is it a two-mode-squeezed thermal state?"];*)0,	% % Is the CM inseparable? Is it a two-mode-squeezed thermal state?"];*)0,
93		%
	93		%
94	94	% %%	% %%
95	95	% b0 = -scriptdtilda*pm;	% b0 = -scriptdtilda*pm;
96	96	% b1 = -2*sqrt(pm)*((abs(cm)*(a*b -cp^2) + cp/4)*pm + (cp*(a*b - cm^2) + abs(cm)/4));	% b1 = -2*sqrt(pm)*((abs(cm)*(a*b -cp^2) + cp/4)*pm + (cp*(a*b - cm^2) + abs(cm)/4));
97	97	% b2 = (a*b - cp^2)*pm^2 + scriptz*pm+(a*b -cm^2);	% b2 = (a*b - cp^2)*pm^2 + scriptz*pm+(a*b -cm^2);
98		%
	98		%
99	99	% %%	% %%
100	100	% yCandidates = roots([b2 b1 b0]);	% yCandidates = roots([b2 b1 b0]);
101	101	% ym = min(yCandidates(~imag(yCandidates)));	% ym = min(yCandidates(~imag(yCandidates)));

File misc/covmats/gausssfcm.m changed (mode: 100644) (index 85ff29a..620fbd8)
...	...	sfcm = [a, 0, cplus, 0;...
22	22	cplus, 0, b, 0;...	cplus, 0, b, 0;...
23	23	0, cminus, 0, b];	0, cminus, 0, b];
24	24	end	end
25

File misc/covmats/gausssfcm2.m changed (mode: 100644) (index 8f07eae..8e937c3)
...	...	sfcm = [n1, 0, c1, 0;...
25	25	c1, 0, m1, 0;...	c1, 0, m1, 0;...
26	26	0, c2, 0, m2];	0, c2, 0, m2];
27	27	end	end
28

File misc/covmats/gausssfparams.m changed (mode: 100644) (index a13866c..7ec8181)
...	...	if ~isreal(sfparams)
33	33	end	end
34	34
35	35	end	end
36

File misc/covmats/gausssfparams2.m changed (mode: 100644) (index 0e0fcf0..ae6a2f6)
...	...	m = sfparams1(2);
18	18	c = sfparams1(3);	c = sfparams1(3);
19	19	cprime = sfparams1(4);	cprime = sfparams1(4);
20	20
21		%% calculate parameters for standard form 2
	21		%% calculate parameters for standard form 2
22	22	if ( n == m )&& (abs(c) == abs(cprime))	if ( n == m )&& (abs(c) == abs(cprime))
23		r = [1, 1];
	23		r = [1, 1];
24	24	else % solve the system described by auxfct1	else % solve the system described by auxfct1
25	25	r = fsolve(@auxfct1, [1, 1]);	r = fsolve(@auxfct1, [1, 1]);
26	26	end	end
...	...	function [x] = auxfct1(r)
29	29	x(1) = ( n/r(1) - 1 )/(n*r(1) - 1) - (m/r(2) - 1)/(m*r(2) - 1);	x(1) = ( n/r(1) - 1 )/(n*r(1) - 1) - (m/r(2) - 1)/(m*r(2) - 1);
30	30	x(2) = sqrt(r(1)*r(2))*abs(c)-abs(cprime)/sqrt(r(1)*r(2)) - sqrt((n*r(1) - 1)*(m*r(2) - 1)) + sqrt((n/r(1) - 1)*(m/r(2) - 1));	x(2) = sqrt(r(1)*r(2))*abs(c)-abs(cprime)/sqrt(r(1)*r(2)) - sqrt((n*r(1) - 1)*(m*r(2) - 1)) + sqrt((n/r(1) - 1)*(m/r(2) - 1));
31	31	end	end
32		%% calculate parameters for standard form 2
	32		%% calculate parameters for standard form 2
33	33	n1 = n*r(1);	n1 = n*r(1);
34	34	n2 = n/r(1);	n2 = n/r(1);
35	35	m1 = m*r(2);	m1 = m*r(2);

File misc/covmats/isphyscov.m changed (mode: 100644) (index d3c189e..ce3e7f5)
1	1	function bool = isphyscov(mat,varargin)	function bool = isphyscov(mat,varargin)
2	2	% bool = isphyscov(M,...)	% bool = isphyscov(M,...)
3		%
	3		%
4	4	% Test if M+I*Σ/2 ≥ 0, where Σ is	% Test if M+I*Σ/2 ≥ 0, where Σ is
5	5	% the symplectic matrix.	% the symplectic matrix.
6	6	%	%

File misc/covmats/logneg.m changed (mode: 100644) (index 4fc07b0..b2bd46c)
...	...	function ln = logneg(cm, varargin)
16	16	checkCMdimensions(cm, 'multimode');	checkCMdimensions(cm, 'multimode');
17	17	modeindices_unspecified = true;	modeindices_unspecified = true;
18	18	for k=1:2:length(varargin)	for k=1:2:length(varargin)
19		switch varargin{k}
20		case 'modeindices'
21		modeindices = varargin{k+1};
	19		switch varargin{k}
	20		case 'modeindices'
	21		modeindices = varargin{k+1};
22	22	modeindices_unspecified = false;	modeindices_unspecified = false;
23		otherwise
24		error('Unrecognized varargin-input.')
25		end
	23		otherwise
	24		error('Unrecognized varargin-input.')
	25		end
26	26	end	end
27	27
28	28	mode_no = length(cm)/2;	mode_no = length(cm)/2;
29	29	if mode_no==2	if mode_no==2
30		% for definition of logarithmic negativity see
31		% Adesso & Illuminati, doi:10.1088/1751-8113/40/28/S01, eq. (80)
	30		% for definition of logarithmic negativity see
	31		% Adesso & Illuminati, doi:10.1088/1751-8113/40/28/S01, eq. (80)
32	32
33		d = det(cm(1:2,1:2)) + det(cm(3:4,3:4)) - 2*det(cm(1:2,3:4));
34		m = 1/sqrt(det(cm));
35		% corrected by a factor of 2 due to commutation relations
36		ln = -log(sqrt(d-sqrt(d^2-4/m^2))/sqrt(2)*2);
	33		d = det(cm(1:2,1:2)) + det(cm(3:4,3:4)) - 2*det(cm(1:2,3:4));
	34		m = 1/sqrt(det(cm));
	35		% corrected by a factor of 2 due to commutation relations
	36		ln = -log(sqrt(d-sqrt(d^2-4/m^2))/sqrt(2)*2);
37	37
38	38	elseif mode_no>2	elseif mode_no>2
39	39	% for definition of logarithmic negativity see	% for definition of logarithmic negativity see
40		% Adesso & Illuminati, doi:10.1088/1751-8113/40/28/S01, eq. (68)
	40		% Adesso & Illuminati, doi:10.1088/1751-8113/40/28/S01, eq. (68)
41	41	if modeindices_unspecified	if modeindices_unspecified
42	42	if mod(mode_no,2)==0	if mod(mode_no,2)==0
43	43	%default choice for even no of modes: split symmetrically	%default choice for even no of modes: split symmetrically
...	...	elseif mode_no>2
49	49	end	end
50	50	end	end
51	51
52		sympevalvec = symevals(partialtranspose(cm, modeindices));
	52		sympevalvec = symevals(partialtranspose(cm, modeindices));
53	53
54	54	negativeSympEvals = sympevalvec(sympevalvec<0.5);	negativeSympEvals = sympevalvec(sympevalvec<0.5);
55	55	if ~isempty(negativeSympEvals)	if ~isempty(negativeSympEvals)
...	...	elseif mode_no>2
58	58	% NOTE: if all symplectic eigenvalues are larger than 0.5, we have an AMBIGUITY (in the multimode case)	% NOTE: if all symplectic eigenvalues are larger than 0.5, we have an AMBIGUITY (in the multimode case)
59	59	% * do we take only the smallest symplectic eigenvalue?	% * do we take only the smallest symplectic eigenvalue?
60	60	% * or do we sum over a certain subset or all symplectic eigenvalues?	% * or do we sum over a certain subset or all symplectic eigenvalues?
61		% Whether we see curves of log.neg. vs other parameters which are continuous across 0
	61		% Whether we see curves of log.neg. vs other parameters which are continuous across 0
62	62	% or not depends on this arbitrary choice!	% or not depends on this arbitrary choice!
63	63	% We choose to take only the smallest symplectic eigenvalue into account	% We choose to take only the smallest symplectic eigenvalue into account
64	64	% since in our application most of the times at most one symplectic eigenvalue is below 0.5	% since in our application most of the times at most one symplectic eigenvalue is below 0.5

File misc/covmats/logneg_test.m changed (mode: 100644) (index ce07591..d18cb7a)
1	1	function test_suite = logneg_test %#ok<STOUT>	function test_suite = logneg_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	% test cases:	% test cases:
5	5	% - vacuum state:	% - vacuum state:
...	...	function twomode_tmss_test
22	22	% r=.5; tmssOnModes1and3 = combineCMs(tmss(r),[1,3]); logneg(tmssOnModes1and3 , 'modeindices', [1]) % equals 2*r	% r=.5; tmssOnModes1and3 = combineCMs(tmss(r),[1,3]); logneg(tmssOnModes1and3 , 'modeindices', [1]) % equals 2*r
23	23
24	24	function multimode_vac_test	function multimode_vac_test
25		N = 8;
	25		N = 8;
26	26	vac = eye(2*N)/2;	vac = eye(2*N)/2;
27		assertEqual(logneg(vac, 'modeindices', [1, 2]), 0)
28		assertEqual(logneg(vac, 'modeindices', [1, 2, 3, 4]), 0)
29		assertEqual(logneg(vac), 0)
30		assertEqual(logneg(eye(2*3)/2), 0)
	27		assertEqual(logneg(vac, 'modeindices', [1, 2]), 0)
	28		assertEqual(logneg(vac, 'modeindices', [1, 2, 3, 4]), 0)
	29		assertEqual(logneg(vac), 0)
	30		assertEqual(logneg(eye(2*3)/2), 0)
31	31
32	32	function multimode_tmss_test	function multimode_tmss_test
33	33	r = 1;	r = 1;
34		tmssOnModes1and4 = combineCMs(tmss(r),[1,4]);
	34		tmssOnModes1and4 = combineCMs(tmss(r),[1,4]);
35	35	assertAlmostEqual(logneg(tmssOnModes1and4 , 'modeindices', [1]), 2*r)	assertAlmostEqual(logneg(tmssOnModes1and4 , 'modeindices', [1]), 2*r)
36		assertAlmostEqual(logneg(tmssOnModes1and4 , 'modeindices', [1, 2]), 2*r)
37		%% check equality for different, but equivalent ways of specifying the partition
38		assertEqual(logneg(tmssOnModes1and4 , 'modeindices', [1, 2]),...
39		logneg(tmssOnModes1and4 , 'modeindices', [3, 4]))
40		assertEqual(logneg(tmssOnModes1and4 , 'modeindices', [1, 2]),...
41		logneg(tmssOnModes1and4))
	36		assertAlmostEqual(logneg(tmssOnModes1and4 , 'modeindices', [1, 2]), 2*r)
	37		%% check equality for different, but equivalent ways of specifying the partition
	38		assertEqual(logneg(tmssOnModes1and4 , 'modeindices', [1, 2]),...
	39		logneg(tmssOnModes1and4 , 'modeindices', [3, 4]))
	40		assertEqual(logneg(tmssOnModes1and4 , 'modeindices', [1, 2]),...
	41		logneg(tmssOnModes1and4))

File misc/covmats/ptSymEvals.m changed (mode: 100644) (index 5014c0a..6200cd3)
...	...	function [ptSympEvals] = ptSymEvals( cm, modeIndices)
7	7	ptSympEvals = symevals(partialtranspose(cm, modeIndices));	ptSympEvals = symevals(partialtranspose(cm, modeIndices));
8	8
9	9	end	end
10

File misc/covmats/purity_test.m changed (mode: 100644) (index 2d7953e..8785a23)
1	1	function test_suite = purity_test %#ok<STOUT>	function test_suite = purity_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function smss_test	function smss_test
5	5	r = 5;	r = 5;

File misc/covmats/quadIndicesFromModeIndices.m changed (mode: 100644) (index a5566d1..7f52fd4)
...	...	function quadIndices = quadIndicesFromModeIndices(modeIndices)
5	5	qIndices = pIndices - 1;	qIndices = pIndices - 1;
6	6	quadIndices = sort([pIndices, qIndices]);	quadIndices = sort([pIndices, qIndices]);
7	7	end	end
8

File misc/covmats/smss.m changed (mode: 100644) (index 591950d..20a94b3)
...	...	s = sinh(2*r);
10	10	cm = (1/2)*[c, s;	cm = (1/2)*[c, s;
11	11	s, c];	s, c];
12	12	end	end
13

File misc/covmats/states_test.m changed (mode: 100644) (index 6260690..892c30e)
1	1	function test_suite = states_test %#ok<STOUT>	function test_suite = states_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function smss_vac_test	function smss_vac_test
5	5	vac = eye(2)/2;	vac = eye(2)/2;

File misc/covmats/symevals.m changed (mode: 100644) (index 0c54671..4feb215)
...	...	function symevalvector = symevals(cm)
2	2	% symevalvector = symevals(cm) takes a covariance matrix and returns the vector of symplectic eigenvalues	% symevalvector = symevals(cm) takes a covariance matrix and returns the vector of symplectic eigenvalues
3	3	N = length(cm);	N = length(cm);
4	4	assert(N>2&&mod(N,2)==0&&all(size(cm)==[N,N]),...	assert(N>2&&mod(N,2)==0&&all(size(cm)==[N,N]),...
5		'Expecting NxN covariance matrix with N>2');
	5		'Expecting NxN covariance matrix with N>2');
6	6
7	7	symevalvector = sort(abs(eig(complex(0,1)*symform(N)*cm)));	symevalvector = sort(abs(eig(complex(0,1)*symform(N)*cm)));
8	8	% for definition of symplectic eigenvalues see	% for definition of symplectic eigenvalues see

File misc/covmats/test_symplectic_functions.m changed (mode: 100644) (index 8ee4f0e..29572d1)
1	1	function test_symplectic_functions( cm )	function test_symplectic_functions( cm )
2		% test_symplectic_functions( cm ) takes a cm,
	2		% test_symplectic_functions( cm ) takes a cm,
3	3	% performs a number of symplectic operations on them	% performs a number of symplectic operations on them
4	4	% and pretty-prints the results.	% and pretty-prints the results.
5	5	%	%
6		% This can be used to test the implementation of the
	6		% This can be used to test the implementation of the
7	7	% functions operating on covariance matrices.	% functions operating on covariance matrices.
8	8	%	%
9		test_symplectic_functions2(cm);
10
11		input('Continue? [Enter to confirm]')
12		disp([10,'Second round for the matrix obtained by mixing mode 2 of your input ',...
13		'with the single-mode vacuum on a 50:50 beam splitter.',10])
14		disp('The beam splitter matrix:')
15		bs = blkdiag(eye(2),beam_splitter(sqrt(0.5)));
16		disp(bs)
17		cm_bs = bs*blkdiag(cm, diag([0.5,0.5]))*bs';
18		test_symplectic_functions2(cm_bs);
19
20		input('Continue? [Enter to confirm]')
21		disp([10,'Third round for the matrix obtained by tracing out all but mode 1 of your CM ',10])
22		cm_1 = traceOutCM(cm, [1]);
23		test_symplectic_functions2(cm_1);
	9		test_symplectic_functions2(cm);
	10
	11		input('Continue? [Enter to confirm]')
	12		disp([10,'Second round for the matrix obtained by mixing mode 2 of your input ',...
	13		'with the single-mode vacuum on a 50:50 beam splitter.',10])
	14		disp('The beam splitter matrix:')
	15		bs = blkdiag(eye(2),beam_splitter(sqrt(0.5)));
	16		disp(bs)
	17		cm_bs = bs*blkdiag(cm, diag([0.5,0.5]))*bs';
	18		test_symplectic_functions2(cm_bs);
	19
	20		input('Continue? [Enter to confirm]')
	21		disp([10,'Third round for the matrix obtained by tracing out all but mode 1 of your CM ',10])
	22		cm_1 = traceOutCM(cm, [1]);
	23		test_symplectic_functions2(cm_1);
24	24
25	25	end	end
26	26
27	27
28		%% test_symplectic_functions2: the actual function
	28		%% test_symplectic_functions2: the actual function
29	29	%(can be called with several inputs from main function)	%(can be called with several inputs from main function)
30	30	function test_symplectic_functions2(cm)	function test_symplectic_functions2(cm)
31		home
32		mode_no = length(cm)/2;
	31		home
	32		mode_no = length(cm)/2;
33	33
34		disp([10,'***********************************',10,...
35		'MUT (matrix under test):'])
36		disp(cm)
	34		disp([10,'***********************************',10,...
	35		'MUT (matrix under test):'])
	36		disp(cm)
37	37
38		disp('is physical?')
39		disp(isphyscov(cm))
	38		disp('is physical?')
	39		disp(isphyscov(cm))
40	40
41		disp([10,'Symplectic eigenvalues:'])
42		se = symp_evals(cm);
43		disp(se')
	41		disp([10,'Symplectic eigenvalues:'])
	42		se = symp_evals(cm);
	43		disp(se')
44	44
45		disp([10,'Williamson normal form:'])
46		wf = williamson_form(cm);
47		disp(wf)
	45		disp([10,'Williamson normal form:'])
	46		wf = williamson_form(cm);
	47		disp(wf)
48	48
49		disp([10,'Williamson diagonalizing matrix wd:'])
50		wd = williamson_diagonalization(cm);
51		disp(wd)
	49		disp([10,'Williamson diagonalizing matrix wd:'])
	50		wd = williamson_diagonalization(cm);
	51		disp(wd)
52	52
53		disp([10,'symplecticity check: wd*symplForm*wd'''])
54		disp(wd*symplectic_form(mode_no)*wd')
	53		disp([10,'symplecticity check: wd*symplForm*wd'''])
	54		disp(wd*symplectic_form(mode_no)*wd')
55	55
56		disp([10,'wd*cm*wd'''])
57		disp(wd*cm*wd')
	56		disp([10,'wd*cm*wd'''])
	57		disp(wd*cm*wd')
58	58	end	end
59

File misc/covmats/tmss.m changed (mode: 100644) (index d010640..313a0dc)
...	...	cm = (1/2)*[c, 0, s, 0;...
12	12	s, 0, c, 0;...	s, 0, c, 0;...
13	13	0, -s, 0, c];	0, -s, 0, c];
14	14	end	end
15

File misc/covmats/traceOutCM_test.m changed (mode: 100644) (index 2134c10..91068c9)
1	1	function test_suite = traceOutCM_test %#ok<STOUT>	function test_suite = traceOutCM_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function single_remaining_mode_test	function single_remaining_mode_test
5	5	CMfull = magic(8);	CMfull = magic(8);

File misc/covmats/wigner.m changed (mode: 100644) (index 26df61e..292e489)
1	1	function wrf = wigner(Xpos,X,CM)	function wrf = wigner(Xpos,X,CM)
2	2	% wrf = wigner(X,X0,CM)	% wrf = wigner(X,X0,CM)
3		%
	3		%
4	4	% Wigner function for a Gaussian state at position X with given mean	% Wigner function for a Gaussian state at position X with given mean
5	5	% X0 and covariance matrix CM. X and X0 must be row vectors, e.g.,	% X0 and covariance matrix CM. X and X0 must be row vectors, e.g.,
6	6	% X=(x1,y1,x2,y2).	% X=(x1,y1,x2,y2).
7
	7
8	8	% See e.g. Olivares, arXiv:1111.0786v2, eq. 13	% See e.g. Olivares, arXiv:1111.0786v2, eq. 13
9	9
10	10	%% test for proper dimensions	%% test for proper dimensions
11		if size(Xpos,1)~=size(X,1) || size(CM,1)~=size(CM,2) || size(CM,1)~=size(X,1)
	11		if size(Xpos,1)~=size(X,1) || size(CM,1)~=size(CM,2) || size(CM,1)~=size(X,1)
12	12	error('State dimensions are not consistent');	error('State dimensions are not consistent');
13	13	end	end
14	14

File misc/covmats/williamson_diagonalization.m changed (mode: 100644) (index 8fb085b..dbc5eb6)
...	...	root_W = diag(sqrt(sym_spectrum));
47	47
48	48	% (5) Compute R=Gamma*U^†	% (5) Compute R=Gamma*U^†
49	49	Gamma = (1/sqrt(2))*kron(eye(mode_no),...	Gamma = (1/sqrt(2))*kron(eye(mode_no),...
50		[sqrt(-1), -sqrt(-1);...
51		1, 1]);
	50		[sqrt(-1), -sqrt(-1);...
	51		1, 1]);
52	52	R = Gamma*eig_vecs_X.';	R = Gamma*eig_vecs_X.';
53	53
54	54	% (6) Compute diagonalizing_mat = W^1/2 * R * V^−1/2	% (6) Compute diagonalizing_mat = W^1/2 * R * V^−1/2

File misc/covmats/williamson_test.m changed (mode: 100644) (index 52fa376..46abd9a)
1	1	function test_suite = williamson_test %#ok<STOUT>	function test_suite = williamson_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function smss_test	function smss_test
5	5	cm = smss(2);	cm = smss(2);

File misc/covupdate.m changed (mode: 100644) (index 744bbf6..a854c02)
1	1	function cov = covupdate(m1,c1,N1,m2,c2,N2)	function cov = covupdate(m1,c1,N1,m2,c2,N2)
2	2	% cov = covupdate(mean_1,cov_1,N_1,mean_2,cov_2,N_2)	% cov = covupdate(mean_1,cov_1,N_1,mean_2,cov_2,N_2)
3		%
	3		%
4	4	% Calculate update covariance matrix from two samples of same	% Calculate update covariance matrix from two samples of same
5	5	% distribution, where mean mean_i, cov_i and N_i are the mean,	% distribution, where mean mean_i, cov_i and N_i are the mean,
6	6	% covariance and sample size of each sample respectively. mean_i can	% covariance and sample size of each sample respectively. mean_i can

File misc/dfilt2ess.m changed (mode: 100644) (index 09ded92..b9ed1e4)
...	...	function esm=dfilt2ess(Hd,h)
2	2	% esm=dfilt2ess(Hd,h)	% esm=dfilt2ess(Hd,h)
3	3	%	%
4	4	% Converts Hd object to ess object, h time step	% Converts Hd object to ess object, h time step
5		%
	5		%
6	6	% uses convert for conversion (accepts many filter implementations)	% uses convert for conversion (accepts many filter implementations)
7	7
8	8

File misc/filtervarargs.m changed (mode: 100644) (index ab0ff40..5be3fc6)
...	...	function argsin = filtervarargs(argsin,filtnames)
6	6
7	7	for ii = 1:numel(filtnames)	for ii = 1:numel(filtnames)
8	8	% key/value pair has to be filtered =	% key/value pair has to be filtered =
9		% delete two consecutive positiions
	9		% delete two consecutive positiions
10	10	idx = strcmp(filtnames{ii},argsin); % find occurence	idx = strcmp(filtnames{ii},argsin); % find occurence
11	11	idx = circshift(idx,[0,1]) | idx; % create mask	idx = circshift(idx,[0,1]) | idx; % create mask
12	12	argsin(idx) = []; % delete elements	argsin(idx) = []; % delete elements

File misc/gui/boundedline.m changed (mode: 100644) (index 3fd9f6c..a088e9c)
...	...	function varargout = boundedline(varargin)
47	47	% 0.2.	% 0.2.
48	48	%	%
49	49	% orient: 'vert': add bounds in vertical (y) direction (default)	% orient: 'vert': add bounds in vertical (y) direction (default)
50		% 'horiz': add bounds in horizontal (x) direction
	50		% 'horiz': add bounds in horizontal (x) direction
51	51	%	%
52	52	% cmap: n x 3 colormap array. If included, lines will be colored	% cmap: n x 3 colormap array. If included, lines will be colored
53	53	% (in order of plotting) according to this colormap,	% (in order of plotting) according to this colormap,
54		% overriding any linespec or default colors.
	54		% overriding any linespec or default colors.
55	55	%	%
56	56	% Output variables:	% Output variables:
57	57	%	%
...	...	function varargout = boundedline(varargin)
66	66	% y2 = cos(x);	% y2 = cos(x);
67	67	% e1 = rand(size(y1))*.5+.5;	% e1 = rand(size(y1))*.5+.5;
68	68	% e2 = [.25 .5];	% e2 = [.25 .5];
69		%
	69		%
70	70	% ax(1) = subplot(2,2,1);	% ax(1) = subplot(2,2,1);
71	71	% [l,p] = boundedline(x, y1, e1, '-b*', x, y2, e2, '--ro');	% [l,p] = boundedline(x, y1, e1, '-b*', x, y2, e2, '--ro');
72	72	% outlinebounds(l,p);	% outlinebounds(l,p);
73	73	% title('Opaque bounds, with outline');	% title('Opaque bounds, with outline');
74		%
	74		%
75	75	% ax(2) = subplot(2,2,2);	% ax(2) = subplot(2,2,2);
76	76	% boundedline(x, [y1;y2], rand(length(y1),2,2)*.5+.5, 'alpha');	% boundedline(x, [y1;y2], rand(length(y1),2,2)*.5+.5, 'alpha');
77	77	% title('Transparent bounds');	% title('Transparent bounds');
78		%
	78		%
79	79	% ax(3) = subplot(2,2,3);	% ax(3) = subplot(2,2,3);
80	80	% boundedline([y1;y2], x, e1(1), 'orientation', 'horiz')	% boundedline([y1;y2], x, e1(1), 'orientation', 'horiz')
81	81	% title('Horizontal bounds');	% title('Horizontal bounds');
82		%
	82		%
83	83	% ax(4) = subplot(2,2,4);	% ax(4) = subplot(2,2,4);
84	84	% boundedline(x, repmat(y1, 4,1), permute(0.5:-0.1:0.2, [3 1 2]), ...	% boundedline(x, repmat(y1, 4,1), permute(0.5:-0.1:0.2, [3 1 2]), ...
85	85	% 'cmap', cool(4), 'transparency', 0.5);	% 'cmap', cool(4), 'transparency', 0.5);
...	...	while ~isempty(varargin)
166	166	varargin(1) = [];	varargin(1) = [];
167	167	else	else
168	168	linespec = [linespec {[]}];	linespec = [linespec {[]}];
169		end
170		end
	169		end
	170		end
171	171
172	172	%--------------------	%--------------------
173	173	% Reformat x and y	% Reformat x and y
...	...	plotdata = cell(0,7);
181	181
182	182	htemp = figure('visible', 'off');	htemp = figure('visible', 'off');
183	183	for ix = 1:length(x)	for ix = 1:length(x)
184
	184
185	185	% Get full x, y, and linespec data for each line (easier to let plot	% Get full x, y, and linespec data for each line (easier to let plot
186	186	% check for properly-sized x and y and expand values than to try to do	% check for properly-sized x and y and expand values than to try to do
187		% it myself)
188
	187		% it myself)
	188
189	189	try	try
190	190	if isempty(linespec{ix})	if isempty(linespec{ix})
191	191	hltemp = plot(x{ix}, y{ix});	hltemp = plot(x{ix}, y{ix});
...	...	for ix = 1:length(x)
196	196	close(htemp);	close(htemp);
197	197	error('X and Y matrices and/or linespec not appropriate for line plot');	error('X and Y matrices and/or linespec not appropriate for line plot');
198	198	end	end
199
	199
200	200	linedata = get(hltemp, {'xdata', 'ydata', 'marker', 'linestyle', 'color'});	linedata = get(hltemp, {'xdata', 'ydata', 'marker', 'linestyle', 'color'});
201
	201
202	202	nline = size(linedata,1);	nline = size(linedata,1);
203
	203
204	204	% Expand bounds matrix if necessary	% Expand bounds matrix if necessary
205
	205
206	206	if nline > 1	if nline > 1
207	207	if ndims(err{ix}) == 3	if ndims(err{ix}) == 3
208	208	err2 = squeeze(num2cell(err{ix},[1 2]));	err2 = squeeze(num2cell(err{ix},[1 2]));
...	...	for ix = 1:length(x)
212	212	else	else
213	213	err2 = err(ix);	err2 = err(ix);
214	214	end	end
215
	215
216	216	% Figure out upper and lower bounds	% Figure out upper and lower bounds
217
	217
218	218	[lo, hi] = deal(cell(nline,1));	[lo, hi] = deal(cell(nline,1));
219	219	for iln = 1:nline	for iln = 1:nline
220
	220
221	221	x2 = linedata{iln,1};	x2 = linedata{iln,1};
222	222	y2 = linedata{iln,2};	y2 = linedata{iln,2};
223	223	nx = length(x2);	nx = length(x2);
224
	224
225	225	if isvert	if isvert
226	226	lineval = y2;	lineval = y2;
227	227	else	else
228	228	lineval = x2;	lineval = x2;
229	229	end	end
230
	230
231	231	sz = size(err2{iln});	sz = size(err2{iln});
232
	232
233	233	if isequal(sz, [nx 2])	if isequal(sz, [nx 2])
234	234	lo{iln} = lineval - err2{iln}(:,1)';	lo{iln} = lineval - err2{iln}(:,1)';
235	235	hi{iln} = lineval + err2{iln}(:,2)';	hi{iln} = lineval + err2{iln}(:,2)';
...	...	for ix = 1:length(x)
253	253	hi{iln} = lineval + err2{iln}(2);	hi{iln} = lineval + err2{iln}(2);
254	254	else	else
255	255	error('Error bounds must be npt x nside x nline array');	error('Error bounds must be npt x nside x nline array');
256		end
257
	256		end
	257
258	258	end	end
259
	259
260	260	% Combine all data (xline, yline, marker, linestyle, color, lower bound	% Combine all data (xline, yline, marker, linestyle, color, lower bound
261		% (x or y), upper bound (x or y)
262
	261		% (x or y), upper bound (x or y)
	262
263	263	plotdata = [plotdata; linedata lo hi];	plotdata = [plotdata; linedata lo hi];
264
	264
265	265	end	end
266	266	close(htemp);	close(htemp);
267	267
...	...	for iln = 1:nline
294	294	% xp{iln} = [plotdata{iln,6} fliplr(plotdata{iln,7})];	% xp{iln} = [plotdata{iln,6} fliplr(plotdata{iln,7})];
295	295	% yp{iln} = [plotdata{iln,2} fliplr(plotdata{iln,2})];	% yp{iln} = [plotdata{iln,2} fliplr(plotdata{iln,2})];
296	296	% end	% end
297
	297
298	298	[xp{iln}, yp{iln}] = calcpatch(plotdata{iln,1}, plotdata{iln,2}, isvert, plotdata{iln,6}, plotdata{iln,7});	[xp{iln}, yp{iln}] = calcpatch(plotdata{iln,1}, plotdata{iln,2}, isvert, plotdata{iln,6}, plotdata{iln,7});
299
	299
300	300	marker{iln} = plotdata{iln,3};	marker{iln} = plotdata{iln,3};
301	301	lnsty{iln} = plotdata{iln,4};	lnsty{iln} = plotdata{iln,4};
302
	302
303	303	if usealpha	if usealpha
304	304	lncol{iln} = plotdata{iln,5};	lncol{iln} = plotdata{iln,5};
305	305	ptchcol{iln} = plotdata{iln,5};	ptchcol{iln} = plotdata{iln,5};
...	...	for iln = 1:nline
310	310	alpha{iln} = 1;	alpha{iln} = 1;
311	311	end	end
312	312	end	end
313
	313
314	314	% Plot patches and lines	% Plot patches and lines
315	315
316	316	[hp,hl] = deal(zeros(nline,1));	[hp,hl] = deal(zeros(nline,1));
...	...	if nargout == 2
341	341	end	end
342	342
343	343	%--------------------	%--------------------
344		% Parse optional
	344		% Parse optional
345	345	% parameters	% parameters
346	346	%--------------------	%--------------------
347	347
...	...	function [xp, yp] = calcpatch(xl, yl, isvert, lo, hi)
371	371
372	372	ismissing = any(isnan([xl;yl;lo;hi]),2);	ismissing = any(isnan([xl;yl;lo;hi]),2);
373	373	if any(ismissing)	if any(ismissing)
374
	374
375	375	else	else
376	376	if isvert	if isvert
377	377	xp = [xl fliplr(xl)];	xp = [xl fliplr(xl)];
...	...	else
381	381	yp = [yl fliplr(yl)];	yp = [yl fliplr(yl)];
382	382	end	end
383	383	end	end
384
385

File misc/io/io_test.m changed (mode: 100644) (index 872775e..f485b7a)
1	1	function test_suite = io_test %#ok<STOUT>	function test_suite = io_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function shared = setup	function shared = setup
5		shared.startstr = '.__test';
6		shared.endstr = '__.datshared.';
7		shared.file1 = [shared.startstr, '1', shared.endstr];
8		shared.file2 = [shared.startstr, '2', shared.endstr];
9		%shared.data1 = [1,2,3];
10		shared.data1 = randn(2e6,1);
11		shared.precision1 = 'double';
12		shared.data2 = [1,2,3, 4];
13		shared.precision2 = 'single';
14		fid1 = fopen(shared.file1, 'w');
15		fid2 = fopen(shared.file2, 'w');
16		fwrite(fid1, shared.data1, shared.precision1);
17		fwrite(fid2, shared.data2, shared.precision2);
	5		shared.startstr = '.__test';
	6		shared.endstr = '__.datshared.';
	7		shared.file1 = [shared.startstr, '1', shared.endstr];
	8		shared.file2 = [shared.startstr, '2', shared.endstr];
	9		%shared.data1 = [1,2,3];
	10		shared.data1 = randn(2e6,1);
	11		shared.precision1 = 'double';
	12		shared.data2 = [1,2,3, 4];
	13		shared.precision2 = 'single';
	14		fid1 = fopen(shared.file1, 'w');
	15		fid2 = fopen(shared.file2, 'w');
	16		fwrite(fid1, shared.data1, shared.precision1);
	17		fwrite(fid2, shared.data2, shared.precision2);
18	18
19	19	function teardown(shared)	function teardown(shared)
20		delete(shared.file1);
21		delete(shared.file2);
	20		delete(shared.file1);
	21		delete(shared.file2);
22	22
23	23	function byteSizeFromPrecision_test(shared)	function byteSizeFromPrecision_test(shared)
24	24	assertEqual(byteSizeFromPrecision('double'), 8)	assertEqual(byteSizeFromPrecision('double'), 8)
...	...	function dtsize_test(shared)
45	45	assertEqual(dtsize('uint8'), 1)	assertEqual(dtsize('uint8'), 1)
46	46
47	47	function fsize_test(shared)	function fsize_test(shared)
48		[s n] = fsize([shared.startstr, '*', shared.endstr]);
49		assertEqual(s(1),...
50		byteSizeFromPrecision(shared.precision1)*length(shared.data1))
51		assertEqual(s(2),...
52		byteSizeFromPrecision(shared.precision2)*length(shared.data2))
53		assertEqual(n{1}, shared.file1)
54		assertEqual(n{2}, shared.file2)
	48		[s n] = fsize([shared.startstr, '*', shared.endstr]);
	49		assertEqual(s(1),...
	50		byteSizeFromPrecision(shared.precision1)*length(shared.data1))
	51		assertEqual(s(2),...
	52		byteSizeFromPrecision(shared.precision2)*length(shared.data2))
	53		assertEqual(n{1}, shared.file1)
	54		assertEqual(n{2}, shared.file2)
55	55
56	56	function sampleNoInFile_test(shared)	function sampleNoInFile_test(shared)
57	57
58		assertEqual(sampleNoInFile(shared.file1, shared.precision1),...
59		length(shared.data1))
60		assertEqual(sampleNoInFile(shared.file2, shared.precision2),...
61		length(shared.data2))
	58		assertEqual(sampleNoInFile(shared.file1, shared.precision1),...
	59		length(shared.data1))
	60		assertEqual(sampleNoInFile(shared.file2, shared.precision2),...
	61		length(shared.data2))
62	62
63	63	function varAndMeanFromFile_test(shared)	function varAndMeanFromFile_test(shared)
64		[cumVar, cumMean, cumSize] = varAndMeanFromFile(shared.file1, shared.precision1);
65		assertAlmostEqual(cumVar, var(shared.data1))
66		assertAlmostEqual(cumMean, mean(shared.data1))
67		assertAlmostEqual(cumSize, length(shared.data1))
	64		[cumVar, cumMean, cumSize] = varAndMeanFromFile(shared.file1, shared.precision1);
	65		assertAlmostEqual(cumVar, var(shared.data1))
	66		assertAlmostEqual(cumMean, mean(shared.data1))
	67		assertAlmostEqual(cumSize, length(shared.data1))
68	68
69		[cumVar, cumMean, cumSize] = varAndMeanFromFile(shared.file2, shared.precision2);
70		assertAlmostEqual(cumVar, var(shared.data2))
71		assertAlmostEqual(cumMean, mean(shared.data2))
72		assertAlmostEqual(cumSize, length(shared.data2))
	69		[cumVar, cumMean, cumSize] = varAndMeanFromFile(shared.file2, shared.precision2);
	70		assertAlmostEqual(cumVar, var(shared.data2))
	71		assertAlmostEqual(cumMean, mean(shared.data2))
	72		assertAlmostEqual(cumSize, length(shared.data2))

File misc/io/sampleNoInFile.m changed (mode: 100644) (index 4fec935..198d501)
...	...	assert(mod(byteNo, bytesPerSample)==0,...
13	13	sampleNo = byteNo/bytesPerSample;	sampleNo = byteNo/bytesPerSample;
14	14
15	15	end	end
16

File misc/io/varAndMeanFromFile.m changed (mode: 100644) (index 8a403b4..cbadd6a)
...	...	cumSize = 0;
18	18	for blockInd=1:blockNo	for blockInd=1:blockNo
19	19	perccount(blockInd, blockNo);	perccount(blockInd, blockNo);
20	20	data = fread(fid, blockSize, precision);	data = fread(fid, blockSize, precision);
21
	21
22	22	blockVar = var(data);	blockVar = var(data);
23	23	blockMean = mean(data);	blockMean = mean(data);
24
	24
25	25	if blockInd==1	if blockInd==1
26	26	cumVar = blockVar;	cumVar = blockVar;
27		cumMean = blockMean;
	27		cumMean = blockMean;
28	28	cumSize = blockSize;	cumSize = blockSize;
29
	29
30	30	else	else
31	31	cumVar = covupdate(cumMean, cumVar, cumSize,...	cumVar = covupdate(cumMean, cumVar, cumSize,...
32	32	blockMean, blockVar, blockSize);	blockMean, blockVar, blockSize);

File misc/matrices/createTestMatrix.m changed (mode: 100644) (index 78fb143..0d5eded)
...	...	for k=1:n
11	11	end	end
12	12
13	13	end	end
14

File misc/matrices/matrixfct_test.m changed (mode: 100644) (index c5a34e1..a2395fa)
1	1	function test_suite = matrixfct_test %#ok<STOUT>	function test_suite = matrixfct_test %#ok<STOUT>
2		initTestSuite;
	2		initTestSuite;
3	3
4	4	function unity_orthogonal_test	function unity_orthogonal_test
5	5	unitmat = eye(5);	unitmat = eye(5);

File misc/meanupdate.m changed (mode: 100644) (index ad411df..5290c04)
1	1	function m = meanupdate(m1,N1,m2,N2)	function m = meanupdate(m1,N1,m2,N2)
2	2	% mean = meanupdate(mean_1,N_1,mean_2,N_2)	% mean = meanupdate(mean_1,N_1,mean_2,N_2)
3		%
	3		%
4	4	% Calculate updated mean vector from two sample of same distribution,	% Calculate updated mean vector from two sample of same distribution,
5	5	% where mean mean_i and N_i are the mean and sample	% where mean mean_i and N_i are the mean and sample
6	6	% size of each sample respectively. mean_i can be column or row	% size of each sample respectively. mean_i can be column or row

File misc/nssv.m changed (mode: 100644) (index 9cbc48c..9b5c51b)
1	1	function [sv,dv] = nssv(cov,part)	function [sv,dv] = nssv(cov,part)
2	2	% [sv,dv] = nssv(cov,part)	% [sv,dv] = nssv(cov,part)
3		%
	3		%
4	4	% Returns normalized singular values of the correlation block given by the	% Returns normalized singular values of the correlation block given by the
5	5	% cell array part.	% cell array part.
6	6	% eg to specify the off-diagonal block for a 2mode covariance matrix,	% eg to specify the off-diagonal block for a 2mode covariance matrix,

File misc/plottools/figure.m changed (mode: 100644) (index 25ee4c1..ead1368)
1	1	function varargout = figure(varargin)	function varargout = figure(varargin)
2		[varargout{1:nargout}] = builtin('figure', varargin{:});
3		set(gca,'FontName','Palatino');
	2		[varargout{1:nargout}] = builtin('figure', varargin{:});
	3		set(gca,'FontName','Palatino');
4	4	set(gca, 'FontSize', 11);	set(gca, 'FontSize', 11);
5	5	myColorOrder = cbrewer('qual','Set1',9);	myColorOrder = cbrewer('qual','Set1',9);
6	6	set(gca, 'ColorOrder', myColorOrder, 'NextPlot', 'replacechildren');	set(gca, 'ColorOrder', myColorOrder, 'NextPlot', 'replacechildren');

File misc/plottools/labels.m changed (mode: 100644) (index 2cc2ba3..036a93a)
1	1	function labels(x_label, y_label)	function labels(x_label, y_label)
2		set(gca,'FontName','Palatino');
	2		set(gca,'FontName','Palatino');
3	3	set(gca, 'FontSize', 12);	set(gca, 'FontSize', 12);
4		xlabel(sentence_case(x_label), 'Interpreter','latex');
5		ylabel(sentence_case(y_label), 'Interpreter','latex');
	4		xlabel(sentence_case(x_label), 'Interpreter','latex');
	5		ylabel(sentence_case(y_label), 'Interpreter','latex');
6	6	set(gca, 'FontSize', 11);	set(gca, 'FontSize', 11);

File misc/plottools/sentence_case.m changed (mode: 100644) (index d18342a..7d7d932)
1	1	function Sentence_string = sentence_case(string)	function Sentence_string = sentence_case(string)
2		% Capitalizes first word of the input string
3		assert(ischar(string), 'Expected string.')
4		if length(string)>0
5		Sentence_string = [upper(string(1)), string(2:end)];
6		else
7		Sentence_string = '';
8		end
	2		% Capitalizes first word of the input string
	3		assert(ischar(string), 'Expected string.')
	4		if length(string)>0
	5		Sentence_string = [upper(string(1)), string(2:end)];
	6		else
	7		Sentence_string = '';
	8		end

File misc/plottools/tex_escape.m changed (mode: 100644) (index f89de0d..6317d6c)
1	1	function escaped_strings = tex_escape(strings)	function escaped_strings = tex_escape(strings)
2		% see https://tex.stackexchange.com/questions/34580/escape-character-in-latex for background
3		if iscell(strings)
4		escaped_strings = cellfun(@tex_escape_single, strings, 'UniformOutput', false);
5		else
6		escaped_strings = tex_escape_single(strings);
7		end
	2		% see https://tex.stackexchange.com/questions/34580/escape-character-in-latex for background
	3		if iscell(strings)
	4		escaped_strings = cellfun(@tex_escape_single, strings, 'UniformOutput', false);
	5		else
	6		escaped_strings = tex_escape_single(strings);
	7		end
8	8
9	9	function escaped_string = tex_escape_single(string)	function escaped_string = tex_escape_single(string)
10		replacements = {...
11		{'\', 'PRIVATEBACKSLASHESCAPED'};...
12		{'&', '\&'};...
13		{'%', '\%'};...
14		{'$', '\$'};...
15		{'#', '\#'};...
16		{'_', '\_'};...
17		{'{', '\{}'};...
18		{'}', '\'};...
19		{'~', '\textasciitilde '};...
20		{'^', '\textasciicircum '};...
21		{'PRIVATEBACKSLASHESCAPED', '\textbackslash '}};
	10		replacements = {...
	11		{'\', 'PRIVATEBACKSLASHESCAPED'};...
	12		{'&', '\&'};...
	13		{'%', '\%'};...
	14		{'$', '\$'};...
	15		{'#', '\#'};...
	16		{'_', '\_'};...
	17		{'{', '\{}'};...
	18		{'}', '\'};...
	19		{'~', '\textasciitilde '};...
	20		{'^', '\textasciicircum '};...
	21		{'PRIVATEBACKSLASHESCAPED', '\textbackslash '}};
22	22
23		escaped_string = string;
24		for k=1:length(replacements)
25		rep = replacements{k};
26		escaped_string = strrep(escaped_string, rep{1}, rep{2});
27		end
	23		escaped_string = string;
	24		for k=1:length(replacements)
	25		rep = replacements{k};
	26		escaped_string = strrep(escaped_string, rep{1}, rep{2});
	27		end

File misc/plottools/tex_escape_test.m changed (mode: 100644) (index 2d09cfa..e2675ad)
1	1	function test_suite = tex_escape_test %#ok<STOUT>	function test_suite = tex_escape_test %#ok<STOUT>
2		localFunctionHandles = cellfun(@str2func,...
3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
	2		localFunctionHandles = cellfun(@str2func,...
	3		which('-subfun', mfilename('fullpath')), 'UniformOutput', false);
	4		test_suite = buildFunctionHandleTestSuite(localFunctionHandles);
5	5
6	6	function pt = backslash_test	function pt = backslash_test
7		input_string = 'string with a \ backslash';
8		assertEqual(tex_escape(input_string),...
9		'string with a \textbackslash backslash')
	7		input_string = 'string with a \ backslash';
	8		assertEqual(tex_escape(input_string),...
	9		'string with a \textbackslash backslash')
10	10
11	11	function pt = mixed_test	function pt = mixed_test
12		input_string = 'string with & and a \ backslash ^';
13		assertEqual(tex_escape(input_string),...
14		'string with \& and a \textbackslash backslash \textasciicircum ')
	12		input_string = 'string with & and a \ backslash ^';
	13		assertEqual(tex_escape(input_string),...
	14		'string with \& and a \textbackslash backslash \textasciicircum ')
15	15
16	16	function pt = cell_array_test	function pt = cell_array_test
17		input_strings = {'string with & and a \ backslash ^',...
18		' & and a \ backslash ^ x_y'};
19		assertEqual(tex_escape(input_strings),...
20		{'string with \& and a \textbackslash backslash \textasciicircum ',...
21		' \& and a \textbackslash backslash \textasciicircum x\_y'})
	17		input_strings = {'string with & and a \ backslash ^',...
	18		' & and a \ backslash ^ x_y'};
	19		assertEqual(tex_escape(input_strings),...
	20		{'string with \& and a \textbackslash backslash \textasciicircum ',...
	21		' \& and a \textbackslash backslash \textasciicircum x\_y'})
22	22
23	23	function pt = plot_test	function pt = plot_test
24		input_string = 'x_y r^z string with & and a \ backslash ^';
25		fig = figure; plot([1,2],[1,2])
26		title(tex_escape(input_string), 'Interpreter', 'latex')
27		legend({tex_escape(input_string)}, 'Interpreter', 'latex')
28		xlabel(tex_escape(input_string), 'Interpreter', 'latex')
29		ylabel(tex_escape(input_string), 'Interpreter', 'latex')
30		close(fig)
	24		input_string = 'x_y r^z string with & and a \ backslash ^';
	25		fig = figure; plot([1,2],[1,2])
	26		title(tex_escape(input_string), 'Interpreter', 'latex')
	27		legend({tex_escape(input_string)}, 'Interpreter', 'latex')
	28		xlabel(tex_escape(input_string), 'Interpreter', 'latex')
	29		ylabel(tex_escape(input_string), 'Interpreter', 'latex')
	30		close(fig)

File misc/running_stats.m changed (mode: 100644) (index 9a0afad..8d1e6bc)
1	1	function [covs, means] = running_stats(array, interval)	function [covs, means] = running_stats(array, interval)
2	2	% [covs, means] = running_stats(array, interval) takes a (sampleno, dim)-sized array	% [covs, means] = running_stats(array, interval) takes a (sampleno, dim)-sized array
3		% and computes covariance matrices
	3		% and computes covariance matrices
4	4
5	5	sampleno = size(array, 1);	sampleno = size(array, 1);
6	6	dim = size(array, 2);	dim = size(array, 2);
...	...	means{1} = mean(data);
16	16	samples = interval;	samples = interval;
17	17
18	18	for k=2:stepno	for k=2:stepno
19		data = array(samples+1:samples+interval,:);
	19		data = array(samples+1:samples+interval,:);
20	20
21	21	% mean = meanupdate(mean_1,N_1,mean_2,N_2)	% mean = meanupdate(mean_1,N_1,mean_2,N_2)
22		means{k} = meanupdate(means{k-1}, samples,...
23		mean(data), interval);
	22		means{k} = meanupdate(means{k-1}, samples,...
	23		mean(data), interval);
24	24
25	25	% cov = covupdate(mean_1,cov_1,N_1,mean_2,cov_2,N_2)	% cov = covupdate(mean_1,cov_1,N_1,mean_2,cov_2,N_2)
26		covs{k} = covupdate(means{k-1}, covs{k-1}, samples,...
27		means{k}, cov(data), interval);
	26		covs{k} = covupdate(means{k-1}, covs{k-1}, samples,...
	27		means{k}, cov(data), interval);
28	28
29		samples = samples + interval;
	29		samples = samples + interval;
30	30	end	end
31	31
32	32	%% test code	%% test code

File misc/ui/perccount.m changed (mode: 100644) (index 09454ce..cd02cda)
...	...	function perccount(jj,maxjj)
8	8	% Do not print anything to the screen between calls of this function!	% Do not print anything to the screen between calls of this function!
9	9	%	%
10	10
11		% title - s cmspike/perccount vr - 1.2
	11		% title - s cmspike/perccount vr - 1.2
12	12	% author - bodc/alead date - 2006may16	% author - bodc/alead date - 2006may16
13	13
14	14	% Updated 2009May14	% Updated 2009May14
15		% At suggestion of John D'Errico renamed internal variable "max" to
	15		% At suggestion of John D'Errico renamed internal variable "max" to
16	16	% "maxjj"	% "maxjj"
17	17	% Also following D'Errico's suggestions the following functionality has	% Also following D'Errico's suggestions the following functionality has
18	18	% been added:	% been added:

File misc/varargs2cell.m changed (mode: 100644) (index a54068a..77c98d6)
1	1	function [varout,nargs] = varargs2cell(varin)	function [varout,nargs] = varargs2cell(varin)
2	2	% varout = varargs2cell(varin)	% varout = varargs2cell(varin)
3		%
	3		%
4	4	% Parse varargin: If varin has one element which is a structure unfold	% Parse varargin: If varin has one element which is a structure unfold
5	5	% the structure to a key/value type cell, else return varin	% the structure to a key/value type cell, else return varin
6	6	% unmodified.	% unmodified.
7	7
8	8	if ~iscell(varin)	if ~iscell(varin)
9	9	error('input argument must be cell')	error('input argument must be cell')
10		end
	10		end
11	11
12	12	if numel(varin) == 1 && isstruct(varin{1})	if numel(varin) == 1 && isstruct(varin{1})
13	13	c = struct2cell(varin{1});	c = struct2cell(varin{1});

File misc/varargs2struct.m changed (mode: 100644) (index 177a265..182a0d5)
...	...	function argS = varargs2struct(varargin)
3	3	% argS = varargs2struct(argNamesC,argC)	% argS = varargs2struct(argNamesC,argC)
4	4	% argS = varargs2struct(argNamesC,argC,nReq)	% argS = varargs2struct(argNamesC,argC,nReq)
5	5	%	%
6		% One input argument:
7		%
	6		% One input argument:
	7		%
8	8	% Input must either be a cell in key/value-pair	% Input must either be a cell in key/value-pair
9	9	% which is returned as a struct or can be a struct which is returned	% which is returned as a struct or can be a struct which is returned
10	10	% unmodified.	% unmodified.
11	11	%	%
12	12	% Two or more input arguments:	% Two or more input arguments:
13		%
	13		%
14	14	% Copy arguments from cell argC to struct argS, where fieldnames are	% Copy arguments from cell argC to struct argS, where fieldnames are
15	15	% given in argNamesC in the form {'arg1','arg2',...}. argC must have	% given in argNamesC in the form {'arg1','arg2',...}. argC must have
16	16	% at most the same length as argNamesC. As a last argument the number	% at most the same length as argNamesC. As a last argument the number
...	...	function argS = varargs2struct(varargin)
26	26	if nargin == 1	if nargin == 1
27	27	% one layer of {} has to be unwrapped	% one layer of {} has to be unwrapped
28	28	% (varargin{1} is equal to original input from calling function)	% (varargin{1} is equal to original input from calling function)
29		if isstruct(varargin{1})
30		% if single struct is given, return unmodified
	29		if isstruct(varargin{1})
	30		% if single struct is given, return unmodified
31	31	argS = varargin{1};	argS = varargin{1};
32	32	elseif iscell(varargin{1})	elseif iscell(varargin{1})
33	33	% if single cell is given it must be in key/value pair form	% if single cell is given it must be in key/value pair form

File misc/varupdate.m changed (mode: 100644) (index d676a18..9853ed8)
1	1	function var = varupdate(m1,v1,N1,m2,v2,N2)	function var = varupdate(m1,v1,N1,m2,v2,N2)
2	2	% var = varupdate(mean_1,var_1,N_1,mean_2,var_2,N_2)	% var = varupdate(mean_1,var_1,N_1,mean_2,var_2,N_2)
3		%
	3		%
4	4	% Calculate update variance from two samples of same distribution,	% Calculate update variance from two samples of same distribution,
5	5	% where mean mean_i, var_i and N_i are the mean, variance and sample	% where mean mean_i, var_i and N_i are the mean, variance and sample
6	6	% size of each sample respectively. mean_i and var_i must be arrays of	% size of each sample respectively. mean_i and var_i must be arrays of

File statisticstests/archive/jarque_bera_test.m changed (mode: 100644) (index 38f58ac..f0b72cf)
1	1	function [jbstat, msig] = jarque_bera_test(y,k)	function [jbstat, msig] = jarque_bera_test(y,k)
2	2	% [jbstat, msig] = jarque_bera_test(y,k)	% [jbstat, msig] = jarque_bera_test(y,k)
3		%
	3		%
4	4	% Jarque-Bera statistic with marginal significance level, y input, k	% Jarque-Bera statistic with marginal significance level, y input, k
5	5	% regressors	% regressors
6	6

File statisticstests/neestest.m changed (mode: 100644) (index 6a688fa..027e3c1)
...	...	switch method
141	141	neesCov=cov(NEES');	neesCov=cov(NEES');
142	142	if ~statv.chi2.h, result='passed'; else result='failed'; end	if ~statv.chi2.h, result='passed'; else result='failed'; end
143	143	fprintf('NEES test %s (p=%g)\n',result,statv.chi2.p);	fprintf('NEES test %s (p=%g)\n',result,statv.chi2.p);
144
145
	144
	145
146	146	case 'NMEE'	case 'NMEE'
147	147	% test NMEE against normal distribution	% test NMEE against normal distribution
148	148	% NMEE should be Gaussian and white (???) with normalized variance	% NMEE should be Gaussian and white (???) with normalized variance
149
	149
150	150	fprintf('\nDo NMEE test...');	fprintf('\nDo NMEE test...');
151	151	NEES=zeros(NT,sn);	NEES=zeros(NT,sn);
152	152	h=zeros(1,sn);	h=zeros(1,sn);
...	...	if show_plots
228	228	[~,r,bound] = chi2Test(NEES,sn,alpha,confint);	[~,r,bound] = chi2Test(NEES,sn,alpha,confint);
229	229	plot(xvec,NEES,xvec,bound(1)*ones(1,NT),'k-.',xvec,bound(2)*ones(1,NT),'k-.');	plot(xvec,NEES,xvec,bound(1)*ones(1,NT),'k-.',xvec,bound(2)*ones(1,NT),'k-.');
230	230	title(sprintf('NEES + %s %g confidence interval: r=%f',confint,alpha,r));	title(sprintf('NEES + %s %g confidence interval: r=%f',confint,alpha,r));
231
	231
232	232	figure();	figure();
233	233	[~,cdfstats]=cdfplot(NEES);hold on;	[~,cdfstats]=cdfplot(NEES);hold on;
234	234	xx=(0:1/NT:1)*cdfstats.max;	xx=(0:1/NT:1)*cdfstats.max;
235	235	plot(xx,chi2cdf(xx,sn),'r');	plot(xx,chi2cdf(xx,sn),'r');
236	236	title(sprintf('CDF NEES: h=%d, p=%f',h,p))	title(sprintf('CDF NEES: h=%d, p=%f',h,p))
237
	237
238	238	case 'NMEE'	case 'NMEE'
239	239	xx=(-1:1/NT:1)*5;	xx=(-1:1/NT:1)*5;
240	240	fNMEE=zeros(sn,1);	fNMEE=zeros(sn,1);
...	...	if show_plots
245	245	plot(xx,cdffunc(xx),'r');	plot(xx,cdffunc(xx),'r');
246	246	title(sprintf('CDF NMEE(%d): h=%d, p=%f',mm,h(mm),p(mm)))	title(sprintf('CDF NMEE(%d): h=%d, p=%f',mm,h(mm),p(mm)))
247	247	end	end
248
	248
249	249	% plot histogram of NMEE values	% plot histogram of NMEE values
250	250	figure();	figure();
251	251	histbins=100;	histbins=100;

File statisticstests/nistest.m changed (mode: 100644) (index 08fecdc..acee28e)
...	...	end
67	67	% (this makes evaluation of if's faster)	% (this makes evaluation of if's faster)
68	68	if sequcalc	if sequcalc
69	69	fprintf('\n******** NIS/NMI test ********\n');	fprintf('\n******** NIS/NMI test ********\n');
70
	70
71	71	H_NTD = (size(H,3)==1);	H_NTD = (size(H,3)==1);
72	72	P_NTD = (size(pmvec,3)==1);	P_NTD = (size(pmvec,3)==1);
73
	73
74	74	% skip data points	% skip data points
75	75	if tsstart>1	if tsstart>1
76	76	fprintf('\nNIS/NMI test: drop first %d datapoints',tsstart-1);	fprintf('\nNIS/NMI test: drop first %d datapoints',tsstart-1);
...	...	if sequcalc
82	82	H=H(:,:,tsstart:end);	H=H(:,:,tsstart:end);
83	83	end	end
84	84	end	end
85
	85
86	86	%%% run NIS tests	%%% run NIS tests
87	87	% (Heijden, p. 294)	% (Heijden, p. 294)
88
	88
89	89	statv = struct();	statv = struct();
90	90	[os,sn,~] = size(H);	[os,sn,~] = size(H);
91	91	NT = length(innov);	NT = length(innov);
92
	92
93	93	fprintf('\nNIS/NMI test with %d DOF (#measurements) and with %d state vaiables\n',os,sn);	fprintf('\nNIS/NMI test with %d DOF (#measurements) and with %d state vaiables\n',os,sn);
94
	94
95	95	else	else
96	96	fprintf('\n******** NIS/NMI test: sequence given ********\n');	fprintf('\n******** NIS/NMI test: sequence given ********\n');
97
	97
98	98	os=size(innov,2);	os=size(innov,2);
99	99	NT = length(innov);	NT = length(innov);
100
	100
101	101	fprintf('\nNIS/NMI test with %d DOF (#measurements)\n',os);	fprintf('\nNIS/NMI test with %d DOF (#measurements)\n',os);
102	102	end	end
103	103
...	...	switch method
154	154	covnischi2=cov(NIS);	covnischi2=cov(NIS);
155	155	if ~statv.chi2.h, result='passed'; else result='failed'; end	if ~statv.chi2.h, result='passed'; else result='failed'; end
156	156	fprintf('NIS test %s (p=%g)\n',result,statv.chi2.p);	fprintf('NIS test %s (p=%g)\n',result,statv.chi2.p);
157
	157
158	158	xx=(0:1/NT:1)*25;	xx=(0:1/NT:1)*25;
159
	159
160	160	case 'NMI'	case 'NMI'
161	161	if sequcalc	if sequcalc
162	162	% calculate NIS sequence	% calculate NIS sequence
...	...	switch method
166	166	NIS=zeros(NT,os);	NIS=zeros(NT,os);
167	167	h=zeros(1,os); p=h;	h=zeros(1,os); p=h;
168	168	droptol=1E-15;	droptol=1E-15;
169
	169
170	170	% initialize variables for time-independent case	% initialize variables for time-independent case
171	171	if H_NTD	if H_NTD
172	172	ht=H;	ht=H;
...	...	switch method
177	177	icopts = struct('type','ict','droptol',droptol);	icopts = struct('type','ict','droptol',droptol);
178	178	if H_NTD && P_NTD	if H_NTD && P_NTD
179	179	S=ht*pt*ht'+R;	S=ht*pt*ht'+R;
180		C=ichol(sparse(S),icopts);
	180		C=ichol(sparse(S),icopts);
181	181	for mm=1:NT	for mm=1:NT
182	182	%NIS=C\innov; %WITLEF	%NIS=C\innov; %WITLEF
183	183	NIS(mm,:)=C\innov(mm,:)';	NIS(mm,:)=C\innov(mm,:)';
...	...	switch method
195	195	NIS(mm,:)=C\innov(mm,:)';	NIS(mm,:)=C\innov(mm,:)';
196	196	end	end
197	197	end	end
198
	198
199	199	else	else
200	200	% NIS sequence given	% NIS sequence given
201	201	NIS=innov;	NIS=innov;
202	202	end	end
203
	203
204	204	for mm=1:os	for mm=1:os
205	205	switch hyptest	switch hyptest
206	206	case 'chi2gof'	case 'chi2gof'
...	...	switch method
210	210	[~,NISxx,~,~,~] = cdfcalc(NIS(:,mm));	[~,NISxx,~,~,~] = cdfcalc(NIS(:,mm));
211	211	nistestf=@(x) kstest(x,[NISxx,normcdf(NISxx,0,1)],alpha);	nistestf=@(x) kstest(x,[NISxx,normcdf(NISxx,0,1)],alpha);
212	212	[h(mm),p(mm)]=nistestf(NIS(:,mm));	[h(mm),p(mm)]=nistestf(NIS(:,mm));
213		case 'ksstat'
	213		case 'ksstat'
214	214	h(mm)=2; % not relevant	h(mm)=2; % not relevant
215	215	p(mm)=ksstatistic(NIS(:,mm)); % p is here the test statistic!	p(mm)=ksstatistic(NIS(:,mm)); % p is here the test statistic!
216	216	case 'none'	case 'none'
...	...	switch method
225	225	if ~statv.norm.h(mm), result='passed'; else result='failed'; end	if ~statv.norm.h(mm), result='passed'; else result='failed'; end
226	226	fprintf('NMI(%d) test %s (p=%g)\n',mm,result,statv.norm.p(mm));	fprintf('NMI(%d) test %s (p=%g)\n',mm,result,statv.norm.p(mm));
227	227	end	end
228
	228
229	229	xx=(-1:1/NT:1)*5;	xx=(-1:1/NT:1)*5;
230
	230
231	231	otherwise	otherwise
232	232	warning(sprintf('unknown method %s',method));	warning(sprintf('unknown method %s',method));
233	233	show_plots = false;	show_plots = false;
...	...	if show_info
267	267	if ~statv.norm.h(mm), result='passed'; else result='failed'; end	if ~statv.norm.h(mm), result='passed'; else result='failed'; end
268	268	fprintf('NMI(%d) test: %s (p=%g)\n',mm,result,statv.norm.p(mm));	fprintf('NMI(%d) test: %s (p=%g)\n',mm,result,statv.norm.p(mm));
269	269	fprintf('NMI(%d) test: %g inside (%s) %g bound(s)\n',mm,r(mm),confint,1-alpha);	fprintf('NMI(%d) test: %g inside (%s) %g bound(s)\n',mm,r(mm),confint,1-alpha);
270		fprintf('NMI(%d) test: mean(NMI) (should be 0): %g\n',mm,meannisnorm(mm));
	270		fprintf('NMI(%d) test: mean(NMI) (should be 0): %g\n',mm,meannisnorm(mm));
271	271	end	end
272	272	fprintf('NMI test: cov(NMI) (should be eye(%g)):\n',size(innov,2));	fprintf('NMI test: cov(NMI) (should be eye(%g)):\n',size(innov,2));
273	273	fprintfarray(1,'%g\t',covnisnorm);	fprintfarray(1,'%g\t',covnisnorm);
274	274	end	end
275
	275
276	276	% whiteness	% whiteness
277	277	if wttest	if wttest
278	278	for mm=1:os	for mm=1:os
...	...	if show_info
286	286	fprintf('Whiteness test(%d) (%s) - smoothed spectral density - %g inside (%s) %g bound(s)\n',mm,method,rwt,confint,1-alpha);	fprintf('Whiteness test(%d) (%s) - smoothed spectral density - %g inside (%s) %g bound(s)\n',mm,method,rwt,confint,1-alpha);
287	287	end	end
288	288	end	end
289
	289
290	290	end	end
291	291
292	292	if show_plots	if show_plots
...	...	if show_plots
298	298	bound = chi2inv(confInterval(alpha,confint),os);	bound = chi2inv(confInterval(alpha,confint),os);
299	299	plot(xvec,NIS,xvec,bound(1)*ones(1,NT),'k-.',xvec,bound(2)*ones(1,NT),'k-.');	plot(xvec,NIS,xvec,bound(1)*ones(1,NT),'k-.',xvec,bound(2)*ones(1,NT),'k-.');
300	300	title(sprintf('NIS + %s %g confidence interval: r=%f',confint,alpha,r));	title(sprintf('NIS + %s %g confidence interval: r=%f',confint,alpha,r));
301
	301
302	302	figure();	figure();
303	303	cdfplot(NIS);hold on;	cdfplot(NIS);hold on;
304	304	plot(xx,chi2cdf(xx,os),'r');	plot(xx,chi2cdf(xx,os),'r');
305	305	title(sprintf('CDF NIS^2: h=%d, p=%f',statv.chi2.h,statv.chi2.p))	title(sprintf('CDF NIS^2: h=%d, p=%f',statv.chi2.h,statv.chi2.p))
306
307		case 'NMI'
308		for mm=1:os
309		figure();
	306
	307		case 'NMI'
	308		for mm=1:os
	309		figure();
310	310	cdfplot(NIS(:,mm)); hold on;	cdfplot(NIS(:,mm)); hold on;
311	311	plot(xx,normcdf(xx),'-r');	plot(xx,normcdf(xx),'-r');
312	312	title(sprintf('CDF NMI(%d): h=%d, p=%f',mm,statv.norm.h(mm),statv.norm.p(mm)))	title(sprintf('CDF NMI(%d): h=%d, p=%f',mm,statv.norm.h(mm),statv.norm.p(mm)))
313	313	end	end
314
	314
315	315	% plot histogram of NMI values	% plot histogram of NMI values
316	316	figure();	figure();
317	317	histbins=100;	histbins=100;
...	...	function kstat = ksstatistic(vec)
373	373	% function computes the Kolmogorov-Smirnov test statistics	% function computes the Kolmogorov-Smirnov test statistics
374	374	% max(|F(x)-G(x)|), where F(x) is the empirical df and	% max(|F(x)-G(x)|), where F(x) is the empirical df and
375	375	% G(x) the standard normal cdf	% G(x) the standard normal cdf
376		[yCDF,xCDF] = cdfcalc(vec);
	376		[yCDF,xCDF] = cdfcalc(vec);
377	377	xCDF = [xCDF;1.1*xCDF(end)];	xCDF = [xCDF;1.1*xCDF(end)];
378	378	yncdf = normcdf(xCDF,0,1);	yncdf = normcdf(xCDF,0,1);
379		kstat = max(abs(yCDF-yncdf));
	379		kstat = max(abs(yCDF-yncdf));

File statisticstests/whitenesstest.m changed (mode: 100644) (index a5771cb..0a19374)
1	1	function [h p varargout] = whitenesstest(x,varargin)	function [h p varargout] = whitenesstest(x,varargin)
2	2	% [h p] = whitenesstest(x,...)	% [h p] = whitenesstest(x,...)
3		%
	3		%
4	4	% Test if stochastic process x (single column vector) is white. The result h=0	% Test if stochastic process x (single column vector) is white. The result h=0
5	5	% means that the null hypothesis (i.e. x is white) cannot be rejected	% means that the null hypothesis (i.e. x is white) cannot be rejected
6	6	% at the given significance level α (default: 0.05). The p value is	% at the given significance level α (default: 0.05). The p value is
...	...	function [h p varargout] = whitenesstest(x,varargin)
8	8	% observing the given statistic or one more extreme.	% observing the given statistic or one more extreme.
9	9	%	%
10	10	% Note: Data must be zero mean in order for these tests to work! Use	% Note: Data must be zero mean in order for these tests to work! Use
11		% 'detrend' if needed.
	11		% 'detrend' if needed.
12	12	%	%
13	13	% optional arguments (key/value pairs):	% optional arguments (key/value pairs):
14	14	% - 'alpha': significance level (default: 0.05)	% - 'alpha': significance level (default: 0.05)
...	...	switch method
69	69	case 'lbq'	case 'lbq'
70	70	%% Ljung-Box Q-test	%% Ljung-Box Q-test
71	71	% see McNelis, p. 86	% see McNelis, p. 86
72		%
	72		%
73	73	% Note:	% Note:
74	74	% - the rejection value for Q is given by the	% - the rejection value for Q is given by the
75	75	% (1-alpha)-quantile of the chi^2 distribution for maxlag d.o.f	% (1-alpha)-quantile of the chi^2 distribution for maxlag d.o.f
...	...	switch method
92	92	%% Bartnett's method	%% Bartnett's method
93	93	% calculate periodogram by averaging over segments	% calculate periodogram by averaging over segments
94	94	% see Shiavi, p. 244f; Hayes, p. 412;	% see Shiavi, p. 244f; Hayes, p. 412;
95		%
	95		%
96	96	% Note:	% Note:
97	97	% - Only use rectangular, non-overlapping windows,	% - Only use rectangular, non-overlapping windows,
98	98	% otherwise variances can't be evaluated.	% otherwise variances can't be evaluated.

File stochasticintegration/discretize.m changed (mode: 100644) (index 088b515..0e2cfba)
...	...	function [F,G,H,D,L,Q,R,M]=discretize(FC,GC,HC,DC,LC,QC,RC,MC,dt,varargin)
32	32	% Convert to discrete system	% Convert to discrete system
33	33	%	%
34	34	% Notes:	% Notes:
35		%
	35		%
36	36	% - By default this uses the built-in c2d function, which is probably	% - By default this uses the built-in c2d function, which is probably
37	37	% the safest to use if prescaling of the ss model may be an issue.	% the safest to use if prescaling of the ss model may be an issue.
38		%
	38		%
39	39	% - A first order approximation for the noise term would be:	% - A first order approximation for the noise term would be:
40	40	% L=(F-eye(length(F)))/FC*LC,	% L=(F-eye(length(F)))/FC*LC,
41	41	% with appropriate scaling of Q,M!	% with appropriate scaling of Q,M!
42		%
	42		%
43	43	% - If needed (e.g. for time-variant systems), this can be used to	% - If needed (e.g. for time-variant systems), this can be used to
44	44	% find the exact value for Q(t):	% find the exact value for Q(t):
45	45	% qf=@(u) expm(FC*u)*LC*QC*LC'*expm(FC'*u); Q=quadv(qf,0,dt);	% qf=@(u) expm(FC*u)*LC*QC*LC'*expm(FC'*u); Q=quadv(qf,0,dt);
46	46	% Ref. [3] gives an explicit form of Q in terms of matrix	% Ref. [3] gives an explicit form of Q in terms of matrix
47	47	% exponentials for LTI systems.	% exponentials for LTI systems.
48		%
	48		%
49	49	% - An alternative first order approximation of F would be F=id+dt*FC	% - An alternative first order approximation of F would be F=id+dt*FC
50	50	% (explicit Euler), but may be unstable.	% (explicit Euler), but may be unstable.
51		%
	51		%
52	52	% - The (old) scaling Q = QC*dt and R = RC/dt [1] amounts to the definition of	% - The (old) scaling Q = QC*dt and R = RC/dt [1] amounts to the definition of
53	53	% - n_k = int_{t_{k-1}}^{t_k} n(s) ds/dt (rescaled by dt)	% - n_k = int_{t_{k-1}}^{t_k} n(s) ds/dt (rescaled by dt)
54	54	% - w_k = int_{t_{k-1}}^{t_k} n(s) ds (no rescaling)	% - w_k = int_{t_{k-1}}^{t_k} n(s) ds (no rescaling)
55	55	% From above scaling M = MC follows, which is consistent with the	% From above scaling M = MC follows, which is consistent with the
56	56	% requirement that positive definitenes must not depent on dt!	% requirement that positive definitenes must not depent on dt!
57		%
	57		%
58	58	% - The (new) scaling Q = QC/dt and R = RC/dt [5] amounts to the definition of	% - The (new) scaling Q = QC/dt and R = RC/dt [5] amounts to the definition of
59	59	% - n_k = int_{t_{k-1}}^{t_k} n(s) ds/dt (rescaled by dt)	% - n_k = int_{t_{k-1}}^{t_k} n(s) ds/dt (rescaled by dt)
60	60	% - w_k = int_{t_{k-1}}^{t_k} n(s) ds/dt (rescaled by dt)	% - w_k = int_{t_{k-1}}^{t_k} n(s) ds/dt (rescaled by dt)
...	...	function [F,G,H,D,L,Q,R,M]=discretize(FC,GC,HC,DC,LC,QC,RC,MC,dt,varargin)
62	62	% advantage that G and L = LC*dt are treated identically, which is important	% advantage that G and L = LC*dt are treated identically, which is important
63	63	% for connecting them! Additionally, this is supposed to be more	% for connecting them! Additionally, this is supposed to be more
64	64	% exact [5]. This method is/seems to be the same as c2d with 'zoh'!	% exact [5]. This method is/seems to be the same as c2d with 'zoh'!
65		%
	65		%
66	66	% References:	% References:
67	67	% [1] Simon, p.231f;	% [1] Simon, p.231f;
68	68	% [2] Stengel, p.326f;	% [2] Stengel, p.326f;

File stochasticintegration/simgauss.m changed (mode: 100644) (index 8c54a13..a73d84d)
...	...	switch method
115	115	% Note: this is the prefered method as	% Note: this is the prefered method as
116	116	% its most robust to numerical errors which	% its most robust to numerical errors which
117	117	% lead to non positive definiteness	% lead to non positive definiteness
118		% Does NOT work for SEMI-DEFINITE matrices!
	118		% Does NOT work for SEMI-DEFINITE matrices!
119	119	alpha = max(0,max(sum(abs(sigma),2)./diag(sigma))-2);	alpha = max(0,max(sum(abs(sigma),2)./diag(sigma))-2);
120	120	C = ichol(sparse(sigma),struct('droptol',droptol,'type','ict'));	C = ichol(sparse(sigma),struct('droptol',droptol,'type','ict'));
121	121	seq = C*randn(size(C,1),NT)+mum;	seq = C*randn(size(C,1),NT)+mum;

File stochasticintegration/simlinsys.m changed (mode: 100644) (index 2938192..8bbc262)
1	1	function [z,x,varargout]=simlinsys(F,G,H,D,L,Q,R,M,u,xinit,NT,varargin)	function [z,x,varargout]=simlinsys(F,G,H,D,L,Q,R,M,u,xinit,NT,varargin)
2	2	% [z,x] = simlinsys(F,G,H,D,L,Q,R,M,u,xinit,NT,...)	% [z,x] = simlinsys(F,G,H,D,L,Q,R,M,u,xinit,NT,...)
3	3	% [z,x,statenoise,obsnoise] = simlinsys(F,G,H,D,L,Q,R,M,u,xinit,NT,...)	% [z,x,statenoise,obsnoise] = simlinsys(F,G,H,D,L,Q,R,M,u,xinit,NT,...)
4		%
	4		%
5	5	% Sample a linear discrete system	% Sample a linear discrete system
6		%
	6		%
7	7	% x(k+1) = F(k) x(k) + G u(k) + L(k) w(k)	% x(k+1) = F(k) x(k) + G u(k) + L(k) w(k)
8	8	% z(k) = H(k) x(k) + D u(k) + n(k)	% z(k) = H(k) x(k) + D u(k) + n(k)
9		%
	9		%
10	10	% with time-dependent state-space matrices F(k), G(k), H(k), D(k),	% with time-dependent state-space matrices F(k), G(k), H(k), D(k),
11	11	% L(k). The noise statistics are given by	% L(k). The noise statistics are given by
12		%
	12		%
13	13	% w ~ N(0,Q)	% w ~ N(0,Q)
14	14	% n ~ N(0,R)	% n ~ N(0,R)
15	15	% E[w*n'] = M	% E[w*n'] = M
16		%
	16		%
17	17	% Note that time-dependent cross correlations in M are not sampled	% Note that time-dependent cross correlations in M are not sampled
18	18	% correctly!	% correctly!
19	19	%	%
...	...	end
76	76
77	77	% check if inputs are time-dependent	% check if inputs are time-dependent
78	78	% x_TD=true <> x is time dependent!	% x_TD=true <> x is time dependent!
79		if size(F,3)==1, F_TD=false; f=F; else F_TD=true; end
	79		if size(F,3)==1, F_TD=false; f=F; else F_TD=true; end
80	80	if size(G,3)==1, D_TD=false; g=G; else G_TD=true; end	if size(G,3)==1, D_TD=false; g=G; else G_TD=true; end
81	81	if size(H,3)==1, H_TD=false; h=H; else H_TD=true; end	if size(H,3)==1, H_TD=false; h=H; else H_TD=true; end
82	82	if size(D,3)==1, G_TD=false; d=D; else D_TD=true; end	if size(D,3)==1, G_TD=false; d=D; else D_TD=true; end
...	...	for tt = 1:NT
131	131	if H_TD, h=H(:,:,tt); end;	if H_TD, h=H(:,:,tt); end;
132	132	if D_TD, d=D(:,:,tt); end;	if D_TD, d=D(:,:,tt); end;
133	133	if L_TD, l=L(:,:,tt); end;	if L_TD, l=L(:,:,tt); end;
134
	134
135	135	x(:,tt) = x0;	x(:,tt) = x0;
136	136	z(:,tt) = h*x0 + d*u(tt,:).' + obsnoise(:,tt);	z(:,tt) = h*x0 + d*u(tt,:).' + obsnoise(:,tt);
137	137	x0 = f*x0 + l*statenoise(:,tt) + g*u(tt,:).';	x0 = f*x0 + l*statenoise(:,tt) + g*u(tt,:).';

File tests/kalmanfilter_test.m changed (mode: 100644) (index b7819cd..3dce5e1)
...	...	tsMSESS(1).label = 'actual means square error';
161	161	% figure();	% figure();
162	162	% plot(tvec(PLOT_TIMEDATA)/dao.refF,tsEstSS(2).yData(PLOT_TIMEDATA,1),'g',tvec(PLOT_TIMEDATA)/dao.refF,tsSim(2).yData(PLOT_TIMEDATA,1),'k'); hold on;	% plot(tvec(PLOT_TIMEDATA)/dao.refF,tsEstSS(2).yData(PLOT_TIMEDATA,1),'g',tvec(PLOT_TIMEDATA)/dao.refF,tsSim(2).yData(PLOT_TIMEDATA,1),'k'); hold on;
163	163	% plot(tvec(PLOT_TIMEDATA)/dao.refF,tsEstSS(2).yData(PLOT_TIMEDATA,2),'r',tvec(PLOT_TIMEDATA)/dao.refF,tsSim(2).yData(PLOT_TIMEDATA,2),'y'); hold off;	% plot(tvec(PLOT_TIMEDATA)/dao.refF,tsEstSS(2).yData(PLOT_TIMEDATA,2),'r',tvec(PLOT_TIMEDATA)/dao.refF,tsSim(2).yData(PLOT_TIMEDATA,2),'y'); hold off;
164		%
	164		%
165	165	% frStart=100;	% frStart=100;
166	166	% [frSim1,w]=pwelch(tsSim(2).yData(frStart:end,1));	% [frSim1,w]=pwelch(tsSim(2).yData(frStart:end,1));
167	167	% frSim2=pwelch(tsSim(2).yData(frStart:end,2));	% frSim2=pwelch(tsSim(2).yData(frStart:end,2));
...	...	tsMSESS(1).label = 'actual means square error';
173	173
174	174
175	175	%% time-dependent kalman filter	%% time-dependent kalman filter
176		if RUN_TDF
	176		if RUN_TDF
177	177	fprintf('******** t-dependent kalmanfilter ********\n');	fprintf('******** t-dependent kalmanfilter ********\n');
178	178	% initialize data structures	% initialize data structures
179	179	tsEst = dadac(2,'xData',tvec);	tsEst = dadac(2,'xData',tvec);
180		tsErr = dadac(2,'xData',tvec);
	180		tsErr = dadac(2,'xData',tvec);
181	181	tsMSE = dadac(2,'xData',tvec);	tsMSE = dadac(2,'xData',tvec);
182
	182
183	183	% run Kalman filter	% run Kalman filter
184	184	if isempty(x0est)	if isempty(x0est)
185	185	x0est = [0;0];	x0est = [0;0];
186	186	end	end
187	187	if isempty(p0est)	if isempty(p0est)
188	188	p0est = kfsscov(sys,'posterior');	p0est = kfsscov(sys,'posterior');
189		end
190
	189		end
	190
191	191	[tsEst(2).yData,tsMSE(1).yData,tsEst(1).yData] = kalmanfilter(sys,tsSim(1).yData,[],x0est,p0est,nisOpts);	[tsEst(2).yData,tsMSE(1).yData,tsEst(1).yData] = kalmanfilter(sys,tsSim(1).yData,[],x0est,p0est,nisOpts);
192	192	% $$$ [tsEst(2).yData,tsMSE(1).yData,tsEst(1).yData] = informationfilter(sys,tsSim(1).yData,[],x0est,[]);	% $$$ [tsEst(2).yData,tsMSE(1).yData,tsEst(1).yData] = informationfilter(sys,tsSim(1).yData,[],x0est,[]);
193	193	Ptend=tsMSE(1).yData(:,:,end);	Ptend=tsMSE(1).yData(:,:,end);
194	194	tsEst(1).label = 'estimated measurement';	tsEst(1).label = 'estimated measurement';
195	195	tsEst(2).label = 'estimated statevector';	tsEst(2).label = 'estimated statevector';
196	196	tsMSE(1).label = 'estimated mean square error';	tsMSE(1).label = 'estimated mean square error';
197
	197
198	198	tsErr(2) = tsSim(2)-tsEst(2);	tsErr(2) = tsSim(2)-tsEst(2);
199	199	tsErr(2).label = 'estimation error sequence';	tsErr(2).label = 'estimation error sequence';
200		Ptreal=cov(tsErr(2).yData(teststart:end,:));
	200		Ptreal=cov(tsErr(2).yData(teststart:end,:));
201	201	tsMSE(2).yData = zeros(NT,sn);	tsMSE(2).yData = zeros(NT,sn);
202	202	tsMSE(2).yData(:,1) = tsErr(2).yData(:,1).^2;	tsMSE(2).yData(:,1) = tsErr(2).yData(:,1).^2;
203	203	tsMSE(2).yData(:,2) = tsErr(2).yData(:,2).^2;	tsMSE(2).yData(:,2) = tsErr(2).yData(:,2).^2;
204	204	tsMSE(2).yData(:,3) = tsErr(2).yData(:,1).*tsErr(2).yData(:,2);	tsMSE(2).yData(:,3) = tsErr(2).yData(:,1).*tsErr(2).yData(:,2);
205		tsMSE(2).label = 'actual means square error';
	205		tsMSE(2).label = 'actual means square error';
206	206	% NEES test	% NEES test
207	207	% if NEES test of ss filter has been run	% if NEES test of ss filter has been run
208	208	% this one is not required	% this one is not required
...	...	fprintf('\ncov Sim:\n');
239	239	fprintfarray(1,'%g\t',cvSim);	fprintfarray(1,'%g\t',cvSim);
240	240
241	241	%% export to file	%% export to file
242		if export_results
243		save(exportfile,'Pss','Pssreal','Pss2','Ptend','Ptreal','-ascii');
	242		if export_results
	243		save(exportfile,'Pss','Pssreal','Pss2','Ptend','Ptreal','-ascii');
244	244	end	end
245	245
246	246	%% plots	%% plots
...	...	if show_plots
250	250	plot(tvec,tsSim(1).yData,tvec,tsSim(2).yData,tvec,tsEst(2).yData)	plot(tvec,tsSim(1).yData,tvec,tsSim(2).yData,tvec,tsEst(2).yData)
251	251	title('timetraces: simulation + estimation')	title('timetraces: simulation + estimation')
252	252	legend('simulated measurement','simulated statevector','simulated statevector','estimated statevector','estimated statevector')	legend('simulated measurement','simulated statevector','simulated statevector','estimated statevector','estimated statevector')
253
	253
254	254	fssMSE=figure();	fssMSE=figure();
255	255	plot(tvec,tsMSESS(1).yData);	plot(tvec,tsMSESS(1).yData);
256	256	title('ss actual mean square error')	title('ss actual mean square error')
257
	257
258	258	if export_pics	if export_pics
259	259	saveas(ftt,strcat(imagefilehead,'_ftt',imageft));	saveas(ftt,strcat(imagefilehead,'_ftt',imageft));
260	260	saveas(fssMSE,strcat(imagefilehead,'_fssMSE',imageft));	saveas(fssMSE,strcat(imagefilehead,'_fssMSE',imageft));
261	261	end	end
262
	262
263	263	if RUN_TDF	if RUN_TDF
264	264	% actual mean square error	% actual mean square error
265	265	ftMSE=figure();	ftMSE=figure();
266	266	plot(tvec,tsMSE(2).yData);	plot(tvec,tsMSE(2).yData);
267	267	title('actual mean square error')	title('actual mean square error')
268
	268
269	269	% show actual z and zpred	% show actual z and zpred
270	270	tsZ = dadac(1,'xData',tvec);	tsZ = dadac(1,'xData',tvec);
271	271	tsZ(1).label = 'predicted measurement';	tsZ(1).label = 'predicted measurement';
272		tsZ(1).yData = tsEst(2).yData*sys.c';
273		fzzpred=figure();
	272		tsZ(1).yData = tsEst(2).yData*sys.c';
	273		fzzpred=figure();
274	274	plot(tvec,tsSim(1).yData,'+m');hold on;	plot(tvec,tsSim(1).yData,'+m');hold on;
275		plot(tvec,tsZ(1).yData,'-r');
	275		plot(tvec,tsZ(1).yData,'-r');
276	276	title('actual z and zpred')	title('actual z and zpred')
277	277	legend('actual z','zpred')	legend('actual z','zpred')
278
279		% estimated mean square error
	278
	279		% estimated mean square error
280	280	fMSE=figure();hold on;	fMSE=figure();hold on;
281	281	ov = ones(NT,1);	ov = ones(NT,1);
282	282	plot(tvec,squeeze(tsMSE(1).yData(1,1,:)),tvec,squeeze(tsMSE(1).yData(2,2,:)),tvec,squeeze(tsMSE(1).yData(1,2,:))); % time-dependent	plot(tvec,squeeze(tsMSE(1).yData(1,1,:)),tvec,squeeze(tsMSE(1).yData(2,2,:)),tvec,squeeze(tsMSE(1).yData(1,2,:))); % time-dependent
...	...	if show_plots
290	290	't-dep actual MSE','t-dep actual MSE','t-dep actual MSE',...	't-dep actual MSE','t-dep actual MSE','t-dep actual MSE',...
291	291	'ss MSE','ss MSE','ss MSE',...	'ss MSE','ss MSE','ss MSE',...
292	292	'ss actual MSE','ss actual MSE','ss actual MSE')	'ss actual MSE','ss actual MSE','ss actual MSE')
293
	293
294	294	if export_pics	if export_pics
295	295	saveas(ftMSE,strcat(imagefilehead,'_ftMSE',imageft));	saveas(ftMSE,strcat(imagefilehead,'_ftMSE',imageft));
296	296	saveas(fzzpred,strcat(imagefilehead,'_fzzpred',imageft));	saveas(fzzpred,strcat(imagefilehead,'_fzzpred',imageft));
297	297	saveas(fMSE,strcat(imagefilehead,'_fMSE',imageft));	saveas(fMSE,strcat(imagefilehead,'_fMSE',imageft));
298		end
	298		end
299	299	end	end
300	300	end	end
301	301

File tests/kfcomparison_test.m changed (mode: 100644) (index ac8fa60..0cee07d)
1	1	% Kalmanfilter comparison:	% Kalmanfilter comparison:
2		%
	2		%
3	3	% Compare different filters (kfss, kalman, kalmanfilter).	% Compare different filters (kfss, kalman, kalmanfilter).
4	4	% - kalman and kalmanfilter should be the same in steady-state (Note	% - kalman and kalmanfilter should be the same in steady-state (Note
5	5	% that kalman returns the prior estimation error while kalmanfilter	% that kalman returns the prior estimation error while kalmanfilter
...	...	end
68	68	[kf2,~,Pss2] = kalman(sys,'current');	[kf2,~,Pss2] = kalman(sys,'current');
69	69	% compare prior error variances	% compare prior error variances
70	70	disp('filter steady state error variances')	disp('filter steady state error variances')
71		Pss1 - Pss2
	71		Pss1 - Pss2
72	72	% apply filters to simulates timeseries and calculate errors	% apply filters to simulates timeseries and calculate errors
73	73	if isempty(u)	if isempty(u)
74	74	xest1 = lsim(kf1('StateEstimate','Measurement'),zvec,tvec,x0est);	xest1 = lsim(kf1('StateEstimate','Measurement'),zvec,tvec,x0est);

File tests/montecarlo_test.m changed (mode: 100644) (index 3b5bd41..a5db4a5)
...	...	pss
50	50	% $$$ neesAv = sum(nees);	% $$$ neesAv = sum(nees);
51	51	% $$$ neesTestF = @(x) chi2gof(x,'cdf',@(z)chi2cdf(z,NM*sn),'alpha',alpha);	% $$$ neesTestF = @(x) chi2gof(x,'cdf',@(z)chi2cdf(z,NM*sn),'alpha',alpha);
52	52	% $$$ [h,p] = neesTestF(neesAv)	% $$$ [h,p] = neesTestF(neesAv)
53		% $$$
	53		% $$$
54	54	% $$$ figure;	% $$$ figure;
55	55	% $$$ xx=(0:1/NT:1)*60;	% $$$ xx=(0:1/NT:1)*60;
56	56	% $$$ cdfplot(neesAv);hold on;	% $$$ cdfplot(neesAv);hold on;

File tests/qfactor_test.m changed (mode: 100644) (index b9cf64b..a88ecd6)
1	1	% Test Q-factor of sampled timeseries	% Test Q-factor of sampled timeseries
2		%
	2		%
3	3	% Initial condition must be far out from equilibrium such that we can	% Initial condition must be far out from equilibrium such that we can
4	4	% actually see a real ringdown!!	% actually see a real ringdown!!
5	5