Added overload subsasgn method in dynSeries class.
Following syntax: ts{'GDP_US'} = ts.{'GDP_US'}.log applies the log function to the variable GDP_US in the dynSeries object ts. Note that this works on an arbitrary number of variables and that regular expressions can be used (see previous commits).time-shift
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function A = subsasgn(A,S,B)
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%@info:
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%! @deftypefn {Function File} {@var{A} =} subsasgn (@var{A}, @var{S}, @var{B})
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%! @anchor{@dynSeries/subsasgn}
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%! @sp 1
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%! Overloads the subsasgn method for the Dynare time series class (@ref{dynSeries}).
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%! @end deftypefn
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%@eod:
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% Copyright (C) 2012 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
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if isa(A,'dynSeries') && isa(B,'dynSeries')
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if length(S)==1 && isequal(S.type,'{}')
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if isequal(A.nobs,B.nobs) && isequal(A.init,B.init)
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id = NaN(length(S.subs),1);
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for i=1:length(S.subs)
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tmp = strmatch(S.subs{i},A.name,'exact');
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if isempty(tmp)
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error(['dynSeries::subsasgn: variable ' S.subs{i} ' is not a member of ' inputname(1) ' dynSeries object!'])
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else
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id(i) = tmp;
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end
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end
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if isequal(B.vobs,length(S.subs))
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A.name(id) = B.name;
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A.data(:,id) = B.data;
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return
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end
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end
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end
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end
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error('dynSeries::subsasgn: Wrong calling sequence!')
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%@test:1
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%$ % Define a datasets.
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%$ A = rand(10,3); B = rand(10,1);
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%$
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%$ % Instantiate two dynSeries object.
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%$ ts1 = dynSeries(A,[],{'A1';'A2';'A3'},[]);
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%$ ts2 = dynSeries(B,[],{'B1'},[]);
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%$
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%$ % modify first object.
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%$ ts1{'A2'} = ts2;
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%$ t(1) = 1;
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%$ % Instantiate a time series object.
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%$
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%$ t(2) = dyn_assert(ts1.vobs,3);
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%$ t(3) = dyn_assert(ts1.nobs,10);
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%$ t(4) = dyn_assert(ts1.name{2},'B1');
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%$ t(5) = dyn_assert(ts1.name{1},'A1');
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%$ t(6) = dyn_assert(ts1.name{3},'A3');
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%$ t(7) = dyn_assert(ts1.data,[A(:,1), B, A(:,3)],1e-15);
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%$ T = all(t);
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%@eof:1
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%@test:2
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%$ % Define a datasets.
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%$ A = rand(10,3);
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%$
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%$ % Instantiate two dynSeries object.
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%$ ts1 = dynSeries(A,[],{'A1';'A2';'A3'},[]);
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%$
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%$ % Apply the exponential function to the second variable.
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%$ ts1{'A2'} = ts1{'A2'}.exp;
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%$
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%$ % Instantiate a time series object.
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%$
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%$ t(1) = dyn_assert(ts1.vobs,3);
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%$ t(2) = dyn_assert(ts1.nobs,10);
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%$ t(3) = dyn_assert(ts1.name{2},'A2');
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%$ t(4) = dyn_assert(ts1.name{1},'A1');
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%$ t(5) = dyn_assert(ts1.name{3},'A3');
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%$ t(6) = dyn_assert(ts1.data,[A(:,1), exp(A(:,2)), A(:,3)],1e-15);
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%$ T = all(t);
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%@eof:2
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%@test:3
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%$ % Define a datasets.
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%$ A = rand(10,3);
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%$
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%$ % Instantiate two dynSeries object.
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%$ ts1 = dynSeries(A,[],{'A1';'A2';'A3'},[]);
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%$
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%$ % Apply the logarithm function to the first and third variables.
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%$ ts1{'A1'} = ts1{'A1'}.log;
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%$ ts1{'A3'} = ts1{'A3'}.log;
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%$
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%$ % Instantiate a time series object.
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%$
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%$ t(1) = dyn_assert(ts1.vobs,3);
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%$ t(2) = dyn_assert(ts1.nobs,10);
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%$ t(3) = dyn_assert(ts1.name{2},'A2');
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%$ t(4) = dyn_assert(ts1.name{1},'A1');
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%$ t(5) = dyn_assert(ts1.name{3},'A3');
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%$ t(6) = dyn_assert(ts1.data,[log(A(:,1)), A(:,2), log(A(:,3))],1e-15);
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%$ T = all(t);
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%@eof:3
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@ -125,6 +125,13 @@ if (length(S)==1) && isequal(S(1).type,'{}')
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return
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end
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if (length(S)==2) && isequal(S(1).type,'{}')
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us = extract(ts,S(1).subs{:});
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us = subsref(us, S(2));
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return
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end
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if (length(S)==2) && isequal(S(1).subs,'save') && isequal(S(1).type,'.') && isequal(S(2).type,'()')
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us = NaN;
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save(ts,S(2).subs{:});
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