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349 | with System ;
with Ada.Text_IO; use Ada.Text_IO;
with Ada.Text_IO.C_Streams;
with Ada.Long_Float_Text_IO; use Ada.Long_Float_Text_IO;
with Ada.Integer_Text_IO; use Ada.Integer_Text_IO;
with Interfaces.C; use Interfaces.C;
with Interfaces.C.Strings; use Interfaces.C.Strings;
with Ada.Strings.Unbounded; use Ada.Strings.Unbounded;
with Ada.Command_Line; use Ada.Command_Line;
with Ada.Numerics.Elementary_Functions ; use Ada.Numerics.Elementary_Functions;
with GNAT.Source_Info; use GNAT.Source_Info;
with gsl ;
with gsl.math ;
with gsl.rng ;
with gsl.randist ;
with gsl.vector_double ;
with gsl.vector_int ;
with gsl.filter ;
procedure Filter is
Verbose : boolean := true;
N : size_t := 500 ; -- Time Series length
K : size_t := 51 ; -- Kernel Size
alpha : gsl.double_array := ( 0.5 , 3.0 , 10.0 );
Status : Int ;
procedure Test1 is
--
-- 24.5.1 Gaussian Example 1
--
rng : access gsl.rng.gsl_rng := gsl.rng.alloc(gsl.rng.default) ;
x, y1, y2, y3, k1, k2, k3 : access gsl.vector_double.gsl_vector ;
gws : access gsl.filter.gsl_filter_gaussian_workspace :=
gsl.filter.gaussian_alloc (K) ;
sum : double := 0.0 ;
ui : double ;
kernelfile, datafile : File_Type ;
begin
Put_Line(enclosing_entity);
x := gsl.vector_double.alloc(N);
y1 := gsl.vector_double.alloc(N);
y2 := gsl.vector_double.alloc(N);
y3 := gsl.vector_double.alloc(N);
k1 := gsl.vector_double.alloc(K);
k2 := gsl.vector_double.alloc(K);
k3 := gsl.vector_double.alloc(K);
for i in 0..N-1
loop
ui := gsl.randist.gaussian(rng,1.0);
sum := sum + ui ;
gsl.vector_double.set(x, i , sum );
end loop ;
Status := gsl.filter.gaussian_kernel(alpha(0) , 0 , 0 , k1 );
Status := gsl.filter.gaussian_kernel(alpha(1) , 0 , 0 , k2 );
Status := gsl.filter.gaussian_kernel(alpha(2) , 0 , 0 , k3 );
Status := gsl.filter.gaussian(gsl.filter.GSL_FILTER_END_PADVALUE,alpha(0),0,x,y1,gws) ;
Status := gsl.filter.gaussian(gsl.filter.GSL_FILTER_END_PADVALUE,alpha(1),0,x,y2,gws) ;
Status := gsl.filter.gaussian(gsl.filter.GSL_FILTER_END_PADVALUE,alpha(2),0,x,y3,gws) ;
Ada.Long_Float_Text_IO.Default_aft := 4 ;
Ada.Long_Float_Text_IO.Default_exp := 0 ;
Create(kernelfile,Out_File,enclosing_entity & ".kernel.csv");
Set_Output(kernelfile);
for i in 0..K-1
loop
Put(Integer(i)); Put(" ; ");
Put(Long_Float(gsl.vector_double.get(k1,i))); Put(" ; ");
Put(Long_Float(gsl.vector_double.get(k2,i))); Put(" ; ");
Put(Long_Float(gsl.vector_double.get(k3,i))); Put(" ; ");
New_Line;
end loop ;
Set_Output(Standard_Output);
Close(kernelfile);
Create(datafile,Out_File,enclosing_entity & ".data.csv");
Set_Output(datafile);
for i in 0..N-1
loop
Put(Integer(i)); Put(" ; ");
Put(Long_Float(gsl.vector_double.get(x,i))); Put(" ; ");
Put(Long_Float(gsl.vector_double.get(y1,i))); Put(" ; ");
Put(Long_Float(gsl.vector_double.get(y2,i))); Put(" ; ");
Put(Long_Float(gsl.vector_double.get(y3,i))); Put(" ; ");
New_Line;
end loop ;
Set_Output(Standard_Output);
Close(datafile);
end Test1 ;
procedure Test2 is
--
-- 24.5.2 Gaussian Example 2
--
myname : String := gnat.Source_Info.enclosing_entity ;
logfile : File_Type ;
x ,
dxi ,
y ,
dy ,
d2y : access gsl.vector_double.gsl_vector ;
gws : access gsl.filter.gsl_filter_gaussian_workspace ;
rng : access gsl.rng.gsl_rng := gsl.rng.alloc(gsl.rng.default);
xi, ei : double ;
begin
if Verbose
then
Put_Line(myname);
end if ;
N := 1000 ;
x := gsl.vector_double.alloc(N) ;
y := gsl.vector_double.alloc(N) ;
dy := gsl.vector_double.alloc(N) ;
d2y := gsl.vector_double.alloc(N) ;
dxi := gsl.vector_double.alloc(N) ;
gws := gsl.filter.gaussian_alloc (K) ;
for i in 1..N
loop
if i > N/2
then
xi := 0.5;
else
xi := 0.0 ;
end if;
ei := gsl.randist.gaussian(rng,0.1);
gsl.vector_double.set(x,size_t(i-1),xi+ei);
end loop ;
for i in 1..N
loop
if i = 1
then
gsl.vector_double.set(dxi,i-1,
gsl.vector_double.get(x,i) - gsl.vector_double.get(x,i-1));
elsif i = n
then
gsl.vector_double.set(dxi,i-1,gsl.vector_double.get(x,i-1) - gsl.vector_double.get(x,i-2));
else
gsl.vector_double.set(dxi,i-1,0.5*(gsl.vector_double.get(x,i) - gsl.vector_double.get(x,i-2)));
end if;
end loop ;
gsl.vector_double.WriteCSV(x,myname & ".data.csv");
Status := gsl.filter.gaussian(gsl.filter.GSL_FILTER_END_PADVALUE,alpha(2),0,x,y,gws);
Status := gsl.filter.gaussian(gsl.filter.GSL_FILTER_END_PADVALUE,alpha(2),1,x,dy,gws);
Status := gsl.filter.gaussian(gsl.filter.GSL_FILTER_END_PADVALUE,alpha(2),2,x,d2y,gws);
gsl.vector_double.WriteCSV( x , myname & ".filtered.csv" ,
y , dy , d2y , dxi );
gsl.vector_double.free(x);
gsl.vector_double.free(y);
gsl.vector_double.free(dy);
gsl.vector_double.free(d2y);
gsl.vector_double.free(dxi);
gsl.rng.free(rng);
gsl.filter.gaussian_free(gws);
end Test2 ;
procedure Test3 is
-- 24.5.3 Square Wave Signal Example
myname : String := gnat.Source_Info.enclosing_entity ;
logfile : File_Type ;
gws : access gsl.filter.gsl_filter_gaussian_workspace ;
rng : access gsl.rng.gsl_rng := gsl.rng.alloc(gsl.rng.default);
t,
x,
y_median,
y_rmedian : access gsl.vector_double.gsl_vector ;
medianws : access gsl.filter.gsl_filter_median_workspace ;
rmedianws : access gsl.filter.gsl_filter_rmedian_workspace ;
freq : double := 5.0 ;
ti : double ;
tmp : double ;
xi, ei : double ;
begin
if Verbose
then
Put_Line(myname);
end if ;
N := 1000 ;
K := 7 ;
gws := gsl.filter.gaussian_alloc(K);
t := gsl.vector_double.alloc(N);
x := gsl.vector_double.alloc(N);
y_median := gsl.vector_double.alloc(N) ;
y_rmedian := gsl.vector_double.alloc(N) ;
medianws := gsl.filter.median_alloc(K) ;
rmedianws := gsl.filter.rmedian_alloc(K) ;
for i in 1..N
loop
ti := double(i) / double(N-1) ;
tmp := double(Ada.Numerics.Elementary_Functions.Sin( 2.0 * Ada.Numerics.PI * float(freq) * float(ti)));
if tmp >= 0.0
then
xi := 1.0 ;
else
xi := -1.0 ;
end if ;
ei := gsl.randist.gaussian(rng,0.1);
gsl.vector_double.set(t,i-1,ti);
gsl.vector_double.set(x,i-1,xi+ei);
end loop ;
Status := gsl.filter.median(gsl.filter.GSL_FILTER_END_PADVALUE,x,y_median,medianws);
Status := gsl.filter.rmedian(gsl.filter.GSL_FILTER_END_PADVALUE,x,y_rmedian,rmedianws);
gsl.vector_double.WriteCSV(t,myname & ".csv" ,x,y_median,y_rmedian) ;
gsl.vector_double.free(t);
gsl.vector_double.free(x);
gsl.vector_double.free(y_median) ;
gsl.vector_double.free(y_rmedian) ;
gsl.filter.median_free(medianws);
gsl.filter.rmedian_free(rmedianws);
end Test3 ;
procedure Test4 is
myname : String := gnat.Source_Info.enclosing_entity ;
logfile : File_Type ;
t : double ;
x,
y,
xmedian ,
xsigma ,
lupper,
llower,
watch : access gsl.vector_double.gsl_vector ;
ioutlier : access gsl.vector_int.gsl_vector_int ;
rng : access gsl.rng.gsl_rng := gsl.rng.alloc(gsl.rng.default) ;
iws : access gsl.filter.gsl_filter_impulse_workspace ;
xi, ei, u, outlier : double ;
noutlier : aliased size_t ;
begin
-- 24.5.4 Impulse Detection Example
if Verbose
then
Put_Line(myname);
end if ;
N := 1000 ;
K := 25 ;
t := 4.0 ;
x := gsl.vector_double.alloc(N);
y := gsl.vector_double.alloc(N);
xmedian := gsl.vector_double.alloc(N);
xsigma := gsl.vector_double.alloc(N);
ioutlier := gsl.vector_int.alloc(N) ;
lupper := gsl.vector_double.alloc(N) ;
llower := gsl.vector_double.alloc(N) ;
watch := gsl.vector_double.alloc(N) ;
iws := gsl.filter.impulse_alloc(K) ;
for i in 1..N
loop
u := gsl.rng.uniform(rng) ;
ei := gsl.randist.gaussian(rng,2.0);
xi := 10.0 *
double(Ada.Numerics.Elementary_Functions.sin( 2.0 *
Ada.Numerics.PI *
Float(i) / Float (N))) ;
if u < 0.01
then
outlier := 15.0 * double(gsl.math.GSL_SIGN(ei)) ;
else
outlier := 0.0;
end if ;
gsl.vector_double.set(x, i-1 , xi+ei+outlier);
end loop ;
Status := gsl.filter.impulse(gsl.filter.GSL_FILTER_END_TRUNCATE,
gsl.filter.GSL_FILTER_SCALE_QN, t, x, y,
xmedian, xsigma, noutlier'access , ioutlier, iws );
for i in 1..N
loop
gsl.vector_double.set(lupper,i-1,
gsl.vector_double.get(xmedian,i-1) + t * gsl.vector_double.get(xsigma,i-1));
gsl.vector_double.set(llower,i-1,
gsl.vector_double.get(xmedian,i-1) - t * gsl.vector_double.get(xsigma,i-1));
gsl.vector_double.set
(watch,
i - 1,
gsl.vector_double.get(xmedian,i-1) * double(gsl.vector_int.get(ioutlier,i-1)) );
end loop ;
gsl.vector_double.WriteCSV
(x,
myname & ".csv",
y , xmedian, xsigma, llower, lupper, watch );
gsl.vector_int.WriteCSV
(ioutlier, myname & ".outlier.csv" );
gsl.vector_double.free(x);
gsl.vector_double.free(y);
gsl.vector_double.free(xmedian);
gsl.vector_double.free(xsigma );
gsl.vector_double.free(lupper);
gsl.vector_double.free(llower);
gsl.vector_int.free(ioutlier);
gsl.rng.free(rng) ;
gsl.filter.impulse_free(iws) ;
end Test4 ;
procedure Template is
myname : String := gnat.Source_Info.enclosing_entity ;
logfile : File_Type ;
begin
if Verbose
then
Put_Line(myname);
end if ;
Create(logfile,Out_File,myname & ".csv");
Set_Output(logfile);
Set_Output(Standard_Output);
Close(logfile);
end Template ;
begin
if Argument_Count > 0
then
N := size_t'Value( Argument(1) );
if Argument_Count > 1
then
K := size_t'Value( Argument(2) );
end if ;
end if ;
Template ;
Test1 ;
Test2 ;
Test3 ;
Test4 ;
end Filter;
|