TD_CONVOLVE Function Example | Teradata Vantage - TD_CONVOLVE Example

TD_CONVOLVE Function Example | Teradata Vantage - TD_CONVOLVE Example - Teradata Vantage

Database Unbounded Array Framework Time Series Functions

Deployment

VantageCloud

VantageCore

Edition

Enterprise

IntelliFlex

VMware

Product

Teradata Vantage

Release Number

17.20

Published

June 2022

Language

English (United States)

Last Update

2024-10-04

dita:mapPath

ncd1634149624743.ditamap

dita:ditavalPath

ruu1634160136230.ditaval

dita:id

ncd1634149624743

Example: TD_CONVOLVE Call with XCONVOLVE_COMPLEX_LEFT Input and CONV_SUMMATION Algorithm

Input Table for XCONVOLVE_COMPLEX_LEFT (Time Series) for CONV_SUMMATION
ID	SEQ	A_REAL	A_IMAG
33	0	1.0	0.2
33	1	2.0	0.8
33	2	-1.0	-0.9
33	3	-2.0	1.9
33	4	-3.0	-0.5

Input Table for Input Table HCONVOLVE_COMPLEX_RIGHT (Filter Series) for CONV_SUMMATION
ID	SEQ	A_REAL	A_IMAG
33	0	1.0	0.55
33	1	2.0	0.65
33	2	0.0	0.45
33	3	-3.0	0.11

TD_CONVOLVE Call with XCONVOLVE_COMPLEX_LEFT Input and CONV_SUMMATION Algorithm

EXECUTE FUNCTION INTO VOLATILE ART(ConvCmplxResB)
         TD_CONVOLVE(
         SERIES_SPEC(TABLE_NAME(XCONVOLVE_COMPLEX_LEFT),SERIES_ID(ID),ROW_AXIS(SEQUENCE(SEQ)),
         PAYLOAD( FIELDS(A_REAL,A_IMAG), CONTENT(MULTIVAR_REAL))),
         SERIES_SPEC(TABLE_NAME(HCONVOLVE_COMPLEX_RIGHT),SERIES_ID(ID),ROW_AXIS(SEQUENCE(SEQ)),
         PAYLOAD( FIELDS(A_REAL,A_IMAG), CONTENT(COMPLEX))),
         FUNC_PARAMS(ALGORITHM("CONV_SUMMATION")),
         INPUT_FMT(INPUT_MODE(MATCH)));

TD_CONVOLVE Output for XCONVOLVE_COMPLEX_LEFT Input and CONV_SUMMATION Algorithm

Display the primary RETURNS TABLE with this statement:

SELECT * FROM ConvCmplxResB;

ID  ROW_I  REAL_A_REAL  IMAG_A_REAL   REAL_A_IMAG         IMAG_A_IMAG
-------------------------------------------------------------------------
33  0        2.0         0.0      0.11000000000000001     0.0
33  1        5.0         0.0      0.5700000000000001      0.0 
33  2        1.0         0.0      0.11499999999999994     0.0
33  3        -10.0       0.0      0.8419999999999999      0.0
33  4        -10.0       0.0      0.6429999999999999      0.0
33  5        -3.0        0.0      0.431000                0.0
33  6        6.0         0.0      -0.016000000000000014   0.0
33  7        9.0         0.0      9.05500000000000E       0.0

Example: TD_CONVOLVE with CONV_DFFT Algorithm

Input Table for XCONVOLVE_COMPLEX_LEFT (Time Series) for CONV_DFFT Algorithm
ID	SEQ	A_REAL	A_IMAG	B_REAL	B_IMAG	C_REAL	C_IMAG
33	0	2.0	0.2	2.0	0.2	2.0	0.2
33	1	1.0	0.8	1.0	0.8	1.0	0.8
33	2	-1.0	-0.9	-1.0	-0.9	-1.0	-0.9
33	3	-2.0	1.9	-2.0	1.9	-2.0	1.9
33	4	-3.0	-0.5	-3.0	-0.5	-3.0	-0.5

Input Table for Input Table HCONVOLVE_COMPLEX_RIGHT (Filter Series) for CONV_DFFT Algorithm
ID	SEQ	A_REAL	A_IMAG	B_REAL	B_IMAG	C_REAL	C_IMAG
33	0	1.0	0.55	1.0	0.55	1.0	0.55
33	1	2.0	0.65	2.0	0.65	2.0	0.65
33	2	0.0	0.45	0.0	0.45	0.0	0.45
33	3	-3.0	0.11	-3.0	0.11	-3.0	0.11

TD_CONVOLVE Call with CONV_DFFT Algorithm

EXECUTE FUNCTION INTO VOLATILE ART(ConvCmplxResB)
   TD_CONVOLVE(
   SERIES_SPEC(TABLE_NAME(XCONVOLVE_COMPLEX_LEFTMULTI),SERIES_ID(ID),ROW_AXIS(SEQUENCE(SEQ)),
   PAYLOAD( FIELDS(A_REAL,A_IMAG,B_REAL,B_IMAG,C_REAL,C_IMAG), CONTENT(MULTIVAR_COMPLEX))),
   SERIES_SPEC(TABLE_NAME(HCONVOLVE_COMPLEX_RIGHTMULTI),SERIES_ID(ID),ROW_AXIS(SEQUENCE(SEQ)),
   PAYLOAD( FIELDS(A_REAL,A_IMAG,B_REAL,B_IMAG,C_REAL,C_IMAG), CONTENT(COMPLEX))),
   FUNC_PARAMS(ALGORITHM("CONV_DFFT")),
   INPUT_FMT(INPUT_MODE(MATCH)));

TD_CONVOLVE Output with CONV_DFFT Algorithm

Display the primary RETURNS TABLE with this statement:

SELECT * FROM ConvCmplxResB;

TD_CONVOLVE Output for CONV_DFFT Algorithm

ID	SEQ	REAL_A_REAL	IMAG_A_REAL	REAL_B_REAL	IMAG_B_REAL	REAL_C_REAL	IMAG_C_REAL
33	0	1.8899999999999997	1.3000000000000003	1.8899999999999997	1.3000000000000003	1.8899999999999997	1.3000000000000003
33	1	4.430000000000001	3.05	4.430000000000001	3.05	4.430000000000001	3.05
33	2	0.8849999999999998	1.700000000000001	0.8849999999999998	1.700000000000001	0.8849999999999998	1.700000000000001
33	3	-10.842	-1.5799999999999992	-10.842	-1.5799999999999992	-10.842	-1.5799999999999992
33	4	-10.643000000000004	-2.390000000000004	-10.643000000000004	-2.390000000000004	-10.643000000000004	-2.390000000000004
33	5	-3.4310000000000005	-1.2599999999999991	-3.4310000000000005	-1.2599999999999991	-3.4310000000000005	-1.2599999999999991
33	6	6.016000000000003	-7.269999999999998	6.016000000000003	-7.269999999999998	6.016000000000003	-7.269999999999998
33	7	9.055000000000001	1.169999999999999	9.055000000000001	1.169999999999999	9.055000000000001	1.169999999999999