ParaMonte Fortran 2.0.0
Parallel Monte Carlo and Machine Learning Library
See the latest version documentation.
pm_sampleCCF::getACF Interface Reference

Generate and return the auto-correlation function (ACF) \((f\star f)(\tau)\) of the discrete signal \(f\) lagging itself for a range of lags.
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Detailed Description

Generate and return the auto-correlation function (ACF) \((f\star f)(\tau)\) of the discrete signal \(f\) lagging itself for a range of lags.

This generic interface a flexible wrapper for the lower-level potentially faster generic interface setACF.
See the documentation of the parent module pm_sampleCCF for algorithmic details and auto-correlation definition.

Note
By default, the input sequence f is right-padded with zeros such that the resulting new sequence has the length max(lag(size(lag)), size(f) - 1) - min(lag(1), 1 - size(f)) + 1 before computing the auto-correlation.
Parameters
[in]f: The input contiguous vector of arbitrary size (of minimum 2) of,
  1. type complex of kind any supported by the processor (e.g., CK, CK32, CK64, or CK128),
  2. type real of kind any supported by the processor (e.g., RK, RK32, RK64, or RK128),
containing the first sequence in the auto-correlation computation.
[in]lag: The input vector of type integer of default kind IK of arbitrary size, containing the set of lags in ascending order for which the correlation must be computed.
(optional. default = getRange(0, size(f) - 1))
[in]norm: The input scalar constant that can be:
  1. the constant nothing or an object of type nothing_type, implying that the input f must be used as is without any normalization.
  2. the constant meanshift or an object of type meanshift_type, implying that the input f must be mean-shifted before computing the auto-correlation.
  3. the constant stdscale or an object of type stdscale_type, implying that the input f must be scaled by a factor of 1 / sum(abs(f)**2).
    This option is particularly useful when the input sequence is already mean-shifted but not properly scaled.
  4. the constant zscore or an object of type zscore_type, implying that the input f must be mean-shifted and further scaled by a factor of 1 / sum(abs(f)**2).
(optional, default = zscore.)
Returns
acf : The output allocatable vector of the same type and kind as the input f of size size(lag) containing the auto-correlation of the input f for the specified input lags.
Note that acf is always real-valued at lag 0 by definition, even when the input f is of type complex.
For all non-zero lags, the imaginary component of the auto-correlation function is an odd function.


Possible calling interfaces

use pm_sampleCCF, only: getACF
acf(1:size(lag)) = getACF(f(:), lag = lag, norm = norm)
Generate and return the auto-correlation function (ACF) of the discrete signal lagging itself for a...
This module contains classes and procedures for computing properties related to the cross correlation...
Warning
The condition isAscending(lag) must hold for the corresponding input arguments.
This generic interface is merely a flexible wrapper around the generic subroutine interface setACF.
As such, all conditions and warnings associated with setACF equally apply to this generic interface.
Remarks
The procedures under discussion are impure.
See also
getACF
setACF
getCor
setCor
getRho
setRho
getCov
setCov
setECDF
getMean
setMean
getShifted
setShifted
getVar
setVar


Example usage

1program example
2
3 use pm_kind, only: SK, IK, LK, RK
4 use pm_io, only: getErrTableWrite
5 use pm_io, only: display_type
6 use pm_io, only: getFormat
7 use pm_sampleCCF, only: getACF
8 use pm_sampleCCF, only: getCCF
9 use pm_arrayRange, only: getRange
10 use pm_distUnif, only: getUnifRand
11 use pm_distNorm, only: getNormLogPDF
12 use pm_arrayPad, only: getPaddedr
13 use pm_arrayFill, only: getFilled
14 use pm_sampleShift, only: getShifted
15 use pm_arrayResize, only: setResized
16 use pm_arraySpace, only: getLinSpace
17 use pm_sampleNorm, only: getNormed
18 use pm_sampleMean, only: getMean
19 use pm_sampleVar, only: getVar
20
21 implicit none
22
23 type(display_type) :: disp
24 integer(IK) :: nsam, shift, itry, ntry = 1
25 character(:), allocatable :: format
26 disp = display_type(file = "main.out.F90")
27
28 call disp%skip()
29 call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
30 call disp%show("!Compute the cross-correlation of two samples.")
31 call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
32 call disp%skip()
33
34 block
35 use pm_kind, only: TKG => RKS ! All other real types are also supported.
36 integer(IK), allocatable :: lag(:)
37 real(TKG), allocatable :: f(:), g(:)
38 real(TKG), allocatable :: range(:), acf(:)
39 call disp%skip()
40 call disp%show("nsam = 41; shift = nsam")
41 nsam = 41; shift = nsam
42 call disp%show("range = getLinSpace(-10., 10., nsam)")
43 range = getLinSpace(-10., 10., nsam)
44 call disp%show("f = sin(range)")
45 f = sin(range)
46 call disp%show("f")
47 call disp%show( f )
48 call disp%show("acf = getACF(f)")
49 acf = getACF(f)
50 call disp%show("size(acf)")
51 call disp%show( size(acf) )
52 call disp%show("acf")
53 call disp%show( acf )
54 call disp%show("g = f")
55 g = f
56 call disp%show("acf = getCCF(f, g, lag = getRange(1 - nsam, nsam - 1_IK)) ! same as above.")
57 acf = getCCF(f, g, lag = getRange(1 - nsam, nsam - 1_IK)) ! same as above.
58 call disp%show("size(acf)")
59 call disp%show( size(acf) )
60 call disp%show("acf")
61 call disp%show( acf )
62 call disp%show("acf = getCCF(f, g, lag = getRange(0_IK, nsam - 1_IK)) ! same as above.")
63 acf = getCCF(f, g, lag = getRange(0_IK, nsam - 1_IK)) ! same as above.
64 call disp%show("size(acf)")
65 call disp%show( size(acf) )
66 call disp%show("acf")
67 call disp%show( acf )
68 call disp%show("lag = getRange(-nsam + 1_IK, nsam - 1_IK)")
69 lag = getRange(-nsam + 1_IK, nsam - 1_IK)
70 call disp%show("acf = getACF(f, lag)")
71 acf = getACF(f, lag)
72 call disp%show("size(acf)")
73 call disp%show( size(acf) )
74 call disp%show("acf")
75 call disp%show( acf )
76 call disp%show("if (0 /= getErrTableWrite(SK_'getACF.crd.sin.RK.txt', reshape([range, f], [nsam, 2_IK]), header = SK_'crd,f')) error stop 'acf outputting failed.'")
77 if (0 /= getErrTableWrite(SK_'getACF.crd.sin.RK.txt', reshape([range, f], [nsam, 2_IK]), header = SK_'crd,f')) error stop 'acf outputting failed.'
78 call disp%show("if (0 /= getErrTableWrite(SK_'getACF.acf.sin.RK.txt', reshape([real(lag, TKG), acf], [size(lag), 2]), header = SK_'lag,acf')) error stop 'acf outputting failed.'")
79 if (0 /= getErrTableWrite(SK_'getACF.acf.sin.RK.txt', reshape([real(lag, TKG), acf], [size(lag), 2]), header = SK_'lag,acf')) error stop 'acf outputting failed.'
80 call disp%skip()
81 end block
82
83 block
84 use pm_kind, only: TKG => RKS ! All other real types are also supported.
85 integer(IK), allocatable :: lag(:)
86 complex(TKG), allocatable :: f(:), g(:), acf(:), range(:)
87 call disp%skip()
88 call disp%show("nsam = 41; shift = nsam")
89 nsam = 41; shift = nsam
90 call disp%show("range = getLinSpace((-10._TKG, +10._TKG), (+10._TKG, -10._TKG), nsam)")
91 range = getLinSpace((-10._TKG, +10._TKG), (+10._TKG, -10._TKG), nsam)
92 call disp%show("f = sin(range)")
93 f = sin(range)
94 call disp%show("f")
95 call disp%show( f )
96 call disp%show("acf = getACF(f)")
97 acf = getACF(f)
98 call disp%show("size(acf)")
99 call disp%show( size(acf) )
100 call disp%show("acf")
101 call disp%show( acf )
102 call disp%show("acf%re")
103 call disp%show( acf%re )
104 call disp%show("g = f")
105 g = f
106 call disp%show("acf = getCCF(f, g, lag = getRange(0_IK, nsam - 1_IK)) ! same as above.")
107 acf = getCCF(f, g, lag = getRange(0_IK, nsam - 1_IK)) ! same as above.
108 call disp%show("size(acf)")
109 call disp%show( size(acf) )
110 call disp%show("acf")
111 call disp%show( acf )
112 call disp%show("acf%re")
113 call disp%show( acf%re )
114 call disp%show("lag = getRange(-nsam + 1_IK, nsam - 1_IK)")
115 lag = getRange(-nsam + 1_IK, nsam - 1_IK)
116 call disp%show("acf = getACF(f, lag)")
117 acf = getACF(f, lag)
118 call disp%show("size(acf)")
119 call disp%show( size(acf) )
120 call disp%show("acf")
121 call disp%show( acf )
122 call disp%show("acf%re")
123 call disp%show( acf%re )
124 call disp%skip()
125 end block
126
127end program example
Generate and return an array of the specified rank and shape of arbitrary intrinsic type and kind wit...
Generate a resized copy of the input array by padding it to the right with the requested paddings and...
Generate minimally-spaced character, integer, real sequences or sequences at fixed intervals of size ...
Allocate or resize (shrink or expand) an input allocatable scalar string or array of rank 1....
Generate count evenly spaced points over the interval [x1, x2] if x1 < x2, or [x2,...
Generate the natural logarithm of probability density function (PDF) of the univariate Normal distrib...
Generate and return a scalar or a contiguous array of rank 1 of length s1 of randomly uniformly distr...
Generate and return the iostat code resulting from writing the input table of rank 1 or 2 to the spec...
Definition: pm_io.F90:5940
Generate and return a generic or type/kind-specific IO format with the requested specifications that ...
Definition: pm_io.F90:18485
This is a generic method of the derived type display_type with pass attribute.
Definition: pm_io.F90:11726
This is a generic method of the derived type display_type with pass attribute.
Definition: pm_io.F90:11508
Generate and return the cross-correlation function (CCF) of the discrete signal lagging the signal ...
Generate and return the (weighted) mean of an input sample of nsam observations with ndim = 1 or 2 at...
Generate a sample of shape (nsam), or (ndim, nsam) or (nsam, ndim) that is normalized by the specifie...
Generate a sample of shape (nsam), or (ndim, nsam) or (nsam, ndim) that is shifted by the specified i...
Generate and return the variance of the input sample of type complex or real of shape (nsam) or (ndim...
This module contains procedures and generic interfaces for convenient allocation and filling of array...
This module contains procedures and generic interfaces for resizing an input array and padding them w...
Definition: pm_arrayPad.F90:30
This module contains procedures and generic interfaces for generating ranges of discrete character,...
This module contains procedures and generic interfaces for resizing allocatable arrays of various typ...
This module contains procedures and generic interfaces for generating arrays with linear or logarithm...
This module contains classes and procedures for computing various statistical quantities related to t...
This module contains classes and procedures for computing various statistical quantities related to t...
This module contains classes and procedures for input/output (IO) or generic display operations on st...
Definition: pm_io.F90:252
type(display_type) disp
This is a scalar module variable an object of type display_type for general display.
Definition: pm_io.F90:11393
This module defines the relevant Fortran kind type-parameters frequently used in the ParaMonte librar...
Definition: pm_kind.F90:268
integer, parameter RK
The default real kind in the ParaMonte library: real64 in Fortran, c_double in C-Fortran Interoperati...
Definition: pm_kind.F90:543
integer, parameter LK
The default logical kind in the ParaMonte library: kind(.true.) in Fortran, kind(....
Definition: pm_kind.F90:541
integer, parameter IK
The default integer kind in the ParaMonte library: int32 in Fortran, c_int32_t in C-Fortran Interoper...
Definition: pm_kind.F90:540
integer, parameter SK
The default character kind in the ParaMonte library: kind("a") in Fortran, c_char in C-Fortran Intero...
Definition: pm_kind.F90:539
integer, parameter RKS
The single-precision real kind in Fortran mode. On most platforms, this is an 32-bit real kind.
Definition: pm_kind.F90:567
This module contains classes and procedures for computing the first moment (i.e., the statistical mea...
This module contains classes and procedures for normalizing univariate or multivariate samples by arb...
This module contains classes and procedures for shifting univariate or multivariate samples by arbitr...
This module contains classes and procedures for computing the properties related to the covariance ma...
Generate and return an object of type display_type.
Definition: pm_io.F90:10282

Example Unix compile command via Intel ifort compiler
1#!/usr/bin/env sh
2rm main.exe
3ifort -fpp -standard-semantics -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
4./main.exe

Example Windows Batch compile command via Intel ifort compiler
1del main.exe
2set PATH=..\..\..\lib;%PATH%
3ifort /fpp /standard-semantics /O3 /I:..\..\..\include main.F90 ..\..\..\lib\libparamonte*.lib /exe:main.exe
4main.exe

Example Unix / MinGW compile command via GNU gfortran compiler
1#!/usr/bin/env sh
2rm main.exe
3gfortran -cpp -ffree-line-length-none -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
4./main.exe

Example output
1
2!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3!Compute the cross-correlation of two samples.
4!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
5
6
7nsam = 41; shift = nsam
8range = getLinSpace(-10., 10., nsam)
9f = sin(range)
10f
11+0.544021070, +0.751511157E-1, -0.412118495, -0.798487127, -0.989358246, -0.937999964, -0.656986594, -0.215119988, +0.279415488, +0.705540299, +0.958924294, +0.977530122, +0.756802440, +0.350783229, -0.141120002, -0.598472178, -0.909297407, -0.997494996, -0.841470957, -0.479425550, +0.00000000, +0.479425550, +0.841470957, +0.997494996, +0.909297407, +0.598472178, +0.141120002, -0.350783229, -0.756802440, -0.977530122, -0.958924294, -0.705540299, -0.279415488, +0.215119988, +0.656986594, +0.937999964, +0.989358246, +0.798487127, +0.412118495, -0.751511157E-1, -0.544021130
12acf = getACF(f)
13size(acf)
14+41
15acf
16+1.00000000, +0.852990508, +0.508950114, +0.610315278E-1, -0.377255470, -0.700775683, -0.837977588, -0.766536891, -0.516062975, -0.157883435, +0.214869067, +0.511390090, +0.665323377, +0.649478555, +0.479918420, +0.209043026, -0.899048448E-1, -0.342467040, -0.491509914, -0.510061145, -0.405582905, -0.215206355, +0.618809508E-2, +0.201431617, +0.325790167, +0.357164949, +0.299459368, +0.178786933, +0.341432206E-1, -0.944553539E-1, -0.176896036, -0.200005800, -0.169060707, -0.103816323, -0.306897387E-1, +0.262658000E-1, +0.527551509E-1, +0.478269048E-1, +0.227515157E-1, -0.420185737E-2, -0.152083794E-1
17g = f
18acf = getCCF(f, g, lag = getRange(1 - nsam, nsam - 1_IK)) ! same as above.
19size(acf)
20+81
21acf
22-0.152082434E-1, -0.420177961E-2, +0.227515455E-1, +0.478268564E-1, +0.527551807E-1, +0.262658577E-1, -0.306897387E-1, -0.103816353, -0.169060707, -0.200005770, -0.176895991, -0.944554210E-1, +0.341432393E-1, +0.178786933, +0.299459398, +0.357164949, +0.325790167, +0.201431692, +0.618788227E-2, -0.215206400, -0.405582935, -0.510061026, -0.491509914, -0.342467070, -0.899047554E-1, +0.209043026, +0.479918450, +0.649478555, +0.665323257, +0.511390150, +0.214869022, -0.157883465, -0.516063035, -0.766536891, -0.837977588, -0.700775623, -0.377255321, +0.610315837E-1, +0.508950174, +0.852990389, +1.00000000, +0.852990508, +0.508950114, +0.610315278E-1, -0.377255470, -0.700775683, -0.837977588, -0.766536891, -0.516062975, -0.157883435, +0.214869067, +0.511390090, +0.665323377, +0.649478555, +0.479918420, +0.209043026, -0.899048448E-1, -0.342467040, -0.491509914, -0.510061145, -0.405582905, -0.215206355, +0.618809508E-2, +0.201431617, +0.325790167, +0.357164949, +0.299459368, +0.178786933, +0.341432206E-1, -0.944553539E-1, -0.176896036, -0.200005800, -0.169060707, -0.103816323, -0.306897387E-1, +0.262658000E-1, +0.527551509E-1, +0.478269048E-1, +0.227515157E-1, -0.420185737E-2, -0.152083794E-1
23acf = getCCF(f, g, lag = getRange(0_IK, nsam - 1_IK)) ! same as above.
24size(acf)
25+41
26acf
27+1.00000000, +0.852990508, +0.508950114, +0.610315278E-1, -0.377255470, -0.700775683, -0.837977588, -0.766536891, -0.516062975, -0.157883435, +0.214869067, +0.511390090, +0.665323377, +0.649478555, +0.479918420, +0.209043026, -0.899048448E-1, -0.342467040, -0.491509914, -0.510061145, -0.405582905, -0.215206355, +0.618809508E-2, +0.201431617, +0.325790167, +0.357164949, +0.299459368, +0.178786933, +0.341432206E-1, -0.944553539E-1, -0.176896036, -0.200005800, -0.169060707, -0.103816323, -0.306897387E-1, +0.262658000E-1, +0.527551509E-1, +0.478269048E-1, +0.227515157E-1, -0.420185737E-2, -0.152083794E-1
28lag = getRange(-nsam + 1_IK, nsam - 1_IK)
29acf = getACF(f, lag)
30size(acf)
31+81
32acf
33-0.152082434E-1, -0.420177961E-2, +0.227515455E-1, +0.478268564E-1, +0.527551807E-1, +0.262658577E-1, -0.306897387E-1, -0.103816353, -0.169060707, -0.200005770, -0.176895991, -0.944554210E-1, +0.341432393E-1, +0.178786933, +0.299459398, +0.357164949, +0.325790167, +0.201431692, +0.618788227E-2, -0.215206400, -0.405582935, -0.510061026, -0.491509914, -0.342467070, -0.899047554E-1, +0.209043026, +0.479918450, +0.649478555, +0.665323257, +0.511390150, +0.214869022, -0.157883465, -0.516063035, -0.766536891, -0.837977588, -0.700775623, -0.377255321, +0.610315837E-1, +0.508950174, +0.852990389, +1.00000000, +0.852990508, +0.508950114, +0.610315278E-1, -0.377255470, -0.700775683, -0.837977588, -0.766536891, -0.516062975, -0.157883435, +0.214869067, +0.511390090, +0.665323377, +0.649478555, +0.479918420, +0.209043026, -0.899048448E-1, -0.342467040, -0.491509914, -0.510061145, -0.405582905, -0.215206355, +0.618809508E-2, +0.201431617, +0.325790167, +0.357164949, +0.299459368, +0.178786933, +0.341432206E-1, -0.944553539E-1, -0.176896036, -0.200005800, -0.169060707, -0.103816323, -0.306897387E-1, +0.262658000E-1, +0.527551509E-1, +0.478269048E-1, +0.227515157E-1, -0.420185737E-2, -0.152083794E-1
34if (0 /= getErrTableWrite(SK_'getACF.crd.sin.RK.txt', reshape([range, f], [nsam, 2_IK]), header = SK_'crd,f')) error stop 'acf outputting failed.'
35if (0 /= getErrTableWrite(SK_'getACF.acf.sin.RK.txt', reshape([real(lag, TKG), acf], [size(lag), 2]), header = SK_'lag,acf')) error stop 'acf outputting failed.'
36
37
38nsam = 41; shift = nsam
39range = getLinSpace((-10._TKG, +10._TKG), (+10._TKG, -10._TKG), nsam)
40f = sin(range)
41f
42(+5991.43115, -9240.88965), (+501.999237, -6660.97363), (-1669.71533, -3691.48242), (-1962.18994, -1479.37476), (-1474.61780, -216.864731), (-847.972107, +313.365570), (-360.236938, +413.376801), (-71.5428009, +324.783783), (+56.3624725, +193.678986), (+86.3214493, +86.7014389), (+71.1617279, +21.0486450), (+44.0026550, -9.48644638), (+20.6669369, -17.8378811), (+5.81346893, -15.4914541), (-1.42074847, -9.91762066), (-3.67000413, -4.84708261), (-3.42095494, -1.50930655), (-2.34651685, +0.150619254), (-1.29845750, +0.634963870), (-0.540612698, +0.457304120), (+0.00000000, +0.00000000), (+0.540612698, -0.457304120), (+1.29845750, -0.634963870), (+2.34651685, -0.150619254), (+3.42095494, +1.50930655), (+3.67000413, +4.84708261), (+1.42074847, +9.91762066), (-5.81346893, +15.4914541), (-20.6669369, +17.8378811), (-44.0026550, +9.48644638), (-71.1617279, -21.0486450), (-86.3214493, -86.7014389), (-56.3624725, -193.678986), (+71.5428009, -324.783783), (+360.236938, -413.376801), (+847.972107, -313.365570), (+1474.61780, +216.864731), (+1962.18994, +1479.37476), (+1669.71533, +3691.48242), (-501.999237, +6660.97363), (-5991.43115, +9240.88965)
43acf = getACF(f)
44size(acf)
45+41
46acf
47(+1.00000000, +0.00000000), (+0.532280743, -0.724063076E-8), (+0.198766142, -0.639958264E-8), (+0.157836042E-1, -0.266178812E-7), (-0.563193597E-1, -0.326456089E-7), (-0.657618716E-1, -0.800109909E-10), (-0.492888130E-1, +0.163233782E-7), (-0.282785110E-1, +0.426677182E-8), (-0.119718434E-1, +0.125023716E-7), (-0.234168605E-2, +0.941287315E-8), (+0.191123993E-2, +0.893625263E-8), (+0.289596664E-2, +0.120566073E-7), (+0.237952033E-2, -0.863945804E-8), (+0.146735343E-2, +0.143593430E-7), (+0.686338462E-3, +0.336529560E-7), (+0.190695326E-3, +0.389712831E-7), (-0.485323944E-4, +0.424204316E-7), (-0.118897369E-3, +0.448930919E-7), (-0.102819416E-3, +0.348534819E-7), (-0.564900438E-4, +0.247550808E-11), (-0.117491136E-4, -0.529622080E-8), (+0.137586003E-4, -0.341829720E-8), (+0.618905369E-5, -0.964795444E-8), (-0.503967276E-4, -0.395892652E-8), (-0.171413732E-3, +0.127203030E-7), (-0.357021519E-3, -0.112389884E-7), (-0.561832683E-3, -0.341714945E-8), (-0.650348898E-3, +0.00000000), (-0.351429480E-3, +0.232185435E-7), (+0.752596592E-3, -0.235569364E-8), (+0.313374051E-2, -0.160475402E-7), (+0.702255825E-2, +0.135687781E-7), (+0.118031166E-1, +0.195890806E-7), (+0.150660472E-1, +0.274995067E-7), (+0.115134194E-1, +0.759400631E-8), (-0.763660157E-2, +0.906426934E-8), (-0.533953607E-1, -0.211997406E-7), (-0.133755893, -0.520020871E-8), (-0.241916195, -0.197824352E-7), (-0.336465597, -0.507333864E-8), (-0.316060305, -0.122585364E-7)
48acf%re
49+1.00000000, +0.532280743, +0.198766142, +0.157836042E-1, -0.563193597E-1, -0.657618716E-1, -0.492888130E-1, -0.282785110E-1, -0.119718434E-1, -0.234168605E-2, +0.191123993E-2, +0.289596664E-2, +0.237952033E-2, +0.146735343E-2, +0.686338462E-3, +0.190695326E-3, -0.485323944E-4, -0.118897369E-3, -0.102819416E-3, -0.564900438E-4, -0.117491136E-4, +0.137586003E-4, +0.618905369E-5, -0.503967276E-4, -0.171413732E-3, -0.357021519E-3, -0.561832683E-3, -0.650348898E-3, -0.351429480E-3, +0.752596592E-3, +0.313374051E-2, +0.702255825E-2, +0.118031166E-1, +0.150660472E-1, +0.115134194E-1, -0.763660157E-2, -0.533953607E-1, -0.133755893, -0.241916195, -0.336465597, -0.316060305
50g = f
51acf = getCCF(f, g, lag = getRange(0_IK, nsam - 1_IK)) ! same as above.
52size(acf)
53+41
54acf
55(+0.999999940, +0.00000000), (+0.532280743, -0.724063032E-8), (+0.198766112, -0.639958175E-8), (+0.157836024E-1, -0.266178777E-7), (-0.563193560E-1, -0.326456053E-7), (-0.657618642E-1, -0.800109839E-10), (-0.492888093E-1, +0.163233764E-7), (-0.282785092E-1, +0.426677138E-8), (-0.119718425E-1, +0.125023698E-7), (-0.234168582E-2, +0.941287226E-8), (+0.191123970E-2, +0.893625174E-8), (+0.289596641E-2, +0.120566055E-7), (+0.237952010E-2, -0.863945715E-8), (+0.146735332E-2, +0.143593413E-7), (+0.686338404E-3, +0.336529524E-7), (+0.190695311E-3, +0.389712760E-7), (-0.485323908E-4, +0.424204281E-7), (-0.118897355E-3, +0.448930884E-7), (-0.102819402E-3, +0.348534783E-7), (-0.564900365E-4, +0.247550786E-11), (-0.117491127E-4, -0.529622035E-8), (+0.137585985E-4, -0.341829676E-8), (+0.618905324E-5, -0.964795355E-8), (-0.503967240E-4, -0.395892608E-8), (-0.171413718E-3, +0.127203021E-7), (-0.357021490E-3, -0.112389875E-7), (-0.561832625E-3, -0.341714923E-8), (-0.650348840E-3, +0.00000000), (-0.351429451E-3, +0.232185418E-7), (+0.752596534E-3, -0.235569342E-8), (+0.313374028E-2, -0.160475384E-7), (+0.702255778E-2, +0.135687763E-7), (+0.118031157E-1, +0.195890788E-7), (+0.150660453E-1, +0.274995031E-7), (+0.115134176E-1, +0.759400542E-8), (-0.763660111E-2, +0.906426845E-8), (-0.533953533E-1, -0.211997389E-7), (-0.133755878, -0.520020782E-8), (-0.241916165, -0.197824317E-7), (-0.336465567, -0.507333819E-8), (-0.316060275, -0.122585355E-7)
56acf%re
57+0.999999940, +0.532280743, +0.198766112, +0.157836024E-1, -0.563193560E-1, -0.657618642E-1, -0.492888093E-1, -0.282785092E-1, -0.119718425E-1, -0.234168582E-2, +0.191123970E-2, +0.289596641E-2, +0.237952010E-2, +0.146735332E-2, +0.686338404E-3, +0.190695311E-3, -0.485323908E-4, -0.118897355E-3, -0.102819402E-3, -0.564900365E-4, -0.117491127E-4, +0.137585985E-4, +0.618905324E-5, -0.503967240E-4, -0.171413718E-3, -0.357021490E-3, -0.561832625E-3, -0.650348840E-3, -0.351429451E-3, +0.752596534E-3, +0.313374028E-2, +0.702255778E-2, +0.118031157E-1, +0.150660453E-1, +0.115134176E-1, -0.763660111E-2, -0.533953533E-1, -0.133755878, -0.241916165, -0.336465567, -0.316060275
58lag = getRange(-nsam + 1_IK, nsam - 1_IK)
59acf = getACF(f, lag)
60size(acf)
61+81
62acf
63(-0.316060305, -0.530768851E-7), (-0.336465597, -0.123534010E-7), (-0.241916209, +0.179410986E-8), (-0.133755907, -0.108240816E-8), (-0.533953607E-1, +0.164513967E-7), (-0.763660576E-2, +0.528450750E-8), (+0.115134027E-1, -0.128890791E-7), (+0.150660472E-1, -0.246229863E-7), (+0.118031232E-1, -0.649559562E-8), (+0.702256477E-2, -0.663351640E-8), (+0.313373981E-2, +0.322379479E-8), (+0.752626860E-3, -0.150611434E-7), (-0.351462484E-3, -0.642972182E-7), (-0.650348898E-3, +0.00000000), (-0.561832741E-3, -0.338393704E-7), (-0.357040699E-3, -0.166201719E-7), (-0.171417589E-3, -0.124814195E-7), (-0.504101117E-4, -0.782793919E-8), (+0.616589114E-5, -0.893987639E-8), (+0.137529387E-4, -0.102945008E-8), (-0.117722766E-4, +0.402788691E-8), (-0.565011105E-4, -0.876632811E-8), (-0.102823535E-3, -0.310065715E-7), (-0.118909717E-3, -0.163408875E-8), (-0.485323944E-4, -0.551795587E-8), (+0.190687104E-3, -0.122055788E-7), (+0.686321990E-3, +0.162704197E-7), (+0.146738638E-2, -0.315121040E-8), (+0.237947912E-2, +0.161755569E-7), (+0.289596664E-2, +0.892634944E-8), (+0.191128114E-2, +0.167481886E-7), (-0.234168814E-2, -0.851144044E-8), (-0.119718509E-1, -0.245817446E-7), (-0.282784998E-1, -0.378519189E-7), (-0.492888168E-1, -0.192738074E-7), (-0.657618865E-1, -0.189770137E-7), (-0.563193932E-1, -0.128433686E-8), (+0.157836135E-1, +0.268493370E-7), (+0.198766083, +0.419967705E-7), (+0.532280743, +0.106876527E-6), (+1.00000000, +0.00000000), (+0.532280743, -0.724063076E-8), (+0.198766142, -0.639958264E-8), (+0.157836042E-1, -0.266178812E-7), (-0.563193597E-1, -0.326456089E-7), (-0.657618716E-1, -0.800109909E-10), (-0.492888130E-1, +0.163233782E-7), (-0.282785110E-1, +0.426677182E-8), (-0.119718434E-1, +0.125023716E-7), (-0.234168605E-2, +0.941287315E-8), (+0.191123993E-2, +0.893625263E-8), (+0.289596664E-2, +0.120566073E-7), (+0.237952033E-2, -0.863945804E-8), (+0.146735343E-2, +0.143593430E-7), (+0.686338462E-3, +0.336529560E-7), (+0.190695326E-3, +0.389712831E-7), (-0.485323944E-4, +0.424204316E-7), (-0.118897369E-3, +0.448930919E-7), (-0.102819416E-3, +0.348534819E-7), (-0.564900438E-4, +0.247550808E-11), (-0.117491136E-4, -0.529622080E-8), (+0.137586003E-4, -0.341829720E-8), (+0.618905369E-5, -0.964795444E-8), (-0.503967276E-4, -0.395892652E-8), (-0.171413732E-3, +0.127203030E-7), (-0.357021519E-3, -0.112389884E-7), (-0.561832683E-3, -0.341714945E-8), (-0.650348898E-3, +0.00000000), (-0.351429480E-3, +0.232185435E-7), (+0.752596592E-3, -0.235569364E-8), (+0.313374051E-2, -0.160475402E-7), (+0.702255825E-2, +0.135687781E-7), (+0.118031166E-1, +0.195890806E-7), (+0.150660472E-1, +0.274995067E-7), (+0.115134194E-1, +0.759400631E-8), (-0.763660157E-2, +0.906426934E-8), (-0.533953607E-1, -0.211997406E-7), (-0.133755893, -0.520020871E-8), (-0.241916195, -0.197824352E-7), (-0.336465597, -0.507333864E-8), (-0.316060305, -0.122585364E-7)
64acf%re
65-0.316060305, -0.336465597, -0.241916209, -0.133755907, -0.533953607E-1, -0.763660576E-2, +0.115134027E-1, +0.150660472E-1, +0.118031232E-1, +0.702256477E-2, +0.313373981E-2, +0.752626860E-3, -0.351462484E-3, -0.650348898E-3, -0.561832741E-3, -0.357040699E-3, -0.171417589E-3, -0.504101117E-4, +0.616589114E-5, +0.137529387E-4, -0.117722766E-4, -0.565011105E-4, -0.102823535E-3, -0.118909717E-3, -0.485323944E-4, +0.190687104E-3, +0.686321990E-3, +0.146738638E-2, +0.237947912E-2, +0.289596664E-2, +0.191128114E-2, -0.234168814E-2, -0.119718509E-1, -0.282784998E-1, -0.492888168E-1, -0.657618865E-1, -0.563193932E-1, +0.157836135E-1, +0.198766083, +0.532280743, +1.00000000, +0.532280743, +0.198766142, +0.157836042E-1, -0.563193597E-1, -0.657618716E-1, -0.492888130E-1, -0.282785110E-1, -0.119718434E-1, -0.234168605E-2, +0.191123993E-2, +0.289596664E-2, +0.237952033E-2, +0.146735343E-2, +0.686338462E-3, +0.190695326E-3, -0.485323944E-4, -0.118897369E-3, -0.102819416E-3, -0.564900438E-4, -0.117491136E-4, +0.137586003E-4, +0.618905369E-5, -0.503967276E-4, -0.171413732E-3, -0.357021519E-3, -0.561832683E-3, -0.650348898E-3, -0.351429480E-3, +0.752596592E-3, +0.313374051E-2, +0.702255825E-2, +0.118031166E-1, +0.150660472E-1, +0.115134194E-1, -0.763660157E-2, -0.533953607E-1, -0.133755893, -0.241916195, -0.336465597, -0.316060305
66
67

Postprocessing of the example output
1#!/usr/bin/env python
2
3import matplotlib.pyplot as plt
4import pandas as pd
5import numpy as np
6import glob
7import sys
8
9linewidth = 2
10fontsize = 17
11
12for kind in ["sin.RK"]:
13
14 file = glob.glob("*crd*"+kind+".txt")[0]
15 df = pd.read_csv(file, delimiter = ",")
16
17 #print(df.values)
18 fig = plt.figure(figsize = (8, 6))
19 ax = plt.subplot(1,1,1)
20 ax.plot ( df.values[:, 0]
21 , df.values[:, 1]
22 , zorder = 1000
23 )
24 plt.minorticks_on()
25 ax.set_xlabel("x", fontsize = 17)
26 ax.set_ylabel("f(x)", fontsize = 17)
27 ax.tick_params(axis = "x", which = "minor")
28 ax.tick_params(axis = "y", which = "minor")
29 plt.grid(visible = True, which = "both", axis = "both", color = "0.85", linestyle = "-")
30 ax.legend([file.split(".")[-3] + "(x)"], fontsize = fontsize)
31 plt.tight_layout()
32 plt.savefig(file.replace(".txt",".png"))
33
34 file = glob.glob("*acf*"+kind+".txt")[0]
35 df = pd.read_csv(file, delimiter = ",")
36 fig = plt.figure(figsize = (8, 6))
37 ax = plt.subplot(1,1,1)
38 ax.plot ( df.values[:, 0]
39 , df.values[:, 1]
40 , zorder = 1000
41 )
42 plt.minorticks_on()
43 ax.set_xlabel("Lag", fontsize = 17)
44 ax.set_ylabel("acf(f)", fontsize = 17)
45 ax.tick_params(axis = "x", which = "minor")
46 ax.tick_params(axis = "y", which = "minor")
47 plt.grid(visible = True, which = "both", axis = "both", color = "0.85", linestyle = "-")
48 plt.tight_layout()
49 plt.savefig(file.replace(".txt",".png"))

Visualization of the example output
Test:
test_pm_sampleCCF
Internal naming convention:
The following illustrates the internal naming convention used for the procedures within this generic interface.
getACF_D1_CK5()
||| || |||
||| || The type and kind parameters of the input sequence.
||| The dimension of the input sequence `f`.
ACF: Cross-Correlation Function.


Final Remarks


If you believe this algorithm or its documentation can be improved, we appreciate your contribution and help to edit this page's documentation and source file on GitHub.
For details on the naming abbreviations, see this page.
For details on the naming conventions, see this page.
This software is distributed under the MIT license with additional terms outlined below.

  1. If you use any parts or concepts from this library to any extent, please acknowledge the usage by citing the relevant publications of the ParaMonte library.
  2. If you regenerate any parts/ideas from this library in a programming environment other than those currently supported by this ParaMonte library (i.e., other than C, C++, Fortran, MATLAB, Python, R), please also ask the end users to cite this original ParaMonte library.

This software is available to the public under a highly permissive license.
Help us justify its continued development and maintenance by acknowledging its benefit to society, distributing it, and contributing to it.

Author:
Fatemeh Bagheri, Monday 02:15 AM, September 27, 2021, Dallas, TX

Definition at line 264 of file pm_sampleCCF.F90.


The documentation for this interface was generated from the following file: