MVLib - Short Writeup - Version 4.0
Example Number 35 - Fourier Filtering and Autocorrelation
This example show hout to use the subroutines for Fourier filtering and autocorrelation plots
The fortran code is available here and the complete script running the code is available here.
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	parameter (ndat=981)
	common /mannetal/ v(ndat),x(ndat),wk1(ndat)
	character title*80
	title='Mann et al. Time Series - Average Temperature (\S\o\N\C)'
	call mvsetflags('Palette di Colori',8.0)
	call mvsetflags('size delle linee',2.)
	call mvsetflags('Colore Plotta',6.0)
	do i=1,ndat ; x(i)=i+999 ; enddo
	call plottavw(x,v,ndat,' ')
	call displayexample('example35',title,' ')
	call mvftfilt(v,wk1,ndat,1,50)
	call plottavw(x,v,ndat,' ')
	call mvsetflags('Colore Plotta',8.0)
	call plottavw(x,wk1,ndat,' ')
	call displayexample('example35',title,'Fouries filter with components 1-50')
	call autocorrplot(v,ndat,wk1,18)
	call displayexample('example35',title,' ')
	end

	blockdata manning
	parameter (ndat=981)
	common /mannetal/ v(ndat),x(ndat)
	data (v(i),i=1,100) / 
	1 -0.084,-0.274,-0.271,-0.330,-0.221,-0.283,-0.214,-0.146,-0.279,-0.180,
	1 -0.031,-0.275,-0.313,-0.229,-0.262,-0.265,-0.271,-0.231,-0.353,-0.119,
	2 -0.040,-0.126,-0.224,-0.182,-0.193,-0.176,-0.243,-0.130,-0.237,-0.211,
	3 -0.241,-0.279,-0.166,-0.322,-0.327,-0.288,-0.178,-0.072,-0.245,-0.243,
	4 -0.218,-0.202,-0.179,-0.166,-0.066,-0.023,-0.161,-0.301,-0.156,-0.224,
	5 -0.228,-0.034,-0.138,-0.240,-0.316,-0.324,-0.259,-0.144,-0.154,-0.424,
	6 -0.259,-0.130,-0.161,-0.280,-0.135,-0.283,-0.081,-0.112,-0.337,-0.240,
	7 -0.140,-0.158,-0.056,-0.169,-0.176,-0.100,-0.183,-0.273,-0.411,-0.031,
	8 -0.036,-0.122,-0.271,-0.229,-0.031,-0.125, 0.040,-0.235,-0.215,-0.296,
	9 -0.046,-0.013, 0.014,-0.257,-0.271,-0.148,-0.133,-0.229,-0.296,-0.114/

	data (v(i),i=101,200) / 
	1 -0.172,-0.016,-0.166,-0.153,-0.181,-0.201,-0.098,-0.040,-0.082,-0.214,
	1 -0.361,-0.115,-0.141,-0.236,-0.331,-0.289,-0.193,-0.162,-0.241,-0.247,
	2 -0.353,-0.375,-0.161,-0.292,-0.147,-0.105,-0.203,-0.312,-0.220,-0.175,
	3 -0.317,-0.244,-0.275,-0.247,-0.170,-0.237,-0.280,-0.242,-0.243,-0.341,
	4 -0.273,-0.236,-0.405,-0.176,-0.366,-0.347,-0.296,-0.226,-0.142,-0.215,
	5 -0.259,-0.244,-0.074,-0.083,-0.088,-0.217,-0.211,-0.136,-0.111,-0.284,
	6 -0.150,-0.083,-0.273,-0.209,-0.061,-0.118,-0.057,-0.077,-0.002,-0.055,
	7 -0.008, 0.016, 0.000,-0.101,-0.074,-0.093,-0.172,-0.277,-0.261,-0.258,
	8  0.020,-0.050,-0.242,-0.161,-0.074,-0.298,-0.228,-0.169,-0.098,-0.184,
	9  0.061,-0.308,-0.336, 0.005,-0.417,-0.274,-0.425,-0.369,-0.201,-0.271/
 
	data (v(i),i=201,300) / 
	1 -0.350,-0.471,-0.236,-0.111,-0.287,-0.292,-0.415,-0.356,-0.468,-0.291,
	1 -0.377,-0.171,-0.186,-0.268,-0.212,-0.148,-0.216,-0.373,-0.241,-0.395,
	2 -0.195,-0.059,-0.054,-0.148, 0.028,-0.083,-0.319,-0.327,-0.266,-0.341,
	3 -0.322,-0.124,-0.262,-0.245,-0.308,-0.226,-0.277,-0.315,-0.199,-0.308,
	4 -0.179,-0.320,-0.258,-0.251,-0.383,-0.242,-0.267,-0.353,-0.004, 0.122,
	5 -0.112,-0.305,-0.047, 0.107,-0.105,-0.151,-0.301,-0.338,-0.256,-0.135,
	6 -0.285,-0.332,-0.389,-0.405,-0.222,-0.262,-0.283,-0.289,-0.304,-0.201,
	7 -0.241,-0.180,-0.158,-0.156,-0.196,-0.095,-0.190,-0.265,-0.277,-0.218,
	8 -0.181,-0.269,-0.167,-0.047,-0.256,-0.264,-0.165,-0.221,-0.347,-0.250,
	9 -0.232,-0.164,-0.216,-0.324,-0.218,-0.188,-0.345,-0.411,-0.198,-0.269/
 
	data (v(i),i=301,400) / 
	1 -0.179,-0.170,-0.303,-0.226,-0.175,-0.153,-0.055,-0.154,-0.158,-0.397,
	1 -0.237,-0.001,-0.136,-0.281, 0.015,-0.076,-0.039,-0.144,-0.089,-0.176,
	2 -0.230,-0.068,-0.205,-0.214,-0.126,-0.036,-0.089,-0.209,-0.192,-0.277,
	3 -0.327,-0.002,-0.315,-0.328,-0.333,-0.326,-0.296,-0.262,-0.294,-0.358,
	4 -0.510,-0.466,-0.411,-0.313,-0.300,-0.479,-0.409,-0.410,-0.459,-0.507,
	5 -0.454,-0.384,-0.393,-0.247,-0.189,-0.224,-0.175,-0.236,-0.277,-0.209,
	6 -0.320,-0.276,-0.150,-0.300,-0.214,-0.158, 0.091,-0.226, 0.004,-0.287,
	7 -0.152,-0.054,-0.011,-0.150,-0.100,-0.226,-0.195,-0.266,-0.224,-0.195,
	8 -0.233,-0.098,-0.109,-0.093,-0.331,-0.235,-0.246,-0.188,-0.032,-0.096,
	9 -0.130,-0.129,-0.171,-0.139,-0.214,-0.109,-0.103,-0.175,-0.162,-0.318/
 
	data (v(i),i=401,500) / 
	1 -0.455,-0.389,-0.282,-0.242,-0.367,-0.302,-0.200,-0.263,-0.252,-0.236,
	1 -0.259,-0.226,-0.329,-0.307,-0.288,-0.240,-0.153,-0.231,-0.256,-0.297,
	2 -0.315,-0.436,-0.291,-0.230,-0.166,-0.277,-0.224,-0.358,-0.304,-0.332,
	3 -0.272,-0.351,-0.327,-0.196,-0.247,-0.182,-0.179,-0.159,-0.295,-0.326,
	4 -0.190,-0.135,-0.243,-0.170,-0.180,-0.341,-0.344,-0.261,-0.338,-0.384,
	5 -0.401,-0.350,-0.299,-0.388,-0.467,-0.466,-0.504,-0.477,-0.563,-0.537,
	6 -0.492,-0.540,-0.609,-0.481,-0.606,-0.532,-0.489,-0.497,-0.550,-0.465,
	7 -0.432,-0.565,-0.550,-0.503,-0.303,-0.474,-0.391,-0.343,-0.350,-0.285,
	8 -0.302,-0.282,-0.406,-0.336,-0.241,-0.241,-0.323,-0.321,-0.256,-0.229,
	9 -0.250,-0.223,-0.422,-0.231,-0.280,-0.374,-0.458,-0.496,-0.383,-0.369/
 
	data (v(i),i=501,600) / 
	1 -0.341,-0.354,-0.409,-0.258,-0.246,-0.327,-0.323,-0.216,-0.192,-0.162,
	1 -0.087,-0.119,-0.206,-0.317,-0.273,-0.309,-0.224,-0.283,-0.413,-0.356,
	2 -0.294,-0.249,-0.382,-0.271,-0.257,-0.196,-0.238,-0.183,-0.345,-0.323,
	3 -0.195,-0.168,-0.227,-0.308,-0.311,-0.209,-0.262,-0.308,-0.346,-0.209,
	4 -0.294,-0.329,-0.262,-0.345,-0.322,-0.283,-0.216,-0.369,-0.360,-0.374,
	5 -0.315,-0.297,-0.312,-0.329,-0.202,-0.205,-0.180,-0.268,-0.349,-0.255,
	6 -0.286,-0.318,-0.241,-0.146,-0.111,-0.167,-0.128,-0.259,-0.179,-0.217,
	7 -0.217,-0.231,-0.309,-0.323,-0.170,-0.276,-0.264,-0.374,-0.444,-0.445,
	8 -0.383,-0.326,-0.242,-0.278,-0.262,-0.276,-0.349,-0.432,-0.377,-0.389,
	9 -0.447,-0.448,-0.353,-0.310,-0.328,-0.374,-0.317,-0.298,-0.306,-0.305/
 
	data (v(i),i=601,700) / 
	1 -0.279,-0.516,-0.344,-0.400,-0.417,-0.318,-0.413,-0.349,-0.373,-0.302,
	1 -0.252,-0.382,-0.342,-0.274,-0.376,-0.358,-0.293,-0.213,-0.230,-0.185,
	2 -0.183,-0.225,-0.315,-0.399,-0.339,-0.398,-0.494,-0.402,-0.316,-0.329,
	3 -0.268,-0.410,-0.223,-0.306,-0.174,-0.147,-0.064,-0.239,-0.299,-0.288,
	4 -0.172,-0.431,-0.532,-0.343,-0.405,-0.357,-0.350,-0.356,-0.299,-0.314,
	5 -0.257,-0.268,-0.211,-0.232,-0.280,-0.190,-0.167,-0.174,-0.207,-0.250,
	6 -0.271,-0.245,-0.250,-0.346,-0.447,-0.408,-0.282,-0.479,-0.413,-0.455,
	7 -0.358,-0.317,-0.444,-0.533,-0.455,-0.443,-0.495,-0.345,-0.449,-0.448,
	8 -0.489,-0.275,-0.326,-0.414,-0.405,-0.428,-0.397,-0.314,-0.394,-0.222,
	9 -0.207,-0.222,-0.310,-0.245,-0.213,-0.375,-0.522,-0.409,-0.497,-0.512/
 
	data (v(i),i=701,800) / 
	1 -0.543,-0.465,-0.322,-0.339,-0.386,-0.512,-0.419,-0.296,-0.322,-0.346,
	1 -0.326,-0.387,-0.405,-0.361,-0.414,-0.337,-0.407,-0.391,-0.295,-0.311,
	2 -0.234,-0.246,-0.227,-0.093,-0.178,-0.423,-0.213,-0.164,-0.340,-0.200,
	3 -0.252,-0.315,-0.402,-0.326,-0.407,-0.271,-0.263,-0.267,-0.291,-0.243,
	4 -0.232,-0.231,-0.375,-0.401,-0.425,-0.349,-0.405,-0.331,-0.207,-0.156,
	5 -0.271,-0.288,-0.295,-0.299,-0.266,-0.306,-0.183,-0.321,-0.289,-0.171,
	6 -0.113,-0.294,-0.187,-0.316,-0.381,-0.227,-0.198,-0.128,-0.213,-0.369,
	7 -0.059,-0.138,-0.028,-0.191,-0.106,-0.098,-0.161,-0.262,-0.236,-0.333,
	8 -0.356,-0.209,-0.401,-0.241,-0.278,-0.417,-0.417,-0.412,-0.270,-0.316,
	9 -0.415,-0.212,-0.168,-0.228,-0.123,-0.267,-0.248,-0.342,-0.247,-0.330/
 
	data (v(i),i=801,900) / 
	1 -0.226,-0.248,-0.318,-0.405,-0.182,-0.332,-0.200,-0.292,-0.229,-0.312,
	1 -0.280,-0.277,-0.389,-0.407,-0.424,-0.412,-0.475,-0.490,-0.409,-0.468,
	2 -0.592,-0.361,-0.367,-0.397,-0.419,-0.355,-0.205,-0.299,-0.259,-0.314,
	3 -0.290,-0.269,-0.387,-0.269,-0.086,-0.457,-0.364,-0.520,-0.575,-0.366,
	4 -0.525,-0.348,-0.514,-0.395,-0.395,-0.462,-0.238,-0.426,-0.354,-0.447,
	5 -0.396,-0.437,-0.286,-0.377,-0.405,-0.320,-0.247,-0.310,-0.407,-0.275,
	6 -0.436,-0.345,-0.342,-0.287,-0.539,-0.290,-0.347,-0.353,-0.317,-0.383,
	7 -0.540,-0.478,-0.386,-0.359,-0.407,-0.386,-0.339,-0.164,-0.237,-0.446,
	8 -0.387,-0.348,-0.354,-0.399,-0.363,-0.340,-0.258,-0.477,-0.319,-0.284,
	9 -0.461,-0.325,-0.469,-0.474,-0.347,-0.308,-0.230,-0.204,-0.370,-0.499/
 
	data (v(i),i=901,981) / 
	1 -0.275,-0.308,-0.516,-0.385,-0.535,-0.349,-0.280,-0.496,-0.512,-0.561,
	1 -0.427,-0.362,-0.476,-0.379,-0.325,-0.257,-0.346,-0.469,-0.363,-0.233,
	2 -0.337,-0.306,-0.430,-0.193,-0.218,-0.314,-0.031,-0.182,-0.082,-0.320,
	3  0.003,-0.167,-0.126,-0.264,-0.076,-0.017,-0.043, 0.090, 0.038, 0.017,
	4 -0.147, 0.012,-0.046, 0.012, 0.128,-0.014,-0.014, 0.075, 0.015,-0.154,
	5 -0.093,-0.170,-0.059,-0.122,-0.075,-0.036,-0.216,-0.021, 0.114, 0.039,
	6  0.110, 0.015,-0.068,-0.012,-0.042,-0.110, 0.091,-0.048, 0.023, 0.039,
	7 -0.017,-0.258,-0.111,-0.044,-0.251,-0.108,-0.104, 0.019,-0.052, 0.107,
	8 -0.029/
	end