I have lists of floats from measurements which vary in length (they grow with the duration of the measurements) What would be a good way to map those to a list of, say, 150 items? In concreto: -When variable list A has 15 elements and fixed list B is 150 long, I can simply repeat each value of A 10 times to get a 150 elements list. -When list A has 1500 elements, and B 150, I could take list A 10 elements at a time, and write the averages thereof to list B. but how do I generalize this for _any_ length of list A? and how do I patch this up in pd? :) Thanks, Tim
hello,
put list A in a table, and use tabread at position : int(list_A_size * X / 149) when X is integer varing from 0 to 149, to create the list_B
cheers Cyrille
Le 21/05/2012 21:13, tim vets a écrit :
I have lists of floats from measurements which vary in length (they grow with the duration of the measurements) What would be a good way to map those to a list of, say, 150 items? In concreto: -When variable list A has 15 elements and fixed list B is 150 long, I can simply repeat each value of A 10 times to get a 150 elements list. -When list A has 1500 elements, and B 150, I could take list A 10 elements at a time, and write the averages thereof to list B. but how do I generalize this for _any_ length of list A? and how do I patch this up in pd? :) Thanks, Tim
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On 22/05/12 02:13, tim vets wrote:
I have lists of floats from measurements which vary in length (they grow with the duration of the measurements) What would be a good way to map those to a list of, say, 150 items? In concreto: -When variable list A has 15 elements and fixed list B is 150 long, I can simply repeat each value of A 10 times to get a 150 elements list. -When list A has 1500 elements, and B 150, I could take list A 10 elements at a time, and write the averages thereof to list B. but how do I generalize this for _any_ length of list A?
look into resampling, eg libsamplerate[1]
when #A > #B you want to low pass filter to antialias when downsampling when #A < #B you want to interpolate to antialias when upsampling
and how do I patch this up in pd? :)
very temperamental/buggy version attached, especially the filtering is a bit rubbish in this one - it just uses a couple of simple causal lop[2] instead of doing it properly
for upsampling, tabread4 does one kind of interpolation (but you might want to use a different interpolation function, refer to the list archives about tabread4c etc)
for downsampling, you might want an acausal filter (like an image blur that works in all directions without shifting the image sideways) - most audio filters are causal (one direction only - the direction in this case is time, and the future can't influence the past, so there's some delay)
you might want to store statistics too, depending on the type of data (eg: in an audio scope like audacity you can see long-scale peaks/troughs even though the average antialiased visible signal would be a line at 0 assuming no DC offset) - you might pick up some ideas from the description of zoom-cache[3]
[1] http://www.mega-nerd.com/SRC/ [2] http://en.wikipedia.org/wiki/Low_pass_filter#Discrete-time_realization [3] http://hackage.haskell.org/package/zoom-cache
Claude
thanks for the suggestions, here's the solution I came up with. gr, Tim
2012/5/22 Claude Heiland-Allen claude@goto10.org
On 22/05/12 02:13, tim vets wrote:
I have lists of floats from measurements which vary in length (they grow with the duration of the measurements) What would be a good way to map those to a list of, say, 150 items? In concreto: -When variable list A has 15 elements and fixed list B is 150 long, I can simply repeat each value of A 10 times to get a 150 elements list. -When list A has 1500 elements, and B 150, I could take list A 10 elements at a time, and write the averages thereof to list B. but how do I generalize this for _any_ length of list A?
look into resampling, eg libsamplerate[1]
when #A > #B you want to low pass filter to antialias when downsampling when #A < #B you want to interpolate to antialias when upsampling
and how do I patch this up in pd? :)
very temperamental/buggy version attached, especially the filtering is a bit rubbish in this one - it just uses a couple of simple causal lop[2] instead of doing it properly
for upsampling, tabread4 does one kind of interpolation (but you might want to use a different interpolation function, refer to the list archives about tabread4c etc)
for downsampling, you might want an acausal filter (like an image blur that works in all directions without shifting the image sideways) - most audio filters are causal (one direction only - the direction in this case is time, and the future can't influence the past, so there's some delay)
you might want to store statistics too, depending on the type of data (eg: in an audio scope like audacity you can see long-scale peaks/troughs even though the average antialiased visible signal would be a line at 0 assuming no DC offset) - you might pick up some ideas from the description of zoom-cache[3]
[1] http://www.mega-nerd.com/SRC/ [2] http://en.wikipedia.org/wiki/**Low_pass_filter#Discrete-time_** realizationhttp://en.wikipedia.org/wiki/Low_pass_filter#Discrete-time_realization [3] http://hackage.haskell.org/**package/zoom-cachehttp://hackage.haskell.org/package/zoom-cache
Claude
-
Claude Heiland-Allen -
Cyrille Henry -
tim vets