LTPDA Toolbox™ contents Constructor examples of the SMODEL class   Constructor examples of the MIIR class

Constructor examples of the MFIR class

Copy an MFIR object
Construct a MFIR object by loading the object from a file
Construct a MFIR object from an Analysis Object (AO)
Construct a MFIR object from a pole/zero model (PZMODEL)
Construct a MFIR object from a standard type
Construct a MFIR object from an existing filter model
Construct a MFIR object from a difference equation

Copy an FIR filter object

The following example creates a copy of an FIR filter object (blue command).

>> fir1 = mfir(plist('type', 'lowpass'));
>> fir2 = mfir(fir2)
------ mfir/1 -------
     gd: 32.5
version: $Id: mfir.m,v 1.84 2009/02/24 17:02:44 ingo Exp
  ntaps: 65
     fs: 1
 infile:
      a: [0.0007 0.0004 -6.9093e-19 -0.0006 -0.0012 -0.0014 -0.0011 1.4147e-18 ...
histout: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  ...
 iunits: [] [1x1 unit]
 ounits: [] [1x1 unit]
   hist: mfir.hist  [1x1 history]
   name: lowpass
---------------------


REMARK: The following command copies only the handle of an object and doesn't create a copy of the object (as above). This means that everything that happens to the copy or original happens to the other object.

>> fir1 = mfir()
------ mfir/1 -------
     gd: []
version: $Id: mfir.m,v 1.84 2009/02/24 17:02:44 ingo Exp
  ntaps: 0
     fs: []
 infile:
      a: []
histout: []
 iunits: [] [1x1 unit]
 ounits: [] [1x1 unit]
   hist: mfir.hist  [1x1 history]
   name: none
---------------------
>> fir2 = fir1;
>> fir2.setName('my new name')
------ mfir/1 -------
     gd: []
version: $Id: mfir.m,v 1.84 2009/02/24 17:02:44 ingo Exp
  ntaps: 0
     fs: []
 infile:
      a: []
histout: []
 iunits: [] [1x1 unit]
 ounits: [] [1x1 unit]
   hist: mfir.hist  [1x1 history]
   name: my new name
---------------------


If we display fir1 again then we see that the property 'name' was changed although we only have changed fir2.

>> fir1
------ mfir/1 -------
     gd: []
version: $Id: mfir.m,v 1.84 2009/02/24 17:02:44 ingo Exp
  ntaps: 0
     fs: []
 infile:
      a: []
histout: []
 iunits: [] [1x1 unit]
 ounits: [] [1x1 unit]
   hist: mfir.hist  [1x1 history]
   name: my new name
---------------------

Construct a MFIR object by loading the object from a file

The following example creates a new mfir object by loading the mfir object from disk.

fir = mfir('fir.mat')
fir = mfir('fir.xml')

or in a PLIST

pl  = plist('filename', 'fir.xml');
fir = mfir(pl)

Construct a MFIR object from an Analysis Object

An FIR filter object can be generated based on the magnitude of the input AO/fsdata object. In the following example an AO/fsdata object is first generated and then passed to the mfir constructor to obtain the equivalent FIR filter.

a1  = ao(plist('fsfcn', '1./(50+f)', 'fs', 1000, 'f', linspace(0, 500, 1000)));
fir = mfir(a1);
iplot(a1, resp(fir));

or in a PLIST with the relevant parameters:

Key Description

'method'

the design method:

'frequency-sampling' - uses fir2()

'least-squares' - uses firls()

'Parks-McClellan' - uses firpm()

[default: 'frequency-sampling']

'win'

Window function for frequency-sampling method [default: 'Hanning']

'N'

Filter order [default: 512]

The following example creates a mfir object from an analysis object.

a1  = ao(plist('fsfcn', '1./(50+f)', 'fs', 1000, 'f', linspace(0, 500, 1000)));
pl  = plist('ao', a1);
fir = mfir(pl)

Construct an FIR filter object from a pole/zero model

The following example creates a new FIR filter object from a pole/zero model.

>> pzm = pzmodel(1, {1 2 3}, {4 5}, 'my pzmodel')
---- pzmodel 1 ----
    name: my pzmodel
    gain: 1
   delay: 0
  iunits: []
  ounits: []
pole 001: (f=1 Hz,Q=NaN)
pole 002: (f=2 Hz,Q=NaN)
pole 003: (f=3 Hz,Q=NaN)
zero 001: (f=4 Hz,Q=NaN)
zero 002: (f=5 Hz,Q=NaN)
-------------------
>> fir = mfir(pzm) % Use the default sample rate fs=8 * frequency of the highest pole or zero in the model
>> fir = mfir(pzm, plist('fs', 100))
------ mfir/1 -------
     gd: 257
version: $Id: mfir.m,v 1.84 2009/02/24 17:02:44 ingo Exp
  ntaps: 513
     fs: 100
 infile:
      a: [-0 -3.013e-10 -1.2486e-09 -2.8506e-09 -5.1166e-09 -7.8604e-09 -1.1133e-08 ...
histout: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
 iunits: [] [1x1 unit]
 ounits: [] [1x1 unit]
   hist: mfir.hist  [1x1 history]
   name: my pzmodel
---------------------

or in a PLIST with the relevant parameters:

Key Description

'pzmodel'

A pzmodel object to construct the filter from [default: empty pzmodel]

'fs'

Sample rate [default: 8 * frequency of the highest pole or zero in the model]

 

>> pzm = pzmodel(1, {1 2 3}, {4 5}, 'my pzmodel')
>> pl  = plist('pzmodel', pzm, 'fs', 100)
>> fir = mfir(pl)

Construct a MFIR object from a standard type

Construct an FIR filter object from a standard type: 'lowpass', 'highpass', 'bandpass' or 'bandreject'

The relevant parameters are:

Key Description

'type'

one of the types: 'highpass', 'lowpass', 'bandpass', 'bandreject'

[default 'lowpass']

You can also specify optional parameters:

Key Description

'gain'

The gain of the filter [default: 1]

'fc'

The roll-off frequency [default: 0.1 Hz]

'fs'

The sampling frequency to design for [default: 1 Hz]

'order'

The filter order [default: 64]

'win'

Specify window function used in filter design [default: 'Hamming']

'iunits'

the input unit of the filter

'ounits'

the output unit of the filter

The following example creates an order 64 highpass filter with high frequency gain 2. Filter is designed for 1 Hz sampled data and has a cut-off frequency of 0.2 Hz.

pl = plist('type', 'highpass',   ...
                   'order', 64,  ...
                   'gain',  2.0, ...
                   'fs',    1,   ...
                   'fc',    0.2);
f = mfir(pl)

Furthermore it is possible to specify a spectral window.

win = specwin('Kaiser', 11, 150);
pl = plist('type', 'lowpass', ...
           'Win',   win,      ...
           'fs',    100,      ...
           'fc',    20,       ...
           'order', 10);
f = mfir(pl)

Construct a MFIR object from an existing filter

The mfir constructor also accepts as an input existing filters stored in different formats:

LISO files

f = mfir('foo_fir.fil')

XML files

f = mfir('foo_fir.xml')

MAT files

f = mfir('foo_fir.mat')

From an LTPDA repository

The relevant parameters for retrieving a FIR filter from a LTPDA repository are:

Key Description

'hostname'

the repository hostname. [default: 'localhost']

'database'

The database name [default: 'ltpda']

'id'

A vector of object IDs. [default: []]

'cid'

Retrieve all rational objects from a particular collection

'binary'

Set to true to retrieve from stored binary representation (not always available). [default: true]

 

f = mfir(plist('hostname', 'localhost', 'database', 'ltpda', 'ID', []))

Construct a MFIR object from a difference equation

The filter can be defined in terms of two vectors specifying the coefficients of the filter and the sampling frequency. The following example creates a FIR filter with sampling frequency 1 Hz and the following recursive equation:

a  = [-0.8 10];
fs = 1;

f = mfir(a,fs)

or in a PLIST

The relevant parameters are:

Key Description

'a'

vector of A coefficients. (see note ** below) [default: empty]

'fs'

sampling frequency of the filter [default: empty]

'name'

name of filter [default: 'None']

 

a  = [-0.8 10];
fs = 1;
pl = plist('a', a, 'fs', fs);

fir = mfir(pl)


NOTES:

** The convention used here for naming the filter coefficients is the opposite to MATLAB's convention. The recursion formula for this convention is

y(n) = a(1)*x(n) + a(2)*x(n-1) + ... + a(na+1)*x(n-na).


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