Forgot to say that I get the same values when I pass samples one by one
from my OOT block to the filter (instead of passing them in vectors of 8
samples)
On Wed, Jan 8, 2020 at 2:24 PM Laura Arjona <arjo...@uw.edu> wrote:
> Hi community! First, thanks for dedicating time to help me
>
> I have a problem with using an *IIR filter* in my OOT module, which
> cannot solve
> I get different outputs using the
> a)IIR_filter in grc and
> b)using the filter as a block in my OOT module.
> A
> nd the outputs I get with b) are wrong.
>
> Any advice or hint why I get wrong IIR-filter output values with my OOT
> module design?
>
> ------------------
>
> In both scenarios, I set the input vector and taps to be the same. I have
> checked that.* The input is a vector with 8 float values*
> The only difference is that in a) the input is a *vector_source block*,
> and in b) the input is the output of an OOT block that I created in C++.
>
>
> *a) **top_block.py*
> self.iir_filter_xxx_0 = filter.iir_filter_ffd(((0.0173, 0,-0.0520,
> 0,0.0520,0,-0.0173)), ((1.0000,-3.0140,4.8671,-4.8327,3.2074
> ,-1.2968,0.2844)), False)
> self.blocks_vector_source_x_0 = blocks.vector_source_f((-7.82476e-06 ,
> 6.03754e-06 , -4.76197e-06 , -2.3359e-05 , 4.91579e-06 , -9.69721e-06 ,
> -5.10805e-06 , 2.64173e-05 ), False, 1, [])
> self.connect((self.blocks_vector_source_x_0, 0),
> (self.iir_filter_xxx_0, 0))
> self.connect((self.iir_filter_xxx_0, 0), (self.blocks_file_sink_0, 0))
>
> *b) **/apps/main.py*
> self.bpf_filter0 = filter.iir_filter_ffd(((0.0173, 0,-0.0520,
> 0,0.0520,0,-0.0173)), ((1.0000,-3.0140,4.8671,-4.8327,3.2074
> ,-1.2968,0.2844)), False)
> self.connect((self.my_OOT_block,0), (self.bpf_filter0,0))
>
> * /lib/my_OOT_block_Impl.cc*
> //main parts of my OOT block
> void
> my_OOT_block_impl::forecast (int noutput_items, gr_vector_int
> &ninput_items_required)
> {
> ninput_items_required[0] = noutput_items;
> }
>
> int
> car_filter_impl::general_work (int noutput_items,
> gr_vector_int &ninput_items,
> gr_vector_const_void_star &input_items,
> gr_vector_void_star &output_items)
> {
> const float *in0 = (const float *) input_items[0];
> float *out0 = (float *) output_items[0];
> int consumed = ninput_items[0];
> int written = 0;
>
> //Send 8 float values to the filter
> for (int i = 0; i<8; i ++){
> out0[i] = my_vector[i];
> written ++;
>
> }
>
> produce(0,written);
> consume_each (consumed);
>
> return WORK_CALLED_PRODUCE;
> }
>
>
>
> --
> *Laura Arjona *
> Washington Research Foundation Innovation Postdoctoral Fellow in
> Neuroengineering
>
> *Paul G. Allen School of Computer Science & Engineering*
> 185 E Stevens Way NE
> University of Washington
> Seattle, WA 98195-2350
>
--
*Laura Arjona *
Washington Research Foundation Innovation Postdoctoral Fellow in
Neuroengineering
*Paul G. Allen School of Computer Science & Engineering*
185 E Stevens Way NE
University of Washington
Seattle, WA 98195-2350
