Hi Chad,
so in that case, the trick would probably be writing good unit tests!
For example, you could take the `for` loop out of your work, and put it
into a method of its own:
import numpy
from gnuradio import gr
from yall1 import *
class yall1_reconstruction_cc(gr.sync_block):
"""
Yall1_reconstruction_block
"""
def __init__(self,n,m):
self.N = n
self.M = m
phi =
np.load("/home/chad/Desktop/PROJECT/Python/Matrices/phi_mtx%(M)dx%(N)d.npy"
%{"M":self.M,"N":self.N})
psi =
np.load("/home/chad/Desktop/PROJECT/Python/Matrices/psi_mtx%(N)dx%(N)d.npy"
%{"N":self.N})
self.alpha = np.dot(phi,psi)
gr.sync_block.__init__(self,
name="yall1_reconstruction",
in_sig=[(np.complex64,self.M)],
out_sig=[(np.complex64,self.N)])
def work(self, input_items, output_items):
in0 = input_items[0]
output_items[0][:] = self._algo(in0)
return len(output_items[0])
def _algo(self, in):
out = np.zeros((size,self.N),dtype = np.complex64)
for i, vector in enumerate(in):
recon = yall1(self.alpha, vector.reshape(self.M,1))*4.6
out[i] = recon.reshape(self.N,)
return out
That way, you can write a unit test that first checks whether your for
loop does exactly what you think it does for "artificial" input, without
having to use the block in a GNU Radio flow graph.
It is does, the next step would be to write a unit test that uses
well-known input in a vector source, connects that to your block, and
connects that to a vector sink; validate .data() of that sink.
Best regards,
Marcus
On 10/30/2015 11:38 AM, Chad R wrote:
> Hi Marcus
>
> It's and adaption of the YALL1 Basis Pursuit Algorithm[1]. I
> simplified it a lot as I knew the case of signals I would be using so
> I have no need to parse it opts at run-time and can just leave those
> parameters constant. So I've adapted it that all I need to pass is the
> compressed vector and compression matrix. My Python implementation of
> the YALL1 algorithm works fine when I run it in a Python program but
> gives funny results in the GRC implementation. I'll look at what you
> said and get back to you if I don't understand something. Thanks for
> your help.
>
> Kind Regards
> Chad Richts
>
> [1] User’s Guide for YALL1: Your ALgorithms for L1
> Optimization: http://www.caam.rice.edu/~zhang/reports/tr0917.pdf
> <http://www.caam.rice.edu/%7Ezhang/reports/tr0917.pdf>
>
> On Thu, Oct 29, 2015 at 9:04 PM, Marcus Müller
> <marcus.muel...@ettus.com <mailto:marcus.muel...@ettus.com>> wrote:
>
> Hi Chad,
>
> thanks for re-posting your code; it's much clearer to read now.
> I'm not quite sure what I'm looking at now: Is this your own
> implementation of the yall1 minimization function, or something
> that can be tested?
> If I remember correctly, the matlab call has a matrix input, and a
> "target" vector, and an options argument. Is there some accessible
> documentation.
>
> Other than my confusion whether this is a GNU Radio or a general
> yall1 question, your code looks pretty good. A few thing's I'd note:
>
> * self.alpha.shape should be right
> * check that input_items.shape and in0.shape are as you expect them
>
> Best regards,
> Marcus
>
>
> On 10/29/2015 01:27 PM, Chad R wrote:
>> Sorry. Something went wrong when I copied pasted it but my actual
>> code is:
>>
>> import numpy
>> from gnuradio import gr
>> from yall1 import *
>>
>> class yall1_reconstruction_cc(gr.sync_block):
>> """
>> Yall1_reconstruction_block
>> """
>> def __init__(self,n,m):
>> self.N = n
>> self.M = m
>> phi =
>> np.load("/home/chad/Desktop/PROJECT/Python/Matrices/phi_mtx%(M)dx%(N)d.npy"
>> %{"M":self.M,"N":self.N})
>> psi =
>> np.load("/home/chad/Desktop/PROJECT/Python/Matrices/psi_mtx%(N)dx%(N)d.npy"
>> %{"N":self.N})
>> self.alpha = np.dot(phi,psi)
>> gr.sync_block.__init__(self,
>> name="yall1_reconstruction",
>> in_sig=[(np.complex64,self.M)],
>> out_sig=[(np.complex64,self.N)])
>>
>> def work(self, input_items, output_items):
>> in0 = input_items[0]
>> size = np.shape(in0)[0]
>> out = np.zeros((size,self.N),dtype = np.complex64)
>> #out = yall1(self.alpha,in0[0]).reshape(self.N,)
>> for i in range(0,size):
>> recon = yall1(self.alpha, in0[i].reshape(self.M,1))*4.6
>> out[i] = recon.reshape(self.N,)
>> output_items[0][:] = out
>> return len(output_items[0])
>>
>>
>> On Thu, Oct 29, 2015 at 2:08 PM, Marcus Müller
>> <marcus.muel...@ettus.com <mailto:marcus.muel...@ettus.com>> wrote:
>>
>> Hi Chad,
>>
>> there's something wrong with the indention of the lines
>> between "def __init__" and "g.sync_block", and the same goes
>> for your work function; so that's my first stab at explaining
>> misbehaviour.
>>
>>
>> Best regards,
>> Marcus
>>
>>
>> On 29.10.2015 13:01, Chad R wrote:
>>> Good day every one
>>>
>>> I have implemented a Python block but I am not getting the
>>> results I expected. I get the results I expect at any
>>> frequency=samp_rate/2^n where n is any integer. My block
>>> makes use of a yall1 reconstruction algorithm to reconstruct
>>> a signal from M=100 to N=1024 vector.
>>> The code for my block is shown below:
>>>
>>> class yall1_reconstruction_cc(gr.sync_block):
>>> """
>>> Yall1_reconstruction_block
>>> """
>>> def __init__(self,n,m):
>>> self.N = n
>>> self.M = m
>>> phi =
>>>
>>> np.load("/home/chad/Desktop/PROJECT/Python/Matrices/phi_mtx%(M)dx%(N)d.npy"
>>> %{"M":self.M,"N":self.N})
>>> psi =
>>>
>>> np.load("/home/chad/Desktop/PROJECT/Python/Matrices/psi_mtx%(N)dx%(N)d.npy"
>>> %{"N":self.N})
>>> self.alpha = np.dot(phi,psi)
>>> gr.sync_block.__init__(self,
>>> name="yall1_reconstruction",
>>> in_sig=[(np.complex64,self.M)],
>>> out_sig=[(np.complex64,self.N)])
>>>
>>> def work(self, input_items, output_items):
>>> in0 = input_items[0]
>>> size = np.shape(in0)[0]
>>> out = np.zeros((size,self.N),dtype = np.complex64)
>>> #out = yall1(self.alpha,in0[0]).reshape(self.N,)
>>> for i in range(0,size):
>>> recon = yall1(self.alpha, in0[i].reshape(self.M,1))*4.7
>>> out[i] = recon.reshape(self.N,)
>>> output_items[0][:] = out
>>> return len(output_items[0])
>>>
>>> Have I implemented it right? or is my issue with my
>>> implementation? I read through the tutorials but I feel some
>>> aspects are quiet hard to follow.
>>>
>>> Thank you in advance for your help
>>>
>>> Chad Richs
>>>
>>>
>>> _______________________________________________
>>> Discuss-gnuradio mailing list
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>>> https://lists.gnu.org/mailman/listinfo/discuss-gnuradio
>>
>>
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>>
>
>
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