diff options
Diffstat (limited to 'simulation/QAM_Fading/epy_block_0.py')
| -rw-r--r-- | simulation/QAM_Fading/epy_block_0.py | 74 |
1 files changed, 0 insertions, 74 deletions
diff --git a/simulation/QAM_Fading/epy_block_0.py b/simulation/QAM_Fading/epy_block_0.py deleted file mode 100644 index 48fe6e9..0000000 --- a/simulation/QAM_Fading/epy_block_0.py +++ /dev/null @@ -1,74 +0,0 @@ -""" -Embedded Python Blocks: - -Each time this file is saved, GRC will instantiate the first class it finds -to get ports and parameters of your block. The arguments to __init__ will -be the parameters. All of them are required to have default values! -""" - -import numpy as np -from numpy.fft import fft,ifft,fftshift -from gnuradio import gr - - -class blk(gr.sync_block): # other base classes are basic_block, decim_block, interp_block - """Embedded Python Block example - a simple multiply const""" - - def __init__(self, amplitudes=[], delays=[], los=True): # only default arguments here - """arguments to this function show up as parameters in GRC""" - gr.sync_block.__init__( - self, - name='Embedded Python Block', # will show up in GRC - in_sig=[np.complex64], - out_sig=[np.complex64] - ) - # if an attribute with the same name as a parameter is found, - # a callback is registered (properties work, too). - self.amplitudes = amplitudes - self.delays = delays - self.temp = [0] - # if los: - # self.amplitudes.append(1) - # self.delays.append(0) - self.los= 1 - #self.fir = - - def work(self, input_items, output_items): - """example: multiply with constant""" - inp = input_items[0] - oup = output_items[0] - - if len(self.amplitudes) != len(self.delays): - raise Exception("Amplitudes and Delay length dont match") - - # raise Exception("Delay length can't be one") - #if np.min(self.delays)<=1: - # raise Exception("Delay length can't be one") - max_len = np.max(self.delays) - sum_x = np.zeros(max_len) - for(a,d) in zip(self.amplitudes,self.delays): - # if d-1 <= 0: - # x = np.concatenate([[a], np.zeros(max_len-1)]) - # else: - x = np.concatenate([np.zeros(d-1), [a], np.zeros(max_len-d)]) - sum_x += x - - sum_x[0] = self.los - print(sum_x) - - #H_int = fft(sum_x) - - #h = ifft(H_int) - - #h[0]=1 - - y = np.convolve(inp, sum_x) - - y+=np.concatenate([self.temp,np.zeros(len(y)-len(self.temp))]) - - - oup[:] = y[:len(inp)] - self.temp = y[len(inp):] - - - return len(oup)
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