Merge remote-tracking branch 'origin/master'

1 files changed, 48 insertions, 19 deletions

diff --git a/src/gr-fadingui/python/multipath_fading.py b/src/gr-fadingui/python/multipath_fading.py
index 01f2dbe..02b3bb4 100644
--- a/src/gr-fadingui/python/multipath_fading.py
+++ b/src/gr-fadingui/python/multipath_fading.py
@@ -44,45 +44,74 @@ class multipath_fading(gr.sync_block):
         self.amplitudes = amplitudes
         self.delays = delays
         self.temp = [0]
-        # if los:
-        #     self.amplitudes.append(1)
-        #     self.delays.append(0)
-        self.los= 1
-        #self.fir = 
+        log.debug(los) #TO DO: True False unterscheidung 
+        if los == True:
+            self.los = 1
+            log.debug("Los True")
+        else: 
+            self.los = 0
+            log.debug("Los False")
+
+
+        
+        
 
     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):
+        if len(self.amplitudes) != len(self.delays):    # Test: Es muss gleich viele Werte für Delays und Amplituden haben.
             raise Exception("Amplitudes and Delay length dont match")
 
+
+        #TO DO negativ check 
+
+        
+
         #    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):
+
+        #max_len = np.max(self.delays) #Max Werte herausfinden für länge 
+        #sum_x = np.zeros(max_len)
+
+        max_order = 2 * np.floor(np.max(self.delays)) + 1
+        max_samples = np.arange(0, max_order +1) 
+        max_len = len(max_samples) #Für Filter
+
+        sum_x = np.zeros(int(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
+            # else:    
+            order = 2 * np.floor(d) + 1
+
+            skip = np.floor(d) - (order - 1) / 2 #M sollte immer 0 sein 
+            assert skip >= 0
+
+            samples = np.arange(0, order +1)  
+
+            h = a*(np.sinc(samples-d)) #sinc
+            h_len = np.concatenate([h, np.zeros(max_len-len(h))])
+
+            sum_x += h_len
+
+            #x = np.concatenate([np.zeros(d-1), [a], np.zeros(max_len-d)])
+            #sum_x += x
         
         sum_x[0] = self.los
-        log.debug(sum_x)
-        
-        #H_int = fft(sum_x)
-
-        #h = ifft(H_int)
+        #log.debug(sum_x)
 
-        #h[0]=1
 
         y = np.convolve(inp, sum_x)
         
+        # signal_shifted = np.convolve(h, inp, mode='full')
+        # y = signal_shifted
+
+        #y+=np.concatenate([self.temp,np.zeros(len(y)-len(self.temp))])
         y+=np.concatenate([self.temp,np.zeros(len(y)-len(self.temp))])
-        
 
         oup[:] = y[:len(inp)]
         self.temp = y[len(inp):]