Moved QAM into it's own file

main.py

@@ -7,42 +7,13 @@ from channel import channel_sim
 
 from serpar import parallelise, serialise
 
-
+import qam
-
-def qam(n, in_data):
-    """
-    Modulates into 4-QAM encoding, might change that number later.
-
-    Parameters:
-    n - number of channels to operate on
-    in_data -  m X n array, m symbols to run on
-
-    Output:
-    data 
-    """
-
-    #initialise output array
-    out_data = np.ndarray((len(in_data), n), dtype=np.csingle)
-
-    for i in range(len(in_data)):
-        for j in range(n):
-            
-            #4-QAM is nice
-            out_data[i][j] = 1 + 1j
-
-            # Just rotate 90 degrees for every number and you've got your encoding
-            for k in range(in_data[i][j]):
-                out_data[i][j] = out_data[i][j] * (1j)
-
-    return out_data
 
 def cyclic_prefix(n, in_data, prefix_len):
     out_data = np.ndarray((len(in_data), n + prefix_len), dtype=np.csingle)
 
 
 
-
-
 if __name__ == '__main__':
     with open('data.txt', 'r') as file:
         data = file.read()
@@ -57,11 +28,6 @@ if __name__ == '__main__':
 
     rx = channel_sim(tx)
     
-    plt.plot(tx[0], 'r')
-    plt.plot(rx[0], 'b')
-    plt.show()
-
-    print(channel_sim(pre_modulated))
 
 
 

qam.py

@@ -0,0 +1,52 @@
+import numpy as np
+from scipy.spatial.distances import euclidean
+
+qam_mapping_table = {
+    0 : 1 + 1j,
+    1 : -1 + 1j,
+    2 : -1 - 1j,
+    3 : 1 - 1j
+}
+
+def qam_demapping_table = { x, y for y, x in qam_mapping_table.items() }
+
+def modulate(in_data):
+    """
+    Modulates into 4-QAM encoding, might change that number later.
+
+    Parameters:
+    in_data -  m X n array, m symbols to run on
+
+    Output:
+    data 
+    """
+
+    #initialise output array
+    out_data = np.ndarray((len(in_data), len(in_data[0])), dtype=np.csingle)
+
+    for i in range(len(in_data)):
+        for j in range(len(in_data[0])):
+            out_data[i][j] = qam_mapping_table[in_data[i][j]]
+
+    return out_data
+
+def demodulate(n, in_data):
+    out_data = np.ndarray((len(in_data), len(in_data[0])), dtype=np.uint8)
+
+    # Just pull the constellation array data out
+    constellation = { x for x in qam_demapping_table.keys() }
+
+    for i in range(len(in_data)):
+        for j in range(len(in_data[0])):
+            distances = np.ndarray((len(constellation)), dtype=np.single)
+
+            # Here we have to map to the closest constellation point,
+            # because floating point error
+            for k in range(len(constellation)):
+                distances[k] = euclidean(in_data[i][j], constellation[i][j])
+
+            # output is the index of the constellation, essentially
+            # this may have to change if I want to generalise
+            out_data[i][j] = np.argmin(distances)
+
+    return out_data