Added utility scripts (RRC filtering)

utils/filter_preamble.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import matplotlib.pyplot as pp
+import rrc
+
+pre = [1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, -1, 1,
+        -1, -1, -1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1,
+        1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1, -1,
+        -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1,
+        -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1,
+        -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1,
+        1, -1, -1, 1, 1, 1, 1]
+
+pre_up = np.zeros(2 * len(pre) + 1)
+
+pre_up[1::2] = pre
+
+rrc_coef = rrc.rrc_design(150, 2)
+
+pre_flt = np.convolve(pre_up, rrc_coef, mode='same')
+
+print(list(pre_flt))
+
+pp.plot(pre_up)
+pp.plot(pre_flt)
+pp.show()

utils/rrc.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+
+import numpy as np
+
+def rrc_design(N, samples_per_symbol, roll_off = 0.35):
+    coef = np.zeros(N)
+
+    k0 = N/2
+    Ts = samples_per_symbol
+
+    for k in range(len(coef)):
+        t = k - k0
+
+        if t == 0:
+            coef[k] = 1 / np.sqrt(Ts) * (1 - roll_off + 4 * roll_off / np.pi)
+        elif abs(t) == (Ts / (4*roll_off)):
+            coef[k] = roll_off / np.sqrt(2 * Ts) * \
+                    ( (1 + 2/np.pi) * np.sin(np.pi / (4 * roll_off)) + \
+                        (1 - 2/np.pi) * np.cos(np.pi / (4 * roll_off)) )
+        else:
+            coef[k] = 1 / np.sqrt(Ts) * \
+                    ( np.sin(np.pi * t / Ts * (1 - roll_off)) + 4 * roll_off * t / Ts * np.cos(np.pi * t / Ts * (1 + roll_off)) ) / \
+                    ( np.pi * t / Ts * (1 - (4 * roll_off * t / Ts)**2) )
+
+    return coef
+
+if __name__ == "__main__":
+    import matplotlib.pyplot as pp
+
+    c = rrc_design(50, 5, 0.35)
+    print(np.sum(c))
+    pp.plot(c)
+    pp.show()