test: Various fixes on python simulation code

test/ltpf.py

@@ -133,15 +133,6 @@ class Ltpf:
 
         (self.pitch_present, self.pitch_index) = (None, None)
 
-    def get_data(self):
-
-        return { 'active' : self.active,
-                 'pitch_index' : self.pitch_index }
-
-    def get_nbits(self):
-
-        return 1 + 10 * int(self.pitch_present)
-
 
 class LtpfAnalysis(Ltpf):
 
@@ -160,6 +151,15 @@ class LtpfAnalysis(Ltpf):
         self.pitch = 0
         self.nc = np.zeros(2)
 
+    def get_data(self):
+
+        return { 'active' : self.active,
+                 'pitch_index' : self.pitch_index }
+
+    def get_nbits(self):
+
+        return 1 + 10 * int(self.pitch_present)
+
     def correlate(self, x, n, k0, k1):
 
         return [ np.dot(x[:n], np.take(x, np.arange(n) - k)) \

test/run.py

@@ -37,4 +37,4 @@ for m in [ ( mdct    , "MDCT"                   ),
 
     ok = ok and ret
 
-exit(0 if ok else 1);
+exit(0 if ok else 1)

test/spec.py

@@ -68,6 +68,9 @@ class SpectrumAnalysis(SpectrumQuantization):
 
         self.g_idx = None
 
+        (noise_factor, xq, lastnz, nbits_residual_max, xg) = \
+            (None, None, None, None, None)
+
     def estimate_gain(self, x, nbits_spec, nbits_off, g_off):
 
         nbits = int(nbits_spec + nbits_off + 0.5)
@@ -402,6 +405,9 @@ class SpectrumSynthesis(SpectrumQuantization):
 
         super().__init__(dt, sr)
 
+        (lastnz, lsb_mode, g_idx) = \
+            (None, None, None)
+
     def fill_noise(self, bw, x, lastnz, f_nf, nf_seed):
 
         (i_nf, nf_start, nf_stop) = self.get_noise_indices(bw, x, lastnz)
@@ -537,7 +543,7 @@ class SpectrumSynthesis(SpectrumQuantization):
         nf_seed = sum(abs(x.astype(np.int)) * range(len(x)))
 
         zero_frame = (self.lastnz <= 2 and x[0] == 0 and x[1] == 0
-                      and self.g_idx <= 0 and nf >= 7)
+                      and self.g_idx <= 0 and f_nf >= 7)
 
         if self.lsb_mode == 0:
 

test/tns.py

@@ -68,7 +68,7 @@ class Tns:
         self.dt = dt
 
         (self.nfilters, self.lpc_weighting, self.rc_order, self.rc) = \
-            (None, None, None, None)
+            (None, None, [ None, None ], [ None, None ])
 
     def get_data(self):
 
@@ -133,7 +133,7 @@ class TnsAnalysis(Tns):
 
         return (r[0] / err, a)
 
-    def lpc_weighting(self, pred_gain, a):
+    def lpc_weight(self, pred_gain, a):
 
         gamma = 1 - (1 - 0.85) * (2 - pred_gain) / (2 - 1.5)
         return a * np.power(gamma, np.arange(len(a)))
@@ -199,7 +199,7 @@ class TnsAnalysis(Tns):
                 continue
 
             if self.lpc_weighting and pred_gain < 2:
-                a = self.lpc_weighting(pred_gain, a)
+                a = self.lpc_weight(pred_gain, a)
 
             rc = self.coeffs_reflexion(a)