Patched FFT_arr_inplace(). Now it uses only simple arrays. re and im are stored like arr[2*i] = re, arr[2*i + 1] = im

FFT.py

@@ -28,8 +28,19 @@ def FFT_real(inp):
 #    out = [2 for val in out]
   if (mode == 2):
   
-  
-    out = FFT_arr_inplace([[val, 0] for val  in inp])
+    tmp = []
+    for re in inp:
+      tmp.append(re)
+      tmp.append(0)
+      
+    out = FFT_arr_inplace(tmp)
+    
+    tmp = out.copy()
+    out = []
+    for i in range(len(tmp)//2):
+      out.append([tmp[2*i], tmp[2*i+1]])
+    
+    
     #out = [val for val in np.abs(out)]
     print("FFT_calculated!")
     print("output:", out)
@@ -93,7 +104,8 @@ def FFT_arr_inplace(buf):
     In-place radix-2 DIT FFT для списка buf длины N=2^m, где каждый элемент — [re, im].
     Без массивов twiddle: твиддл на уровне обновляется рекуррентно.
     """
-  N = len(buf)/2
+
+  N = len(buf)//2
   # --- bit-reverse перестановка (чтобы бабочки шли последовательно) ---
   j = 0
   for i in range(1, N):
@@ -110,8 +122,8 @@ def FFT_arr_inplace(buf):
   while m <= N:
     half = m // 2
     # шаг угла Δ = 2π/m, базовый твиддл w_m = e^{-jΔ} => (cw, sw)
-    cΔ = math.cos(2.0 * math.pi / m)
-    sΔ = -math.sin(2.0 * math.pi / m)
+    c_delta = cos(2.0 * math.pi / m)
+    s_delta = -sin(2.0 * math.pi / m)
 
     for start in range(0, N, m):
       # w = e^{-j*0*Δ} = 1 + j0
@@ -121,6 +133,7 @@ def FFT_arr_inplace(buf):
         v_re, v_im = buf[(start + k + half)*2], buf[(start + k + half)*2 +1]
 
         # t = w * v
+        print("wr, v_re, wi, v_im:",wr, v_re, wi, v_im)
         t_re = wr * v_re - wi * v_im
         t_im = wr * v_im + wi * v_re
 
@@ -132,7 +145,7 @@ def FFT_arr_inplace(buf):
 
         # w *= w_m  (поворот на Δ с помощью рекуррентной формулы)
         # (wr + j wi) * (cΔ + j sΔ)
-        wr, wi = wr * cΔ - wi * sΔ, wr * sΔ + wi * cΔ
+        wr, wi = wr * c_delta - wi * s_delta, wr * s_delta + wi * c_delta
 
     m <<= 1
   return buf