Replaced for loop by vector calculation, limit memory size of smoothing matrix

src/lasp/tools/tools.py

@@ -102,7 +102,7 @@ def smoothCalcMatrix(freq, sw: SmoothingWidth):
 
     # Initialize
     L = len(freq)
-    Q = np.zeros(shape=(L, L))
+    Q = np.zeros(shape=(L, L), dtype=np.float16)  # float16: keep size small
 
     x0 = 1 if freq[0] == 0 else 0  # Skip first data point if zero frequency
     # Loop over indices of raw frequency vector
@@ -136,13 +136,11 @@ def smoothCalcMatrix(freq, sw: SmoothingWidth):
         xl = xu-1 if xu-xl <= 0 else xl  # Guarantee window length of at least 1
 
         # Calculate window
-        gs = np.zeros(xu-xl)
-        for n, xi in enumerate(range(xl, xu)):
-            fi = freq[xi]  # current frequency
-            gs[n] = np.sqrt( 1/ ((1+((fi/fc - fc/fi)*(1.507*Noct))**6)) )
-
-        gs /= np.sum(gs)  # normalize: integral=1
-        Q[x, xl:xu] = gs  # add to matrix
+        xg = np.arange(xl, xu)  # indices
+        fg = freq[xg]           # [Hz] corresponding freq
+        gs = np.sqrt( 1/ ((1+((fg/fc - fc/fg)*(1.507*Noct))**6)) )  # raw windw
+        gs /= np.sum(gs)        # normalize: integral=1
+        Q[x, xl:xu] = gs        # add to matrix
 
     return Q
 
@@ -209,6 +207,8 @@ def smoothSpectralData(freq, M, sw: SmoothingWidth,
                                    # ..point is skipped. Not plotted any way.
 
     # Find smoothing matrix. Look it up, otherwise calculate and store.
+    # TODO: find a way to reduce file size, loading time, a good place to store
+    # TODO: also save the last table in memory
     fname = 'smoothing_tables.json'    # storage file
     nfft = int(2*(len(freq)-1))
     key = f"nfft{nfft}_Noct{Noct}"     # name