Smoothing matrix stored in memory instead of file

src/lasp/tools/tools.py

@@ -34,11 +34,8 @@ __all__ = ['SmoothingType', 'smoothSpectralData', 'SmoothingWidth']
 
 from enum import Enum, unique
 import bisect
-import codecs
 import copy
-import json
 import numpy as np
-import os
 
 
 @unique
@@ -206,29 +203,20 @@ def smoothSpectralData(freq, M, sw: SmoothingWidth,
         Psm[0] = P[0]              # Reuse old value in case first data..
                                    # ..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
+    # Re-use smoothing matrix Q if available. Otherwise, calculate.
+    # Store in dict 'Qdict'
     nfft = int(2*(len(freq)-1))
-    key = f"nfft{nfft}_Noct{Noct}"     # name
-    if os.path.isfile(fname):
-        with open(fname) as f:
-            Qdict = json.load(f)
-    else:
-        Qdict = {'Help': 'This file contains smoothing tables'}
-        json.dump(Qdict, codecs.open(fname, 'w', encoding='utf-8'),
-                  separators=(',', ':'), sort_keys=True, indent=4)
+    key = f"nfft{nfft}_Noct{Noct}"  # matrix name
+
+    if 'Qdict' not in globals():  # Guarantee Qdict exists
+        global Qdict
+        Qdict = {}
 
     if key in Qdict:
-        # Load = fast
-        Q = np.asarray(Qdict[key])
+        Q = Qdict[key]
     else:
-        # Calculate new matrix; store
         Q = smoothCalcMatrix(freq, sw)
-        Qdict[key] = Q.tolist()  # json cannot handle ndarray
-        json.dump(Qdict, codecs.open(fname, 'w', encoding='utf-8'),
-                  separators=(',', ':'), sort_keys=True, indent=4)
+        Qdict[key] = Q
 
     # Apply smoothing
     Psm = np.matmul(Q, P)