feat: find_beat(): bias with f_hint - once we know the beat freq

beat.py

@@ -1,5 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt  # for debug only
+from sqi import gauss
 
 # note: may be called ZxingDetector instead?
 class SsfZxing:
@@ -161,14 +162,25 @@ class RegularBeatFinder:
     num_freqs = 200  #: number of freq steps to evaluate
     range_f1 = 0.5  #: lowest detection frequency in Hz
     range_f2 = 4.0  #: highest detection frequency in Hz
+    f_bias_width = 0.2  #: gaussian std relative to num_freqs within f1..f2
 
     def __init__(self): pass
 
-    def find_beat(self, fs, ssf_zxings, debug_fe=False, debug_i=None):
+    def find_beat(self, fs, ssf_zxings, f_hint=None, debug_fe=False, debug_i=None):
         """Find the optimal beat frequency."""
         act_ibis = np.diff(ssf_zxings)
         # evaluate mean absolute errors for all frequencies
         freqs, freq_errs = self._get_opt_ibi_freq_2(fs, act_ibis, debug_i)
+        # bias with f_hint - once we know the beat freq, make it more likely for it to be found everywhere
+        nf, f1, f2 = RegularBeatFinder.num_freqs, RegularBeatFinder.range_f2, RegularBeatFinder.range_f1
+        bias = gauss(
+            nf,
+            (f_hint - f1) / (f2 - f1) * nf,
+            RegularBeatFinder.f_bias_width * nf
+        )
+        freqs_bias = 1.0 / (np.max(bias)+bias)  # make 'f_hint' at most 2x more likely -- (1+bias) if normalized
+        freq_errs *= freqs_bias
+        #
         if debug_fe:
             plt.figure(figsize=(8,2))
             plt.plot(freqs, freq_errs)