shaper_calibrate: Ignore vibrations below max_psd / 20 threshold

The input shaper can only reduce vibrations by 20x. Therefore,
the best-case achievable vibrations level is max(psd) / 20.
Any vibrations below that level can be ignored for the prupose
of input shaper scoring. This allows tuning script to ignore
wide tails of resonances, because it is not helpful to try to
reduce them with input shaping.

Signed-off-by: Dmitry Butyugin <dmbutyugin@google.com>

1 files changed, 16 insertions, 8 deletions

diff --git a/klippy/extras/shaper_calibrate.py b/klippy/extras/shaper_calibrate.py
index 8e7bb8e6..d948ae3e 100644
--- a/klippy/extras/shaper_calibrate.py
+++ b/klippy/extras/shaper_calibrate.py
@@ -10,6 +10,7 @@ MAX_FREQ = 200.
 WINDOW_T_SEC = 0.5
 MAX_SHAPER_FREQ = 150.
 
+SHAPER_VIBRATION_REDUCTION=20.
 TEST_DAMPING_RATIOS=[0.075, 0.1, 0.15]
 SHAPER_DAMPING_RATIO = 0.1
 
@@ -50,7 +51,7 @@ def get_mzv_shaper(shaper_freq, damping_ratio):
     return (A, T)
 
 def get_ei_shaper(shaper_freq, damping_ratio):
-    v_tol = 0.05 # vibration tolerance
+    v_tol = 1. / SHAPER_VIBRATION_REDUCTION # vibration tolerance
     df = math.sqrt(1. - damping_ratio**2)
     K = math.exp(-damping_ratio * math.pi / df)
     t_d = 1. / (shaper_freq * df)
@@ -64,7 +65,7 @@ def get_ei_shaper(shaper_freq, damping_ratio):
     return (A, T)
 
 def get_2hump_ei_shaper(shaper_freq, damping_ratio):
-    v_tol = 0.05 # vibration tolerance
+    v_tol = 1. / SHAPER_VIBRATION_REDUCTION # vibration tolerance
     df = math.sqrt(1. - damping_ratio**2)
     K = math.exp(-damping_ratio * math.pi / df)
     t_d = 1. / (shaper_freq * df)
@@ -81,7 +82,7 @@ def get_2hump_ei_shaper(shaper_freq, damping_ratio):
     return (A, T)
 
 def get_3hump_ei_shaper(shaper_freq, damping_ratio):
-    v_tol = 0.05 # vibration tolerance
+    v_tol = 1. / SHAPER_VIBRATION_REDUCTION # vibration tolerance
     df = math.sqrt(1. - damping_ratio**2)
     K = math.exp(-damping_ratio * math.pi / df)
     t_d = 1. / (shaper_freq * df)
@@ -302,8 +303,14 @@ class ShaperCalibrate:
     def _estimate_remaining_vibrations(self, shaper, test_damping_ratio,
                                        freq_bins, psd):
         vals = self._estimate_shaper(shaper, test_damping_ratio, freq_bins)
-        remaining_vibrations = (vals * psd).sum() / psd.sum()
-        return (remaining_vibrations, vals)
+        # The input shaper can only reduce the amplitude of vibrations by
+        # SHAPER_VIBRATION_REDUCTION times, so all vibrations below that
+        # threshold can be igonred
+        vibrations_threshold = psd.max() / SHAPER_VIBRATION_REDUCTION
+        remaining_vibrations = self.numpy.maximum(
+                vals * psd - vibrations_threshold, 0).sum()
+        all_vibrations = self.numpy.maximum(psd - vibrations_threshold, 0).sum()
+        return (remaining_vibrations / all_vibrations, vals)
 
     def fit_shaper(self, shaper_cfg, calibration_data, max_smoothing):
         np = self.numpy
@@ -335,7 +342,8 @@ class ShaperCalibrate:
             # The score trying to minimize vibrations, but also accounting
             # the growth of smoothing. The formula itself does not have any
             # special meaning, it simply shows good results on real user data
-            shaper_score = shaper_vibrations**1.5 * shaper_smoothing
+            shaper_score = shaper_smoothing * (shaper_vibrations**1.5 +
+                                               shaper_vibrations * .2 + .01)
             results.append(
                     CalibrationResult(
                         name=shaper_cfg.name, freq=test_freq, vals=shaper_vals,
@@ -392,10 +400,10 @@ class ShaperCalibrate:
                            shaper.name, round(shaper.max_accel / 100.) * 100.))
             all_shapers.append(shaper)
             if (best_shaper is None or shaper.score * 1.2 < best_shaper.score or
-                    (shaper.score * 1.1 < best_shaper.score and
+                    (shaper.score * 1.05 < best_shaper.score and
                         shaper.smoothing * 1.1 < best_shaper.smoothing)):
                 # Either the shaper significantly improves the score (by 20%),
-                # or it improves both the score and smoothing (by 10%)
+                # or it improves the score and smoothing (by 5% and 10% resp.)
                 best_shaper = shaper
         return best_shaper, all_shapers