diff options
Diffstat (limited to 'klippy/extras/shaper_calibrate.py')
| -rw-r--r-- | klippy/extras/shaper_calibrate.py | 24 |
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 |