shaper_calibrate: Estimate max_accel to avoid excessive smoothing
Signed-off-by: Dmitry Butyugin <dmbutyugin@google.com>
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@ -97,10 +97,8 @@ def get_3hump_ei_shaper(shaper_freq, damping_ratio):
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T = [0., .5*t_d, t_d, 1.5*t_d, 2.*t_d]
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T = [0., .5*t_d, t_d, 1.5*t_d, 2.*t_d]
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return (A, T)
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return (A, T)
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def get_shaper_smoothing(shaper):
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def get_shaper_smoothing(shaper, accel=5000, scv=5.):
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# Smoothing calculation params
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half_accel = accel * .5
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HALF_ACCEL = 2500.
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SCV = 5.
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A, T = shaper
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A, T = shaper
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inv_D = 1. / sum(A)
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inv_D = 1. / sum(A)
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@ -113,8 +111,8 @@ def get_shaper_smoothing(shaper):
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for i in range(n):
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for i in range(n):
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if T[i] >= ts:
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if T[i] >= ts:
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# Calculate offset for one of the axes
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# Calculate offset for one of the axes
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offset_90 += A[i] * (SCV + HALF_ACCEL * (T[i]-ts)) * (T[i]-ts)
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offset_90 += A[i] * (scv + half_accel * (T[i]-ts)) * (T[i]-ts)
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offset_180 += A[i] * HALF_ACCEL * (T[i]-ts)**2
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offset_180 += A[i] * half_accel * (T[i]-ts)**2
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offset_90 *= inv_D * math.sqrt(2.)
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offset_90 *= inv_D * math.sqrt(2.)
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offset_180 *= inv_D
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offset_180 *= inv_D
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return max(offset_90, offset_180)
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return max(offset_90, offset_180)
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@ -164,7 +162,7 @@ class CalibrationData:
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CalibrationResult = collections.namedtuple(
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CalibrationResult = collections.namedtuple(
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'CalibrationResult',
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'CalibrationResult',
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('name', 'freq', 'vals', 'vibrs', 'smoothing', 'score'))
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('name', 'freq', 'vals', 'vibrs', 'smoothing', 'score', 'max_accel'))
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class ShaperCalibrate:
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class ShaperCalibrate:
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def __init__(self, printer):
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def __init__(self, printer):
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@ -333,6 +331,7 @@ class ShaperCalibrate:
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shaper_vals = np.maximum(shaper_vals, vals)
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shaper_vals = np.maximum(shaper_vals, vals)
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if vibrations > shaper_vibrations:
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if vibrations > shaper_vibrations:
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shaper_vibrations = vibrations
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shaper_vibrations = vibrations
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max_accel = self.find_shaper_max_accel(shaper)
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# The score trying to minimize vibrations, but also accounting
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# The score trying to minimize vibrations, but also accounting
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# the growth of smoothing. The formula itself does not have any
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# the growth of smoothing. The formula itself does not have any
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# special meaning, it simply shows good results on real user data
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# special meaning, it simply shows good results on real user data
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@ -341,7 +340,7 @@ class ShaperCalibrate:
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CalibrationResult(
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CalibrationResult(
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name=shaper_cfg.name, freq=test_freq, vals=shaper_vals,
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name=shaper_cfg.name, freq=test_freq, vals=shaper_vals,
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vibrs=shaper_vibrations, smoothing=shaper_smoothing,
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vibrs=shaper_vibrations, smoothing=shaper_smoothing,
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score=shaper_score))
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score=shaper_score, max_accel=max_accel))
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if best_res is None or best_res.vibrs > results[-1].vibrs:
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if best_res is None or best_res.vibrs > results[-1].vibrs:
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# The current frequency is better for the shaper.
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# The current frequency is better for the shaper.
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best_res = results[-1]
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best_res = results[-1]
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@ -353,6 +352,30 @@ class ShaperCalibrate:
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selected = res
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selected = res
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return selected
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return selected
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def _bisect(self, func):
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left = right = 1.
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while not func(left):
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right = left
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left *= .5
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if right == left:
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while func(right):
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right *= 2.
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while right - left > 1e-8:
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middle = (left + right) * .5
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if func(middle):
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left = middle
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else:
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right = middle
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return left
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def find_shaper_max_accel(self, shaper):
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# Just some empirically chosen value which produces good projections
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# for max_accel without much smoothing
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TARGET_SMOOTHING = 0.12
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max_accel = self._bisect(lambda test_accel: get_shaper_smoothing(
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shaper, test_accel) <= TARGET_SMOOTHING)
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return max_accel
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def find_best_shaper(self, calibration_data, max_smoothing, logger=None):
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def find_best_shaper(self, calibration_data, max_smoothing, logger=None):
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best_shaper = None
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best_shaper = None
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all_shapers = []
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all_shapers = []
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@ -364,6 +387,9 @@ class ShaperCalibrate:
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"(vibrations = %.1f%%, smoothing ~= %.3f)" % (
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"(vibrations = %.1f%%, smoothing ~= %.3f)" % (
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shaper.name, shaper.freq, shaper.vibrs * 100.,
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shaper.name, shaper.freq, shaper.vibrs * 100.,
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shaper.smoothing))
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shaper.smoothing))
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logger("To avoid too much smoothing with '%s', suggested "
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"max_accel <= %.0f mm/sec^2" % (
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shaper.name, round(shaper.max_accel / 100.) * 100.))
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all_shapers.append(shaper)
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all_shapers.append(shaper)
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if (best_shaper is None or shaper.score * 1.2 < best_shaper.score or
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if (best_shaper is None or shaper.score * 1.2 < best_shaper.score or
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(shaper.score * 1.1 < best_shaper.score and
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(shaper.score * 1.1 < best_shaper.score and
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@ -100,9 +100,10 @@ def plot_freq_response(lognames, calibration_data, shapers,
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ax2.set_ylabel('Shaper vibration reduction (ratio)')
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ax2.set_ylabel('Shaper vibration reduction (ratio)')
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best_shaper_vals = None
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best_shaper_vals = None
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for shaper in shapers:
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for shaper in shapers:
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label = "%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f)" % (
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label = "%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)" % (
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shaper.name.upper(), shaper.freq,
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shaper.name.upper(), shaper.freq,
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shaper.vibrs * 100., shaper.smoothing)
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shaper.vibrs * 100., shaper.smoothing,
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round(shaper.max_accel / 100.) * 100.)
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linestyle = 'dotted'
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linestyle = 'dotted'
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if shaper.name == selected_shaper:
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if shaper.name == selected_shaper:
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linestyle = 'dashdot'
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linestyle = 'dashdot'
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