mirror of https://github.com/Desuuuu/klipper.git
graph_extruder: Internal updates to graph calculation
Signed-off-by: Kevin O'Connor <kevin@koconnor.net>
This commit is contained in:
Kevin O'Connor
parent
17f2b4e960
commit
0e37f8c9b3
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@ -1,7 +1,7 @@
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#!/usr/bin/env python2
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# Generate extruder pressure advance motion graphs
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#
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# Copyright (C) 2019 Kevin O'Connor <kevin@koconnor.net>
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# Copyright (C) 2019-2020 Kevin O'Connor <kevin@koconnor.net>
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#
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# This file may be distributed under the terms of the GNU GPLv3 license.
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import math, optparse, datetime
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@ -17,7 +17,7 @@ INV_SEG_TIME = 1. / SEG_TIME
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# List of moves: [(start_v, end_v, move_t), ...]
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Moves = [
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(0., 0., .200),
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(0., 0., .100),
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(0., 100., None), (100., 100., .200), (100., 60., None),
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(60., 100., None), (100., 100., .200), (100., 0., None),
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(0., 0., .300)
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@ -47,85 +47,111 @@ def gen_positions():
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start_t = end_t
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return out
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######################################################################
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# List helper functions
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######################################################################
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MARGIN_TIME = 0.050
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def time_to_index(t):
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return int(t * INV_SEG_TIME + .5)
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def indexes(positions):
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drop = time_to_index(MARGIN_TIME)
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return range(drop, len(positions)-drop)
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def trim_lists(*lists):
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keep = len(lists[0]) - time_to_index(2. * MARGIN_TIME)
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for l in lists:
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del l[keep:]
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######################################################################
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# Common data filters
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######################################################################
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# Generate estimated first order derivative
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def gen_deriv(data):
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return [0.] + [(data[i+1] - data[i]) * INV_SEG_TIME
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for i in range(len(data)-1)]
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def time_to_index(t):
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return int(t * INV_SEG_TIME + .5)
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# Simple average between two points smooth_time away
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def calc_average(positions, smooth_time):
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offset = time_to_index(smooth_time * .5)
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out = [0.] * len(positions)
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for i in indexes(positions):
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out[i] = .5 * (positions[i-offset] + positions[i+offset])
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return out
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# Average (via integration) of smooth_time range
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def calc_smooth(positions, smooth_time):
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offset = time_to_index(smooth_time * .5)
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weight = 1. / (2*offset - 1)
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out = [0.] * len(positions)
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for i in indexes(positions):
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out[i] = sum(positions[i-offset+1:i+offset]) * weight
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return out
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# Time weighted average (via integration) of smooth_time range
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def calc_weighted(positions, smooth_time):
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offset = time_to_index(smooth_time * .5)
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weight = 1. / offset**2
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out = [0.] * len(positions)
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for i in indexes(positions):
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weighted_data = [positions[j] * (offset - abs(j-i))
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for j in range(i-offset, i+offset)]
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out[i] = sum(weighted_data) * weight
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return out
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######################################################################
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# Pressure advance
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######################################################################
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PA_HALF_SMOOTH_T = .040 / 2.
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SMOOTH_TIME = .040
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PRESSURE_ADVANCE = .045
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# Calculate raw pressure advance positions
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def calc_pa_raw(t, positions):
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def calc_pa_raw(positions):
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pa = PRESSURE_ADVANCE * INV_SEG_TIME
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i = time_to_index(t)
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return positions[i] + pa * (positions[i+1] - positions[i])
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out = [0.] * len(positions)
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for i in indexes(positions):
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out[i] = positions[i] + pa * (positions[i+1] - positions[i])
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return out
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# Pressure advance smoothed using average velocity (for reference only)
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def calc_pa_average(t, positions):
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pa_factor = PRESSURE_ADVANCE / (2. * PA_HALF_SMOOTH_T)
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base_pos = positions[time_to_index(t)]
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start_pos = positions[time_to_index(t - PA_HALF_SMOOTH_T)]
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end_pos = positions[time_to_index(t + PA_HALF_SMOOTH_T)]
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return base_pos + (end_pos - start_pos) * pa_factor
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# Pressure advance with simple time smoothing (for reference only)
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def calc_pa_smooth(t, positions):
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start_index = time_to_index(t - PA_HALF_SMOOTH_T) + 1
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end_index = time_to_index(t + PA_HALF_SMOOTH_T)
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pa = PRESSURE_ADVANCE * INV_SEG_TIME
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pa_data = [positions[i] + pa * (positions[i+1] - positions[i])
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for i in range(start_index, end_index)]
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return sum(pa_data) / (end_index - start_index)
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# Calculate pressure advance smoothed using a "weighted average"
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def calc_pa_weighted(t, positions):
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base_index = time_to_index(t)
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start_index = time_to_index(t - PA_HALF_SMOOTH_T) + 1
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end_index = time_to_index(t + PA_HALF_SMOOTH_T)
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diff = .5 * (end_index - start_index)
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pa = PRESSURE_ADVANCE * INV_SEG_TIME
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pa_data = [(positions[i] + pa * (positions[i+1] - positions[i]))
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* (diff - abs(i-base_index))
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for i in range(start_index, end_index)]
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return sum(pa_data) / diff**2
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# Pressure advance after smoothing
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def calc_pa(positions):
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return calc_weighted(calc_pa_raw(positions), SMOOTH_TIME)
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######################################################################
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# Plotting and startup
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######################################################################
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MARGIN_TIME = 0.100
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def plot_motion():
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# Nominal motion
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positions = gen_positions()
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drop = int(MARGIN_TIME * INV_SEG_TIME)
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times = [SEG_TIME * t for t in range(len(positions))][drop:-drop]
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velocities = gen_deriv(positions[drop:-drop])
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velocities = gen_deriv(positions)
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accels = gen_deriv(velocities)
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# Motion with pressure advance
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pa_positions = [calc_pa_raw(t, positions) for t in times]
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pa_positions = calc_pa_raw(positions)
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pa_velocities = gen_deriv(pa_positions)
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# Smoothed motion
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sm_positions = [calc_pa_weighted(t, positions) for t in times]
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sm_positions = calc_pa(positions)
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sm_velocities = gen_deriv(sm_positions)
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# Build plot
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shift_times = [t - MARGIN_TIME for t in times]
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times = [SEG_TIME * i for i in range(len(positions))]
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trim_lists(times, velocities, accels,
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pa_positions, pa_velocities,
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sm_positions, sm_velocities)
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fig, ax1 = matplotlib.pyplot.subplots(nrows=1, sharex=True)
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ax1.set_title("Extruder Velocity")
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ax1.set_ylabel('Velocity (mm/s)')
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pa_plot, = ax1.plot(shift_times, pa_velocities, 'r',
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pa_plot, = ax1.plot(times, pa_velocities, 'r',
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label='Pressure Advance', alpha=0.3)
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nom_plot, = ax1.plot(shift_times, velocities, 'black', label='Nominal')
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sm_plot, = ax1.plot(shift_times, sm_velocities, 'g', label='Smooth PA',
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alpha=0.9)
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nom_plot, = ax1.plot(times, velocities, 'black', label='Nominal')
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sm_plot, = ax1.plot(times, sm_velocities, 'g', label='Smooth PA', alpha=0.9)
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fontP = matplotlib.font_manager.FontProperties()
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fontP.set_size('x-small')
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ax1.legend(handles=[nom_plot, pa_plot, sm_plot], loc='best', prop=fontP)
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