diff --git a/scripts/graph_extruder.py b/scripts/graph_extruder.py
index 1a5bfb6c2..241f727f2 100755
--- a/scripts/graph_extruder.py
+++ b/scripts/graph_extruder.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python2
# Generate extruder pressure advance motion graphs
#
-# Copyright (C) 2019 Kevin O'Connor <kevin@koconnor.net>
+# Copyright (C) 2019-2020 Kevin O'Connor <kevin@koconnor.net>
#
# This file may be distributed under the terms of the GNU GPLv3 license.
import math, optparse, datetime
@@ -17,7 +17,7 @@ INV_SEG_TIME = 1. / SEG_TIME
# List of moves: [(start_v, end_v, move_t), ...]
Moves = [
- (0., 0., .200),
+ (0., 0., .100),
(0., 100., None), (100., 100., .200), (100., 60., None),
(60., 100., None), (100., 100., .200), (100., 0., None),
(0., 0., .300)
@@ -47,85 +47,111 @@ def gen_positions():
start_t = end_t
return out
+
+######################################################################
+# List helper functions
+######################################################################
+
+MARGIN_TIME = 0.050
+
+def time_to_index(t):
+ return int(t * INV_SEG_TIME + .5)
+
+def indexes(positions):
+ drop = time_to_index(MARGIN_TIME)
+ return range(drop, len(positions)-drop)
+
+def trim_lists(*lists):
+ keep = len(lists[0]) - time_to_index(2. * MARGIN_TIME)
+ for l in lists:
+ del l[keep:]
+
+
+######################################################################
+# Common data filters
+######################################################################
+
+# Generate estimated first order derivative
def gen_deriv(data):
return [0.] + [(data[i+1] - data[i]) * INV_SEG_TIME
for i in range(len(data)-1)]
-def time_to_index(t):
- return int(t * INV_SEG_TIME + .5)
+# Simple average between two points smooth_time away
+def calc_average(positions, smooth_time):
+ offset = time_to_index(smooth_time * .5)
+ out = [0.] * len(positions)
+ for i in indexes(positions):
+ out[i] = .5 * (positions[i-offset] + positions[i+offset])
+ return out
+
+# Average (via integration) of smooth_time range
+def calc_smooth(positions, smooth_time):
+ offset = time_to_index(smooth_time * .5)
+ weight = 1. / (2*offset - 1)
+ out = [0.] * len(positions)
+ for i in indexes(positions):
+ out[i] = sum(positions[i-offset+1:i+offset]) * weight
+ return out
+
+# Time weighted average (via integration) of smooth_time range
+def calc_weighted(positions, smooth_time):
+ offset = time_to_index(smooth_time * .5)
+ weight = 1. / offset**2
+ out = [0.] * len(positions)
+ for i in indexes(positions):
+ weighted_data = [positions[j] * (offset - abs(j-i))
+ for j in range(i-offset, i+offset)]
+ out[i] = sum(weighted_data) * weight
+ return out
######################################################################
# Pressure advance
######################################################################
-PA_HALF_SMOOTH_T = .040 / 2.
+SMOOTH_TIME = .040
PRESSURE_ADVANCE = .045
# Calculate raw pressure advance positions
-def calc_pa_raw(t, positions):
+def calc_pa_raw(positions):
pa = PRESSURE_ADVANCE * INV_SEG_TIME
- i = time_to_index(t)
- return positions[i] + pa * (positions[i+1] - positions[i])
+ out = [0.] * len(positions)
+ for i in indexes(positions):
+ out[i] = positions[i] + pa * (positions[i+1] - positions[i])
+ return out
-# Pressure advance smoothed using average velocity (for reference only)
-def calc_pa_average(t, positions):
- pa_factor = PRESSURE_ADVANCE / (2. * PA_HALF_SMOOTH_T)
- base_pos = positions[time_to_index(t)]
- start_pos = positions[time_to_index(t - PA_HALF_SMOOTH_T)]
- end_pos = positions[time_to_index(t + PA_HALF_SMOOTH_T)]
- return base_pos + (end_pos - start_pos) * pa_factor
-
-# Pressure advance with simple time smoothing (for reference only)
-def calc_pa_smooth(t, positions):
- start_index = time_to_index(t - PA_HALF_SMOOTH_T) + 1
- end_index = time_to_index(t + PA_HALF_SMOOTH_T)
- pa = PRESSURE_ADVANCE * INV_SEG_TIME
- pa_data = [positions[i] + pa * (positions[i+1] - positions[i])
- for i in range(start_index, end_index)]
- return sum(pa_data) / (end_index - start_index)
-
-# Calculate pressure advance smoothed using a "weighted average"
-def calc_pa_weighted(t, positions):
- base_index = time_to_index(t)
- start_index = time_to_index(t - PA_HALF_SMOOTH_T) + 1
- end_index = time_to_index(t + PA_HALF_SMOOTH_T)
- diff = .5 * (end_index - start_index)
- pa = PRESSURE_ADVANCE * INV_SEG_TIME
- pa_data = [(positions[i] + pa * (positions[i+1] - positions[i]))
- * (diff - abs(i-base_index))
- for i in range(start_index, end_index)]
- return sum(pa_data) / diff**2
+# Pressure advance after smoothing
+def calc_pa(positions):
+ return calc_weighted(calc_pa_raw(positions), SMOOTH_TIME)
######################################################################
# Plotting and startup
######################################################################
-MARGIN_TIME = 0.100
-
def plot_motion():
# Nominal motion
positions = gen_positions()
- drop = int(MARGIN_TIME * INV_SEG_TIME)
- times = [SEG_TIME * t for t in range(len(positions))][drop:-drop]
- velocities = gen_deriv(positions[drop:-drop])
+ velocities = gen_deriv(positions)
+ accels = gen_deriv(velocities)
# Motion with pressure advance
- pa_positions = [calc_pa_raw(t, positions) for t in times]
+ pa_positions = calc_pa_raw(positions)
pa_velocities = gen_deriv(pa_positions)
# Smoothed motion
- sm_positions = [calc_pa_weighted(t, positions) for t in times]
+ sm_positions = calc_pa(positions)
sm_velocities = gen_deriv(sm_positions)
# Build plot
- shift_times = [t - MARGIN_TIME for t in times]
+ times = [SEG_TIME * i for i in range(len(positions))]
+ trim_lists(times, velocities, accels,
+ pa_positions, pa_velocities,
+ sm_positions, sm_velocities)
fig, ax1 = matplotlib.pyplot.subplots(nrows=1, sharex=True)
ax1.set_title("Extruder Velocity")
ax1.set_ylabel('Velocity (mm/s)')
- pa_plot, = ax1.plot(shift_times, pa_velocities, 'r',
+ pa_plot, = ax1.plot(times, pa_velocities, 'r',
label='Pressure Advance', alpha=0.3)
- nom_plot, = ax1.plot(shift_times, velocities, 'black', label='Nominal')
- sm_plot, = ax1.plot(shift_times, sm_velocities, 'g', label='Smooth PA',
- alpha=0.9)
+ nom_plot, = ax1.plot(times, velocities, 'black', label='Nominal')
+ sm_plot, = ax1.plot(times, sm_velocities, 'g', label='Smooth PA', alpha=0.9)
fontP = matplotlib.font_manager.FontProperties()
fontP.set_size('x-small')
ax1.legend(handles=[nom_plot, pa_plot, sm_plot], loc='best', prop=fontP)