-
Notifications
You must be signed in to change notification settings - Fork 4
/
fill_2d_shape.py
585 lines (504 loc) · 23.5 KB
/
fill_2d_shape.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
"""
Fill a 2D shape with an oriented cycle. See Algorithm 1 in Chermain et al.
2023.
"""
import argparse
import gc
import os
import time
import jax.numpy as jnp
import jax.random
import numpy as np
from jax import block_until_ready, device_get, device_put
from svgpathtools import svg2paths
import cglib.backend
import cglib.cycle
import cglib.fdm_aa
import cglib.gabor_filter
import cglib.grid
import cglib.grid.cell
import cglib.grid.edge
import cglib.limits
import cglib.math
import cglib.point_data
import cglib.polyline
import cglib.scalar
import cglib.sine_wave
import cglib.texture
import cglib.tree_util
import cglib.type
# Estimation of the number of polylines in the output of the algorithm. It is
# one, as the article's objective is to have a closed polyline filling a 2D
# shape. If the stitching step is skipped, this value needs to be increased.
POLYLINE_COUNT_ESTIMATION = 1
# If true, the starting point of the cycle will be the cycle's leftest point.
START_AT_LEFT = True
def run():
device_cpu, device_gpu = cglib.backend.get_cpu_and_gpu_devices()
parser = argparse.ArgumentParser(
description='Input: A json file containing all the input parameters. Output: A cycle with varying width.'
)
parser.add_argument("input_filename", help="Use the inputs in the file specified here.")
parser.add_argument("--nodumping", help="To not save intermediate data.")
args = parser.parse_args()
input_params_filename = args.input_filename
dump_intermediate_data = not args.nodumping
parameters = cglib.fdm_aa.Parameters()
parameters.load(input_params_filename)
log_file = open(parameters.log_filename, 'w', encoding="utf-8")
nozzle_width_derived_param = \
cglib.fdm_aa.compute_nozzle_width_derived_parameters(
parameters.nozzle_width,
parameters.layer_height_rt_nozzle_width)
# Load svg paths
# Transformations inside the SVG are ignored, so only SVG files without
# transformations are valid. Using Inkscape, ensure your contour is not
# associated with a layer to avoid implicit transformations. Be sure that
# the contour is a clockwise-oriented closed polyline. Holes are
# represented with counter-clockwise oriented closed polylines.
paths, _, svg_attributes = svg2paths(
parameters.svg_path, return_svg_attributes=True)
# The shape domain size is determined by the SVG width and heigh
# -2: remove the unit
svg_width = float(svg_attributes['width'][:-2])
svg_height = float(svg_attributes['height'][:-2])
# [x, y]
shape_domain_size = np.array([svg_width, svg_height])
# There are three different grids used to do the computation.
# 1. The first, i.e., `shape_domain_grid`, discretizes the 2D shape domain
# and is used by the sine wave evaluation algorithm and scalar field
# contouring algorithm.
# 2. The second, i.e., `shape_domain_grid_sqr`, is `shape_domain_grid`
# round up to the next power of two cell 2D count. It is used by the
# sine wave aligning algorithm. The power of two is required for the
# multigrid build.
# 3. The third grid, i.e., `trajectory_grid``, is the grid implicitly
# returned by the contouring algorithm. This grid has each edge
# associated with nothing or a unique contour point.
shape_domain_grid = cglib.fdm_aa.discretize_2dshape_domain_with_cells(
parameters.nozzle_width, shape_domain_size)
shape_domain_grid: cglib.grid.Grid = device_put(
shape_domain_grid, device_cpu)
shape_domain_grid_sqr = cglib.grid.roundup_power_of_2(shape_domain_grid)
trajectory_grid = cglib.scalar.grid2_contour_get_output_grid(
shape_domain_grid)
str_tmp = f"\nInput filename: {input_params_filename}\n\n"
str_tmp += f"shape_domain_grid: {shape_domain_grid}\n"
str_tmp += f"shape_domain_grid_sqr: {shape_domain_grid_sqr}"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
# Each point will be associated with a line (2 floats), a phase (1 float),
# and a constraint (1 float).
POINT_DATA_SIZE = 4
cycle_polyline, cycle_polyline_shape_dtype = cglib.polyline.create_from_2dgrid(
trajectory_grid.cell_ndcount,
device_cpu,
POLYLINE_COUNT_ESTIMATION,
POINT_DATA_SIZE)
# We have to compute the points associated with each cell of the shape
# domain grid.
# Then, we must compute the signed distance from the boundary for the
# points and the closest normal on the boundary.
shape_domain_grid_data = cglib.fdm_aa.ShapeDomainGridData()
parameters.create_SDF_filename(shape_domain_grid)
sdf_exist = os.path.exists(parameters.sdf_filename)
do_sdf_computation = not sdf_exist or parameters.force_sdf_computation
execution_times = cglib.fdm_aa.ExecutionTime()
# If the sdf is not cached or if the user forces the SDF computation
if do_sdf_computation:
str_tmp = f"shape_domain_grid_data compute cell points started"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
start = time.perf_counter()
shape_domain_grid_data.grid = shape_domain_grid
shape_domain_grid_data.cell_center_points = \
cglib.grid.cell.center_points(shape_domain_grid)
jitter_distance = \
nozzle_width_derived_param.domain_grid_points_perturbation_width / \
(parameters.nozzle_width * 0.5)
seed_jax = jax.random.PRNGKey(parameters.seed)
shape_domain_grid_data.cell_center_points_jittered = \
cglib.grid.cell.center_points_jittered(
shape_domain_grid,
seed_jax,
jitter_distance)
stop = time.perf_counter()
execution_times.cell_point_computation += stop - start
print("compute_boundary_cycles_from_svg_paths started")
start = time.perf_counter()
boundary_cycles = cglib.fdm_aa.compute_boundary_cycles_from_svg_paths(
shape_domain_size, paths, parameters.nozzle_width, device_cpu)
stop = time.perf_counter()
execution_times.boundary_cycles_from_svg_paths = stop - start
str_tmp = f"compute_boundary_cycles_from_svg_paths took {stop - start} s"
print(str_tmp)
log_file.write(str_tmp+'\n')
# Pack each cycle into one polygonal data in order to compute signed
# distance thereafter
boundary_polydata, boundary_normal_glyph = cglib.fdm_aa.cycle_to_polydata(
boundary_cycles, nozzle_width_derived_param.layer_height)
boundary_polydata.save(parameters.boundary_polydata_filename)
boundary_normal_glyph.save(parameters.boundary_normal_glyph_filename)
# Compute signed distance from boundary. More precisely, compute domain grid
# perturbed points distance from boundary, and sign the distance using
# nearest boundary normals
# Return also the normals
str_tmp = f"compute_signed_distance_from_boundary started"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
start = time.perf_counter()
signed_distance_from_boundary, closest_boundary_normal = \
cglib.fdm_aa.compute_signed_distance_from_boundary(
shape_domain_grid_data.cell_center_points_jittered,
boundary_polydata)
# Group data in the class
shape_domain_grid_data.ccpj_signed_distance_from_boundary = \
signed_distance_from_boundary
shape_domain_grid_data.ccpj_closest_boundary_normal = \
closest_boundary_normal[:, :2]
stop = time.perf_counter()
execution_times.compute_sdf = stop - start
str_tmp = "compute_signed_distance_from_boundary took "
str_tmp += f"{execution_times.compute_sdf} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
shape_domain_grid_data.save(parameters.sdf_filename)
else:
shape_domain_grid_data.load(parameters.sdf_filename)
# Convert from ndarray to tuple because a tuple is hashable, a property
# needed by the compilation process.
shape_domain_grid_data.cell_ndcount_tuple = tuple(
device_get(shape_domain_grid_data.grid.cell_ndcount))
borders = cglib.fdm_aa.create_borders(
parameters.nozzle_width, parameters.perimeter_count)
# Load the image textures
line_field_tex, line_mode_field_tex = \
cglib.fdm_aa.load_line_field_textures(
parameters.line_field_path,
parameters.line_mode_field_path,
device_cpu)
# Convert them to useable data
direction, direction_mode = cglib.fdm_aa.eval_textures(
shape_domain_grid_data.cell_center_points_jittered,
line_mode_field_tex,
line_field_tex,
shape_domain_size,
cglib.texture.InterpolationType.NEAREST)
# Put everything on GPU
direction = device_put(direction, device_gpu)
direction_mode = device_put(direction_mode, device_gpu)
shape_domain_grid_data.device_put(device_gpu)
compile_functions_param = cglib.fdm_aa.CompileFunctionParam(
parameters,
shape_domain_grid,
shape_domain_grid_sqr,
trajectory_grid,
cycle_polyline_shape_dtype,
device_cpu,
device_gpu)
compiled_functions, _ = \
cglib.fdm_aa.compile_functions(compile_functions_param, log_file)
print("\nEXECUTION STARTS\n")
# Init lines, phases and constraints based on inputs
start = time.perf_counter()
# DEBUG
# res = cglib.fdm_aa.init_grid_data(shape_domain_grid_data.ccpj_signed_distance_from_boundary,
# shape_domain_grid_data.cell_center_points_jittered,
# direction_mode,
# parameters.perimeter_count,
# shape_domain_grid_data.ccpj_closest_boundary_normal,
# parameters.nozzle_width,
# direction)
res = compiled_functions.init_grid_data(
shape_domain_grid_data.ccpj_signed_distance_from_boundary,
shape_domain_grid_data.cell_center_points_jittered,
direction_mode,
parameters.perimeter_count,
shape_domain_grid_data.ccpj_closest_boundary_normal,
parameters.nozzle_width,
direction)
shape_domain_grid_data.cell_center_points_jittered = res[0]
shape_domain_grid_data.ccpj_data = res[1]
one_run = res[2]
# Square the grid and its data
grid_sines = compiled_functions.shape_grid_data_sqr(
shape_domain_grid_data.cell_center_points_jittered,
shape_domain_grid_data.ccpj_data,
shape_domain_grid_data.grid
)
grid_sines = block_until_ready(grid_sines)
stop = time.perf_counter()
execution_times.init_grid_data = stop - start
str_tmp = f"Init grid data took {execution_times.init_grid_data} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
# Align sines
str_tmp = f"Align sines started"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
start = time.perf_counter()
# DEBUG
# grid_sines_aligned: cglib.point_data.GridPointData = cglib.sine_wave.multigrid_align(int(shape_domain_grid_sqr.cell_ndcount[0]), grid_sines, nozzle_width_derived_param.frequency, 32, True)
grid_sines_aligned: cglib.point_data.GridPointData = \
compiled_functions.sine_wave_multigrid_align(
grid_sines, nozzle_width_derived_param.frequency)
grid_sines_aligned = block_until_ready(grid_sines_aligned)
stop = time.perf_counter()
execution_times.sine_wave_multigrid_align = stop - start
str_tmp = f"Align sines took {execution_times.sine_wave_multigrid_align} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
# If needed, realigned
if not one_run:
start = time.perf_counter()
grid_sines = compiled_functions.prepare_grid_data_for_second_run(
grid_sines_aligned,
parameters.perimeter_count,
parameters.nozzle_width,
shape_domain_grid_data.ccpj_signed_distance_from_boundary,
direction_mode,
direction)
grid_sines = block_until_ready(grid_sines)
stop = time.perf_counter()
execution_times.prepare_grid_data_for_second_run = stop - start
str_tmp = "Prepare data for 2nd run took "
str_tmp += f"{execution_times.prepare_grid_data_for_second_run} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
# Align sines second time
start = time.perf_counter()
grid_sines_aligned: cglib.point_data.GridPointData = \
compiled_functions.sine_wave_multigrid_align(
grid_sines, nozzle_width_derived_param.frequency)
grid_sines_aligned = block_until_ready(grid_sines_aligned)
stop = time.perf_counter()
time_tmp = stop - start
execution_times.sine_wave_multigrid_align_round2 += time_tmp
str_tmp = f"Align sines took (2nd round) {time_tmp} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
del grid_sines
gc.collect()
# Save grid of sines aligned
if dump_intermediate_data:
cglib.point_data.grid_save(
parameters.grid_sines_aligned_filename, grid_sines_aligned)
# Evaluate the sine values at slice grid perturbed points
start = time.perf_counter()
scalar_field = \
compiled_functions.gabor_filter_grid_eval(
shape_domain_grid_data.cell_center_points,
nozzle_width_derived_param.frequency,
grid_sines_aligned)
scalar_field = block_until_ready(scalar_field)
stop = time.perf_counter()
execution_times.gabor_filter_grid_eval = stop - start
str_tmp = "gabor_field_value_with_mask_x took "
str_tmp += f"{execution_times.gabor_filter_grid_eval} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
start = time.perf_counter()
del grid_sines_aligned
gc.collect()
# Put max sine values at the exterior of the shape
scalar_field = jnp.where(
shape_domain_grid_data.ccpj_signed_distance_from_boundary >= borders.perimeter_shifted_outside,
shape_domain_grid_data.ccpj_signed_distance_from_boundary *
2. / parameters.nozzle_width + 1.,
scalar_field)
scalar_field = jnp.where(
shape_domain_grid_data.ccpj_signed_distance_from_boundary >= 0., 1., scalar_field)
stop = time.perf_counter()
time_tmp = stop - start
execution_times.set_scalar_field_boundary += time_tmp
# Dump scalar field
if dump_intermediate_data:
shape_domain_grid_cell_ndcount_tuple = tuple(
device_get(shape_domain_grid.cell_ndcount))
scalar_field_2dshape = scalar_field.reshape(
shape_domain_grid_cell_ndcount_tuple)
jnp.save(parameters.scalar_field_filename, scalar_field_2dshape)
# Compute the contour of the sine field
start = time.perf_counter()
contour: cglib.point_data.PointData = \
compiled_functions.scalar_grid2_contour(
scalar_field, shape_domain_grid)
contour = block_until_ready(contour)
contour_cpu = device_put(contour, device=device_cpu)
del scalar_field
del contour
gc.collect()
stop = time.perf_counter()
execution_times.scalar_grid2_contour = stop - start
str_tmp = "scalar_grid2_contour took "
str_tmp += f"{execution_times.scalar_grid2_contour} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
if dump_intermediate_data:
cglib.point_data.save(
parameters.contour_graph_filename, device_get(contour_cpu))
start = time.perf_counter()
cycles_cpu: cglib.cycle.Cycle = compiled_functions.cycle_create_from_graph(
contour_cpu)
cycles_cpu = block_until_ready(cycles_cpu)
stop = time.perf_counter()
execution_times.cycle_create_from_graph = stop - start
str_tmp = "cycles_create_from_graph took "
str_tmp += f"{execution_times.cycle_create_from_graph} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
if dump_intermediate_data:
cglib.cycle.save(parameters.cycles_filename, cycles_cpu)
# Cycles: match and stitch
start = time.perf_counter()
for _ in range(cycles_cpu.cycle_count-1):
# cycles_data[:, 1]: Cycle edge count
# Sort with increasing cycle edge count
cycle_id_with_min_edge_count = jnp.argmin(cycles_cpu.cycle_data[:, 1])
best_edge_pair, best_edge_cost = \
compiled_functions.cycle_neighboring_edge_with_minimum_patching_energy(
cycle_id_with_min_edge_count,
cycles_cpu,
trajectory_grid.cell_ndcount)
no_neighboring_cycle = best_edge_cost == cglib.limits.FLOAT_MAX
if no_neighboring_cycle:
print("no neighboring cycle (different connex component)")
best_edge_pair, best_edge_cost = \
compiled_functions.cycle_edge_with_minimum_patching_energy(
cycle_id_with_min_edge_count, cycles_cpu)
cycles_cpu: cglib.cycle.Cycle = \
compiled_functions.cycle_stitch_two_edges(
best_edge_pair[0], best_edge_pair[1], cycles_cpu)
# Debug
cycles_cpu = block_until_ready(cycles_cpu)
stop = time.perf_counter()
execution_times.match_and_stitch = stop - start
str_tmp = "cycles_match_and_stitch took "
str_tmp += f"{execution_times.match_and_stitch} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
if START_AT_LEFT:
tmp = jnp.where(jnp.isnan(cycles_cpu.point_data.point[:, :]),
cglib.limits.FLOAT_MAX,
cycles_cpu.point_data.point[:, :])
leftest_point_id = jnp.argmin(tmp[:, 0])
cycle_id_with_min_edge_count = jnp.argmin(cycles_cpu.cycle_data[:, 1])
cycles_cpu = cglib.cycle.Cycle(
cglib.point_data.PointData(
cycles_cpu.point_data.point, cycles_cpu.point_data.data),
cycles_cpu.cycle_data.at[cycle_id_with_min_edge_count, 0].set(
leftest_point_id),
cycles_cpu.cycle_count)
# START REPULSION
if parameters.repulse_curves:
start = time.perf_counter()
cycle_point_data = cglib.point_data.PointData(
cycles_cpu.point_data.point, cycles_cpu.point_data.data[:, :2])
arg0 = device_put(cycle_point_data, device=device_gpu)
arg2 = device_put(trajectory_grid, device=device_gpu)
arg3 = device_put(parameters.nozzle_width*0.5, device=device_gpu)
arg4 = device_put(
np.full((cycles_cpu.point_data.point.shape[0],), False), device=device_gpu)
cycle_point_data = compiled_functions.repulse_points_n_times(
arg0,
arg2,
arg3,
arg4)
cycle_point_data = block_until_ready(cycle_point_data)
cycle_point_data = device_put(cycle_point_data, device=device_cpu)
cycles_cpu = device_put(
cglib.cycle.Cycle(
cglib.point_data.PointData(
cycle_point_data.point,
cycles_cpu.point_data.data),
cycles_cpu.cycle_data,
cycles_cpu.cycle_count),
device=device_cpu)
del arg0
del arg4
gc.collect()
stop = time.perf_counter()
execution_times.repulsion = stop - start
str_tmp = "uniform_2dgrid_repulse_edge_points_n_times took "
str_tmp += f"{execution_times.repulsion} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
# END REPULSION
start = time.perf_counter()
cycles_only_adj = cglib.cycle.Cycle(
cglib.point_data.PointData(
cycles_cpu.point_data.point,
cycles_cpu.point_data.data[:, :2]),
cycles_cpu.cycle_data,
cycles_cpu.cycle_count)
cycles_gpu = device_put(cycles_only_adj, device=device_gpu)
cycles_gpu = block_until_ready(cycles_gpu)
min_radius_circle = compiled_functions.tangent_distance_to_neighbors(
cycles_gpu, trajectory_grid)
min_radius_circle = block_until_ready(min_radius_circle)
del cycles_gpu
gc.collect()
min_radius_circle = device_put(min_radius_circle, device=device_cpu)
stop = time.perf_counter()
execution_times.tangent_distance_to_neighbors = stop - start
str_tmp = "tangent_distance_to_neighbors took "
str_tmp += f"{execution_times.tangent_distance_to_neighbors} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
# Constant radius
# min_radius_circle = jnp.full((cycles_cpu.points_data.point.shape[0]), parameters.nozzle_width*0.5)
# Convert cycle data from uint to float
start = time.perf_counter()
cycles_cpu_points_data_data_float = cycles_cpu.point_data.data.astype(
cglib.type.float_)
# Append the minimum circle radius
cycles_cpu_points_data_data_float = jnp.concatenate(
(cycles_cpu_points_data_data_float,
min_radius_circle.reshape((-1, 1))),
axis=1)
# Cycles with minimum circle radius per point
cycles_cpu = cglib.cycle.Cycle(
cglib.point_data.PointData(
cycles_cpu.point_data.point,
cycles_cpu_points_data_data_float),
cycles_cpu.cycle_data,
cycles_cpu.cycle_count)
cycles_cpu = block_until_ready(cycles_cpu)
stop = time.perf_counter()
execution_times.radius_concatenation = stop - start
str_tmp = f"Radius concatenation: {execution_times.radius_concatenation} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
# Convert to polylines to remove the indirect accesses to points
start = time.perf_counter()
cycle_polyline = compiled_functions.cycle_to_polyline(
cycles_cpu, cycle_polyline)
cycle_polyline: cglib.polyline.Polyline = block_until_ready(cycle_polyline)
stop = time.perf_counter()
execution_times.cycle_to_polyline = stop - start
str_tmp = "cycles_to_polylines took: "
str_tmp += f"{execution_times.cycle_to_polyline} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
del cycles_cpu
gc.collect()
execution_times.compute_total_time()
str_tmp = "Total exec time (without sdf computation): "
str_tmp += f"{execution_times.total} s"
cglib.fdm_aa.write_str_in_file_and_print(str_tmp, log_file)
# Point count per polyline
point_count_per_polyline = np.array(
jax.device_get(cycle_polyline.data[:, 1]))
point_count_per_polyline_max = int(point_count_per_polyline.max())
polyline_point_2d = np.array(jax.device_get(cycle_polyline.point))
# Slice to remove most of the nan.
polyline_point_2d = polyline_point_2d[:,:point_count_per_polyline_max,:]
# At this point, the data associated with each point has 4 floats. The
# first three are useless here and are removed. It was the adjacency
# list (2 floats) and the cycle ID (1 float). The last float is the
# radius of the trajectory.
polyline_point_radius = np.array(
jax.device_get(cycle_polyline.point_data[:, :, 3]))
# Slice to remove most of the nan.
polyline_point_radius = polyline_point_radius[
:,:point_count_per_polyline_max]
# Clamp radius
polyline_point_radius = np.minimum(polyline_point_radius, np.full_like(
polyline_point_radius, nozzle_width_derived_param.max_radius))
polyline_point_radius = np.maximum(polyline_point_radius, np.full_like(
polyline_point_radius, nozzle_width_derived_param.min_radius))
cycle_data_final = point_count_per_polyline.reshape((-1, 1))
cycle_data_final = np.concatenate(
(cycle_data_final,
np.full_like(cycle_data_final,
nozzle_width_derived_param.layer_height)),
axis=1)
cycle_polyline = cglib.polyline.Polyline(
polyline_point_2d, polyline_point_radius, cycle_data_final)
cglib.polyline.save(parameters.cycle_polyline_filename, cycle_polyline)
log_file.close()
if __name__ == "__main__":
run()