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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
|
# -*- coding: utf-8 -*-
from collections import namedtuple
import warnings
import numpy as np
import meep as mp
from meep.geom import Vector3, init_do_averaging
from meep.source import EigenModeSource, check_positive
# ------------------------------------------------------- #
# Visualization
# ------------------------------------------------------- #
# Contains all necesarry visualation routines for use with
# pymeep and pympb.
# ------------------------------------------------------- #
# Functions used to define the default plotting parameters
# for the different plotting routines.
default_source_parameters = {
'color':'r',
'edgecolor':'r',
'facecolor':'none',
'hatch':'/',
'linewidth':2
}
default_monitor_parameters = {
'color':'b',
'edgecolor':'b',
'facecolor':'none',
'hatch':'/',
'linewidth':2
}
default_field_parameters = {
'interpolation':'spline36',
'cmap':'RdBu',
'alpha':0.6,
'post_process':np.real
}
default_eps_parameters = {
'interpolation':'spline36',
'cmap':'binary',
'alpha':1.0,
'contour':False,
'contour_linewidth':1
}
default_boundary_parameters = {
'color':'g',
'edgecolor':'g',
'facecolor':'none',
'hatch':'/'
}
default_volume_parameters = {
'alpha':1.0,
'color':'k',
'linestyle':'-',
'linewidth':1,
'marker':'.',
'edgecolor':'k',
'facecolor':'none',
'hatch':'/'
}
default_label_parameters = {
'label_color':'r',
'offset':20,
'label_alpha':0.3
}
# Used to remove the elements of a dictionary (dict_to_filter) that
# don't correspond to the keyword arguments of a particular
# function (func_with_kwargs.)
# Adapted from https://stackoverflow.com/questions/26515595/how-does-one-ignore-unexpected-keyword-arguments-passed-to-a-function/44052550
def filter_dict(dict_to_filter, func_with_kwargs):
import inspect
filter_keys = []
try:
# Python3 ...
sig = inspect.signature(func_with_kwargs)
filter_keys = [param.name for param in sig.parameters.values() if param.kind == param.POSITIONAL_OR_KEYWORD]
except:
# Python2 ...
filter_keys = inspect.getargspec(func_with_kwargs)[0]
filtered_dict = {filter_key:dict_to_filter[filter_key] for filter_key in filter_keys if filter_key in dict_to_filter}
return filtered_dict
# ------------------------------------------------------- #
# Routines to add legends to plot
def place_label(ax,label_text,x,y,centerx,centery,label_parameters=None):
label_parameters = default_label_parameters if label_parameters is None else dict(default_label_parameters, **label_parameters)
offset = label_parameters['offset']
alpha = label_parameters['label_alpha']
color = label_parameters['label_color']
if x > centerx:
xtext = -offset
else:
xtext = offset
if y > centery:
ytext = -offset
else:
ytext = offset
ax.annotate(label_text, xy=(x, y), xytext=(xtext,ytext),
textcoords='offset points', ha='center', va='bottom',
bbox=dict(boxstyle='round,pad=0.2', fc=color, alpha=alpha),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5',
color=color))
return ax
# ------------------------------------------------------- #
# Helper functions used to plot volumes on a 2D plane
# Returns the intersection points of 2 Volumes.
# Volumes must be a line, plane, or rectangular prism
# (since they are volume objects)
def intersect_volume_volume(volume1,volume2):
# volume1 ............... [volume]
# volume2 ............... [volume]
# Represent the volumes by an "upper" and "lower" coordinate
U1 = [volume1.center.x+volume1.size.x/2,volume1.center.y+volume1.size.y/2,volume1.center.z+volume1.size.z/2]
L1 = [volume1.center.x-volume1.size.x/2,volume1.center.y-volume1.size.y/2,volume1.center.z-volume1.size.z/2]
U2 = [volume2.center.x+volume2.size.x/2,volume2.center.y+volume2.size.y/2,volume2.center.z+volume2.size.z/2]
L2 = [volume2.center.x-volume2.size.x/2,volume2.center.y-volume2.size.y/2,volume2.center.z-volume2.size.z/2]
# Evaluate intersection
U = np.min([U1,U2],axis=0)
L = np.max([L1,L2],axis=0)
# For single points we have to check manually
if np.all(U-L == 0):
if (not volume1.pt_in_volume(Vector3(*U))) or (not volume2.pt_in_volume(Vector3(*U))):
return []
# Check for two volumes that don't intersect
if np.any(U-L < 0):
return []
# Pull all possible vertices
vertices = []
for x_vals in [L[0],U[0]]:
for y_vals in [L[1],U[1]]:
for z_vals in [L[2],U[2]]:
vertices.append(Vector3(x_vals,y_vals,z_vals))
# Remove any duplicate points caused by coplanar lines
vertices = [vertices[i] for i, x in enumerate(vertices) if x not in vertices[i+1:]]
return vertices
# All of the 2D plotting routines need an output plane over which to plot.
# The user has many options to specify this output plane. They can pass
# the output_plane parameter, which is a 2D volume object. They can specify
# a volume using in_volume, which stores the volume as a C volume, not a python
# volume. They can also do nothing and plot the XY plane through Z=0.
#
# Not only do we need to check for all of these possibilities, but we also need
# to check if the user accidentally specifies a plane that stretches beyond the
# simulation domain.
def get_2D_dimensions(sim,output_plane):
from meep.simulation import Volume
# Pull correct plane from user
if output_plane:
plane_center, plane_size = (output_plane.center, output_plane.size)
elif sim.output_volume:
plane_center, plane_size = mp.get_center_and_size(sim.output_volume)
else:
plane_center, plane_size = (sim.geometry_center, sim.cell_size)
plane_volume = Volume(center=plane_center,size=plane_size)
if plane_size.x!=0 and plane_size.y!=0 and plane_size.z!=0:
raise ValueError("Plane volume must be 2D (a plane).")
check_volume = Volume(center=sim.geometry_center,size=sim.cell_size)
vertices = intersect_volume_volume(check_volume,plane_volume)
if len(vertices) == 0:
raise ValueError("The specified user volume is completely outside of the simulation domain.")
intersection_vol = Volume(vertices=vertices)
if (intersection_vol.size != plane_volume.size) or (intersection_vol.center != plane_volume.center):
warnings.warn('The specified user volume is larger than the simulation domain and has been truncated.')
sim_center, sim_size = (intersection_vol.center, intersection_vol.size)
return sim_center, sim_size
# ------------------------------------------------------- #
# actual plotting routines
def plot_volume(sim,ax,volume,output_plane=None,plotting_parameters=None,label=None):
if not sim._is_initialized:
sim.init_sim()
import matplotlib.patches as patches
from matplotlib import pyplot as plt
from meep.simulation import Volume
# Set up the plotting parameters
plotting_parameters = default_volume_parameters if plotting_parameters is None else dict(default_volume_parameters, **plotting_parameters)
# Get domain measurements
sim_center, sim_size = get_2D_dimensions(sim,output_plane)
plane = Volume(center=sim_center,size=sim_size)
# Pull volume parameters
size = volume.size
center = volume.center
xmax = center.x+size.x/2
xmin = center.x-size.x/2
ymax = center.y+size.y/2
ymin = center.y-size.y/2
zmax = center.z+size.z/2
zmin = center.z-size.z/2
# Add labels if requested
if label is not None and mp.am_master():
if sim_size.x == 0:
ax = place_label(ax,label,center.y,center.z,sim_center.y,sim_center.z,label_parameters=plotting_parameters)
elif sim_size.y == 0:
ax = place_label(ax,label,center.x,center.z,sim_center.x,sim_center.z,label_parameters=plotting_parameters)
elif sim_size.z == 0:
ax = place_label(ax,label,center.x,center.y,sim_center.x,sim_center.y,label_parameters=plotting_parameters)
# Intersect plane with volume
intersection = intersect_volume_volume(volume,plane)
# Sort the points in a counter clockwise manner to ensure convex polygon is formed
def sort_points(xy):
xy = np.squeeze(xy)
xy_mean = np.mean(xy,axis=0)
theta = np.arctan2(xy[:,1]-xy_mean[1],xy[:,0]-xy_mean[0])
return xy[np.argsort(theta,axis=0),:]
if mp.am_master():
# Point volume
if len(intersection) == 1:
point_args = {key:value for key, value in plotting_parameters.items() if key in ['color','marker','alpha','linewidth']}
if sim_size.y==0:
ax.scatter(center.x,center.z, **point_args)
return ax
elif sim_size.x==0:
ax.scatter(center.y,center.z, **point_args)
return ax
elif sim_size.z==0:
ax.scatter(center.x,center.y, **point_args)
return ax
else:
return ax
# Line volume
elif len(intersection) == 2:
line_args = {key:value for key, value in plotting_parameters.items() if key in ['color','linestyle','linewidth','alpha']}
# Plot YZ
if sim_size.x==0:
ax.plot([a.y for a in intersection],[a.z for a in intersection], **line_args)
return ax
#Plot XZ
elif sim_size.y==0:
ax.plot([a.x for a in intersection],[a.z for a in intersection], **line_args)
return ax
# Plot XY
elif sim_size.z==0:
ax.plot([a.x for a in intersection],[a.y for a in intersection], **line_args)
return ax
else:
return ax
# Planar volume
elif len(intersection) > 2:
planar_args = {key:value for key, value in plotting_parameters.items() if key in ['edgecolor','linewidth','facecolor','hatch','alpha']}
# Plot YZ
if sim_size.x==0:
ax.add_patch(patches.Polygon(sort_points([[a.y,a.z] for a in intersection]), **planar_args))
return ax
#Plot XZ
elif sim_size.y==0:
ax.add_patch(patches.Polygon(sort_points([[a.x,a.z] for a in intersection]), **planar_args))
return ax
# Plot XY
elif sim_size.z==0:
ax.add_patch(patches.Polygon(sort_points([[a.x,a.y] for a in intersection]), **planar_args))
return ax
else:
return ax
else:
return ax
return ax
def plot_eps(sim,ax,output_plane=None,eps_parameters=None,frequency=0):
if sim.structure is None:
sim.init_sim()
# consolidate plotting parameters
eps_parameters = default_eps_parameters if eps_parameters is None else dict(default_eps_parameters, **eps_parameters)
# Get domain measurements
sim_center, sim_size = get_2D_dimensions(sim,output_plane)
xmin = sim_center.x - sim_size.x/2
xmax = sim_center.x + sim_size.x/2
ymin = sim_center.y - sim_size.y/2
ymax = sim_center.y + sim_size.y/2
zmin = sim_center.z - sim_size.z/2
zmax = sim_center.z + sim_size.z/2
center = Vector3(sim_center.x,sim_center.y,sim_center.z)
cell_size = Vector3(sim_size.x,sim_size.y,sim_size.z)
if sim_size.x == 0:
# Plot y on x axis, z on y axis (YZ plane)
extent = [ymin,ymax,zmin,zmax]
xlabel = 'Y'
ylabel = 'Z'
elif sim_size.y == 0:
# Plot x on x axis, z on y axis (XZ plane)
extent = [xmin,xmax,zmin,zmax]
xlabel = 'X'
ylabel = 'Z'
elif sim_size.z == 0:
# Plot x on x axis, y on y axis (XY plane)
extent = [xmin,xmax,ymin,ymax]
xlabel = 'X'
ylabel = 'Y'
else:
raise ValueError("A 2D plane has not been specified...")
eps_data = np.rot90(np.real(sim.get_array(center=center, size=cell_size, component=mp.Dielectric, frequency=frequency)))
if mp.am_master():
if eps_parameters['contour']:
ax.contour(eps_data, 0, colors='black', origin='upper', extent=extent, linewidths=eps_parameters['contour_linewidth'])
else:
ax.imshow(eps_data, extent=extent, **filter_dict(eps_parameters, ax.imshow))
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
return ax
def plot_boundaries(sim,ax,output_plane=None,boundary_parameters=None):
if not sim._is_initialized:
sim.init_sim()
# consolidate plotting parameters
boundary_parameters = default_boundary_parameters if boundary_parameters is None else dict(default_boundary_parameters, **boundary_parameters)
def get_boundary_volumes(thickness,direction,side):
from meep.simulation import Volume
thickness = boundary.thickness
# Get domain measurements
sim_center, sim_size = (sim.geometry_center, sim.cell_size)
xmin = sim_center.x - sim_size.x/2
xmax = sim_center.x + sim_size.x/2
ymin = sim_center.y - sim_size.y/2
ymax = sim_center.y + sim_size.y/2
zmin = sim_center.z - sim_size.z/2
zmax = sim_center.z + sim_size.z/2
cell_x = sim.cell_size.x
cell_y = sim.cell_size.y
cell_z = sim.cell_size.z
if direction == mp.X and side == mp.Low:
return Volume(center=Vector3(xmin+thickness/2,sim.geometry_center.y,sim.geometry_center.z),
size=Vector3(thickness,cell_y,cell_z))
elif direction == mp.X and side == mp.High:
return Volume(center=Vector3(xmax-thickness/2,sim.geometry_center.y,sim.geometry_center.z),
size=Vector3(thickness,cell_y,cell_z))
elif direction == mp.Y and side == mp.Low:
return Volume(center=Vector3(sim.geometry_center.x,ymin+thickness/2,sim.geometry_center.z),
size=Vector3(cell_x,thickness,cell_z))
elif direction == mp.Y and side == mp.High:
return Volume(center=Vector3(sim.geometry_center.x,ymax-thickness/2,sim.geometry_center.z),
size=Vector3(cell_x,thickness,cell_z))
elif direction == mp.Z and side == mp.Low:
return Volume(center=Vector3(sim.geometry_center.x,sim.geometry_center.y,zmin+thickness/2),
size=Vector3(cell_x,cell_y,thickness))
elif direction == mp.Z and side == mp.High:
return Volume(center=Vector3(sim.geometry_center.x,sim.geometry_center.y,zmax-thickness/2),
size=Vector3(cell_x,cell_y,thickness))
else:
raise ValueError("Invalid boundary type")
import itertools
for boundary in sim.boundary_layers:
# All 4 side are the same
if boundary.direction == mp.ALL and boundary.side == mp.ALL:
if sim.dimensions == 1:
dims = [mp.X]
elif sim.dimensions == 2:
dims = [mp.X,mp.Y]
elif sim.dimensions == 3:
dims = [mp.X,mp.Y,mp.Z]
else:
raise ValueError("Invalid simulation dimensions")
for permutation in itertools.product(dims, [mp.Low, mp.High]):
vol = get_boundary_volumes(boundary.thickness,*permutation)
ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters)
# 2 sides are the same
elif boundary.side == mp.ALL:
for side in [mp.Low, mp.High]:
vol = get_boundary_volumes(boundary.thickness,boundary.direction,side)
ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters)
# only one side
else:
vol = get_boundary_volumes(boundary.thickness,boundary.direction,boundary.side)
ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters)
return ax
def plot_sources(sim,ax,output_plane=None,labels=False,source_parameters=None):
if not sim._is_initialized:
sim.init_sim()
from meep.simulation import Volume
# consolidate plotting parameters
source_parameters = default_source_parameters if source_parameters is None else dict(default_source_parameters, **source_parameters)
label = 'source' if labels else None
for src in sim.sources:
vol = Volume(center=src.center,size=src.size)
ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=source_parameters,label=label)
return ax
def plot_monitors(sim,ax,output_plane=None,labels=False,monitor_parameters=None):
if not sim._is_initialized:
sim.init_sim()
from meep.simulation import Volume
# consolidate plotting parameters
monitor_parameters = default_monitor_parameters if monitor_parameters is None else dict(default_monitor_parameters, **monitor_parameters)
label = 'monitor' if labels else None
for mon in sim.dft_objects:
for reg in mon.regions:
vol = Volume(center=reg.center,size=reg.size)
ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=monitor_parameters,label=label)
return ax
def plot_fields(sim,ax=None,fields=None,output_plane=None,field_parameters=None):
if not sim._is_initialized:
sim.init_sim()
if fields is None:
return ax
field_parameters = default_field_parameters if field_parameters is None else dict(default_field_parameters, **field_parameters)
# user specifies a field component
if fields in [mp.Ex, mp.Ey, mp.Ez, mp.Hx, mp.Hy, mp.Hz]:
# Get domain measurements
sim_center, sim_size = get_2D_dimensions(sim,output_plane)
xmin = sim_center.x - sim_size.x/2
xmax = sim_center.x + sim_size.x/2
ymin = sim_center.y - sim_size.y/2
ymax = sim_center.y + sim_size.y/2
zmin = sim_center.z - sim_size.z/2
zmax = sim_center.z + sim_size.z/2
center = Vector3(sim_center.x,sim_center.y,sim_center.z)
cell_size = Vector3(sim_size.x,sim_size.y,sim_size.z)
if sim_size.x == 0:
# Plot y on x axis, z on y axis (YZ plane)
extent = [ymin,ymax,zmin,zmax]
xlabel = 'Y'
ylabel = 'Z'
elif sim_size.y == 0:
# Plot x on x axis, z on y axis (XZ plane)
extent = [xmin,xmax,zmin,zmax]
xlabel = 'X'
ylabel = 'Z'
elif sim_size.z == 0:
# Plot x on x axis, y on y axis (XY plane)
extent = [xmin,xmax,ymin,ymax]
xlabel = 'X'
ylabel = 'Y'
fields = sim.get_array(center=center, size=cell_size, component=fields)
else:
raise ValueError('Please specify a valid field component (mp.Ex, mp.Ey, ...')
fields = field_parameters['post_process'](fields)
# Either plot the field, or return the array
if ax:
if mp.am_master():
ax.imshow(np.rot90(fields), extent=extent, **filter_dict(field_parameters,ax.imshow))
return ax
else:
return np.rot90(fields)
return ax
def plot2D(sim,ax=None, output_plane=None, fields=None, labels=False,
eps_parameters=None,boundary_parameters=None,
source_parameters=None,monitor_parameters=None,
field_parameters=None, frequency=None,
plot_eps_flag=True, plot_sources_flag=True,
plot_monitors_flag=True, plot_boundaries_flag=True):
# Initialize the simulation
if sim.structure is None:
sim.init_sim()
# Ensure a figure axis exists
if ax is None and mp.am_master():
from matplotlib import pyplot as plt
ax = plt.gca()
# Determine a frequency to plot all epsilon
if frequency is None:
try:
# Continuous sources
frequency = sim.sources[0].frequency
except:
try:
# Gaussian sources
frequency = sim.sources[0].src.frequency
except:
try:
# Custom sources
frequency = sim.sources[0].src.center_frequency
except:
# No sources
frequency = 0
# validate the output plane to ensure proper 2D coordinates
from meep.simulation import Volume
sim_center, sim_size = get_2D_dimensions(sim,output_plane)
output_plane = Volume(center=sim_center,size=sim_size)
# Plot geometry
if plot_eps_flag:
ax = plot_eps(sim,ax,output_plane=output_plane,eps_parameters=eps_parameters,frequency=frequency)
# Plot boundaries
if plot_boundaries_flag:
ax = plot_boundaries(sim,ax,output_plane=output_plane,boundary_parameters=boundary_parameters)
# Plot sources
if plot_sources_flag:
ax = plot_sources(sim,ax,output_plane=output_plane,labels=labels,source_parameters=source_parameters)
# Plot monitors
if plot_monitors_flag:
ax = plot_monitors(sim,ax,output_plane=output_plane,labels=labels,monitor_parameters=monitor_parameters)
# Plot fields
ax = plot_fields(sim,ax,fields,output_plane=output_plane,field_parameters=field_parameters)
return ax
def plot3D(sim):
from mayavi import mlab
if not sim._is_initialized:
sim.init_sim()
if sim.dimensions < 3:
raise ValueError("Simulation must have 3 dimensions to visualize 3D")
eps_data = sim.get_epsilon()
s = mlab.contour3d(eps_data, colormap="YlGnBu")
return s
def visualize_chunks(sim):
if sim.structure is None:
sim.init_sim()
import matplotlib.pyplot as plt
import matplotlib.cm
import matplotlib.colors
if sim.structure.gv.dim == 2:
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
else:
from matplotlib.collections import PolyCollection
vols = sim.structure.get_chunk_volumes()
owners = sim.structure.get_chunk_owners()
def plot_box(box, proc, fig, ax):
if sim.structure.gv.dim == 2:
low = mp.Vector3(box.low.x, box.low.y, box.low.z)
high = mp.Vector3(box.high.x, box.high.y, box.high.z)
points = [low, high]
x_len = mp.Vector3(high.x) - mp.Vector3(low.x)
y_len = mp.Vector3(y=high.y) - mp.Vector3(y=low.y)
xy_len = mp.Vector3(high.x, high.y) - mp.Vector3(low.x, low.y)
points += [low + x_len]
points += [low + y_len]
points += [low + xy_len]
points += [high - x_len]
points += [high - y_len]
points += [high - xy_len]
points = np.array([np.array(v) for v in points])
edges = [
[points[0], points[2], points[4], points[3]],
[points[1], points[5], points[7], points[6]],
[points[0], points[3], points[5], points[7]],
[points[1], points[4], points[2], points[6]],
[points[3], points[4], points[1], points[5]],
[points[0], points[7], points[6], points[2]]
]
faces = Poly3DCollection(edges, linewidths=1, edgecolors='k')
color_with_alpha = matplotlib.colors.to_rgba(chunk_colors[proc], alpha=0.2)
faces.set_facecolor(color_with_alpha)
ax.add_collection3d(faces)
# Plot the points themselves to force the scaling of the axes
ax.scatter(points[:, 0], points[:, 1], points[:, 2], s=0)
else:
low = mp.Vector3(box.low.x, box.low.y)
high = mp.Vector3(box.high.x, box.high.y)
points = [low, high]
x_len = mp.Vector3(high.x) - mp.Vector3(low.x)
y_len = mp.Vector3(y=high.y) - mp.Vector3(y=low.y)
points += [low + x_len]
points += [low + y_len]
points = np.array([np.array(v)[:-1] for v in points])
edges = [
[points[0], points[2], points[1], points[3]]
]
faces = PolyCollection(edges, linewidths=1, edgecolors='k')
color_with_alpha = matplotlib.colors.to_rgba(chunk_colors[proc])
faces.set_facecolor(color_with_alpha)
ax.add_collection(faces)
# Plot the points themselves to force the scaling of the axes
ax.scatter(points[:, 0], points[:, 1], s=0)
if mp.am_master():
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d' if sim.structure.gv.dim == 2 else None)
chunk_colors = matplotlib.cm.rainbow(np.linspace(0, 1, mp.count_processors()))
for i, v in enumerate(vols):
plot_box(mp.gv2box(v.surroundings()), owners[i], fig, ax)
ax.set_xlabel('x')
ax.set_ylabel('y')
cell_box = mp.gv2box(sim.structure.gv.surroundings())
if sim.structure.gv.dim == 2:
ax.set_xlim3d(left=cell_box.low.x,right=cell_box.high.x)
ax.set_ylim3d(bottom=cell_box.low.y,top=cell_box.high.y)
ax.set_zlim3d(bottom=cell_box.low.z,top=cell_box.high.z)
ax.set_zlabel('z')
else:
ax.set_xlim(left=cell_box.low.x,right=cell_box.high.x)
ax.set_ylim(bottom=cell_box.low.y,top=cell_box.high.y)
ax.set_aspect('equal')
plt.tight_layout()
plt.show()
# ------------------------------------------------------- #
# Animate2D
# ------------------------------------------------------- #
# An extensive run function used to visualize the fields
# of a 2D simulation after every specified time step.
# ------------------------------------------------------- #
# Required arguments
# sim ................. [Simulation object]
# fields .............. [mp.Ex, mp.Ey, ..., mp. Hz]
# ------------------------------------------------------- #
# Optional arguments
# f ................... [matplotlib figure object]
# realtime ............ [bool] Update plot in each step
# normalize ........... [bool] saves fields to normalize
# after simulation ends.
# plot_modifiers ...... [list] additional functions to
# modify plot
# customization_args .. [dict] other customization args
# to pass to plot2D()
class Animate2D(object):
"""
A class used to record the fields during timestepping (i.e., a [`run`](#run-functions)
function). The object is initialized prior to timestepping by specifying the
simulation object and the field component. The object can then be passed to any
[step-function modifier](#step-function-modifiers). For example, one can record the
E<sub>z</sub> fields at every one time unit using:
```py
animate = mp.Animate2D(sim,
fields=mp.Ez,
realtime=True,
field_parameters={'alpha':0.8, 'cmap':'RdBu', 'interpolation':'none'},
boundary_parameters={'hatch':'o', 'linewidth':1.5, 'facecolor':'y', 'edgecolor':'b', 'alpha':0.3})
sim.run(mp.at_every(1,animate),until=25)
```
By default, the object saves each frame as a PNG image into memory (not disk). This is
typically more memory efficient than storing the actual fields. If the user sets the
`normalize` argument, then the object will save the actual field information as a
NumPy array to be normalized for post processing. The fields of a figure can also be
updated in realtime by setting the `realtime` flag. This does not work for
IPython/Jupyter notebooks, however.
Once the simulation is run, the animation can be output as an interactive JSHTML
object, an mp4, or a GIF.
Multiple `Animate2D` objects can be initialized and passed to the run function to
track different volume locations (using `mp.in_volume`) or field components.
"""
def __init__(self, sim, fields, f=None, realtime=False, normalize=False,
plot_modifiers=None, **customization_args):
"""
Construct an `Animate2D` object.
+ **`sim`** — Simulation object.
+ **`fields`** — Field component to record at each time instant.
+ **`f=None`** — Optional `matplotlib` figure object that the routine will update
on each call. If not supplied, then a new one will be created upon
initialization.
+ **`realtime=False`** — Whether or not to update a figure window in realtime as
the simulation progresses. Disabled by default. Not compatible with
IPython/Jupyter notebooks.
+ **`normalize=False`** — Records fields at each time step in memory in a NumPy
array and then normalizes the result by dividing by the maximum field value at a
single point in the cell over all the time snapshots.
+ **`plot_modifiers=None`** — A list of functions that can modify the figure's
`axis` object. Each function modifier accepts a single argument, an `axis`
object, and must return that same axis object. The following modifier changes
the `xlabel`:
```py
def mod1(ax):
ax.set_xlabel('Testing')
return ax
plot_modifiers = [mod1]
```
+ **`**customization_args`** — Customization keyword arguments passed to
`plot2D()` (i.e. `labels`, `eps_parameters`, `boundary_parameters`, etc.)
"""
self.fields = fields
if f:
self.f = f
self.ax = self.f.gca()
elif mp.am_master():
from matplotlib import pyplot as plt
self.f = plt.figure()
self.ax = self.f.gca()
else:
self.f = None
self.ax = None
self.realtime = realtime
self.normalize = normalize
self.plot_modifiers = plot_modifiers
self.customization_args = customization_args
self.cumulative_fields = []
self._saved_frames = []
self.frame_format = 'png' # format in which each frame is saved in memory
self.codec = 'h264' # encoding of mp4 video
self.default_mode = 'loop' # html5 video control mode
self.init = False
# Needed for step functions
self.__code__ = namedtuple('gna_hack',['co_argcount'])
self.__code__.co_argcount=2
def __call__(self,sim,todo):
from matplotlib import pyplot as plt
if todo == 'step':
# Initialize the plot
if not self.init:
filtered_plot2D = filter_dict(self.customization_args, plot2D)
ax = sim.plot2D(ax=self.ax,fields=self.fields,**filtered_plot2D)
# Run the plot modifier functions
if self.plot_modifiers:
for k in range(len(self.plot_modifiers)):
ax = self.plot_modifiers[k](self.ax)
# Store the fields
if mp.am_master():
fields = ax.images[-1].get_array()
self.ax = ax
self.w, self.h = self.f.get_size_inches()
self.init = True
else:
# Update the plot
filtered_plot_fields= filter_dict(self.customization_args, plot_fields)
fields = sim.plot_fields(fields=self.fields,**filtered_plot_fields)
if mp.am_master():
self.ax.images[-1].set_data(fields)
self.ax.images[-1].set_clim(vmin=0.8*np.min(fields), vmax=0.8*np.max(fields))
if self.realtime and mp.am_master():
# Redraw the current figure if requested
plt.pause(0.05)
if self.normalize and mp.am_master():
# Save fields as a numpy array to be normalized
# and saved later.
self.cumulative_fields.append(fields)
elif mp.am_master():
# Capture figure as a png, but store the png in memory
# to avoid writing to disk.
self.grab_frame()
return
elif todo == 'finish':
# Normalize the frames, if requested, and export
if self.normalize and mp.am_master():
if mp.verbosity.meep > 0:
print("Normalizing field data...")
fields = np.array(self.cumulative_fields) / np.max(np.abs(self.cumulative_fields),axis=(0,1,2))
for k in range(len(self.cumulative_fields)):
self.ax.images[-1].set_data(fields[k,:,:])
self.ax.images[-1].set_clim(vmin=-0.8, vmax=0.8)
self.grab_frame()
return
@property
def frame_size(self):
# A tuple ``(width, height)`` in pixels of a movie frame.
# modified from matplotlib library
w, h = self.f.get_size_inches()
return int(w * self.f.dpi), int(h * self.f.dpi)
def grab_frame(self):
# Saves the figures frame to memory.
# modified from matplotlib library
from io import BytesIO
bin_data = BytesIO()
self.f.savefig(bin_data, format=self.frame_format)
#imgdata64 = base64.encodebytes(bin_data.getvalue()).decode('ascii')
self._saved_frames.append(bin_data.getvalue())
def _embedded_frames(self, frame_list, frame_format):
# converts frame data stored in memory to html5 friendly format
# frame_list should be a list of base64-encoded png files
# modified from matplotlib
import base64
template = ' frames[{0}] = "data:image/{1};base64,{2}"\n'
return "\n" + "".join(
template.format(i, frame_format, base64.encodebytes(frame_data).decode('ascii').replace('\n', '\\\n'))
for i, frame_data in enumerate(frame_list))
def to_jshtml(self,fps):
"""
Outputs an interactable JSHTML animation object that is embeddable in Jupyter
notebooks. The object is packaged with controls to manipulate the video's
playback. User must specify a frame rate `fps` in frames per second.
"""
# Exports a javascript enabled html object that is
# ready for jupyter notebook embedding.
# modified from matplotlib/animation.py code.
# Only works with Python3 and matplotlib > 3.1.0
from distutils.version import LooseVersion
import matplotlib
if LooseVersion(matplotlib.__version__) < LooseVersion("3.1.0"):
print('-------------------------------')
print('Warning: JSHTML output is not supported with your current matplotlib build. Consider upgrading to 3.1.0+')
print('-------------------------------')
return
if mp.am_master():
from uuid import uuid4
from matplotlib._animation_data import (DISPLAY_TEMPLATE, INCLUDED_FRAMES, JS_INCLUDE, STYLE_INCLUDE)
# save the frames to an html file
fill_frames = self._embedded_frames(self._saved_frames, self.frame_format)
Nframes = len(self._saved_frames)
mode_dict = dict(once_checked='',
loop_checked='',
reflect_checked='')
mode_dict[self.default_mode + '_checked'] = 'checked'
interval = 1000 // fps
html_string = ""
html_string += JS_INCLUDE
html_string += STYLE_INCLUDE
html_string += DISPLAY_TEMPLATE.format(id=uuid4().hex,
Nframes=Nframes,
fill_frames=fill_frames,
interval=interval,
**mode_dict)
return JS_Animation(html_string)
def to_gif(self,fps,filename):
"""
Generates and outputs a GIF file of the animation with the filename, `filename`,
and the frame rate, `fps`. Note that GIFs are significantly larger than mp4 videos
since they don't use any compression. Artifacts are also common because the GIF
format only supports 256 colors from a _predefined_ color palette. Requires
`ffmpeg`.
"""
# Exports a gif of the recorded animation
# requires ffmpeg to be installed
# modified from the matplotlib library
if mp.am_master():
from subprocess import Popen, PIPE
from io import TextIOWrapper, BytesIO
FFMPEG_BIN = 'ffmpeg'
command = [FFMPEG_BIN,
'-f', 'image2pipe', # force piping of rawvideo
'-vcodec', self.frame_format, # raw input codec
'-s', '%dx%d' % (self.frame_size),
'-r', str(fps), # frame rate in frames per second
'-i', 'pipe:', # The input comes from a pipe
'-vcodec', 'gif', # output gif format
'-r', str(fps), # frame rate in frames per second
'-y',
'-vf', 'pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2',
'-an', filename # output filename
]
if mp.verbosity.meep > 0:
print("Generating GIF...")
proc = Popen(command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
for i in range(len(self._saved_frames)):
proc.stdin.write(self._saved_frames[i])
out, err = proc.communicate() # pipe in images
proc.stdin.close()
proc.wait()
return
def to_mp4(self,fps,filename):
"""
Generates and outputs an mp4 video file of the animation with the filename,
`filename`, and the frame rate, `fps`. Default encoding is h264 with yuv420p
format. Requires `ffmpeg`.
"""
# Exports an mp4 of the recorded animation
# requires ffmpeg to be installed
# modified from the matplotlib library
if mp.am_master():
from subprocess import Popen, PIPE
from io import TextIOWrapper, BytesIO
FFMPEG_BIN = 'ffmpeg'
command = [FFMPEG_BIN,
'-f', 'image2pipe', # force piping of rawvideo
'-vcodec', self.frame_format, # raw input codec
'-s', '%dx%d' % (self.frame_size),
#'-pix_fmt', self.frame_format,
'-r', str(fps), # frame rate in frames per second
'-i', 'pipe:', # The input comes from a pipe
'-vcodec', self.codec, # output mp4 format
'-pix_fmt','yuv420p',
'-r', str(fps), # frame rate in frames per second
'-y',
'-vf', 'pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2',
'-an', filename # output filename
]
if mp.verbosity.meep > 0:
print("Generating MP4...")
proc = Popen(command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
for i in range(len(self._saved_frames)):
proc.stdin.write(self._saved_frames[i])
out, err = proc.communicate() # pipe in images
proc.stdin.close()
proc.wait()
return
def reset(self):
self.cumulative_fields = []
self.ax = None
self.f = None
def set_figure(self,f):
self.f = f
# ------------------------------------------------------- #
# JS_Animation
# ------------------------------------------------------- #
# A helper class used to make jshtml animations embed
# seamlessly within Jupyter notebooks.
class JS_Animation():
def __init__(self,jshtml):
self.jshtml = jshtml
def _repr_html_(self):
return self.jshtml
def get_jshtml(self):
return self.jshtml
|