def plot_line(xdata,
ydata,
title,
xlbl,
ylbl,
out_name,
color,
lbl,
stdev=None,
do_save=True,
black_bg=False):
#####
# Plot
#####
plt.plot(xdata, ydata, 'o', markersize=5, markeredgewidth=0, color=color)
#hdl, =
plt.plot(xdata, ydata, '-', linewidth=2, color=color, label=lbl)
# Plot error bars
# http://matplotlib.org/1.2.1/examples/pylab_examples/errorbar_demo.html
# Plot error bar without line, fmt='':
# http://stackoverflow.com/questions/18498742/how-do-you-make-an-errorbar-plot-in-matplotlib-using-linestyle-none-in-rcparams
if stdev:
plt.errorbar(xdata, ydata, fmt='', capthick=2, yerr=stdev, color=color)
plt.grid(True, color='gray')
#plt.grid (False)
if title:
plt.title(title)
plt.xlabel(xlbl)
plt.ylabel(ylbl)
if black_bg:
black_background()
# Save to file
if do_save:
plt.savefig(out_name, bbox_inches='tight')
print('Plot saved to %s' % out_name)
def plot_hist(
hist,
edges,
ax,
ylbl,
strict_lims=True, #, xlbl_suffix=''):
bg_color='white',
fg_color='g',
tick_rot=0):
# MATLAB "hold on"
ax.hold(True)
# MATLAB "grid on"
ax.grid(True, color='gray')
# Ref font size of labels: http://stackoverflow.com/questions/12444716/how-do-i-set-figure-title-and-axes-labels-font-size-in-matplotlib
#ax.set_xlabel ('Bins' + xlbl_suffix, fontsize=6)
ax.set_ylabel(ylbl)
if strict_lims:
ax.set_xlim(edges[0], edges[len(edges) - 1])
# Get histogram centers from edges. n+1 edges means there are n centers.
centers = (edges[0:len(edges) - 1] + edges[1:len(edges)]) * 0.5
width = (edges[1] - edges[0]) * .5
#print (edges)
#print (centers)
#print (hist)
ax.bar(centers, hist, width=width, color=fg_color, edgecolor='none')
# Rotate tick orientation
# Diagonal 45-degree slanted tick labels
# Ref: https://stackoverflow.com/questions/14852821/aligning-rotated-xticklabels-with-their-respective-xticks/14854007#14854007
# axes API: https://matplotlib.org/api/axes_api.html
if tick_rot != 0:
ax.set_xticklabels(ax.get_xticks(), rotation=tick_rot)
if bg_color == 'black':
black_background(ax=ax)
def main():
arg_parser = argparse.ArgumentParser()
# Variable number of args http://stackoverflow.com/questions/13219910/argparse-get-undefined-number-of-arguments
arg_parser.add_argument(
'--display',
action="store_true",
help=
'Specify for debugging one by one. Displays matplotlib plots. This will block program flow and require user interaction to close window to move on to next data sample.'
)
arg_parser.add_argument('--no-save',
action='store_true',
help='Specify to NOT save any visualized images.')
arg_parser.add_argument(
'--uinput',
action='store_true',
help='Specify to use keyboard interaction, useful for debugging.')
arg_parser.add_argument(
'--scale-heatmaps',
action='store_true',
help=
'Specify the flag if it was specified to occlusion_test.cpp to generate the heatmaps. Rescale heatmaps back to min/max depth values.'
)
arg_parser.add_argument('--black-bg',
action='store_true',
help='Generate plots with black background.')
args = arg_parser.parse_args()
SCALE_HEATMAPS = args.scale_heatmaps
BLACK_BG = args.black_bg
DISPLAY_IMAGES = args.display
UINPUT = args.uinput
SAVE_IMG = not args.no_save
# Number of grasps per object to save images
N_TO_SAVE = 1
# User adjust parameter. Save just a single axis, for paper or presentation
# Indices in the 3 subplots
DEPTH_IDX = 1
VIS_IDX = 2
OCC_IDX = 3
# NOTE: Make sure these names match with the subplot indices!
SUBPLOT_NAMES = [get_vis_depth_fmt(), get_vis_vis_fmt(), get_vis_occ_fmt()]
#SAVE_SUBPLOTS = [DEPTH_IDX]
SAVE_SUBPLOTS = [DEPTH_IDX, VIS_IDX, OCC_IDX]
pkg_path = rospkg.RosPack().get_path('depth_scene_rendering')
scene_list_path = os.path.join(pkg_path, "config/scenes_noisy.yaml")
scene_list_f = open(scene_list_path, 'rb')
renders_dir = get_renders_data_path()
heatmaps_dir = get_heatmaps_data_path()
heatmap_fmts = get_heatmap_blob_fmt()
vis_fmt = heatmap_fmts[0]
occ_fmt = heatmap_fmts[1]
scaler = RawDepthScaling()
success, depth_range = scaler.load_depth_range()
if not success:
return
MIN_DEPTH = depth_range[0]
MAX_DEPTH = depth_range[1]
vis_dir = get_vis_path()
contacts_dir = get_contacts_path()
# scenes.yaml
objs = ConfigReadYAML.read_scene_paths()
# String
obj_names = objs[0]
# List of list of strings, paths to .pcd scene files
scene_paths = objs[2]
# For each object
terminate = False
for o_i in range(len(obj_names)):
#for o_i in [7]:
obj_name = obj_names[o_i]
# Contacts meta file, number of elements in the list is number of grasps
obj_meta_path = os.path.join(contacts_dir, obj_name + '_meta.csv')
with open(obj_meta_path, 'rb') as obj_meta_f:
obj_meta_reader = csv.reader(obj_meta_f)
# Only 1 row in file. List of strings separated by comma
for row in obj_meta_reader:
n_grasps = len(row)
skip_obj = False
# For each scene for this object
for s_i in range(len(scene_paths[o_i])):
# For each grasp for this object
for g_i in range(n_grasps):
scene_path = scene_paths[o_i][s_i]
scene_base = os.path.basename(scene_path)
print(
'Object [%d], Scene [%d], Grasp [%d], Loading triplet files for %s'
% (o_i, s_i, g_i, scene_path))
depth_name = os.path.join(
renders_dir,
os.path.splitext(scene_base)[0] + 'crop.png')
vis_name = os.path.join(
heatmaps_dir,
vis_fmt % (os.path.splitext(scene_base)[0], g_i))
occ_name = os.path.join(
heatmaps_dir,
occ_fmt % (os.path.splitext(scene_base)[0], g_i))
depth_im = np_from_depth(depth_name)
vis_im = np_from_depth(vis_name)
occ_im = np_from_depth(occ_name)
if depth_im is None or vis_im is None or occ_im is None:
print(
'%sERROR: One or more of (depth, vis_tac, occ_tac) images does not exist. Did you forget to run rosrun depth_scene_generation postprocess_scenes? Run it to generate the cropped images. Terminating...%s'
% (ansi.FAIL, ansi.ENDC))
return
# Calculate raw depths from the integers in image
depth_im = scaler.scale_ints_to_depths(depth_im)
if SCALE_HEATMAPS:
vis_im = scaler.scale_ints_to_depths(vis_im)
occ_im = scaler.scale_ints_to_depths(occ_im)
else:
vis_im = vis_im.astype(np.float32) / 255.0
occ_im = occ_im.astype(np.float32) / 255.0
fig = plt.figure(figsize=(15, 6))
ax = plt.subplot(1, 3, 1)
# gray_r
depth_obj = plt.imshow(depth_im[:, :, 0], cmap=plt.cm.jet)
#clim=[MIN_DEPTH, MAX_DEPTH])
tt1 = plt.title('Raw Depth')
cb1 = plt.colorbar(depth_obj, fraction=0.046, pad=0.01)
if BLACK_BG:
black_background(title_hdl=tt1)
black_colorbar(cb1)
ax = plt.subplot(1, 3, 2)
# Plot a white background first, so that black background plots produce
# the same heatmap appearances as white background ones.
# Ref plot white image https://stackoverflow.com/questions/28234416/plotting-a-white-grayscale-image-in-python-matplotlib
plt.imshow(np.ones((depth_im.shape[0], depth_im.shape[1])),
cmap='gray',
vmin=0,
vmax=1,
alpha=1.0)
plt.imshow(depth_im[:, :, 0], cmap=plt.cm.jet, alpha=0.4)
vis_obj = plt.imshow(vis_im[:, :, 0],
cmap=plt.cm.jet,
alpha=0.7)
#clim=[MIN_DEPTH, MAX_DEPTH])
tt2 = plt.title('Visible')
cb2 = plt.colorbar(vis_obj, fraction=0.046, pad=0.01)
if BLACK_BG:
black_background(title_hdl=tt2)
black_colorbar(cb2)
ax = plt.subplot(1, 3, 3)
plt.imshow(np.ones((depth_im.shape[0], depth_im.shape[1])),
cmap='gray',
vmin=0,
vmax=1,
alpha=1.0)
plt.imshow(depth_im[:, :, 0], cmap=plt.cm.jet, alpha=0.4)
occ_obj = plt.imshow(occ_im[:, :, 0],
cmap=plt.cm.jet,
alpha=0.7)
#clim=[MIN_DEPTH, MAX_DEPTH])
tt3 = plt.title('Occluded')
# Flush colorbar with image
cb3 = plt.colorbar(occ_obj, fraction=0.046, pad=0.01)
if BLACK_BG:
black_background(title_hdl=tt3)
black_colorbar(cb3)
fig.tight_layout()
if SAVE_IMG:
dest = os.path.join(
vis_dir,
get_vis_heatmap_fmt() % (os.path.splitext(
os.path.basename(scene_base))[0], g_i))
if BLACK_BG:
plt.savefig(dest,
bbox_inches='tight',
facecolor=fig.get_facecolor(),
edgecolor='none',
transparent=True)
else:
fig.savefig(dest)
print('%sWritten entire plot to %s%s' %
(ansi.OKCYAN, dest, ansi.ENDC))
# Save an individual axis
for subplot_i in SAVE_SUBPLOTS:
# For depth image, only need to save 1st one, `.` all the same
if subplot_i == DEPTH_IDX and g_i != 0:
continue
ax = plt.subplot(1, 3, subplot_i)
ax.set_aspect(1)
plt.axis('off')
# To save individual axis cleanly
depth_dest = os.path.join(
vis_dir, SUBPLOT_NAMES[subplot_i - 1] %
(os.path.splitext(os.path.basename(scene_base))[0]))
extent = ax.get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
fig.savefig(depth_dest, bbox_inches=extent)
print('%sWritten depth image axis to %s%s' %
(ansi.OKCYAN, depth_dest, ansi.ENDC))
if DISPLAY_IMAGES:
plt.show()
plt.close(fig)
if g_i >= N_TO_SAVE - 1:
break
if UINPUT:
uinput = raw_input(
'Press s to skip to next scene, o to skip to next objet, q to quit, or anything else to go to next grasp in this scene: '
)
if uinput.lower() == 's':
break
elif uinput.lower() == 'o':
skip_obj = True
break
elif uinput.lower() == 'q':
terminate = True
break
# Break out of s_i
if skip_obj or terminate:
break
# Break out of o_i
if terminate:
break
def main():
ORDERED = False
TOP_ONLY = True
BLACK_BG = True
plot_all = False
if ORDERED:
pos, quats = test_ordered_poses(topOnly=TOP_ONLY)
if TOP_ONLY:
out_name = 'test_spherical_pose_generation_top'
else:
out_name = 'test_spherical_pose_generation_full'
else:
n_rand_pts = 100
pos, quats = test_rand_poses(n_rand_pts, topOnly=TOP_ONLY)
if TOP_ONLY:
out_name = 'test_spherical_pose_generation_rand_top'
else:
out_name = 'test_spherical_pose_generation_rand_full'
#print quats.shape
n_pts = quats.shape[1]
#####
# Use quaternion rotation only, plot rotations on unit sphere
# 3 x n
XYZ = np.tile(np.array([[0, 0, 0]]).T, (1, n_pts))
# 3 x n
UVW = np.zeros((3, n_pts))
for i in range(n_pts):
mat = quaternion_matrix(quats[:, i])
# Multiply the rotation by x-axis, to get a vector
UVW[:, i] = np.dot(mat[0:3, 0:3], [1, 0, 0])
if plot_all:
fig = plt.figure(figsize=(15, 6))
# Ref 3D plot https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html
ax = fig.add_subplot(131, projection='3d')
# Ref https://matplotlib.org/examples/mplot3d/quiver3d_demo.html
# First three xyzs are start point, last three are vector.
# pivot specifies the part of the arrow that is at the grid point, 1st xyzs
# passed in. Arrow rotates about this point. Default is 'tip', regardless
# of what API says. Arrowheads end at first set of xyzs passed in. Other
# option is 'tail' or 'middle'.
ax.quiver(UVW[0, :],
UVW[1, :],
UVW[2, :],
-UVW[0, :],
-UVW[1, :],
-UVW[2, :],
color='orange',
length=0.4,
arrow_length_ratio=0.5,
pivot='tail')
ax.scatter(UVW[0, :], UVW[1, :], UVW[2, :], c='orange')
ax.set_title('By quaternion only')
ax.set_aspect(1)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_zlim(-1, 1)
#####
# Use position only, plot positions on sphere
ax = fig.add_subplot(132, projection='3d')
ax.scatter(pos[0, :], pos[1, :], pos[2, :], c='red')
ax.set_title('By position only')
ax.set_aspect(1)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_zlim(-1, 1)
#####
# Overlap position and quaternion, to see if they overlap exactly
# Result: They do NOT overlap exactly. So the best option to generate both
# position and quaternion is using the quaternion way, where quats variable
# gives the quaternions, UVW gives the positions.
ax = fig.add_subplot(133, projection='3d')
# Quaternion
#ax.quiver (XYZ[0, :], XYZ[1, :], XYZ[2, :], UVW[0, :], UVW[1, :], UVW[2, :],
# color='orange', length=1, arrow_length_ratio=0.1, pivot='tail')
ax.scatter(UVW[0, :], UVW[1, :], UVW[2, :], c='orange', alpha=0.5)
# Position
ax.scatter(pos[0, :], pos[1, :], pos[2, :], c='red', alpha=0.5)
# Text label, for easier debugging of mismatches between positions and quats
for i in range(pos.shape[1]):
ax.text(UVW[0, i], UVW[1, i], UVW[2, i], str(i), color='orange')
ax.text(pos[0, i], pos[1, i], pos[2, i], str(i), color='red')
ax.set_title('Overlay quaternion and position')
ax.set_aspect(1)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_zlim(-1, 1)
fig.tight_layout()
'''
# Save png
fig.savefig (out_name + '.png')
print ('Written plot to %s.png' % out_name)
# Save eps
fig.savefig (out_name + '.eps')
print ('Written plot to %s.eps' % out_name)
'''
plt.show()
# Plot an individual image for thesis writing
fig2 = plt.figure()
ax = fig2.add_subplot(111, projection='3d')
cm_name = custom_colormap_neon()
color = mpl_color(1, 8, colormap_name=cm_name)
#color = np.array ((252, 149, 11)) / 255.0
# Ref https://matplotlib.org/examples/mplot3d/quiver3d_demo.html
# First three xyzs are start point, last three are vector.
# pivot specifies the part of the arrow that is at the grid point, 1st xyzs
# passed in. Arrow rotates about this point. Default is 'tip', regardless
# of what API says. Arrowheads end at first set of xyzs passed in. Other
# option is 'tail' or 'middle'.
ax.quiver(UVW[0, :],
UVW[1, :],
UVW[2, :],
-UVW[0, :],
-UVW[1, :],
-UVW[2, :],
color=color,
length=0.4,
arrow_length_ratio=0.5,
pivot='tail')
# 1, 4 go together well, dark blue, light green
# 1, 3 okay too, dark blue, cyan
# 1, 5 good too, dark blue, yellow
color = mpl_color(4, 8, colormap_name=cm_name)
ax.scatter(UVW[0, :], UVW[1, :], UVW[2, :], c=color)
ax.set_title('Random poses')
ax.set_aspect(1)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_zlim(-1, 1)
fig2.tight_layout()
if BLACK_BG:
black_3d_background(ax)
black_background(ax)
single_out_base = out_name + '_single_black'
single_out_name = single_out_base + '.eps'
fig2.savefig(single_out_name,
bbox_inches='tight',
facecolor=fig2.get_facecolor(),
edgecolor='none',
transparent=True)
print('Written plot to %s' % single_out_name)
single_out_name = single_out_base + '.png'
fig2.savefig(single_out_name,
bbox_inches='tight',
facecolor=fig2.get_facecolor(),
edgecolor='none',
transparent=True)
print('Written plot to %s' % single_out_name)
plt.show()
def plot_bars(obj_ids, obj_errs, obj_names_ordered, lbls_by_value, out_name):
truetype()
cm_name = custom_colormap_neon()
# 2 (light sky blue) and 7 (noen orange) look good
color = mpl_color(2, 8, colormap_name=cm_name)
_, title_hdl = plot_line(obj_ids,
obj_errs,
'Per-Object-Class Errors',
'Object',
'Error',
out_name='',
color=color,
lbl='',
style='bar',
dots=False,
grid=True,
do_save=False,
do_show=False,
return_title_hdl=True)
black_background(title_hdl=title_hdl)
# Limit ticks to object IDs, not the extra columns on left and right
mpl_diagonal_xticks(plt.gca(), obj_ids, obj_names_ordered, rot_degs=0)
## Plot a horizontal line across, at average per-class error
# Dashed line
# API https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.axhline.html
mean_err = np.mean(obj_errs)
plt.gca().axhline(y=mean_err,
linewidth=1,
color='w',
linestyle='--',
label='Mean per-class')
mean_err_obj = plt.text(0, mean_err + 0.01, '%.3f' % mean_err, color='w')
if len(lbls_by_value) > 0:
smaller_proportion = np.min(lbls_by_value) / float(
np.sum(lbls_by_value))
plt.gca().axhline(y=smaller_proportion,
linewidth=1,
color='r',
linestyle='--',
label='Labels portion')
plt.text(0,
smaller_proportion + 0.01,
'%.3f' % smaller_proportion,
color='r')
# Positive is good, negative is bad
mean_err_obj.set_text (mean_err_obj.get_text () + \
' (%.3f lower)' % (smaller_proportion - mean_err))
legend_hdl = plt.legend()
black_legend(legend_hdl)
# y-axis is percentage
plt.gca().set_ylim([0.0, 1.0])
## Plot
plt.savefig(out_name,
bbox_inches='tight',
facecolor=plt.gcf().get_facecolor(),
edgecolor='none',
transparent=True)
print('%sWritten plot to %s%s' % (ansi.OKCYAN, out_name, ansi.ENDC))
plt.show()
def draw_confusion_matrix_per_sample(unsorted_idx,
distance_matrix,
ticks=[],
img_name='',
title_prefix='',
draw_title=True,
draw_xylbls=True,
draw_xticks=True,
draw_yticks=True,
fontsize=10,
bg_color='white',
cmap_name='jet_r'):
# Matrix is n x n
nSamples = np.shape(unsorted_idx)[0]
dists_sorted = np.zeros(np.shape(distance_matrix))
for i in range(0, nSamples):
# Sort the ith row of sample IDs. Save the indexes from sorting.
# Ref: http://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
sorted_idx_rowi = np.argsort(unsorted_idx[i, :])
# Take ith row of distance matrix, index it using the sorted indices
dists_sorted[i, :] = distance_matrix[i, :][sorted_idx_rowi]
print('')
print ('Max distance: %f. Min distance: %f. Mean distance: %f' % \
(np.max (dists_sorted), np.min (dists_sorted), np.mean (dists_sorted)))
print('Confusion matrix:')
print(dists_sorted)
# Show confusion matrix in a separate window
# Using a figure makes colorbar same length as main plot!
fig = plt.figure()
plt.matshow(dists_sorted, fignum=fig.number)
if draw_title:
if ticks:
plt.title(title_prefix + 'Confusion matrix', y=1.3)
else:
plt.title(title_prefix + 'Confusion matrix')
# Use jet_r. Reverse colors, so that small dists are hot, far dists are cold.
# http://stackoverflow.com/questions/3279560/invert-colormap-in-matplotlib
# Save orig cmap so can set it back for future plots
#orig_cmap = plt.get_cmap ()
plt.set_cmap(cmap_name)
colorbar = plt.colorbar()
if draw_xylbls:
plt.ylabel('Objects')
plt.xlabel('Objects')
ax = plt.gca()
# Display class names on the axes ticks
if draw_xticks or draw_yticks:
# Custom ticks passed in
if len(ticks) > 0:
draw_classname_ticks(dists_sorted,
ticks,
draw_x=draw_xticks,
draw_y=draw_yticks,
fontsize=fontsize)
# Automatically generate numerical ticks, every 20 units
else:
units_per_tick = 20.0
if draw_xticks:
# You can adjust these to make as frequent or as rare ticks as you want
plt.xticks(np.arange(0, nSamples, units_per_tick))
if draw_yticks:
plt.yticks(np.arange(0, nSamples, units_per_tick))
#print (nSamples)
#print (np.arange (0, nSamples, units_per_tick))
if not draw_xticks:
# http://stackoverflow.com/questions/2176424/hiding-axis-text-in-matplotlib-plots
#ax.get_xaxis ().set_visible (False)
ax.get_xaxis().set_ticks([])
if not draw_yticks:
#ax.get_yaxis ().set_visible (False)
ax.get_yaxis().set_ticks([])
for tick in ax.xaxis.get_major_ticks():
# http://stackoverflow.com/questions/6390393/matplotlib-make-tick-labels-font-size-smaller
tick.label.set_fontsize(10)
# specify integer or one of preset strings, e.g.
#tick.label.set_fontsize('x-small')
#tick.label.set_rotation('vertical')
# Set background color
if bg_color == 'black':
black_background()
black_colorbar(colorbar)
if img_name:
# Ref savefig() black background: https://stackoverflow.com/questions/4804005/matplotlib-figure-facecolor-background-color
plt.savefig(img_name,
bbox_inches='tight',
facecolor=fig.get_facecolor(),
edgecolor='none',
transparent=True)
print('Plot saved to %s' % img_name)
plt.show()
def draw_confusion_matrix(true_lbls,
predicted_lbls,
ticks=[],
img_name='',
title_prefix='',
draw_title=True,
raw=False,
bg_color='white'):
# Compute confusion matrix
# Make sure you keep order the same. In (true, predicted) order, the y-axis
# is true label, the x-axis is predicted. If you swap these, then the
# plot's x- and y-labels need to swap as well.
# Ref: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
cm = confusion_matrix(true_lbls, predicted_lbls)
print('Confusion matrix:')
print(cm)
# Do percentages, instead of raw numbers that are returned by default
if not raw:
# Sum each row to get total number of objects in each category (y-axis
# label of confusion mat is true labels, so sum rows, not columns).
# Need floating point so cm divided by this array gives floats, not ints!
nTtl = np.sum(cm, axis=1).astype(np.float32)
# Reshape to column vector, so element-wise divide would divide each row of
# conf mat by total object sum in that row.
nTtl = nTtl.reshape((np.size(nTtl), 1))
# Element-wise divide each row of confusion matrix by the total # objects
cm = np.divide(cm, nTtl)
#print (cm)
# Show confusion matrix in a separate window
# Using a figure makes colorbar same length as main plot!
fig = plt.figure()
plt.matshow(cm, fignum=fig.number)
plt.set_cmap('jet')
colorbar = plt.colorbar()
if draw_title:
# Ref move title up: http://stackoverflow.com/questions/12750355/python-matplotlib-figure-title-overlaps-axes-label-when-using-twiny
plt.title(title_prefix + 'Confusion matrix', y=1.2)
plt.ylabel('True label')
plt.xlabel('Predicted label')
# Display class names on the axes ticks
if ticks:
draw_classname_ticks(cm, ticks)
# Set background color
if bg_color == 'black':
black_background()
black_colorbar(colorbar)
if img_name:
# Ref savefig() black background: https://stackoverflow.com/questions/4804005/matplotlib-figure-facecolor-background-color
plt.savefig(img_name,
bbox_inches='tight',
facecolor=fig.get_facecolor(),
edgecolor='none',
transparent=True)
print('Plot saved to %s' % img_name)
plt.show()
def save_individual_subplots(figs,
ylbls,
ylbl_to_save,
nRows,
nCols,
scale_xmin=1.1,
scale_x=1.2,
scale_y=1.15,
ndims=3,
show=True,
bg_color='white',
tick_rot=0):
x_expand_idx = []
y_expand_idx = []
# Expand horizontal space for plots on left column
for i in range(0, nRows):
# Subplots on left have indices 0*n, 1*n, 2*n, ..., (n-1)*n. Add margin on
# x on left.
x_expand_idx.append(i * nRows)
# Expand vertical space for plots on botom row
for i in range(0, nCols):
# Subplots at bottom have indices (n-1)*n+0, (n-1)*n+1, (n-1)*n+2, ...,
# (n-1)*n+(n-1). Add margin on y at bottom.
y_expand_idx.append((nRows - 1) * nCols + i)
# Constants for scaling bbox, tuned for ticks and labels with fontsize 6
SCALE_XMIN = scale_xmin
SCALE_YMIN = 1.1
SCALE_X = scale_x
SCALE_Y = scale_y
# Find the figure that matches the desired ylbl
for f_i in range(0, len(ylbls)):
if ylbls[f_i] != ylbl_to_save:
continue
# Found the figure with desired ylbl
else:
imgpath = get_img_path('hists')
imgname = os.path.join(
imgpath, 'triangle_' + str(ndims) + 'Dhist_' + ylbls[f_i])
axes = figs[f_i].get_axes()
for sp_i in range(0, len(axes)):
curr_imgname = imgname + ('_%02d' % sp_i) + '.eps'
# Ref save a subplot (axis) in a figure to file:
# http://stackoverflow.com/questions/4325733/save-a-subplot-in-matplotlib
extent = axes[sp_i].get_window_extent().transformed(
figs[f_i].dpi_scale_trans.inverted())
# Default. Need 1.05 to show entire plot, 1 doesn't show whole thing!
# 1.3 shows axes labels (font size 6) perfectly, for x or y
bbox_inches = extent.expanded(SCALE_XMIN, SCALE_YMIN)
# Record original x y position of box, before expanding
# http://matplotlib.org/devel/transformations.html
xmax_orig = bbox_inches.xmax
ymax_orig = bbox_inches.ymax
# expanded() works outwards from center only, can't make box fixed
# in one corner. So have to manually move box back after expand.
# extent is a matplotlib.transforms.BboxBase type
# Ref http://matplotlib.org/devel/transformations.html
# Subplot on lower-left needs to expand in both x and y
if sp_i in x_expand_idx and sp_i in y_expand_idx:
bbox_inches = extent.expanded(SCALE_X, SCALE_Y)
# Only expand x, on left
elif sp_i in x_expand_idx:
# x should be same as above, so last row is same width as prev rows
bbox_inches = extent.expanded(SCALE_X, SCALE_YMIN)
# Only expand y, on bottom
elif sp_i in y_expand_idx:
# Need extra y to show bottom x label, need extra x to show left-most
# tick.
bbox_inches = extent.expanded(SCALE_XMIN, SCALE_Y)
# Shift content in bbox to sit at upper-right of the expanded bbox
xmax_new = bbox_inches.xmax
ymax_new = bbox_inches.ymax
bbox_inches = bbox_inches.translated(xmax_orig - xmax_new,
ymax_orig - ymax_new)
if bg_color == 'black':
black_background(axes[sp_i])
# Ref savefig() black background: https://stackoverflow.com/questions/4804005/matplotlib-figure-facecolor-background-color
figs[f_i].savefig(curr_imgname,
bbox_inches=bbox_inches,
facecolor=bg_color,
edgecolor='none',
transparent=True)
print ('Individual axes %d in dimension %s saved to %s' % ( \
sp_i, ylbl_to_save, curr_imgname))
break