def test_bbox_image_inverted():
# This is just used to produce an image to feed to BboxImage
image = np.arange(100).reshape((10, 10))
fig, ax = plt.subplots()
bbox_im = BboxImage(
TransformedBbox(Bbox([[100, 100], [0, 0]]), ax.transData))
bbox_im.set_data(image)
bbox_im.set_clip_on(False)
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
ax.add_artist(bbox_im)
image = np.identity(10)
bbox_im = BboxImage(
TransformedBbox(Bbox([[0.1, 0.2], [0.3, 0.25]]),
ax.figure.transFigure))
bbox_im.set_data(image)
bbox_im.set_clip_on(False)
ax.add_artist(bbox_im)
def make_artists(self, axes, show_intruder):
'make self.artists_dict'
assert self.vec_list
self.img = get_airplane_img()
posa_list = [(v[0], v[1], v[2]) for v in self.vec_list]
posb_list = [(v[3], v[4], v[5]) for v in self.vec_list]
pos_lists = [posa_list, posb_list]
if show_intruder:
pos_lists.append(posb_list)
for i, pos_list in enumerate(pos_lists):
x, y, theta = pos_list[0]
l = axes.plot(*zip(*pos_list), f'c-', lw=0, zorder=1)[0]
l.set_visible(False)
self.artists_dict[f'line{i}'] = l
if i == 0:
lc = LineCollection([], lw=2, animated=True, color='k', zorder=1)
axes.add_collection(lc)
self.artists_dict[f'lc{i}'] = lc
int_lc = LineCollection([], lw=2, animated=True, color='k', zorder=1)
axes.add_collection(int_lc)
self.artists_dict[f'int_lc{i}'] = int_lc
# only sim_index = 0 gets intruder aircraft
if i == 0 or (i == 1 and show_intruder):
size = State.plane_size
box = Bbox.from_bounds(x - size/2, y - size/2, size, size)
tbox = TransformedBbox(box, axes.transData)
box_image = BboxImage(tbox, zorder=2)
theta_deg = (theta - np.pi / 2) / np.pi * 180 # original image is facing up, not right
img_rotated = ndimage.rotate(self.img, theta_deg, order=1)
box_image.set_data(img_rotated)
axes.add_artist(box_image)
self.artists_dict[f'plane{i}'] = box_image
if i == 0:
dot = axes.plot([x], [y], 'k.', markersize=6.0, zorder=2)[0]
self.artists_dict[f'dot{i}'] = dot
rad = 1500
c = patches.Ellipse((x, y), rad, rad, color='k', lw=3.0, fill=False)
axes.add_patch(c)
self.artists_dict[f'circle{i}'] = c
def plotImage(x, y, im):
# TODO hard-coded size of images, breaks in some cases
width = 1
height = 2
bb = Bbox.from_bounds(x,y,width, height)
bb2 = TransformedBbox(bb,plot.transData)
bbox_image = BboxImage(bb2,
norm = None,
origin=None,
clip_on=False)
bbox_image.set_data(im)
plot.add_artist(bbox_image)
def plotImagesInWeightSpace( fig, wspace, orig_images, img_dims, margin_percent = 0.03, img_ratio = 0.035 ):
# Author: C. Howard
# function to robustly plot images in the weight space
#
# fig: Figure handle for plotting
# wspace : The lower dimensional features such that wspace = W*X where W maps
# the input data X into lower dim data
# orig_images : This is basically X where each column is a vectorized image
# img_dims : This is the rectangular dimensions of each image in the data set
# margin_percent: What percent of the data span we want to add margin in the plot
# img_ratio : What ratio we use to decide image sizes to be plotted, relative to the data span
# get number of original images
(d,num_images) = orig_images.shape
# get max dims of data
x1max = np.max(wspace[0, :])
x1min = np.min(wspace[0, :])
x2max = np.max(wspace[1, :])
x2min = np.min(wspace[1, :])
# get the center
x1c = 0.5 * (x1max + x1min)
x2c = 0.5 * (x2max + x2min)
# get width and height
w = x1max - x1min
h = x2max - x2min
# compute how much to scale the width/height from the
# center of the data to set the xlimits/ylimits
scale = 0.5 + margin_percent
# plot the images in the weight space
ax = fig.add_subplot(111)
for k in range(0, num_images):
bb = Bbox.from_bounds(wspace[0, k], wspace[1, k], w * img_ratio, h * img_ratio)
bb2 = TransformedBbox(bb, ax.transData)
bbox_img = BboxImage(bb2, norm=None, origin=None, clip_on=False)
bbox_img.set_data(orig_images[:, k].reshape(img_dims[0], img_dims[0]))
ax.add_artist(bbox_img)
# set the axis limits
ax.set_xlim(x1c - scale * w, x1c + scale * w)
ax.set_ylim(x2c - scale * h, x2c + scale * h)
# return the fig handle and axis handle
return (fig,ax)
def test_bbox_image_inverted():
# This is just used to produce an image to feed to BboxImage
fig = plt.figure()
axes = fig.add_subplot(111)
axes.plot([1, 2, 3])
im_buffer = io.BytesIO()
fig.savefig(im_buffer)
im_buffer.seek(0)
image = imread(im_buffer)
bbox_im = BboxImage(Bbox([[100, 100], [0, 0]]))
bbox_im.set_data(image)
axes.add_artist(bbox_im)
def show(data, eps=0.0):
# Make box slightly larger
box_extent = data.space.partition.extent * (1.0 + eps)
box_min = data.space.min_pt - data.space.partition.extent * eps / 2.0
bbox0 = Bbox.from_bounds(*box_min, *box_extent)
bbox = TransformedBbox(bbox0, ax.transData)
# TODO: adapt interpolation
bbox_image = BboxImage(bbox,
norm=normalization,
cmap='bone',
interpolation='nearest',
origin=False)
bbox_image.set_data(np.asarray(data).T)
ax.add_artist(bbox_image)
def create_artists(self, legend, orig_handle,
xdescent, ydescent, width, height, fontsize, trans):
scale = 1.5
bb = Bbox.from_bounds(
xdescent + self.offset,
ydescent,
height * self.image_data.shape[1] / self.image_data.shape[0] * scale,
height * scale)
tbb = TransformedBbox(bb, trans)
image = BboxImage(tbb)
image.set_data(self.image_data)
self.update_prop(image, orig_handle, legend)
return [image]
def create_artists(self, legend, orig_handle, xdescent, ydescent, width,
height, fontsize, trans):
# enlarge the image by these margins
sx, sy = self.image_stretch
# create a bounding box to house the image
bb = Bbox.from_bounds(xdescent - sx, ydescent - sy, width + sx,
height + sy)
tbb = TransformedBbox(bb, trans)
image = BboxImage(tbb)
image.set_data(self.image_data)
self.update_prop(image, orig_handle, legend)
return [image]
def create_artists(self, legend, orig_handle,
xdescent, ydescent, width, height, fontsize, trans):
l = matplotlib.lines.Line2D([xdescent+self.offset,xdescent+(width-self.space)/3.+self.offset],
[ydescent+height/2., ydescent+height/2.])
l.update_from(orig_handle)
l.set_clip_on(False)
l.set_transform(trans)
bb = Bbox.from_bounds(xdescent +(width+self.space)/3.+self.offset,
ydescent,
height*self.image_data.shape[1]/self.image_data.shape[0],
height)
tbb = TransformedBbox(bb, trans)
image = BboxImage(tbb)
image.set_data(self.image_data)
self.update_prop(image, orig_handle, legend)
return [l,image]
def plot_wind_data2(ax, so, time_nums):
so.wind_speed = np.array(so.wind_speed)
max_index = so.wind_speed.argmax()
wind_speed_max = so.wind_speed[max_index]
print('max speed', wind_speed_max, len(so.wind_speed))
logo = image.imread('north.png', None)
bbox2 = Bbox.from_bounds(210, 330, 30, 40)
# trans_bbox2 = bbox2.transformed(ax.transData)
bbox_image2 = BboxImage(bbox2)
bbox_image2.set_data(logo)
ax.add_image(bbox_image2)
# for x in range(0,len(time_nums),100):
U = so.u
V = so.v
# if x == max_index:
Q = ax.quiver(time_nums,
-.15,
U,
V,
headlength=0,
headwidth=0,
headaxislength=0,
alpha=1,
color='#045a8d',
width=.0015,
scale=wind_speed_max * 5)
ax.quiverkey(
Q,
0.44,
0.84,
wind_speed_max * .6,
labelpos='N',
label=
' 0 mph %.2f mph'
% wind_speed_max,
# fontproperties={'weight': 'bold'}
)
def create_artists(self, legend, orig_handle, xdescent, ydescent, width,
height, fontsize, trans):
# save original visibility and then make it visible
orig_vis = orig_handle.get_visible()
orig_handle.set_visible(1)
# set correct state and image data
if not orig_vis:
image_data = VisibilityHandler._unchecked
self.state = False
else:
image_data = VisibilityHandler._checked
self.state = True
# ratio for square checkbox
image_ratio = image_data.shape[1] / image_data.shape[0]
# create a checkbox artist
bb = Bbox.from_bounds(xdescent, ydescent, height * image_ratio, height)
tbb = TransformedBbox(bb, trans)
image = BboxImage(tbb)
image.set_data(image_data)
# update self
self.update_prop(image, orig_handle, legend)
self.set_events(image, orig_handle)
# artists to be returned
artists = [image]
# if a handler is given, create artists to be return
if self.handler is not None:
artists += self.handler.create_artists(legend, orig_handle,
xdescent - (height * 2.),
ydescent,
width - (height * 2.),
height, fontsize, trans)
# revert visibility
orig_handle.set_visible(orig_vis)
return artists
def plotBWImage(x, y, im, ax, lims=(3, 3)):
"""
Plots image im on a x,y scatter plot
:param x: x plot location
:param y: y plot location
:param im: bw image to be plotted, should be a 2D numpy matrix
:param ax: axis to plot the image on
:param lims: (xlim,ylim) tuple where |x| > xlim and |y| > ylim are not
plotted
:returns: matplotlib.image.BboxImage handle to the plotted image
or None if x,y outside of lims
"""
if np.abs(x) > lims[0] or np.abs(y) > lims[1]:
return
bb = Bbox.from_bounds(x, y, 0.1, 0.1)
bb2 = TransformedBbox(bb, ax.transData)
bbox_image = BboxImage(bb2, norm=None, origin=None, clip_on=False)
bbox_image.set_data(im)
ax.add_artist(bbox_image)
def imagesAsTickMarks(ax, images):
TICKYPOS = -.6
lowerCorner = ax.transData.transform((.8, TICKYPOS - .2))
upperCorner = ax.transData.transform((1.2, TICKYPOS + .2))
print(lowerCorner)
print(upperCorner)
bbox_image = BboxImage(
Bbox([
lowerCorner[0],
lowerCorner[1],
upperCorner[0],
upperCorner[1],
]),
norm=None,
origin=None,
clip_on=False,
)
image = imread(images[0])
print('img loaded')
bbox_image.set_data(image)
ax.add_artist(bbox_image)
def scatter_image(feature_x, feature_y, image_paths, title, save=None, code_list=None): """ Args: feature_x: x座標 feature_y: y座標 image_paths: """ global Scale fig = plt.figure() ax = fig.add_subplot(111) xlim = [np.min(feature_x)-5, np.max(feature_x)+5] ylim = [feature_y.min()-5, feature_y.max()+5] for (x, y, path) in zip(feature_x, feature_y, image_paths): img = plt.imread(path) if EmpCode != "" and get_class ( path ) == EmpCode : img = frame_image ( img, 30, 0 ) elif code_list != None : idx = code_list.index ( get_class (path) ) img = frame_image ( img, 30, float(idx) / len(code_list), cmap=cm ) disp_size = max ( xlim[1]-xlim[0], ylim[1]-ylim[0] ) / Num bb = Bbox.from_bounds(x, y, disp_size*Scale, disp_size * Scale) bb2 = TransformedBbox(bb, ax.transData) bbox_image = BboxImage(bb2, cmap=None, norm=None, origin=None, clip_on=False) bbox_image.set_data(img) ax.add_artist(bbox_image) ax.set_ylim(*ylim) ax.set_xlim(*xlim) plt.title(title) if save is not None: plt.savefig(save, dpi=600) plt.show()
def __init__(self, ax, snapshot):
self.snapshot = snapshot
self.ax = ax
self.ax.set_aspect('equal', 'box-forced')
self.grass = BboxImage(ax.bbox, interpolation='bicubic', zorder=-1000)
self.ax.add_artist(self.grass)
self.grass.set_data(GRASS)
for lane in self.snapshot.lanes:
path = Path([
lane.p-LANE_SCALE*lane.m-lane.n*lane.w*0.5,
lane.p-LANE_SCALE*lane.m+lane.n*lane.w*0.5,
lane.q+LANE_SCALE*lane.m+lane.n*lane.w*0.5,
lane.q+LANE_SCALE*lane.m-lane.n*lane.w*0.5,
lane.p-LANE_SCALE*lane.m-lane.n*lane.w*0.5
], [
Path.MOVETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.CLOSEPOLY
])
ax.add_artist(PathPatch(path, facecolor=LANE_COLOR, lw=0.5, edgecolor=LANE_BCOLOR, zorder=-100))
self.robots = [AxesImage(self.ax, interpolation='bicubic', zorder=0) for robot in self.snapshot.robots]
for robot in self.robots:
self.ax.add_artist(robot)
self.human = AxesImage(self.ax, interpolation='bicubic', zorder=100)
self.ax.add_artist(self.human)
self.ts = np.linspace(0., self.snapshot.human.T, STEPS)
xs = self.snapshot.human.ix(self.ts)
self.h_future = ax.plot(xs[:, 0], xs[:, 1], zorder=50, linestyle='--', color='white', linewidth=1.)[0]
self.h_past = ax.plot(xs[:, 0], xs[:, 1], zorder=40, linestyle='-', color='blue', linewidth=2.)[0]
self.ax.set_xlim(-0.5, 0.5)
self.ax.set_ylim(-0.2, 0.8)
def graph_update(i):
ax1.clear()
ax1.set_ylim(myGraph.ymin, myGraph.ymax)
ax1.set_xlim(myGraph.xmin, myGraph.xmax)
champ_count = df['name'][:i].value_counts()
champs = champ_count.index.values
play_count = champ_count.values
for j, champ in enumerate(champs):
lowerCorner = ax1.transData.transform((-.4 + j, .85 * myGraph.ymin))
upperCorner = ax1.transData.transform((.4 + j, 0.1 * myGraph.ymin))
bbox_image = BboxImage(
Bbox([
[lowerCorner[0], lowerCorner[1]],
[upperCorner[0], upperCorner[1]],
]),
norm=None,
origin=None,
clip_on=False,
)
bbox_image.set_data(imread('{}.png'.format(champs[j])))
ax1.add_artist(bbox_image)
ax1.bar(champs, play_count)
myGraph.update_graph(champ_count[0])
def main(argv):
parser = argparse.ArgumentParser(prog='VIZ')
parser.add_argument('source', help='path to the source metadata file')
args = parser.parse_args(argv[1:])
# read in the data file
data = pandas.read_csv(args.source, sep='\t')
# load up data
vis_x = data['x'].tolist()
vis_y = data['y'].tolist()
img_data = data['filename'].tolist()
# Create figure
fig = plt.figure()
ax = fig.add_subplot(111)
for x, y, filepath in zip(vis_x, vis_y, img_data):
im = plt.imread(filepath)
bb = Bbox.from_bounds(x, y, 1, 1)
bb2 = TransformedBbox(bb, ax.transData)
bbox_image = BboxImage(bb2, norm=None, origin=None, clip_on=False)
bbox_image.set_data(im)
ax.add_artist(bbox_image)
#plt.scatter(vis_x, vis_y, marker='s', c=vis_y)
#plt.colorbar(ticks=range(10))
#plt.clim(-0.5, 9.5)
# Set the x and y limits
ax.set_ylim(-50, 50)
ax.set_xlim(-50, 50)
plt.show()
def create_artists(self, legend, orig_handle, xdescent, ydescent, width,
height, fontsize, trans):
l = Line2D([xdescent + self.offset], [ydescent + height / 2.],
c=self.color,
ls="",
marker="o",
mfc=self.color,
mec=self.color)
l.set_clip_on(False)
bb = Bbox.from_bounds(
xdescent + (width + self.space) / 3. + self.offset, ydescent,
height * self.image_data.shape[1] / self.image_data.shape[0],
height)
tbb = TransformedBbox(bb, trans)
image = BboxImage(tbb)
image.set_data(self.image_data)
image.set_alpha(1.0)
legend.set_alpha(1.0)
self.update_prop(image, orig_handle, legend)
return [l, image]
import matplotlib.pyplot as plt from matplotlib.transforms import Bbox, TransformedBbox from matplotlib.image import BboxImage from pathlib import Path c = Path.cwd() / "checked.png" # Create figure and axes fig, ax = plt.subplots() # Add the Bbox Image to add the checked.png image bb = Bbox.from_bounds(0.2, 0.2, 0.6, 0.6) image_data = plt.imread(c, 0) tbb = TransformedBbox(bb, ax.transData) bbox_image = BboxImage(tbb) bbox_image.set_data(image_data) ax.add_artist(bbox_image) plt.show()
fmt = ScalarFormatter(useOffset=False)
ax.xaxis.set_major_formatter(fmt)
for year, h, bc in zip(years, heights, box_colors):
bbox0 = Bbox.from_extents(year - 0.4, 0., year + 0.4, h)
bbox = TransformedBbox(bbox0, ax.transData)
rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")
ax.add_artist(rb_patch)
ax.annotate(r"%d" % (int(h / 100.) * 100), (year, h),
va="bottom",
ha="center")
patch_gradient = BboxImage(
ax.bbox,
interpolation="bicubic",
zorder=0.1,
)
gradient = np.zeros((2, 2, 4), dtype=float)
gradient[:, :, :3] = [1, 1, 0.]
gradient[:, :, 3] = [[0.1, 0.3], [0.3, 0.5]] # alpha channel
patch_gradient.set_array(gradient)
ax.add_artist(patch_gradient)
ax.set_xlim(years[0] - 0.5, years[-1] + 0.5)
ax.set_ylim(0, 10000)
fig.savefig('ribbon_box.png')
plt.show()
def plotImage(x, y, im):
bb = Bbox.from_bounds(x, y, 1, 1)
bb2 = TransformedBbox(bb, ax.transData)
bbox_image = BboxImage(bb2, norm=None, origin=None, clip_on=False)
bbox_image.set_data(im)
ax.add_artist(bbox_image)
def display_patches_ticklabels():
ytickspace = 0
S0_ticks = np.array([
dist_echo[echodepth > -1000],
[ytickspace] * len(dist_echo[echodepth > -1000])
]).T
S1_ticks = np.array([
dist_echo[(echodepth < -1000) & (echodepth > -1500)], [ytickspace] *
len(dist_echo[(echodepth < -1000) & (echodepth > -1500)])
]).T
S2_ticks = np.array([
dist_echo[(echodepth < -1500) & (echodepth > -2000)], [ytickspace] *
len(dist_echo[(echodepth < -1500) & (echodepth > -2000)])
]).T
S3_ticks = np.array([
dist_echo[(echodepth < -2000) & (echodepth > -3000)], [ytickspace] *
len(dist_echo[(echodepth < -2000) & (echodepth > -3000)])
]).T
S4_ticks = np.array([
dist_echo[(echodepth < -3000)],
[ytickspace] * len(dist_echo[(echodepth < -3000)])
]).T
xaxislen = np.max(dist_gline) + 20 - (np.min(dist_gline) - 20)
ytickspace = 50
for i in range(len(S0_ticks)):
lowerCorner = contour_ax.transData.transform(
(S0_ticks[i][0] - 0.05 * xaxislen, S0_ticks[i][1] - ytickspace))
upperCorner = contour_ax.transData.transform(
(S0_ticks[i][0] + 0.05 * xaxislen, S0_ticks[i][1] + ytickspace))
print([lowerCorner[0], lowerCorner[1], upperCorner[0], upperCorner[1]])
bbox_image = BboxImage(Bbox([[lowerCorner[0], lowerCorner[1]],
[upperCorner[0], upperCorner[1]]]),
norm=None,
origin=None,
clip_on=False)
bbox_image.set_data(plt.imread('./Images/acrossASF/markers/cross.png'))
contour_ax.add_artist(bbox_image)
for i in range(len(S1_ticks)):
lowerCorner = contour_ax.transData.transform(
(S1_ticks[i][0] - 0.05 * xaxislen, S0_ticks[i][1] - ytickspace))
upperCorner = contour_ax.transData.transform(
(S1_ticks[i][0] + 0.05 * xaxislen, S0_ticks[i][1] + ytickspace))
bbox_image = BboxImage(Bbox([[lowerCorner[0], lowerCorner[1]],
[upperCorner[0], upperCorner[1]]]),
norm=None,
origin=None,
clip_on=False)
bbox_image.set_data(
plt.imread('./Images/acrossASF/markers/circle.png'))
contour_ax.add_artist(bbox_image)
for i in range(len(S2_ticks)):
lowerCorner = contour_ax.transData.transform(
(S2_ticks[i][0] - 0.05 * xaxislen, S0_ticks[i][1] - ytickspace))
upperCorner = contour_ax.transData.transform(
(S2_ticks[i][0] + 0.05 * xaxislen, S0_ticks[i][1] + ytickspace))
bbox_image = BboxImage(Bbox([[lowerCorner[0], lowerCorner[1]],
[upperCorner[0], upperCorner[1]]]),
norm=None,
origin=None,
clip_on=False)
bbox_image.set_data(
plt.imread('./Images/acrossASF/markers/square.png'))
contour_ax.add_artist(bbox_image)
for i in range(len(S3_ticks)):
lowerCorner = contour_ax.transData.transform(
(S3_ticks[i][0] - 0.05 * xaxislen, S0_ticks[i][1] - ytickspace))
upperCorner = contour_ax.transData.transform(
(S3_ticks[i][0] + 0.05 * xaxislen, S0_ticks[i][1] + ytickspace))
bbox_image = BboxImage(Bbox([[lowerCorner[0], lowerCorner[1]],
[upperCorner[0], upperCorner[1]]]),
norm=None,
origin=None,
clip_on=False)
bbox_image.set_data(
plt.imread('./Images/acrossASF/markers/triangle.png'))
contour_ax.add_artist(bbox_image)
for i in range(len(S4_ticks)):
lowerCorner = contour_ax.transData.transform(
(S4_ticks[i][0] - 0.05 * xaxislen, S0_ticks[i][1] - ytickspace))
upperCorner = contour_ax.transData.transform(
(S4_ticks[i][0] + 0.05 * xaxislen, S0_ticks[i][1] + ytickspace))
bbox_image = BboxImage(Bbox([[lowerCorner[0], lowerCorner[1]],
[upperCorner[0], upperCorner[1]]]),
norm=None,
origin=None,
clip_on=False)
bbox_image.set_data(plt.imread('./Images/acrossASF/markers/plus.png'))
contour_ax.add_artist(bbox_image)
images = torch.from_numpy(raw_data.astype('float32'))
images = [image.view(-1) for image in images]
for image in images:
state = vae_append(state, image)
bb_ans_compressed = bytes_to_img(rans.flatten(state).tobytes())
raw_data = np.concatenate(~raw_data, axis=1)
labels = ["BB-ANS", "bz2", "PNG", "MNIST"]
data = [bb_ans_compressed, bz2_compressed, png_compressed, raw_data ]
yticks = []
for i, d in enumerate(data):
bbox = Bbox.from_bounds(0, 28 * (spacing + i * (h + spacing)),
28 * d.shape[1], 28 * h)
bbox = TransformedBbox(bbox, ax.transData)
bbox_image = BboxImage(bbox, cmap='gray', origin=None)
bbox_image.set_data(d)
ax.add_artist(bbox_image)
yticks.append(28 * (spacing + i * (h + spacing) + h // 2))
ax.set_xlim(0, max(28 * d.shape[1] for d in data))
ax.set_yticks(yticks)
ax.set_yticklabels(labels)
ax.set_ylim(0, 28 * (spacing + (i + 1) * (h + spacing)))
ax.set_xlabel('Size (bits)')
ax.set_aspect('equal')
plt.savefig('compression_plot.png', dpi=800, bbox_inches='tight')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage
from matplotlib.transforms import Bbox, TransformedBbox
fig, (ax1, ax2) = plt.subplots(ncols=2)
# ----------------------------
# Create a BboxImage with Text
# ----------------------------
txt = ax1.text(0.5, 0.5, "test", size=30, ha="center", color="w")
kwargs = dict()
bbox_image = BboxImage(txt.get_window_extent,
norm=None,
origin=None,
clip_on=False,
**kwargs)
a = np.arange(256).reshape(1, 256) / 256.
bbox_image.set_data(a)
ax1.add_artist(bbox_image)
# ------------------------------------
# Create a BboxImage for each colormap
# ------------------------------------
a = np.linspace(0, 1, 256).reshape(1, -1)
a = np.vstack((a, a))
# List of all colormaps; skip reversed colormaps.
maps = sorted(m for m in plt.colormaps() if not m.endswith("_r"))
Y = np.array(Y)
print(X.shape, Y.shape)
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(X)
transformed = pca.transform(X)
print(transformed.shape)
plt.scatter(transformed[:, 0], transformed[:, 1], c=Y)
plt.colorbar()
plt.show()
X = transformed
from matplotlib.image import BboxImage
from matplotlib.transforms import Bbox, TransformedBbox
fig = plt.figure()
ax = fig.add_subplot(111)
for (r, label, path) in zip(X, Y, Z):
x, y = r
img = plt.imread(path)
bb = Bbox.from_bounds(x, y, 1, 1)
bb2 = TransformedBbox(bb, ax.transData)
bbox_image = BboxImage(bb2, norm=None, origin=None, clip_on=False)
bbox_image.set_data(img)
ax.add_artist(bbox_image)
xlim = [X[:, 0].min() - 2, X[:, 0].max() + 2]
ylim = [X[:, 1].min() - 2, X[:, 1].max() + 2]
ax.set_xlim(*xlim)
ax.set_ylim(*ylim)
plt.show()
# plots T-SNE embeddings as a scatter plot (for reference)
plt.clf()
plt.scatter(embedded[:, 0], embedded[:, 1], marker='.', color='blue')
plt.savefig("tests/tsne_test.png")
plt.clf()
# generates 2d T-SNE visualization of image embeddings
plt.clf()
fig = plt.figure(figsize=(25, 25))
plt.xlim([-20, 20])
plt.ylim([-20, 20])
ax = fig.add_subplot(111)
plt.axis('off')
# adapted from https://stackoverflow.com/questions/25329583/matplotlib-using-image-for-points-on-plot
for i in range(0, len(img_filepaths)):
bb = Bbox.from_bounds(embedded[i][0], embedded[i][1], 1.0, 1.0)
bb2 = TransformedBbox(bb, ax.transData)
bbox_image = BboxImage(bb2,
norm=None,
origin=None,
clip_on=False,
cmap='gray')
im = plt.imread(img_filepaths[i])
bbox_image.set_data(im)
ax.add_artist(bbox_image)
# saves figure
plt.savefig("tests/testfig.png", dpi=500, bbox_inches='tight')
plt.clf()
def draw(self, renderer, *args, **kwargs):
bbox = self.get_window_extent(renderer)
stretch_factor = bbox.height / bbox.width
ny = int(stretch_factor*self._ribbonbox.nx)
if self._cached_ny != ny:
arr = self._ribbonbox.get_stretched_image(stretch_factor)
self.set_array(arr)
self._cached_ny = ny
BboxImage.draw(self, renderer, *args, **kwargs)
if 1:
from matplotlib.transforms import Bbox, TransformedBbox
from matplotlib.ticker import ScalarFormatter
fig, ax = plt.subplots()
years = np.arange(2004, 2009)
box_colors = [(0.8, 0.2, 0.2),
(0.2, 0.8, 0.2),
(0.2, 0.2, 0.8),
(0.7, 0.5, 0.8),
(0.3, 0.8, 0.7),
]
heights = np.random.random(years.shape) * 7000 + 3000
fmt = ScalarFormatter(useOffset=False)
ax.xaxis.set_major_formatter(fmt)
for year, h, bc in zip(years, heights, box_colors):
bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)
bbox = TransformedBbox(bbox0, ax.transData)
rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")
ax.add_artist(rb_patch)
ax.annotate(r"%d" % (int(h/100.)*100),
(year, h), va="bottom", ha="center")
patch_gradient = BboxImage(ax.bbox,
interpolation="bicubic",
zorder=0.1,
)
gradient = np.zeros((2, 2, 4), dtype=np.float)
gradient[:,:,:3] = [1, 1, 0.]
gradient[:,:,3] = [[0.1, 0.3],[0.3, 0.5]] # alpha channel
patch_gradient.set_array(gradient)
ax.add_artist(patch_gradient)
ax.set_xlim(years[0]-0.5, years[-1]+0.5)
ax.set_ylim(0, 10000)
fig.savefig('ribbon_box.png')
plt.show()
def __init__(self, path, bbox_image, **kwargs):
super().__init__(path, **kwargs)
self.bbox_image = BboxImage(self.get_window_extent,
norm=None,
origin=None)
self.bbox_image.set_data(bbox_image)
for name_color in args.players_colors)
players = [{'name': name_color.split('_')[0][:17].upper(),
'color': name_color.split('_')[1],
'wedges': set()}
for name_color in args.players_colors]
# rectangular axes for legend, die rolls, and key to topics
fig, ax = plt.subplots(1, 1, figsize=(11, 9))
plt.subplots_adjust(left=-.15)
ax.set_axis_off()
# images for rolling die
dice = {i: plt.imread(f'images/{i}.png') for i in range(7)}
bb = Bbox.from_bounds(.84, .46, .065, .1)
bb2 = TransformedBbox(bb, ax.transData)
bbox_image = BboxImage(bb2)
# perimiter spaces
p_coords = [(6, i * (2 * np.pi / 42)) for i in range(42)]
p_coords.append((0, 0))
# will plot 'wedge' spaces a second time with larger markers
w_coords = [(6, i * (2 * np.pi / 42)) for i in range(42) if i % 7 == 0]
# 'spoke' spaces
s_coords = [(k, j * (2 * np.pi / 42)) for k in range(1, 6) for j in range(0, 42, 7)]
# coordinates for plotting
p_r, p_th = zip(*p_coords)
p_r = np.asarray(p_r)
p_th = np.asarray(p_th)
