def make_artists(self, axes, show_intruder):
'make self.artists_dict'
assert self.vec_list is not None
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
# 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 - math.pi / 2) / math.pi * 180 # original image is facing up, not right
img_rotated = ndimage.rotate(State.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], f'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 _compute_bbox(self, fig, kw):
"""
Compute the tight bounding box for each figure once, reducing
number of required canvas draw calls from N*2 to N+1 as a
function of the number of frames.
Tight bounding box computing code here mirrors:
matplotlib.backend_bases.FigureCanvasBase.print_figure
as it hasn't been factored out as a function.
"""
fig_id = id(fig)
if kw['bbox_inches'] == 'tight':
if not fig_id in MPLRenderer.drawn:
fig.set_dpi(self.dpi)
renderer = fig._cachedRenderer
bbox_inches = fig.get_tightbbox(renderer)
bbox_artists = kw.pop("bbox_extra_artists", [])
bbox_artists += fig.get_default_bbox_extra_artists()
bbox_filtered = []
for a in bbox_artists:
bbox = a.get_window_extent(renderer)
if isinstance(bbox, tuple):
continue
if a.get_clip_on():
clip_box = a.get_clip_box()
if clip_box is not None:
bbox = Bbox.intersection(bbox, clip_box)
clip_path = a.get_clip_path()
if clip_path is not None and bbox is not None:
clip_path = clip_path.get_fully_transformed_path()
bbox = Bbox.intersection(bbox,
clip_path.get_extents())
if bbox is not None and (bbox.width != 0
or bbox.height != 0):
bbox_filtered.append(bbox)
if bbox_filtered:
_bbox = Bbox.union(bbox_filtered)
trans = Affine2D().scale(1.0 / self.dpi)
bbox_extra = TransformedBbox(_bbox, trans)
bbox_inches = Bbox.union([bbox_inches, bbox_extra])
pad = plt.rcParams['savefig.pad_inches']
bbox_inches = bbox_inches.padded(pad)
MPLRenderer.drawn[fig_id] = bbox_inches
kw['bbox_inches'] = bbox_inches
else:
kw['bbox_inches'] = MPLRenderer.drawn[fig_id]
return kw
def mark_inset(parent_axes, inset_axes, loc1, loc2, **kwargs):
"""
Draw a box to mark the location of an area represented by an inset axes.
This function draws a box in *parent_axes* at the bounding box of
*inset_axes*, and shows a connection with the inset axes by drawing lines
at the corners, giving a "zoomed in" effect.
Parameters
----------
parent_axes : `matplotlib.axes.Axes`
Axes which contains the area of the inset axes.
inset_axes : `matplotlib.axes.Axes`
The inset axes.
loc1, loc2 : {1, 2, 3, 4}
Corners to use for connecting the inset axes and the area in the
parent axes.
**kwargs
Patch properties for the lines and box drawn:
%(Patch)s
Returns
-------
pp : `matplotlib.patches.Patch`
The patch drawn to represent the area of the inset axes.
p1, p2 : `matplotlib.patches.Patch`
The patches connecting two corners of the inset axes and its area.
"""
rect = TransformedBbox(inset_axes.viewLim, parent_axes.transData)
fill = kwargs.pop("fill", False)
pp = BboxPatch(rect, fill=fill, **kwargs)
parent_axes.add_patch(pp)
p1 = BboxConnector(inset_axes.bbox, rect, loc1=loc1, **kwargs)
inset_axes.add_patch(p1)
p1.set_clip_on(False)
p2 = BboxConnector(inset_axes.bbox, rect, loc1=loc2, **kwargs)
inset_axes.add_patch(p2)
p2.set_clip_on(False)
return pp, p1, p2
def update_artists(self, axes):
'''update artists in self.artists_dict to be consistant with self.vec, returns a list of artists'''
assert self.artists_dict
rv = []
x1, y1, theta1, x2, y2, theta2, _ = self.vec
for i, x, y, theta in zip([0, 1], [x1, x2], [y1, y2], [theta1, theta2]):
key = f'plane{i}'
if key in self.artists_dict:
plane = self.artists_dict[key]
rv.append(plane)
if plane.get_visible():
theta_deg = (theta - math.pi / 2) / math.pi * 180 # original image is facing up, not right
original_size = list(State.img.shape)
img_rotated = ndimage.rotate(State.img, theta_deg, order=1)
rotated_size = list(img_rotated.shape)
ratios = [r / o for r, o in zip(rotated_size, original_size)]
plane.set_data(img_rotated)
size = State.plane_size
width = size * ratios[0]
height = size * ratios[1]
box = Bbox.from_bounds(x - width/2, y - height/2, width, height)
tbox = TransformedBbox(box, axes.transData)
plane.bbox = tbox
key = f'dot{i}'
if key in self.artists_dict:
dot = self.artists_dict[f'dot{i}']
cir = self.artists_dict[f'circle{i}']
rv += [dot, cir]
dot.set_data([x], [y])
cir.set_center((x, y))
# line collection
lc = self.artists_dict['lc0']
rv.append(lc)
self.update_lc_artist(lc)
return rv
def adjust_tight_bbox(self, pad=0.1, extra_artists=None):
bbox_inches = self.figure.get_tightbbox(self.renderer)
bbox_artists = self.figure.get_default_bbox_extra_artists()
if extra_artists is None:
extra_artists = []
extra_artists.extend([ax.get_legend() for ax in self.figure.axes if ax.get_legend()])
bbox_artists.extend(extra_artists)
bbox_filtered = []
for a in bbox_artists:
bbox = a.get_window_extent(self.renderer)
if a.get_clip_on():
clip_box = a.get_clip_box()
if clip_box is not None:
bbox = Bbox.intersection(bbox, clip_box)
clip_path = a.get_clip_path()
if clip_path is not None and bbox is not None:
clip_path = clip_path.get_fully_transformed_path()
bbox = Bbox.intersection(bbox,
clip_path.get_extents())
if bbox is not None and (bbox.width != 0 or
bbox.height != 0):
bbox_filtered.append(bbox)
if bbox_filtered:
_bbox = Bbox.union(bbox_filtered)
trans = Affine2D().scale(1.0 / self.figure.dpi)
bbox_extra = TransformedBbox(_bbox, trans)
bbox_inches = Bbox.union([bbox_inches, bbox_extra])
if pad:
bbox_inches = bbox_inches.padded(pad)
rect = (np.array(bbox_inches.bounds).reshape(-1,2) / self.figure.get_size_inches()).flatten()
# Adjust the rect; values <0 to +; + to zero
xpad = -np.min((rect[0], (1-rect[2])))
xpad = 0 if xpad < 0 else xpad
ypad = -np.min((rect[1], (1-rect[3])))
ypad = 0 if ypad < 0 else ypad
rect = np.array([ xpad, ypad, 1-xpad, 1-ypad ])
self.figure.tight_layout(rect=np.abs(rect))
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 get_transform(self):
# If we don't override this, the transform includes LogTransforms
# and the final image gets warped to be 'correct' in data space
# since Matplotlib 2.x:
#
# https://matplotlib.org/users/prev_whats_new/whats_new_2.0.0.html#non-linear-scales-on-image-plots
#
# However, we want pixels to always visually be the same size, so we
# override the transform to not include the LogTransform components.
xmin, xmax = self._ax.get_xlim()
ymin, ymax = self._ax.get_ylim()
bbox = BboxTransformFrom(TransformedBbox(Bbox([[xmin, ymin], [xmax, ymax]]),
IDENTITY))
return bbox + self._ax.transAxes
def set_bbox_to_anchor(self, bbox, transform=None):
"""
Set the bbox that the legend will be anchored to.
Parameters
----------
bbox : `~matplotlib.transforms.BboxBase` or tuple
The bounding box can be specified in the following ways:
- A `.BboxBase` instance
- A tuple of ``(left, bottom, width, height)`` in the given
transform (normalized axes coordinate if None)
- A tuple of ``(left, bottom)`` where the width and height will be
assumed to be zero.
- *None*, to remove the bbox anchoring, and use the parent bbox.
transform : `~matplotlib.transforms.Transform`, optional
A transform to apply to the bounding box. If not specified, this
will use a transform to the bounding box of the parent.
"""
if bbox is None:
self._bbox_to_anchor = None
return
elif isinstance(bbox, BboxBase):
self._bbox_to_anchor = bbox
else:
try:
l = len(bbox)
except TypeError as err:
raise ValueError("Invalid argument for bbox : %s" %
str(bbox)) from err
if l == 2:
bbox = [bbox[0], bbox[1], 0, 0]
self._bbox_to_anchor = Bbox.from_bounds(*bbox)
if transform is None:
transform = BboxTransformTo(self.parent.bbox)
self._bbox_to_anchor = TransformedBbox(self._bbox_to_anchor,
transform)
self.stale = True
def _draw_text_with_rotation(self,
x0,
y0,
w,
h,
txt,
offset_x=0,
offset_y=0,
fg="black",
rotation=0,
debug=False):
'''
draw text txt at coordinates (x0, y0) and width w, height h
(additional x offset offset_x and y offset offset_y if desired)
with rotation and color fg
'''
if debug:
self.draw_rectangle(x0 - w / 2.0,
y0 - h / 2,
x0 + w / 2.0,
y0 + h / 2.0,
fill='none',
outline='red')
clip_rect = mpatches.Rectangle(xy=[x0 - w / 2.0, y0 - h / 2.0],
width=w,
height=0.1,
transform=self._ax.transData)
textobj = plt.text(x0 + offset_x,
y0 + offset_y,
txt,
color=fg,
ha='left',
va='center',
clip_path=clip_rect,
clip_on=True,
rotation=rotation)
# Let's store the clipping box in the object, so that we can use it
# when clipping text
textobj._clip = TransformedBbox(bbox=Bbox(
((x0 - w / 2.0, y0 - h / 2.0), (x0 + w / 2, y0 + h / 2.0))),
transform=self._ax.transData)
textobj.set_rotation_mode('anchor')
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 connect_bbox(bbox1, bbox2, loc1, loc2=None):
"""
Construct a `.Path` connecting corner *loc1* of *bbox1* to corner
*loc2* of *bbox2*, where parameters behave as documented as for the
`.BboxConnector` constructor.
"""
if isinstance(bbox1, Rectangle):
bbox1 = TransformedBbox(Bbox.unit(), bbox1.get_transform())
if isinstance(bbox2, Rectangle):
bbox2 = TransformedBbox(Bbox.unit(), bbox2.get_transform())
if loc2 is None:
loc2 = loc1
x1, y1 = BboxConnector.get_bbox_edge_pos(bbox1, loc1)
x2, y2 = BboxConnector.get_bbox_edge_pos(bbox2, loc2)
return Path([[x1, y1], [x2, y2]])
def initalize():
mpl.rcParams['lines.linewidth'] = 1
plt.rcParams.update({'font.size': 6})
theta = np.linspace(0, 2 * np.pi, 150)
x1 = r1 * np.cos(theta)
x2 = r1 * np.sin(theta)
ax1.plot(x1, x2)
x1 = r2 * np.cos(theta)
x2 = r2 * np.sin(theta)
ax1.plot(x1, x2)
x = np.linspace(0, 3, 100)
for i in range(51):
y = np.repeat(i, 100)
ax2.plot(x, y, color='black')
y = np.linspace(0, 50, 100)
for i in range(4):
x = np.repeat(i, 100)
ax2.plot(x, y, color='black')
x = np.linspace(3, 4, 10)
y = np.repeat(49, 10)
ax2.plot(x, y, color='black')
y = np.repeat(50, 10)
ax2.plot(x, y, color='black')
x = np.repeat(4, 10)
y = np.linspace(49, 50, 10)
ax2.plot(x, y, color='black')
mpl.rcParams['lines.linewidth'] = 0.5
for i, m in enumerate(sorted_meals):
x = i // 50 + 0.01
y = 49 - i % 50 + 0.1
x2 = x + 1
y2 = y + 1
box = TransformedBbox(Bbox([[x, y], [x2, y2]]), ax2.transData)
t = ax2.annotate(m, xy=(x, y), clip_box=box)
bb = t.get_window_extent(renderer=rend)
if bb.width > max_width:
overs.append(i)
clipped.append(t)
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 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 zoom_effect(ax1, ax2, **kwargs):
"""
ax1 : the main axes
ax1 : the zoomed axes
Connect ax1 and ax2. The xmin & xmax will be taken from the
ax1.viewLim.
"""
tt = ax1.transScale + (ax1.transLimits + ax2.transAxes)
trans = blended_transform_factory(ax2.transData, tt)
mybbox1 = ax1.bbox
mybbox2 = TransformedBbox(ax1.viewLim, trans)
prop_patches = kwargs.copy()
prop_patches["ec"] = "none"
prop_patches["alpha"] = 0.2
c1, c2, bbox_patch1, bbox_patch2, patch = connect_bbox(
mybbox1,
mybbox2,
loc1a=3,
loc2a=2,
loc1b=4,
loc2b=1,
prop_lines=kwargs,
prop_patches=prop_patches,
)
ax1.add_patch(bbox_patch1)
ax2.add_patch(bbox_patch2)
ax2.add_patch(c1)
ax2.add_patch(c2)
ax2.add_patch(patch)
return c1, c2, bbox_patch1, bbox_patch2, patch
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]
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 connect_bbox(bbox1, bbox2, loc1, loc2=None):
"""
Helper function to obtain a Path from one bbox to another.
Parameters
----------
bbox1, bbox2 : `matplotlib.transforms.Bbox`
Bounding boxes to connect.
loc1 : {1, 2, 3, 4}
Corner of *bbox1* to use. Valid values are::
'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4
loc2 : {1, 2, 3, 4}, optional
Corner of *bbox2* to use. If None, defaults to *loc1*.
Valid values are::
'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4
Returns
-------
path : `matplotlib.path.Path`
A line segment from the *loc1* corner of *bbox1* to the *loc2*
corner of *bbox2*.
"""
if isinstance(bbox1, Rectangle):
bbox1 = TransformedBbox(Bbox.unit(), bbox1.get_transform())
if isinstance(bbox2, Rectangle):
bbox2 = TransformedBbox(Bbox.unit(), bbox2.get_transform())
if loc2 is None:
loc2 = loc1
x1, y1 = BboxConnector.get_bbox_edge_pos(bbox1, loc1)
x2, y2 = BboxConnector.get_bbox_edge_pos(bbox2, loc2)
return Path([[x1, y1], [x2, y2]])
ncol = 2
nrow = len(maps) // ncol + 1
xpad_fraction = 0.3
dx = 1. / (ncol + xpad_fraction * (ncol - 1))
ypad_fraction = 0.3
dy = 1. / (nrow + ypad_fraction * (nrow - 1))
for i, m in enumerate(maps):
ix, iy = divmod(i, nrow)
bbox0 = Bbox.from_bounds(ix * dx * (1 + xpad_fraction),
1. - iy * dy * (1 + ypad_fraction) - dy, dx, dy)
bbox = TransformedBbox(bbox0, ax2.transAxes)
bbox_image = BboxImage(bbox,
cmap=plt.get_cmap(m),
norm=None,
origin=None,
**kwargs)
bbox_image.set_data(a)
ax2.add_artist(bbox_image)
plt.show()
#############################################################################
#
# .. admonition:: References
def plot_uncertainty(y_true, y_pred, background_data):
""" Plots the samples over background data
:param y_true: ground truth
:param y_pred: model samples
:param background_data: numpy array containing the positions with data
"""
_, ax = plt.subplots()
# Flips y (imshow assumes 0 on top, growing down)
y_true[:, 1] = -y_true[:, 1] + GRID_SIZE
y_pred[:, 1] = -y_pred[:, 1] + GRID_SIZE
# Plots the background in black and black (white = positions with data)
ax.imshow(-background_data,
cmap='Greys',
vmin=-1.0,
vmax=0.0,
alpha=MAP_ALPHA)
# Plots the MC Dropout samples in blue, with some transparency
ax.scatter(x=y_pred[:, 0, :],
y=y_pred[:, 1, :],
c='b',
s=3,
alpha=0.15,
edgecolors='none',
label="MC Dropout samples")
# Plots the true position in solid red
ax.scatter(x=y_true[:, 0],
y=y_true[:, 1],
c='r',
s=5,
edgecolors='none',
label="True positions")
ax.legend(loc="upper right", labelcolor=['b', 'r'], fontsize="small")
# Plots a zoomed detail
axins = zoomed_inset_axes(ax, zoom=3, loc='lower left')
axins.imshow(-background_data,
cmap='Greys',
vmin=-1.0,
vmax=0.0,
alpha=MAP_ALPHA)
rand_idx = np.random.randint(low=0, high=y_true.shape[0], size=1)
axins.scatter(
x=y_pred[rand_idx, 0, :],
y=y_pred[rand_idx, 1, :],
c='b',
s=4,
alpha=0.15,
edgecolors='none',
)
axins.scatter(x=y_true[rand_idx, 0],
y=y_true[rand_idx, 1],
c='r',
edgecolors='none')
# Limit the region for zoom
axins.set_xlim(y_true[rand_idx, 0] - ZOOM_SIZE,
y_true[rand_idx, 0] + ZOOM_SIZE)
axins.set_ylim(y_true[rand_idx, 1] - ZOOM_SIZE,
y_true[rand_idx, 1] + ZOOM_SIZE)
# Hides ticks
plt.xticks(visible=False)
plt.yticks(visible=False)
# draw a bbox of the region of the inset axes in the parent axes and connecting lines between
# the bbox and the inset axes area. Also inverts the y of the inner plot, as the outer plot
# is inverted (imshow)
axins.invert_yaxis()
# ================================================================
# (adapted from `mark_inset`, removing the connectors)
parent_axes = ax
inset_axes = axins
kwargs = {"fc": "none", "ec": "0.0"}
rect = TransformedBbox(inset_axes.viewLim, parent_axes.transData)
fill = bool({'fc', 'facecolor', 'color'}.intersection(kwargs))
pp = BboxPatch(rect, fill=fill, **kwargs)
parent_axes.add_patch(pp)
# ================================================================
plt.draw()
plt.savefig(PLOT_PATH, dpi=300)
logging.info("Plot written to %s", PLOT_PATH)
def auto_adjust_subplotpars(fig,
renderer,
nrows_ncols,
num1num2_list,
subplot_list,
ax_bbox_list=None,
pad=1.08,
h_pad=None,
w_pad=None,
rect=None):
"""
Return a dict of subplot parameters to adjust spacing between subplots
or ``None`` if resulting axes would have zero height or width.
Note that this function ignores geometry information of subplot
itself, but uses what is given by the *nrows_ncols* and *num1num2_list*
parameters. Also, the results could be incorrect if some subplots have
``adjustable=datalim``.
Parameters
----------
nrows_ncols : Tuple[int, int]
Number of rows and number of columns of the grid.
num1num2_list : List[int]
List of numbers specifying the area occupied by the subplot
subplot_list : list of subplots
List of subplots that will be used to calculate optimal subplot_params.
pad : float
Padding between the figure edge and the edges of subplots, as a
fraction of the font size.
h_pad, w_pad : float
Padding (height/width) between edges of adjacent subplots, as a
fraction of the font size. Defaults to *pad*.
rect : Tuple[float, float, float, float]
[left, bottom, right, top] in normalized (0, 1) figure coordinates.
"""
rows, cols = nrows_ncols
font_size_inches = (
FontProperties(size=rcParams["font.size"]).get_size_in_points() / 72)
pad_inches = pad * font_size_inches
vpad_inches = h_pad * font_size_inches if h_pad is not None else pad_inches
hpad_inches = w_pad * font_size_inches if w_pad is not None else pad_inches
if len(num1num2_list) != len(subplot_list) or len(subplot_list) == 0:
raise ValueError
if rect is None:
margin_left = margin_bottom = margin_right = margin_top = None
else:
margin_left, margin_bottom, _right, _top = rect
margin_right = 1 - _right if _right else None
margin_top = 1 - _top if _top else None
vspaces = np.zeros((rows + 1, cols))
hspaces = np.zeros((rows, cols + 1))
if ax_bbox_list is None:
ax_bbox_list = [
Bbox.union([ax.get_position(original=True) for ax in subplots])
for subplots in subplot_list
]
for subplots, ax_bbox, (num1, num2) in zip(subplot_list, ax_bbox_list,
num1num2_list):
if all(not ax.get_visible() for ax in subplots):
continue
bb = []
for ax in subplots:
if ax.get_visible():
try:
bb += [ax.get_tightbbox(renderer, for_layout_only=True)]
except TypeError:
bb += [ax.get_tightbbox(renderer)]
tight_bbox_raw = Bbox.union(bb)
tight_bbox = TransformedBbox(tight_bbox_raw,
fig.transFigure.inverted())
row1, col1 = divmod(num1, cols)
if num2 is None:
num2 = num1
row2, col2 = divmod(num2, cols)
for row_i in range(row1, row2 + 1):
hspaces[row_i, col1] += ax_bbox.xmin - tight_bbox.xmin # left
hspaces[row_i, col2 + 1] += tight_bbox.xmax - ax_bbox.xmax # right
for col_i in range(col1, col2 + 1):
vspaces[row1, col_i] += tight_bbox.ymax - ax_bbox.ymax # top
vspaces[row2 + 1, col_i] += ax_bbox.ymin - tight_bbox.ymin # bot.
fig_width_inch, fig_height_inch = fig.get_size_inches()
# margins can be negative for axes with aspect applied, so use max(, 0) to
# make them nonnegative.
if not margin_left:
margin_left = (max(hspaces[:, 0].max(), 0) +
pad_inches / fig_width_inch)
if not margin_right:
margin_right = (max(hspaces[:, -1].max(), 0) +
pad_inches / fig_width_inch)
if not margin_top:
margin_top = (max(vspaces[0, :].max(), 0) +
pad_inches / fig_height_inch)
suptitle = fig._suptitle
if suptitle and suptitle.get_in_layout():
rel_suptitle_height = fig.transFigure.inverted().transform_bbox(
suptitle.get_window_extent(renderer)).height
margin_top += rel_suptitle_height + pad_inches / fig_height_inch
if not margin_bottom:
margin_bottom = (max(vspaces[-1, :].max(), 0) +
pad_inches / fig_height_inch)
if margin_left + margin_right >= 1:
cbook._warn_external('Tight layout not applied. The left and right '
'margins cannot be made large enough to '
'accommodate all axes decorations. ')
return None
if margin_bottom + margin_top >= 1:
cbook._warn_external('Tight layout not applied. The bottom and top '
'margins cannot be made large enough to '
'accommodate all axes decorations. ')
return None
kwargs = dict(left=margin_left,
right=1 - margin_right,
bottom=margin_bottom,
top=1 - margin_top)
if cols > 1:
hspace = hspaces[:, 1:-1].max() + hpad_inches / fig_width_inch
# axes widths:
h_axes = (1 - margin_right - margin_left - hspace * (cols - 1)) / cols
if h_axes < 0:
cbook._warn_external('Tight layout not applied. tight_layout '
'cannot make axes width small enough to '
'accommodate all axes decorations')
return None
else:
kwargs["wspace"] = hspace / h_axes
if rows > 1:
vspace = vspaces[1:-1, :].max() + vpad_inches / fig_height_inch
v_axes = (1 - margin_top - margin_bottom - vspace * (rows - 1)) / rows
if v_axes < 0:
cbook._warn_external('Tight layout not applied. tight_layout '
'cannot make axes height small enough to '
'accommodate all axes decorations')
return None
else:
kwargs["hspace"] = vspace / v_axes
return kwargs
def auto_adjust_subplotpars(fig,
renderer,
nrows_ncols,
num1num2_list,
subplot_list,
ax_bbox_list=None,
pad=1.08,
h_pad=None,
w_pad=None,
rect=None):
"""
Return a dict of subplot parameters to adjust spacing between subplots
or ``None`` if resulting axes would have zero height or width.
Note that this function ignores geometry information of subplot
itself, but uses what is given by the *nrows_ncols* and *num1num2_list*
parameters. Also, the results could be incorrect if some subplots have
``adjustable=datalim``.
Parameters
----------
nrows_ncols : Tuple[int, int]
Number of rows and number of columns of the grid.
num1num2_list : List[int]
List of numbers specifying the area occupied by the subplot
subplot_list : list of subplots
List of subplots that will be used to calculate optimal subplot_params.
pad : float
Padding between the figure edge and the edges of subplots, as a
fraction of the font size.
h_pad, w_pad : float
Padding (height/width) between edges of adjacent subplots, as a
fraction of the font size. Defaults to *pad*.
rect : Tuple[float, float, float, float]
[left, bottom, right, top] in normalized (0, 1) figure coordinates.
"""
rows, cols = nrows_ncols
font_size_inches = (
FontProperties(size=rcParams["font.size"]).get_size_in_points() / 72)
pad_inches = pad * font_size_inches
if h_pad is not None:
vpad_inches = h_pad * font_size_inches
else:
vpad_inches = pad_inches
if w_pad is not None:
hpad_inches = w_pad * font_size_inches
else:
hpad_inches = pad_inches
if len(num1num2_list) != len(subplot_list) or len(subplot_list) == 0:
raise ValueError
if rect is None:
margin_left = margin_bottom = margin_right = margin_top = None
else:
margin_left, margin_bottom, _right, _top = rect
if _right:
margin_right = 1 - _right
else:
margin_right = None
if _top:
margin_top = 1 - _top
else:
margin_top = None
vspaces = [[] for i in range((rows + 1) * cols)]
hspaces = [[] for i in range(rows * (cols + 1))]
union = Bbox.union
if ax_bbox_list is None:
ax_bbox_list = []
for subplots in subplot_list:
ax_bbox = union(
[ax.get_position(original=True) for ax in subplots])
ax_bbox_list.append(ax_bbox)
for subplots, ax_bbox, (num1, num2) in zip(subplot_list, ax_bbox_list,
num1num2_list):
if all(not ax.get_visible() for ax in subplots):
continue
tight_bbox_raw = union([
ax.get_tightbbox(renderer) for ax in subplots if ax.get_visible()
])
tight_bbox = TransformedBbox(tight_bbox_raw,
fig.transFigure.inverted())
row1, col1 = divmod(num1, cols)
if num2 is None:
# left
hspaces[row1 * (cols + 1) + col1].append(
_get_left(tight_bbox, ax_bbox))
# right
hspaces[row1 * (cols + 1) + (col1 + 1)].append(
_get_right(tight_bbox, ax_bbox))
# top
vspaces[row1 * cols + col1].append(_get_top(tight_bbox, ax_bbox))
# bottom
vspaces[(row1 + 1) * cols + col1].append(
_get_bottom(tight_bbox, ax_bbox))
else:
row2, col2 = divmod(num2, cols)
for row_i in range(row1, row2 + 1):
# left
hspaces[row_i * (cols + 1) + col1].append(
_get_left(tight_bbox, ax_bbox))
# right
hspaces[row_i * (cols + 1) + (col2 + 1)].append(
_get_right(tight_bbox, ax_bbox))
for col_i in range(col1, col2 + 1):
# top
vspaces[row1 * cols + col_i].append(
_get_top(tight_bbox, ax_bbox))
# bottom
vspaces[(row2 + 1) * cols + col_i].append(
_get_bottom(tight_bbox, ax_bbox))
fig_width_inch, fig_height_inch = fig.get_size_inches()
# margins can be negative for axes with aspect applied. And we
# append + [0] to make minimum margins 0
if not margin_left:
margin_left = max([sum(s) for s in hspaces[::cols + 1]] + [0])
margin_left += pad_inches / fig_width_inch
if not margin_right:
margin_right = max([sum(s) for s in hspaces[cols::cols + 1]] + [0])
margin_right += pad_inches / fig_width_inch
if not margin_top:
margin_top = max([sum(s) for s in vspaces[:cols]] + [0])
margin_top += pad_inches / fig_height_inch
if not margin_bottom:
margin_bottom = max([sum(s) for s in vspaces[-cols:]] + [0])
margin_bottom += pad_inches / fig_height_inch
if margin_left + margin_right >= 1:
warnings.warn('Tight layout not applied. The left and right margins '
'cannot be made large '
'enough to accommodate all axes decorations. ')
return None
if margin_bottom + margin_top >= 1:
warnings.warn('Tight layout not applied. '
'The bottom and top margins cannot be made large '
'enough to accommodate all axes decorations. ')
return None
kwargs = dict(left=margin_left,
right=1 - margin_right,
bottom=margin_bottom,
top=1 - margin_top)
if cols > 1:
hspace = (max(
sum(s) for i in range(rows)
for s in hspaces[i * (cols + 1) + 1:(i + 1) * (cols + 1) - 1]) +
hpad_inches / fig_width_inch)
# axes widths:
h_axes = (1 - margin_right - margin_left - hspace * (cols - 1)) / cols
if h_axes < 0:
warnings.warn('Tight layout not applied. '
'tight_layout cannot make axes width small enough '
'to accommodate all axes decorations')
return None
else:
kwargs["wspace"] = hspace / h_axes
if rows > 1:
vspace = (max(sum(s) for s in vspaces[cols:-cols]) +
vpad_inches / fig_height_inch)
v_axes = (1 - margin_top - margin_bottom - vspace * (rows - 1)) / rows
if v_axes < 0:
warnings.warn('Tight layout not applied. '
'tight_layout cannot make axes height small enough '
'to accommodate all axes decorations')
return None
else:
kwargs["hspace"] = vspace / v_axes
return kwargs
def auto_adjust_subplotpars(fig,
renderer,
nrows_ncols,
num1num2_list,
subplot_list,
ax_bbox_list=None,
pad=1.2,
h_pad=None,
w_pad=None,
rect=None):
"""
Return a dictionary of subplot parameters so that spacing between
subplots are adjusted. Note that this function ignore geometry
information of subplot itself, but uses what is given by
*nrows_ncols* and *num1num2_list* parameteres. Also, the results could be
incorrect if some subplots have ``adjustable=datalim``.
Parameters:
nrows_ncols
number of rows and number of columns of the grid.
num1num2_list
list of numbers specifying the area occupied by the subplot
subplot_list
list of subplots that will be used to calcuate optimal subplot_params.
pad : float
padding between the figure edge and the edges of subplots, as a fraction of the font-size.
h_pad, w_pad : float
padding (height/width) between edges of adjacent subplots.
Defaults to `pad_inches`.
rect
[left, bottom, right, top] in normalized (0, 1) figure coordinates.
"""
rows, cols = nrows_ncols
pad_inches = pad * FontProperties(
size=rcParams["font.size"]).get_size_in_points() / renderer.dpi
if h_pad is not None:
vpad_inches = h_pad * FontProperties(
size=rcParams["font.size"]).get_size_in_points() / renderer.dpi
else:
vpad_inches = pad_inches
if w_pad is not None:
hpad_inches = w_pad * FontProperties(
size=rcParams["font.size"]).get_size_in_points() / renderer.dpi
else:
hpad_inches = pad_inches
if len(subplot_list) == 0:
raise RuntimeError("")
if len(num1num2_list) != len(subplot_list):
raise RuntimeError("")
if rect is None:
margin_left, margin_bottom, margin_right, margin_top = None, None, None, None
else:
margin_left, margin_bottom, _right, _top = rect
if _right: margin_right = 1. - _right
else: margin_right = None
if _top: margin_top = 1. - _top
else: margin_top = None
vspaces = [[] for i in range((rows + 1) * cols)]
hspaces = [[] for i in range(rows * (cols + 1))]
union = Bbox.union
if ax_bbox_list is None:
ax_bbox_list = []
for subplots in subplot_list:
ax_bbox = union(
[ax.get_position(original=True) for ax in subplots])
ax_bbox_list.append(ax_bbox)
for subplots, ax_bbox, (num1, num2) in zip(subplot_list, ax_bbox_list,
num1num2_list):
#ax_bbox = union([ax.get_position(original=True) for ax in subplots])
tight_bbox_raw = union([ax.get_tightbbox(renderer) for ax in subplots])
tight_bbox = TransformedBbox(tight_bbox_raw,
fig.transFigure.inverted())
row1, col1 = divmod(num1, cols)
if num2 is None:
# left
hspaces[row1 * (cols + 1) + col1].append(
_get_left(tight_bbox, ax_bbox))
# right
hspaces[row1 * (cols + 1) + (col1 + 1)].append(
_get_right(tight_bbox, ax_bbox))
# top
vspaces[row1 * cols + col1].append(_get_top(tight_bbox, ax_bbox))
# bottom
vspaces[(row1 + 1) * cols + col1].append(
_get_bottom(tight_bbox, ax_bbox))
else:
row2, col2 = divmod(num2, cols)
for row_i in range(row1, row2 + 1):
# left
hspaces[row_i * (cols + 1) + col1].append(
_get_left(tight_bbox, ax_bbox))
# right
hspaces[row_i * (cols + 1) + (col2 + 1)].append(
_get_right(tight_bbox, ax_bbox))
for col_i in range(col1, col2 + 1):
# top
vspaces[row1 * cols + col_i].append(
_get_top(tight_bbox, ax_bbox))
# bottom
vspaces[(row2 + 1) * cols + col_i].append(
_get_bottom(tight_bbox, ax_bbox))
fig_width_inch, fig_height_inch = fig.get_size_inches()
# margins can be negative for axes with aspect applied. And we
# append + [0] to make minimum margins 0
if not margin_left:
margin_left = max([sum(s) for s in hspaces[::cols + 1]] + [0])
margin_left += pad_inches / fig_width_inch
if not margin_right:
margin_right = max([sum(s) for s in hspaces[cols::cols + 1]] + [0])
margin_right += pad_inches / fig_width_inch
if not margin_top:
margin_top = max([sum(s) for s in vspaces[:cols]] + [0])
margin_top += pad_inches / fig_height_inch
if not margin_bottom:
margin_bottom = max([sum(s) for s in vspaces[-cols:]] + [0])
margin_bottom += pad_inches / fig_height_inch
kwargs = dict(left=margin_left,
right=1 - margin_right,
bottom=margin_bottom,
top=1 - margin_top)
if cols > 1:
hspace = max([
sum(s) for i in range(rows)
for s in hspaces[i * (cols + 1) + 1:(i + 1) * (cols + 1) - 1]
])
hspace += hpad_inches / fig_width_inch
h_axes = ((1 - margin_right - margin_left) - hspace *
(cols - 1)) / cols
kwargs["wspace"] = hspace / h_axes
if rows > 1:
vspace = max([sum(s) for s in vspaces[cols:-cols]])
vspace += vpad_inches / fig_height_inch
v_axes = ((1 - margin_top - margin_bottom) - vspace *
(rows - 1)) / rows
kwargs["hspace"] = vspace / v_axes
return kwargs
def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True):
"""
Normalize, rescale and color the image `A` from the given
in_bbox (in data space), to the given out_bbox (in pixel
space) clipped to the given clip_bbox (also in pixel space),
and magnified by the magnification factor.
`A` may be a greyscale image (MxN) with a dtype of `float32`,
`float64`, `uint16` or `uint8`, or an RGBA image (MxNx4) with
a dtype of `float32`, `float64`, or `uint8`.
If `unsampled` is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.
If `round_to_pixel_border` is True, the output image size will
be rounded to the nearest pixel boundary. This makes the
images align correctly with the axes. It should not be used
in cases where you want exact scaling, however, such as
FigureImage.
Returns the resulting (image, x, y, trans), where (x, y) is
the upper left corner of the result in pixel space, and
`trans` is the affine transformation from the image to pixel
space.
"""
if A is None:
raise RuntimeError('You must first set the image'
' array or the image attribute')
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
if clipped_bbox is None:
return None, 0, 0, None
out_width_base = clipped_bbox.width * magnification
out_height_base = clipped_bbox.height * magnification
if out_width_base == 0 or out_height_base == 0:
return None, 0, 0, None
if self.origin == 'upper':
# Flip the input image using a transform. This avoids the
# problem with flipping the array, which results in a copy
# when it is converted to contiguous in the C wrapper
t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
else:
t0 = IdentityTransform()
t0 += (
Affine2D()
.scale(
in_bbox.width / A.shape[1],
in_bbox.height / A.shape[0])
.translate(in_bbox.x0, in_bbox.y0)
+ self.get_transform())
t = (t0
+ Affine2D().translate(
-clipped_bbox.x0,
-clipped_bbox.y0)
.scale(magnification, magnification))
# So that the image is aligned with the edge of the axes, we want
# to round up the output width to the next integer. This also
# means scaling the transform just slightly to account for the
# extra subpixel.
if (t.is_affine and round_to_pixel_border and
(out_width_base % 1.0 != 0.0 or
out_height_base % 1.0 != 0.0)):
out_width = int(ceil(out_width_base) + 1)
out_height = int(ceil(out_height_base) + 1)
extra_width = (out_width - out_width_base) / out_width_base
extra_height = (out_height - out_height_base) / out_height_base
t += Affine2D().scale(
1.0 + extra_width, 1.0 + extra_height)
else:
out_width = int(out_width_base)
out_height = int(out_height_base)
if not unsampled:
if A.ndim == 2:
A = self.norm(A)
if A.dtype.kind == 'f':
# For floating-point greyscale images, we treat negative
# numbers as transparent.
# TODO: Use np.full when we support Numpy 1.9 as a
# minimum
output = np.empty((out_height, out_width), dtype=A.dtype)
output[...] = -100.0
else:
output = np.zeros((out_height, out_width), dtype=A.dtype)
alpha = 1.0
elif A.ndim == 3:
# Always convert to RGBA, even if only RGB input
if A.shape[2] == 3:
A = _rgb_to_rgba(A)
elif A.shape[2] != 4:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
output = np.zeros((out_height, out_width, 4), dtype=A.dtype)
alpha = self.get_alpha()
if alpha is None:
alpha = 1.0
else:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
_image.resample(
A, output, t, _interpd_[self.get_interpolation()],
self.get_resample(), alpha,
self.get_filternorm() or 0.0, self.get_filterrad() or 0.0)
output = self.to_rgba(output, bytes=True, norm=False)
# Apply alpha *after* if the input was greyscale
if A.ndim == 2:
alpha = self.get_alpha()
if alpha is not None and alpha != 1.0:
alpha_channel = output[:, :, 3]
alpha_channel[:] = np.asarray(
np.asarray(alpha_channel, np.float32) * alpha,
np.uint8)
else:
if self._imcache is None:
self._imcache = self.to_rgba(A, bytes=True, norm=(A.ndim == 2))
output = self._imcache
# Subset the input image to only the part that will be
# displayed
subset = TransformedBbox(
clip_bbox, t0.frozen().inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
int(min(subset.ymax + 1, output.shape[0])),
int(max(subset.xmin, 0)):
int(min(subset.xmax + 1, output.shape[1]))]
t = Affine2D().translate(
int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
return output, clipped_bbox.x0, clipped_bbox.y0, t
def test_fill_facecolor():
fig, ax = plt.subplots(1, 5)
fig.set_size_inches(5, 5)
for i in range(1, 4):
ax[i].yaxis.set_visible(False)
ax[4].yaxis.tick_right()
bbox = Bbox.from_extents(0, 0.4, 1, 0.6)
# fill with blue by setting 'fc' field
bbox1 = TransformedBbox(bbox, ax[0].transData)
bbox2 = TransformedBbox(bbox, ax[1].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox1, bbox2, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", fc="b")
p.set_clip_on(False)
ax[0].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[0], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.xaxis.set_ticks([])
plt.gca().axes.yaxis.set_ticks([])
mark_inset(ax[0], axins, loc1=2, loc2=4, fc="b", ec="0.5")
# fill with yellow by setting 'facecolor' field
bbox3 = TransformedBbox(bbox, ax[1].transData)
bbox4 = TransformedBbox(bbox, ax[2].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox3, bbox4, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", facecolor="y")
p.set_clip_on(False)
ax[1].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[1], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.xaxis.set_ticks([])
plt.gca().axes.yaxis.set_ticks([])
mark_inset(ax[1], axins, loc1=2, loc2=4, facecolor="y", ec="0.5")
# fill with green by setting 'color' field
bbox5 = TransformedBbox(bbox, ax[2].transData)
bbox6 = TransformedBbox(bbox, ax[3].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox5, bbox6, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", color="g")
p.set_clip_on(False)
ax[2].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[2], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.xaxis.set_ticks([])
plt.gca().axes.yaxis.set_ticks([])
mark_inset(ax[2], axins, loc1=2, loc2=4, color="g", ec="0.5")
# fill with green but color won't show if set fill to False
bbox7 = TransformedBbox(bbox, ax[3].transData)
bbox8 = TransformedBbox(bbox, ax[4].transData)
# BboxConnectorPatch won't show green
p = BboxConnectorPatch(
bbox7, bbox8, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", fc="g", fill=False)
p.set_clip_on(False)
ax[3].add_patch(p)
# marked area won't show green
axins = zoomed_inset_axes(ax[3], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
axins.xaxis.set_ticks([])
axins.yaxis.set_ticks([])
mark_inset(ax[3], axins, loc1=2, loc2=4, fc="g", ec="0.5", fill=False)
def __init__(self,
figsize = None, # defaults to rc figure.figsize
dpi = None, # defaults to rc figure.dpi
facecolor = None, # defaults to rc figure.facecolor
edgecolor = None, # defaults to rc figure.edgecolor
linewidth = 0.0, # the default linewidth of the frame
frameon = True, # whether or not to draw the figure frame
subplotpars = None, # default to rc
tight_layout = None, # default to rc figure.autolayout
):
"""
*figsize*
w,h tuple in inches
*dpi*
Dots per inch
*facecolor*
The figure patch facecolor; defaults to rc ``figure.facecolor``
*edgecolor*
The figure patch edge color; defaults to rc ``figure.edgecolor``
*linewidth*
The figure patch edge linewidth; the default linewidth of the frame
*frameon*
If *False*, suppress drawing the figure frame
*subplotpars*
A :class:`SubplotParams` instance, defaults to rc
*tight_layout*
If *False* use *subplotpars*; if *True* adjust subplot
parameters using :meth:`tight_layout`. Defaults to
rc ``figure.autolayout``.
"""
Artist.__init__(self)
self.callbacks = cbook.CallbackRegistry()
if figsize is None : figsize = rcParams['figure.figsize']
if dpi is None : dpi = rcParams['figure.dpi']
if facecolor is None: facecolor = rcParams['figure.facecolor']
if edgecolor is None: edgecolor = rcParams['figure.edgecolor']
self.dpi_scale_trans = Affine2D()
self.dpi = dpi
self.bbox_inches = Bbox.from_bounds(0, 0, *figsize)
self.bbox = TransformedBbox(self.bbox_inches, self.dpi_scale_trans)
self.frameon = frameon
self.transFigure = BboxTransformTo(self.bbox)
# the figurePatch name is deprecated
self.patch = self.figurePatch = Rectangle(
xy=(0,0), width=1, height=1,
facecolor=facecolor, edgecolor=edgecolor,
linewidth=linewidth,
)
self._set_artist_props(self.patch)
self.patch.set_aa(False)
self._hold = rcParams['axes.hold']
self.canvas = None
if subplotpars is None:
subplotpars = SubplotParams()
self.subplotpars = subplotpars
self.set_tight_layout(tight_layout)
self._axstack = AxesStack() # track all figure axes and current axes
self.clf()
self._cachedRenderer = None
def __call__(self, ax, renderer):
bbox_parent = self.parent.get_position(original=False)
trans = BboxTransformTo(bbox_parent)
bbox_inset = Bbox.from_bounds(*self.lbwh)
bb = TransformedBbox(bbox_inset, trans)
return bb
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies figure.bbox, figure.bbox_inches,
figure.transFigure._boxout, and figure.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0, bbox_inches.width,
bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1, fig.bbox.width / w1,
fig.bbox.height / h1)
return restore_bbox
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=float)
gradient[:, :, :3] = [1, 1, 0.]
def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True):
"""
Normalize, rescale and color the image `A` from the given
in_bbox (in data space), to the given out_bbox (in pixel
space) clipped to the given clip_bbox (also in pixel space),
and magnified by the magnification factor.
`A` may be a greyscale image (MxN) with a dtype of `float32`,
`float64`, `uint16` or `uint8`, or an RGBA image (MxNx4) with
a dtype of `float32`, `float64`, or `uint8`.
If `unsampled` is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.
If `round_to_pixel_border` is True, the output image size will
be rounded to the nearest pixel boundary. This makes the
images align correctly with the axes. It should not be used
in cases where you want exact scaling, however, such as
FigureImage.
Returns the resulting (image, x, y, trans), where (x, y) is
the upper left corner of the result in pixel space, and
`trans` is the affine transformation from the image to pixel
space.
"""
if A is None:
raise RuntimeError('You must first set the image'
' array or the image attribute')
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
if clipped_bbox is None:
return None, 0, 0, None
out_width_base = clipped_bbox.width * magnification
out_height_base = clipped_bbox.height * magnification
if out_width_base == 0 or out_height_base == 0:
return None, 0, 0, None
if self.origin == 'upper':
# Flip the input image using a transform. This avoids the
# problem with flipping the array, which results in a copy
# when it is converted to contiguous in the C wrapper
t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
else:
t0 = IdentityTransform()
t0 += (
Affine2D()
.scale(
in_bbox.width / A.shape[1],
in_bbox.height / A.shape[0])
.translate(in_bbox.x0, in_bbox.y0)
+ self.get_transform())
t = (t0
+ Affine2D().translate(
-clipped_bbox.x0,
-clipped_bbox.y0)
.scale(magnification, magnification))
# So that the image is aligned with the edge of the axes, we want
# to round up the output width to the next integer. This also
# means scaling the transform just slightly to account for the
# extra subpixel.
if (t.is_affine and round_to_pixel_border and
(out_width_base % 1.0 != 0.0 or out_height_base % 1.0 != 0.0)):
out_width = int(ceil(out_width_base))
out_height = int(ceil(out_height_base))
extra_width = (out_width - out_width_base) / out_width_base
extra_height = (out_height - out_height_base) / out_height_base
t += Affine2D().scale(
1.0 + extra_width, 1.0 + extra_height)
else:
out_width = int(out_width_base)
out_height = int(out_height_base)
if not unsampled:
created_rgba_mask = False
if A.ndim not in (2, 3):
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
if A.ndim == 2:
A = self.norm(A)
if A.dtype.kind == 'f':
# If the image is greyscale, convert to RGBA and
# use the extra channels for resizing the over,
# under, and bad pixels. This is needed because
# Agg's resampler is very aggressive about
# clipping to [0, 1] and we use out-of-bounds
# values to carry the over/under/bad information
rgba = np.empty((A.shape[0], A.shape[1], 4), dtype=A.dtype)
rgba[..., 0] = A # normalized data
rgba[..., 1] = A < 0 # under data
rgba[..., 2] = A > 1 # over data
rgba[..., 3] = ~A.mask # bad data
A = rgba
output = np.zeros((out_height, out_width, 4),
dtype=A.dtype)
alpha = 1.0
created_rgba_mask = True
else:
# colormap norms that output integers (ex NoNorm
# and BoundaryNorm) to RGBA space before
# interpolating. This is needed due to the
# Agg resampler only working on floats in the
# range [0, 1] and because interpolating indexes
# into an arbitrary LUT may be problematic.
#
# This falls back to interpolating in RGBA space which
# can produce it's own artifacts of colors not in the map
# showing up in the final image.
A = self.cmap(A, alpha=self.get_alpha(), bytes=True)
if not created_rgba_mask:
# Always convert to RGBA, even if only RGB input
if A.shape[2] == 3:
A = _rgb_to_rgba(A)
elif A.shape[2] != 4:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
output = np.zeros((out_height, out_width, 4), dtype=A.dtype)
alpha = self.get_alpha()
if alpha is None:
alpha = 1.0
_image.resample(
A, output, t, _interpd_[self.get_interpolation()],
self.get_resample(), alpha,
self.get_filternorm() or 0.0, self.get_filterrad() or 0.0)
if created_rgba_mask:
# Convert back to a masked greyscale array so
# colormapping works correctly
hid_output = output
output = np.ma.masked_array(
hid_output[..., 0], hid_output[..., 3] < 0.5)
# relabel under data
output[hid_output[..., 1] > .5] = -1
# relabel over data
output[hid_output[..., 2] > .5] = 2
output = self.to_rgba(output, bytes=True, norm=False)
# Apply alpha *after* if the input was greyscale without a mask
if A.ndim == 2 or created_rgba_mask:
alpha = self.get_alpha()
if alpha is not None and alpha != 1.0:
alpha_channel = output[:, :, 3]
alpha_channel[:] = np.asarray(
np.asarray(alpha_channel, np.float32) * alpha,
np.uint8)
else:
if self._imcache is None:
self._imcache = self.to_rgba(A, bytes=True, norm=(A.ndim == 2))
output = self._imcache
# Subset the input image to only the part that will be
# displayed
subset = TransformedBbox(
clip_bbox, t0.frozen().inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
int(min(subset.ymax + 1, output.shape[0])),
int(max(subset.xmin, 0)):
int(min(subset.xmax + 1, output.shape[1]))]
t = Affine2D().translate(
int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
return output, clipped_bbox.x0, clipped_bbox.y0, t
class Figure(Artist):
"""
The Figure instance supports callbacks through a *callbacks*
attribute which is a :class:`matplotlib.cbook.CallbackRegistry`
instance. The events you can connect to are 'dpi_changed', and
the callback will be called with ``func(fig)`` where fig is the
:class:`Figure` instance.
*patch*
The figure patch is drawn by a
:class:`matplotlib.patches.Rectangle` instance
*suppressComposite*
For multiple figure images, the figure will make composite
images depending on the renderer option_image_nocomposite
function. If suppressComposite is True|False, this will
override the renderer.
"""
def __str__(self):
return "Figure(%gx%g)" % tuple(self.bbox.size)
def __init__(self,
figsize = None, # defaults to rc figure.figsize
dpi = None, # defaults to rc figure.dpi
facecolor = None, # defaults to rc figure.facecolor
edgecolor = None, # defaults to rc figure.edgecolor
linewidth = 0.0, # the default linewidth of the frame
frameon = True, # whether or not to draw the figure frame
subplotpars = None, # default to rc
tight_layout = None, # default to rc figure.autolayout
):
"""
*figsize*
w,h tuple in inches
*dpi*
Dots per inch
*facecolor*
The figure patch facecolor; defaults to rc ``figure.facecolor``
*edgecolor*
The figure patch edge color; defaults to rc ``figure.edgecolor``
*linewidth*
The figure patch edge linewidth; the default linewidth of the frame
*frameon*
If *False*, suppress drawing the figure frame
*subplotpars*
A :class:`SubplotParams` instance, defaults to rc
*tight_layout*
If *False* use *subplotpars*; if *True* adjust subplot
parameters using :meth:`tight_layout`. Defaults to
rc ``figure.autolayout``.
"""
Artist.__init__(self)
self.callbacks = cbook.CallbackRegistry()
if figsize is None : figsize = rcParams['figure.figsize']
if dpi is None : dpi = rcParams['figure.dpi']
if facecolor is None: facecolor = rcParams['figure.facecolor']
if edgecolor is None: edgecolor = rcParams['figure.edgecolor']
self.dpi_scale_trans = Affine2D()
self.dpi = dpi
self.bbox_inches = Bbox.from_bounds(0, 0, *figsize)
self.bbox = TransformedBbox(self.bbox_inches, self.dpi_scale_trans)
self.frameon = frameon
self.transFigure = BboxTransformTo(self.bbox)
# the figurePatch name is deprecated
self.patch = self.figurePatch = Rectangle(
xy=(0,0), width=1, height=1,
facecolor=facecolor, edgecolor=edgecolor,
linewidth=linewidth,
)
self._set_artist_props(self.patch)
self.patch.set_aa(False)
self._hold = rcParams['axes.hold']
self.canvas = None
if subplotpars is None:
subplotpars = SubplotParams()
self.subplotpars = subplotpars
self.set_tight_layout(tight_layout)
self._axstack = AxesStack() # track all figure axes and current axes
self.clf()
self._cachedRenderer = None
def _get_axes(self):
return self._axstack.as_list()
axes = property(fget=_get_axes, doc="Read-only: list of axes in Figure")
def _get_dpi(self):
return self._dpi
def _set_dpi(self, dpi):
self._dpi = dpi
self.dpi_scale_trans.clear().scale(dpi, dpi)
self.callbacks.process('dpi_changed', self)
dpi = property(_get_dpi, _set_dpi)
def get_tight_layout(self):
"""
Return the Boolean flag, True to use :meth`tight_layout` when drawing.
"""
return self._tight
def set_tight_layout(self, tight):
"""
Set whether :meth:`tight_layout` is used upon drawing.
If None, the rcParams['figure.autolayout'] value will be set.
ACCEPTS: [True | False | None]
"""
if tight is None:
tight = rcParams['figure.autolayout']
tight = bool(tight)
self._tight = tight
def autofmt_xdate(self, bottom=0.2, rotation=30, ha='right'):
"""
Date ticklabels often overlap, so it is useful to rotate them
and right align them. Also, a common use case is a number of
subplots with shared xaxes where the x-axis is date data. The
ticklabels are often long, and it helps to rotate them on the
bottom subplot and turn them off on other subplots, as well as
turn off xlabels.
*bottom*
The bottom of the subplots for :meth:`subplots_adjust`
*rotation*
The rotation of the xtick labels
*ha*
The horizontal alignment of the xticklabels
"""
allsubplots = np.alltrue([hasattr(ax, 'is_last_row') for ax in self.axes])
if len(self.axes)==1:
for label in self.axes[0].get_xticklabels():
label.set_ha(ha)
label.set_rotation(rotation)
else:
if allsubplots:
for ax in self.get_axes():
if ax.is_last_row():
for label in ax.get_xticklabels():
label.set_ha(ha)
label.set_rotation(rotation)
else:
for label in ax.get_xticklabels():
label.set_visible(False)
ax.set_xlabel('')
if allsubplots:
self.subplots_adjust(bottom=bottom)
def get_children(self):
'get a list of artists contained in the figure'
children = [self.patch]
children.extend(self.artists)
children.extend(self.axes)
children.extend(self.lines)
children.extend(self.patches)
children.extend(self.texts)
children.extend(self.images)
children.extend(self.legends)
return children
def contains(self, mouseevent):
"""
Test whether the mouse event occurred on the figure.
Returns True,{}
"""
if callable(self._contains): return self._contains(self,mouseevent)
#inside = mouseevent.x >= 0 and mouseevent.y >= 0
inside = self.bbox.contains(mouseevent.x,mouseevent.y)
return inside,{}
def get_window_extent(self, *args, **kwargs):
'get the figure bounding box in display space; kwargs are void'
return self.bbox
def suptitle(self, t, **kwargs):
"""
Add a centered title to the figure.
kwargs are :class:`matplotlib.text.Text` properties. Using figure
coordinates, the defaults are:
*x* : 0.5
The x location of the text in figure coords
*y* : 0.98
The y location of the text in figure coords
*horizontalalignment* : 'center'
The horizontal alignment of the text
*verticalalignment* : 'top'
The vertical alignment of the text
A :class:`matplotlib.text.Text` instance is returned.
Example::
fig.suptitle('this is the figure title', fontsize=12)
"""
x = kwargs.pop('x', 0.5)
y = kwargs.pop('y', 0.98)
if ('horizontalalignment' not in kwargs) and ('ha' not in kwargs):
kwargs['horizontalalignment'] = 'center'
if ('verticalalignment' not in kwargs) and ('va' not in kwargs):
kwargs['verticalalignment'] = 'top'
t = self.text(x, y, t, **kwargs)
return t
def set_canvas(self, canvas):
"""
Set the canvas the contains the figure
ACCEPTS: a FigureCanvas instance
"""
self.canvas = canvas
def hold(self, b=None):
"""
Set the hold state. If hold is None (default), toggle the
hold state. Else set the hold state to boolean value b.
Eg::
hold() # toggle hold
hold(True) # hold is on
hold(False) # hold is off
"""
if b is None: self._hold = not self._hold
else: self._hold = b
def figimage(self, X,
xo=0,
yo=0,
alpha=None,
norm=None,
cmap=None,
vmin=None,
vmax=None,
origin=None,
**kwargs):
"""
Adds a non-resampled image to the figure.
call signatures::
figimage(X, **kwargs)
adds a non-resampled array *X* to the figure.
::
figimage(X, xo, yo)
with pixel offsets *xo*, *yo*,
*X* must be a float array:
* If *X* is MxN, assume luminance (grayscale)
* If *X* is MxNx3, assume RGB
* If *X* is MxNx4, assume RGBA
Optional keyword arguments:
========= ==========================================================
Keyword Description
========= ==========================================================
xo or yo An integer, the *x* and *y* image offset in pixels
cmap a :class:`matplotlib.colors.Colormap` instance, eg cm.jet.
If *None*, default to the rc ``image.cmap`` value
norm a :class:`matplotlib.colors.Normalize` instance. The
default is normalization(). This scales luminance -> 0-1
vmin|vmax are used to scale a luminance image to 0-1. If either is
*None*, the min and max of the luminance values will be
used. Note if you pass a norm instance, the settings for
*vmin* and *vmax* will be ignored.
alpha the alpha blending value, default is *None*
origin [ 'upper' | 'lower' ] Indicates where the [0,0] index of
the array is in the upper left or lower left corner of
the axes. Defaults to the rc image.origin value
========= ==========================================================
figimage complements the axes image
(:meth:`~matplotlib.axes.Axes.imshow`) which will be resampled
to fit the current axes. If you want a resampled image to
fill the entire figure, you can define an
:class:`~matplotlib.axes.Axes` with size [0,1,0,1].
An :class:`matplotlib.image.FigureImage` instance is returned.
.. plot:: mpl_examples/pylab_examples/figimage_demo.py
Additional kwargs are Artist kwargs passed on to
:class:`~matplotlib.image.FigureImage`
"""
if not self._hold: self.clf()
im = FigureImage(self, cmap, norm, xo, yo, origin, **kwargs)
im.set_array(X)
im.set_alpha(alpha)
if norm is None:
im.set_clim(vmin, vmax)
self.images.append(im)
return im
def set_size_inches(self, *args, **kwargs):
"""
set_size_inches(w,h, forward=False)
Set the figure size in inches
Usage::
fig.set_size_inches(w,h) # OR
fig.set_size_inches((w,h) )
optional kwarg *forward=True* will cause the canvas size to be
automatically updated; eg you can resize the figure window
from the shell
ACCEPTS: a w,h tuple with w,h in inches
"""
forward = kwargs.get('forward', False)
if len(args)==1:
w,h = args[0]
else:
w,h = args
dpival = self.dpi
self.bbox_inches.p1 = w, h
if forward:
dpival = self.dpi
canvasw = w*dpival
canvash = h*dpival
manager = getattr(self.canvas, 'manager', None)
if manager is not None:
manager.resize(int(canvasw), int(canvash))
def get_size_inches(self):
return self.bbox_inches.p1
def get_edgecolor(self):
'Get the edge color of the Figure rectangle'
return self.patch.get_edgecolor()
def get_facecolor(self):
'Get the face color of the Figure rectangle'
return self.patch.get_facecolor()
def get_figwidth(self):
'Return the figwidth as a float'
return self.bbox_inches.width
def get_figheight(self):
'Return the figheight as a float'
return self.bbox_inches.height
def get_dpi(self):
'Return the dpi as a float'
return self.dpi
def get_frameon(self):
'get the boolean indicating frameon'
return self.frameon
def set_edgecolor(self, color):
"""
Set the edge color of the Figure rectangle
ACCEPTS: any matplotlib color - see help(colors)
"""
self.patch.set_edgecolor(color)
def set_facecolor(self, color):
"""
Set the face color of the Figure rectangle
ACCEPTS: any matplotlib color - see help(colors)
"""
self.patch.set_facecolor(color)
def set_dpi(self, val):
"""
Set the dots-per-inch of the figure
ACCEPTS: float
"""
self.dpi = val
def set_figwidth(self, val):
"""
Set the width of the figure in inches
ACCEPTS: float
"""
self.bbox_inches.x1 = val
def set_figheight(self, val):
"""
Set the height of the figure in inches
ACCEPTS: float
"""
self.bbox_inches.y1 = val
def set_frameon(self, b):
"""
Set whether the figure frame (background) is displayed or invisible
ACCEPTS: boolean
"""
self.frameon = b
def delaxes(self, a):
'remove a from the figure and update the current axes'
self._axstack.remove(a)
for func in self._axobservers: func(self)
def _make_key(self, *args, **kwargs):
'make a hashable key out of args and kwargs'
def fixitems(items):
#items may have arrays and lists in them, so convert them
# to tuples for the key
ret = []
for k, v in items:
if iterable(v): v = tuple(v)
ret.append((k,v))
return tuple(ret)
def fixlist(args):
ret = []
for a in args:
if iterable(a): a = tuple(a)
ret.append(a)
return tuple(ret)
key = fixlist(args), fixitems(kwargs.iteritems())
return key
@docstring.dedent_interpd
def add_axes(self, *args, **kwargs):
"""
Add an axes at position *rect* [*left*, *bottom*, *width*,
*height*] where all quantities are in fractions of figure
width and height. kwargs are legal
:class:`~matplotlib.axes.Axes` kwargs plus *projection* which
sets the projection type of the axes. (For backward
compatibility, ``polar=True`` may also be provided, which is
equivalent to ``projection='polar'``). Valid values for
*projection* are: %(projection_names)s. Some of these
projections support additional kwargs, which may be provided
to :meth:`add_axes`. Typical usage::
rect = l,b,w,h
fig.add_axes(rect)
fig.add_axes(rect, frameon=False, axisbg='g')
fig.add_axes(rect, polar=True)
fig.add_axes(rect, projection='polar')
fig.add_axes(ax)
If the figure already has an axes with the same parameters,
then it will simply make that axes current and return it. If
you do not want this behavior, e.g. you want to force the
creation of a new Axes, you must use a unique set of args and
kwargs. The axes :attr:`~matplotlib.axes.Axes.label`
attribute has been exposed for this purpose. Eg., if you want
two axes that are otherwise identical to be added to the
figure, make sure you give them unique labels::
fig.add_axes(rect, label='axes1')
fig.add_axes(rect, label='axes2')
In rare circumstances, add_axes may be called with a single
argument, an Axes instance already created in the present
figure but not in the figure's list of axes. For example,
if an axes has been removed with :meth:`delaxes`, it can
be restored with::
fig.add_axes(ax)
In all cases, the :class:`~matplotlib.axes.Axes` instance
will be returned.
In addition to *projection*, the following kwargs are supported:
%(Axes)s
"""
if not len(args): return
# shortcut the projection "key" modifications later on, if an axes
# with the exact args/kwargs exists, return it immediately.
key = self._make_key(*args, **kwargs)
ax = self._axstack.get(key)
if ax is not None:
self.sca(ax)
return ax
if isinstance(args[0], Axes):
a = args[0]
assert(a.get_figure() is self)
else:
rect = args[0]
projection_class, kwargs, key = \
process_projection_requirements(self, *args, **kwargs)
# check that an axes of this type doesn't already exist, if it
# does, set it as active and return it
ax = self._axstack.get(key)
if ax is not None and isinstance(ax, projection_class):
self.sca(ax)
return ax
# create the new axes using the axes class given
a = projection_class(self, rect, **kwargs)
self._axstack.add(key, a)
self.sca(a)
return a
@docstring.dedent_interpd
def add_subplot(self, *args, **kwargs):
"""
Add a subplot. Examples::
fig.add_subplot(111)
# equivalent but more general
fig.add_subplot(1,1,1)
# add subplot with red background
fig.add_subplot(212, axisbg='r')
# add a polar subplot
fig.add_subplot(111, projection='polar')
# add Subplot instance sub
fig.add_subplot(sub)
*kwargs* are legal :class:`~matplotlib.axes.Axes` kwargs plus
*projection*, which chooses a projection type for the axes.
(For backward compatibility, *polar=True* may also be
provided, which is equivalent to *projection='polar'*). Valid
values for *projection* are: %(projection_names)s. Some of
these projections
support additional *kwargs*, which may be provided to
:meth:`add_axes`.
The :class:`~matplotlib.axes.Axes` instance will be returned.
If the figure already has a subplot with key (*args*,
*kwargs*) then it will simply make that subplot current and
return it.
The following kwargs are supported:
%(Axes)s
"""
if not len(args): return
if len(args) == 1 and isinstance(args[0], int):
args = tuple([int(c) for c in str(args[0])])
if isinstance(args[0], SubplotBase):
a = args[0]
assert(a.get_figure() is self)
# make a key for the subplot (which includes the axes object id
# in the hash)
key = self._make_key(*args, **kwargs)
else:
projection_class, kwargs, key = \
process_projection_requirements(self, *args, **kwargs)
# try to find the axes with this key in the stack
ax = self._axstack.get(key)
if ax is not None:
if isinstance(ax, projection_class):
# the axes already existed, so set it as active & return
self.sca(ax)
return ax
else:
# Undocumented convenience behavior:
# subplot(111); subplot(111, projection='polar')
# will replace the first with the second.
# Without this, add_subplot would be simpler and
# more similar to add_axes.
self._axstack.remove(ax)
a = subplot_class_factory(projection_class)(self, *args, **kwargs)
self._axstack.add(key, a)
self.sca(a)
return a
def clf(self, keep_observers=False):
"""
Clear the figure.
Set *keep_observers* to True if, for example,
a gui widget is tracking the axes in the figure.
"""
self.suppressComposite = None
self.callbacks = cbook.CallbackRegistry()
for ax in tuple(self.axes): # Iterate over the copy.
ax.cla()
self.delaxes(ax) # removes ax from self._axstack
toolbar = getattr(self.canvas, 'toolbar', None)
if toolbar is not None:
toolbar.update()
self._axstack.clear()
self.artists = []
self.lines = []
self.patches = []
self.texts=[]
self.images = []
self.legends = []
if not keep_observers:
self._axobservers = []
def clear(self):
"""
Clear the figure -- synonym for :meth:`clf`.
"""
self.clf()
@allow_rasterization
def draw(self, renderer):
"""
Render the figure using :class:`matplotlib.backend_bases.RendererBase`
instance *renderer*.
"""
# draw the figure bounding box, perhaps none for white figure
if not self.get_visible(): return
renderer.open_group('figure')
if self.get_tight_layout() and self.axes:
try:
self.tight_layout(renderer)
except ValueError:
pass
# ValueError can occur when resizing a window.
if self.frameon: self.patch.draw(renderer)
# a list of (zorder, func_to_call, list_of_args)
dsu = []
for a in self.patches:
dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
for a in self.lines:
dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
for a in self.artists:
dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
# override the renderer default if self.suppressComposite
# is not None
not_composite = renderer.option_image_nocomposite()
if self.suppressComposite is not None:
not_composite = self.suppressComposite
if len(self.images)<=1 or not_composite or \
not cbook.allequal([im.origin for im in self.images]):
for a in self.images:
dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
else:
# make a composite image blending alpha
# list of (_image.Image, ox, oy)
mag = renderer.get_image_magnification()
ims = [(im.make_image(mag), im.ox, im.oy)
for im in self.images]
im = _image.from_images(self.bbox.height * mag,
self.bbox.width * mag,
ims)
im.is_grayscale = False
l, b, w, h = self.bbox.bounds
def draw_composite():
gc = renderer.new_gc()
gc.set_clip_rectangle(self.bbox)
gc.set_clip_path(self.get_clip_path())
renderer.draw_image(gc, l, b, im)
gc.restore()
dsu.append((self.images[0].get_zorder(), self.images[0], draw_composite, []))
# render the axes
for a in self.axes:
dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
# render the figure text
for a in self.texts:
dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
for a in self.legends:
dsu.append( (a.get_zorder(), a, a.draw, [renderer]))
dsu = [row for row in dsu if not row[1].get_animated()]
dsu.sort(key=itemgetter(0))
for zorder, a, func, args in dsu:
func(*args)
renderer.close_group('figure')
self._cachedRenderer = renderer
self.canvas.draw_event(renderer)
def draw_artist(self, a):
"""
draw :class:`matplotlib.artist.Artist` instance *a* only --
this is available only after the figure is drawn
"""
assert self._cachedRenderer is not None
a.draw(self._cachedRenderer)
def get_axes(self):
return self.axes
def legend(self, handles, labels, *args, **kwargs):
"""
Place a legend in the figure. Labels are a sequence of
strings, handles is a sequence of
:class:`~matplotlib.lines.Line2D` or
:class:`~matplotlib.patches.Patch` instances, and loc can be a
string or an integer specifying the legend location
USAGE::
legend( (line1, line2, line3),
('label1', 'label2', 'label3'),
'upper right')
The *loc* location codes are::
'best' : 0, (currently not supported for figure legends)
'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4,
'right' : 5,
'center left' : 6,
'center right' : 7,
'lower center' : 8,
'upper center' : 9,
'center' : 10,
*loc* can also be an (x,y) tuple in figure coords, which
specifies the lower left of the legend box. figure coords are
(0,0) is the left, bottom of the figure and 1,1 is the right,
top.
Keyword arguments:
*prop*: [ *None* | FontProperties | dict ]
A :class:`matplotlib.font_manager.FontProperties`
instance. If *prop* is a dictionary, a new instance will be
created with *prop*. If *None*, use rc settings.
*numpoints*: integer
The number of points in the legend line, default is 4
*scatterpoints*: integer
The number of points in the legend line, default is 4
*scatteroffsets*: list of floats
a list of yoffsets for scatter symbols in legend
*markerscale*: [ *None* | scalar ]
The relative size of legend markers vs. original. If *None*, use rc
settings.
*fancybox*: [ *None* | *False* | *True* ]
if *True*, draw a frame with a round fancybox. If *None*, use rc
*shadow*: [ *None* | *False* | *True* ]
If *True*, draw a shadow behind legend. If *None*, use rc settings.
*ncol* : integer
number of columns. default is 1
*mode* : [ "expand" | *None* ]
if mode is "expand", the legend will be horizontally expanded
to fill the axes area (or *bbox_to_anchor*)
*title* : string
the legend title
Padding and spacing between various elements use following keywords
parameters. The dimensions of these values are given as a fraction
of the fontsize. Values from rcParams will be used if None.
================ ==================================================================
Keyword Description
================ ==================================================================
borderpad the fractional whitespace inside the legend border
labelspacing the vertical space between the legend entries
handlelength the length of the legend handles
handletextpad the pad between the legend handle and text
borderaxespad the pad between the axes and legend border
columnspacing the spacing between columns
================ ==================================================================
.. Note:: Not all kinds of artist are supported by the legend.
See LINK (FIXME) for details.
**Example:**
.. plot:: mpl_examples/pylab_examples/figlegend_demo.py
"""
l = Legend(self, handles, labels, *args, **kwargs)
self.legends.append(l)
return l
@docstring.dedent_interpd
def text(self, x, y, s, *args, **kwargs):
"""
Add text to figure.
Call signature::
text(x, y, s, fontdict=None, **kwargs)
Add text to figure at location *x*, *y* (relative 0-1
coords). See :func:`~matplotlib.pyplot.text` for the meaning
of the other arguments.
kwargs control the :class:`~matplotlib.text.Text` properties:
%(Text)s
"""
override = _process_text_args({}, *args, **kwargs)
t = Text(
x=x, y=y, text=s,
)
t.update(override)
self._set_artist_props(t)
self.texts.append(t)
return t
def _set_artist_props(self, a):
if a!= self:
a.set_figure(self)
a.set_transform(self.transFigure)
@docstring.dedent_interpd
def gca(self, **kwargs):
"""
Return the current axes, creating one if necessary
The following kwargs are supported for ensuring the returned axes
adheres to the given projection etc., and for axes creation if
the active axes does not exist:
%(Axes)s
"""
ckey, cax = self._axstack.current_key_axes()
# if there exists an axes on the stack see if it maches
# the desired axes configuration
if cax is not None:
# if no kwargs are given just return the current axes
# this is a convenience for gca() on axes such as polar etc.
if not kwargs:
return cax
# if the user has specified particular projection detail
# then build up a key which can represent this
else:
# we don't want to modify the original kwargs
# so take a copy so that we can do what we like to it
kwargs_copy = kwargs.copy()
projection_class, _, key = \
process_projection_requirements(self, **kwargs_copy)
# let the returned axes have any gridspec by removing it from the key
ckey = ckey[1:]
key = key[1:]
# if the cax matches this key then return the axes, otherwise
# continue and a new axes will be created
if key == ckey and isinstance(cax, projection_class):
return cax
# no axes found, so create one which spans the figure
return self.add_subplot(1, 1, 1, **kwargs)
def sca(self, a):
'Set the current axes to be a and return a'
self._axstack.bubble(a)
for func in self._axobservers: func(self)
return a
def _gci(self):
"""
helper for :func:`~matplotlib.pyplot.gci`;
do not use elsewhere.
"""
for ax in reversed(self.axes):
im = ax._gci()
if im is not None:
return im
return None
def add_axobserver(self, func):
'whenever the axes state change, ``func(self)`` will be called'
self._axobservers.append(func)
def savefig(self, *args, **kwargs):
"""
Save the current figure.
Call signature::
savefig(fname, dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1)
The output formats available depend on the backend being used.
Arguments:
*fname*:
A string containing a path to a filename, or a Python
file-like object, or possibly some backend-dependent object
such as :class:`~matplotlib.backends.backend_pdf.PdfPages`.
If *format* is *None* and *fname* is a string, the output
format is deduced from the extension of the filename. If
the filename has no extension, the value of the rc parameter
``savefig.format`` is used.
If *fname* is not a string, remember to specify *format* to
ensure that the correct backend is used.
Keyword arguments:
*dpi*: [ *None* | ``scalar > 0`` ]
The resolution in dots per inch. If *None* it will default to
the value ``savefig.dpi`` in the matplotlibrc file.
*facecolor*, *edgecolor*:
the colors of the figure rectangle
*orientation*: [ 'landscape' | 'portrait' ]
not supported on all backends; currently only on postscript output
*papertype*:
One of 'letter', 'legal', 'executive', 'ledger', 'a0' through
'a10', 'b0' through 'b10'. Only supported for postscript
output.
*format*:
One of the file extensions supported by the active
backend. Most backends support png, pdf, ps, eps and svg.
*transparent*:
If *True*, the axes patches will all be transparent; the
figure patch will also be transparent unless facecolor
and/or edgecolor are specified via kwargs.
This is useful, for example, for displaying
a plot on top of a colored background on a web page. The
transparency of these patches will be restored to their
original values upon exit of this function.
*bbox_inches*:
Bbox in inches. Only the given portion of the figure is
saved. If 'tight', try to figure out the tight bbox of
the figure.
*pad_inches*:
Amount of padding around the figure when bbox_inches is
'tight'.
*bbox_extra_artists*:
A list of extra artists that will be considered when the
tight bbox is calculated.
"""
kwargs.setdefault('dpi', rcParams['savefig.dpi'])
transparent = kwargs.pop('transparent', False)
if transparent:
kwargs.setdefault('facecolor', 'none')
kwargs.setdefault('edgecolor', 'none')
original_axes_colors = []
for ax in self.axes:
patch = ax.patch
original_axes_colors.append((patch.get_facecolor(),
patch.get_edgecolor()))
patch.set_facecolor('none')
patch.set_edgecolor('none')
else:
kwargs.setdefault('facecolor', rcParams['savefig.facecolor'])
kwargs.setdefault('edgecolor', rcParams['savefig.edgecolor'])
self.canvas.print_figure(*args, **kwargs)
if transparent:
for ax, cc in zip(self.axes, original_axes_colors):
ax.patch.set_facecolor(cc[0])
ax.patch.set_edgecolor(cc[1])
@docstring.dedent_interpd
def colorbar(self, mappable, cax=None, ax=None, **kw):
"""
Create a colorbar for a ScalarMappable instance, *mappable*.
Documentation for the pylab thin wrapper:
%(colorbar_doc)s
"""
if ax is None:
ax = self.gca()
use_gridspec = kw.pop("use_gridspec", True)
if cax is None:
if use_gridspec and isinstance(ax, SubplotBase):
cax, kw = cbar.make_axes_gridspec(ax, **kw)
else:
cax, kw = cbar.make_axes(ax, **kw)
cax.hold(True)
cb = cbar.colorbar_factory(cax, mappable, **kw)
self.sca(ax)
return cb
def subplots_adjust(self, *args, **kwargs):
"""
Call signature::
subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=None, hspace=None)
Update the :class:`SubplotParams` with *kwargs* (defaulting to rc when
*None*) and update the subplot locations
"""
self.subplotpars.update(*args, **kwargs)
import matplotlib.axes
for ax in self.axes:
if not isinstance(ax, matplotlib.axes.SubplotBase):
# Check if sharing a subplots axis
if ax._sharex is not None and isinstance(ax._sharex, matplotlib.axes.SubplotBase):
ax._sharex.update_params()
ax.set_position(ax._sharex.figbox)
elif ax._sharey is not None and isinstance(ax._sharey, matplotlib.axes.SubplotBase):
ax._sharey.update_params()
ax.set_position(ax._sharey.figbox)
else:
ax.update_params()
ax.set_position(ax.figbox)
def ginput(self, n=1, timeout=30, show_clicks=True, mouse_add=1, mouse_pop=3, mouse_stop=2):
"""
Call signature::
ginput(self, n=1, timeout=30, show_clicks=True,
mouse_add=1, mouse_pop=3, mouse_stop=2)
Blocking call to interact with the figure.
This will wait for *n* clicks from the user and return a list of the
coordinates of each click.
If *timeout* is zero or negative, does not timeout.
If *n* is zero or negative, accumulate clicks until a middle click
(or potentially both mouse buttons at once) terminates the input.
Right clicking cancels last input.
The buttons used for the various actions (adding points, removing
points, terminating the inputs) can be overriden via the
arguments *mouse_add*, *mouse_pop* and *mouse_stop*, that give
the associated mouse button: 1 for left, 2 for middle, 3 for
right.
The keyboard can also be used to select points in case your mouse
does not have one or more of the buttons. The delete and backspace
keys act like right clicking (i.e., remove last point), the enter key
terminates input and any other key (not already used by the window
manager) selects a point.
"""
blocking_mouse_input = BlockingMouseInput(self, mouse_add =mouse_add,
mouse_pop =mouse_pop,
mouse_stop=mouse_stop)
return blocking_mouse_input(n=n, timeout=timeout,
show_clicks=show_clicks)
def waitforbuttonpress(self, timeout=-1):
"""
Call signature::
waitforbuttonpress(self, timeout=-1)
Blocking call to interact with the figure.
This will return True is a key was pressed, False if a mouse
button was pressed and None if *timeout* was reached without
either being pressed.
If *timeout* is negative, does not timeout.
"""
blocking_input = BlockingKeyMouseInput(self)
return blocking_input(timeout=timeout)
def get_default_bbox_extra_artists(self):
bbox_extra_artists = [t for t in self.texts if t.get_visible()]
for ax in self.axes:
if ax.get_visible():
bbox_extra_artists.extend(ax.get_default_bbox_extra_artists())
return bbox_extra_artists
def get_tightbbox(self, renderer):
"""
Return a (tight) bounding box of the figure in inches.
It only accounts axes title, axis labels, and axis
ticklabels. Needs improvement.
"""
bb = []
for ax in self.axes:
if ax.get_visible():
bb.append(ax.get_tightbbox(renderer))
_bbox = Bbox.union([b for b in bb if b.width!=0 or b.height!=0])
bbox_inches = TransformedBbox(_bbox,
Affine2D().scale(1./self.dpi))
return bbox_inches
def tight_layout(self, renderer=None, pad=1.08, h_pad=None, w_pad=None, rect=None):
"""
Adjust subplot parameters to give specified padding.
Parameters:
*pad* : float
padding between the figure edge and the edges of subplots,
as a fraction of the font-size.
*h_pad*, *w_pad* : float
padding (height/width) between edges of adjacent subplots.
Defaults to `pad_inches`.
*rect* : if rect is given, it is interpreted as a rectangle
(left, bottom, right, top) in the normalized figure
coordinate that the whole subplots area (including
labels) will fit into. Default is (0, 0, 1, 1).
"""
from tight_layout import get_renderer, get_tight_layout_figure
no_go = [ax for ax in self.axes if not isinstance(ax, SubplotBase)]
if no_go:
warnings.Warn("Cannot use tight_layout;"
" all Axes must descend from SubplotBase")
return
if renderer is None:
renderer = get_renderer(self)
kwargs = get_tight_layout_figure(self, self.axes, renderer,
pad=pad, h_pad=h_pad, w_pad=w_pad,
rect=rect)
self.subplots_adjust(**kwargs)