def cursor_info(image: AxesImage,
xdata: float,
ydata: float,
full_bbox: Bbox = None) -> Optional[CursorInfo]:
"""Return information on the image for the given position in
data coordinates.
:param image: An instance of an image type
:param xdata: X data coordinate of cursor
:param xdata: Y data coordinate of cursor
:param full_bbox: Bbox of full workspace dimension to use for transforming mouse position
:return: None if point is not valid on the image else return CursorInfo type
"""
extent = image.get_extent()
xmin, xmax, ymin, ymax = extent
arr = image.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
if full_bbox is None:
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
else:
# If the view is zoomed in and the slice is changed, then the image extents
# and data extents change. This causes the cursor to be transformed to the
# wrong point for certain MDH workspaces (since it cannot be dynamically rebinned).
# This will use the full WS data dimensions to do the transformation
trans = BboxTransform(boxin=full_bbox, boxout=array_extent)
point = trans.transform_point([ydata, xdata])
if any(np.isnan(point)):
return None
point = point.astype(int)
if 0 <= point[0] < arr.shape[0] and 0 <= point[1] < arr.shape[1]:
return CursorInfo(array=arr, extent=extent, point=point)
else:
return None
def test_transform_array():
trans = BboxTransform(SMALL, LARGE)
result = trans.transform(np.array([(SMALL.x0, SMALL.y0),
(SMALL.x1, SMALL.y1)]))
expected = [(LARGE.x0, LARGE.y0),
(LARGE.x1, LARGE.y1)]
assert_allclose(result, expected)
def test_transform_list_of_points():
trans = BboxTransform(SMALL, LARGE)
result = trans.transform([(SMALL.x0, SMALL.y0),
(SMALL.x1, SMALL.y1)])
expected = [(LARGE.x0, LARGE.y0),
(LARGE.x1, LARGE.y1)]
assert_allclose(result, expected)
def update_line_plots(self, x, y):
xmin, xmax, ymin, ymax = self.im.get_extent()
arr = self.im.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
point = trans.transform_point([y, x])
if any(np.isnan(point)):
return
i, j = point.astype(int)
if 0 <= i < arr.shape[0]:
self.plot_x_line(np.linspace(xmin, xmax, arr.shape[1]), arr[i, :])
if 0 <= j < arr.shape[1]:
self.plot_y_line(np.linspace(ymin, ymax, arr.shape[0]), arr[:, j])
def convert_point(self,point_px, stop=False):
'''
given a touch point in the view space, compute the corresponding point in data coords. assumes linear scaling!
TODO: support log scaling
there are basically two bbox transforms: 1) from figure coords to view coords, accounting for sign change in y. this then lets us compute axes box in view coords, and generate 2) transform from view to data coords.
'''
transFig=BboxTransformTo(Bbox([(0,self.height),(self.width,0)]))
bbox_axes=Bbox(transFig.transform(plt.gca().get_position()))
bbox_data=Bbox([(self.xlim[0],self.ylim[0]),(self.xlim[1],self.ylim[1])])
transMPL=BboxTransform(bbox_axes,bbox_data)
self.trans=transMPL
ax_pt=transMPL.transform_point(point_px)
return ax_pt
def get_courser_index(img, event):
xmin, xmax, ymin, ymax = img.get_extent()
if img.origin == 'upper':
ymin, ymax = ymax, ymin
arr = img.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
y, x = event.ydata, event.xdata
point = trans.transform_point([y, x])
if any(np.isnan(point)):
return None
row, col = point.astype(int)
# Clip the coordinates at array bounds
return row, col
def convert_point(self, point_px, stop=False):
'''
given a touch point in the view space, compute the corresponding point in data coords. assumes linear scaling!
TODO: support log scaling
there are basically two bbox transforms: 1) from figure coords to view coords, accounting for sign change in y. this then lets us compute axes box in view coords, and generate 2) transform from view to data coords.
'''
transFig = BboxTransformTo(Bbox([(0, self.height), (self.width, 0)]))
bbox_axes = Bbox(transFig.transform(plt.gca().get_position()))
bbox_data = Bbox([(self.xlim[0], self.ylim[0]),
(self.xlim[1], self.ylim[1])])
transMPL = BboxTransform(bbox_axes, bbox_data)
self.trans = transMPL
ax_pt = transMPL.transform_point(point_px)
return ax_pt
def get_cursor_data(self, event):
"""Get the cursor data for a given event"""
xmin, xmax, ymin, ymax = self.get_extent()
if self.origin == 'upper':
ymin, ymax = ymax, ymin
arr = self.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
y, x = event.ydata, event.xdata
i, j = trans.transform_point([y, x]).astype(int)
# Clip the coordinates at array bounds
if not (0 <= i < arr.shape[0]) or not (0 <= j < arr.shape[1]):
return None
else:
return arr[i, j]
def get_cursor_data(self, event):
"""Get the cursor data for a given event"""
xmin, xmax, ymin, ymax = self.get_extent()
if self.origin == "upper":
ymin, ymax = ymax, ymin
arr = self.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
y, x = event.ydata, event.xdata
i, j = trans.transform_point([y, x]).astype(int)
# Clip the coordinates at array bounds
if not (0 <= i < arr.shape[0]) or not (0 <= j < arr.shape[1]):
return None
else:
return arr[i, j]
def make_image(self, renderer, magnification=1.0, unsampled=False):
width, height = renderer.get_canvas_width_height()
bbox_in = self.get_window_extent(renderer).frozen()
bbox_in._points /= [width, height]
bbox_out = self.get_window_extent(renderer)
clip = Bbox([[0, 0], [width, height]])
self._transform = BboxTransform(Bbox([[0, 0], [1, 1]]), clip)
return self._make_image(
self._A,
bbox_in, bbox_out, clip, magnification, unsampled=unsampled)
def get_cursor_data(self, event):
"""
Return the aggregated data at the event position or *None* if the
event is outside the bounds of the current view.
"""
xmin, xmax, ymin, ymax = self.get_extent()
if self.origin == "upper":
ymin, ymax = ymax, ymin
arr = self.get_ds_data().data
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
y, x = event.ydata, event.xdata
i, j = trans.transform_point([y, x]).astype(int)
# Clip the coordinates at array bounds
if not (0 <= i < arr.shape[0]) or not (0 <= j < arr.shape[1]):
return None
else:
return arr[i, j]
def cursor_info(image: AxesImage, xdata: float, ydata: float) -> Optional[CursorInfo]:
"""Return information on the image for the given position in
data coordinates.
:param image: An instance of an image type
:param xdata: X data coordinate of cursor
:param xdata: Y data coordinate of cursor
:return: None if point is not valid on the image else return CursorInfo type
"""
extent = image.get_extent()
xmin, xmax, ymin, ymax = extent
arr = image.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
point = trans.transform_point([ydata, xdata])
if any(np.isnan(point)):
return None
point = point.astype(int)
if 0 <= point[0] < arr.shape[0] and 0 <= point[1] < arr.shape[1]:
return CursorInfo(array=arr, extent=extent, point=point)
else:
return None
def get_cursor_data(self, event):
"""Get the cursor data for a given event"""
from matplotlib.transforms import Bbox, BboxTransform
aximg = self.image
xmin, xmax, ymin, ymax = aximg.get_extent()
if aximg.origin == 'upper':
ymin, ymax = ymax, ymin
arr = aximg.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
y, x = event.ydata, event.xdata
point = trans.transform_point([y, x])
if any(isnan(point)):
return None
i, j = point.astype(int)
# Clip the coordinates at array bounds
if not (0 <= i < arr.shape[0]) or not (0 <= j < arr.shape[1]):
return None
else:
return i, j, arr[i, j]
def get_cursor_data(self, event):
"""Get the cursor data for a given event"""
from matplotlib.transforms import Bbox, BboxTransform
aximg = self.image
xmin, xmax, ymin, ymax = aximg.get_extent()
if aximg.origin == 'upper':
ymin, ymax = ymax, ymin
arr = aximg.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
y, x = event.ydata, event.xdata
point = trans.transform_point([y, x])
if any(isnan(point)):
return None
i, j = point.astype(int)
# Clip the coordinates at array bounds
if not (0 <= i < arr.shape[0]) or not (0 <= j < arr.shape[1]):
return None
else:
return i, j, arr[i, j]
def _transform_from_image(self):
from matplotlib.transforms import Bbox, BboxTransform
ref_im = np.array([(15.152, 57.079), (15.152, 65.091),
(12.949, 65.091), (12.949, 62.575), (5.613, 62.575),
(5.613, 60.587), (12.949, 60.587),
(12.949, 57.079)])
ref = Vessel().digitizer.xy[2]
bbox_im, bbox = Bbox.unit(), Bbox.unit()
bbox_im.update_from_data_xy(ref_im)
bbox.update_from_data_xy(ref)
trans = BboxTransform(bbox_im, bbox)
return trans
def update_image_data(self, x, y, update_line_plot=False):
xmin, xmax, ymin, ymax = self.image.get_extent()
arr = self.image.get_array()
data_extent = Bbox([[ymin, xmin], [ymax, xmax]])
array_extent = Bbox([[0, 0], arr.shape[:2]])
trans = BboxTransform(boxin=data_extent, boxout=array_extent)
point = trans.transform_point([y, x])
if any(np.isnan(point)):
return
i, j = point.astype(int)
if update_line_plot:
if 0 <= i < arr.shape[0]:
self.plot_x_line(np.linspace(xmin, xmax, arr.shape[1]),
arr[i, :])
if 0 <= j < arr.shape[1]:
self.plot_y_line(np.linspace(ymin, ymax, arr.shape[0]), arr[:,
j])
# Clip the coordinates at array bounds
if not (0 <= i < arr.shape[0]) or not (0 <= j < arr.shape[1]):
return None
else:
return arr[i, j]
def main():
blue = '#4b92db'
# We're drawing a flag with a 3:5 aspect ratio.
fig = plt.figure(figsize=[7.5, 4.5], facecolor=blue)
# Put a blue background on the figure.
blue_background = PathPatch(matplotlib.path.Path.unit_rectangle(),
transform=fig.transFigure,
color=blue,
zorder=-1)
fig.patches.append(blue_background)
# Set up the Azimuthal Equidistant and Plate Carree projections
# for later use.
az_eq = ccrs.AzimuthalEquidistant(central_latitude=90)
pc = ccrs.PlateCarree()
# Pick a suitable location for the map (which is in an Azimuthal
# Equidistant projection).
ax = fig.add_axes([0.25, 0.24, 0.5, 0.54], projection=az_eq)
# The background patch is not needed in this example.
ax.background_patch.set_facecolor('none')
# The Axes frame produces the outer meridian line.
for spine in ax.spines.values():
spine.update({'edgecolor': 'white', 'linewidth': 2})
# We want the map to go down to -60 degrees latitude.
ax.set_extent([-180, 180, -60, 90], ccrs.PlateCarree())
# Importantly, we want the axes to be circular at the -60 latitude
# rather than cartopy's default behaviour of zooming in and becoming
# square.
_, patch_radius = az_eq.transform_point(0, -60, pc)
circular_path = matplotlib.path.Path.circle(0, patch_radius)
ax.set_boundary(circular_path)
if filled_land:
ax.add_feature(cfeature.LAND, facecolor='white', edgecolor='none')
else:
ax.stock_img()
gl = ax.gridlines(crs=pc, linewidth=2, color='white', linestyle='-')
# Meridians every 45 degrees, and 4 parallels.
gl.xlocator = matplotlib.ticker.FixedLocator(np.arange(-180, 181, 45))
parallels = np.arange(-30, 70, 30)
gl.ylocator = matplotlib.ticker.FixedLocator(parallels)
# Now add the olive branches around the axes. We do this in normalised
# figure coordinates
olive_leaf = olive_path()
olives_bbox = Bbox.null()
olives_bbox.update_from_path(olive_leaf)
# The first olive branch goes from left to right.
olive1_axes_bbox = Bbox([[0.45, 0.15], [0.725, 0.75]])
olive1_trans = BboxTransform(olives_bbox, olive1_axes_bbox)
# THe second olive branch goes from right to left (mirroring the first).
olive2_axes_bbox = Bbox([[0.55, 0.15], [0.275, 0.75]])
olive2_trans = BboxTransform(olives_bbox, olive2_axes_bbox)
olive1 = PathPatch(olive_leaf,
facecolor='white',
edgecolor='none',
transform=olive1_trans + fig.transFigure)
olive2 = PathPatch(olive_leaf,
facecolor='white',
edgecolor='none',
transform=olive2_trans + fig.transFigure)
fig.patches.append(olive1)
fig.patches.append(olive2)
plt.show()
ax = subplot(212)
xlim(0, 10)
xticks(arange(10))
boxin = Bbox.from_extents(ax.viewLim.x0, -20, ax.viewLim.x1, 20)
height = ax.bbox.height
boxout = Bbox.from_extents(ax.bbox.x0, -1.0 * height, ax.bbox.x1,
1.0 * height)
transOffset = BboxTransformTo(
Bbox.from_extents(0.0, ax.bbox.y0, 1.0, ax.bbox.y1))
for i in range(numRows):
# effectively a copy of transData
trans = BboxTransform(boxin, boxout)
offset = (i + 1) / (numRows + 1)
trans += Affine2D().translate(*transOffset.transform_point((0,
offset)))
thisLine = Line2D(
t,
data[:, i] - data[0, i],
)
thisLine.set_transform(trans)
ax.add_line(thisLine)
ticklocs.append(offset)