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492 | class ImageManager:
"""
Manages the placement and storage of image overlays within the energy diagram.
Supports two modes: standalone images placed at an explicit data-coordinate
position, and image series where one image is placed per x-position with
automatic collision avoidance against energy bars, number annotations, and
x-axis labels. Series images are redrawn whenever number annotations change.
All rendered artists are stored in ``mpl_objects`` for later access.
"""
def __init__(
self,
figure_manager: FigureManager,
) -> None:
self.figure_manager = figure_manager
self.image_series_data: dict = {}
self.has_image_series = False
self.solo_image_data: dict = {}
self.mpl_objects: dict = {}
def add_image_in_plot(
self,
img_path: str,
position: tuple[float, float] | list[float],
margins: dict[str, tuple],
figsize: tuple[float, float],
img_name: str | None = None,
width: float | None = None,
height: float | None = None,
framed: bool = False,
frame_color: str = "black",
horizontal_alignment: str = "center",
vertical_alignment: str = "center",
) -> None:
# Sanity checks
Validators.validate_numeric_sequence(position, "position", required_length=2)
if img_name is not None:
if not isinstance(img_name, str):
raise TypeError("img_series_name must be a string or None.")
Validators.validate_number(width, "width", allow_none=True, min_value=0)
Validators.validate_number(height, "height", allow_none=True, min_value=0)
if not isinstance(framed, bool):
raise TypeError("framed must be a bool.")
# Construct the image
img_object = self._construct_image(
img_path=img_path,
position=position,
margins=margins,
figsize=figsize,
vertical_alignment=vertical_alignment,
horizontal_alignment=horizontal_alignment,
width=width,
height=height,
framed=framed,
frame_color=frame_color,
)
# Save mpl objects
if img_name is None:
img_name = f"__Image_{len(self.mpl_objects)}"
self.mpl_objects[img_name] = img_object
# Save underlying data
self.solo_image_data[img_name] = {
"img_name": img_name,
"img_path": img_path,
"position": position,
"width": width,
"height": height,
"framed": framed,
"frame_color": frame_color,
"horizontal_alignment": horizontal_alignment,
"vertical_alignment": vertical_alignment,
}
def add_image_series_in_plot(
self,
img_paths: Sequence[str],
margins: dict[str, tuple],
figsize: tuple[float, float],
path_data: dict,
number_mpl_objects: dict,
xlabel_mpl_objects: dict,
path_mpl_objects: dict,
img_x_places: Sequence[float] | None = None,
y_placement: Sequence[str] | str = "auto",
y_offsets: Sequence[float] | float = 0,
img_series_name: str | None = None,
width: Sequence[float | None] | float | None = None,
height: Sequence[float | None] | float | None = None,
framed: Sequence[bool] | bool = False,
frame_colors: Sequence[str] | str = "black",
) -> None:
# Sanity checks
Validators.validate_string_sequence(img_paths, "img_paths")
Validators.validate_numeric_sequence(img_x_places, "img_places", allow_none=True)
# Sanity checks y_placement
ALLOWED_Y_PLACEMENT = ["top", "bottom", "auto"]
if isinstance(y_placement, (list, tuple)):
Validators.validate_string_sequence(y_placement, "y_placement")
if len(img_paths) != len(y_placement):
raise ValueError(
"There must be the same number of images and elements in y_placement."
)
for value in y_placement:
if value not in ALLOWED_Y_PLACEMENT:
raise ValueError(
f"All values of y_placement must be oneof {ALLOWED_Y_PLACEMENT}."
)
else:
if y_placement not in ALLOWED_Y_PLACEMENT:
raise ValueError(f"y_placement must be one of {ALLOWED_Y_PLACEMENT}.")
y_placement = [str(y_placement)] * len(img_paths)
# Sanity checks y_offsets
if isinstance(y_offsets, Sequence):
Validators.validate_numeric_sequence(y_offsets, "y_offsets")
if len(img_paths) != len(y_offsets):
raise ValueError(
"There must be the same number of images and elements in y_offsets."
)
else:
if not isinstance(y_offsets, (int, float)):
raise ValueError("y_offsets must be a float.")
y_offsets = [y_offsets] * len(img_paths)
# Sanity checks img_series_name
if img_series_name is not None:
if not isinstance(img_series_name, str):
raise TypeError("img_series_name must be a string or None.")
# Sanity checks img_x_places
if img_x_places is not None:
if len(img_paths) != len(img_x_places):
raise ValueError("There must be the same number of images and img_x_places.")
else:
img_x_places = list(range(len(img_paths)))
# Sanity checks height
if isinstance(height, Sequence):
Validators.validate_numeric_sequence(
height,
"height",
allow_none_elements=True,
)
if len(img_paths) != len(height):
raise ValueError("height must have the same length as img_paths.")
elif isinstance(height, (int, float)):
height = [height] * len(img_paths)
elif height is None:
height = [None] * len(img_paths)
else:
raise TypeError("height must be a Sequence, numeric value or None.")
# Sanity checks width
if isinstance(width, Sequence):
Validators.validate_numeric_sequence(
width,
"width",
allow_none_elements=True,
)
if len(img_paths) != len(width):
raise ValueError("width must have the same length as img_paths.")
elif isinstance(width, (int, float)):
width = [width] * len(img_paths)
elif width is None:
# Only set width to default if no height value for same image
width = [constants.IMAGE_WIDTH if value is None else None for value in height]
else:
raise TypeError("width must be a Sequence, numeric value or None.")
# Sanity checks framed
if isinstance(framed, (list, tuple)):
if any([not isinstance(entry, bool) for entry in framed]):
raise TypeError("Elements in framed must be a bool.")
if len(framed) != len(height):
raise ValueError("framed must have the same length as img_paths.")
elif isinstance(framed, bool):
framed = [framed] * len(img_paths)
else:
raise TypeError("framed must be a Sequence of bools, or a bool.")
# Sanity checks frame colors
if isinstance(frame_colors, (list, tuple)):
if len(img_paths) != len(frame_colors):
raise ValueError("frame_colors must have the same length as img_paths.")
elif isinstance(frame_colors, str):
frame_colors = [frame_colors] * len(img_paths)
else:
raise TypeError("frame_colors must be a Sequence, or a string")
self.has_image_series = True
# Print the image for each x
series_mpl_objects = {}
for index in range(len(img_paths)):
x = img_x_places[index]
# Avoid collision with plateaus
diff_to_plateau = DifferenceManager._get_diff_img_plateau(margins, figsize)
all_values_at_x = NumberManager._get_all_values_at_x(path_data, x)
if all_values_at_x:
y_min_top = max(all_values_at_x) + diff_to_plateau
y_max_bottom = min(all_values_at_x) - diff_to_plateau
else:
print(f"Warning: No plateaus at x = {x}. Placing image at y = 0.")
y_min_top = 0
y_max_bottom = 0
# Avoid collision with numbers
for _, numbers in number_mpl_objects.items():
try:
number_fontsize = numbers[f"{x:.1f}"].get_fontsize()
number_y = numbers[f"{x:.1f}"].get_position()[1]
diff_to_number = DifferenceManager._get_diff_img_number(
margins, figsize, number_fontsize
)
if number_y + diff_to_number > y_min_top:
y_min_top = number_y + diff_to_number
if number_y - diff_to_number < y_max_bottom:
y_max_bottom = number_y - diff_to_number
except KeyError:
pass
# Avoid collision with x-labels
try:
label_fontsize = xlabel_mpl_objects[f"{x:.1f}"].get_fontsize()
label_y = xlabel_mpl_objects[f"{x:.1f}"].get_position()[1]
labeltext = xlabel_mpl_objects[f"{x:.1f}"].get_text()
diff_to_label = DifferenceManager._get_diff_img_label(
margins, figsize, label_fontsize, labeltext
)
if label_y + diff_to_label > y_min_top:
y_min_top = label_y + diff_to_label
if label_y - diff_to_label < y_max_bottom:
y_max_bottom = label_y - diff_to_label
except KeyError:
pass
# Avoid collision with path labels
for _, paths_obj in path_mpl_objects.items():
try:
label_fontsize = paths_obj.labels[f"{x:.1f}"].get_fontsize()
label_y = paths_obj.labels[f"{x:.1f}"].get_position()[1]
labeltext = paths_obj.labels[f"{x:.1f}"].get_text()
diff_to_label = DifferenceManager._get_diff_img_label(
margins, figsize, label_fontsize, labeltext
)
if label_y + diff_to_label > y_min_top:
y_min_top = label_y + diff_to_label
if label_y - diff_to_label < y_max_bottom:
y_max_bottom = label_y - diff_to_label
except KeyError:
pass
# Determine current vertival alignment and position
if y_placement[index] == "auto":
space_on_top = margins["y"][1] - y_min_top
space_on_bottom = y_max_bottom - margins["y"][0]
if space_on_top > space_on_bottom:
vertical_alignment_current = "bottom"
position_current = (x, y_min_top + y_offsets[index])
else:
vertical_alignment_current = "top"
position_current = (x, y_max_bottom - y_offsets[index])
elif y_placement[index] == "top":
vertical_alignment_current = "bottom"
position_current = (x, y_min_top + y_offsets[index])
elif y_placement[index] == "bottom":
vertical_alignment_current = "top"
position_current = (x, y_max_bottom - y_offsets[index])
# Construct the image
img_object = self._construct_image(
img_path=img_paths[index],
position=position_current,
margins=margins,
figsize=figsize,
vertical_alignment=vertical_alignment_current,
width=width[index],
height=height[index],
framed=framed[index],
frame_color=frame_colors[index],
)
series_mpl_objects[f"{x:.1f}"] = img_object
# Save mpl objects
if img_series_name is None:
img_series_name = f"__Series_{len(self.mpl_objects)}"
self.mpl_objects[img_series_name] = series_mpl_objects
# Save underlying data if redrawing is neccesary
self.image_series_data[img_series_name] = {
"img_series_name": img_series_name,
"img_paths": img_paths,
"img_x_places": img_x_places,
"y_placement": y_placement,
"y_offsets": y_offsets,
"width": width,
"height": height,
"framed": framed,
"frame_colors": frame_colors,
}
def recalculate_image_series(
self,
margins: dict[str, tuple],
figsize: tuple[float, float],
path_data: dict,
number_mpl_objects: dict,
xlabel_mpl_objects: dict,
path_mpl_objects: dict,
) -> None:
# Series images are removed and redrawn; standalone images are permanent
self._remove_image_series()
for _, image_series in self.image_series_data.items():
self.add_image_series_in_plot(
margins=margins,
figsize=figsize,
path_data=path_data,
number_mpl_objects=number_mpl_objects,
xlabel_mpl_objects=xlabel_mpl_objects,
path_mpl_objects=path_mpl_objects,
**image_series,
)
############################################################
# Internal helper methods
############################################################
def _remove_image_series(self) -> None:
for _, series in self.mpl_objects.items():
if isinstance(series, dict):
for __, image in series.items():
image.remove()
def _construct_image(
self,
img_path: str,
position: tuple[float, float] | list[float],
margins: dict[str, tuple],
figsize: tuple[float, float],
vertical_alignment: str = "bottom",
horizontal_alignment: str = "center",
width: float | None = None,
height: float | None = None,
framed: bool = False,
frame_color: str = "black",
) -> ImageObject:
def draw_frame_part(x_coords, y_coords):
return self.figure_manager.ax.plot(
x_coords,
y_coords,
zorder=constants.ZORDER_IMAGE_FRAME,
ls="-",
lw=constants.LW_IMAGE_FRAME,
color=frame_color,
)[0]
# Sanity checks
Validators.validate_numeric_sequence(position, "position", required_length=2)
Validators.validate_number(width, "width", allow_none=True, min_value=0)
Validators.validate_number(height, "height", allow_none=True, min_value=0)
ALLOWED_VA_VALUES = ["top", "bottom", "center"]
if vertical_alignment not in ALLOWED_VA_VALUES:
raise ValueError(f"vertical_alignment must be in {ALLOWED_VA_VALUES}.")
ALLOWED_HA_VALUES = ["left", "center", "right"]
if horizontal_alignment not in ALLOWED_HA_VALUES:
raise ValueError(f"horizontal alignment must be in {ALLOWED_HA_VALUES}")
# Read and scale image
img_file = mpimg.imread(img_path)
positimg_height_px = img_file.shape[0]
img_width_px = img_file.shape[1]
if width is None and height is None:
width = constants.IMAGE_WIDTH
height = (
width
* positimg_height_px
/ img_width_px
* (margins["y"][1] - margins["y"][0])
/ (margins["x"][1] - margins["x"][0])
* figsize[0]
/ figsize[1]
)
elif width is None:
width = (
height
* img_width_px
/ positimg_height_px
/ (margins["y"][1] - margins["y"][0])
* (margins["x"][1] - margins["x"][0])
/ figsize[0]
* figsize[1]
)
elif height is None:
height = (
width
* positimg_height_px
/ img_width_px
* (margins["y"][1] - margins["y"][0])
/ (margins["x"][1] - margins["x"][0])
* figsize[0]
/ figsize[1]
)
assert width is not None
assert height is not None
if horizontal_alignment == "center":
img_x_extent = (
position[0] - width / 2,
position[0] + width / 2,
)
elif horizontal_alignment == "left":
img_x_extent = (
position[0],
position[0] + width,
)
elif horizontal_alignment == "right":
img_x_extent = (
position[0] - width,
position[0],
)
if vertical_alignment == "bottom":
img_y_extent = (
position[1],
position[1] + height,
)
elif vertical_alignment == "top":
img_y_extent = (
position[1] - height,
position[1],
)
elif vertical_alignment == "center":
img_y_extent = (
position[1] - height / 2,
position[1] + height / 2,
)
img_extent = img_x_extent + img_y_extent
# Draw image
img_artist = self.figure_manager.ax.imshow(
img_file,
extent=img_extent,
interpolation="bilinear", # nearest/bilinear/bicubic (nearest ugly)
aspect="auto",
)
# Draw borders
border_objects = {}
if framed:
border_objects["top"] = draw_frame_part(
(img_extent[0], img_extent[1]), (img_extent[3], img_extent[3])
)
border_objects["bottom"] = draw_frame_part(
(img_extent[0], img_extent[1]), (img_extent[2], img_extent[2])
)
border_objects["left"] = draw_frame_part(
(img_extent[0], img_extent[0]), (img_extent[2], img_extent[3])
)
border_objects["right"] = draw_frame_part(
(img_extent[1], img_extent[1]), (img_extent[2], img_extent[3])
)
return ImageObject(img_artist, border_objects)
|