# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
DataLab Datasets
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
from __future__ import annotations
import abc
import enum
import json
import sys
from collections.abc import Callable, Iterable
from copy import deepcopy
from typing import TYPE_CHECKING, Any, Literal
import guidata.dataset as gds
import numpy as np
import pandas as pd
from guidata.configtools import get_font
from guidata.dataset import update_dataset
from guidata.io import JSONReader, JSONWriter
from plotpy.builder import make
from plotpy.io import load_items, save_items
from plotpy.items import (
AbstractLabelItem,
AnnotatedPoint,
AnnotatedSegment,
AnnotatedShape,
LabelItem,
PolygonShape,
)
from cdl.algorithms import coordinates
from cdl.algorithms.datatypes import is_integer_dtype
from cdl.config import PLOTPY_CONF, Conf, _
if TYPE_CHECKING:
from plotpy.items import (
AbstractShape,
AnnotatedCircle,
AnnotatedEllipse,
AnnotatedRectangle,
CurveItem,
Marker,
MaskedImageItem,
)
from plotpy.styles import AnnotationParam, ShapeParam
ROI_KEY = "_roi_"
ANN_KEY = "_ann_"
def deepcopy_metadata(metadata: dict[str, Any]) -> dict[str, Any]:
"""Deepcopy metadata, except keys starting with "_" (private keys)
with the exception of "_roi_" and "_ann_" keys."""
mdcopy = deepcopy(metadata)
for key, value in metadata.items():
rshape = ResultShape.from_metadata_entry(key, value)
if rshape is None and key.startswith("_") and key not in (ROI_KEY, ANN_KEY):
mdcopy.pop(key)
return mdcopy
@enum.unique
class Choices(enum.Enum):
"""Object associating an enum to guidata.dataset.ChoiceItem choices"""
# Reimplement enum.Enum method as suggested by Python documentation:
# https://docs.python.org/3/library/enum.html#enum.Enum._generate_next_value_
# Here, it is only needed for ImageDatatypes (see core/model/image.py).
# pylint: disable=unused-argument,no-self-argument,no-member
def _generate_next_value_(name, start, count, last_values):
return name.lower()
@classmethod
def get_choices(cls):
"""Return tuple of (key, value) choices to be used as parameter of
guidata.dataset.ChoiceItem"""
return tuple((member, member.value) for member in cls)
[docs]
class BaseProcParam(gds.DataSet):
"""Base class for processing parameters"""
def __init__(self, title=None, comment=None, icon=""):
super().__init__(title, comment, icon)
self.set_global_prop("data", min=None, max=None)
[docs]
def apply_integer_range(self, vmin, vmax): # pylint: disable=unused-argument
"""Do something in case of integer min-max range"""
[docs]
def apply_float_range(self, vmin, vmax): # pylint: disable=unused-argument
"""Do something in case of float min-max range"""
[docs]
def set_from_datatype(self, dtype):
"""Set min/max range from NumPy datatype"""
if is_integer_dtype(dtype):
info = np.iinfo(dtype)
self.apply_integer_range(info.min, info.max)
else:
info = np.finfo(dtype)
self.apply_float_range(info.min, info.max)
self.set_global_prop("data", min=info.min, max=info.max)
class BaseRandomParam(BaseProcParam):
"""Random signal/image parameters"""
seed = gds.IntItem(_("Seed"), default=1)
[docs]
class NormalRandomParam(BaseRandomParam):
"""Normal-law random signal/image parameters"""
DEFAULT_RELATIVE_MU = 0.1
DEFAULT_RELATIVE_SIGMA = 0.02
[docs]
def apply_integer_range(self, vmin, vmax):
"""Do something in case of integer min-max range"""
delta = vmax - vmin
self.mu = int(self.DEFAULT_RELATIVE_MU * delta + vmin)
self.sigma = int(self.DEFAULT_RELATIVE_SIGMA * delta)
mu = gds.FloatItem(
"μ", default=DEFAULT_RELATIVE_MU, help=_("Normal distribution mean")
)
sigma = gds.FloatItem(
"σ",
default=DEFAULT_RELATIVE_SIGMA,
help=_("Normal distribution standard deviation"),
).set_pos(col=1)
[docs]
@enum.unique
class ShapeTypes(enum.Enum):
"""Shape types for image metadata"""
# Reimplement enum.Enum method as suggested by Python documentation:
# https://docs.python.org/3/library/enum.html#enum.Enum._generate_next_value_
# pylint: disable=unused-argument,no-self-argument,no-member
def _generate_next_value_(name, start, count, last_values):
return f"_{name.lower()[:3]}_"
#: Rectangle shape
RECTANGLE = enum.auto()
#: Circle shape
CIRCLE = enum.auto()
#: Ellipse shape
ELLIPSE = enum.auto()
#: Segment shape
SEGMENT = enum.auto()
#: Marker shape
MARKER = enum.auto()
#: Point shape
POINT = enum.auto()
#: Polygon shape
POLYGON = enum.auto()
def config_annotated_shape(
item: AnnotatedShape,
fmt: str,
lbl: bool,
section: str,
option: str,
cmp: bool | None = None,
):
"""Configurate annotated shape.
Args:
item: Annotated shape item
fmt: Format string
lbl: Show label
section: Shape style section (e.g. "plot")
option: Shape style option (e.g. "shape/drag")
cmp: Show computations
"""
param: AnnotationParam = item.annotationparam
param.format = fmt
param.show_label = lbl
if cmp is not None:
param.show_computations = cmp
if isinstance(item, AnnotatedSegment):
item.label.labelparam.anchor = "T"
item.label.labelparam.update_item(item.label)
param.update_item(item)
item.set_style(section, option)
# TODO: [P3] Move this function as a method of plot items in PlotPy
def set_plot_item_editable(
item: AbstractShape | AbstractLabelItem | AnnotatedShape, state
):
"""Set plot item editable state.
Args:
item: Plot item
state: State
"""
item.set_movable(state)
item.set_resizable(state and not isinstance(item, AbstractLabelItem))
item.set_rotatable(state and not isinstance(item, AbstractLabelItem))
item.set_readonly(not state)
item.set_selectable(state)
class BaseResult(abc.ABC):
"""Base class for results, i.e. objects returned by computation functions
used by :py:class`cdl.core.gui.processor.base.BaseProcessor.compute_10` method.
Args:
title: result title
category: result category
array: result array (one row per ROI, first column is ROI index)
labels: result labels (one label per column of result array)
"""
PREFIX = "" # To be overriden in children classes
METADATA_ATTRS = () # To be overriden in children classes
def __init__(
self,
title: str,
array: np.ndarray,
labels: list[str] | None = None,
) -> None:
assert isinstance(title, str)
self.title = title
self.array = array
self.xunit: str = ""
self.yunit: str = ""
self.__labels = labels
self.check_array()
@property
@abc.abstractmethod
def category(self) -> str:
"""Return result category"""
def check_array(self) -> None:
"""Check if array attribute is valid
Raises:
AssertionError: invalid array
"""
# Allow to pass a list of lists or a NumPy array.
# For instance, the following are equivalent:
# array = [[1, 2], [3, 4]]
# array = np.array([[1, 2], [3, 4]])
# Or, for only one line (one single result), the following are equivalent:
# array = [1, 2]
# array = [[1, 2]]
# array = np.array([[1, 2]])
if isinstance(self.array, (list, tuple)):
if isinstance(self.array[0], (list, tuple)):
self.array = np.array(self.array)
else:
self.array = np.array([self.array])
assert isinstance(self.array, np.ndarray)
assert len(self.array.shape) == 2
@property
def labels(self) -> list[str] | None:
"""Return result labels (one label per column of result array)"""
return self.__labels
@property
def headers(self) -> list[str] | None:
"""Return result headers (one header per column of result array)"""
# Default implementation: return labels
return self.__labels
def to_dataframe(self) -> pd.DataFrame:
"""Return DataFrame from properties array"""
return pd.DataFrame(self.shown_array, columns=list(self.headers))
@property
@abc.abstractmethod
def shown_array(self) -> np.ndarray:
"""Return array of shown results, i.e. including complementary array (if any)
Returns:
Array of shown results
"""
@property
def raw_data(self):
"""Return raw data (array without ROI informations)"""
return self.array[:, 1:]
@property
def key(self) -> str:
"""Return metadata key associated to result"""
return self.PREFIX + self.title
@classmethod
def from_metadata_entry(cls, key: str, value: dict[str, Any]) -> BaseResult | None:
"""Create metadata shape object from (key, value) metadata entry"""
if (
isinstance(key, str)
and key.startswith(cls.PREFIX)
and isinstance(value, dict)
):
try:
title = key[len(cls.PREFIX) :]
instance = cls(title, **value)
return instance
except (ValueError, TypeError):
pass
return None
@classmethod
def match(cls, key, value) -> bool:
"""Return True if metadata dict entry (key, value) is a metadata result"""
return cls.from_metadata_entry(key, value) is not None
def add_to(self, obj: BaseObj) -> None:
"""Add result to object metadata
Args:
obj: object (signal/image)
"""
self.set_obj_metadata(obj)
def set_obj_metadata(self, obj: BaseObj) -> None:
"""Set object metadata with properties
Args:
obj: object
"""
obj.metadata[self.key] = {
key: getattr(self, key) for key in self.METADATA_ATTRS
}
[docs]
class ResultProperties(BaseResult):
"""Object representing properties serializable in signal/image metadata.
Result `array` is a NumPy 2-D array: each row is a list of properties, optionnally
associated to a ROI (first column value).
ROI index is starting at 0 (or is simply 0 if there is no ROI).
Args:
title: properties title
array: properties array
labels: properties labels (one label per column of result array)
item_json: JSON string of label item associated to this obj
.. note::
The `array` argument can be a list of lists or a NumPy array. For instance,
the following are equivalent:
- ``array = [[1, 2], [3, 4]]``
- ``array = np.array([[1, 2], [3, 4]])``
Or for only one line (one single result), the following are equivalent:
- ``array = [1, 2]``
- ``array = [[1, 2]]``
- ``array = np.array([[1, 2]])``
"""
PREFIX = "_properties_"
METADATA_ATTRS = ("array", "labels", "item_json")
def __init__(
self,
title: str,
array: np.ndarray,
labels: list[str] | None,
item_json: str = "",
) -> None:
super().__init__(title, array, labels)
if labels is not None:
assert len(labels) == self.array.shape[1] - 1
self.item_json = item_json # JSON string of label item associated to this obj
@property
def category(self) -> str:
"""Return result category"""
return _("Properties") + f" | {self.title}"
@property
def headers(self) -> list[str] | None:
"""Return result headers (one header per column of result array)"""
# ResultProperties implementation: return labels without units or "=" sign
return [label.split("=")[0].strip() for label in self.labels]
@property
def shown_array(self) -> np.ndarray:
"""Return array of shown results, i.e. including complementary array (if any)
Returns:
Array of shown results
"""
return self.raw_data
@property
def label_contents(self) -> tuple[tuple[int, str], ...]:
"""Return label contents, i.e. a tuple of couples of (index, text)
where index is the column of raw_data and text is the associated
label format string"""
return tuple(enumerate(self.labels))
[docs]
def create_label_item(self, obj: BaseObj) -> LabelItem | None:
"""Create label item
Args:
obj: object (signal/image)
Returns:
Label item
.. note::
The signal or image object is required as argument to create the label
item because the label text may contain format strings that need to be
filled with the object properties. For instance, the label text may contain
the signal or image units.
"""
text = ""
for i_row in range(self.array.shape[0]):
suffix = f"|ROI{i_row}" if i_row > 0 else ""
text += f"<u>{self.title}{suffix}</u>:"
for i_col, label in self.label_contents:
# "label" may contains "<" and ">" characters which are interpreted
# as HTML tags by the LabelItem. We must escape them.
label = label.replace("<", "<").replace(">", ">")
if "%" not in label:
label += " = %g"
text += (
"<br>" + label.strip().format(obj) % self.shown_array[i_row, i_col]
)
if i_row < self.shown_array.shape[0] - 1:
text += "<br><br>"
item = make.label(text, "TL", (0, 0), "TL", title=self.title)
font = get_font(PLOTPY_CONF, "properties", "label/font")
item.set_style("properties", "label")
item.labelparam.font.update_param(font)
item.labelparam.update_item(item)
return item
[docs]
def get_label_item(self, obj: BaseObj) -> LabelItem | None:
"""Return label item associated to this result
Args:
obj: object (signal/image)
Returns:
Label item
.. note::
The signal or image object is required as argument to eventually create
the label item if it has not been created yet.
See :py:meth:`create_label_item`.
"""
if not self.item_json:
# Label item has not been created yet
item = self.create_label_item(obj)
if item is not None:
self.update_obj_metadata_from_item(obj, item)
if self.item_json:
item = json_to_items(self.item_json)[0]
assert isinstance(item, LabelItem)
return item
return None
[docs]
class ResultShape(ResultProperties):
"""Object representing a geometrical shape serializable in signal/image metadata.
Result `array` is a NumPy 2-D array: each row is a result, optionnally associated
to a ROI (first column value).
ROI index is starting at 0 (or is simply 0 if there is no ROI).
Args:
title: result shape title
array: shape coordinates (multiple shapes: one shape per row),
first column is ROI index (0 if there is no ROI)
shape: shape kind
item_json: JSON string of label item associated to this obj
add_label: if True, add a label item (and the geometrical shape) to plot
(default to False)
Raises:
AssertionError: invalid argument
.. note::
The `array` argument can be a list of lists or a NumPy array. For instance,
the following are equivalent:
- ``array = [[1, 2], [3, 4]]``
- ``array = np.array([[1, 2], [3, 4]])``
Or for only one line (one single result), the following are equivalent:
- ``array = [1, 2]``
- ``array = [[1, 2]]``
- ``array = np.array([[1, 2]])``
"""
PREFIX = "_shapes_"
METADATA_ATTRS = ("array", "shape", "item_json", "add_label")
def __init__(
self,
title: str,
array: np.ndarray,
shape: Literal[
"rectangle", "circle", "ellipse", "segment", "marker", "point", "polygon"
],
item_json: str = "",
add_label: bool = False,
) -> None:
self.shape = shape
try:
self.shapetype = ShapeTypes[shape.upper()]
except KeyError as exc:
raise ValueError(f"Invalid shapetype {shape}") from exc
self.add_label = add_label
super().__init__(title, array, labels=None, item_json=item_json)
@property
def category(self) -> str:
"""Return result category"""
return self.shape.upper()
[docs]
def check_array(self) -> None:
"""Check if array attribute is valid
Raises:
AssertionError: invalid array
"""
super().check_array()
if self.shapetype is ShapeTypes.POLYGON:
# Polygon is a special case: the number of data columns is variable
# (2 columns per point). So we only check if the number of columns
# is odd, which means that the first column is the ROI index, followed
# by an even number of data columns (flattened x, y coordinates).
assert self.array.shape[1] % 2 == 1
else:
data_colnb = len(self.__get_coords_labels())
# `data_colnb` is the number of data columns depends on the shape type,
# not counting the ROI index, hence the +1 in the following assertion
assert self.array.shape[1] == data_colnb + 1
def __get_coords_labels(self) -> tuple[str]:
"""Return shape coordinates labels
Returns:
Shape coordinates labels
"""
if self.shapetype is ShapeTypes.POLYGON:
labels = []
for i in range(0, self.array.shape[1] - 1, 2):
labels += [f"x{i//2}", f"y{i//2}"]
return tuple(labels)
try:
return {
ShapeTypes.MARKER: ("x", "y"),
ShapeTypes.POINT: ("x", "y"),
ShapeTypes.RECTANGLE: ("x0", "y0", "x1", "y1"),
ShapeTypes.CIRCLE: ("x", "y", "r"),
ShapeTypes.SEGMENT: ("x0", "y0", "x1", "y1"),
ShapeTypes.ELLIPSE: ("x", "y", "a", "b", "θ"),
}[self.shapetype]
except KeyError as exc:
raise NotImplementedError(
f"Unsupported shapetype {self.shapetype}"
) from exc
def __get_complementary_xlabels(self) -> tuple[str] | None:
"""Return complementary labels for result array columns
Returns:
Complementary labels for result array columns, or None if there is no
complementary labels
"""
if self.shapetype is ShapeTypes.SEGMENT:
return ("L", "Xc", "Yc")
if self.shapetype in (ShapeTypes.CIRCLE, ShapeTypes.ELLIPSE):
return ("A",)
return None
def __get_complementary_array(self) -> np.ndarray | None:
"""Return the complementary array of results, e.g. the array of lengths
for a segment result shape, or the array of areas for a circle result shape
Returns:
Complementary array of results, or None if there is no complementary array
"""
array = self.array
if self.shapetype is ShapeTypes.SEGMENT:
dx1, dy1 = array[:, 3] - array[:, 1], array[:, 4] - array[:, 2]
length = np.linalg.norm(np.vstack([dx1, dy1]).T, axis=1)
xc = (array[:, 1] + array[:, 3]) / 2
yc = (array[:, 2] + array[:, 4]) / 2
return np.vstack([length, xc, yc]).T
if self.shapetype is ShapeTypes.CIRCLE:
area = np.pi * array[:, 3] ** 2
return area.reshape(-1, 1)
if self.shapetype is ShapeTypes.ELLIPSE:
area = np.pi * array[:, 3] * array[:, 4]
return area.reshape(-1, 1)
return None
@property
def headers(self) -> list[str] | None:
"""Return result headers (one header per column of result array)"""
labels = self.__get_coords_labels() + (self.__get_complementary_xlabels() or ())
return labels[-self.shown_array.shape[1] :]
@property
def shown_array(self) -> np.ndarray:
"""Return array of shown results, i.e. including complementary array (if any)
Returns:
Array of shown results
"""
comp_array = self.__get_complementary_array()
if comp_array is None:
return self.raw_data
return np.hstack([self.raw_data, comp_array])
@property
def label_contents(self) -> tuple[tuple[int, str], ...]:
"""Return label contents, i.e. a tuple of couples of (index, text)
where index is the column of raw_data and text is the associated
label format string"""
contents = []
for idx, lbl in enumerate(self.__get_complementary_xlabels()):
contents.append((idx + self.raw_data.shape[1], lbl))
return tuple(contents)
[docs]
def create_label_item(self, obj: BaseObj) -> LabelItem | None:
"""Create label item
Returns:
Label item
"""
if self.add_label:
return super().create_label_item(obj)
return None
[docs]
def merge_with(self, obj: BaseObj, other_obj: BaseObj | None = None):
"""Merge object resultshape with another's: obj <-- other_obj
or simply merge this resultshape with obj if other_obj is None"""
if other_obj is None:
other_obj = obj
other_value = other_obj.metadata.get(self.key)
if other_value is not None:
other = ResultShape.from_metadata_entry(self.key, other_value)
assert other is not None
other_array = np.array(other.array, copy=True)
other_array[:, 0] += self.array[-1, 0] + 1 # Adding ROI index offset
if other_array.shape[1] != self.array.shape[1]:
# This can only happen if the shape is a polygon
assert self.shapetype is ShapeTypes.POLYGON
# We must padd the array with NaNs
max_colnb = max(self.array.shape[1], other_array.shape[1])
new_array = np.full(
(self.array.shape[0] + other_array.shape[0], max_colnb), np.nan
)
new_array[: self.array.shape[0], : self.array.shape[1]] = self.array
new_array[self.array.shape[0] :, : other_array.shape[1]] = other_array
self.array = new_array
else:
self.array = np.vstack([self.array, other_array])
self.add_to(obj)
[docs]
def iterate_plot_items(
self, fmt: str, lbl: bool, option: Literal["s", "i"]
) -> Iterable:
"""Iterate over metadata shape plot items.
Args:
fmt: numeric format (e.g. "%.3f")
lbl: if True, show shape labels
option: shape style option ("s" for signal, "i" for image)
Yields:
Plot item
"""
for coords in self.raw_data:
yield self.create_shape_item(coords, fmt, lbl, option)
[docs]
def create_shape_item(
self, coords: np.ndarray, fmt: str, lbl: bool, option: Literal["s", "i"]
) -> (
AnnotatedPoint
| Marker
| AnnotatedRectangle
| AnnotatedCircle
| AnnotatedSegment
| AnnotatedEllipse
| PolygonShape
| None
):
"""Make geometrical shape plot item adapted to the shape type.
Args:
coords: shape data
fmt: numeric format (e.g. "%.3f")
lbl: if True, show shape labels
option: shape style option ("s" for signal, "i" for image)
Returns:
Plot item
"""
if self.shapetype is ShapeTypes.MARKER:
x0, y0 = coords
item = self.__make_marker_item(x0, y0, fmt)
elif self.shapetype is ShapeTypes.POINT:
x0, y0 = coords
item = AnnotatedPoint(x0, y0)
sparam: ShapeParam = item.shape.shapeparam
sparam.symbol.marker = "Ellipse"
sparam.symbol.size = 6
sparam.sel_symbol.marker = "Ellipse"
sparam.sel_symbol.size = 6
aparam = item.annotationparam
aparam.title = self.title
sparam.update_item(item.shape)
aparam.update_item(item)
elif self.shapetype is ShapeTypes.RECTANGLE:
x0, y0, x1, y1 = coords
item = make.annotated_rectangle(x0, y0, x1, y1, title=self.title)
elif self.shapetype is ShapeTypes.CIRCLE:
xc, yc, r = coords
x0, y0, x1, y1 = coordinates.circle_to_diameter(xc, yc, r)
item = make.annotated_circle(x0, y0, x1, y1, title=self.title)
elif self.shapetype is ShapeTypes.SEGMENT:
x0, y0, x1, y1 = coords
item = make.annotated_segment(x0, y0, x1, y1, title=self.title)
elif self.shapetype is ShapeTypes.ELLIPSE:
xc, yc, a, b, t = coords
coords = coordinates.ellipse_to_diameters(xc, yc, a, b, t)
x0, y0, x1, y1, x2, y2, x3, y3 = coords
item = make.annotated_ellipse(
x0, y0, x1, y1, x2, y2, x3, y3, title=self.title
)
elif self.shapetype is ShapeTypes.POLYGON:
x, y = coords[::2], coords[1::2]
item = make.polygon(x, y, title=self.title, closed=False)
else:
print(f"Warning: unsupported item {self.shapetype}", file=sys.stderr)
return None
if isinstance(item, AnnotatedShape):
config_annotated_shape(item, fmt, lbl, "results", option)
set_plot_item_editable(item, False)
return item
def __make_marker_item(self, x0: float, y0: float, fmt: str) -> Marker:
"""Make marker item
Args:
x0: x coordinate
y0: y coordinate
fmt: numeric format (e.g. '%.3f')
"""
if np.isnan(x0):
mstyle = "-"
def label(x, y): # pylint: disable=unused-argument
return (self.title + ": " + fmt) % y
elif np.isnan(y0):
mstyle = "|"
def label(x, y): # pylint: disable=unused-argument
return (self.title + ": " + fmt) % x
else:
mstyle = "+"
txt = self.title + ": (" + fmt + ", " + fmt + ")"
def label(x, y):
return txt % (x, y)
return make.marker(
position=(x0, y0),
markerstyle=mstyle,
label_cb=label,
linestyle="DashLine",
color="yellow",
)
def make_roi_item(
func,
coords: list,
title: str,
fmt: str,
lbl: bool,
editable: bool,
option: Literal["s", "i"],
):
"""Make ROI item shape.
Args:
func: function to create ROI item
coords: coordinates
title: title
fmt: numeric format (e.g. "%.3f")
lbl: if True, show shape labels
editable: if True, make shape editable
option: shape style option ("s" for signal, "i" for image)
Returns:
Plot item
"""
item = func(*coords, title)
option += "/" + ("editable" if editable else "readonly")
if not editable:
if isinstance(item, AnnotatedShape):
config_annotated_shape(item, fmt, lbl, "roi", option, cmp=editable)
item.set_movable(False)
item.set_resizable(False)
item.set_readonly(True)
item.set_style("roi", option)
return item
def items_to_json(items: list) -> str | None:
"""Convert plot items to JSON string.
Args:
items: list of plot items
Returns:
JSON string or None if items is empty
"""
if items:
writer = JSONWriter(None)
save_items(writer, items)
return writer.get_json(indent=4)
return None
def json_to_items(json_str: str | None) -> list:
"""Convert JSON string to plot items.
Args:
json_str: JSON string or None
Returns:
List of plot items
"""
items = []
if json_str:
try:
for item in load_items(JSONReader(json_str)):
items.append(item)
except json.decoder.JSONDecodeError:
pass
return items
class BaseObjMeta(abc.ABCMeta, gds.DataSetMeta):
"""Mixed metaclass to avoid conflicts"""
class BaseObj(metaclass=BaseObjMeta):
"""Object (signal/image) interface"""
PREFIX = "" # This is overriden in children classes
DEFAULT_FMT = "s" # This is overriden in children classes
CONF_FMT = Conf.view.sig_format # This is overriden in children classes
# This is overriden in children classes with a gds.DictItem instance:
metadata: dict[str, Any] = {}
VALID_DTYPES = ()
def __init__(self):
self.__onb = 0
self.__roi_changed: bool | None = None
self.__metadata_options: dict[str, Any] | None = None
self.reset_metadata_to_defaults()
@property
def number(self) -> int:
"""Return object number (used for short ID)"""
return self.__onb
@number.setter
def number(self, onb: int):
"""Set object number (used for short ID).
Args:
onb: object number
"""
self.__onb = onb
@property
def short_id(self):
"""Short object ID"""
return f"{self.PREFIX}{self.__onb:03d}"
@property
@abc.abstractmethod
def data(self):
"""Data"""
@classmethod
def get_valid_dtypenames(cls) -> list[str]:
"""Get valid data type names
Returns:
Valid data type names supported by this class
"""
return [
dtname
for dtname in np.sctypeDict
if dtname in (dtype.__name__ for dtype in cls.VALID_DTYPES)
]
def check_data(self):
"""Check if data is valid, raise an exception if that's not the case
Raises:
TypeError: if data type is not supported
"""
if self.data is not None:
if self.data.dtype not in self.VALID_DTYPES:
raise TypeError(f"Unsupported data type: {self.data.dtype}")
def iterate_roi_indexes(self):
"""Iterate over object ROI indexes ([0] if there is no ROI)"""
if self.roi is None:
yield 0
else:
yield from range(len(self.roi))
@abc.abstractmethod
def get_data(self, roi_index: int | None = None) -> np.ndarray:
"""
Return original data (if ROI is not defined or `roi_index` is None),
or ROI data (if both ROI and `roi_index` are defined).
Args:
roi_index: ROI index
Returns:
Data
"""
@abc.abstractmethod
def copy(self, title: str | None = None, dtype: np.dtype | None = None) -> BaseObj:
"""Copy object.
Args:
title: title
dtype: data type
Returns:
Copied object
"""
@abc.abstractmethod
def set_data_type(self, dtype):
"""Change data type.
Args:
dtype: data type
"""
@abc.abstractmethod
def make_item(self, update_from=None):
"""Make plot item from data.
Args:
update_from: update
Returns:
Plot item
"""
@abc.abstractmethod
def update_item(self, item, data_changed: bool = True) -> None:
"""Update plot item from data.
Args:
item: plot item
data_changed: if True, data has changed
"""
@abc.abstractmethod
def roi_coords_to_indexes(self, coords: list) -> np.ndarray:
"""Convert ROI coordinates to indexes.
Args:
coords: coordinates
Returns:
Indexes
"""
@abc.abstractmethod
def get_roi_param(self, title, *defaults: int) -> gds.DataSet:
"""Return ROI parameters dataset.
Args:
title: title
*defaults: default values
"""
def roidata_to_params(
self, roidata: np.ndarray | list[list[int]]
) -> gds.DataSetGroup:
"""Convert ROI array data to ROI dataset group.
Args:
roidata: ROI array data (array or list of lists, floating point values
are accepted and will be converted to integers)
Returns:
ROI dataset group
"""
roi_params = []
try:
data = np.array(roidata, int)
except (ValueError, TypeError) as exc:
raise TypeError(f"Invalid ROI data: {roidata}") from exc
if len(data.shape) != 2 and data.size != 0:
raise ValueError(f"Invalid ROI data shape: {data.shape}")
for index, parameters in enumerate(data):
roi_param = self.get_roi_param(f"ROI{index:02d}", *parameters)
roi_params.append(roi_param)
group = gds.DataSetGroup(roi_params, title=_("Regions of interest"))
return group
@abc.abstractmethod
def params_to_roidata(self, params: gds.DataSetGroup) -> np.ndarray:
"""Convert ROI dataset group to ROI array data.
Args:
params: ROI dataset group
Returns:
ROI array data
"""
def roi_has_changed(self) -> bool:
"""Return True if ROI has changed since last call to this method.
The first call to this method will return True if ROI has not yet been set,
or if ROI has been set and has changed since the last call to this method.
The next call to this method will always return False if ROI has not changed
in the meantime.
Returns:
True if ROI has changed
"""
if self.__roi_changed is None:
self.__roi_changed = True
returned_value = self.__roi_changed
self.__roi_changed = False
return returned_value
@property
def roi(self) -> np.ndarray | None:
"""Return object regions of interest array (one ROI per line).
Returns:
Regions of interest array
"""
roidata = self.metadata.get(ROI_KEY)
assert roidata is None or isinstance(roidata, np.ndarray)
return roidata
@roi.setter
def roi(self, roidata: np.ndarray | None) -> None:
"""Set object regions of interest array, using a list or ROI dataset params.
Args:
roidata: regions of interest array
"""
if roidata is None:
if ROI_KEY in self.metadata:
self.metadata.pop(ROI_KEY)
else:
self.metadata[ROI_KEY] = np.array(roidata, int)
self.__roi_changed = True
def iterate_resultshapes(self) -> Iterable[ResultShape]:
"""Iterate over object result shapes.
Yields:
Result shape
"""
for key, value in self.metadata.items():
if ResultShape.match(key, value):
yield ResultShape.from_metadata_entry(key, value)
def iterate_resultproperties(self) -> Iterable[ResultProperties]:
"""Iterate over object result properties.
Yields:
Result properties
"""
for key, value in self.metadata.items():
if ResultProperties.match(key, value):
yield ResultProperties.from_metadata_entry(key, value)
def delete_results(self) -> None:
"""Delete all object results (shapes and properties)"""
for key in list(self.metadata.keys()):
if ResultShape.match(key, self.metadata[key]) or ResultProperties.match(
key, self.metadata[key]
):
self.metadata.pop(key)
def update_resultshapes_from(self, other: BaseObj) -> None:
"""Update geometric shape from another object (merge metadata).
Args:
other: other object, from which to update this object
"""
# The following code is merging the result shapes of the `other` object
# with the result shapes of this object, but it is merging only the result
# shapes of the same type (`mshape.key`). Thus, if the `other` object has
# a result shape that is not present in this object, it will not be merged,
# and we will have to add it to this object manually.
for mshape in self.iterate_resultshapes():
assert mshape is not None
mshape.merge_with(self, other)
# Iterating on `other` object result shapes to find result shapes that are
# not present in this object, and add them to this object.
for mshape in other.iterate_resultshapes():
assert mshape is not None
if mshape.key not in self.metadata:
mshape.add_to(self)
def transform_shapes(self, orig, func, param=None):
"""Apply transform function to result shape / annotations coordinates.
Args:
orig: original object
func: transform function
param: transform function parameter
"""
def transform(coords: np.ndarray):
"""Transform coordinates"""
if param is None:
func(self, orig, coords)
else:
func(self, orig, coords, param)
for mshape in self.iterate_resultshapes():
assert mshape is not None
mshape.transform_coordinates(transform)
items = []
for item in json_to_items(self.annotations):
if isinstance(item, AnnotatedShape):
transform(item.shape.points)
item.set_label_position()
elif isinstance(item, LabelItem):
x, y = item.G
points = np.array([[x, y]], float)
transform(points)
x, y = points[0]
item.set_pos(x, y)
items.append(item)
if items:
self.annotations = items_to_json(items)
@abc.abstractmethod
def iterate_roi_items(self, fmt: str, lbl: bool, editable: bool = True):
"""Make plot item representing a Region of Interest.
Args:
fmt: format string
lbl: if True, add label
editable: if True, ROI is editable
Yields:
Plot item
"""
def __set_annotations(self, annotations: str | None) -> None:
"""Set object annotations (JSON string describing annotation plot items)
Args:
annotations: JSON string describing annotation plot items,
or None to remove annotations
"""
if annotations is None:
if ANN_KEY in self.metadata:
self.metadata.pop(ANN_KEY)
else:
self.metadata[ANN_KEY] = annotations
def __get_annotations(self) -> str:
"""Get object annotations (JSON string describing annotation plot items)"""
return self.metadata.get(ANN_KEY, "")
annotations = property(__get_annotations, __set_annotations)
def add_annotations_from_items(self, items: list) -> None:
"""Add object annotations (annotation plot items).
Args:
items: annotation plot items
"""
ann_items = json_to_items(self.annotations)
ann_items.extend(items)
if ann_items:
self.annotations = items_to_json(ann_items)
def add_annotations_from_file(self, filename: str) -> None:
"""Add object annotations from file (JSON).
Args:
filename: filename
"""
with open(filename, "r", encoding="utf-8") as file:
json_str = file.read()
if self.annotations:
json_str = self.annotations[:-1] + "," + json_str[1:]
self.annotations = json_str
@abc.abstractmethod
def add_label_with_title(self, title: str | None = None) -> None:
"""Add label with title annotation
Args:
title: title (if None, use object title)
"""
def iterate_shape_items(self, editable: bool = False):
"""Iterate over shape items encoded in metadata (if any).
Args:
editable: if True, annotations are editable
Yields:
Plot item
"""
fmt = self.get_metadata_option("format")
lbl = self.get_metadata_option("showlabel")
for key, value in self.metadata.items():
if key == ROI_KEY:
yield from self.iterate_roi_items(fmt=fmt, lbl=lbl, editable=False)
elif ResultShape.match(key, value):
mshape: ResultShape = ResultShape.from_metadata_entry(key, value)
yield from mshape.iterate_plot_items(fmt, lbl, self.PREFIX)
if self.annotations:
try:
for item in json_to_items(self.annotations):
section, option = "annotations", f"{self.PREFIX}/shape"
if isinstance(item, AnnotatedShape):
config_annotated_shape(item, fmt, lbl, section, option)
elif isinstance(item, PolygonShape):
item.set_style(section, option)
set_plot_item_editable(item, editable)
yield item
except json.decoder.JSONDecodeError:
pass
def remove_all_shapes(self) -> None:
"""Remove metadata shapes and ROIs"""
for key, value in list(self.metadata.items()):
resultshape = ResultShape.from_metadata_entry(key, value)
if resultshape is not None or key == ROI_KEY:
# Metadata entry is a metadata shape or a ROI
self.metadata.pop(key)
self.annotations = None
def get_metadata_option(self, name: str) -> Any:
"""Return metadata option value
A metadata option is a metadata entry starting with an underscore.
It is a way to store application-specific options in object metadata.
Args:
name: option name
Returns:
Option value
Valid option names:
'format': format string
'showlabel': show label
"""
if name not in self.__metadata_options:
raise ValueError(f"Invalid metadata option name `{name}`")
default = self.__metadata_options[name]
return self.metadata.get(f"__{name}", default)
def set_metadata_option(self, name: str, value: Any) -> None:
"""Set metadata option value
A metadata option is a metadata entry starting with an underscore.
It is a way to store application-specific options in object metadata.
Args:
name: option name
value: option value
Valid option names:
'format': format string
'showlabel': show label
"""
if name not in self.__metadata_options:
raise ValueError(f"Invalid metadata option name `{name}`")
self.metadata[f"__{name}"] = value
def save_attr_to_metadata(self, attrname: str, new_value: Any) -> None:
"""Save attribute to metadata
Args:
attrname: attribute name
new_value: new value
"""
value = getattr(self, attrname)
if value:
self.metadata[f"orig_{attrname}"] = value
setattr(self, attrname, new_value)
def restore_attr_from_metadata(self, attrname: str, default: Any) -> None:
"""Restore attribute from metadata
Args:
attrname: attribute name
default: default value
"""
value = self.metadata.pop(f"orig_{attrname}", default)
setattr(self, attrname, value)
def reset_metadata_to_defaults(self) -> None:
"""Reset metadata to default values"""
self.__metadata_options = {
"format": "%" + self.CONF_FMT.get(self.DEFAULT_FMT),
"showlabel": Conf.view.show_label.get(False),
}
self.metadata = {}
for name, value in self.__metadata_options.items():
self.set_metadata_option(name, value)
self.update_metadata_view_settings()
def __get_def_dict(self) -> dict[str, Any]:
"""Return default visualization settings dictionary"""
return Conf.view.get_def_dict(self.__class__.__name__[:3].lower())
def update_metadata_view_settings(self) -> None:
"""Update metadata view settings from Conf.view"""
self.metadata.update(self.__get_def_dict())
def update_plot_item_parameters(self, item: CurveItem | MaskedImageItem) -> None:
"""Update plot item parameters from object data/metadata
Takes into account a subset of plot item parameters. Those parameters may
have been overriden by object metadata entries or other object data. The goal
is to update the plot item accordingly.
This is *almost* the inverse operation of `update_metadata_from_plot_item`.
Args:
item: plot item
"""
# Subclasses have to override this method to update plot item parameters,
# then call this implementation of the method to update plot item.
update_dataset(item.param, self.metadata)
item.param.update_item(item)
if item.selected:
item.select()
def update_metadata_from_plot_item(self, item: CurveItem | MaskedImageItem) -> None:
"""Update metadata from plot item.
Takes into account a subset of plot item parameters. Those parameters may
have been modified by the user through the plot item GUI. The goal is to
update the metadata accordingly.
This is *almost* the inverse operation of `update_plot_item_parameters`.
Args:
item: plot item
"""
for key in self.__get_def_dict():
if hasattr(item.param, key): # In case the PlotPy version is not up-to-date
self.metadata[key] = getattr(item.param, key)
# Subclasses may override this method to update metadata from plot item,
# then call this implementation of the method to update metadata standard
# entries.
