# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
.. Common computation objects (see parent package :mod:`cdl.computation`)
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
# Note:
# ----
# All dataset classes must also be imported in the cdl.computation.param module.
from __future__ import annotations
from typing import TYPE_CHECKING
import guidata.dataset as gds
import numpy as np
from cdl.config import _
from cdl.obj import ResultProperties, create_signal
if TYPE_CHECKING:
from typing import Callable
from cdl.obj import ImageObj, SignalObj
[docs]
class ArithmeticParam(gds.DataSet):
"""Arithmetic parameters"""
[docs]
def get_operation(self) -> str:
"""Return the operation string"""
o, a, b = self.operator, self.factor, self.constant
b_added = False
if a == 0.0:
if o in ("+", "-"):
txt = "obj3 = obj1"
elif b == 0.0:
txt = "obj3 = 0"
else:
txt = f"obj3 = {b}"
b_added = True
elif a == 1.0:
txt = f"obj3 = obj1 {o} obj2"
else:
txt = f"obj3 = (obj1 {o} obj2) × {a}"
if b != 0.0 and not b_added:
txt += f" + {b}"
return txt
[docs]
def update_operation(self, _item, _value): # pylint: disable=unused-argument
"""Update the operation item"""
self.operation = self.get_operation()
operators = ("+", "-", "×", "/")
operator = gds.ChoiceItem(_("Operator"), list(zip(operators, operators))).set_prop(
"display", callback=update_operation
)
factor = (
gds.FloatItem(_("Factor"), default=1.0)
.set_pos(col=1)
.set_prop("display", callback=update_operation)
)
constant = (
gds.FloatItem(_("Constant"), default=0.0)
.set_pos(col=1)
.set_prop("display", callback=update_operation)
)
operation = gds.StringItem(_("Operation"), default="").set_prop(
"display", active=False
)
restore_dtype = gds.BoolItem(
_("Convert to `obj1` data type"), label=_("Result"), default=True
)
[docs]
class GaussianParam(gds.DataSet):
"""Gaussian filter parameters"""
sigma = gds.FloatItem("σ", default=1.0)
HELP_MODE = _("""Mode of the filter:
- 'reflect': Reflect the data at the boundary
- 'constant': Pad with a constant value
- 'nearest': Pad with the nearest value
- 'mirror': Reflect the data at the boundary with the data itself
- 'wrap': Circular boundary""")
[docs]
class MovingAverageParam(gds.DataSet):
"""Moving average parameters"""
n = gds.IntItem(_("Size of the moving window"), default=3, min=1)
modes = ("reflect", "constant", "nearest", "mirror", "wrap")
mode = gds.ChoiceItem(
_("Mode"), list(zip(modes, modes)), default="reflect", help=HELP_MODE
)
[docs]
class ClipParam(gds.DataSet):
"""Data clipping parameters"""
lower = gds.FloatItem(_("Lower clipping value"), check=False)
upper = gds.FloatItem(_("Upper clipping value"), check=False)
[docs]
class NormalizeParam(gds.DataSet):
"""Normalize parameters"""
methods = (
("maximum", _("Maximum")),
("amplitude", _("Amplitude")),
("area", _("Area")),
("energy", _("Energy")),
("rms", _("RMS")),
)
method = gds.ChoiceItem(_("Normalize with respect to"), methods)
[docs]
class HistogramParam(gds.DataSet):
"""Histogram parameters"""
[docs]
def get_suffix(self, data: np.ndarray) -> str:
"""Return suffix for the histogram computation
Args:
data: data array
"""
suffix = f"bins={self.bins:d}"
if self.lower is not None:
suffix += f", ymin={self.lower:.3f}"
else:
self.lower = np.min(data)
if self.upper is not None:
suffix += f", ymax={self.upper:.3f}"
else:
self.upper = np.max(data)
bins = gds.IntItem(_("Number of bins"), default=256, min=1)
lower = gds.FloatItem(_("Lower limit"), default=None, check=False)
upper = gds.FloatItem(_("Upper limit"), default=None, check=False)
[docs]
class ROIDataParam(gds.DataSet):
"""ROI Editor data"""
roidata = gds.FloatArrayItem(
_("ROI data"),
help=_(
"For convenience, this item accepts a 2D NumPy array, a list of list "
"of numbers, or None. In the end, the data is converted to a 2D NumPy "
"array of integers (if not None)."
),
)
singleobj = gds.BoolItem(
_("Single object"),
help=_("Whether to extract the ROI as a single object or not."),
)
@property
def is_empty(self) -> bool:
"""Return True if there is no ROI"""
return self.roidata is None or np.array(self.roidata).size == 0
[docs]
class FFTParam(gds.DataSet):
"""FFT parameters"""
shift = gds.BoolItem(_("Shift"), help=_("Shift zero frequency to center"))
[docs]
class SpectrumParam(gds.DataSet):
"""Spectrum parameters"""
log = gds.BoolItem(_("Logarithmic scale"), default=False)
[docs]
class ConstantParam(gds.DataSet):
"""Parameter used to set a constant value to used in operations"""
value = gds.FloatItem(_("Constant value"))
# MARK: Helper functions for creating result objects -----------------------------------
[docs]
def dst_11(
src: SignalObj | ImageObj, name: str, suffix: str | None = None
) -> SignalObj | ImageObj:
"""Create a result object, as returned by the callback function of the
:func:`cdl.core.gui.processor.base.BaseProcessor.compute_11` method
Args:
src: source signal or image object
name: name of the function. If provided, the title of the result object
will be `{name}({src.short_id})|{suffix})`, unless the name is a single
character, in which case the title will be `{src.short_id}{name}{suffix}`
where `name` is an operator and `suffix` is the other term of the operation.
suffix: suffix to add to the title. Optional.
Returns:
Result signal or image object
"""
if len(name) == 1: # This is an operator
title = f"{src.short_id}{name}"
else:
title = f"{name}({src.short_id})"
if suffix: # suffix may be None or an empty string
title += "|"
if suffix: # suffix may be None or an empty string
title += suffix
return src.copy(title=title)
[docs]
def dst_n1n(
src1: SignalObj | ImageObj,
src2: SignalObj | ImageObj,
name: str,
suffix: str | None = None,
) -> SignalObj | ImageObj:
"""Create a result object, as returned by the callback function of the
:func:`cdl.core.gui.processor.base.BaseProcessor.compute_n1n` method
Args:
src1: input signal or image object
src2: input signal or image object
name: name of the processing function
Returns:
Output signal or image object
"""
if len(name) == 1: # This is an operator
title = f"{src1.short_id}{name}{src2.short_id}"
else:
title = f"{name}({src1.short_id}, {src2.short_id})"
if suffix is not None:
title += "|" + suffix
return src1.copy(title=title)
[docs]
def new_signal_result(
src: SignalObj | ImageObj,
name: str,
suffix: str | None = None,
units: tuple[str, str] | None = None,
labels: tuple[str, str] | None = None,
) -> SignalObj:
"""Create new signal object as a result of a compute_11 function
As opposed to the `dst_11` functions, this function creates a new signal object
without copying the original object metadata, except for the "source" entry.
Args:
src: input signal or image object
name: name of the processing function
suffix: suffix to add to the title
units: units of the output signal
labels: labels of the output signal
Returns:
Output signal object
"""
title = f"{name}({src.short_id})"
dst = create_signal(title=title, units=units, labels=labels)
if suffix is not None:
dst.title += "|" + suffix
if "source" in src.metadata:
dst.metadata["source"] = src.metadata["source"] # Keep track of the source
return dst
[docs]
def calc_resultproperties(
title: str, obj: SignalObj | ImageObj, labeledfuncs: dict[str, Callable]
) -> ResultProperties:
"""Calculate result properties by executing a computation function
on a signal/image object.
Args:
title: title of the result properties
obj: signal or image object
labeledfuncs: dictionary of labeled computation functions. The keys are
the labels of the computation functions and the values are the functions
themselves (each function must take a single argument - which is the data
of the ROI or the whole signal/image - and return a float)
Returns:
Result properties object
"""
if not all(isinstance(k, str) for k in labeledfuncs.keys()):
raise ValueError("Keys of labeledfuncs must be strings")
if not all(callable(v) for v in labeledfuncs.values()):
raise ValueError("Values of labeledfuncs must be functions")
res = []
roi_nb = 0 if obj.roi is None else obj.roi.shape[0]
for i_roi in [None] + list(range(roi_nb)):
data_roi = obj.get_data(i_roi)
val_roi = -1 if i_roi is None else i_roi
res.append([val_roi] + [fn(data_roi) for fn in labeledfuncs.values()])
return ResultProperties(title, np.array(res), list(labeledfuncs.keys()))
