✨ Add coherent artifact plot functionality

pyglotaran_extras/__init__.py

@@ -1,6 +1,7 @@
 """Pyglotaran extension package with convenience functionality such as plotting."""
 from pyglotaran_extras.io.load_data import load_data
 from pyglotaran_extras.io.setup_case_study import setup_case_study
+from pyglotaran_extras.plotting.plot_coherent_artifact import plot_coherent_artifact
 from pyglotaran_extras.plotting.plot_data import plot_data_overview
 from pyglotaran_extras.plotting.plot_guidance import plot_guidance
 from pyglotaran_extras.plotting.plot_irf_dispersion_center import plot_irf_dispersion_center
@@ -12,6 +13,7 @@
 __all__ = [
     "load_data",
     "setup_case_study",
+    "plot_coherent_artifact",
     "plot_data_overview",
     "plot_overview",
     "plot_simple_overview",

pyglotaran_extras/plotting/plot_coherent_artifact.py

@@ -0,0 +1,123 @@
+"""Module containing coherent artifact plot functionality."""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+from warnings import warn
+
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+from cycler import Cycler
+
+from pyglotaran_extras.plotting.utils import abs_max
+from pyglotaran_extras.plotting.utils import add_cycler_if_not_none
+
+if TYPE_CHECKING:
+    from matplotlib.figure import Figure
+    from matplotlib.pyplot import Axes
+
+
+def plot_coherent_artifact(
+    res: xr.Dataset,
+    *,
+    time_range: tuple[float, float] | None = None,
+    spectral: float = 0,
+    normalize: bool = False,
+    figsize: tuple[int, int] = (18, 7),
+    show_zero_line: bool = True,
+    cycler: Cycler | None = None,
+    title: str | None = "Coherent Artifact",
+) -> tuple[Figure, Axes]:
+    """Plot coherent artifact as IRF derivative components over time and IRFAS over spectral dim.
+
+    The IRFAS are the IRF (Instrument Response Function) Associated Spectra.
+
+    Parameters
+    ----------
+    res: xr.Dataset
+        Result dataset from a pyglotaran optimization.
+    time_range: tuple[float, float] | None
+        Start and end time for the IRF derivative plot. Defaults to None which means that
+        the full time range is used
+    spectral: float
+        Value of the spectral axis that should be used to select the data for the IRF derivative
+        plot this value does not need to be an exact existing value and only has a effect if the
+        IRF has dispersion. Defaults to 0 which means that the IRF derivative plot at lowest
+        spectral value will be shown.
+    normalize: bool
+        Whether or not to normalize the IRF derivative plot.If the IRF derivative is normalized,
+        the IRFAS is scaled with the reciprocal of the normalization to compensate for this.
+        Defaults to False.
+    figsize: tuple[int, int]
+        Size of the figure (N, M) in inches. Defaults to (18, 7)
+    show_zero_line: bool
+        Whether or not to add a horizontal line at zero. Defaults to True.
+    cycler: Cycler | None
+        Plot style cycler to use. Defaults to None, which means that the matplotlib default style
+        will be used.
+    title: str | None
+        Title of the figure. Defaults to "Coherent Artifact".
+
+    Returns
+    -------
+    tuple[Figure, Axes]
+        Figure object which contains the plots and the Axes.
+    """
+    fig, axes = plt.subplots(1, 2, figsize=figsize)
+    add_cycler_if_not_none(axes, cycler)
+
+    if (
+        "coherent_artifact_response" not in res
+        or "coherent_artifact_associated_spectra" not in res
+    ):
+        warn(
+            UserWarning(f"Dataset does not contain coherent artifact data:\n {res.data_vars}"),
+            stacklevel=2,
+        )
+        return fig, axes
+
+    irf_max = abs_max(res.coherent_artifact_response, result_dims=("coherent_artifact_order"))
+    irfas_max = abs_max(
+        res.coherent_artifact_associated_spectra, result_dims=("coherent_artifact_order")
+    )
+    scales = np.sqrt(irfas_max * irf_max)
+    norm_factor = 1
+    irf_y_label = "amplitude"
+    irfas_y_label = "ΔA"
+
+    if normalize is True:
+        norm_factor = scales.max()
+        irf_y_label = f"normalized {irf_y_label}"
+
+    plot_slice_irf = (
+        res.coherent_artifact_response.sel(spectral=spectral, method="nearest")
+        / irf_max
+        * scales
+        / norm_factor
+    )
+    irf_sel_kwargs = (
+        {"time": slice(time_range[0], time_range[1])} if time_range is not None else {}
+    )
+    plot_slice_irf.sel(**irf_sel_kwargs).plot.line(x="time", ax=axes[0])
+    axes[0].set_title("IRF Derivatives")
+    axes[0].set_ylabel(f"{irf_y_label} (a.u.)")
+
+    plot_slice_irfas = res.coherent_artifact_associated_spectra / irfas_max * scales * norm_factor
+    plot_slice_irfas.plot.line(x="spectral", ax=axes[1])
+    axes[1].get_legend().remove()
+    axes[1].set_title("IRFAS")
+    axes[1].set_ylabel(f"{irfas_y_label} (mOD)")
+
+    if show_zero_line is True:
+        axes[0].axhline(0, color="k", linewidth=1)
+        axes[1].axhline(0, color="k", linewidth=1)
+
+    #
+    if res.coords["coherent_artifact_order"][0] == 1:
+        axes[0].legend(
+            [f"{int(ax_label)-1}" for ax_label in res.coords["coherent_artifact_order"]],
+            title="coherent_artifact_order",
+        )
+    if title:
+        fig.suptitle(title, fontsize=16)
+    return fig, axes

pyglotaran_extras/plotting/utils.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
+from typing import Iterable
 from warnings import warn
 
 import numpy as np
@@ -10,7 +11,7 @@
 from pyglotaran_extras.io.utils import result_dataset_mapping
 
 if TYPE_CHECKING:
-    from typing import Iterable
+    from typing import Hashable
 
     from cycler import Cycler
     from matplotlib.axis import Axis
@@ -360,7 +361,7 @@ def get_shifted_traces(
     return shift_time_axis_by_irf_location(traces, irf_location)
 
 
-def add_cycler_if_not_none(axis: Axis, cycler: Cycler | None) -> None:
+def add_cycler_if_not_none(axis: Axis | Axes, cycler: Cycler | None) -> None:
     """Add cycler to and axis if it is not None.
 
     This is a convenience function that allow to opt out of using
@@ -370,10 +371,38 @@ def add_cycler_if_not_none(axis: Axis, cycler: Cycler | None) -> None:
 
     Parameters
     ----------
-    axis: Axis
-        Axis to plot the data and fits on.
+    axis: Axis | Axes
+        Axis to plot on.
     cycler: Cycler | None
         Plot style cycler to use.
     """
     if cycler is not None:
-        axis.set_prop_cycle(cycler)
+        # We can't use `Axis` in isinstance so we check for the np.ndarray attribute of `Axes`
+        if hasattr(axis, "flatten") is False:
+            axis = np.array([axis])
+        for ax in axis.flatten():
+            ax.set_prop_cycle(cycler)
+
+
+def abs_max(
+    data: xr.DataArray, *, result_dims: Hashable | Iterable[Hashable] = ()
+) -> xr.DataArray:
+    """Calculate the absolute maximum values of ``data`` along all dims except ``result_dims``.
+
+    Parameters
+    ----------
+    data: xr.DataArray
+        Data for which the absolute maximum should be calculated.
+    result_dims: Hashable | Iterable[Hashable]
+        Dimensions of ``data`` which should be preserved and part of the resulting DataArray.
+        Defaults to () which results in the absolute maximum of all values
+
+    Returns
+    -------
+    xr.DataArray
+        Absolute maximum values of ``data`` with dimensions ``result_dims``.
+    """
+    if not isinstance(result_dims, Iterable):
+        result_dims = (result_dims,)
+    reduce_dims = (dim for dim in data.dims if dim not in result_dims)
+    return np.abs(data).max(dim=reduce_dims)

tests/plotting/test_utils.py

@@ -1,13 +1,18 @@
 """Tests for pyglotaran_extras.plotting.utils"""
 from __future__ import annotations
 
+from typing import Hashable
+from typing import Iterable
+
 import matplotlib
 import matplotlib.pyplot as plt
 import pytest
+import xarray as xr
 from cycler import Cycler
 from cycler import cycle
 
 from pyglotaran_extras.plotting.style import PlotStyle
+from pyglotaran_extras.plotting.utils import abs_max
 from pyglotaran_extras.plotting.utils import add_cycler_if_not_none
 
 matplotlib.use("Agg")
@@ -18,10 +23,45 @@
     "cycler,expected_cycler",
     ((None, DEFAULT_CYCLER()), (PlotStyle().cycler, PlotStyle().cycler())),
 )
-def test_add_cycler_if_not_none(cycler: Cycler | None, expected_cycler: cycle):
-    """Default cycler inf None and cycler otherwise"""
+def test_add_cycler_if_not_none_single_axis(cycler: Cycler | None, expected_cycler: cycle):
+    """Default cycler if None and cycler otherwise on a single axis"""
     ax = plt.subplot()
     add_cycler_if_not_none(ax, cycler)
 
     for _ in range(10):
-        assert next(ax._get_lines.prop_cycler) == next(expected_cycler)
+        expected = next(expected_cycler)
+        assert next(ax._get_lines.prop_cycler) == expected
+
+
+@pytest.mark.parametrize(
+    "cycler,expected_cycler",
+    ((None, DEFAULT_CYCLER()), (PlotStyle().cycler, PlotStyle().cycler())),
+)
+def test_add_cycler_if_not_none_multiple_axes(cycler: Cycler | None, expected_cycler: cycle):
+    """Default cycler if None and cycler otherwise on all axes"""
+    _, axes = plt.subplots(1, 2)
+    add_cycler_if_not_none(axes, cycler)
+
+    for _ in range(10):
+        expected = next(expected_cycler)
+        assert next(axes[0]._get_lines.prop_cycler) == expected
+        assert next(axes[1]._get_lines.prop_cycler) == expected
+
+
+@pytest.mark.parametrize(
+    "result_dims, expected",
+    (
+        ((), xr.DataArray(40)),
+        ("dim1", xr.DataArray([20, 40], coords={"dim1": [1, 2]})),
+        ("dim2", xr.DataArray([30, 40], coords={"dim2": [3, 4]})),
+        (("dim1",), xr.DataArray([20, 40], coords={"dim1": [1, 2]})),
+        (
+            ("dim1", "dim2"),
+            xr.DataArray([[10, 20], [30, 40]], coords={"dim1": [1, 2], "dim2": [3, 4]}),
+        ),
+    ),
+)
+def test_abs_max(result_dims: Hashable | Iterable[Hashable], expected: xr.DataArray):
+    """Result values are positive and dimensions are preserved if result_dims is not empty."""
+    data = xr.DataArray([[-10, 20], [-30, 40]], coords={"dim1": [1, 2], "dim2": [3, 4]})
+    assert abs_max(data, result_dims=result_dims).equals(expected)