👌 Make plot_data_overview able to plot single trace data (#137)

This change allows plot_data_overview to be able to plot single trace data without crashing on a single axis dataset.
The SVD part of the plot will be skipped since it does not make sense.

* ✨ Implemented not_single_element_dims util function

* 👌 Made plot_data_overview able to plot single trace data

pyglotaran_extras/plotting/plot_data.py

@@ -12,11 +12,15 @@
 from pyglotaran_extras.plotting.plot_svd import plot_lsv_data
 from pyglotaran_extras.plotting.plot_svd import plot_rsv_data
 from pyglotaran_extras.plotting.plot_svd import plot_sv_data
+from pyglotaran_extras.plotting.utils import not_single_element_dims
 from pyglotaran_extras.plotting.utils import shift_time_axis_by_irf_location
 
 __all__ = ["plot_data_overview"]
 
 if TYPE_CHECKING:
+    from typing import Hashable
+
+    import xarray as xr
     from glotaran.project.result import Result
     from matplotlib.figure import Figure
     from matplotlib.pyplot import Axes
@@ -33,7 +37,7 @@ def plot_data_overview(
     nr_of_data_svd_vectors: int = 4,
     show_data_svd_legend: bool = True,
     irf_location: float | None = None,
-) -> tuple[Figure, Axes]:
+) -> tuple[Figure, Axes] | tuple[Figure, Axis]:
     """Plot data as filled contour plot and SVD components.
 
     Parameters
@@ -59,10 +63,21 @@ def plot_data_overview(
 
     Returns
     -------
-    tuple[Figure, Axes]
+    tuple[Figure, Axes]|tuple[Figure,Axis]
         Figure and axes which can then be refined by the user.
     """
     dataset = load_data(dataset, _stacklevel=3)
+    data = shift_time_axis_by_irf_location(dataset.data, irf_location)
+
+    if len(not_single_element_dims(data)) == 1:
+        return _plot_single_trace(
+            data,
+            not_single_element_dims(data)[0],
+            title="Single trace data",
+            linlog=linlog,
+            linthresh=linthresh,
+            figsize=figsize,
+        )
 
     fig = plt.figure(figsize=figsize)
     data_ax = cast(Axis, plt.subplot2grid((4, 3), (0, 0), colspan=3, rowspan=3, fig=fig))
@@ -71,8 +86,6 @@ def plot_data_overview(
     sv_ax = cast(Axis, plt.subplot2grid((4, 3), (3, 1), fig=fig))
     rsv_ax = cast(Axis, plt.subplot2grid((4, 3), (3, 2), fig=fig))
 
-    data = shift_time_axis_by_irf_location(dataset.data, irf_location)
-
     if len(data.time) > 1:
         data.plot(x="time", ax=data_ax, center=False)
     else:
@@ -97,3 +110,44 @@ def plot_data_overview(
     if linlog:
         data_ax.set_xscale("symlog", linthresh=linthresh)
     return fig, (data_ax, lsv_ax, sv_ax, rsv_ax)
+
+
+def _plot_single_trace(
+    data_array: xr.DataArray,
+    x_dim: Hashable,
+    *,
+    title: str = "Single trace data",
+    linlog: bool = False,
+    linthresh: float = 1,
+    figsize: tuple[int, int] = (15, 10),
+) -> tuple[Figure, Axis]:
+    """Plot single trace data in case ``plot_data_overview`` gets passed ingle trace data.
+
+    Parameters
+    ----------
+    data_array: xr.DataArray
+        DataArray containing only data  of a single trace.
+    x_dim: Hashable
+        Name of the x dimension.
+    title: str
+        Title to add to the figure. Defaults to "Data overview".
+    linlog: bool
+        Whether to use 'symlog' scale or not. Defaults to False.
+    linthresh: float
+        A single float which defines the range (-x, x), within which the plot is linear.
+        This avoids having the plot go to infinity around zero. Defaults to 1.
+    figsize: tuple[int, int]
+        Size of the figure (N, M) in inches. Defaults to (15, 10).
+
+    Returns
+    -------
+    tuple[Figure, Axis]
+        Figure and axis which can then be refined by the user.
+    """
+    fig, ax = plt.subplots(1, 1, figsize=figsize)
+    data_array.plot.line(x=x_dim, ax=ax)
+    fig.suptitle(title, fontsize=16)
+
+    if linlog:
+        ax.set_xscale("symlog", linthresh=linthresh)
+    return fig, ax

pyglotaran_extras/plotting/utils.py

@@ -463,3 +463,22 @@ def calculate_ticks_in_units_of_pi(
     return tick_labels * np.pi, (
         str(val) for val in pretty_format_numerical_iterable(tick_labels, decimal_places=1)
     )
+
+
+def not_single_element_dims(data_array: xr.DataArray) -> list[Hashable]:
+    """Names of dimensions in ``data`` which don't have a size equal to one.
+
+    This helper function is for example used to determine if a data only have a single trace,
+    since this requires different plotting code (e.g. ``data_array.plot.line(x="time")``).
+
+    Parameters
+    ----------
+    data_array: xr.DataArray
+        _description_
+
+    Returns
+    -------
+    list[Hashable]
+        Names of dimensions in ``data`` which don't have a size equal to one.
+    """
+    return [dim for dim, values in data_array.coords.items() if values.size != 1]

tests/plotting/test_utils.py

@@ -16,6 +16,7 @@
 from pyglotaran_extras.plotting.utils import abs_max
 from pyglotaran_extras.plotting.utils import add_cycler_if_not_none
 from pyglotaran_extras.plotting.utils import calculate_ticks_in_units_of_pi
+from pyglotaran_extras.plotting.utils import not_single_element_dims
 
 matplotlib.use("Agg")
 DEFAULT_CYCLER = plt.rcParams["axes.prop_cycle"]
@@ -85,3 +86,22 @@ def test_calculate_ticks_in_units_of_pi(
 
     assert np.allclose(list(tick_values), expected_tick_values)
     assert list(tick_labels) == expected_tick_labels
+
+
+@pytest.mark.parametrize(
+    "data_array, expected",
+    (
+        (xr.DataArray([1]), []),
+        (xr.DataArray([1], coords={"dim1": [1]}), []),
+        (xr.DataArray([[1], [1]], coords={"dim1": [1, 2], "dim2": [1]}), ["dim1"]),
+        (
+            xr.DataArray(
+                [[[1, 1]], [[1, 1]]], coords={"dim1": [1, 2], "dim2": [1], "dim3": [1, 2]}
+            ),
+            ["dim1", "dim3"],
+        ),
+    ),
+)
+def test_not_single_element_dims(data_array: xr.DataArray, expected: list[Hashable]):
+    """Only get dim with more than one element."""
+    assert not_single_element_dims(data_array) == expected