def file_vs_value_plot(
a_x: Axis, field_name: str, row: pd.DataFrame, range_columns: List[str], fontsize: float, pad: float
) -> None:
"""Create a dot plot with one point per file"""
assert field_name in ["rt_peak", "peak_height"]
a_x.tick_params(direction="in", length=1, pad=pad, width=0.1, labelsize=fontsize)
num_files = len(range_columns)
a_x.scatter(range(num_files), row[:num_files], s=0.2)
if field_name == "rt_peak":
a_x.axhline(y=row["atlas RT peak"], color="r", linestyle="-", linewidth=0.2)
range_columns += ["atlas RT peak"]
a_x.set_ylim(np.nanmin(row.loc[range_columns]) - 0.12, np.nanmax(row.loc[range_columns]) + 0.12)
else:
a_x.set_yscale("log")
a_x.set_ylim(bottom=1e4, top=1e10)
a_x.set_xlim(-0.5, num_files + 0.5)
a_x.xaxis.set_major_locator(mticker.FixedLocator(np.arange(0, num_files, 1.0)))
_ = [s.set_linewidth(0.1) for s in a_x.spines.values()]
# truncate name so it fits above a single subplot
a_x.set_title(row.name[:33], pad=pad, fontsize=fontsize)
a_x.set_xlabel("Files", labelpad=pad, fontsize=fontsize)
ylabel = "Actual RTs" if field_name == "rt_peak" else "Peak Height"
a_x.set_ylabel(ylabel, labelpad=pad, fontsize=fontsize)
def plot_data_and_fits(
result: ResultLike,
wavelength: float,
axis: Axis,
center_λ: float | None = None,
main_irf_nr: int = 0,
linlog: bool = False,
linthresh: float = 1,
divide_by_scale: bool = True,
per_axis_legend: bool = False,
y_label: str = "a.u.",
cycler: Cycler | None = PlotStyle().data_cycler_solid,
) -> None:
"""Plot data and fits for a given ``wavelength`` on a given ``axis``.
If the wavelength isn't part of a dataset, that dataset will be skipped.
Parameters
----------
result : ResultLike
Data structure which can be converted to a mapping.
wavelength : float
Wavelength to plot data and fits for.
axis: Axis
Axis to plot the data and fits on.
center_λ: float | None
Center wavelength (λ in nm)
main_irf_nr : int
Index of the main ``irf`` component when using an ``irf``
parametrized with multiple peaks. Defaults to 0.
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.
divide_by_scale : bool
Whether or not to divide the data by the dataset scale used for optimization.
Defaults to True.
per_axis_legend: bool
Whether to use a legend per plot or for the whole figure. Defaults to False.
y_label: str
Label used for the y-axis of each subplot.
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().data_cycler_solid.
See Also
--------
plot_fit_overview
"""
result_map = result_dataset_mapping(result)
add_cycler_if_not_none(axis, cycler)
for dataset_name in result_map.keys():
spectral_coords = result_map[dataset_name].coords["spectral"].values
if spectral_coords.min() <= wavelength <= spectral_coords.max():
result_data = result_map[dataset_name].sel(spectral=[wavelength],
method="nearest")
scale = extract_dataset_scale(result_data, divide_by_scale)
irf_loc = extract_irf_location(result_data, center_λ, main_irf_nr)
result_data = result_data.assign_coords(
time=result_data.coords["time"] - irf_loc)
(result_data.data / scale).plot(x="time",
ax=axis,
label=f"{dataset_name}_data")
(result_data.fitted_data / scale).plot(x="time",
ax=axis,
label=f"{dataset_name}_fit")
else:
[next(axis._get_lines.prop_cycler) for _ in range(2)]
if linlog:
axis.set_xscale("symlog", linthresh=linthresh)
axis.set_ylabel(y_label)
if per_axis_legend is True:
axis.legend()