def plot_norm_sas(res: xr.Dataset,
ax: Axis,
title: str = "norm SAS",
cycler: Cycler | None = PlotStyle().cycler) -> None:
"""Plot normalized SAS (Species Associated Spectra) on ``ax``.
Parameters
----------
res : xr.Dataset
Result dataset
ax : Axis
Axis to plot on.
title : str
Title of the plot. Defaults to "norm SAS".
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
"""
add_cycler_if_not_none(ax, cycler)
keys = [
v for v in res.data_vars
if v.startswith(("species_associated_spectra", "species_spectra"))
]
for key in keys:
sas = res[key]
# sas = res.species_associated_spectra
(sas / np.abs(sas).max(dim="spectral")).plot.line(x="spectral", ax=ax)
ax.set_title(title)
ax.get_legend().remove()
def plot_das(res: xr.Dataset,
ax: Axis,
title: str = "DAS",
cycler: Cycler | None = PlotStyle().cycler) -> None:
"""Plot DAS (Decay Associated Spectra) on ``ax``.
Parameters
----------
res : xr.Dataset
Result dataset
ax : Axis
Axis to plot on.
title : str
Title of the plot. Defaults to "DAS".
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
"""
add_cycler_if_not_none(ax, cycler)
keys = [
v for v in res.data_vars
if v.startswith(("decay_associated_spectra", "species_spectra"))
]
for key in keys:
das = res[key]
das.plot.line(x="spectral", ax=ax)
ax.set_title(title)
ax.get_legend().remove()
def plot_sv_residual(
res: xr.Dataset,
ax: Axis,
indices: Sequence[int] = range(10),
cycler: Cycler | None = PlotStyle().cycler,
) -> None:
"""Plot singular values of the residual matrix.
Parameters
----------
res : xr.Dataset
Result dataset
ax : Axis
Axis to plot on.
indices : Sequence[int]
Indices of the singular vector to plot. Defaults to range(4).
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
"""
add_cycler_if_not_none(ax, cycler)
if "weighted_residual_singular_values" in res:
rSV = res.weighted_residual_singular_values
else:
rSV = res.residual_singular_values
rSV.sel(singular_value_index=indices[:len(rSV.singular_value_index)]
).plot.line("ro-", yscale="log", ax=ax)
ax.set_title("res. log(SV)")
def plot_rsv_residual(
res: xr.Dataset,
ax: Axis,
indices: Sequence[int] = range(2),
cycler: Cycler | None = PlotStyle().cycler,
show_legend: bool = True,
) -> None:
"""Plot right singular vectors (spectra) of the residual matrix.
Parameters
----------
res : xr.Dataset
Result dataset
ax : Axis
Axis to plot on.
indices : Sequence[int]
Indices of the singular vector to plot. Defaults to range(4).
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
show_legend: bool
Whether or not to show the legend. Defaults to True.
"""
add_cycler_if_not_none(ax, cycler)
if "weighted_residual_right_singular_vectors" in res:
rRSV = res.weighted_residual_right_singular_vectors
else:
rRSV = res.residual_right_singular_vectors
_plot_svd_vetors(rRSV, indices, "right_singular_value_index", ax,
show_legend)
ax.set_title("res. RSV")
def plot_concentrations(
res: xr.Dataset,
ax: Axis,
center_λ: float | None,
linlog: bool = False,
linthresh: float = 1,
linscale: float = 1,
main_irf_nr: int = 0,
cycler: Cycler | None = PlotStyle().cycler,
title: str = "Concentrations",
) -> None:
"""Plot traces on the given axis ``ax``.
Parameters
----------
res: xr.Dataset
Result dataset from a pyglotaran optimization.
ax: Axis
Axis to plot the traces on
center_λ: float | None
Center wavelength (λ in nm)
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.
linscale: float
This allows the linear range (-linthresh to linthresh) to be stretched
relative to the logarithmic range.
Its value is the number of decades to use for each half of the linear range.
For example, when linscale == 1.0 (the default), the space used for the
positive and negative halves of the linear range will be equal to one
decade in the logarithmic range. Defaults to 1.
main_irf_nr: int
Index of the main ``irf`` component when using an ``irf``
parametrized with multiple peaks. Defaults to 0.
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().data_cycler_solid.
title: str
Title used for the plot axis. Defaults to "Concentrations".
See Also
--------
get_shifted_traces
"""
add_cycler_if_not_none(ax, cycler)
traces = get_shifted_traces(res, center_λ, main_irf_nr)
if "spectral" in traces.coords:
traces.sel(spectral=center_λ, method="nearest").plot.line(x="time",
ax=ax)
else:
traces.plot.line(x="time", ax=ax)
ax.set_title(title)
if linlog:
ax.set_xscale("symlog", linthresh=linthresh, linscale=linscale)
def plot_spectra(res: xr.Dataset,
axes: Axes,
cycler: Cycler | None = PlotStyle().cycler) -> None:
"""Plot spectra such as SAS and DAS as well as their normalize version on ``axes``.
Parameters
----------
res : xr.Dataset
Result dataset
axes : Axes
Axes to plot the spectra on (needs to be at least 2x2).
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
"""
plot_sas(res, axes[0, 0], cycler=cycler)
plot_das(res, axes[0, 1], cycler=cycler)
plot_norm_sas(res, axes[1, 0], cycler=cycler)
plot_norm_das(res, axes[1, 1], cycler=cycler)
def plot_residual(
res: xr.Dataset,
ax: Axis,
linlog: bool = False,
linthresh: float = 1,
show_data: bool = False,
cycler: Cycler | None = PlotStyle().cycler,
) -> None:
"""Plot data or residual on a 2D contour plot.
Parameters
----------
res : xr.Dataset
Result dataset
ax : Axis
Axis to plot on.
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.
show_data : bool
Whether to show the data or the residual. Defaults to False.
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
"""
add_cycler_if_not_none(ax, cycler)
data = res.data if show_data else res.residual
title = "dataset" if show_data else "residual"
shape = np.array(data.shape)
dims = data.coords.dims
# Handle different dimensionality of data
if min(shape) == 1:
data.plot.line(x=dims[shape.argmax()], ax=ax)
elif min(shape) < 5:
data.plot(x="time", ax=ax)
else:
data.plot(x="time", ax=ax, add_colorbar=False)
if linlog:
ax.set_xscale("symlog", linthresh=linthresh)
ax.set_title(title)
def load_and_plot_results():
# %% Plot and save as PDF
# This set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
parameter_file = output_folder.joinpath("optimized_parameters.csv")
parameters = read_parameters_from_csv_file(str(parameter_file))
print(f"Optimized parameters loaded:\n {parameters}")
result1 = output_folder.joinpath("dataset1.nc")
fig1 = plot_overview(result1, linlog=True, show_data=True)
timestamp = datetime.today().strftime("%y%m%d_%H%M")
fig1.savefig(output_folder.joinpath(f"plot_overview_1of2_{timestamp}.pdf"),
bbox_inches="tight")
result2 = output_folder.joinpath("dataset2.nc")
fig2 = plot_overview(result2, linlog=True)
timestamp = datetime.today().strftime("%y%m%d_%H%M")
fig2.savefig(output_folder.joinpath(f"plot_overview_2of2_{timestamp}.pdf"),
bbox_inches="tight")
plt.show()
def plot_lsv_residual(
res: xr.Dataset,
ax: Axis,
indices: Sequence[int] = range(2),
linlog: bool = False,
linthresh: float = 1,
cycler: Cycler | None = PlotStyle().cycler,
show_legend: bool = True,
) -> None:
"""Plot left singular vectors (time) of the residual matrix.
Parameters
----------
res : xr.Dataset
Result dataset
ax : Axis
Axis to plot on.
indices : Sequence[int]
Indices of the singular vector to plot. Defaults to range(4).
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.
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
show_legend: bool
Whether or not to show the legend. Defaults to True.
"""
add_cycler_if_not_none(ax, cycler)
if "weighted_residual_left_singular_vectors" in res:
rLSV = res.weighted_residual_left_singular_vectors
else:
rLSV = res.residual_left_singular_vectors
_plot_svd_vetors(rLSV, indices, "left_singular_value_index", ax,
show_legend)
ax.set_title("res. LSV")
if linlog:
ax.set_xscale("symlog", linthresh=linthresh)
def plot_overview(
result: DatasetConvertible,
center_λ: float | None = None,
linlog: bool = True,
linthresh: float = 1,
linscale: float = 1,
show_data: bool = False,
main_irf_nr: int = 0,
figsize: tuple[int, int] = (18, 16),
cycler: Cycler | None = PlotStyle().cycler,
figure_only: bool = True,
nr_of_data_svd_vectors: int = 4,
nr_of_residual_svd_vectors: int = 2,
show_data_svd_legend: bool = True,
show_residual_svd_legend: bool = True,
) -> Figure | tuple[Figure, Axes]:
"""Plot overview of the optimization result.
Parameters
----------
result: DatasetConvertible
Result from a pyglotaran optimization as dataset, Path or Result object.
center_λ: float | None
Center wavelength (λ in nm)
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.
linscale: float
This allows the linear range (-linthresh to linthresh) to be stretched
relative to the logarithmic range.
Its value is the number of decades to use for each half of the linear range.
For example, when linscale == 1.0 (the default), the space used for the
positive and negative halves of the linear range will be equal to one
decade in the logarithmic range. Defaults to 1.
show_data: bool
Whether to show the input data or residual. Defaults to False.
main_irf_nr: int
Index of the main ``irf`` component when using an ``irf``
parametrized with multiple peaks. Defaults to 0.
figsize : tuple[int, int]
Size of the figure (N, M) in inches. Defaults to (18, 16).
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
figure_only: bool
Whether or not to only return the figure.
This is a deprecation helper argument to transition to a consistent return value
consisting of the :class:`Figure` and the :class:`Axes`. Defaults to True.
nr_of_data_svd_vectors: int
Number of data SVD vector to plot. Defaults to 4.
nr_of_residual_svd_vectors: int
Number of residual SVD vector to plot. Defaults to 2.
show_data_svd_legend: bool
Whether or not to show the data SVD legend. Defaults to True.
show_residual_svd_legend: bool
Whether or not to show the residual SVD legend. Defaults to True.
Returns
-------
Figure|tuple[Figure, Axes]
If ``figure_only`` is True, Figure object which contains the plots (deprecated).
If ``figure_only`` is False, Figure object which contains the plots and the Axes.
"""
res = load_data(result)
# Plot dimensions
M = 4
N = 3
fig, axes = plt.subplots(M, N, figsize=figsize, constrained_layout=True)
if center_λ is None: # center wavelength (λ in nm)
center_λ = min(res.dims["spectral"], round(res.dims["spectral"] / 2))
# First and second row: concentrations - SAS/EAS - DAS
plot_concentrations(
res,
axes[0, 0],
center_λ,
linlog=linlog,
linthresh=linthresh,
linscale=linscale,
main_irf_nr=main_irf_nr,
cycler=cycler,
)
plot_spectra(res, axes[0:2, 1:3], cycler=cycler)
plot_svd(
res,
axes[2:4, 0:3],
linlog=linlog,
linthresh=linthresh,
cycler=cycler,
nr_of_data_svd_vectors=nr_of_data_svd_vectors,
nr_of_residual_svd_vectors=nr_of_residual_svd_vectors,
show_data_svd_legend=show_data_svd_legend,
show_residual_svd_legend=show_residual_svd_legend,
)
plot_residual(
res, axes[1, 0], linlog=linlog, linthresh=linthresh, show_data=show_data, cycler=cycler
)
# plt.tight_layout(pad=3, w_pad=4.0, h_pad=4.0)
if figure_only is True:
warn(PyglotaranExtrasApiDeprecationWarning(FIG_ONLY_WARNING), stacklevel=2)
return fig
else:
return fig, axes
def plot_simple_overview(
result: DatasetConvertible,
title: str | None = None,
figsize: tuple[int, int] = (12, 6),
cycler: Cycler | None = PlotStyle().cycler,
figure_only: bool = True,
) -> Figure | tuple[Figure, Axes]:
"""Plot simple overview.
Parameters
----------
result: DatasetConvertible
Result from a pyglotaran optimization as dataset, Path or Result object.
title: str | None
Title of the figure. Defaults to None.
figsize : tuple[int, int]
Size of the figure (N, M) in inches. Defaults to (18, 16).
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
figure_only: bool
Whether or not to only return the figure.
This is a deprecation helper argument to transition to a consistent return value
consisting of the :class:`Figure` and the :class:`Axes`. Defaults to True.
Returns
-------
Figure|tuple[Figure, Axes]
If ``figure_only`` is True, Figure object which contains the plots (deprecated).
If ``figure_only`` is False, Figure object which contains the plots and the Axes.
"""
res = load_data(result)
fig, axes = plt.subplots(2, 3, figsize=figsize, constrained_layout=True)
for ax in axes.flatten():
add_cycler_if_not_none(ax, cycler)
if title:
fig.suptitle(title, fontsize=16)
sas = res.species_associated_spectra
traces = res.species_concentration
if "spectral" in traces.coords:
traces.sel(spectral=res.spectral.values[0], method="nearest").plot.line(
x="time", ax=axes[0, 0]
)
else:
traces.plot.line(x="time", ax=axes[0, 0])
sas.plot.line(x="spectral", ax=axes[0, 1])
rLSV = res.residual_left_singular_vectors
rLSV.isel(left_singular_value_index=range(min(2, len(rLSV)))).plot.line(
x="time", ax=axes[1, 0]
)
axes[1, 0].set_title("res. LSV")
rRSV = res.residual_right_singular_vectors
rRSV.isel(right_singular_value_index=range(min(2, len(rRSV)))).plot.line(
x="spectral", ax=axes[1, 1]
)
axes[1, 1].set_title("res. RSV")
res.data.plot(x="time", ax=axes[0, 2])
axes[0, 2].set_title("data")
res.residual.plot(x="time", ax=axes[1, 2])
axes[1, 2].set_title("residual")
if figure_only is True:
warn(PyglotaranExtrasApiDeprecationWarning(FIG_ONLY_WARNING), stacklevel=2)
return fig
else:
return fig, axes
def plot_doas(
result: DatasetConvertible,
figsize: tuple[int, int] = (25, 25),
cycler: Cycler | None = PlotStyle().cycler,
figure_only: bool = True,
) -> Figure | tuple[Figure, Axes]:
"""Plot Damped oscillation associated spectra (DOAS).
Parameters
----------
result: DatasetConvertible
Result from a pyglotaran optimization as dataset, Path or Result object.
figsize : tuple[int, int]
Size of the figure (N, M) in inches. Defaults to (18, 16).
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
figure_only: bool
Whether or not to only return the figure.
This is a deprecation helper argument to transition to a consistent return value
consisting of the :class:`Figure` and the :class:`Axes`. Defaults to True.
Returns
-------
Figure|tuple[Figure, Axes]
If ``figure_only`` is True, Figure object which contains the plots (deprecated).
If ``figure_only`` is False, Figure object which contains the plots and the Axes.
"""
dataset = load_data(result)
# Create M x N plotting grid
M = 6
N = 3
fig, axes = plt.subplots(M, N, figsize=figsize)
for ax in axes.flatten():
add_cycler_if_not_none(ax, cycler)
# Plot data
dataset.species_associated_spectra.plot.line(x="spectral", ax=axes[0, 0])
dataset.decay_associated_spectra.plot.line(x="spectral", ax=axes[0, 1])
if "spectral" in dataset.species_concentration.coords:
dataset.species_concentration.isel(spectral=0).plot.line(x="time",
ax=axes[1, 0])
else:
dataset.species_concentration.plot.line(x="time", ax=axes[1, 0])
axes[1, 0].set_xscale("symlog", linthreshx=1)
if "dampened_oscillation_associated_spectra" in dataset:
dataset.dampened_oscillation_cos.isel(spectral=0).sel(
time=slice(-1, 10)).plot.line(x="time", ax=axes[1, 1])
dataset.dampened_oscillation_associated_spectra.plot.line(x="spectral",
ax=axes[2,
0])
dataset.dampened_oscillation_phase.plot.line(x="spectral",
ax=axes[2, 1])
dataset.residual_left_singular_vectors.isel(
left_singular_value_index=0).plot(ax=axes[0, 2])
dataset.residual_singular_values.plot.line("ro-",
yscale="log",
ax=axes[1, 2])
dataset.residual_right_singular_vectors.isel(
right_singular_value_index=0).plot(ax=axes[2, 2])
interval = int(dataset.spectral.size / 11)
for i in range(0):
axi = axes[i % 3, int(i / 3) + 3]
index = (i + 1) * interval
dataset.data.isel(spectral=index).plot(ax=axi)
dataset.residual.isel(spectral=index).plot(ax=axi)
dataset.fitted_data.isel(spectral=index).plot(ax=axi)
plt.tight_layout(pad=5, w_pad=2.0, h_pad=2.0)
if figure_only is True:
warn(PyglotaranExtrasApiDeprecationWarning(FIG_ONLY_WARNING),
stacklevel=2)
return fig
else:
return fig, axes
"""Tests for pyglotaran_extras.plotting.utils"""
from __future__ import annotations
import matplotlib
import matplotlib.pyplot as plt
import pytest
from cycler import Cycler
from cycler import cycle
from pyglotaran_extras.plotting.style import PlotStyle
from pyglotaran_extras.plotting.utils import add_cycler_if_not_none
matplotlib.use("Agg")
DEFAULT_CYCLER = plt.rcParams["axes.prop_cycle"]
@pytest.mark.parametrize(
"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"""
ax = plt.subplot()
add_cycler_if_not_none(ax, cycler)
for _ in range(10):
assert next(ax._get_lines.prop_cycler) == next(expected_cycler)
def plot_svd(
res: xr.Dataset,
axes: Axes,
linlog: bool = False,
linthresh: float = 1,
cycler: Cycler | None = PlotStyle().cycler,
nr_of_data_svd_vectors: int = 4,
nr_of_residual_svd_vectors: int = 2,
show_data_svd_legend: bool = True,
show_residual_svd_legend: bool = True,
) -> None:
"""Plot SVD (Singular Value Decomposition) of data and residual.
Parameters
----------
res : xr.Dataset
Result dataset
axes : Axes
Axes to plot the SVDs on (needs to be at least 2x3).
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.
cycler : Cycler | None
Plot style cycler to use. Defaults to PlotStyle().cycler.
nr_of_data_svd_vectors: int
Number of data SVD vector to plot. Defaults to 4.
nr_of_residual_svd_vectors: int
Number of residual SVD vector to plot. Defaults to 2.
show_data_svd_legend: bool
Whether or not to show the data SVD legend. Defaults to True.
show_residual_svd_legend: bool
Whether or not to show the residual SVD legend. Defaults to True.
"""
if "weighted_residual" in res:
add_svd_to_dataset(dataset=res, name="weighted_residual")
else:
add_svd_to_dataset(dataset=res, name="residual")
plot_lsv_residual(
res,
axes[0, 0],
linlog=linlog,
linthresh=linthresh,
cycler=cycler,
indices=range(nr_of_residual_svd_vectors),
show_legend=show_residual_svd_legend,
)
plot_rsv_residual(
res,
axes[0, 1],
cycler=cycler,
indices=range(nr_of_residual_svd_vectors),
show_legend=show_residual_svd_legend,
)
plot_sv_residual(res, axes[0, 2], cycler=cycler)
add_svd_to_dataset(dataset=res, name="data")
plot_lsv_data(
res,
axes[1, 0],
linlog=linlog,
linthresh=linthresh,
cycler=cycler,
indices=range(nr_of_data_svd_vectors),
show_legend=show_data_svd_legend,
)
plot_rsv_data(
res,
axes[1, 1],
cycler=cycler,
indices=range(nr_of_data_svd_vectors),
show_legend=show_data_svd_legend,
)
plot_sv_data(res, axes[1, 2], cycler=cycler)
def plot_fitted_traces(
result: ResultLike,
wavelengths: Iterable[float],
axes_shape: tuple[int, int] = (4, 4),
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,
figsize: tuple[int, int] = (30, 15),
title: str = "Fit overview",
y_label: str = "a.u.",
cycler: Cycler | None = PlotStyle().data_cycler_solid,
) -> tuple[Figure, Axes]:
"""Plot data and their fit in per wavelength plot grid.
Parameters
----------
result : ResultLike
Data structure which can be converted to a mapping of datasets.
axes_shape : tuple[int, int]
Shape of the plot grid (N, M). Defaults to (4, 4).
wavelengths: Iterable[float]
Wavelength which should be used for each subplot, should to be of length N*M
with ``axes_shape`` being of shape (N, M), else it will result in missing plots.
center_λ: float | None
Center wavelength of the IRF (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.
figsize : tuple[int, int]
Size of the figure (N, M) in inches. Defaults to (30, 15).
title : str
Title to add to the figure. Defaults to "Fit overview".
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.
Returns
-------
tuple[Figure, Axes]
Figure and axes which can then be refined by the user.
See Also
--------
maximum_coordinate_range
add_unique_figure_legend
plot_data_and_fits
calculate_wavelengths
"""
result_map = result_dataset_mapping(result)
fig, axes = plt.subplots(*axes_shape, figsize=figsize)
nr_of_plots = len(axes.flatten())
max_spectral_values = max(
len(result_map[dataset_name].coords["spectral"])
for dataset_name in result_map.keys())
if nr_of_plots > max_spectral_values:
warn(
PlotDuplicationWarning(
f"The number of plots ({nr_of_plots}) exceeds the maximum number of "
f"spectral data points ({max_spectral_values}), "
"which will lead in duplicated plots."),
stacklevel=2,
)
for wavelength, axis in zip(wavelengths, axes.flatten()):
plot_data_and_fits(
result=result_map,
wavelength=wavelength,
axis=axis,
center_λ=center_λ,
main_irf_nr=main_irf_nr,
linlog=linlog,
linthresh=linthresh,
divide_by_scale=divide_by_scale,
per_axis_legend=per_axis_legend,
y_label=y_label,
cycler=cycler,
)
if per_axis_legend is False:
add_unique_figure_legend(fig, axes)
fig.suptitle(title, fontsize=28)
fig.tight_layout()
return fig, axes
"dataset1": dataset1,
"dataset2": dataset2,
"dataset3": dataset3
},
maximum_number_function_evaluations=99,
non_negative_least_squares=True,
# optimization_method="Levenberg-Marquardt",
)
# optimize
result = optimize(scheme)
# %% Save results
result.save(str(output_folder))
# %% Plot results
# Set subsequent plots to the glotaran style
plot_style = PlotStyle()
plt.rc("axes", prop_cycle=plot_style.cycler)
# TODO: enhance plot_overview to handle multiple datasets
result_datafile1 = output_folder.joinpath("dataset1.nc")
result_datafile2 = output_folder.joinpath("dataset2.nc")
result_datafile3 = output_folder.joinpath("dataset3.nc")
fig1 = plot_overview(result_datafile1, linlog=True, linthresh=1)
fig1.savefig(
output_folder.joinpath("plot_overview_sim3d_d1.pdf"),
bbox_inches="tight",
)
fig2 = plot_overview(result_datafile2, linlog=True, linthresh=1)
fig2.savefig(
output_folder.joinpath("plot_overview_sim3d_d2.pdf"),
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()