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_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_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_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 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 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 control_plot(data: (List[int], List[float], pd.Series, np.array),
upper_control_limit: (int, float),
lower_control_limit: (int, float),
highlight_beyond_limits: bool = True,
highlight_zone_a: bool = True,
highlight_zone_b: bool = True,
highlight_zone_c: bool = True,
highlight_trend: bool = True,
highlight_mixture: bool = True,
highlight_stratification: bool = True,
highlight_overcontrol: bool = True,
ax: Axis = None):
"""
Create a control plot based on the input data.
:param data: a list, pandas.Series, or numpy.array representing the data set
:param upper_control_limit: an integer or float which represents the upper control limit, commonly called the UCL
:param lower_control_limit: an integer or float which represents the upper control limit, commonly called the UCL
:param highlight_beyond_limits: True if points beyond limits are to be highlighted
:param highlight_zone_a: True if points that are zone A violations are to be highlighted
:param highlight_zone_b: True if points that are zone B violations are to be highlighted
:param highlight_zone_c: True if points that are zone C violations are to be highlighted
:param highlight_trend: True if points that are trend violations are to be highlighted
:param highlight_mixture: True if points that are mixture violations are to be highlighted
:param highlight_stratification: True if points that are stratification violations are to be highlighted
:param highlight_overcontrol: True if points that are overcontrol violations are to be hightlighted
:param ax: an instance of matplotlib.axis.Axis
:return: None
"""
data = coerce(data)
if ax is None:
fig, ax = plt.subplots()
ax.plot(data)
ax.set_title('Zone Control Chart')
spec_range = (upper_control_limit - lower_control_limit) / 2
spec_center = lower_control_limit + spec_range
zone_c_upper_limit = spec_center + spec_range / 3
zone_c_lower_limit = spec_center - spec_range / 3
zone_b_upper_limit = spec_center + 2 * spec_range / 3
zone_b_lower_limit = spec_center - 2 * spec_range / 3
zone_a_upper_limit = spec_center + spec_range
zone_a_lower_limit = spec_center - spec_range
ax.axhline(spec_center, linestyle='--', color='red', alpha=0.6)
ax.axhline(zone_c_upper_limit, linestyle='--', color='red', alpha=0.5)
ax.axhline(zone_c_lower_limit, linestyle='--', color='red', alpha=0.5)
ax.axhline(zone_b_upper_limit, linestyle='--', color='red', alpha=0.3)
ax.axhline(zone_b_lower_limit, linestyle='--', color='red', alpha=0.3)
ax.axhline(zone_a_upper_limit, linestyle='--', color='red', alpha=0.2)
ax.axhline(zone_a_lower_limit, linestyle='--', color='red', alpha=0.2)
left, right = ax.get_xlim()
right_plus = (right - left) * 0.01 + right
ax.text(right_plus, upper_control_limit, s='UCL', va='center')
ax.text(right_plus, lower_control_limit, s='LCL', va='center')
ax.text(right_plus, (spec_center + zone_c_upper_limit) / 2,
s='Zone C',
va='center')
ax.text(right_plus, (spec_center + zone_c_lower_limit) / 2,
s='Zone C',
va='center')
ax.text(right_plus, (zone_b_upper_limit + zone_c_upper_limit) / 2,
s='Zone B',
va='center')
ax.text(right_plus, (zone_b_lower_limit + zone_c_lower_limit) / 2,
s='Zone B',
va='center')
ax.text(right_plus, (zone_a_upper_limit + zone_b_upper_limit) / 2,
s='Zone A',
va='center')
ax.text(right_plus, (zone_a_lower_limit + zone_b_lower_limit) / 2,
s='Zone A',
va='center')
plot_params = {'alpha': 0.3, 'zorder': -10, 'markersize': 14}
if highlight_beyond_limits:
beyond_limits_violations = control_beyond_limits(
data=data,
upper_control_limit=upper_control_limit,
lower_control_limit=lower_control_limit)
if len(beyond_limits_violations):
plot_params['zorder'] -= 1
plot_params['markersize'] -= 1
ax.plot(beyond_limits_violations,
'o',
color='red',
label='beyond limits',
**plot_params)
if highlight_zone_a:
zone_a_violations = control_zone_a(
data=data,
upper_control_limit=upper_control_limit,
lower_control_limit=lower_control_limit)
if len(zone_a_violations):
plot_params['zorder'] -= 1
plot_params['markersize'] -= 1
ax.plot(zone_a_violations,
'o',
color='orange',
label='zone a violations',
**plot_params)
if highlight_zone_b:
zone_b_violations = control_zone_b(
data=data,
upper_control_limit=upper_control_limit,
lower_control_limit=lower_control_limit)
if len(zone_b_violations):
plot_params['zorder'] -= 1
plot_params['markersize'] -= 1
ax.plot(zone_b_violations,
'o',
color='blue',
label='zone b violations',
**plot_params)
if highlight_zone_c:
zone_c_violations = control_zone_c(
data=data,
upper_control_limit=upper_control_limit,
lower_control_limit=lower_control_limit)
if len(zone_c_violations):
plot_params['zorder'] -= 1
plot_params['markersize'] -= 1
ax.plot(zone_c_violations,
'o',
color='green',
label='zone c violations',
**plot_params)
if highlight_trend:
zone_trend_violations = control_zone_trend(data=data)
if len(zone_trend_violations):
plot_params['zorder'] -= 1
plot_params['markersize'] -= 1
ax.plot(zone_trend_violations,
'o',
color='purple',
label='trend violations',
**plot_params)
if highlight_mixture:
zone_mixture_violations = control_zone_mixture(
data=data,
upper_control_limit=upper_control_limit,
lower_control_limit=lower_control_limit)
if len(zone_mixture_violations):
plot_params['zorder'] -= 1
plot_params['markersize'] -= 1
ax.plot(zone_mixture_violations,
'o',
color='brown',
label='mixture violations',
**plot_params)
if highlight_stratification:
zone_stratification_violations = control_zone_stratification(
data=data,
upper_control_limit=upper_control_limit,
lower_control_limit=lower_control_limit)
if len(zone_stratification_violations):
plot_params['zorder'] -= 1
plot_params['markersize'] -= 1
ax.plot(zone_stratification_violations,
'o',
color='orange',
label='stratification violations',
**plot_params)
if highlight_overcontrol:
zone_overcontrol_violations = control_zone_overcontrol(
data=data,
upper_control_limit=upper_control_limit,
lower_control_limit=lower_control_limit)
if len(zone_overcontrol_violations):
plot_params['zorder'] -= 1
plot_params['markersize'] -= 1
ax.plot(zone_overcontrol_violations,
'o',
color='blue',
label='overcontrol violations',
**plot_params)
ax.legend()