Visualization/Diagnostics

Creates diagnostic plots for visualizing imager event groupings.

Example

Load an event list and use the clustering algorithm to label each:

data = np.loadtxt("imager-event-list.txt")
group_ids = cluster_method.fit(data)

Then create a diagnostic and use it to plot the labels:

diagnostic = FullDiagnostic()
diagnostic.plot_diagnostic(data, group_ids)
diagnostic.savefig('diagnostic.png')
class x_ray_imager_bagriff.identify_lines.plot.GenericIdentifyDiagnostic

Generic figure for visualizing imager event groupings.

A list of events and optionally, their group ID should be passed to plot_diagnostic(). This diagnostic can then be saved or displayed like any other pyplot figure. The specific diagnostic should be implemented as a subclass.

__init__(*args, rc_params=None, **kwargs)

Initialize the diagnostic figure.

Parameters:

rc_params (dict | None) – A dictionary of matplotlib rc_params to use for this figure. If not provided, there is default style in this package. It’s linked here by the variable STYLE_FILE.

Return type:

None

plot_diagnostic(X, labels=None)

Visualize the events and classifications.

This calls _diagnostic which should be overloaded with the specific diagnostic.

Parameters:

  • X (ArrayLike) – Array of measurements. Shape is (n events, n detectors). See _identify.find_lines() for specifics.

  • labels (NDArray[int64] | None) – Integer index of cluster identified for each measurement.

Return type:

None

set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, canvas=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, constrained_layout=<UNSET>, constrained_layout_pads=<UNSET>, dpi=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, figheight=<UNSET>, figwidth=<UNSET>, frameon=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, layout_engine=<UNSET>, linewidth=<UNSET>, mouseover=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, rasterized=<UNSET>, size_inches=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, tight_layout=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)

Set multiple properties at once.

a.set(a=A, b=B, c=C)

is equivalent to

a.set_a(A)
a.set_b(B)
a.set_c(C)

In addition to the full property names, aliases are also supported, e.g. set(lw=2) is equivalent to set(linewidth=2), but it is an error to pass both simultaneously.

The order of the individual setter calls matches the order of parameters in set(). However, most properties do not depend on each other so that order is rarely relevant.

Supported properties are

Properties:

agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image alpha: float or None animated: bool canvas: FigureCanvas clip_box: ~matplotlib.transforms.BboxBase or None clip_on: bool clip_path: Patch or (Path, Transform) or None constrained_layout: unknown constrained_layout_pads: unknown dpi: float edgecolor: color facecolor: color figheight: float figure: unknown figwidth: float frameon: bool gid: str in_layout: bool label: object layout_engine: {‘constrained’, ‘compressed’, ‘tight’, ‘none’, .LayoutEngine, None} linewidth: number mouseover: bool path_effects: list of .AbstractPathEffect picker: None or bool or float or callable rasterized: bool size_inches: (float, float) or float sketch_params: (scale: float, length: float, randomness: float) snap: bool or None tight_layout: unknown transform: ~matplotlib.transforms.Transform url: str visible: bool zorder: float

class x_ray_imager_bagriff.identify_lines.plot.AngerDiagnostic

Scatter plot of events by Anger imager position, colored by id.

For details, see position_estimation._anger.anger().

anger(ax, X, labels=None, limit_points=1000)

Plot the anger diagnostic on the Axis provided.

Parameters:

  • ax (Axes) – Axis to use for the scatter plot.

  • X (ArrayLike) – Array of measurements. Shape is (n events, n detectors).

  • labels (NDArray[int64] | None) – Integer index of cluster identified for each measurement.

  • limit_points (int | None)

Return type:

None

set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, canvas=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, constrained_layout=<UNSET>, constrained_layout_pads=<UNSET>, dpi=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, figheight=<UNSET>, figwidth=<UNSET>, frameon=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, layout_engine=<UNSET>, linewidth=<UNSET>, mouseover=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, rasterized=<UNSET>, size_inches=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, tight_layout=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)

Set multiple properties at once.

a.set(a=A, b=B, c=C)

is equivalent to

a.set_a(A)
a.set_b(B)
a.set_c(C)

In addition to the full property names, aliases are also supported, e.g. set(lw=2) is equivalent to set(linewidth=2), but it is an error to pass both simultaneously.

The order of the individual setter calls matches the order of parameters in set(). However, most properties do not depend on each other so that order is rarely relevant.

Supported properties are

Properties:

agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image alpha: float or None animated: bool canvas: FigureCanvas clip_box: ~matplotlib.transforms.BboxBase or None clip_on: bool clip_path: Patch or (Path, Transform) or None constrained_layout: unknown constrained_layout_pads: unknown dpi: float edgecolor: color facecolor: color figheight: float figure: unknown figwidth: float frameon: bool gid: str in_layout: bool label: object layout_engine: {‘constrained’, ‘compressed’, ‘tight’, ‘none’, .LayoutEngine, None} linewidth: number mouseover: bool path_effects: list of .AbstractPathEffect picker: None or bool or float or callable rasterized: bool size_inches: (float, float) or float sketch_params: (scale: float, length: float, randomness: float) snap: bool or None tight_layout: unknown transform: ~matplotlib.transforms.Transform url: str visible: bool zorder: float

class x_ray_imager_bagriff.identify_lines.plot.AmplitudeDiagnostic

Histogram of the sum of all detectors for each event, colored by id.

amplitude_hist(ax, X, labels=None)

Plot the event amplitude histogram on the Axis provided.

Parameters:

  • ax (Axes) – Axis to use for the scatter plot.

  • X (ArrayLike) – Array of measurements. Shape is (n events, n detectors).

  • labels (NDArray[int64] | None) – Integer index of cluster identified for each measurement.

Return type:

None

set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, canvas=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, constrained_layout=<UNSET>, constrained_layout_pads=<UNSET>, dpi=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, figheight=<UNSET>, figwidth=<UNSET>, frameon=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, layout_engine=<UNSET>, linewidth=<UNSET>, mouseover=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, rasterized=<UNSET>, size_inches=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, tight_layout=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)

Set multiple properties at once.

a.set(a=A, b=B, c=C)

is equivalent to

a.set_a(A)
a.set_b(B)
a.set_c(C)

In addition to the full property names, aliases are also supported, e.g. set(lw=2) is equivalent to set(linewidth=2), but it is an error to pass both simultaneously.

The order of the individual setter calls matches the order of parameters in set(). However, most properties do not depend on each other so that order is rarely relevant.

Supported properties are

Properties:

agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image alpha: float or None animated: bool canvas: FigureCanvas clip_box: ~matplotlib.transforms.BboxBase or None clip_on: bool clip_path: Patch or (Path, Transform) or None constrained_layout: unknown constrained_layout_pads: unknown dpi: float edgecolor: color facecolor: color figheight: float figure: unknown figwidth: float frameon: bool gid: str in_layout: bool label: object layout_engine: {‘constrained’, ‘compressed’, ‘tight’, ‘none’, .LayoutEngine, None} linewidth: number mouseover: bool path_effects: list of .AbstractPathEffect picker: None or bool or float or callable rasterized: bool size_inches: (float, float) or float sketch_params: (scale: float, length: float, randomness: float) snap: bool or None tight_layout: unknown transform: ~matplotlib.transforms.Transform url: str visible: bool zorder: float

class x_ray_imager_bagriff.identify_lines.plot.FullDiagnostic

Combine the Anger and Amplitude diagnostics in one figure.

set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, canvas=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, constrained_layout=<UNSET>, constrained_layout_pads=<UNSET>, dpi=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, figheight=<UNSET>, figwidth=<UNSET>, frameon=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, layout_engine=<UNSET>, linewidth=<UNSET>, mouseover=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, rasterized=<UNSET>, size_inches=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, tight_layout=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)

Set multiple properties at once.

a.set(a=A, b=B, c=C)

is equivalent to

a.set_a(A)
a.set_b(B)
a.set_c(C)

In addition to the full property names, aliases are also supported, e.g. set(lw=2) is equivalent to set(linewidth=2), but it is an error to pass both simultaneously.

The order of the individual setter calls matches the order of parameters in set(). However, most properties do not depend on each other so that order is rarely relevant.

Supported properties are

Properties:

agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image alpha: float or None animated: bool canvas: FigureCanvas clip_box: ~matplotlib.transforms.BboxBase or None clip_on: bool clip_path: Patch or (Path, Transform) or None constrained_layout: unknown constrained_layout_pads: unknown dpi: float edgecolor: color facecolor: color figheight: float figure: unknown figwidth: float frameon: bool gid: str in_layout: bool label: object layout_engine: {‘constrained’, ‘compressed’, ‘tight’, ‘none’, .LayoutEngine, None} linewidth: number mouseover: bool path_effects: list of .AbstractPathEffect picker: None or bool or float or callable rasterized: bool size_inches: (float, float) or float sketch_params: (scale: float, length: float, randomness: float) snap: bool or None tight_layout: unknown transform: ~matplotlib.transforms.Transform url: str visible: bool zorder: float