Trial visualization: services

FiltersService

Returned by: TrialVisualization.filters

Member	Kind	Description
`list`	method	Return all currently configured filter payloads.
`replace`	method	Replace the full filter list with raw filter payloads.
`add_numeric`	method	Append one numeric descriptor filter to the visualization.
`add_categorical`	method	Append one categorical descriptor filter to the visualization.
`add_patient_list`	method	Append one patient-list filter to the visualization.
`get`	method	Return the current stored payload for this visualization section.
`clear`	method	Clear this visualization section from the trial visualization.

list

list() -> list[dict[str, Any]]

Return all currently configured filter payloads.

Examples:

>>> trial_viz.filters.list()
[{'tag': 'DescriptorFilter', 'contents': [...]}]

replace

replace(
    filters: Sequence[dict[str, Any]], *, version: str | dict | None = None
) -> TrialVisualization

Replace the full filter list with raw filter payloads.

Use this when you already know the backend payload shape and want to set all filters in one call.

Examples:

>>> trial_viz.filters.replace([
...     {
...         'tag': 'DescriptorFilter',
...         'contents': [
...             {'descriptorId': 'Blood.Drug.tend', 'arm': 'identity', 'operator': 'Gte', 'value': 0.0}
...         ],
...     }
... ])

add_numeric

add_numeric(
    descriptor: DescriptorRef,
    *,
    operator: Literal["Eq", "Neq", "Lt", "Lte", "Gt", "Gte"],
    value: float,
    arm: str | None = None,
    version: str | dict | None = None
) -> TrialVisualization

Append one numeric descriptor filter to the visualization.

Pass either a raw descriptor id string, a descriptor handle from trial.descriptors, or a dict with id / descriptorId and optional arm.

Examples:

>>> age = trial.descriptors.scalars.get('Blood.Drug.tend', arm='identity')
>>> trial_viz.filters.add_numeric(age, operator='Gte', value=0.0)

With a raw id:

>>> trial_viz.filters.add_numeric('Blood.Drug.tend', operator='Lte', value=10.0, arm='identity')

add_categorical

add_categorical(
    descriptor: DescriptorRef,
    *,
    levels: Sequence[str],
    arm: str | None = None,
    version: str | dict | None = None
) -> TrialVisualization

Append one categorical descriptor filter to the visualization.

Pass either a categorical descriptor handle, a raw descriptor id, or a dict with id / descriptorId and optional arm.

Examples:

>>> sex = trial.descriptors.categoricals.get('sex', arm='identity')
>>> trial_viz.filters.add_categorical(sex, levels=['female'])

add_patient_list

add_patient_list(
    patients: Sequence[str], *, version: str | dict | None = None
) -> TrialVisualization

Append one patient-list filter to the visualization.

Examples:

>>> trial_viz.filters.add_patient_list(['patient-001', 'patient-002'])

get

get() -> dict[str, Any] | list[dict[str, Any]] | None

Return the current stored payload for this visualization section.

Examples:

>>> trial_viz.timeseries.get()
{'selectors': ['Blood.Drug'], 'layout': {}, ...}

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Clear this visualization section from the trial visualization.

Examples:

>>> trial_viz.scatter_plots.clear()

GroupsService

Returned by: TrialVisualization.groups

Member	Kind	Description
`get`	method	Return the current grouping configuration.
`replace`	method	Replace the full grouping configuration.
`clear`	method	Reset grouping to the default behavior: group by arm only.
`set_group_by_arm`	method	Enable or disable grouping by arm.
`add_scalar_distinct_grouping`	method	Add scalar grouping by distinct values.
`add_scalar_bin_width_grouping`	method	Add scalar grouping by fixed bin width.
`add_scalar_bin_count_grouping`	method	Add scalar grouping by number of bins.
`add_scalar_quantile_grouping`	method	Add scalar grouping by quantiles.
`add_scalar_explicit_grouping`	method	Add scalar grouping with explicit cut points.
`add_categorical_grouping`	method	Add categorical grouping.

get

get() -> list[dict[str, Any]]

Return the current grouping configuration.

Examples:

>>> trial_viz.groups.get()
[{'tag': 'Scalar', 'contents': {...}}]

Use this when you want to inspect the exact configured groupings or make a local copy before editing it and sending it back with replace.

replace

replace(
    groups: Sequence[dict[str, Any]], *, version: str | dict | None = None
) -> TrialVisualization

Replace the full grouping configuration.

This is the advanced editing path for groupings. The intended workflow is:

read the current configuration with get
edit that list locally
write it back with replace

Examples:

>>> groups = trial_viz.groups.get()
>>> groups.pop(3) # remove the fourth grouping
>>> trial_viz.groups.replace(groups)

groups should be a list of grouping dictionaries in the same shape as returned by get.

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Reset grouping to the default behavior: group by arm only.

Examples:

>>> trial_viz.groups.clear()

set_group_by_arm

set_group_by_arm(
    enabled: bool, *, version: str | dict | None = None
) -> TrialVisualization

Enable or disable grouping by arm.

This controls the same concept as the web UI's "group by arm" toggle. Enabling it ensures one arm-grouping entry is present. Disabling it removes any existing arm-grouping entries while leaving the other configured groupings unchanged.

Examples:

>>> trial_viz.groups.set_group_by_arm(True)
>>> trial_viz.groups.set_group_by_arm(False)

add_scalar_distinct_grouping

add_scalar_distinct_grouping(
    descriptor: DescriptorRef,
    *,
    reference_arm: ReferenceArmRef = "crossArms",
    version: str | dict | None = None
) -> TrialVisualization

Add scalar grouping by distinct values.

Pass either a trial scalar descriptor handle or a raw descriptor id. reference_arm may be:

an arm id string
"crossArms"
"identity"
a protocol-arm handle

Examples:

>>> auc = trial.descriptors.scalars.get('Tumor.Drug.auc', arm='identity')
>>> trial_viz.groups.add_scalar_distinct_grouping(auc, reference_arm='identity')

add_scalar_bin_width_grouping

add_scalar_bin_width_grouping(
    descriptor: DescriptorRef,
    *,
    reference_arm: ReferenceArmRef = "crossArms",
    offset: float,
    width: float,
    version: str | dict | None = None
) -> TrialVisualization

Add scalar grouping by fixed bin width.

offset is the lower bound of the first bin. width is the size of each bin and must be strictly positive.

Examples:

>>> auc = trial.descriptors.scalars.get('Tumor.Drug.auc', arm='identity')
>>> trial_viz.groups.add_scalar_bin_width_grouping(
...     auc,
...     reference_arm='identity',
...     offset=0.0,
...     width=2.5,
... )

add_scalar_bin_count_grouping

add_scalar_bin_count_grouping(
    descriptor: DescriptorRef,
    *,
    reference_arm: ReferenceArmRef = "crossArms",
    count: int,
    version: str | dict | None = None
) -> TrialVisualization

Add scalar grouping by number of bins.

count is the requested number of bins and must be strictly positive.

Examples:

>>> tend = trial.descriptors.scalars.get('Blood.Drug.tend', arm='identity')
>>> trial_viz.groups.add_scalar_bin_count_grouping(
...     tend,
...     reference_arm='identity',
...     count=3,
... )

add_scalar_quantile_grouping

add_scalar_quantile_grouping(
    descriptor: DescriptorRef,
    *,
    reference_arm: ReferenceArmRef = "crossArms",
    count: int,
    version: str | dict | None = None
) -> TrialVisualization

Add scalar grouping by quantiles.

count is the requested number of quantile groups and must be strictly positive.

Examples:

>>> auc = trial.descriptors.scalars.get('Tumor.Drug.auc', arm='identity')
>>> trial_viz.groups.add_scalar_quantile_grouping(
...     auc,
...     reference_arm='identity',
...     count=4,
... )

add_scalar_explicit_grouping

add_scalar_explicit_grouping(
    descriptor: DescriptorRef,
    *,
    reference_arm: ReferenceArmRef = "crossArms",
    breaks: Sequence[float],
    version: str | dict | None = None
) -> TrialVisualization

Add scalar grouping with explicit cut points.

breaks defines the cut points between groups. Values must be strictly increasing.

Examples:

>>> tend = trial.descriptors.scalars.get('Blood.Drug.tend', arm='identity')
>>> trial_viz.groups.add_scalar_explicit_grouping(
...     tend,
...     reference_arm='identity',
...     breaks=[0.0, 2.0, 5.0],
... )

add_categorical_grouping

add_categorical_grouping(
    descriptor: DescriptorRef,
    *,
    reference_arm: ReferenceArmRef = "crossArms",
    version: str | dict | None = None
) -> TrialVisualization

Add categorical grouping.

Pass either a trial categorical descriptor handle or a raw descriptor id. reference_arm may be an arm id string, "crossArms", "identity", or a protocol-arm handle.

Examples:

>>> sex = trial.descriptors.categoricals.get('sex', arm='identity')
>>> trial_viz.groups.add_categorical_grouping(sex, reference_arm='identity')

TimeseriesService

Returned by: TrialVisualization.timeseries

Member	Kind	Description
`set_selectors`	method	Set the time-series selector list, preserving existing section options.
`add_selectors`	method	Append selectors to the current time-series selection.
`replace`	method	Replace this section with the provided raw payload. The intended workflow is:
`get`	method	Return the current stored payload for this visualization section.
`clear`	method	Clear this visualization section from the trial visualization.

set_selectors

set_selectors(
    selectors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Set the time-series selector list, preserving existing section options.

Pass raw ids, descriptor handles, or dicts with id / descriptorId.

Examples:

>>> trial_viz.timeseries.set_selectors(['Blood.Drug', 'Tumor.Drug'])

add_selectors

add_selectors(
    selectors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Append selectors to the current time-series selection.

Examples:

>>> trial_viz.timeseries.add_selectors(['Tumor.Drug'])

replace

replace(
    payload: dict[str, Any], *, version: str | dict | None = None
) -> TrialVisualization

Replace this section with the provided raw payload. The intended workflow is:

read the current configuration with get
edit it locally
write it back with replace

Examples:

>>> timeseries=trial_viz.timeseries.get()
>>> timeseries["selectors"].pop(2) # edit timeseries
>>> trial_viz.timeseries.replace(timeseries)

get

get() -> dict[str, Any] | list[dict[str, Any]] | None

Return the current stored payload for this visualization section.

Examples:

>>> trial_viz.timeseries.get()
{'selectors': ['Blood.Drug'], 'layout': {}, ...}

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Clear this visualization section from the trial visualization.

Examples:

>>> trial_viz.scatter_plots.clear()

ScalarsService

Returned by: TrialVisualization.scalars

Member	Kind	Description
`set_selectors`	method	Set the scalar selector list, preserving existing section options.
`add_selectors`	method	Append selectors to the current scalar selection.
`replace`	method	Replace this section with the provided raw payload. The intended workflow is:
`get`	method	Return the current stored payload for this visualization section.
`clear`	method	Clear this visualization section from the trial visualization.

set_selectors

set_selectors(
    selectors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Set the scalar selector list, preserving existing section options.

Examples:

>>> auc = trial.descriptors.scalars.get('Tumor.Drug.auc', arm='identity')
>>> trial_viz.scalars.set_selectors([auc])

add_selectors

add_selectors(
    selectors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Append selectors to the current scalar selection.

Examples:

>>> trial_viz.scalars.add_selectors(['Tumor.Drug.auc'])

replace

replace(
    payload: dict[str, Any], *, version: str | dict | None = None
) -> TrialVisualization

Replace this section with the provided raw payload. The intended workflow is:

read the current configuration with get
edit it locally
write it back with replace

Examples:

>>> timeseries=trial_viz.timeseries.get()
>>> timeseries["selectors"].pop(2) # edit timeseries
>>> trial_viz.timeseries.replace(timeseries)

get

get() -> dict[str, Any] | list[dict[str, Any]] | None

Return the current stored payload for this visualization section.

Examples:

>>> trial_viz.timeseries.get()
{'selectors': ['Blood.Drug'], 'layout': {}, ...}

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Clear this visualization section from the trial visualization.

Examples:

>>> trial_viz.scatter_plots.clear()

ScatterPlotsService

Returned by: TrialVisualization.scatter_plots

Member	Kind	Description
`set_config`	method	Replace the scatter-plot configuration list and optional regression settings.
`add_x_vs_x_plot`	method	Append one X-vs-X scatter plot configuration.
`add_x_vs_y_plot`	method	Append one X-vs-Y scatter plot configuration.
`set_regression`	method	Set scatter-plot regression options.
`replace`	method	Replace this section with the provided raw payload. The intended workflow is:
`get`	method	Return the current stored payload for this visualization section.
`clear`	method	Clear this visualization section from the trial visualization.

set_config

set_config(
    scatter_plot_config: Sequence[dict[str, Any]],
    *,
    regression: dict[str, Any] | None = None,
    version: str | dict | None = None
) -> TrialVisualization

Replace the scatter-plot configuration list and optional regression settings.

scatter_plot_config is currently a raw payload surface because the backend shape is fairly specific and we do not yet have a richer typed builder.

Examples:

>>> trial_viz.scatter_plots.set_config([
...     {
...         'id': 'auc_vs_cmax',
...         'mode': 'XvsY',
...         'variables': {'x': 'Tumor.Drug.auc', 'y': 'Blood.Drug.max'},
...         'arms': ['identity'],
...         'groupedByArm': True,
...     }
... ])

Note

The exact payload contract for every scatter mode is not fully documented beyond OpenAPI/examples and existing UI behavior.

add_x_vs_x_plot

add_x_vs_x_plot(
    variable: DescriptorRef,
    *,
    reference_arm: ReferenceArmRef,
    compare_arms: Sequence[ReferenceArmRef],
    grouped_by_arm: bool = True,
    plot_id: str | None = None,
    version: str | dict | None = None
) -> TrialVisualization

Append one X-vs-X scatter plot configuration.

This compares one variable in a reference arm against the same variable in one or more other arms.

Examples:

>>> trial_viz.scatter_plots.add_x_vs_x_plot(
...     'Blood.Drug.tend',
...     reference_arm='identity',
...     compare_arms=['treated'],
... )

add_x_vs_y_plot

add_x_vs_y_plot(
    x_variable: DescriptorRef,
    y_variable: DescriptorRef,
    *,
    arms: Sequence[ReferenceArmRef],
    grouped_by_arm: bool = True,
    plot_id: str | None = None,
    version: str | dict | None = None
) -> TrialVisualization

Append one X-vs-Y scatter plot configuration.

This compares one variable against another over one or more arms.

Examples:

>>> trial_viz.scatter_plots.add_x_vs_y_plot(
...     'Tumor.Drug.auc',
...     'Blood.Drug.max',
...     arms=['identity'],
... )

set_regression

set_regression(
    *,
    show_line_equation: bool,
    regression_type: Literal["linear", "quadratic", "cubic"] | None = None,
    line_equation_legend: dict[str, Any] | None = None,
    version: str | dict | None = None
) -> TrialVisualization

Set scatter-plot regression options.

Examples:

>>> trial_viz.scatter_plots.set_regression(
...     show_line_equation=True,
...     regression_type='linear',
... )

replace

replace(
    payload: dict[str, Any], *, version: str | dict | None = None
) -> TrialVisualization

Replace this section with the provided raw payload. The intended workflow is:

read the current configuration with get
edit it locally
write it back with replace

Examples:

>>> timeseries=trial_viz.timeseries.get()
>>> timeseries["selectors"].pop(2) # edit timeseries
>>> trial_viz.timeseries.replace(timeseries)

get

get() -> dict[str, Any] | list[dict[str, Any]] | None

Return the current stored payload for this visualization section.

Examples:

>>> trial_viz.timeseries.get()
{'selectors': ['Blood.Drug'], 'layout': {}, ...}

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Clear this visualization section from the trial visualization.

Examples:

>>> trial_viz.scatter_plots.clear()

SurvivalAnalysisService

Returned by: TrialVisualization.survival_analysis

Member	Kind	Description
`set_selectors`	method	Set the survival-analysis selector list, preserving existing section options.
`add_selectors`	method	Append selectors to the current survival-analysis selection.
`set_observation_window_from_start_until_end`	method	Use the full simulated period as the survival observation window.
`set_observation_window_from_start_until_time`	method	Restrict survival analysis to a period ending at `period_end`.
`set_confidence_interval`	method	Show or hide confidence intervals on survival plots.
`replace`	method	Replace this section with the provided raw payload. The intended workflow is:
`get`	method	Return the current stored payload for this visualization section.
`clear`	method	Clear this visualization section from the trial visualization.

set_selectors

set_selectors(
    selectors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Set the survival-analysis selector list, preserving existing section options.

Examples:

>>> trial_viz.survival_analysis.set_selectors(['TimeToProgression'])

Note

The exact backend expectations for survival-analysis-compatible selectors are not fully documented in this repository. In practice these should be time-to-event-like outputs accepted by the web UI.

add_selectors

add_selectors(
    selectors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Append selectors to the current survival-analysis selection.

Examples:

>>> trial_viz.survival_analysis.add_selectors(['TimeToProgression'])

set_observation_window_from_start_until_end

set_observation_window_from_start_until_end(
    *, version: str | dict | None = None
) -> TrialVisualization

Use the full simulated period as the survival observation window.

Examples:

>>> trial_viz.survival_analysis.set_observation_window_from_start_until_end()

set_observation_window_from_start_until_time

set_observation_window_from_start_until_time(
    period_end: str, *, version: str | dict | None = None
) -> TrialVisualization

Restrict survival analysis to a period ending at period_end.

period_end should be a valid time measure accepted by the platform, such as an ISO 8601 duration string.

Examples:

>>> trial_viz.survival_analysis.set_observation_window_from_start_until_time('PT30D')

Note

The exact accepted time formats are only partially documented in this repository.

set_confidence_interval

set_confidence_interval(
    enabled: bool, *, version: str | dict | None = None
) -> TrialVisualization

Show or hide confidence intervals on survival plots.

Examples:

>>> trial_viz.survival_analysis.set_confidence_interval(True)

replace

replace(
    payload: dict[str, Any], *, version: str | dict | None = None
) -> TrialVisualization

Replace this section with the provided raw payload. The intended workflow is:

read the current configuration with get
edit it locally
write it back with replace

Examples:

>>> timeseries=trial_viz.timeseries.get()
>>> timeseries["selectors"].pop(2) # edit timeseries
>>> trial_viz.timeseries.replace(timeseries)

get

get() -> dict[str, Any] | list[dict[str, Any]] | None

Return the current stored payload for this visualization section.

Examples:

>>> trial_viz.timeseries.get()
{'selectors': ['Blood.Drug'], 'layout': {}, ...}

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Clear this visualization section from the trial visualization.

Examples:

>>> trial_viz.scatter_plots.clear()

ContributionAnalysisService

Returned by: TrialVisualization.contribution_analysis

Member	Kind	Description
`set_selectors`	method	Set the contribution-analysis selector list, preserving existing section options.
`add_selectors`	method	Append selectors to the current contribution-analysis selection.
`set_quantile`	method	Set the contribution-analysis quantile.
`set_input_baseline_only`	method	Use only input baseline descriptors in contribution analysis.
`set_all_baseline`	method	Use all baseline descriptors in contribution analysis.
`set_custom_baseline`	method	Use an explicit list of baseline descriptor ids.
`replace`	method	Replace this section with the provided raw payload. The intended workflow is:
`get`	method	Return the current stored payload for this visualization section.
`clear`	method	Clear this visualization section from the trial visualization.

set_selectors

set_selectors(
    selectors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Set the contribution-analysis selector list, preserving existing section options.

Examples:

>>> auc = trial.descriptors.scalars.get('Tumor.Drug.auc', arm='identity')
>>> trial_viz.contribution_analysis.set_selectors([auc])

add_selectors

add_selectors(
    selectors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Append selectors to the current contribution-analysis selection.

Examples:

>>> trial_viz.contribution_analysis.add_selectors(['Tumor.Drug.auc'])

set_quantile

set_quantile(
    quantile: float, *, version: str | dict | None = None
) -> TrialVisualization

Set the contribution-analysis quantile.

quantile should be between 0 and 1.

Examples:

>>> trial_viz.contribution_analysis.set_quantile(0.5)

set_input_baseline_only

set_input_baseline_only(
    *, version: str | dict | None = None
) -> TrialVisualization

Use only input baseline descriptors in contribution analysis.

Examples:

>>> trial_viz.contribution_analysis.set_input_baseline_only()

set_all_baseline

set_all_baseline(
    *, version: str | dict | None = None
) -> TrialVisualization

Use all baseline descriptors in contribution analysis.

Examples:

>>> trial_viz.contribution_analysis.set_all_baseline()

set_custom_baseline

set_custom_baseline(
    descriptors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Use an explicit list of baseline descriptor ids.

Examples:

>>> trial_viz.contribution_analysis.set_custom_baseline(['WT'])

Note

The exact expected descriptor family for custom baseline selection is not fully documented in this repository.

replace

replace(
    payload: dict[str, Any], *, version: str | dict | None = None
) -> TrialVisualization

Replace this section with the provided raw payload. The intended workflow is:

read the current configuration with get
edit it locally
write it back with replace

Examples:

>>> timeseries=trial_viz.timeseries.get()
>>> timeseries["selectors"].pop(2) # edit timeseries
>>> trial_viz.timeseries.replace(timeseries)

get

get() -> dict[str, Any] | list[dict[str, Any]] | None

Return the current stored payload for this visualization section.

Examples:

>>> trial_viz.timeseries.get()
{'selectors': ['Blood.Drug'], 'layout': {}, ...}

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Clear this visualization section from the trial visualization.

Examples:

>>> trial_viz.scatter_plots.clear()

DataOverlayService

Returned by: TrialVisualization.data_overlay

Member	Kind	Description
`set_tables`	method	Replace the full data-overlay table list.
`set_ranges_enabled`	method	Show or hide observed ranges in the data overlay.
`add_table`	method	Append one data table to the current data-overlay configuration.
`replace`	method	Replace this section with the provided raw payload. The intended workflow is:
`get`	method	Return the current stored payload for this visualization section.
`clear`	method	Clear this visualization section from the trial visualization.

set_tables

set_tables(
    tables: Sequence[DataOverlayTableInput],
    *,
    ranges: bool = True,
    version: str | dict | None = None
) -> TrialVisualization

Replace the full data-overlay table list.

Each entry may be a DataTable handle or a raw stored-reference dict with coreItemId and snapshotId.

Examples:

>>> table = client.get_data_table('dt-...')
>>> trial_viz.data_overlay.set_tables([table], ranges=True)

set_ranges_enabled

set_ranges_enabled(
    enabled: bool, *, version: str | dict | None = None
) -> TrialVisualization

Show or hide observed ranges in the data overlay.

Examples:

>>> trial_viz.data_overlay.set_ranges_enabled(True)

add_table

add_table(
    table: DataOverlayTableInput,
    *,
    include: bool = True,
    label: str | None = None,
    time_tolerance: float | None = None,
    weight: float | None = None,
    log_transform_wide_bounds: Sequence[str] | None = None,
    version: str | dict | None = None
) -> TrialVisualization

Append one data table to the current data-overlay configuration.

label, when provided, must use only letters, digits, dash -, underscore _, or colon :.

Examples:

>>> table = client.get_data_table('dt-...')
>>> trial_viz.data_overlay.add_table(table, label='ObservedData', include=True)

With a raw stored reference:

>>> trial_viz.data_overlay.add_table(
...     {'coreItemId': '...', 'snapshotId': '...'},
...     label='ObservedData',
... )

Note

The exact semantics of timeTolerance, weight, and logTransformWideBounds are only partially documented in this repository. The shapes are taken from OpenAPI and the existing raw skill script.

replace

replace(
    payload: dict[str, Any], *, version: str | dict | None = None
) -> TrialVisualization

Replace this section with the provided raw payload. The intended workflow is:

read the current configuration with get
edit it locally
write it back with replace

Examples:

>>> timeseries=trial_viz.timeseries.get()
>>> timeseries["selectors"].pop(2) # edit timeseries
>>> trial_viz.timeseries.replace(timeseries)

get

get() -> dict[str, Any] | list[dict[str, Any]] | None

Return the current stored payload for this visualization section.

Examples:

>>> trial_viz.timeseries.get()
{'selectors': ['Blood.Drug'], 'layout': {}, ...}

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Clear this visualization section from the trial visualization.

Examples:

>>> trial_viz.scatter_plots.clear()

OverlayService

Returned by: TrialVisualization.patients_overlay

Member	Kind	Description
`set`	method	Replace the individual-patients overlay configuration.
`add_patients`	method	Append patients to the current patient overlay.
`add_descriptors`	method	Append descriptors to the current patient overlay.
`replace`	method	Replace this section with the provided raw payload. The intended workflow is:
`get`	method	Return the current stored payload for this visualization section.
`clear`	method	Clear this visualization section from the trial visualization.

set

set(
    *,
    patients: Sequence[str],
    descriptors: Sequence[DescriptorRef],
    version: str | dict | None = None
) -> TrialVisualization

Replace the individual-patients overlay configuration.

descriptors accepts raw ids, descriptor handles, or dicts with an id key and optional arms list.

Examples:

>>> auc = trial.descriptors.scalars.get('Tumor.Drug.auc', arm='identity')
>>> trial_viz.patients_overlay.set(
...     patients=['patient-001'],
...     descriptors=[auc],
... )

With explicit arms:

>>> trial_viz.patients_overlay.set(
...     patients=['patient-001'],
...     descriptors=[{'id': 'Tumor.Drug.auc', 'arms': ['identity']}],
... )

Note

The backend-expected descriptor type for patient overlays is not completely clear from contracts alone. Existing examples suggest descriptor ids plus optional arms are accepted.

add_patients

add_patients(
    patients: Sequence[str], *, version: str | dict | None = None
) -> TrialVisualization

Append patients to the current patient overlay.

Examples:

>>> trial_viz.patients_overlay.add_patients(['patient-002'])

add_descriptors

add_descriptors(
    descriptors: Sequence[DescriptorRef], *, version: str | dict | None = None
) -> TrialVisualization

Append descriptors to the current patient overlay.

Examples:

>>> trial_viz.patients_overlay.add_descriptors(['Tumor.Drug.auc'])

replace

replace(
    payload: dict[str, Any], *, version: str | dict | None = None
) -> TrialVisualization

Replace this section with the provided raw payload. The intended workflow is:

read the current configuration with get
edit it locally
write it back with replace

Examples:

>>> timeseries=trial_viz.timeseries.get()
>>> timeseries["selectors"].pop(2) # edit timeseries
>>> trial_viz.timeseries.replace(timeseries)

get

get() -> dict[str, Any] | list[dict[str, Any]] | None

Return the current stored payload for this visualization section.

Examples:

>>> trial_viz.timeseries.get()
{'selectors': ['Blood.Drug'], 'layout': {}, ...}

clear

clear(*, version: str | dict | None = None) -> TrialVisualization

Clear this visualization section from the trial visualization.

Examples:

>>> trial_viz.scatter_plots.clear()

FiltersService​

list​

replace​

add_numeric​

add_categorical​

add_patient_list​

get​

clear​

GroupsService​

get​

replace​

clear​

set_group_by_arm​

add_scalar_distinct_grouping​

add_scalar_bin_width_grouping​

add_scalar_bin_count_grouping​

add_scalar_quantile_grouping​

add_scalar_explicit_grouping​

add_categorical_grouping​

TimeseriesService​

set_selectors​

add_selectors​

replace​

get​

clear​

ScalarsService​

set_selectors​

add_selectors​

replace​

get​

clear​

ScatterPlotsService​

set_config​

add_x_vs_x_plot​

add_x_vs_y_plot​

set_regression​

replace​

get​

clear​

SurvivalAnalysisService​

set_selectors​

add_selectors​

set_observation_window_from_start_until_end​

set_observation_window_from_start_until_time​

set_confidence_interval​

replace​

get​

clear​

ContributionAnalysisService​

set_selectors​

add_selectors​

set_quantile​

set_input_baseline_only​

set_all_baseline​

set_custom_baseline​

replace​

get​

clear​

DataOverlayService​

set_tables​

set_ranges_enabled​

add_table​

replace​

get​

clear​

OverlayService​

set​

add_patients​

add_descriptors​

replace​

get​

clear​

FiltersService

list

replace

add_numeric

add_categorical

add_patient_list

get

clear

GroupsService

get

replace

clear

set_group_by_arm

add_scalar_distinct_grouping

add_scalar_bin_width_grouping

add_scalar_bin_count_grouping

add_scalar_quantile_grouping

add_scalar_explicit_grouping

add_categorical_grouping

TimeseriesService

set_selectors

add_selectors

replace

get

clear

ScalarsService

set_selectors

add_selectors

replace

get

clear

ScatterPlotsService

set_config

add_x_vs_x_plot

add_x_vs_y_plot

set_regression

replace

get

clear

SurvivalAnalysisService

set_selectors

add_selectors

set_observation_window_from_start_until_end

set_observation_window_from_start_until_time

set_confidence_interval

replace

get

clear

ContributionAnalysisService

set_selectors

add_selectors

set_quantile

set_input_baseline_only

set_all_baseline

set_custom_baseline

replace

get

clear

DataOverlayService

set_tables

set_ranges_enabled

add_table

replace

get

clear

OverlayService

set

add_patients

add_descriptors

replace

get

clear