Why Drive Diagnostic Suites From Configuration?
A diagnostic report often needs several plots, and different runs or review settings may call for different subsets. One report may need plots grouped by study; another may need the same plot types grouped by occasion. If those choices are embedded directly in plotting code, even a small change can require code edits, create duplicated routines, and make it harder to determine exactly what was requested.
A configuration-driven suite separates what to produce from how a plotting routine produces it. The configuration names the requested diagnostics and their options. A stable runner reads and validates that request, maps it to routines, and records or exports the resulting artifacts. The plotting routines remain unchanged when users select a different supported diagnostic, grouping variable, label, scale, or output format.
This is an automation design topic, not a guide to interpreting diagnostic panels or choosing a plot theme. A configuration can request a scale and label, but the plotting routine still needs to implement them correctly, and the resulting plot still needs to be interpreted appropriately.
Designing a Configuration Contract
A configuration file is useful only if its structure has an explicit contract. The contract defines which fields are required, which are optional, the permitted values for each field, and how omitted values are handled. A small, versioned contract is easier to validate and maintain than an unrestricted collection of settings.
One practical structure has top-level fields for a configuration version, requested plots, and output settings. Each plot entry has an identifier, optional grouping, axis-scale choices, and labels. Output settings specify supported file formats and, if needed, a destination. The names below are illustrative; consistency and validation matter more than a particular file syntax.
| Configuration area | Purpose | Validation question |
|---|---|---|
| Version | Identifies the contract the file follows | Is this version recognized by the runner? |
| Plot identifier | Selects a registered diagnostic routine | Is this diagnostic supported? |
| Grouping | Requests separate panels or groups using a data field | Does the field exist and can it be used for grouping? |
| Scales and labels | Sets supported axis behavior and displayed text | Are the options valid for this plot and data? |
| Output | Chooses a file format and destination | Is the format allowed and can the destination be written? |
The runner should validate configuration against the canonical diagnostic data schema and the capabilities of each registered routine. A valid field name in the data is not automatically valid for every plot: a routine may not support grouping, or a requested scale may be incompatible with its values. Conversely, labels are presentation text, not data-field names. Keeping those concepts distinct prevents a label such as “Time after dose (h)” from being mistakenly treated as a request to find a column with that name.
Use explicit defaults rather than relying on hidden behavior. For example, if grouping is optional, document whether an omitted grouping field means one ungrouped plot. If the output format is omitted, either apply a documented default or reject the request; do not let the choice vary according to whichever routine happens to run first. Unsupported options should produce a clear error rather than being silently ignored.
From Request to Artifact
A reliable runner follows a consistent sequence: load the configuration, validate its structure and values, resolve each plot identifier to a registered routine, supply that routine with the validated options and relevant data, and export the returned plot in each requested format. Separating these steps makes it possible to report a configuration error before producing a partial report.
Read the file and check that it declares a recognized configuration version. A parse failure or unknown version stops the run before plotting begins.
Check required keys, allowed values, data-field references, and plot-specific constraints. Collect useful error messages instead of allowing an obscure rendering failure later.
Look up each validated plot identifier in a registry and pass its options to the corresponding routine. Do not construct executable code from strings in the file.
Write artifacts in the requested supported formats, record their paths and the configuration used, and report failures with enough context to identify the affected request.
The separation can be represented as a composition. Let \(C\) be a configuration, \(D\) the available diagnostic data, \(V\) the validation operation, \(R\) the registered rendering operation, and \(E\) the export operation. The suite behaves as follows:
This ordering is justified by a simple dependency: rendering needs resolved plot options, and exporting needs rendered artifacts. Validation must therefore precede both. If validation fails, the runner should not proceed as though the request were valid. Also, because the renderer receives options as data rather than being rewritten for each request, changing an allowed option changes the request without changing the rendering routine.
Worked Example: Selecting Plots and Grouping Fields
Suppose a hypothetical diagnostic data set has 240 observation records from 30 subjects. Its canonical fields include observation time, observed concentration, population prediction, study, and occasion. The team wants an observed-versus-population-prediction plot and a residual-versus-time plot, both grouped by study.
The configuration requests two plot identifiers, assigns the grouping field “study” to each, and specifies the desired supported axes and labels for each routine. The runner first checks that both plot identifiers are registered and that “study,” “time,” and the fields needed by the selected routines exist in the data schema. It also checks that grouping by study is supported for these plot types.
Assume the data contain three studies. If each requested plot produces one panel per study, the request yields \(2 \times 3 = 6\) panels. If the configured output formats are PNG and PDF, the runner exports \(2 \times 2 = 4\) files: one file for each plot-format pair. The six panels are contained within those four files; they are not six additional files unless a separate, explicitly supported export option requests one file per panel.
No plotting routine needs to be edited to switch the grouping field from study to occasion, provided occasion is an eligible field and the routines support that grouping. The runner validates the changed request and passes the new grouping option through the same routine interface.
Validation, Diagnostics, and Failure Modes
Validation should be layered. First check the file’s structure and configuration version. Next check that identifiers and enumerated options are recognized. Then check references to data fields and constraints that depend on the data or plot. Finally, check operational conditions such as whether the output destination is writable. Reporting failures at these stages makes it clear whether the problem is a malformed file, unsupported request, incompatible data, or an export problem.
Scale settings need validation too. A routine may allow only a defined set of scale types. If a requested logarithmic axis requires positive values, the runner or routine should check that the plotted values meet that requirement, or provide an explicit, documented handling rule. It should not silently remove nonpositive observations or substitute a different scale. Labels should be checked for the expected type and length limits, while output formats should be selected from a supported allowlist.
Useful run diagnostics include the configuration version, requested plot identifiers, resolved grouping fields, selected formats, and a summary of successful and failed exports. These records help a reviewer distinguish “not requested” from “requested but failed.” They also support reproducibility when the report is regenerated. A configuration file alone may not fully specify a run if data versions, software versions, or other run metadata affect the result; preserve relevant run metadata alongside the report.
Common failure modes include misspelled keys being ignored, an invalid plot identifier being treated as a file name, an unavailable grouping field, a scale incompatible with the data, and a failed export being reported as success. Prefer strict parsing: reject unknown keys unless the contract explicitly permits extensions, identify the exact plot entry and field causing an error, and do not mark the full suite successful when required artifacts are missing.
Worked Example: Catching Invalid Options Before Rendering
Consider a hypothetical request for a concentration-versus-time plot grouped by “site,” using a logarithmic concentration axis and both PNG and SVG output. The data schema contains the fields “time,” “concentration,” and “study,” but not “site.” The supported output formats are PNG and PDF.
The request fails validation for two independent reasons: “site” is not a field in the schema, and SVG is not an allowed output format. The runner should report both issues together, for example: “plot 1: grouping field ‘site’ is unavailable; output format ‘SVG’ is unsupported.” It should not begin rendering and then produce only an incomplete set of files.
Suppose the request is corrected to group by “study” and to request PNG and PDF. Before rendering, the runner checks the scale condition. In this hypothetical data set, all 180 concentration values selected for the plot are positive, so the requested logarithmic axis is compatible with the values. If even one value were zero or negative, the runner would need to apply the documented policy: reject the request, or use a specifically supported transformation or filtering rule. Silently dropping that value would change the data represented by the plot.
After correction, two output formats for one plot produce \(1 \times 2 = 2\) files. If the corrected grouping has four studies, the plot may contain four grouped panels, but the requested file count remains two when each format contains the full plot.
Choosing Configuration Over Other Approaches
Hard-coded plotting choices are straightforward for a one-off analysis, but they become difficult to review when similar reports need different subsets or options. Copying and editing plotting routines multiplies maintenance work and can cause two versions of a diagnostic to behave differently. A validated configuration is a good fit when users need controlled variation while the underlying routines and supported options remain stable.
Command-line arguments can be convenient for a small number of choices, such as selecting a single output directory or configuration file. As the number of plot-specific options grows, a configuration file is generally easier to inspect, version, and review as a complete request. A graphical interface may help users construct requests, but it still needs to produce a request that follows the same validation contract. These approaches can be combined: a command-line argument can identify the configuration file, while a user interface can generate one.
Configuration does not remove the need for software tests. Test the validator against valid and invalid requests, and test each registered routine’s option interface. For a stable request and fixed inputs, tests can check that the same plot identifiers, groupings, labels, and output formats are passed through and that expected artifacts are produced. Such checks verify execution and specification handling; they do not establish that a diagnostic plot is scientifically adequate.
Worked Example: Estimating the Suite Before Running It
A hypothetical report request contains three registered diagnostics. The first is ungrouped, the second is grouped by two treatment categories, and the third is grouped by four studies. The configuration requests PDF and PNG for every diagnostic, with one complete file per plot and format.
The first diagnostic produces one panel, the second two panels, and the third four panels. The total panel count is \(1 + 2 + 4 = 7\). The total file count is the number of plot-format pairs: \(3 \times 2 = 6\). Thus the expected output is six files containing seven panels in total.
This pre-run estimate can be compared with the export manifest. If only five files are recorded, the suite is incomplete even if the five files that exist open successfully. If seven files appear, the runner may have applied an undocumented one-file-per-panel rule. A manifest that records each requested plot, its format, and its destination makes either discrepancy detectable.
Limitations and a Common Mistake
A configuration can select only capabilities the software has implemented. It cannot make an unsupported plot meaningful, make a missing data field available, or guarantee that a requested scale is appropriate for every data set. As options multiply, configuration contracts can become difficult to understand. Keep options specific to a plot where necessary, provide clear validation messages, and introduce a new contract version when a change would otherwise alter the meaning of existing configuration files.
- Mistake: “The request parsed, so the diagnostic suite is valid.” Parsing only establishes that the file can be read. Correction: validate the schema, allowed values, data references, plot-specific constraints, and export conditions before rendering.
- Mistake: “I can add a new plot by writing its name in the configuration.” Correction: a plot identifier must resolve to a registered routine with a defined, tested option interface.
- Mistake: “If an option is unsupported, the runner can ignore it and still report success.” Correction: reject unsupported options or explicitly document a safe default; never imply that the requested output was produced when it was not.
Check Your Understanding
Answer each question, then compare your reasoning with the solutions below.
- A request selects two plots, each grouped by one field with three observed categories, and asks for PDF and PNG. If each plot-format pair contains all its groups, how many files and grouped panels are expected?
- Why should the runner validate a grouping field against the canonical data schema before rendering?
- A request asks for an unsupported output format. Should the runner substitute a supported format without notice? Explain.
- When is a configuration file more useful than hard-coding plot choices, and what does it not replace?
- A log scale is requested, but the plotted field contains a zero. Name an appropriate validation response.
Solutions. (1) There are \(2 \times 2 = 4\) files, one for each plot-format pair, and \(2 \times 3 = 6\) grouped panels in total. (2) Validation catches unavailable or misspelled fields before a routine fails or produces an unintended result. (3) No. It should reject the unsupported format with a clear message, unless a documented rule explicitly defines a substitution; otherwise the output would not match the request. (4) Configuration is useful when the same stable routines must support different controlled selections and options. It does not replace routine tests, validation, or scientific review of the resulting plots. (5) Reject the request or apply an explicitly supported and documented handling rule; do not silently drop or alter the zero.