gcode_reader.emulate.diagnostics

Sanity checks for AdditivePart inputs and derived state.

Surfaces common silent-failure modes that otherwise propagate as zero-valued or oddly-shaped statistics:

  • check_extrusion: extrusion did not register (often a wrong-machine config), no is_depositing rows, or zero depositing distance/volume.

  • check_geometry: degenerate bounding-box dimensions, suspicious aspect ratios.

  • check_layer_grouping: non-monotonic layer ordering, misaligned layer_normal, coordinate-resolution fragmentation, or outlier moves at unrelated heights (the failure mode that motivated the original layer-grouping fix).

  • check_additive_part: runs all of the above plus cross-cutting checks (1 layer despite measurable build extent, implausible implied layer thickness, zero deposition length/volume despite depositing rows).

Each check returns a dict with the raw numbers and a list of human-readable warnings. The format_*_report helpers render those dicts as text.

Functions

check_additive_part(→ dict)

Run the full battery of sanity checks on an AdditivePart.

check_extrusion(→ dict)

Verify that extrusion data is present and non-trivial.

check_geometry(→ dict)

Sanity-check the deposition bounding box and overall geometry of an AdditivePart.

check_layer_grouping(→ dict)

Compute layer-grouping sanity checks for a tagged G-code DataFrame.

format_additive_part_report(→ str)

Render the output of check_additive_part as a human-readable string.

format_layer_grouping_report(→ str)

Render the output of check_layer_grouping as a human-readable string.

Module Contents

gcode_reader.emulate.diagnostics.check_additive_part(part) → dict[source]

Run the full battery of sanity checks on an AdditivePart.

Combines check_extrusion, check_geometry, and check_layer_grouping, plus a few cross-cutting checks (layer count plausibility, deposition length).

Returns a dict with one sub-dict per check and a flat warnings list that aggregates every warning, prefixed by which check raised it.

gcode_reader.emulate.diagnostics.check_extrusion(df: pandas.DataFrame) → dict[source]

Verify that extrusion data is present and non-trivial.

A common silent failure mode is configuring the wrong machine/extruder, in which case deposited_volume stays at zero and is_depositing is empty even though the G-code clearly contains print moves. Downstream stats (layer count, volume, mass, deposition bounds) then come back as zero or missing with no error raised.

Returns a dict with:

  • has_is_depositing_col, has_deposited_volume_col

  • n_depositing_rows: number of rows tagged is_depositing == True

  • depositing_fraction: fraction of rows that are depositing

  • total_deposited_volume: sum of the deposited_volume column

  • n_depositions: distinct deposition_id values among depositing rows

  • warnings: human-readable flags

gcode_reader.emulate.diagnostics.check_geometry(part) → dict[source]

Sanity-check the deposition bounding box and overall geometry of an AdditivePart.

Flags degenerate dimensions (zero extent in any axis), unusually thin parts (aspect ratio > 1000), and missing bounds. A zero extent in the build direction is often a tell-tale of extrusion not registering or of a single-layer parse.

Returns a dict with:

  • bounds: ((xmin, ymin, zmin), (xmax, ymax, zmax)) or None

  • dims: (dx, dy, dz) or None

  • aspect_ratio: max(dim) / min(positive dim), or inf if any dim is zero

  • warnings

gcode_reader.emulate.diagnostics.check_layer_grouping(df: pandas.DataFrame, layer_index_col: str = 'layer_index', layer_height_col: str = 'layer_height', sliver_count: int = 5, max_gap_ratio: float = 10.0) → dict[source]

Compute layer-grouping sanity checks for a tagged G-code DataFrame.

Parameters:

  • df – DataFrame with layer_index and layer_height columns (the output of insert_layer_indices_gcode_dataframe).

  • layer_index_col – Column name for layer indices.

  • layer_height_col – Column name for projected heights.

  • sliver_count – A layer with fewer than this many rows is flagged as a sliver.

  • max_gap_ratio – Gaps between consecutive layers larger than max_gap_ratio * median_gap are flagged as outliers (e.g. a probe move at an unrelated height).

Returns:

  • n_layers: number of distinct layer indices

  • monotonic: True if layer_index is in ascending physical-height order

  • rank_correlation: Pearson correlation between layer_index and physical height (near +1 = healthy, near 0 = scrambled ordering)

  • min_gap, median_gap, max_gap: gaps between consecutive layer heights

  • min_spread, median_spread, max_spread: within-layer height spread (large values relative to gap suggest the normal is misaligned)

  • n_slivers: number of layers with fewer than sliver_count rows

  • n_outlier_gaps: number of gaps larger than max_gap_ratio * median_gap

  • per_layer: DataFrame indexed by layer_index with min/max/count/spread columns

  • warnings: list of human-readable strings flagging anomalies

Return type:

A dict with summary statistics and a per_layer DataFrame. Keys

gcode_reader.emulate.diagnostics.format_additive_part_report(diagnostics: dict) → str[source]

Render the output of check_additive_part as a human-readable string.

gcode_reader.emulate.diagnostics.format_layer_grouping_report(diagnostics: dict) → str[source]

Render the output of check_layer_grouping as a human-readable string.