Where a setting goes¶
Every setting spaCR reads, and the call paths that carry it from an entry point to the functions that actually read it.
Asked for on 2026-09-08: “in the API when a user clicks a settings they should see the setting and the function it goes to and the function(s) that the settings get passed along to. when they click the setting itself they should get the tool tip text.” This is that page. A setting’s tooltip in the interface links straight to its section here, so the answer to “what does this actually change” is one click from the control it is about.
HOW TO READ A BRANCH. Each line is a call, indented under its caller, and a
step marked – reads it is where the value is finally used rather than
passed on. A branch that reaches no reader is not drawn at all, because a
path that carries a setting nowhere is not a path the reader needs. A step
marked [UNRESOLVED] is a call static analysis cannot follow – a
getattr, a dispatch table, a Qt signal, a callback passed as a value –
and it is SHOWN rather than dropped: a tree that silently hides what it
could not follow looks complete when it is not.
The page is generated by tools/settings_flow.py from the source itself,
so it cannot drift from what the code does without the next run saying so.
Generated by tools/settings_flow.py – do not edit.
1092 settings, of 1112 read anywhere.
Each setting lists the call paths that carry it from an entry point
to the functions that read it. Branches that reach no reader are not
drawn. A step marked [UNRESOLVED] is a call static analysis
cannot follow – getattr, a dispatch table, a Qt signal, a
callback passed as a value – and is shown rather than dropped,
because a tree that hides what it could not follow looks complete.
CP_prob¶
(float) - Cellpose cellprob_threshold: the cell-probability cut-off applied to the network output when deciding which pixels belong to an object. Lower it (typically toward -6) to recover dim or partly detected objects and grow existing masks; raise it (toward 6) to drop faint false positives and shrink masks. Default 0.
check_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
CP_probability¶
(int) - Cellpose cellprob_threshold used by the standalone apply/test-model submodules, where it carries this name instead of the per-object <object>_cellprob_threshold used by the Mask module. Only pixels whose predicted cell probability exceeds it join a mask, so raising it shrinks outlines and drops faint objects while lowering it grows them and recovers dim ones. Default 0.
apply_cellpose_model() – reads itget_default_apply_cellpose_model_settings() – reads ittest_cellpose_model() – reads itget_default_test_cellpose_model_settings() – reads itRead by get_default_apply_cellpose_model_settings(), get_default_test_cellpose_model_settings(), apply_cellpose_model(), test_cellpose_model().
FT¶
(int) - Cellpose flow_threshold for the standalone apply/test-model submodules, the counterpart of the Mask module’s per-object <object>_flow_threshold. Masks whose recomputed flows disagree with the network’s prediction by more than this are discarded, so a low value strips ragged or implausible objects and also loses real ones. Default 100, which effectively accepts every candidate.
apply_cellpose_model() – reads itget_default_apply_cellpose_model_settings() – reads ittest_cellpose_model() – reads itget_default_test_cellpose_model_settings() – reads itRead by get_default_apply_cellpose_model_settings(), get_default_test_cellpose_model_settings(), apply_cellpose_model(), test_cellpose_model().
Signal_to_noise¶
(int) - Background multiplier used as the Cellpose normalization threshold (background * Signal_to_noise). Per channel, spaCR selects the first of the 98th, 99th, 99.9th, 99.99th and 99.999th percentiles above it and rescales to that value. Higher values reduce clipping and make output dimmer; lower values reveal faint signal but can saturate bright objects. If no percentile qualifies, the range collapses to the 2nd percentile, indicating that this value is too high. Ignored when percentiles is set. Default 10 (5 in check_cellpose_models).
_concatenate_and_normalize_impl_normalize_img_batch – reads itcheck_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by _normalize_img_batch, get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
Toxoplasma¶
(bool) - Join the bundled Toxoplasma annotation to every exported table and color the volcano plot by it. The annotation includes gene name and product, signal peptide and transmembrane-domain predictions from the project’s DeepTMHMM analysis, hyperLOPIT/TAGM compartment, published CRISPR fitness scores, and tachyzoite, tissue-cyst, and EES1-5 expression. Tables are joined by gene number so TGGT1 and TGME49 identifiers match. Writes supplementary_topology.csv beside the results. Disable this setting for non-Toxoplasma screens. The deprecated alias ‘toxo’ remains accepted. Default True.
_trial_settings – reads itsettings_for_trial() – reads it_perform_regression_perform_regression_annotation_source_toxoplasma_is_on – reads it_toxoplasma_is_on – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _toxoplasma_is_on, _trial_settings, settings_for_trial().
_plot_theme¶
generate_image_umap() – reads it_on_run – reads itRead by generate_image_umap(), _on_run.
_regression_diagnostics¶
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
_regression_exclusions¶
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
_regression_folder¶
_perform_regressionperform_regression() – reads it_perform_regression_perform_regression_set_paths – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_write_fit_resources – reads it_write_regression_panel_packages – reads itRead by _perform_regression_set_paths, _write_fit_resources, _write_regression_panel_packages, perform_regression().
_regression_frame¶
_perform_regressionperform_regression() – reads itRead by perform_regression().
_regression_stage¶
_perform_regression_read_data_stage – reads it_perform_regressionperform_regression() – reads it_perform_regression_perform_regression_set_paths_stage – reads it_stage – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_write_regression_panel_packages – reads itRead by _stage, _write_regression_panel_packages, perform_regression().
_well_geometry¶
Read by _plaque_scale_for, split_wells().
abs¶
_captum_attribute – reads itRead by _captum_attribute.
activation_db¶
plot_region() – reads itRead by plot_region().
activation_mode¶
plot_region() – reads itRead by plot_region().
adjust_cells¶
(bool) - After segmentation, merge cell labels that divide a single pathogen or nucleus, and absorb an anucleate cell fragment into the neighbouring label with which it shares the largest perimeter. Requires cell, nucleus, and pathogen channels and is skipped for timelapse runs. Enable when large infected cells are systematically fragmented by segmentation. Default True.
preprocess_generate_masks() – reads itapply_settings() – reads it_seed_organelle_column(...) [UNRESOLVED]Read by preprocess_generate_masks(), apply_settings().
affinity¶
reduction_and_clustering() – reads itRead by reduction_and_clustering().
agg_type¶
(str) - How per-object scores are collapsed to one value per well before regression: ‘mean’, ‘median’, ‘quantile’ (75th percentile), or None to skip aggregation and regress on individual objects. Median resists a handful of extreme cells; None keeps power but ignores within-well correlation. Forced to a per-well sum for poisson and to None for quantile. Default ‘mean’.
_preparation_key – reads it_perform_regression_perform_regression – reads it_run_guide_permutation_analysis – reads itresolve_auto_inference() – reads it_resolve_regression_analysis_choices – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads itrecord_run() – reads itrefusals() – reads itRead by _perform_regression, _run_guide_permutation_analysis, resolve_auto_inference(), _preparation_key, _run_settings_row, record_run(), _resolve_regression_analysis_choices, refusals().
all_scores¶
Keep every scored pair in the report, not only the ones that passed. Turning it on makes the report larger and lets you see how close a failed stitch came; it changes nothing about the mosaic. Default False.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
allow_rotation¶
Let the fit rotate one tile relative to another. Leave it off for a motorised stage, which does not rotate between fields; turning it on adds a degree of freedom that can absorb a bad match into a plausible-looking angle. Default False.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
allow_scale¶
Let the fit change size between tiles. Off for a single acquisition, where the magnification cannot differ; on, a weak match can be explained away as a scale change. Default False.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
allow_spacr_targets¶
import_project() – reads itRead by import_project().
alpha¶
(float) - Regularisation strength for penalised models only: the L1 penalty for ‘lasso’, the L2 penalty for ‘ridge’, the combined penalty for ‘elasticnet’, and the inverse margin for ‘hinge’. Larger values shrink more coefficients toward zero. Set it to ‘auto’ or None to select the value by five-fold cross-validation; the default 1 may over-regularise fraction-scale designs. Other model families reject a non-default alpha rather than ignoring it. Default 1.
_perform_regression_read_data_reconcile_random_row_column_effects – reads it_reject_impossible_probabilities – reads it_perform_regression_perform_regression – reads it_call_level_hits – reads it_reconcile_random_row_column_effects – reads it_reject_impossible_probabilities – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]refit_settings() – reads itRead by _call_level_hits, _perform_regression, _reconcile_random_row_column_effects, _reject_impossible_probabilities, refit_settings().
amsgrad¶
(bool) - Use the AMSGrad variant of Adam/AdamW, which keeps a running maximum of past squared gradients instead of their decaying average so the effective step size never grows back. Enable when training loss oscillates or stops converging with plain Adam; it costs a little speed and memory. Only honoured by optimizer_type ‘adam’ and ‘adamw’ - ignored by sgd, rmsprop, nadam, radam and adagrad. Default True.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
analysis_excluded_wells¶
(list or None) - Wells dropped before anything is fitted: the sequencing sweep removes them from the count table before it looks for the fraction threshold, and the regression never sees them. It must name the same wells as filter_value or the threshold is fitted on wells the fit has already dropped. Regression initializes it from filter_value plus any declared control blocks (the shipped default is [‘c1’, ‘c2’, ‘c3’]). Set a column (‘c12’), row (‘r1’), well (‘r1_c12’), or complete plate key.
_perform_regression_perform_regression_draw_the_threshold_sweep_graph_sequencing_statsgraph_sequencing_stats() – reads it_graph_sequencing_statsgraph_sequencing_stats() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by graph_sequencing_stats().
analysis_mode¶
(str) - ‘regression’ fits the selected simultaneous model. ‘guide_permutation’ tests each guide as a plate-adjusted marginal association using blocked Freedman–Lane permutations and then corrects the requested support family. This setting is normally derived from inference; set it directly only to override that choice. Default ‘regression’. The Regression module starts with inference=’nonparametric’, so its resolved initial mode is ‘guide_permutation’.
_perform_regression_read_data – reads it_it_will_permute – reads it_perform_regression_perform_regression – reads it_perform_regression_set_pathsresults_folder_kind() – reads itresolve_auto_inference() – reads it_resolve_regression_analysis_choices – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_analysis_path – reads it_run_settings_row_permuted – reads itdestination() – reads itget_setting_dependencies() – reads itpermutation_active() – reads itpermutation_is_certain() – reads itrefusals() – reads itRead by _perform_regression, _perform_regression_read_data, resolve_auto_inference(), results_folder_kind(), _it_will_permute, _analysis_path, _permuted, destination(), _resolve_regression_analysis_choices, get_setting_dependencies(), permutation_active(), permutation_is_certain(), refusals().
analysis_unit¶
(str) - What one row of the model is. ‘well’ collapses each well’s objects into a single value with agg_type first, so the well is the independent unit and the number of cells behind it only affects precision. ‘cell’ regresses the individual objects instead, which keeps power but treats cells from one well as independent when they are not, so standard errors are optimistic unless the model accounts for the clustering (regression_type=’mixed’). This is the explicit spelling of agg_type=None, which used to change the unit of analysis silently. Default ‘well’.
_preparation_key – reads it_perform_regression_perform_regression_run_guide_permutation_analysis – reads itresolve_auto_inference() – reads it_resolve_regression_analysis_choices – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads itrecord_run() – reads itget_setting_dependencies() – reads itrefusals() – reads itRead by _run_guide_permutation_analysis, resolve_auto_inference(), _preparation_key, _run_settings_row, record_run(), _resolve_regression_analysis_choices, get_setting_dependencies(), refusals().
analyze_clusters¶
(bool) - After clustering the embedding, rank every measured feature by cluster separation using random-forest importance and a per-feature ANOVA or Kruskal-Wallis test, then write results/cluster_results.csv. Enable this setting to identify morphology or intensity features associated with each cluster. It adds a full model fit over the feature table. Default False.
generate_image_umap() – reads itRead by generate_image_umap().
anisotropy¶
(float or None) - Ratio of z step to xy pixel size (dz / dxy), used by volumetric mode to represent inter-plane distance. A value of 1.0 on a confocal stack whose z step is 3-10 times the xy pixel size can fuse objects along z. Leave this None and set voxel_size_z_um / voxel_size_xy_um to derive it; if neither is available, volumetric mode raises an error rather than assuming 1.0. Measure also uses it for 3-D region properties and distance transforms. Default None.
_t_stack_planplan_4d_from_settings() – reads it_z_stack_planplan_from_settings() – reads it_measure_crop_core_intensity_measurements_measure_intensity_distanceresolve_measurement_spacing() – reads itresolve_measurement_spacing() – reads it_morphological_measurementsresolve_measurement_spacing() – reads itresolve_measurement_spacing() – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by resolve_measurement_spacing(), get_measure_crop_settings(), plan_4d_from_settings(), plan_from_settings().
anndata_compression¶
HDF5 compression for X and the layers. ‘gzip’ (default) typically halves the file; ‘’ writes it uncompressed.
run_anndata_export() – reads itanndata_export_settings() – reads itanndata_compute_umap¶
Compute obsm[‘X_umap’] during the export, through the same reducer the UMAP app uses. Off by default: it costs minutes on a large table.
run_anndata_export() – reads itanndata_export_settings() – reads itanndata_dtype¶
dtype of X. Default ‘float32’ - the scanpy convention and half the memory of float64.
run_anndata_export() – reads itanndata_export_settings() – reads itanndata_nan_policy¶
What happens to missing values in X: ‘keep’ (default; AnnData stores them, scanpy’s scale/pca/neighbors do not), ‘drop_features’, ‘drop_objects’, ‘zero’ or ‘mean’. The two imputing policies also write a layers[‘missing’] mask.
run_anndata_export() – reads itanndata_export_settings() – reads itanndata_out¶
Path of the .h5ad file to write. An existing file at this path is overwritten, and the export is registered against it in artifacts.db, so writing twice to one path replaces the earlier record rather than adding a second. Default <src>/results/<project>.h5ad.
run_anndata_export() – reads itanndata_export_settings() – reads itanndata_register_artifact¶
Record the written file with spacr.artifacts, so a re-run of Measure marks the export stale. Default True.
run_anndata_export() – reads itanndata_export_settings() – reads itanndata_row_limit¶
Maximum number of objects exported after filtering. 0 disables the limit. When a limit is set, the first N objects in table order are retained. Default 0.
run_anndata_export() – reads itanndata_export_settings() – reads itanndata_single_table¶
Export this one object table instead of the join, one row per object of that type. The only way to get a nucleus-level or pathogen-level matrix: the join averages children onto their parent cell. Empty means the joined export. Default ‘’.
run_anndata_export() – reads itanndata_export_settings() – reads itanndata_tables¶
Object tables joined into the cell-anchored export. Dropping a table drops its features from X: without ‘pathogen’ there are no pathogen columns and no count_pathogen, and without ‘png_list’ the crop paths, annotations and model scores are all absent from obs. Default [‘cell’, ‘cytoplasm’, ‘nucleus’, ‘pathogen’, ‘png_list’].
run_anndata_export() – reads itanndata_export_settings() – reads itannotated_classes¶
(list) - Legacy setting that is not read from a module configuration. It remains available only as a direct parameter of io.training_dataset_from_annotation and io.training_dataset_from_annotation_metadata. Define classes with the Classes editor instead: select metadata values in metadata mode, or annotation columns and values in annotation mode. Default [1, 2], with no effect in module settings.
Read by _rules_from_annotation.
annotation_column¶
(str) - Integer column in the png_list table that stores manual class labels. The Annotate app adds it with ALTER TABLE if it is absent and writes labels to it. This column provides the reference labels when dataset_mode is ‘annotation’ and is the fallback when annotation_columns is unset. Supplying it while dataset_mode is unset also selects annotation mode for compatibility with older settings files. Default None.
_begin_flowview_run_install_classify_collectorresolve_basis() – reads itresolve_basis() – reads itdict(...) [UNRESOLVED]resolve_basis() – reads it_rules_from_annotation – reads itresolve_basis() – reads itdict(...) [UNRESOLVED]generate_training_dataset() – reads it_fold_the_classesfold_into_classes() – reads itannotation_column_of() – reads itresolve_basis() – reads it_fresh_collectorresolve_basis() – reads itdict(...) [UNRESOLVED]load_search_data() – reads itgenerate_ml_scores() – reads itresolve_basis() – reads itresolve_basis() – reads itset_annotate_default_settings() – reads itresolve_basis() – reads itcount_phenotypes() – reads itRead by _rules_from_annotation, annotation_column_of(), fold_into_classes(), load_search_data(), generate_training_dataset(), generate_ml_scores(), set_annotate_default_settings(), count_phenotypes(), resolve_basis().
annotation_columns¶
Read by _rules_from_annotation, generate_training_dataset().
annotation_source¶
(str) - Which organism’s annotation to join onto the regression results. Empty or ‘toxoplasma’ uses the bundled Toxoplasma gondii tables, which need no network and are the default. Any other organism name or NCBI taxon id – ‘human’, ‘Plasmodium falciparum’, ‘Neospora caninum’, ‘9606’ – pulls that organism’s entries from UniProt, and a single accession such as P04637 retrieves that entry. The result is cached beside the outputs, so a rerun needs no network. A name spaCR does not recognise leaves the results unannotated and says which names were close. Default ‘toxoplasma’.
_perform_regression_perform_regression_annotation_source – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _annotation_source.
annotation_values¶
Read by _rules_from_annotation, generate_training_dataset().
apply_model_to_dataset¶
(bool) - After training (or straight away when reusing a saved model_path), pack the object PNGs into a tar, run inference over it, copy the n_top_examples most confident images per class into top_examples/, and merge the per-object scores back into measurements.db. Turn it off to only train and evaluate a model without scoring the screen. Default True.
run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]deep_spacr() – reads itrun_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by deep_spacr(), _check_required_paths.
arr_axes¶
How to read the axis order inside each file. AUTO takes it from the file’s own metadata and falls back to guessing from the shape. Set it explicitly when a stack is being misread as channels or z. Default ‘AUTO’.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
at¶
profile_by() – reads itRead by profile_by().
attribution_baseline¶
(str) - Replacement used when deletion/insertion curves remove a pixel: ‘blur’, ‘zero’, or ‘noise’. Replacing pixels with zero can create an out-of-distribution edge, causing part of the score change to reflect the replacement artifact rather than removed information. ‘blur’ generally produces the smallest distribution shift and is the default. Comparing multiple baselines quantifies the sensitivity of the AUC to this choice. Default ‘blur’.
run_search_for_app() – reads itRead by run_search_for_app(), get_default_generate_activation_map_settings().
attribution_steps¶
(int) - Points along the deletion and insertion curves used to score a map. At each step the highest-ranked remaining pixels are removed or added and the model is re-evaluated. This sets the resolution of the area under the curve used to assess whether the map identifies image features contributing to the prediction. More steps produce a smoother AUC with linearly more forward passes. Default 12.
run_search_for_app() – reads itRead by run_search_for_app(), get_default_generate_activation_map_settings().
augment¶
(bool) - Expand the training split eightfold by adding four 90-degree rotations of each crop and their horizontal reflections; validation and test splits are not augmented. Enable this setting when few annotated objects are available and validation accuracy is below training accuracy. The expanded set is materialised in RAM, requiring approximately eight times the memory and epoch duration. Default False.
_fit_one – reads it_inner_loader – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]train_cellpose() – reads itRead by _cross_validate_model, _fit_one, _inner_loader, train_test_model(), get_train_test_model_settings(), train_cellpose().
average_attn_weights¶
_ask_for_attention_weights – reads itoriginal(...) [UNRESOLVED]dict(...) [UNRESOLVED]_wrap – reads itoriginal(...) [UNRESOLVED]dict(...) [UNRESOLVED]forward() – reads itoriginal(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _ask_for_attention_weights, _wrap, forward().
avg_cells_per_well¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
avg_genes_per_well¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
avg_reads_per_gene¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
axes.edgecolor¶
rc_params() – reads itRead by rc_params().
axes.prop_cycle¶
rc_params() – reads itRead by rc_params().
background¶
(float) - Per-channel background level in raw intensity units. Pixels below it are zeroed when remove_background is on, and it is multiplied by Signal_to_noise to set the upper anchor for normalization. Raise it if faint haze survives; set it too high and dim real objects vanish. Default 100 (200 for Cellpose training and plaque analysis).
_concatenate_and_normalize_impl_normalize_img_batch – reads itcheck_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by _normalize_img_batch, get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
background_correction¶
(str) - Per-object local background subtracted from the outside-stain statistic before thresholding. ‘auto’ uses the median of the five-pixel ring outside the parasite mask, removing a per-field offset without a flat-field image; ‘none’ performs no subtraction. A brightly stained attached parasite may produce an antibody halo that extends into the reference ring, in which case subtraction can reduce the signal required for classification. Default ‘none’.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
balance_to_smallest¶
(bool) - Downsample every generated training class to the size of the smallest class before writing train/test folders. This removes the dataset prior but discards majority examples; disable it and use class_balance during training to retain all images. Default True.
generate_training_dataset() – reads itRead by generate_training_dataset().
band_rows¶
align_folder() – reads itRead by align_folder().
barcode_mismatches¶
(int) - How many mismatched bases a barcode may carry and still be matched. 0, the default and prior behavior, requires an exact match, so a single sequencing error anywhere in a barcode discards the read. A read that falls within the mismatch budget of two barcodes remains unassigned because its source barcode cannot be resolved without risking guide misattribution.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
barcode_qc¶
QC a barcode-mapping run — reads per well, starved wells, unmapped reads, barcode collisions, row/column position effects and library coverage — then state how many gRNAs per well the design intends and let spaCR derive the abundance threshold that delivers it, sweeping around it so the power/attributability trade-off is visible before the number is chosen.
_run_barcode_qc – reads itRead by _run_barcode_qc.
baselines¶
_captum_attribute – reads itRead by _captum_attribute.
batch_column¶
(str) - Metadata column that identifies independent acquisition batches, normally ‘plateID’. Every analyzed row must have a value and at least batch_min_samples rows must occur in each batch. Use an acquisition date or instrument ID only if that is the nuisance source you intend to remove. Default ‘plateID’. API: spacr.batch_correction.correct_batch_effects.
correction_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads it_perform_regression_perform_regressioncorrection_kwargs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by correction_kwargs().
batch_combat_mean_only¶
(bool) - True corrects only the additive batch shift and leaves each batch’s scale alone. Use it when the plates differ in level but not in spread, or when a batch has too few rows for a stable variance estimate. False (the default) corrects both location and scale, which is standard ComBat. Ignored by every method other than combat. API: spacr.batch_correction.correct_batch_effects.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itgenerate_ml_scores() – reads it_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by generate_image_umap(), reducer_hyperparameter_search(), _perform_regression, generate_ml_scores().
batch_control_column¶
(str or None) - Metadata column containing reference-control labels for control_center, normally ‘columnID’ for plate controls. It is ignored by center, zscore, robust_zscore, and none. Blank follows col_to_compare in Image UMAP or location_column in Classify (ML); regression defaults to ‘columnID’. API: spacr.batch_correction.correct_batch_effects.
correction_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads it_perform_regression_perform_regressioncorrection_kwargs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]refusals() – reads itRead by correction_kwargs(), refusals().
batch_control_values¶
(str, number, list or None) - Reference/negative-control value(s) in batch_control_column used by control_center. Each plate needs at least batch_min_samples matching rows. Image UMAP falls back to neg and Classify (ML) to negative_control_id when this field is blank; regression requires an explicit value. Default varies by module. API: spacr.batch_correction.correct_batch_effects.
correction_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads it_perform_regression_perform_regressioncorrection_kwargs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by correction_kwargs().
batch_correction¶
(str) - Plate/batch correction applied before Image UMAP, ML screen classification or phenotype regression. ‘none’ leaves measurements alone; ‘center’ removes each plate’s mean shift; ‘zscore’ aligns plate means and variances; ‘robust_zscore’ uses median/MAD and tolerates outliers; ‘combat’ models the batch effect while protecting the terms named in batch_covariate_column. Correct when plates were stained or imaged separately; leave off when they were not, since every method removes real signal that happens to align with plate. See spacr.batch_correction.correct_batch_effects. Default ‘none’.
correction_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads it_perform_regression_perform_regression – reads itcorrection_kwargs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]get_setting_dependencies() – reads itrefusals() – reads itRead by correction_kwargs(), _perform_regression, get_setting_dependencies(), refusals().
batch_covariate_column¶
(str, list or None) - Metadata column(s) naming the biological effects ComBat must preserve, for example ‘condition’ or ‘condition,timepoint’. ComBat estimates the batch effect from residuals after fitting these terms, so unlisted effects may be removed with the plate effect. Include every treatment effect that must remain in the corrected data. See spacr.batch_correction.correct_batch_effects. Default None.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itgenerate_ml_scores() – reads it_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]refusals() – reads itRead by generate_image_umap(), reducer_hyperparameter_search(), _perform_regression, generate_ml_scores(), refusals().
batch_fields¶
(int) - Streaming pipeline only (pipeline_style=’v2’): how many whole field stacks are loaded into RAM before one Cellpose batch is segmented. Larger values keep the GPU busier and cut the number of read passes over the plate, at a memory cost of roughly one full field stack each. Ignored entirely by the v1 pipeline. Default 8.
Read by preprocess_generate_masks().
batch_min_samples¶
(int) - Minimum number of rows required in every batch, and minimum matching reference controls per batch for control_center. Correction stops with an actionable error below this threshold because a one- or two-object plate estimate is unstable. Default 3. API: spacr.batch_correction.correct_batch_effects.
correction_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads it_perform_regression_perform_regressioncorrection_kwargs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by correction_kwargs().
batch_missing_control¶
(str) - Policy when control_center cannot find enough reference controls on a plate: ‘error’ stops rather than silently mixing corrected and raw plates; ‘skip’ leaves that plate unchanged and records a warning. Default ‘error’. API: spacr.batch_correction.correct_batch_effects.
correction_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads itcorrection_kwargs() – reads it_perform_regression_perform_regressioncorrection_kwargs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by correction_kwargs().
batch_size¶
(int) - How many images are held and processed together in one pass: field stacks during normalization and Cellpose segmentation, crops per step during classifier training and activation maps. Raising it speeds runs up but increases RAM/VRAM roughly linearly; lower it on out-of-memory errors. Defaults: 50 for mask generation, 64 for training.
preprocess_img_data() – reads it_concatenate_and_normalize_impl – reads itgenerate_cellpose_masks_sam() – reads itgenerate_organelle_masks_sam() – reads it_inner_loader – reads itapply_model_to_tar() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itgenerate_activation_map() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]_cellpose_z_segment_fn – reads it_segment – reads it_segment_cellpose – reads itgenerate_cellpose_masks() – reads itcheck_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itapply_cellpose_model() – reads itget_default_apply_cellpose_model_settings() – reads ittest_cellpose_model() – reads itget_default_test_cellpose_model_settings() – reads ittrain_cellpose() – reads itRead by _cross_validate_model, _inner_loader, apply_model_to_tar(), generate_activation_map(), train_test_model(), _concatenate_and_normalize_impl, preprocess_img_data(), _cellpose_z_segment_fn, _segment, _segment_cellpose, generate_cellpose_masks(), generate_cellpose_masks_sam(), generate_organelle_masks_sam(), get_analyze_plaque_settings(), get_default_apply_cellpose_model_settings(), get_default_generate_activation_map_settings(), get_default_test_cellpose_model_settings(), get_train_test_model_settings(), check_cellpose_models(), identify_masks_finetune(), apply_cellpose_model(), test_cellpose_model(), train_cellpose().
bimodality_cutoff¶
(float) - Minimum bimodality coefficient required for an unflagged field- or well-level efficiency estimate. Values below the threshold are retained but flagged because the intensity distribution provides insufficient evidence for two populations. Increasing the threshold requires stronger bimodality. Default 0.5555555555555556.
_invasion_field_thresholds – reads it_invasion_well_table – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_field_thresholds, _invasion_well_table, _set_analyze_invasion_defaults.
black_background¶
(bool) - Choose the standalone/CLI embedding fallback: black canvas with white axes when True, white canvas with black axes when False. In the Qt app, Image UMAP automatically matches its enclosing card in the active theme and uses that theme’s readable foreground color instead. Default True.
generate_image_umap() – reads itRead by generate_image_umap().
blend¶
How overlapping tiles are combined where they meet: ‘max’ takes the brighter pixel, ‘overwrite’ lets the later tile win. ‘max’ hides a seam, ‘overwrite’ shows you where one is. Default ‘max’.
align_folder() – reads it_work – reads itwrite_stack() – reads itget_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by align_folder(), _work, write_stack(), get_preprocess_ops_settings().
blur_sigma¶
Gaussian blur, in pixels, applied before edges are found for the quality-control outlines. Higher ignores texture and follows only the object’s shape; 0 does not smooth at all. Affects the drawn outline, never the mosaic. Default 0.0.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
bounding_box¶
(bool) - Crop geometry used when stream_method is ‘array’. True retains the rectangular region enclosing each labelled object, including local background and neighbouring signal. False retains only pixels within the object mask and sets surrounding pixels to zero. Default True.
generate_annotation_dataset() – reads itstream_dataset() – reads itbystander_measurements¶
(bool) - Split uninfected cells into bystanders and distal cells. A bystander is an uninfected cell within the reach set by bystander_reach_in_diameters of an infected one; everything else uninfected is distal. Without this the two are the same row, so a bystander phenotype cannot be found and the uninfected control is a mixture of two populations whose variance hides the effect being looked for. Adds three columns per cell and costs one distance transform and one KD-tree per field. Default False.
_measure_crop_core_morphological_measurements – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _morphological_measurements, get_measure_crop_settings().
bystander_reach_in_diameters¶
(float) - How close an uninfected cell must be to an infected one to count as a bystander, expressed in measured cell diameters rather than pixels or micrometres, so it means the same thing at 20x and 63x. The diameter is the median of the cells in the field, ignoring those clipped by its edge. Zero or less makes every uninfected cell distal, which turns the split off without a second setting. Ignored unless bystander_measurements is enabled. Default 1.0.
_measure_crop_core_morphological_measurements – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _morphological_measurements, get_measure_crop_settings().
calculate_correlation¶
(bool) - For every pair of measured channels and every object mask, compute a per-object Pearson correlation and the three Manders coefficients (manders_m1, manders_m2, manders_overlap_coefficient), stored as <object>_channel_i_channel_j_* columns. Needs at least two channels. Turn it off to cut measurement time and database size when colocalisation is not part of the phenotype. Default True.
_measure_crop_core_intensity_measurements – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _intensity_measurements, get_measure_crop_settings().
calibrate_fraction_threshold¶
(bool) - Estimate fraction_threshold from control wells instead of using the configured value. The sweep recomputes per-well fractions across candidate cutoffs and selects the cutoff with greatest imaging-sequencing agreement. The plate design must identify pure control wells independently; selecting controls by the measured fraction would be circular. Default False.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
cam_type¶
(str) - Which attribution map is computed. ‘gradcam’ weights the target_layer feature maps by their pooled gradients into a coarse heatmap of the region that drove the call; ‘gradcam_pp’ currently computes the identical map and only changes the output folder and table name. ‘saliency_image’ sums the absolute input gradient into one map; ‘saliency_channel’ keeps it per channel so you can see which stain mattered. Default ‘gradcam’.
generate_activation_map() – reads itactivation_correlations_to_database() – reads itactivation_maps_to_database() – reads itRead by generate_activation_map(), get_default_generate_activation_map_settings(), activation_correlations_to_database(), activation_maps_to_database().
canny¶
Low and high thresholds, in intensity units, for the edge detector that draws quality-control outlines. Lower values find more edge and more noise with it. Default (40, 120).
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
cell_background¶
(int) - Background intensity of the cell channel in raw image units. Pixels below it are zeroed when remove_background_cell is True, and it is multiplied by cell_signal_to_noise to set the intensity the normalisation ceiling must reach. Set it from a genuinely empty region; too high and dim cells are erased. Default 100.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
cell_cellprob_threshold¶
(float) - Cellpose cellprob_threshold: only pixels whose predicted cell probability exceeds it are assigned to a mask. Raise it to shrink outlines and drop faint or spurious cells; lower it to grow outlines and recover dim ones. Valid range roughly -6 to 6, default 0. Lower it first when whole cells are missing.
preprocess_generate_masks() – reads itgenerate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads itgenerate_cellpose_masks() – reads itRead by preprocess_generate_masks(), _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
cell_chann_dim¶
(int) - Recruitment analysis only (analyze_recruitment): the image-channel index paired with the cell mask when drawing outline overlays, and the switch that enables the cell filters - set an integer and cell_size_range, cell_intensity_range and target_intensity_min are applied; leave it None and cells are not filtered at all. Default 3.
analyze_recruitment() – reads itRead by analyze_recruitment().
cell_channel¶
(int or None) - Zero-indexed raw acquisition channel that Cellpose segments into cell masks; it also selects which channel the cell_background, cell_signal_to_noise and remove_background_cell settings are applied to during preprocessing. Set to None and no cell masks, cell table or cell crops are produced. At least one of cell/nucleus/pathogen/organelle_channel must be an integer or the run aborts. Default None.
preprocess_generate_masks() – reads it_concatenate_and_normalize_impl_normalize_img_batch – reads itgenerate_cellpose_masks_sam() – reads itautomated_motility_assay() – reads itdense_mask_channel_positions() – reads it_segment_cellpose_sam – reads itdense_mask_channel_positions() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_get_lists_for_normalization – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_segment_cellpose – reads itgenerate_cellpose_masks() – reads itautomated_motility_assay() – reads itdense_mask_channel_positions() – reads itplot_region() – reads it_channelschannels_from_settings() – reads itapply_settings() – reads it_seed_organelle_column(...) [UNRESOLVED]apply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by preprocess_generate_masks(), channels_from_settings(), _get_lists_for_normalization, _normalize_img_batch, _segment_cellpose, _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam(), plot_region(), apply_settings(), apply_settings(), organelle_measurement_caveats(), automated_motility_assay(), dense_mask_channel_positions(), _describe_objects.
cell_diameter¶
(int or None) - Expected cell diameter in pixels. Cellpose 4 rescales the image by 30/diameter before segmentation, aligning the expected object size with the scale used to train CPSAM; leave it None to segment at native scale. Set it when cells are much larger or smaller than ~30 px and segmentation produces fragmented or merged masks. spacr.diameter.estimate_diameters estimates a value from the selected fields. Default None.
preprocess_generate_masks() – reads itgenerate_cellpose_masks_sam() – reads it_get_object_settings – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_get_object_settings – reads itorganelle_morphology_now() – reads itapply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by preprocess_generate_masks(), generate_cellpose_masks_sam(), organelle_morphology_now(), apply_settings(), _get_object_settings, _describe_objects.
cell_dim¶
annotate_crops() – reads itRead by annotate_crops().
cell_flow_threshold¶
(float) - Cellpose flow_threshold: the maximum allowed error between a candidate mask’s recomputed flows and the network’s predicted flows. Masks above it are discarded, so lowering it strips ragged or implausible cells but also loses real ones; raising it keeps more. Usable range about 0-3; the GUI accepts -1 to 100 so it can hold this default. Default 100, which accepts every candidate.
preprocess_generate_masks() – reads itgenerate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads itgenerate_cellpose_masks() – reads itRead by preprocess_generate_masks(), _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
cell_intensity_merge¶
(bool) - Merge two touching cell labels when the mean intensity along their shared boundary reaches cell_intensity_threshold, which means there is no detectable dark seam between them and the split is spurious. Requires an intensity image. Set the threshold first: with none set the merge refuses and reports the boundary intensities it found. Default False.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cell_intensity_range¶
(list) - Legacy [min, max] bounds used during recruitment analysis when cell_chann_dim is set. Despite the setting name, the current _object_filter call uses index 0 from [nucleus, pathogen, cell] and therefore filters the nucleus-channel mean intensity. Review the filtered object counts when using this setting. Default None.
analyze_recruitment() – reads itRead by analyze_recruitment().
cell_intensity_split¶
(bool) - Split oversized cell labels by distance-transform watershed, before the merge and filter steps. Objects larger than cell_minimum_area_to_split are seeded at local distance maxima cell_min_watershed_distance apart and cut. Despite the name no intensity is used. Enable when several touching cells share one label. Default False.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cell_intensity_threshold¶
(float or None) - Absolute intensity, in the image’s own raw units, that the mean along a shared boundary must reach before cell_intensity_merge joins two touching cell labels. Read it off the image with a pixel inspector rather than guessing. It does NOT carry between acquisitions taken at different exposure or gain, so re-read it for a new imaging session. Leave unset and the merge refuses and reports the boundary intensities it found, which is the number to type. Ignored unless cell_intensity_merge is True. Default None.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cell_loc¶
(list) - One list of well identifiers per entry in cells, specifying the plate locations of each host cell line, for example [[‘c1’,’c2’],[‘c3’]]. Identifiers must start with ‘r’ for a row or ‘c’ for a column; other values are ignored and the corresponding wells remain unannotated. None labels every row with the first entry of cells. No default is set: annotate_filter_vision accesses settings[‘cell_loc’] directly, so the key must be present in the dictionary and explicitly set to None when location mapping is not required.
annotate_filter_vision() – reads itRead by annotate_filter_vision().
cell_mask_dim¶
(int) - Position along the last axis of each merged/*.npy array where the cell label mask sits. Merged arrays are ordered [image channels…, cell, nucleus, pathogen, organelle], so the default 4 assumes the four channels 0-3 were kept; keep fewer channels and every mask dim shifts down. None makes measure_crop skip all cell measurements and cell crops. Default 4.
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]mask_dims_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads it_intensity_measurements – reads it_morphological_measurements – reads itmeasure_crop() – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]plot_merged() – reads itdisplay(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by mask_dims_from_settings(), _crop_shape_overrides, _intensity_measurements, _measure_crop_core, _morphological_measurements, measure_crop(), plot_merged(), get_measure_crop_settings(), organelle_measurement_caveats(), _describe_objects.
cell_max_area¶
(int or None) - Maximum cell area in pixels^2; objects larger than this are deleted after segmentation. Use it to discard clumps or debris blobs that Cellpose labelled as one huge cell. It only deletes, it never splits - use cell_intensity_split for that. 0 or None disables the filter. Default 0.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cell_max_size¶
(int | None) - Drop cells larger than this many pixels. None, the default, disables the filter and preserves prior behavior. The minimum sizes remove debris; only a maximum removes a segmentation artifact, which passes every minimum and carries its area into the classifier and the regression. The run prints how many objects each bound dropped.
filter_selection() – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by filter_selection(), _measure_crop_core, get_measure_crop_settings().
cell_method¶
run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by _describe_objects.
cell_min_area¶
(int) - Minimum cell area in pixels^2. Passed to Cellpose as min_size so undersized masks are dropped during segmentation, then re-applied afterwards to delete any object below it. Raise it to clear debris and fragments; set it too high and genuine small cells disappear. 0 disables. Default 0.
preprocess_generate_masks() – reads itmerge_split_filter_masks() – reads it_get_object_settings – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_get_object_settings – reads itRead by preprocess_generate_masks(), _measure_crop_core, merge_split_filter_masks(), _get_object_settings, get_measure_crop_settings().
cell_min_size¶
(int) - (Deprecated) Pixel-area floor applied to cell labels during measurement: any cell smaller than this is erased from the mask before features are extracted. Superseded by cell_min_area, which filters at segmentation time, but this one still runs if you set it. 0 or None disables it. Default 8000.
filter_selection() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_measure_crop_core – reads itget_measure_crop_settings() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by filter_selection(), _measure_crop_core, get_measure_crop_settings(), _describe_objects.
cell_min_watershed_distance¶
(int) - Minimum separation in pixels between watershed seeds when cell_intensity_split divides an oversized cell; seeds are local maxima of the distance transform. Increase it to produce fewer, larger fragments and reduce over-segmentation of individual cells; decrease it to separate tightly packed cells. Ignored unless cell_intensity_split is True. Default 10.
merge_split_filter_masks() – reads it_run_one – reads itRead by _run_one, merge_split_filter_masks().
cell_minimum_area_to_split¶
(int) - Absolute pixel area above which a cell label is split by cell_intensity_split; objects at or below it are never cut. ABSOLUTE, not a multiple of the median: the threshold no longer moves with whatever else is in the field, so the same object is treated the same way in a sparse dish and a crowded one. Raise it to protect small cells from fragmentation. Default 100.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cell_model_name¶
(str) - Cell-segmentation weights. Cellpose 4 provides the stock ‘cpsam’ model; alternatively, provide a CPSAM checkpoint created by Train Cellpose, loaded as pretrained_model. Legacy names (‘cyto’, ‘cyto2’, ‘cyto3’, ‘nuclei’) remain accepted but resolve to cpsam because Cellpose 4 no longer ships those models. Only diameter changes inference (scaling by 30/diameter); model_type and diam_mean are not used in v4.0.1+. Default ‘cpsam’.
preprocess_generate_masks() – reads it_get_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by preprocess_generate_masks(), _get_object_settings, _get_object_settings.
cell_morphology¶
Read by organelle_morphology_now().
cell_outlier_mads¶
_perform_regression_perform_regressionapply() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by apply().
cell_perimeter_fraction¶
(float) - For each touching pair of cell labels, the shared boundary length divided by the smaller object’s perimeter; pairs at or above this fraction are merged into one cell. Low values such as 0.1 merge aggressively and can fuse true neighbours, high values only rejoin pieces of the same cell. 0 disables perimeter merging. Default 0.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cell_perimiter_fraction¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cell_plate_metadata¶
(list of lists) - Wells occupied by each entry of cell_types, with one inner list per cell type in the same order, for example [[‘c2’,’c3’],[‘c4’]]. Each identifier must start with ‘c’ (column) or ‘r’ (row); invalid identifiers are skipped without an exception and those wells receive no host_cells label. Because ‘condition’ combines the labels that are present, a typographical error changes the comparison without raising an error. Default None.
plot_data_from_db() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_recruitment() – reads itanalyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by plot_data_from_db(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_recruitment(), analyze_replication().
cell_remove_border_objects¶
(bool) - Delete every cell label touching any of the four image edges before measurement. Removes partial cells whose area and total intensity are truncated and would bias per-cell statistics, at the cost of losing objects - a large cost in fields where cells are big relative to the field. Default False.
merge_split_filter_masks() – reads it_apply_size_filter – reads itRead by merge_split_filter_masks(), _apply_size_filter.
cell_resample¶
_segment_cellpose_sam – reads itRead by _segment_cellpose_sam.
cell_restore_type¶
_get_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by _get_object_settings, _get_object_settings.
cell_signal_to_noise¶
(int) - Multiplied by cell_background to define the minimum intensity for the normalisation ceiling. spaCR evaluates the 98th through 99.5th percentiles of the cell channel and uses the first value at or above that product as the upper anchor. Increase it to raise the ceiling and reduce normalised intensity; decrease it to increase the visibility of faint cells. Default 10.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
cell_size_range¶
(list) - [min, max] bounds in pixels^2 on cell_area, used to drop rows from the measurement table during recruitment analysis; only cells strictly between the two values are kept. Both entries must be integers or that bound is silently skipped. Setting it to None widens it to [0, 1e100]. Default [0, 100000].
analyze_recruitment() – reads itRead by analyze_recruitment().
cell_type¶
_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]organelle_morphology_now() – reads itRead by organelle_morphology_now(), get_measure_crop_settings(), organelle_measurement_caveats().
cell_types¶
(list) - Names of the host cell lines in the experiment, e.g. [‘HeLa’]. Each name is written into the host_cells column and folded into the combined condition label used for grouping and plotting; the list is positionally paired with cell_plate_metadata, which says which wells hold each one. Default [‘HeLa’].
plot_data_from_db() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_recruitment() – reads itanalyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by plot_data_from_db(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_recruitment(), analyze_replication().
cellcellpose¶
Read by preprocess_img_data().
cellcellpose__channel¶
generate_cellpose_masks_sam() – reads it_get_cellpose_channels – reads itgenerate_cellpose_masks() – reads it_get_cellpose_channels – reads itorganelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by generate_cellpose_masks(), generate_cellpose_masks_sam(), organelle_settings_view(), _get_cellpose_channels.
cellorganelle¶
organelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by organelle_settings_view().
cellpose_cell_channel¶
_get_cellpose_channels – reads it_get_cellpose_channels – reads itRead by _get_cellpose_channels.
cellpose_diameter¶
Expected nucleus diameter in pixels for the segmentation used to align acquisitions. Empty lets Cellpose estimate it, which is usually right and occasionally very wrong on a sparse field; setting it removes that variance. Default None.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
cellpose_model¶
Which Cellpose model segments the nuclei that the phenotype- to-genotype alignment matches on. Changing it changes which objects are found, and so which points the alignment is solved from. Default ‘cpsam’.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
cellpose_nucleus_channel¶
_get_cellpose_channels – reads it_get_cellpose_channels – reads itRead by _get_cellpose_channels.
cellpose_organelle_channel¶
generate_organelle_masks_sam() – reads itorganelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by generate_organelle_masks_sam(), organelle_settings_view().
cellpose_pathogen_channel¶
_get_cellpose_channels – reads it_get_cellpose_channels – reads itRead by _get_cellpose_channels.
cellprob¶
Read by segment_frame().
cells¶
(list) - Names of the host cell lines on the plate, e.g. [‘HeLa’]. Each name is written to the host_cells column and becomes part of the combined condition label used for grouping in plots and statistics. With cell_loc set the names are mapped well by well; with cell_loc None only the first name is used, applied to every row. No default is set: no set_default_* function fills this key and its readers index settings[‘cells’] directly, so it must be present - use None to skip host-cell annotation.
generate_screen_graphs() – reads itannotate_filter_vision() – reads itcells_per_well¶
(int) - Minimum cells a well must contribute to survive recruitment analysis; wells below it, and every cell in them, are dropped before the by-well plots and CSVs are produced. Raise it to suppress noisy, sparsely populated wells at the cost of losing those wells. Default 0, which keeps every well.
analyze_recruitment() – reads itRead by analyze_recruitment().
change_plate¶
(bool) - Relabel each source directory as plate1, plate2, … instead of trusting the plate ID stored in its database. Use it when several plates were written under the same name, which would otherwise let two plates’ fields pool into one threshold and one well. Default False.
analyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_endodyogeny(), analyze_invasion(), analyze_replication().
channel¶
Read by segment_frame().
channel_arrays¶
(list[int]) - Zero-based intensity-plane indices included in each streamed image, in output-channel order. This setting applies to both stream methods; changing the order changes the channel mapping presented to the model. Default [0, 1, 2].
generate_annotation_dataset() – reads itstream_dataset() – reads itchannel_dims¶
(list) - Recruitment analysis only: image-channel indices in the merged arrays. They determine the channels used for overlays and recruitment measurements, but _calculate_recruitment writes fixed column names without channel identifiers. Runs with different values can therefore produce identically named columns containing measurements from different channels. Default [0, 1, 2, 3].
analyze_recruitment() – reads itRead by analyze_recruitment().
channel_index¶
Which channel the stitcher matches on, zero-indexed. Pick the one with the most structure, usually the nuclear stain: a sparse channel gives the detector nothing to align and every pair is skipped. Default 0.
ops_preprocess() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), ops_preprocess(), stitch_cycle_wells().
channel_indices¶
Which channels go into a multi-channel mosaic, in the order they are written. Empty uses every channel the tiles share. Default None.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
channel_of_interest¶
(int, list, or str) - Measurements available to the model. Specify one channel to train on that channel alone, multiple channels to use their combination of measurements, or ‘shape’ to use outline-derived features. An empty value includes every measurement. A colocalisation feature is associated with both measured channels, so selecting either channel also includes their shared colocalisation features. This setting also selects the channel used for recruitment measurements. Default 3 in machine-learning steps and 1 or 2 elsewhere.
Read by generate_ml_scores(), plot_data_from_db(), analyze_recruitment().
channels¶
(list of int) - Zero-indexed image channels kept in merged/*.npy and measured by measure_crop; each entry produces its own <object>_channel_<n>_* intensity columns. The list length fixes where masks land, so cell/nucleus/pathogen_mask_dim must shift if you change it. Preprocessing silently resets it to range(n) when it does not match the number of channel folders found. Default [0,1,2,3]. External Masks starts with []; there an empty list means every detected intensity channel, not no channels.
preprocess_generate_masks() – reads itv2_channels_from_settings() – reads itpreprocess_img_data() – reads itprepare_illumination_model() – reads itautomated_motility_assay() – reads itrun_preflight()describe_plan() – reads itisinstance(...) [UNRESOLVED]_check_channels – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]apply_model_to_tar() – reads itgenerate_activation_map() – reads itrun_import() – reads itload_activation_data() – reads itillumination_settings() – reads it_measure_crop_core – reads it_morphological_measurements – reads it_with_distances – reads itmeasure_crop() – reads itprepare_illumination_model() – reads itget_measure_crop_settings() – reads itrun_preflight()describe_plan() – reads itisinstance(...) [UNRESOLVED]_check_channels – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]automated_motility_assay() – reads itplot_region() – reads itapply_settings() – reads it_describe_plan_safedescribe_plan() – reads itisinstance(...) [UNRESOLVED]set_annotate_default_settings() – reads itcheck_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]Read by v2_channels_from_settings(), preprocess_generate_masks(), apply_model_to_tar(), generate_activation_map(), run_import(), load_activation_data(), illumination_settings(), prepare_illumination_model(), preprocess_img_data(), _measure_crop_core, _morphological_measurements, _with_distances, measure_crop(), plot_region(), apply_settings(), get_analyze_plaque_settings(), get_default_generate_activation_map_settings(), get_measure_crop_settings(), set_annotate_default_settings(), check_cellpose_models(), identify_masks_finetune(), interpret_vision_model(), automated_motility_assay(), _check_channels, describe_plan().
checkpoint_path¶
(str or None) - Optional explicit path for an atomic resume checkpoint. Format Converter defaults to .spacr_conversion.checkpoint.json in its destination; Image UMAP defaults to results/.spacr_checkpoints/umap_search.json under the project. Keep checkpoints with their outputs. Default None.
Read by convert_folder(), umap_checkpoint_path().
chunk_size¶
(int) - Number of FASTQ reads read into memory and handed to each worker batch. Larger chunks cut per-batch overhead and make the progress bar coarser but raise peak RAM per job; smaller chunks stream more gently on low-memory machines. Also sets how many reads are processed when test is True. Default 100000.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
circularize¶
(bool) - Replace each detected mask with an equal-area circle centred on its centroid before measurement. This can reduce boundary variation for approximately circular objects with noisy segmentation outlines. Do not enable it when shape is an outcome, because circularization removes elongation and other morphological differences. Default False.
apply_cellpose_model() – reads itget_default_apply_cellpose_model_settings() – reads itRead by get_default_apply_cellpose_model_settings(), apply_cellpose_model().
class_balance¶
(str) - Correction for imbalance among training classes. ‘none’ preserves sampling and reports class counts, their ratio and a recommendation. ‘weighted_sampler’ samples classes approximately equally using 1/n. ‘sqrt_weighted_sampler’ uses 1/sqrt(n), reducing the risk that repeated sampling of a very small class causes memorization. ‘weighted_loss’ preserves sampling and weights the loss. Use one of the latter three when the reported ratio exceeds approximately 3:1. Default ‘none’.
_inner_loader – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _inner_loader, train_test_model(), get_train_test_model_settings().
class_column¶
(str) - Column containing the per-object class label used by class-proportion analysis. Missing values are filled with 0 rather than dropping the corresponding rows; selecting an incorrect column can therefore assign class zero to every object without raising an error. The value is also appended to the condition when group_by_class is enabled. Default ‘test’.
analyze_class_proportion() – reads itanalyze_endodyogeny() – reads itclass_folder_names¶
(list of str) - Ordered training folder names. Each must exactly match a subfolder under src/train and src/test; its position becomes the integer label, and the list length sets the classifier-head width. Training raises FileNotFoundError with missing and available folders when a name is absent. Generate Training Dataset replaces this list with the folders it wrote. This setting identifies crop locations; ‘classes’ defines their semantic labels. Default [‘nc’,’pc’].
normalize_settings() – reads it_rules_from_annotationfolder_names() – reads it_rules_from_metadatafolder_names() – reads itfolder_names() – reads itdict(...) [UNRESOLVED]_class_folder_namesfolder_names() – reads it_class_folder_namesfolder_names() – reads it_cross_validate_model_class_folder_namesfolder_names() – reads itget_train_test_model_settings() – reads it_record_generated_folder_names – reads it_default_train_class_folder_namesfolder_names() – reads it_cross_validate_model_class_folder_namesfolder_names() – reads itget_train_test_model_settings() – reads it_class_folder_namesfolder_names() – reads it_cross_validate_model_class_folder_namesfolder_names() – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _record_generated_folder_names, folder_names(), normalize_settings(), get_train_test_model_settings().
class_metadata¶
(list of lists) - One inner list per training class, holding the metadata values that select that class’s objects, for example [[‘c1’],[‘c2’]] for a two-class run keyed on column. Order fixes the class indices the model learns, so reordering the inner lists relabels the whole training set. Values that occur in no row make the generator select nothing and stop. Default [[‘c1’], [‘c2’]].
generate_training_dataset() – reads it_fold_the_classesfold_into_classes() – reads itclass_metadata_of() – reads itRead by class_metadata_of(), fold_into_classes(), generate_training_dataset().
class_names¶
normalize_settings() – reads itdict(...) [UNRESOLVED]Read by normalize_settings().
classes¶
(dict) - Class definitions in the form class name -> {column, value}; for example, ‘pc’ might be {‘column’: ‘columnID’, ‘value’: ‘c3’}. In the Classes editor, select a column and assign names to its distinct values; definitions may span several columns. One row may instead define a random complement containing objects not assigned by any other class, sampled to match the largest class. This setting defines which objects belong to each class; class_folder_names defines the training subfolders. A pre-split settings file contains a plain list and is converted when read. Default {}.
class_rules() – reads itnormalize_settings() – reads it_rules_from_annotationfolder_names() – reads itclass_names() – reads itclass_rules() – reads it_rules_from_metadatafolder_names() – reads itclass_names() – reads itclass_rules() – reads itfolder_names() – reads itclass_names() – reads itclass_rules() – reads itdict(...) [UNRESOLVED]_class_folder_namesfolder_names() – reads itclass_names() – reads itclass_rules() – reads it_class_folder_namesfolder_names() – reads itclass_names() – reads itclass_rules() – reads it_cross_validate_model – reads it_class_folder_namesfolder_names() – reads itclass_names() – reads itclass_rules() – reads itget_train_test_model_settings() – reads it_record_generated_folder_names – reads it_class_column – reads it_fold_the_classesannotation_column_of() – reads itclass_metadata_of() – reads it_default_train_class_folder_namesfolder_names() – reads itclass_names() – reads itclass_rules() – reads it_cross_validate_model – reads it_class_folder_namesfolder_names() – reads itclass_names() – reads itclass_rules() – reads itget_train_test_model_settings() – reads it_class_folder_namesfolder_names() – reads itclass_names() – reads itclass_rules() – reads it_cross_validate_model – reads it_class_folder_namesfolder_names() – reads itclass_names() – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]class_names() – reads itclass_rules() – reads it_describe_classifier_training – reads it_migrate_control_wells – reads itdict(...) [UNRESOLVED]Read by _record_generated_folder_names, annotation_column_of(), class_metadata_of(), class_names(), class_rules(), folder_names(), normalize_settings(), _cross_validate_model, _class_column, _describe_classifier_training, _migrate_control_wells, get_train_test_model_settings().
classifier_accuracy¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
classifier_evaluation¶
(bool) - Build the Classifier Evaluation workbench bundle from out-of-fold predictions after Classify (CV): sample-level predictions, confusion matrices, reliability curves, calibrated probabilities, per-plate metrics, leakage reports and a manifest. It requires cross_validation_folds >= 2; a single train/validation split cannot produce unbiased out-of-fold diagnostics. Default True. API: spacr.classifier_evaluation.evaluate_predictions.
_cross_validate_model – reads it_default_train_cross_validate_model – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model.
classifier_evaluation_path¶
_cross_validate_model – reads it_default_train_cross_validate_model – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model.
classifier_family¶
(str) - Which classifier the merged Classify module runs: ‘cv’ trains a Torch model on object crops, ‘ml’ fits a gradient-boosted model on measured features. The settings the other family uses are greyed out, not hidden - they keep their values. This is the only Classify screen; the two it replaced were removed on 2026-08-23, and their entry points (spacr.deep_spacr.deep_spacr and spacr.ml.generate_ml_scores) are what this dispatches to unchanged. Default ‘cv’.
_begin_flowview_run_install_classify_collectorresolve_family() – reads itresolve_family() – reads it_fresh_collector – reads itresolve_family() – reads itdict(...) [UNRESOLVED]run_search_for_app() – reads itRead by resolve_family(), _fresh_collector, run_search_for_app().
clustering¶
(str) - Algorithm applied to the two-dimensional embedding. ‘dbscan’ identifies density-based clusters from eps and min_samples, labels sparse points as noise (-1), and determines the cluster count from the data. ‘kmeans’ forces exactly min_samples clusters and assigns every point. Use dbscan for distinct phenotypes over a diffuse background and kmeans when a fixed number of groups is required. Default ‘dbscan’.
_compute_umap – reads itgenerate_image_umap() – reads itRead by _compute_umap, generate_image_umap().
cmap¶
(str) - Matplotlib colormap applied to single-channel image previews and plate heatmaps. Perceptually uniform maps (‘viridis’, ‘inferno’, ‘magma’) preserve the relative visibility of intensity differences; ‘gray’ resembles the raw single-channel microscope image. Any registered matplotlib name is accepted, with an ‘_r’ suffix to reverse it. Default ‘inferno’ for image plots and ‘viridis’ for plate heatmaps.
preprocess_generate_masks() – reads itpreprocess_img_data() – reads it_concatenate_and_normalize_impl – reads itgenerate_ml_scores() – reads itplot_merged() – reads itdisplay(...) [UNRESOLVED]analyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]generate_score_heatmap() – reads itgenerate_score_heatmap() – reads itdict(...) [UNRESOLVED]Read by preprocess_generate_masks(), _concatenate_and_normalize_impl, preprocess_img_data(), generate_ml_scores(), plot_merged(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_endodyogeny(), analyze_invasion(), analyze_replication(), generate_score_heatmap(), generate_score_heatmap().
col_to_compare¶
(str) - Metadata column that identifies the control wells when embedding_by_controls is True: rows whose value equals pos or neg are used to train the reducer, and the column is then dropped before fitting. Typically ‘columnID’ or ‘rowID’ depending on where controls sit on the plate. Ignored otherwise. Default ‘columnID’.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itload_search_data() – reads itRead by generate_image_umap(), reducer_hyperparameter_search(), load_search_data().
collision¶
What to do when a destination filename already exists: ‘rename’ the incoming file, ‘skip’ it, or ‘overwrite’ it. Overwrite destroys the earlier file. Default ‘rename’.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
collision_max_distance¶
How many substituted bases still count as a barcode collision. 1 catches the pairs a single miscalled base can turn into each other, which is the common event; 0 reports only exact duplicates. Above 1 the search is pairwise and slow on a full gRNA library. Default 1.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
color¶
_wear_the_print_look – reads itRead by _wear_the_print_look.
color_by¶
(str) - Name of a column in the joined measurement table (e.g. ‘cond’, ‘columnID’, ‘plateID’) used to color embedding points instead of the cluster labels. Set it to see how a known grouping such as condition or plate column falls across the map; leave it None to color by the clustering result. Setting it also disables remove_cluster_noise, plot_outlines and smooth_lines. Default None.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itRead by generate_image_umap(), reducer_hyperparameter_search().
columnID¶
(str) - Plate-map field identifying the plate column, the 1-24 axis of a well name. It is used to group wells and define the heatmap x-axis. Selecting a different field produces an incorrectly defined axis without raising an error. Default None.
Read by generate_score_heatmap().
column_csv¶
(path) - CSV mapping column barcodes to well names; it must have ‘sequence’ and ‘name’ columns. Reads are matched verbatim against it with no reverse-complementing, so the sequences must be in the same orientation as the reads - run barecodes_reverse_complement on the file if they are not. Unmatched reads get NA for columnID. Default the bundled spacr/resources/data/barcodes_column.csv; barcode QC (sequencing_qc) instead defaults this key to empty, where the reference is optional.
Read by _run_barcode_qc, generate_barecode_mapping(), barcode_qc_defaults().
column_map¶
run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]import_project() – reads itrun_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by import_project(), _check_required_paths.
comp_level¶
(int) - complevel passed to the HDF5 store, 0-9. 0 disables compression (fastest write, largest file); higher values shrink annotated_reads.h5 at increasing CPU cost, and at the top of the range saving can take longer than the barcode mapping itself. Ignored when save_h5 is False. Default 5.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
comp_type¶
(str) - PyTables compression library used when writing annotated_reads.h5, passed to pandas HDFStore as complib: ‘zlib’, ‘lzo’, ‘bzip2’ or ‘blosc’. ‘blosc’ is far faster at similar file size, ‘bzip2’ is smallest but slowest. Ignored entirely when save_h5 is False. Default ‘zlib’.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
compartment¶
(str) - Prefix used by per-object measurement columns, so ‘pathogen’ selects pathogen_area and pathogen_channel_1_percentile_95. It must match the object type contained in the table; otherwise the run stops and reports the unresolved area and intensity columns. Default ‘pathogen’.
analyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_endodyogeny(), analyze_invasion(), analyze_replication().
consolidate¶
(bool) - Before processing, recursively scan src for images and copy them into a single <src>/consolidated folder, prefixing each filename with its subfolder names so nothing collides; src is then repointed there. Use it when one plate’s images are split across per-well or per-channel subfolders. Copies, so disk use roughly doubles. Default False.
Read by preprocess_generate_masks().
control_quantile¶
(float) - Quantile of the control wells’ outside-stain distribution used as the threshold. A value of 0.99 classifies approximately one percent of genuinely unstained parasites as attached. Lowering it toward 0.95 reduces false invaded classifications while increasing false attached classifications; raising it has the opposite effect. Default 0.99.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
control_sgrnas¶
(list) - gRNA names treated as controls when computing the mixed-condition fraction; every fraction is measured relative to these controls. An incorrect or incomplete list shifts every fraction on the plate in the same direction without raising an exception. Verify the list against the screening library. Default None.
Read by generate_score_heatmap().
controls¶
generate_screen_graphs() – reads itRead by generate_screen_graphs().
controls_loc¶
generate_screen_graphs() – reads itRead by generate_screen_graphs().
coordinate_columns¶
(list or None) - Database columns used to locate an object for on-demand cropping. Two columns may give a centroid row and column; one column may identify a labeled object whose mask supplies its extent. Coordinate-backed routes support only bounding-box crops. None uses the configured mask plane instead. Default None. Merged Classifier derives one identifier from object_array, initially [‘cell_id’], so the single-column form is valid rather than an incomplete coordinate pair.
validate() – reads itRead by validate().
correlation¶
(bool) - Correlate every input channel with every activation-map channel per image and write the result to the <cam_type>_correlations table: a Pearson coefficient plus Manders M1/M2 at each manders_thresholds percentile (15, 50, and 75 by default). This provides quantitative evidence of stain-specific model attention beyond visual heatmap inspection. save=True is required to write the results to the database. Default True.
generate_activation_map() – reads itRead by generate_activation_map(), get_default_generate_activation_map_settings().
count_data¶
(str or list) - CSV(s) of per-well gRNA read counts from the sequencing step (unique_combinations.csv); each must contain grna, count, rowID and columnID columns or the run raises ValueError. These are the regression’s independent variable. Pass one path per plate, position-aligned with plates_count; results are written under the first file’s folder. Default ‘list of paths’, a placeholder that must be replaced; the barcode QC module defaults this key to ‘path to unique_combinations.csv’.
_perform_regression_read_data – reads itnormalize_regression_input_pairs() – reads it_preparation_key – reads it_announce_the_fit_count_files_of – reads it_it_will_permute(...) [UNRESOLVED]_say_what_the_permutation_will_do(...) [UNRESOLVED]_first_count_file – reads it_perform_regression_perform_regression – reads it_calibrated_fraction_threshold_calibration_inputs – reads it_draw_the_threshold_sweep_graph_sequencing_statsgraph_sequencing_stats() – reads it_graph_sequencing_statsgraph_sequencing_stats() – reads it_perform_regression_set_paths – reads it_screen_figure_folders – reads itminimum_cell_simulation() – reads itnormalize_regression_input_pairs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_first_usable_count_path – reads itbarcode_qc() – reads itbarcode_qc_defaults() – reads itRead by _calibration_inputs, _perform_regression, _perform_regression_read_data, _perform_regression_set_paths, _screen_figure_folders, minimum_cell_simulation(), normalize_regression_input_pairs(), _preparation_key, _first_count_file, _count_files_of, _first_usable_count_path, graph_sequencing_stats(), barcode_qc(), barcode_qc_defaults().
count_grna_column¶
(str) - Name of the column in the count CSV containing the guide identifier. Earlier versions required the name ‘grna’ and rejected files using alternatives such as ‘sgRNA’ or ‘guide’. Set this value to the column produced by the sequencing pipeline. Default ‘grna’.
_perform_regression_perform_regression – reads it_calibrated_fraction_threshold_calibration_inputs – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _calibration_inputs, _perform_regression.
count_value_column¶
(str) - Name of the column in the count CSV holding the read count for one guide in one well; it becomes the per-well fraction the fraction_threshold sweep works on. Hard-coded to ‘count’ until now, so a file naming it ‘reads’ or ‘n’ failed with a message naming only the columns spaCR expected. Default ‘count’.
_perform_regression_perform_regression – reads it_calibrated_fraction_threshold_calibration_inputs – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _calibration_inputs, _perform_regression.
count_well_column¶
_perform_regression_perform_regression_calibrated_fraction_threshold_calibration_inputs – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _calibration_inputs.
cov_type¶
(str) - Heteroscedasticity-robust covariance estimator passed to likelihood fits: ‘HC0’, ‘HC1’, ‘HC2’, ‘HC3’, or None for classical non-robust errors. It changes standard errors and P-values, not coefficients. Use ‘HC3’ when residual variance increases with well cell count. Penalized, robust and quantile fits do not support this estimator and raise an error rather than reporting ordinary errors under a robust label. Default None.
_perform_regression_read_data_reconcile_random_row_column_effects – reads it_perform_regression_perform_regression – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _reconcile_random_row_column_effects.
crop_dtype¶
(str) - Data type used for saved crop files. ‘original’ preserves the pipeline output, including uint16 data from a 16-bit camera. ‘uint8’ converts using the same high-byte rule as the PNG path. ‘uint16’ casts an 8-bit crop without rescaling because multiplication by 257 would change measured intensities without adding information. This setting controls storage size and compatibility with other software. Training precision is unchanged because ToTensor converts the input to floating point in [0,1]. Default ‘original’.
_measure_crop_core – reads itRead by _measure_crop_core.
crop_mode¶
(list) - Mask used to center each PNG crop: ‘cell’, ‘nucleus’, ‘pathogen’, ‘cytoplasm’ or ‘organelle’. One crop set is written per entry into <mode>_png/ folders, so [‘cell’,’nucleus’] doubles the images written and the rows added to png_list. A single png_size such as [224,224] is broadcast to every mode, as are dialate_pngs and dialate_png_ratios; use lists only when modes require different values. A list shorter than crop_mode reuses its final entry for the remaining modes and records a warning. Default [‘cell’].
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()describe_plan() – reads it_describe_outputs – reads itisinstance(...) [UNRESOLVED]describe_resources() – reads itisinstance(...) [UNRESOLVED]_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]crop_spec_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itrun_preflight()describe_plan() – reads it_describe_outputs – reads itisinstance(...) [UNRESOLVED]describe_resources() – reads itisinstance(...) [UNRESOLVED]_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]apply_settings() – reads it_describe_plan_safedescribe_plan() – reads it_describe_outputs – reads itisinstance(...) [UNRESOLVED]Read by crop_spec_from_settings(), _crop_shape_overrides, _measure_crop_core, measure_crop(), apply_settings(), get_measure_crop_settings(), _check_app_specific, _describe_outputs, describe_plan(), describe_resources().
crop_shape¶
(str) - ‘bounding_box’ cuts the smallest rectangle containing the object; ‘object’ masks everything outside it away. Database-sourced crops can only be bounding boxes. Default ‘bounding_box’.
crops_from_merged() – reads itvalidate() – reads itRead by crops_from_merged(), validate().
crop_source¶
(str) - Select where image crops come from. Viewers use ‘png’ (LOAD IMAGES) for exported crops in data/ or ‘merged’ (STREAM IMAGES) to cut from merged/*.npy using the measurements database. These viewer modes correspond to training’s ‘load_images’ and ‘stream_images’ sources. spaCR reports any fallback, and controls that do not apply to the selected source are disabled. Default ‘png’ in viewers and ‘load_images’ in training. Image UMAP starts at ‘auto’, preferring exported PNGs when available and otherwise streaming from merged arrays.
open_crop_source() – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]resolve_source() – reads itresolve_crop_source() – reads itopen_crop_source() – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]open_crop_source() – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]bounding_box_only() – reads it_streaming – reads itget_setting_dependencies() – reads itset_annotate_default_settings() – reads itRead by resolve_source(), resolve_crop_source(), open_crop_source(), bounding_box_only(), _streaming, get_setting_dependencies(), set_annotate_default_settings().
crops¶
import_project() – reads itRead by import_project().
cross_validation¶
(bool) - Score the classifier with 5-fold stratified cross-validation instead of a single train/test split, so every control object receives an out-of-fold prediction and an optimal probability threshold is picked per fold. Gives a far more stable accuracy estimate on small control sets, at roughly 5x the training time. Default True.
generate_ml_scores() – reads itRead by generate_ml_scores().
cross_validation_enabled¶
(bool) - Enable k-fold validation for Classify. If cross_validation_folds is 0 or 1, enabling this uses 5 folds. Use cv_group_by=’plate’ to hold out whole plates, or ‘well’/’field’ for within-plate validation without leaking related crops between training and validation. Default False.
train_test_model() – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by train_test_model(), get_train_test_model_settings().
cross_validation_folds¶
(int) - Number of k-fold splits used to train the vision classifier instead of one val_split holdout. 0 (the default) or 1 uses one random split; 2 or more trains a separate model per fold, evaluates each model on its held-out fold, and reports the mean, fold-to-fold standard deviation, and range. These statistics quantify sensitivity to the data partition. Runtime is approximately proportional to k. Distinct from ‘cross_validation’, which controls the regression pipeline.
train_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_fit – reads itdict(...) [UNRESOLVED]_attribute(...) [UNRESOLVED]classify_cv_fit_fn() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, train_test_model(), _fit, classify_cv_fit_fn(), get_train_test_model_settings().
csv¶
(str, path) - Sequencing-derived CSV of gRNA counts per well, used to compute the mixed-condition fraction the scores are compared against. Distinct from csv_name, which names the per-plate score files; this one is a single file for the whole comparison. Default None.
Read by generate_score_heatmap().
csv_name¶
(str) - Filename searched for within each entry of folders. All plates must use the same name. A plate whose file has a different name is omitted from the heatmap. Default None.
Read by generate_score_heatmap().
csv_path¶
generate_ml_scores() – reads itadd_column_to_database() – reads itRead by generate_ml_scores(), add_column_to_database().
csvs¶
graph_importance() – reads itRead by graph_importance().
custom_model¶
(str) - Path to a saved Cellpose model, loaded as pretrained_model by the mask-finetune tool. When set, model_type is passed as None and diameter as diam_mean (which Cellpose 4.x ignores with a warning). model_name remains active and selects the channel pair sent to model.eval; an incompatible value therefore segments the wrong channels. Default None.
run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]analyze_plaques() – reads itidentify_masks_finetune() – reads itRead by identify_masks_finetune(), analyze_plaques(), _check_required_paths.
custom_model_path¶
(str) - Path to a trained classifier artifact whose model weights initialize a new fine-tuning run. The optimizer and epoch start fresh. Leave empty to initialize from ImageNet or random weights according to init_weights. Default ‘’.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]resolve_model_source() – reads itRead by _cross_validate_model, _fit_one, train_test_model(), resolve_model_source(), get_train_test_model_settings().
custom_regex¶
(str or None) - Python regex with named groups that extracts metadata from raw image filenames. It must supply wellID, fieldID and chanID; plateID is optional (falling back to the source folder name), and timeID or sliceID may be absent. With metadata_type=’custom’, a filename that does not match or lacks a required group is skipped with a warning, which can reduce the dataset. With ‘auto’, the regex is tried first for Yokogawa renaming and requires only wellID; automatic detection is used if it fails. Default None.
preprocess_generate_masks() – reads itpreprocess_img_data() – reads itrun_preflight()_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]describe_resources()_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]describe_resources()_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_on_measure_clicked – reads it_describe_plan_safe_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]Read by preprocess_generate_masks(), preprocess_img_data(), _on_measure_clicked, _candidate_patterns.
cv_best_model_path¶
deep_spacr() – reads it_cross_validate_model – reads it_default_train_cross_validate_model – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, deep_spacr().
cv_csv¶
(str, path) - CSV of cross-validated scores, added to the heatmap as its own row so a held-out score can be read directly beside the in-sample ones. Leave it None to plot the score rows alone. Default None.
Read by generate_score_heatmap().
cv_group_by¶
(str) - Train/test independence: ‘cell’, ‘field’, ‘well’ (default), or ‘plate’. Cell can place sibling crops from one well on both sides; field narrows but does not close that leak; well matches the usual experimental assignment unit; plate holds out a complete batch. Whole groups make the requested fraction approximate, so runs report held-out groups and cells. Legacy ‘none’/’off’ alias ‘cell’. Crop identities come from spaCR’s plate_well_field_object.png names; unverifiable grouped designs are refused rather than silently randomized.
train_test_model() – reads it_cross_validate_model – reads ittrain_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads itgenerate_training_dataset() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads ittrain_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads it_fit – reads itdict(...) [UNRESOLVED]_attribute(...) [UNRESOLVED]run_search_for_app() – reads itclassify_cv_fit_fn() – reads ittrain_test_model() – reads it_cross_validate_model – reads ittrain_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]generate_ml_scores() – reads itset_annotate_default_settings() – reads itRead by _cross_validate_model, train_model(), train_test_model(), _fit, classify_cv_fit_fn(), run_search_for_app(), generate_training_dataset(), generate_ml_scores(), get_train_test_model_settings(), set_annotate_default_settings().
cv_results_path¶
deep_spacr() – reads it_cross_validate_model – reads it_default_train_cross_validate_model – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, deep_spacr().
cytoplasm¶
(bool) - Derive a cytoplasm object per cell (cell mask with nucleus, pathogen and organelle pixels removed) and write it to its own cytoplasm table, which recruitment ratios such as pathogen/cytoplasm intensity are computed from. Requires a cell mask; measure_crop switches it on automatically whenever cell_mask_dim is set, so the value you enter is usually overridden. Default True.
run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_measure_crop_core – reads it_morphological_measurements – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by _measure_crop_core, _morphological_measurements, measure_crop(), get_measure_crop_settings(), _describe_objects.
cytoplasm_cellprob_threshold¶
generate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads itgenerate_cellpose_masks() – reads itRead by _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
cytoplasm_channel¶
preprocess_generate_masks() – reads itgenerate_cellpose_masks_sam() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_record_organelle_caveatsorganelle_measurement_caveats() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]generate_cellpose_masks() – reads it_channelschannels_from_settings() – reads itapply_settings() – reads it_seed_organelle_column(...) [UNRESOLVED]apply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by preprocess_generate_masks(), channels_from_settings(), generate_cellpose_masks(), generate_cellpose_masks_sam(), apply_settings(), apply_settings(), organelle_measurement_caveats(), _describe_objects.
cytoplasm_diameter¶
generate_cellpose_masks_sam() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]organelle_morphology_now() – reads itapply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by generate_cellpose_masks_sam(), organelle_morphology_now(), apply_settings(), _describe_objects.
cytoplasm_flow_threshold¶
generate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads itgenerate_cellpose_masks() – reads itRead by _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
cytoplasm_intensity_merge¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cytoplasm_intensity_split¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cytoplasm_intensity_threshold¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cytoplasm_mask_dim¶
run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]mask_dims_from_settings() – reads it_measure_crop_core – reads it_morphological_measurements – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by mask_dims_from_settings(), _measure_crop_core, _morphological_measurements, get_measure_crop_settings(), organelle_measurement_caveats(), _describe_objects.
cytoplasm_max_area¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cytoplasm_max_size¶
filter_selection() – reads itRead by filter_selection().
cytoplasm_method¶
run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by _describe_objects.
cytoplasm_min_area¶
merge_split_filter_masks() – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _measure_crop_core, merge_split_filter_masks(), get_measure_crop_settings().
cytoplasm_min_size¶
(int) - (Deprecated) Pixel-area floor for the cytoplasm mask, which is the cell mask with nucleus, pathogen and organelle pixels removed. Cytoplasm regions below this are erased before measurement, so their host cell yields no cytoplasm features and any recruitment ratio built on them is lost. 0 or None disables. Default 0.
filter_selection() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_measure_crop_core – reads itget_measure_crop_settings() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by filter_selection(), _measure_crop_core, get_measure_crop_settings(), _describe_objects.
cytoplasm_min_watershed_distance¶
merge_split_filter_masks() – reads it_run_one – reads itRead by _run_one, merge_split_filter_masks().
cytoplasm_minimum_area_to_split¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cytoplasm_model_name¶
_get_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by _get_object_settings, _get_object_settings.
cytoplasm_morphology¶
Read by organelle_morphology_now().
cytoplasm_outlier_mads¶
_perform_regression_perform_regressionapply() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by apply().
cytoplasm_perimeter_fraction¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cytoplasm_perimiter_fraction¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
cytoplasm_remove_border_objects¶
merge_split_filter_masks() – reads it_apply_size_filter – reads itRead by merge_split_filter_masks(), _apply_size_filter.
cytoplasm_resample¶
_segment_cellpose_sam – reads itRead by _segment_cellpose_sam.
cytoplasm_type¶
_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]organelle_morphology_now() – reads itRead by organelle_morphology_now(), get_measure_crop_settings(), organelle_measurement_caveats().
cytoplasmcellpose¶
Read by preprocess_img_data().
cytoplasmcellpose__channel¶
generate_cellpose_masks_sam() – reads it_get_cellpose_channels – reads itgenerate_cellpose_masks() – reads it_get_cellpose_channels – reads itorganelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by generate_cellpose_masks(), generate_cellpose_masks_sam(), organelle_settings_view(), _get_cellpose_channels.
cytoplasmorganelle¶
organelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by organelle_settings_view().
data_column¶
(str) - Column in each per-plate score CSV that provides the heatmap values. It must exist in every file named by csv_name; a plate missing this column is omitted. Selecting an incorrect column produces an unintended visualization without necessarily raising an error. Default None.
graph_importance() – reads itplot_data_from_csv() – reads itplot_data_from_db() – reads itgenerate_score_heatmap() – reads itdict(...) [UNRESOLVED]Read by graph_importance(), plot_data_from_csv(), plot_data_from_db(), generate_score_heatmap().
data_column_cv¶
(str) - Column in cv_csv holding the cross-validated value. Named separately from data_column because a cross-validation file almost always labels its column differently from the score files it came from. Default None.
Read by generate_score_heatmap().
database¶
generate_annotation_dataset() – reads itplot_data_from_db() – reads itdataset¶
(str) - Path to the .tar archive of single-object PNG crops produced by generate_dataset, which the activation-map step opens with TarImageDataset. The plate folder is inferred two levels above it and CAM outputs are written next to it under <tar_name>/<cam_type>/. Provide an absolute or directory-qualified path rather than a filename alone. Default ‘’.
generate_activation_map() – reads itactivation_correlations_to_database() – reads itactivation_maps_to_database() – reads itload_activation_data() – reads itRead by generate_activation_map(), load_activation_data(), get_default_generate_activation_map_settings(), activation_correlations_to_database(), activation_maps_to_database().
dataset_mode¶
(str) - How training classes are defined: ‘metadata’ splits crops by well metadata, ‘annotation’ by the values in one or more annotation columns of png_list. Either way the classes themselves are set in the Classes editor, which names a column and a value per class. A settings file written before the ‘measurement’ basis was removed still loads: it is read as ‘annotation’, which is what its threshold rules resolved to after writing their label column. Any other value aborts and returns no dataset. Default ‘metadata’.
_begin_flowview_run_install_classify_collectorresolve_basis() – reads itnormalize_settings() – reads itresolve_basis() – reads itdict(...) [UNRESOLVED]resolve_basis() – reads itresolve_basis() – reads itdict(...) [UNRESOLVED]resolve_basis() – reads it_fresh_collectorresolve_basis() – reads itdict(...) [UNRESOLVED]resolve_basis() – reads itresolve_basis() – reads itresolve_basis() – reads itRead by normalize_settings(), resolve_basis().
db_path¶
(str) - Exact measurements.db whose objects an analysis reads. Choosing another database is refused when object or crop identities do not match, preventing cross-experiment joins. Default ‘’ requires selection.
align_folder() – reads itconvert_folder() – reads itinvestigate_hit() – reads itgenerate_ml_scores() – reads itwrite_stack() – reads itrun_explain_cv() – reads itexplain_cv_default_settings() – reads itadd_column_to_database() – reads itRead by align_folder(), convert_folder(), investigate_hit(), generate_ml_scores(), write_stack(), explain_cv_default_settings(), run_explain_cv(), add_column_to_database().
db_table_name¶
(str) - Table inside <src>/measurements/measurements.db that holds the pre-QC per-frame measurements. It is rewritten with if_exists=’replace’ on every run, a companion table with the suffix ‘_well_motility’ holds the well summary, and the same name is read back when reuse_existing_measurements is True. Default ‘timelapse_object_measurements’.
automated_motility_assay() – reads itautomated_motility_assay() – reads itRead by automated_motility_assay().
delete_intermediate¶
(bool) - Legacy force-cleanup switch. True overrides keep_intermediate and keep_original_images, removing stack/, masks/, the numeric per-channel folders, and the orig/ raw backup after merged/ is built. Cleanup is already the default; enable this setting only when cleanup must override those retention settings. Deletion is skipped unless every field of view reached merged/. Default False.
Read by preprocess_generate_masks().
dependent_variable¶
(str) - Name of the column in score_data that is modelled as the response, e.g. ‘pred’/’predictions’ from the ML scoring step or a measured feature such as ‘pathogen_nucleus_shortest_distance’. It is aggregated per well by agg_type and then optionally transformed. The run aborts if the column is absent from the score CSV. Default ‘pred’.
_perform_regression_read_data – reads it_preparation_key – reads it_make_screen – reads itapply_settings() – reads itisinstance(...) [UNRESOLVED]apply_settings(...) [UNRESOLVED]_perform_regression_perform_regression – reads it_calibrated_fraction_threshold_calibration_inputs – reads it_show_response_distribution – reads itminimum_cell_simulation() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_write_regression_panel_packages – reads itcolumn_run_settings() – reads it_run_settings_row – reads itrecord_run() – reads itRead by _calibration_inputs, _perform_regression, _perform_regression_read_data, _show_response_distribution, _write_regression_panel_packages, minimum_cell_simulation(), _preparation_key, _make_screen, apply_settings(), column_run_settings(), _run_settings_row, record_run().
detector¶
Which feature detector finds the same landmark in two overlapping tiles. ORB is rotation-invariant and free; changing it changes which pairs match and how long scoring takes. Default ‘ORB’.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
deterministic¶
run_context() – reads ittrain_test_model() – reads it_default_traintrain_test_model() – reads ittrain_test_model() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]run_context() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]run_context() – reads itRead by train_test_model(), run_context().
device¶
_choose_model – reads itRead by _choose_model.
dialate_png_ratios¶
(list of float) - Dilation amount as a fraction of object size: the mask is grown by ratio * sqrt(object area) pixels of binary dilation, so 0.2 expands a cell by roughly 20% of its diameter and pulls in surrounding background. Only used when dialate_pngs is True. A single value applies to every crop_mode entry; pass a list only when the modes need different ratios. Default [0.2].
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]crop_spec_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads itget_measure_crop_settings() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by crop_spec_from_settings(), _crop_shape_overrides, _measure_crop_core, get_measure_crop_settings(), _check_app_specific.
dialate_pngs¶
(bool) - Grow each object mask before cropping so the PNG keeps a rim of surrounding pixels instead of a hard mask edge; the amount comes from dialate_png_ratios. May be a list with one value per crop_mode entry (a single value applies to all of them), and is forced off for crop_mode ‘cytoplasm’. Enable when context around the object helps the classifier. Default False.
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]crop_spec_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads itget_measure_crop_settings() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by crop_spec_from_settings(), _crop_shape_overrides, _measure_crop_core, get_measure_crop_settings(), _check_app_specific.
diameter¶
(float) - Deprecated expected object diameter in pixels, passed to model.eval(diameter=…) by the mask-finetune tool and check_cellpose_models. Cellpose rescales each image by 30/diameter to match its approximately 30-pixel working size; a value below the true diameter upscales the image, whereas a larger value downscales it. Prefer the per-object diameter settings. Default 30.
_describe_cellpose_training – reads it_read_and_segment_sequencesegment_frame() – reads itsegment_frame() – reads itcheck_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by _describe_cellpose_training, segment_frame(), get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
diameter_estimate_n_fields¶
(int) - How many fields spacr.diameter.estimate_diameters reads before it proposes cell_diameter, nucleus_diameter and pathogen_diameter from blob statistics instead of requiring manual estimation. Fields are taken on an even stride across the sorted plate, so rows and columns are both represented rather than the first few wells; each field costs about a second of CPU and loads neither torch nor Cellpose. Increase it to 10–20 when wells are heterogeneous or confidence is low; decrease it to 2–3 for a faster preliminary estimate. Default 5.
_on_measure_clicked – reads itRead by _on_measure_clicked.
dilate_ksize¶
How many pixels to thicken the drawn quality-control outline. 0 leaves it one pixel wide, which is hard to see on a large mosaic. Affects the overlay only. Default 0.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
dims¶
annotate_crops() – reads itRead by annotate_crops().
distance_gaussian_sigma¶
(int or None) - Sigma in pixels of the Gaussian blur applied to each channel before measuring intensity-weighted centroid distances from cells to nuclei and pathogens. Larger values smooth out speckle so the weighted centroid follows broad signal. None or 0 skips these distance features entirely. Needs a cell mask plus a nucleus or pathogen mask. Default 10.
_measure_crop_core_intensity_measurements – reads it_measure_intensity_distance – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _intensity_measurements, _measure_intensity_distance, get_measure_crop_settings().
do_multichannel¶
Write one mosaic holding every channel instead of a single channel. Off gives a smaller file that carries only the channel named by channel_index. Default True.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
do_nuc_stitch¶
Segment nuclei and align on those instead of on raw pixels. More robust when the two acquisitions use different stains, because cells correspond even when pixel intensities do not; costs a segmentation pass. Default True.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
do_organize¶
Move each tile into a per-well folder before stitching. ON MOVES YOUR FILES: run with Dry run first if the current layout matters to you. Off leaves them where they are and stitches in place. Default True.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
dot_size¶
(int) - Matplotlib marker area, in points squared, for each object plotted in the UMAP/tSNE embedding. Increase it when a few hundred points make the scatter look empty; drop it to roughly 5-10 when tens of thousands of points overplot and hide cluster structure. Default 50.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itRead by generate_image_umap(), reducer_hyperparameter_search().
downsample¶
Scale tiles down before matching, for speed. THE MOST COMMON CAUSE OF A RUN THAT FINDS NOTHING: at 0.5 a 256 px tile becomes 128 px and the detector has almost no corners left, so every pair is skipped. Raise it to 1.0 if pairs are being skipped. Default 0.5.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
dropout_rate¶
(float) - Dropout probability (0-1) written into every existing Dropout layer of the backbone and applied to a Dropout inserted before the final linear classifier; 0 or None removes dropout entirely. Raise it (0.2-0.5) when training accuracy runs well ahead of validation accuracy; lower it when the model underfits and training loss stalls high. Default 0.1.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]build_model_from_configuration() – reads itRead by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings(), build_model_from_configuration().
dry_run¶
(bool) - Validate settings against the selected data, report the planned operations, and stop before any compute begins. Checks that src contains the expected files, channel and mask-plane indices are valid, and required models, barcode CSV files, or measurements.db files are present. Each problem is reported with a suggested correction, followed by a summary of the planned segmentation, measurement, and output locations. Nothing is written and no model is loaded. Default False.
preprocess_generate_masks() – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by preprocess_generate_masks(), measure_crop(), get_measure_crop_settings(), get_preprocess_ops_settings(), stitch_cycle_wells().
dst¶
(str) - Folder receiving versioned tables, manifests and figures. Default ‘’ uses a module-specific folder beside the primary input, keeping different analyses separated.
align_folder() – reads itgenerate_annotation_dataset() – reads itconvert_folder() – reads ittrain_test_model() – reads it_cross_validate_model – reads itplan_external_masks() – reads itimport_project() – reads itinvestigate_hit() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads ittrain_test_model() – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]plot_data_from_db() – reads itplates_of() – reads itbarcode_qc() – reads itbarcode_qc_defaults() – reads itanalyze_plaques() – reads itrun_explain_cv() – reads itexplain_cv_default_settings() – reads itgenerate_score_heatmap() – reads itdict(...) [UNRESOLVED]Read by align_folder(), generate_annotation_dataset(), convert_folder(), _cross_validate_model, train_test_model(), plan_external_masks(), import_project(), investigate_hit(), plot_data_from_db(), plates_of(), barcode_qc(), barcode_qc_defaults(), analyze_plaques(), generate_score_heatmap(), explain_cv_default_settings(), run_explain_cv().
dst_root¶
Where the organised wells, mosaics and reports are written. Empty writes beside the source, which mixes outputs with inputs and makes a second run ambiguous about what it is reading. Default None.
ops_preprocess() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), ops_preprocess(), stitch_cycle_wells().
early_exaggeration¶
reduction_and_clustering() – reads itRead by reduction_and_clustering().
early_stopping_patience¶
(int) - Stop training after this many consecutive epochs in which validation accuracy fails to beat the best value so far; the best checkpoint is still kept. 0 (default) disables it and always runs the full ‘epochs’ budget. Set 10-20 on long runs to cut wasted epochs once the model plateaus.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
edge_image¶
(bool) - Draw the object outline over the source image. False draws the outline on a blank background so that boundary geometry can be evaluated independently of image intensity. Default False.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
edge_thickness¶
(float) - Outline width as a fraction of object size rather than a fixed pixel count, preserving relative width across img_size values. Larger values improve boundary visibility in small thumbnails but cover more interior pixels. Default 0.1.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
edge_transparency¶
(float) - Outline opacity on a 0-100 scale: 0 hides the outline and 100 makes it fully opaque. Intermediate values blend the outline with the image. Default 100.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
embedding_by_controls¶
(bool) - Fit the reducer only on control wells - rows whose col_to_compare value equals pos or neg - and then project every object into that space. Use it when the axes should be defined by the control phenotypes so treatments are read relative to them; False fits on all objects. Default False.
generate_image_umap() – reads itRead by generate_image_umap().
endian¶
_v3_codec_chain – reads itRead by _v3_codec_chain.
engine¶
read_table() – reads itRead by read_table().
epochs¶
(int) - Number of full passes over the training set. It also sets the learning-rate schedule horizon - cosine anneals over exactly this many epochs and step_lr drops every epochs/5 - so changing it rescales the schedule. A checkpoint is always written on the final epoch and every 100th. Raise it for small datasets and use early_stopping_patience to cut runs short. Default 100.
_begin_flowview_run_install_classify_collectorclassify_graph() – reads it_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_fresh_collectorclassify_graph() – reads itdict(...) [UNRESOLVED]_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]_describe_classifier_training – reads itRead by _cross_validate_model, _fit_one, train_test_model(), classify_graph(), _describe_classifier_training, get_train_test_model_settings().
eps¶
(float) - DBSCAN neighbourhood radius, expressed in the units of the UMAP/t-SNE embedding and measured with the ‘metric’ setting: two points are neighbours if they lie within this distance. Raise it to merge fragments into fewer, larger clusters and leave less noise; lower it to split clusters and push more points to noise (-1). Ignored when clustering is ‘kmeans’. Default 0.9.
_compute_umap – reads itgenerate_image_umap() – reads itRead by _compute_umap, generate_image_umap().
error_bar_type¶
generate_screen_graphs() – reads itRead by generate_screen_graphs().
evaluation_bins¶
(int) - Number of equal-width probability bins in reliability curves and expected calibration error. Values around 10 balance resolution against noise; use fewer bins for small validation sets and more only when every class has many hundreds of out-of-fold samples. Minimum 2, default 10. API: spacr.classifier_evaluation.calibration_table.
_cross_validate_model – reads it_default_train_cross_validate_model – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model.
evaluation_calibration¶
(str) - Probability calibration written to the evaluation bundle. ‘temperature’ cross-fits one scalar temperature per held-out fold using all other out-of-fold predictions, so a sample never fits its own calibrator; ‘none’ retains raw softmax probabilities. Calibration changes reported probabilities, not the saved model weights. Default ‘temperature’. API: spacr.classifier_evaluation.cross_calibrate_probabilities.
_cross_validate_model – reads it_default_train_cross_validate_model – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model.
evaluation_fail_on_leakage¶
(bool) - Stop Classify (CV) before fitting a fold when the same object, augmentation family, or protected cv_group_by identity appears in both train and validation. False records the problem and continues, which is useful only for diagnosing a legacy dataset because its performance estimate remains invalid. Default True. API: spacr.classifier_evaluation.audit_split_leakage.
train_test_model() – reads it_cross_validate_model – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads ittrain_test_model() – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, train_test_model().
examples_to_plot¶
(int) - How many randomly chosen merged image stacks are rendered as segmentation-overlay previews after mask generation (in timelapse mode, per-channel panels instead). Raise it to check outlines and normalization across more fields of view, at the cost of render time and larger PDFs; 0 skips previews entirely. Default 1.
preprocess_generate_masks() – reads itgenerate_organelle_masks_sam() – reads itRead by preprocess_generate_masks(), generate_organelle_masks_sam().
exclude¶
(str or list) - Names of measurement columns to drop from the feature set before UMAP embedding or ML training, applied after the channel_of_interest selection. Use it to remove features that leak the label or swamp the embedding. It does not filter database rows; use exclude_rows for that. Default None keeps every feature.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itload_search_data() – reads itgenerate_ml_scores() – reads itRead by generate_image_umap(), reducer_hyperparameter_search(), load_search_data(), generate_ml_scores().
exclude_conditions¶
(list) - Condition labels dropped from the image UMAP input, matched against the cond column that map_condition derives from the pos, neg and mix column IDs; the only possible entries are ‘neg’, ‘pos’, ‘mix’ and ‘screen’. A bare string is accepted and wrapped in a list. Use it to embed screen wells only. Default None.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itRead by generate_image_umap(), reducer_hyperparameter_search().
exclude_grnas¶
(list or str) - gRNA or gene identifiers known not to occur in cells, such as primer or plasmid carry-over. These sequences are removed before guide fractions are calculated, and retained guides are renormalized. This differs from background subtraction, which corrects spurious reads assigned to a real guide. A gene identifier matches all of its guides. Default None.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
exclude_rows¶
(dict or None) - General UMAP row exclusions. Choose one or more database columns, then check the values whose rows should be removed. Rules are combined with OR, so a row matching any selected column/value pair is excluded. Default None keeps every row.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itload_search_data() – reads itRead by generate_image_umap(), reducer_hyperparameter_search(), load_search_data().
exclude_starved_wells¶
Leave starved wells out of the population the threshold is derived and swept over. They report one gRNA at any cutoff and pull the target down onto a threshold the healthy wells never needed. They stay in the QC panels regardless. Default True.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
exclude_wells¶
filter_selection() – reads itRead by filter_selection().
expected_sha256¶
install() – reads itRead by install().
experiment¶
(str) - Free-text run label. Its real effect is naming the exported PNG dataset tar as <YYMMDD>_<experiment>.tar (a random-numbered variant is used if that name already exists), so give each screen a distinct value to avoid confusing dataset tars. It is also passed to the measurement-database writer but not stored there. Default ‘experiment’ (the barcode pipeline uses ‘experiment_1’ and a foreign import uses ‘foreign_import’).
generate_dataset() – reads itrun_import() – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]stitch_cycle_wells() – reads itdict(...) [UNRESOLVED]Read by run_import(), generate_dataset(), _measure_crop_core, get_measure_crop_settings(), stitch_cycle_wells().
export_tiffs¶
plot_region() – reads itRead by plot_region().
extracellular_class¶
(str) - Classification policy for parasites that overlap no host cell. ‘attached’ assigns them to the attached class independent of stain intensity because they cannot be intracellular; ‘classify’ uses the stain signal when host-cell segmentation is uncertain; ‘exclude’ removes them before summary calculations. n_no_host_cell reports their count under every policy. Default ‘attached’.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
extract_channels¶
crops_from_merged() – reads itRead by crops_from_merged().
exts¶
Which file extensions count as images. Anything else in the folder is ignored rather than failing the run. Default [‘.tif’, ‘.tiff’].
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
factory¶
register_kwargs() – reads itRead by register_kwargs().
fdr_alpha¶
(float) - Family-level rejection threshold for adjusted P values in guide_permutation mode. Must be between 0 and 1. Default 0.05.
_perform_regression_read_data_reject_impossible_probabilities – reads itrun_sweep() – reads it_perform_regression_perform_regression – reads it_call_level_hits – reads it_reject_impossible_probabilities – reads it_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_write_regression_panel_packages – reads it_run_settings_row – reads itrecord_run() – reads itrefit_settings() – reads itsummarise_trial() – reads itRead by _call_level_hits, _perform_regression, _reject_impossible_probabilities, _run_guide_permutation_analysis, _write_regression_panel_packages, run_sweep(), _run_settings_row, record_run(), refit_settings(), summarise_trial().
feather¶
align_folder() – reads itRead by align_folder().
feature_cache_dir¶
Where the computed feature cache is written. Empty puts it beside the outputs. Point it at a fast local disk when the outputs are on a network share. Default None.
get_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings().
feature_cache_mode¶
Whether features are cached on ‘disk’, held in memory, or not cached. Disk pays the cost once and makes a re-run fast; memory is faster and bounded by max_ram_features. Default ‘disk’.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
feature_importance¶
(bool) - Fit a random forest against the score column and plot its impurity-based importances. Fast and always available, but biased toward high-cardinality and correlated features, so read the result as a shortlist rather than a ranking. Turning it off skips that plot and its two grouped-by-compartment and grouped-by-channel companions. Default True.
interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]feature_mask¶
_captum_attribute – reads itRead by _captum_attribute.
fields¶
(list | str | None) - Fields to process, enabling selected fields to be reprocessed without repeating the complete plate. None, the default, – processes every field found. A list or a comma-separated string of field ids in any spelling spaCR accepts (‘f3’, 3, ‘F003’), or a glob such as ‘f1*’. The run writes only the fields named, into the same folders, so a re-run replaces those and leaves the rest.
Read by preprocess_img_data().
figure.autolayout¶
rc_params() – reads itRead by rc_params().
figure.figsize¶
rc_params() – reads itRead by rc_params().
figuresize¶
(int) - Base figure size in inches; figures are built square as figuresize x figuresize and font sizes are derived from it (legend, axis labels and ticks at 0.75x, overlay text at 0.5x). Raise it when text is unreadable at publication scale, lower it to fit panels on screen. Default 10; cluster grids cap total width at 200 inches.
generate_image_umap() – reads itpreprocess_generate_masks() – reads itpreprocess_img_data() – reads it_concatenate_and_normalize_impl – reads itplot_merged() – reads itdisplay(...) [UNRESOLVED]analyze_recruitment() – reads itRead by generate_image_umap(), preprocess_generate_masks(), _concatenate_and_normalize_impl, preprocess_img_data(), plot_merged(), analyze_recruitment().
file_metadata¶
(str, list, or None) - Substring filter applied to png_path when retrieving crops from the database. Only paths containing the supplied string are included; a list matches any entry rather than requiring all entries. Use this setting to restrict a dataset to one plate, well, or object type, for example ‘plate1_’ or ‘cell_png’. None includes every crop. Default None.
Read by _dataset_crop_refs, generate_dataset().
file_type¶
(str) - Image FORMAT the pre-generated crops are in, as a file extension: ‘png’, ‘tif’, ‘tiff’, ‘jpg’, ‘jpeg’, ‘bmp’ or ‘npy’. It is a format filter and nothing else - WHICH OBJECT a crop is of is path_string’s job, so the pair can express ‘every nucleus crop, whatever format’ and ‘every TIFF, whatever object’, which one combined setting never could. A legacy value of the form ‘<object>_png’ is still accepted and read as its extension, so an old settings file keeps working. Blank accepts any format. Default ‘cell_png’, which is read as ‘png’.
select_crops() – reads itvalidate() – reads itRead by select_crops(), validate().
fill_in¶
(bool) - Post-process each Cellpose mask with fill_holes_in_mask in the mask-finetune and plaque tools. The mask is relabelled by connectivity over all nonzero pixels, then interior holes are filled component by component. Relabelling does not preserve the original label values, so touching objects can merge. Default False. Plaque Analysis starts with this enabled so plaque interiors are filled before scoring.
Read by get_analyze_plaque_settings(), identify_masks_finetune().
fill_na¶
(bool) - When a barcode does not match any entry in its reference CSV, count it under its raw sequence instead of dropping it. When disabled, grouping discards unmatched reads without warning; a reference in the incorrect orientation can therefore produce a reduced table rather than an empty one. Enable this setting to quantify the unmapped fraction. Default False.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
filter¶
(bool) - Legacy switch for the old post-Cellpose cleanup pass, which re-ran size/intensity/border filtering and logged ‘_after_filtration’ object counts to the database. The current Cellpose-SAM segmentation path never reads it, so toggling it changes nothing; use the per-object <object>_min_area, <object>_max_area and <object>_perimeter_fraction settings instead. Default False.
generate_cellpose_masks() – reads itRead by generate_cellpose_masks().
filter_1¶
(list or None) - Two-element [column, minimum] filter applied before counting. Rows whose column value is not strictly greater than minimum are excluded. Use it to remove objects that are too small or dim to score so they do not affect the reported percentage. This strict > filter has one lower bound and no upper bound. Default None, which disables pre-filtering.
analyze_percent_positive() – reads itdefault_settings_analyze_percent_positive() – reads itRead by default_settings_analyze_percent_positive(), analyze_percent_positive().
filter_by¶
(str or None) - Restricts the feature matrix before dimensionality reduction: only columns matching this channel are kept and the other channel_1-channel_4 columns are dropped. Accepts ‘channel_0’-‘channel_3’, an int, a list of channel numbers, or ‘morphology’ to keep only shape features (area, eccentricity, Zernike moments, …). None, ‘None’, ‘all’, and ‘*’ disable filtering. Default ‘channel_0’.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itload_search_data() – reads itRead by generate_image_umap(), reducer_hyperparameter_search(), load_search_data().
filter_column¶
(str) - Metadata column used to drop control wells before regression: every row whose value appears in filter_value is removed from both the score data and the read counts. Use ‘columnID’ (default) when controls sit in plate columns, ‘rowID’ when they sit in rows. In annotate_filter_vision it instead names the score column thresholded by upper_threshold/lower_threshold.
annotate_filter_vision() – reads it_preparation_key – reads it_perform_regression_perform_regression – reads it_draw_the_threshold_sweep_graph_sequencing_statsgraph_sequencing_stats() – reads it_graph_sequencing_statsgraph_sequencing_stats() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by annotate_filter_vision(), _perform_regression, _preparation_key, graph_sequencing_stats().
filter_min_max¶
(list) - Display-only size filter for plot_merged: one [min_area, max_area] pair in pixels per mask dimension, in the order cell, nucleus, pathogen, e.g. [[500,50000],[100,5000],[10,2000]]. Objects outside a pair are erased from that mask before the overlay is drawn. None (default) keeps every object.
plot_merged() – reads itdisplay(...) [UNRESOLVED]Read by plot_merged().
filter_value¶
(list) - Values of filter_column whose rows are removed - not kept - before regression, normally the control columns; default [‘c1’,’c2’,’c3’]. Dropping them stops control wells from dominating the gene and gRNA fits. Only list values take effect: a bare string is silently ignored and nothing is filtered.
_preparation_key – reads it_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _preparation_key.
flow_threshold¶
(float) - Cellpose flow_threshold: the maximum allowed error between the predicted flow field and the flows recomputed from each candidate mask; masks above it are discarded. Raise it to keep more objects, including irregularly shaped ones; lower it to reject poorly formed masks and reduce false positives. Default 0.4.
_read_and_segment_sequencesegment_frame() – reads itsegment_frame() – reads itcheck_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by segment_frame(), get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
focal_alpha¶
(float) - Class-balancing weight for focal loss (read only when loss_type resolves to focal). In the single-logit binary path it scales positives by alpha and negatives by 1-alpha, so raise it toward 1 to emphasise a rare positive class; with two or more output classes a plain float scales the whole loss uniformly. Default None (no alpha weighting).
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
focal_gamma¶
(float) - Focusing exponent in the focal-loss weight (1 - p_t)^gamma, applied only when loss_type is focal. 0 reduces it to cross-entropy; increasing it, typically within 1-5, reduces the contribution of confidently classified crops and increases the relative contribution of difficult samples. Default 2.0. Increase when class imbalance causes training to be dominated by easy examples.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
folders¶
(list) - One folder per plate holding the classification CSVs to combine into a single heatmap; each is searched for csv_name. The order given is the order the plates are laid out in. Default None.
Read by generate_score_heatmap().
fps¶
(int) - Playback rate of the per-channel movies written to <src>/movies from timelapse .npy stacks, and only when timelapse is True. Raise it to skim long acquisitions, lower it to inspect individual frames. Affects the movies only - never tracking, segmentation or measurements. Default 2.
preprocess_img_data() – reads itinstall_ambient() – reads itRead by preprocess_img_data(), install_ambient().
fraction_grna¶
(str) - The single gRNA whose fraction is kept and plotted, selected out of the fraction table once it has been computed. Everything else in that table is discarded, so this picks which gRNA the comparison is about. Default None.
Read by generate_score_heatmap().
fraction_threshold¶
(float or None) - Minimum relative abundance, 0-1, that a gRNA must reach within a well’s total read count to be retained. Increasing it removes low-abundance and bleed-through gRNAs and reduces the mean number of gRNAs per well; if set too high, every row is removed and the run raises an error. Use None to select automatically the cutoff that yields target_unique_count gRNAs per well. Default None. Regression starts at the reproducible fixed cutoff 0.02; enter None only when the automatic sweep is intended.
_preparation_key – reads it_perform_regression_perform_regression – reads it_draw_the_threshold_sweep – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads itrecord_run() – reads itRead by _draw_the_threshold_sweep, _perform_regression, _preparation_key, _run_settings_row, record_run().
frame_interval_s¶
(float or None) - Seconds between consecutive timepoints, obtained from the acquisition metadata. It converts frame indices into physical time in the tracks table and displacement per frame into speed. It does not affect object linking, so an incorrect value rescales reported velocities without changing track identities. None uses the motility module’s seconds_per_frame setting. Default None.
_t_stack_planplan_4d_from_settings() – reads itRead by plan_4d_from_settings().
gene_column¶
select_montage_per_guide() – reads itRead by select_montage_per_guide().
gene_ineq_coeff¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
generate_full_dataset¶
(bool) - Build the full unlabelled inference dataset tar from every selected plate independently of training or model application. Apply model to dataset also creates it automatically when needed. API: spacr.io.generate_dataset. Default False.
deep_spacr() – reads itRead by deep_spacr().
generate_training_dataset¶
(bool) - Rebuild the train/ and test/ PNG folders from the object crops before training, using the dataset_mode rules (annotation_column labels, metadata rules or measurement rules) and splitting off test_split of the images. Turn it off to reuse an existing split; it is only consulted when train or test is True, and a failed build aborts training. Default True.
run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]deep_spacr() – reads itrun_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by deep_spacr(), _check_required_paths.
genotype_source¶
The folder holding the low-magnification acquisition that carries the barcodes. This is the one that gets stitched into per-well mosaics; the phenotype images are placed onto its output. Default None.
ops_preprocess() – reads itget_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]gpu¶
(bool) - Request RAPIDS acceleration for the main dimensionality reduction and Image UMAP hyperparameter search. Controlled by the GPU toggle beside Hyperparameter search; supported for UMAP, t-SNE and PCA, with the actual backend recorded. Default False.
generate_image_umap() – reads itpreview_cellpose_model() – reads itgradient_accumulation_steps¶
(int) - How many batches are summed per optimizer step when gradient_accumulation is on; the loss is divided by this value so gradient magnitude stays comparable. Effective batch size = batch_size x this. Raise it (4-16) to emulate a larger batch on limited VRAM, at the cost of fewer weight updates per epoch. Ignored when gradient_accumulation is False. Default 4.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_fold_gradient_accumulation – reads it_fold_gradient_accumulation – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_fold_gradient_accumulation – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_fold_gradient_accumulation – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]_fold_gradient_accumulation – reads it_fold_gradient_accumulation – reads itRead by _cross_validate_model, _fit_one, train_test_model(), _fold_gradient_accumulation, get_train_test_model_settings().
graph_name¶
plot_data_from_csv() – reads itplot_data_from_db() – reads itRead by plot_data_from_csv(), plot_data_from_db().
graph_type¶
generate_screen_graphs() – reads itgraph_importance() – reads itplot_data_from_csv() – reads itplot_data_from_db() – reads itRead by generate_screen_graphs(), graph_importance(), plot_data_from_csv(), plot_data_from_db().
grayscale¶
(bool) - Force the Cellpose channel pair to [0, 0] so the network treats the input as a single combined channel, overriding the [cytoplasm, nucleus] pair otherwise inferred from model_name (cyto -> [1,0], cyto2 -> [2,1], nucleus -> [0,0]). Leave it on for single-channel inputs; switch it off only when feeding a genuine two-channel stack. Default True.
_describe_cellpose_training – reads itcheck_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by _describe_cellpose_training, get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
grid¶
Read by align_folder(), _work, build_plan().
grid.color¶
rc_params() – reads itRead by rc_params().
grna_csv¶
(path) - CSV mapping gRNA barcode sequences to gRNA names; it must have ‘sequence’ and ‘name’ columns. Reads are matched verbatim with no reverse-complementing, so orientation must match the reads (barecodes_reverse_complement flips a file). Rows whose gRNA does not match are written as NA and dropped from the counts. Default: the bundled spacr/resources/data/grna_barcodes.csv.
Read by _run_barcode_qc, generate_barecode_mapping(), barcode_qc_defaults().
grna_statistic¶
(str) - What the permutation test measures between a gRNA’s well fractions and the well phenotype. ‘pearson’ is a partial correlation, which is linear and is moved by an extreme well in proportion to how extreme it is. ‘rank’ is the same quantity computed on the ranked phenotype, so it responds to order rather than magnitude and no single well can move it far. Both cost one matrix product, so the choice does not change how long the test takes. Default ‘pearson’.
_say_what_the_permutation_will_do – reads it_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]recommend() – reads itRead by _run_guide_permutation_analysis, _say_what_the_permutation_will_do, recommend().
group_by_class¶
(bool) - Whether the endodyogeny condition labels are split by class before proportions are computed: on, the condition string has the class_column value appended, so each condition-class combination becomes its own group; off, classes are pooled within a condition. It changes what the bars count, not how the statistics are weighted. Default False.
analyze_endodyogeny() – reads itRead by analyze_endodyogeny().
group_by_well¶
align_folder() – reads itRead by align_folder().
group_column¶
(str) - Column whose values become the experimental conditions compared against each other; ‘condition’ is the combined host-cell / pathogen / treatment label built from the plate-metadata maps. Point it at ‘pathogen’ or ‘treatment’ to compare on one factor alone. Rows with no value here are dropped before anything is counted. Default ‘condition’.
analyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_replication().
group_lasso_lambda¶
(float) - Penalty weight of the group lasso, which shrinks all of one gene’s guides together rather than one at a time, so a gene enters or leaves the model as a unit instead of on its luckiest guide. Larger values keep fewer genes; 0 leaves the fit unpenalised and negative is refused. Set it to auto to choose it by cross-validation. Default auto.
_perform_regression_perform_regression – reads it_call_level_hits – reads it_reject_a_threshold_that_cannot_mean_what_it_says – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits, _perform_regression, _reject_a_threshold_that_cannot_mean_what_it_says.
grouping¶
(str) - How per-object values collapse to one number per well in the plate heatmap: ‘mean’ averages heatmap_feature over the objects in a well, ‘sum’ totals them, ‘count’ ignores the feature and colors wells by object count. Use ‘count’ to spot uneven seeding or dropout, ‘mean’ for phenotype strength. Default ‘mean’; any other value raises ValueError.
generate_ml_scores() – reads itRead by generate_ml_scores().
grouping_column¶
Read by graph_importance(), plot_data_from_csv(), plot_data_from_db().
guide_column¶
select_montage_per_guide() – reads itRead by select_montage_per_guide().
guide_fractions_file¶
Sequencing-derived table with one fraction per well and guide. Duplicate well-guide rows are refused because they would reweight target evidence ambiguously. Default ‘’ requires selection.
investigate_hit() – reads itRead by investigate_hit().
guide_min_wells¶
(int or list) - Minimum numbers of independent wells containing a guide. A list such as [1, 2, 3, 4] writes one sensitivity-analysis table and volcano plot per threshold; P values are computed once and the multiple-testing correction is repeated within each eligible family. Default [1, 2, 3, 4].
_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _run_guide_permutation_analysis.
guide_nuisance_columns¶
(list) - Additional measured well-level covariates to residualize from both phenotype and guide fraction before testing. Do not put post-treatment outcomes here. Default []. Regression starts with [‘rowID’, ‘columnID’] to remove plate-position structure before the within-plate permutation.
_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_report_exchangeability_usable_nuisance_columns – reads it_usable_nuisance_columns – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]recommend() – reads itRead by _run_guide_permutation_analysis, _usable_nuisance_columns, recommend().
guide_permutation_batch_size¶
(int) - Number of permutation outcomes evaluated together. Lower this if memory is tight; it does not change the result. Default 500.
_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _run_guide_permutation_analysis.
guide_permutation_block¶
(str) - Column defining exchangeability blocks for permutations, normally plateID. Residuals are never shuffled between its levels. Default ‘plateID’.
_say_what_the_permutation_will_do – reads it_perform_regression_perform_regression_identifiability_warning – reads it_run_guide_permutation_analysis – reads it_report_exchangeability – reads it_usable_nuisance_columns – reads it_usable_nuisance_columns – reads it_write_regression_diagnostics_diagnostic_screen_design – reads itresolve_auto_inference() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _diagnostic_screen_design, _identifiability_warning, _report_exchangeability, _run_guide_permutation_analysis, _usable_nuisance_columns, resolve_auto_inference(), _say_what_the_permutation_will_do.
guide_permutation_gene_level¶
_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _run_guide_permutation_analysis.
guide_permutation_plot¶
_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _run_guide_permutation_analysis.
guide_permutation_seed¶
(int) - Random seed for reproducible residual permutations. Keep it fixed to reproduce exact empirical P values; change it to check Monte Carlo sensitivity. Default 0.
_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _run_guide_permutation_analysis.
guide_permutations¶
(int) - Number of plate-blocked Freedman–Lane residual permutations used for empirical two-sided guide P values. The estimator is (exceedances + 1) / (permutations + 1), where exceedances are permuted statistics at least as extreme as the observed statistic; this imposes a hard floor of 1 / (permutations + 1). Values of 1,000, 10,000, and 200,000 resolve floors of approximately 1e-3, 1e-4, and 5e-6, respectively. Increase the count when results accumulate at this resolution floor; runtime increases linearly. Default 200000.
_say_what_the_permutation_will_do – reads it_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads itrecord_run() – reads itRead by _run_guide_permutation_analysis, _say_what_the_permutation_will_do, _run_settings_row, record_run().
guide_presence_threshold¶
(float) - A guide counts as present in a well only when its fraction is above this value. The effect still uses the unthresholded fraction. Default 0.0.
_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_write_regression_diagnostics – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _run_guide_permutation_analysis, _write_regression_diagnostics.
guide_primary_min_wells¶
(int or None) - Which guide_min_wells family supplies results_significant.csv and the returned ‘significant’ table. Default None chooses the smallest requested threshold.
_perform_regression_perform_regression_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _run_guide_permutation_analysis.
hash_inputs¶
cmd_validate() – reads it_on_run – reads ithashing_enabled() – reads itRead by cmd_validate(), _on_run, hashing_enabled().
heatmap_feature¶
(str) - Numeric column that is aggregated per well and color-mapped in the plate heatmap after ML scoring, e.g. ‘predictions’ for the classifier score or ‘recruitment’ for the pathogen/cytoplasm intensity ratio. Must be a numeric column of the scored dataframe or the run raises ValueError listing the valid names. Default ‘predictions’.
generate_ml_scores() – reads itRead by generate_ml_scores().
hi_pct¶
Read by segment_frame().
hinge_n_boot¶
(int) - Number of bootstrap resamples behind the hinge p-values. A support vector machine has no likelihood and so no Wald test; spaCR refits it on this many resamples of the wells and compares each coefficient to its bootstrap standard deviation. Treat the result as a stability statistic, not a hypothesis test. Higher is steadier and linearly slower; below about 50 the standard deviations are too noisy to rank on. Default 200.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
hinge_threshold¶
(float) - Response value above which a well counts as positive for the hinge (linear SVM) fit. Leave it None when the response is already binary, in which case the two values it holds become the two classes. spaCR refuses a continuous response with no threshold rather than splitting it at the mean or median, because a cut chosen by the software decides the hypothesis being tested. Read only by regression_type ‘hinge’. Default None.
_perform_regression_read_data_reconcile_random_row_column_effects – reads it_perform_regression_perform_regression – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _reconcile_random_row_column_effects.
hit_bootstrap¶
Number of well-level bootstrap resamples used for the prevalence-difference confidence interval. More resamples improve tail stability but increase runtime. Default 5000.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_direction¶
Positive ranks larger phenotype scores first; negative ranks smaller scores first. It must match the sign and interpretation of the selected regression effect. Default positive.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]investigate_hit() – reads ithit_investigation_default_settings() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by hit_investigation_default_settings(), investigate_hit(), _check_app_specific.
hit_effect¶
Effect estimate copied from the exact source result for provenance and display. It does not get re-estimated from candidate cells. Default 0.0.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_fdr¶
Adjusted P value copied from the selected regression hit. It records source evidence and never becomes a cell-level confidence value. Default 1.0.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_feature_columns¶
Independent measured morphology features used by the weak-supervision model. Default [] uses guarded numeric selection excluding identifiers, labels, fractions and scores.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_gallery_per_stratum¶
Maximum blinded-review examples drawn from high, borderline, low and control-false-looking strata. Raising it expands manual review without changing attribution. Default 24.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_guide_agreement¶
Agreement among guides supporting the selected hit, copied from the regression result rather than recomputed from candidate cells. Default NaN records unavailable evidence.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_include_original_score¶
Add the original CV phenotype score to model features. Default False preserves an independent check; enabling it makes validation partly circular and records a warning.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_n_guides¶
Number of distinct guides supporting the selected hit in the source regression. It is provenance for interpretation, not a cell-level prediction threshold. Default 0.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_permutations¶
Number of plate-aware well-label permutations used for the enrichment P value. More permutations improve resolution without changing fitted candidate probabilities. Default 10000.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_phenotype¶
Human-readable phenotype copied from the source regression, including the direction and score represented by the selected effect. Default ‘’.
investigate_hit() – reads itRead by investigate_hit().
hit_pipeline_permutations¶
Guide-fraction and well-label null iterations that repeat grouped cross-fitting inside every permutation. Increase it for stronger pipeline-null resolution at substantial runtime cost. Default 100.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_probability_threshold¶
Probability boundary for review calls and optional promotion. It is never a genotype threshold; changing it alters calls but not fitted probabilities. Default 0.8.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_random_seed¶
Seed for grouped cross-fitting, bootstrap intervals and plate-aware permutations. Keep it fixed for reproducible candidate probabilities and uncertainty. Default 0.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_split_by¶
Cross-fitting unit: auto prefers held-out plates when the design supports them and otherwise holds wells out. Cell-level splitting is never allowed. Default auto.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_store_database¶
Store this attribution as a new versioned database run. Disabling it writes portable files only; neither choice overwrites hand annotations. Default True.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
hit_well_support¶
Number of independent wells supporting the selected hit in the source regression. It records experimental replication without changing attribution weights. Default 0.
investigate_hit() – reads ithit_investigation_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit().
holdout_plate¶
(str | list | None) - Train without this plate and score on it. None – the default – splits within the available data. Cross-validation can otherwise learn plate-associated variation rather than the phenotype while retaining apparently strong metrics. Performance on a held-out plate directly evaluates generalization across plates. The run is rejected if either split would omit a class.
generate_ml_scores() – reads itRead by generate_ml_scores().
homogeneity¶
(bool) - Compute grey-level co-occurrence-matrix homogeneity for every object in every channel, adding one homogeneity_distance_<d> column per entry in homogeneity_distances. Homogeneity is high for smooth, evenly filled objects and low for punctate or grainy ones, so keep it on for texture phenotypes; disabling it noticeably speeds up measurement. Default True.
_measure_crop_core_intensity_measurements – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _intensity_measurements, get_measure_crop_settings().
homogeneity_distances¶
(list) - Pixel offsets used to build each object’s grey-level co-occurrence matrix; every entry adds one homogeneity_distance_<d> feature per channel. Small offsets capture fine-grained texture, large ones capture coarse structure, and offsets larger than the object itself carry no signal. More entries means more features and slower measurement. Default [8, 16, 32].
_measure_crop_core_intensity_measurements – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _intensity_measurements, get_measure_crop_settings().
huber_t¶
(float) - Where Huber’s loss switches from squared to linear, in units of the estimated residual scale, for the robust fits. Smaller values downweight more wells and resist heavier contamination; larger values approach ordinary least squares. The default 1.345 gives 95 percent of the efficiency of OLS under normally distributed residuals. Read only by regression_type ‘rlm’ and ‘huber’. Default 1.345.
_perform_regression_read_data_reconcile_random_row_column_effects – reads it_perform_regression_perform_regression – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _reconcile_random_row_column_effects.
id¶
Read by require_codec().
ig_baseline¶
(str) - Reference image used by integrated gradients: ‘zero’ is black, ‘blur’ is a blurred copy of the input image, and ‘noise’ is random. The baseline defines the attribution reference and therefore changes the result. On dark-field images, a zero baseline attributes broadly to bright object signal; a blurred baseline preserves low-frequency content and emphasizes contributions from image detail. Default ‘zero’.
ig_steps¶
(int) - Interpolation steps between the baseline and input image for integrated gradients. The completeness approximation improves with step count; too few steps increase the error without raising an exception. Validate the completeness error when reducing the default of 50. Computational cost is linear in this number. Default 50.
illumination_correction¶
Estimate the uneven illumination of the microscope from the fields themselves and divide it out of the pixels at every enabled stage. Off by default. On, the same cell looks and measures the same wherever it sits in the field of view, which is what removes the position-dependent bias behind plate edge effects. Default False.
preprocess_generate_masks() – reads itload_segmentation_illumination_resume() – reads itpreprocess_img_data() – reads itload_segmentation_illumination_resume() – reads itprepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_model() – reads itvalidate_measurement_illumination_inputs() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_illumination – reads itlen(...) [UNRESOLVED]Read by preprocess_generate_masks(), illumination_settings(), load_segmentation_illumination_resume(), prepare_illumination_model(), validate_measurement_illumination_inputs(), preprocess_img_data(), _illumination.
illumination_dark¶
Camera dark offset in raw counts, subtracted before the gain is applied. Leave at zero unless you measured it from a dark frame: it is not identifiable from the images themselves, and an estimated value can subtract genuine background signal. Default 0.0.
prepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_model() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by illumination_settings(), prepare_illumination_model().
illumination_degree¶
Order of the fitted illumination surface. 4 gives fifteen terms, enough for a lamp profile, a vignette and a tilt. Raising it lets the surface follow finer structure and, past about 6, start absorbing the cells you are trying to measure. Default 4.
prepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_model() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by illumination_settings(), prepare_illumination_model().
illumination_estimator¶
How the smooth field is fitted to the across-field median: ‘polynomial’ fits a low-order surface, which cannot bend around a cell and is the right choice for a lamp profile plus a vignette; ‘smooth’ Gaussian-blurs the median instead and can follow a dust shadow the polynomial would miss. Default polynomial.
prepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_model() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_illumination – reads itlen(...) [UNRESOLVED]Read by illumination_settings(), prepare_illumination_model(), _illumination.
illumination_max_fields¶
How many fields per plate the estimate reads, sampled evenly across the plate. More fields make the across-field median a better object rejector and cost linear time; below about ten, cells start surviving into the gain map. Default 50.
prepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_model() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by illumination_settings(), prepare_illumination_model().
illumination_model¶
Path to an illumination model saved earlier. Empty means estimate a fresh one from the fields in src, which is what you want unless you are re-measuring a plate and must reproduce the exact correction an earlier run applied. Default empty.
load_segmentation_illumination_resume() – reads itload_segmentation_illumination_resume() – reads itprepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_model() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_illumination – reads itlen(...) [UNRESOLVED]Read by illumination_settings(), load_segmentation_illumination_resume(), prepare_illumination_model(), _illumination.
illumination_on_missing¶
What to do with a field whose plate the model does not cover: ‘error’ fails that field and stamps the run incomplete, ‘skip’ measures it uncorrected. Default error, because corrected and uncorrected rows sharing one table is worse than a failed field.
load_segmentation_illumination_resume() – reads itload_segmentation_illumination_resume() – reads itprepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_correction() – reads itprepare_illumination_model() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by illumination_settings(), load_segmentation_illumination_resume(), prepare_illumination_correction(), prepare_illumination_model().
illumination_per_plate¶
Estimate one illumination field per plate rather than one for every plate together. Lamp age, a re-seated filter cube or a different objective change the field between acquisition sessions, so pooling two sessions estimates neither of them well. Default True.
prepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_model() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by illumination_settings(), prepare_illumination_model().
illumination_qc¶
Write the QC figure beside the model: the estimated field as an image, the intensity-versus-position trend before and after, and the percentage of the position bias the correction removed. The figure has low computational cost and provides direct verification of the correction. Default True.
prepare_illumination_model() – reads itillumination_settings() – reads itprepare_illumination_model() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by illumination_settings(), prepare_illumination_model().
image_key¶
import_project() – reads itRead by import_project().
image_nr¶
(int) - How many example object crops to draw on the embedding plot: that many per cluster when plot_by_cluster is on (smaller clusters show all they have), otherwise that many sampled at random overall. It also sets how many images each cluster contributes to the cluster-grid figure. Raise it for a fuller montage, lower it when thumbnails hide the points. Default 16.
generate_image_umap() – reads itRead by generate_image_umap().
image_size¶
(int) - Side length in pixels of the center crop taken from each object PNG before model input. Images are cropped rather than rescaled, so larger values add zero padding and smaller values discard peripheral object pixels. This value also defines the backbone input resolution and must match the crop size used to generate the dataset. Default 224.
crops_from_merged() – reads itvalidate() – reads it_fit_one – reads itapply_model_to_tar() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itgenerate_activation_map() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]load_activation_data() – reads it_describe_classifier_training – reads itbuild_model_from_configuration() – reads itRead by crops_from_merged(), validate(), _cross_validate_model, _fit_one, apply_model_to_tar(), generate_activation_map(), train_test_model(), load_activation_data(), _describe_classifier_training, get_default_generate_activation_map_settings(), get_train_test_model_settings(), build_model_from_configuration().
image_source¶
(str) - Source of classification images. ‘load_images’ reads previously exported object crops; ‘stream_images’ generates crops from merged image and mask arrays during training. Streaming avoids creating a separate export for each object, channel, and crop-shape combination. Default ‘load_images’.
_streaming – reads itget_setting_dependencies() – reads itRead by _streaming, get_setting_dependencies().
image_type¶
(str) - Exported crop folder to read: ‘cell_png’, ‘nucleus_png’, ‘pathogen_png’, or ‘cytoplasm_png’. This setting applies only to pre-generated image loading and is not used when crops are streamed from merged/. Default ‘cell_png’.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
images¶
import_project() – reads itRead by import_project().
img_size¶
(int) - How many pixels across each cell is drawn. One number: the crop is square. Larger fills the tab with fewer cells per page; the pagination follows it. Default 200.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
img_src¶
_describe_cellpose_training – reads itRead by _describe_cellpose_training.
img_zoom¶
(float) - Scale applied to each object thumbnail pasted onto the embedding: 1.0 draws the crop at native pixel size, 0.5 at half. Raise it when crops are too small to judge morphology, lower it when thumbnails overlap and bury the point cloud. Practical range about 0.1-2.0. Default 0.5.
generate_image_umap() – reads itRead by generate_image_umap().
include_all¶
(bool) - When grouping feature importances by compartment and channel, also emit an ‘all’ row containing features that belong to no single compartment or channel. When disabled, those features are omitted and grouped bars may not sum to the total importance. Default False.
interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]Read by interpret_vision_model().
indent¶
to_json() – reads itRead by to_json().
independent_variable_layout¶
(str) - Shape of the independent-variable/count table: ‘long’ means one row per well and guide, ‘wide’ means one row per well with one guide per column, and ‘auto’ detects long from count_grna_column plus count_value_column and otherwise treats the numeric non-metadata columns as guides. Wide input is melted losslessly before filtering. Default ‘auto’.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
independent_variable_layout_resolved¶
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
infection_hist_data¶
_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_infection_qc_xgboost – reads it_infection_qc_histogram – reads it_make_adjusted_qc_panel – reads itRead by _apply_infection_intensity_qc, _infection_qc_histogram, _infection_qc_xgboost, _make_adjusted_qc_panel, _make_intensity_motility_panel.
infection_hist_intensity_col¶
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_plot_hist_qc – reads itRead by _make_intensity_motility_panel, _plot_hist_qc.
infection_hist_percentile¶
_infection_qc_xgboost_infection_qc_histogram – reads itRead by _infection_qc_histogram.
infection_hist_thr_val¶
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_plot_hist_qc – reads itRead by _make_intensity_motility_panel, _plot_hist_qc.
infection_intensity_frac_infected¶
_infection_qc_xgboost_infection_qc_histogram – reads itRead by _infection_qc_histogram.
infection_intensity_log¶
_infection_qc_xgboost_infection_qc_histogram – reads itRead by _infection_qc_histogram.
infection_intensity_mode¶
(str) - Action applied when the quality-control classification disagrees with the mask-based label. ‘relabel’ replaces the label and retains the cell; ‘remove’ excludes cells with discordant mask and intensity evidence. Unknown values fall back to ‘relabel’. Default ‘relabel’.
_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]_infection_qc_xgboost – reads it_infection_qc_histogram – reads itRead by _infection_qc_histogram, _infection_qc_pca_clustering, _infection_qc_xgboost.
infection_intensity_n_bins¶
(int) - Bin count for the pathogen-intensity histogram, clamped to 10-256. The histogram strategy evaluates bins from low to high and uses the first bin whose infected fraction reaches the target as the intensity threshold. More bins provide finer threshold resolution but increase variability in per-bin fractions. This setting also controls the QC-panel histogram. Default 64.
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_infection_qc_xgboost – reads it_infection_qc_histogram – reads it_plot_hist_qc – reads itRead by _infection_qc_histogram, _infection_qc_xgboost, _make_intensity_motility_panel, _plot_hist_qc.
infection_intensity_qc¶
(bool) - Master switch for infection re-calling. While False the mask-based label (cell contains at least one pathogen) is used unchanged and every other infection_* setting is inert; True runs the method chosen by infection_intensity_strategy. A pathogen_channel must also be set. No default is applied anywhere, so it behaves as False until you set it.
automated_motility_assay() – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]automated_motility_assay() – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]Read by _apply_infection_intensity_qc, automated_motility_assay().
infection_intensity_qc_graphs¶
(bool) - Save the infection-intensity histogram PNG and reserve the QC sub-axes (histogram, embedding, or XGBoost probability plus feature importance) inside the combined intensity/motility panel. Set False to skip that plotting work on large runs; the infection relabelling itself is unchanged either way. Default True.
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_infection_qc_xgboost_infection_qc_histogram – reads itRead by _infection_qc_histogram, _make_intensity_motility_panel.
infection_intensity_qc_panel_path¶
(str) - Path of the QC image embedded in the mask panel. The histogram strategy records its generated PNG; other strategies leave this value empty. The pipeline clears any supplied value before QC, so this is a reported output rather than a control. No default; empty until QC has run.
_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]_infection_qc_xgboost – reads it_infection_qc_histogram – reads itRead by _apply_infection_intensity_qc, _infection_qc_histogram, _infection_qc_pca_clustering, _infection_qc_xgboost, _make_intensity_motility_panel.
infection_intensity_qc_panel_type¶
_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]_infection_qc_xgboost – reads it_infection_qc_histogram – reads itRead by _apply_infection_intensity_qc, _infection_qc_histogram, _infection_qc_pca_clustering, _infection_qc_xgboost, _make_intensity_motility_panel.
infection_intensity_qc_scope¶
(str) - Whether infection QC is fitted once or per group: ‘combined’/’global’/’all’ fits one model on everything, ‘plate’/’per_plate’ one per plateID, ‘well’/’per_well’ one per plate-well, and ‘none’/’off’ skips QC; an unrecognised string falls back to combined behaviour with a warning. Per-well fitting absorbs staining and exposure differences but needs enough cells per well; every group still writes its own QC plot, only the QC payload embedded in the summary panel is taken from the first processed group. Default ‘per_well’.
_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]Read by _apply_infection_intensity_qc.
infection_intensity_strategy¶
(str) - How infected vs uninfected is decided once infection_intensity_qc is True: ‘xgboost’ trains a classifier on intensity extremes, ‘histogram’ picks one intensity threshold, and ‘pca’/’umap’/’tsne’ cluster a 2D embedding. Unknown values fall back to histogram, as does xgboost when the package is missing or a class is too small. Default ‘xgboost’.
automated_motility_assay() – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads itautomated_motility_assay() – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]Read by _apply_infection_intensity_qc, _infection_qc_pca_clustering, _make_intensity_motility_panel, automated_motility_assay().
infection_intensity_threshold¶
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_plot_hist_qc – reads itRead by _make_intensity_motility_panel, _plot_hist_qc.
infection_pca_data¶
_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]_infection_qc_xgboost – reads it_make_adjusted_qc_panel – reads itRead by _apply_infection_intensity_qc, _infection_qc_pca_clustering, _infection_qc_xgboost, _make_adjusted_qc_panel, _make_intensity_motility_panel.
infection_pca_log_intensity¶
(bool) - Apply log1p to non-negative features whose names contain ‘intensity’, ‘p75’, ‘p95’, or ‘max’ before standardization and embedding. This compresses the upper tail and reduces the influence of a small number of high-intensity cells. Consider enabling for pathogen stains with a wide dynamic range. Default False.
_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]Read by _infection_qc_pca_clustering.
infection_pca_max_cells¶
(int) - Maximum number of cells included in the embedding. When more cells are available, a random subsample of this size is drawn with a fixed seed of 0, independently of infection_pca_random_state. Decrease the value to reduce UMAP or t-SNE runtime and memory use; increase it to improve representation of rare subpopulations. Applied after removal of non-finite rows. Default 50000.
_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]Read by _infection_qc_pca_clustering.
infection_pca_method¶
(str) - Records the embedding used (‘pca’, ‘umap’ or ‘t-sne’). The pipeline derives and overwrites this value from infection_intensity_strategy during QC; change infection_intensity_strategy to select the embedding. This output remains empty until QC has run. No default.
_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]Read by _infection_qc_pca_clustering.
infection_pca_min_gt_separation¶
(float) - Alert level for the ground-truth separation score - the absolute difference, between the two clusters, in the fraction of intensity-extreme cells that are infected (0-1). Dropping below it only prints a warning; the cluster labels are still applied. Raise it to be told sooner that the embedding is not separating infection. Default 0.2.
_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]Read by _infection_qc_pca_clustering.
infection_pca_min_silhouette¶
(float) - Silhouette value below which the log prints a ‘weak cluster structure’ warning with tuning hints. It does not reject or re-run the clustering - the cluster-derived labels are applied regardless - so treat it purely as an alert level. Silhouette runs from -1 to 1. Default 0.05.
_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]Read by _infection_qc_pca_clustering.
infection_pca_pathogen_weight¶
(float) - Multiplier applied to the standardised pathogen-channel features before embedding. Above 1 it stretches the embedding along pathogen intensity so KMeans splits infected from uninfected rather than by morphology; 1.0 leaves all features weighted equally. Raise it when the log reports weak cluster separation. Default 2.0.
_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]Read by _infection_qc_pca_clustering.
infection_pca_random_state¶
(int) - Seed for KMeans and for the UMAP/t-SNE embeddings in the pca/umap/tsne strategies. Fixing it makes the embedding and the resulting infected/uninfected cluster assignment reproducible; change it to check that the split is not an artifact of one initialisation. Note the max-cells subsample uses its own fixed seed. Default 42.
_infection_qc_pca_clustering – reads it_search_umap(...) [UNRESOLVED]_search_tsne(...) [UNRESOLVED]_infection_qc_xgboost – reads itRead by _infection_qc_pca_clustering, _infection_qc_xgboost.
infection_xgb_ambiguous_high¶
(float) - Upper edge of the discarded probability band, between 0 and 1. Together with infection_xgb_ambiguous_low it defines the interval whose cells are dropped when infection_xgb_drop_ambiguous is True. Lower it toward the threshold to keep more cells, raise it to discard more. Swapped automatically if it falls below the low bound. Default 0.75.
automated_motility_assay() – reads itautomated_motility_assay() – reads it_infection_qc_xgboost – reads itRead by _infection_qc_xgboost, automated_motility_assay().
infection_xgb_ambiguous_low¶
(float) - Lower edge of the discarded probability band, between 0 and 1. Cells whose probability falls between this and infection_xgb_ambiguous_high are dropped when infection_xgb_drop_ambiguous is True. Raise it toward the threshold to keep more cells, lower it to discard more borderline ones. Swapped automatically if it exceeds the high bound. Default 0.25.
automated_motility_assay() – reads itautomated_motility_assay() – reads it_infection_qc_xgboost – reads itRead by _infection_qc_xgboost, automated_motility_assay().
infection_xgb_colsample_bytree¶
(float) - Fraction of feature columns offered to each tree, between 0 and 1. Lowering it stops a couple of dominant pathogen-intensity features from being chosen by every tree, spreading gain across morphology features and reducing overfitting; 1.0 exposes all features to every tree. Default 0.8.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_corr_threshold¶
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_drop_ambiguous¶
(bool) - After prediction, discard cells whose probability lies between infection_xgb_ambiguous_low and infection_xgb_ambiguous_high instead of forcing a call on them. True gives cleaner infected vs uninfected motility comparisons at the cost of sample size; False keeps every cell. Only used by the xgboost strategy. Default True.
automated_motility_assay() – reads itautomated_motility_assay() – reads it_infection_qc_xgboost – reads itRead by _infection_qc_xgboost, automated_motility_assay().
infection_xgb_importance¶
_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_apply_infection_intensity_qc – reads itdict(...) [UNRESOLVED]qc_func(...) [UNRESOLVED]_make_intensity_motility_panel – reads it_infection_qc_xgboost – reads it_make_adjusted_qc_panel – reads itRead by _apply_infection_intensity_qc, _infection_qc_xgboost, _make_adjusted_qc_panel, _make_intensity_motility_panel.
infection_xgb_learning_rate¶
(float) - Shrinkage applied to each boosting round’s contribution (XGBoost eta). Lower values require more rounds but can produce smoother, better-calibrated infection probabilities; higher values converge faster but can yield probabilities concentrated near 0 or 1, reducing the utility of the ambiguous range. Typical range 0.01-0.3; tune together with infection_xgb_n_estimators. Default 0.1.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_margin¶
(float) - Half-width of the confidence band around infection_xgb_proba_threshold, clamped to 0-0.49. In ‘relabel’ mode only cells outside the band get their label overridden, the rest keep the mask-based call; in ‘remove’ mode cells inside the band are spared deletion. Raise it to trust the model less. Default 0.15.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_max_depth¶
(int) - Maximum depth of each boosted tree. Deeper trees capture interactions between morphology and pathogen-intensity features but overfit the quartile-derived training labels; shallower trees generalise better across wells. Typical range 2-8; raise it only when the classifier cannot separate infected from uninfected. Default 3.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_min_cells_per_class¶
(int) - Per well, how many intensity-extreme examples each class must reach before that well’s training data are balanced by subsampling to the smaller class; wells that have both classes but fewer examples contribute all of theirs, unbalanced. Wells with only one class are skipped entirely. No well is ever excluded for being small, so raising it leaves more wells unbalanced and the training set more skewed - lower it towards 1 to force balancing in every usable well. Default 10.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_n_estimators¶
(int) - Number of boosting rounds (trees) trained, passed as num_boost_round. More rounds fit the intensity-extreme training set more tightly and push infection probabilities away from 0.5, which shrinks the ambiguous band, but cost runtime and can overfit small wells. Trade off against infection_xgb_learning_rate. Default 200.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_n_jobs¶
(int) - Threads XGBoost uses for training and prediction (its nthread parameter). -1 uses every available core; set a small positive number to leave CPU free for other work or when several plates run at once. It changes runtime, not the training recipe. Default -1.
Read by apply_worker_budget(), _infection_qc_xgboost.
infection_xgb_proba_column¶
(str) - Column used by both the track-level ambiguous filter and the QC probability plot. If it is absent, both components discover the classifier output column, normally infection_prob. Before 2026-08-12 this fallback was unreachable, so the track-level filter was not applied under the default configuration. Set this explicitly only to override discovery. Default ‘infection_xgb_proba’.
automated_motility_assay() – reads it_make_intensity_motility_panel – reads itautomated_motility_assay() – reads it_make_intensity_motility_panel – reads it_plot_xgb_prob_qc – reads itRead by _make_intensity_motility_panel, _plot_xgb_prob_qc, automated_motility_assay().
infection_xgb_proba_threshold¶
(float) - Predicted probability at or above which a cell is called infected, between 0 and 1. Lowering it makes infection calling more permissive (more cells become infected), raising it more stringent. It is also the centre of the confidence band whose half-width is infection_xgb_margin. Default 0.5.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_random_state¶
(int) - Seed for the generator that balances the per-well training set, i.e. which intensity-extreme cells are sampled for each class. It is not handed to XGBoost itself. Change it and re-run to confirm the adjusted infection calls are stable under a different training draw. Default 42.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_reg_lambda¶
(float) - L2 penalty on leaf weights. Larger values shrink leaf outputs, giving a more conservative model whose probabilities sit closer to 0.5 and therefore more cells inside the ambiguous band; 0 removes the penalty entirely. Raise it when the model fits training cells perfectly yet disagrees wildly with mask-based labels. Default 1.0.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_subsample¶
(float) - Fraction of training rows drawn at random for each boosting round, between 0 and 1. Below 1 it injects stochasticity that limits overfitting to the small set of intensity-extreme cells used for training; 1.0 uses every training row every round. Lower it if the classifier appears to memorise individual wells. Default 0.8.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
infection_xgb_top_features¶
(int) - How many features, ranked by XGBoost gain, are retained for the feature-importance panel of the QC figure. This is a display cut applied after training: it never changes the model or the infection calls. Lower it for a readable bar chart, raise it to inspect more features. Default 20.
_infection_qc_xgboost – reads itRead by _infection_qc_xgboost.
inference¶
(str) - How effects are tested; the readable front end for analysis_mode. Default ‘nonparametric’: each guide is a plate-blocked Freedman-Lane permutation with an empirical P, valid however many guides there are, but no P can be below 1/(guide_permutations + 1). ‘parametric’ fits every guide at once in the chosen regression_type, so it needs more wells than guides or no coefficient is identifiable. ‘auto’ counts guides and wells and takes the simultaneous fit only when the design supports it.
_it_will_permute – reads it_perform_regression_perform_regression – reads itresolve_auto_inference() – reads it_resolve_regression_analysis_choices – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads it_permuted – reads itrecord_run() – reads itrecommend() – reads it_is_nonparametric – reads itget_setting_dependencies() – reads itpermutation_active() – reads itpermutation_is_certain() – reads itrefusals() – reads itRead by _perform_regression, resolve_auto_inference(), _it_will_permute, _permuted, _run_settings_row, record_run(), recommend(), _is_nonparametric, _resolve_regression_analysis_choices, get_setting_dependencies(), permutation_active(), permutation_is_certain(), refusals().
inflation_warn¶
(float) - Additional invasion efficiency, in proportion units, that increasing the threshold by threshold_sensitivity may add to a well before the well is flagged. Only the upward change is monitored because decreasing the threshold can only reclassify invaded parasites as attached and cannot create a positive invasion result. A value of 0.05 flags a well whose efficiency would increase by more than five percentage points. Default 0.05.
_invasion_well_table – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_well_table, _set_analyze_invasion_defaults.
init_weights¶
(bool) - Start the backbone from ImageNet-pretrained weights instead of random initialisation; the spaCR classifier head bolted on top is randomly initialised either way. Leave it on - transfer learning converges in far fewer epochs on the small annotated sets typical here. Turn it off only to train from scratch on a very large dataset, or to measure how much pretraining contributes. Default True.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
input_mean¶
(list or None) - Per-channel means for input_statistics=’custom’ or ‘dataset’, on the 0-1 scale ToTensor produces. One value is broadcast to every channel, which is what a single-stain dataset wants. Compute the dataset ones with spacr.normalization.dataset_statistics rather than guessing. Ignored unless input_statistics asks for them. Default None.
apply_model_to_tar() – reads itgenerate_activation_map() – reads itload_activation_data() – reads itRead by apply_model_to_tar(), generate_activation_map(), load_activation_data().
input_pair_audit¶
_perform_regression_read_data – reads it_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _perform_regression_read_data.
input_statistics¶
(str) - Mean and standard deviation used by the loader when normalize_input is enabled. ‘symmetric’ uses spaCR’s historical 0.5/0.5 convention (Inception and TF-Slim), mapping [0,1] to [-1,1]. ‘imagenet’ and ‘clip’ use the distinct statistics associated with those pretrained backbones. ‘dataset’ estimates per-channel statistics from the current data and may be appropriate for fluorescence images whose intensity distribution differs from photographs. ‘custom’ uses input_mean and input_std. Default ‘symmetric’.
apply_model_to_tar() – reads itgenerate_activation_map() – reads itload_activation_data() – reads itRead by apply_model_to_tar(), generate_activation_map(), load_activation_data().
input_std¶
(list or None) - Per-channel standard deviations paired with input_mean. Zero values are replaced with 1.0 to prevent division by zero and non-finite training losses; a zero standard deviation indicates a constant channel. Default None.
apply_model_to_tar() – reads itgenerate_activation_map() – reads itload_activation_data() – reads itRead by apply_model_to_tar(), generate_activation_map(), load_activation_data().
inputs¶
Image and external label-mask files or folders to import. The preview groups them by source and proposes whether each group is an intensity image, an object mask, or ignored. Default [].
run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]plan_external_masks() – reads itrun_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by plan_external_masks(), _check_required_paths.
intensity_statistic¶
(str) - Per-object statistic of the pre-permeabilisation channel used for thresholding. Because the stain is localized to the parasite surface, the object mean divides rim signal by the full area and can classify a larger parasite as dimmer than a smaller parasite with equivalent surface staining. A percentile of rim-pixel intensity reduces this size-dependent bias. Default ‘mean’. Invasion starts at ‘auto’: it chooses periphery_95 when present, otherwise percentile_95, and uses mean only as a warned last resort.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
intercept¶
(str) - Definition of the fitted intercept. ‘fitted’ estimates it from the data as the predicted response when every predictor is at its reference level; in a one-hot gene design, this reference is the gene omitted by patsy and may not have a useful biological interpretation. ‘control’ subtracts the negative controls’ mean response before fitting and suppresses the intercept term, so every coefficient is a difference from the controls. ‘zero’ fits through the origin. ‘value’ fixes the intercept at intercept_value. Default ‘fitted’.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]get_setting_dependencies() – reads itRead by _perform_regression, get_setting_dependencies().
intercept_value¶
(float) - The number the intercept is pinned to when intercept is ‘value’. The response is shifted by it and the term suppressed, so the fit is exactly y = intercept_value + terms. Read for no other intercept setting, and the panel greys it out for them. Default 0.0.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
intermedeate_save¶
(bool, sequence of float, or None) - Control archival model snapshots on improving epochs. True or None uses validation-accuracy thresholds 0.99, 0.98, 0.95 and 0.94; False disables archival snapshots; and a sequence supplies custom thresholds. Best-model and last-model checkpoints remain enabled independently, so False does not remove those recovery artifacts. Default True.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
invert¶
(bool) - Invert intensities as each image is loaded, pixel -> dtype_max - pixel (255 - x for uint8). Switch it on for brightfield or phase-contrast data where objects are darker than the background, since Cellpose expects bright objects on a dark field; leave it off for fluorescence. Default False.
check_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
invert_dependent_variable¶
(bool or int) - Transform the response before per-well aggregation when lower scores represent a stronger phenotype. False or 0 leaves the response unchanged, True or 1 uses 1 - x (appropriate for probabilities), and -1 uses 1 / x (appropriate for distances or counts). Any other value raises ValueError in process_scores. The transformation changes coefficient signs and therefore the side of the volcano plot on which significant effects appear. Default False.
_preparation_key – reads it_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _preparation_key.
isomap_n_neighbors¶
(int) - Number of neighbors in Isomap’s geodesic graph. Too few can disconnect the graph; too many make the result approach a global linear projection. Used only by Isomap. Default 15.
generate_image_umap() – reads itRead by generate_image_umap().
isomap_path_method¶
(str) - Isomap shortest-path solver: auto chooses, FW uses Floyd-Warshall, and D uses Dijkstra. Used only by Isomap. Default ‘auto’.
generate_image_umap() – reads itRead by generate_image_umap().
keep_groups¶
plot_data_from_csv() – reads itRead by plot_data_from_csv().
keep_intermediate¶
(bool) - Keep the intermediate stack/ and masks/ folders after the merged/ arrays are built. Off by default: only merged/ is kept (masks are embedded in merged and recorded in the database).
Read by preprocess_generate_masks().
keep_npz¶
(bool) - Streaming pipeline only (pipeline_style=’v2’): write each in-memory NPZ batch under merged/_scratch/ instead of discarding it, so intermediate data from a failed run can be inspected. This increases disk usage; enable it only for diagnosis. Default False.
Read by preprocess_generate_masks().
keep_original_images¶
(bool) - Keep the original raw input images (in orig/). Off by default to save disk space; the pixel data lives in merged/.
Read by preprocess_generate_masks().
l1_ratio¶
(float) - How the elastic-net penalty is split between L1 and L2: 1.0 is a pure lasso (sparse, picks one gRNA out of a correlated group), 0.0 is a pure ridge (dense, shares the effect across the group), and values between keep some of both. Use 0.5 when correlated gRNAs of the same gene should be selected together rather than arbitrarily. Read only by regression_type ‘elasticnet’. Default 0.5.
_perform_regression_read_data_reconcile_random_row_column_effects – reads it_perform_regression_perform_regression – reads it_call_level_hits – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits, _perform_regression, _reconcile_random_row_column_effects.
label¶
extract_crop() – reads itreplace(...) [UNRESOLVED]Read by extract_crop().
label_key¶
import_project() – reads itRead by import_project().
label_smoothing¶
(float) - Epsilon passed to cross-entropy when loss_type is label_smoothing: each target retains 1 - eps of its probability mass and distributes the remainder across the other classes. Increase it, typically within 0.05-0.2, when predicted probabilities are overconfident or annotations are noisy; 0 disables smoothing. Ignored by other loss types. Default 0.1.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
lasso_n_boot¶
(int) - Number of bootstrap resamples used to rank lasso and elastic-net hits by how often each gRNA survives the penalty. These models have no valid p-values, so selection frequency replaces the significance test entirely. Higher is steadier and linearly slower; the cost is one full penalised fit per resample, doubled when alpha is ‘auto’ because each resample cross-validates. Default 200.
_perform_regression_perform_regression_call_level_hits – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits.
lasso_selection_threshold¶
(float) - Minimum bootstrap selection frequency, between 0 and 1, for a lasso or elastic-net coefficient to be called a hit. 0.6 means the gRNA kept a non-zero coefficient in at least three fifths of the resamples. Raise it for a shorter, harder-to-argue-with list; lowering it below about 0.5 admits terms the penalty drops as often as it keeps. Default 0.6.
_perform_regression_perform_regression_call_level_hits – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits.
layout¶
Naming-layout hint used to read plate, well, field, channel, Z, and time identifiers from source files. ‘auto’ detects the supported layout from the filenames. Default ‘auto’.
Read by convert_folder(), plan_external_masks(), import_project().
leakage_audit_train_test¶
(bool) - Audit the permanent train/ and test/ boundary before any classifier fit. Checks plate/well/field/object lineage, exported augmentation families and (when enabled) byte-identical renamed copies. Default True. API: spacr.classifier_evaluation.audit_dataset_splits.
train_test_model() – reads it_default_traintrain_test_model() – reads ittrain_test_model() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by train_test_model().
leakage_hash_content¶
(bool) - SHA-256 hash classifier images during leakage audits so an identical crop copied or renamed across train/test or CV boundaries is still detected. Reads files in 1 MiB chunks and never decodes pixels. Default True. API: spacr.classifier_evaluation.audit_cv_folds.
train_test_model() – reads it_cross_validate_model – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads ittrain_test_model() – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, train_test_model().
leakage_require_identity¶
(bool) - Treat filenames that do not encode the protected cv_group_by identity, and files that cannot be hashed, as a failed audit rather than an advisory warning. Default True because independence cannot be claimed when lineage is unknown. API: spacr.classifier_evaluation.audit_split_leakage.
train_test_model() – reads it_cross_validate_model – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads ittrain_test_model() – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, train_test_model().
learning_rate¶
(float) - Initial optimizer step size. Values that are too high may prevent convergence; values that are too low may slow convergence or converge to a suboptimal solution. A value near 1e-3 is commonly used for training from random initialization, while 1e-4 to 1e-5 is appropriate for fine-tuning ImageNet weights (init_weights=True). The selected schedule modifies this initial value during training. Default 0.001.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]generate_ml_scores() – reads ittrain_cellpose() – reads itreduction_and_clustering() – reads itRead by _cross_validate_model, _fit_one, train_test_model(), generate_ml_scores(), get_train_test_model_settings(), train_cellpose(), reduction_and_clustering().
legacy_volcano¶
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
level¶
(str) - Result level. For regression, ‘both’ writes results_grna.csv and results_gene.csv and corrects each family separately; ‘grna’ reports guide effects, and ‘gene’ pools guides by gene. Nonparametric inference also honours this choice. Mixed models disable it because they estimate gene effects with guides nested inside genes. For proportion plots, the same key selects ‘object’, ‘well’, or ‘plate’ aggregation. Default ‘both’ for regression and ‘object’ for proportions.
select_montage_per_guide() – reads it_announce_the_fit – reads it_it_will_permute(...) [UNRESOLVED]_say_what_the_permutation_will_do(...) [UNRESOLVED]_say_what_the_permutation_will_do – reads itsection_explainer() – reads itsection_explainer_html() – reads it_perform_regression_perform_regression – reads it_run_guide_permutation_analysis – reads it_resolve_regression_analysis_choices – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_why_no_rows_at – reads itrefit_settings() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by select_montage_per_guide(), _perform_regression, _run_guide_permutation_analysis, _announce_the_fit, _say_what_the_permutation_will_do, section_explainer(), section_explainer_html(), _why_no_rows_at, refit_settings(), _resolve_regression_analysis_choices, _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_replication().
line_thickness¶
How many pixels wide the quality-control outlines are drawn. Larger is easier to see on a downsampled preview and obscures more of the image under it. Default 1.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
lines.marker¶
rc_params() – reads itRead by rc_params().
lo_pct¶
Read by segment_frame().
location_column¶
(str) - Metadata column searched for positive_control_id and negative_control_id values when labelling rows for machine-learning training, normally ‘columnID’ or ‘rowID’. Set ‘rowID’ when controls are arranged along plate rows instead of columns. annotation_column overrides this setting when specified. Default ‘columnID’.
_rules_from_metadata – reads itdict(...) [UNRESOLVED]load_search_data() – reads itgenerate_ml_scores() – reads itRead by _rules_from_metadata, load_search_data(), generate_ml_scores().
log_data¶
(bool) - Apply log(x + 1e-6) to every numeric feature, after the correlation filter and before standard scaling. Compresses heavy-tailed measurements such as intensity sums and areas so a handful of bright or huge objects stop dominating the embedding. Negative feature values become NaN and are then filled with the column mean. Default False.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itload_search_data() – reads itRead by generate_image_umap(), reducer_hyperparameter_search(), load_search_data().
log_x¶
(bool) - Use a log10 x-axis; for line graphs, the x column is log10-transformed rather than only changing the axis scale. Enable when x spans several orders of magnitude, such as gRNA fraction thresholds or count distributions, to prevent low values from being visually compressed. Values at or below zero cannot be displayed. Default False.
plot_data_from_csv() – reads it_perform_regression_perform_regression_draw_the_threshold_sweep_graph_sequencing_statsgraph_sequencing_stats() – reads it_graph_sequencing_statsgraph_sequencing_stats() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by plot_data_from_csv(), graph_sequencing_stats().
log_y¶
(bool) - Put the y-axis on a log10 scale; for line graphs the y column is log10-transformed instead of the axis being rescaled. Enable it when the measured values span orders of magnitude or a few large wells compress everything else toward the baseline. Values at or below zero cannot be shown. Default False.
plot_data_from_csv() – reads it_perform_regression_perform_regression_draw_the_threshold_sweep_graph_sequencing_statsgraph_sequencing_stats() – reads it_graph_sequencing_statsgraph_sequencing_stats() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by plot_data_from_csv(), graph_sequencing_stats().
logit_adjust_tau¶
(float) - Strength of the Menon-et-al. logit adjustment: tau * log(class prior) is added to the logits during training, pulling decisions toward rare classes. Only used when loss_type resolves to logit_adjust_ce, which ‘auto’ picks when the smallest class is under 10% of the data. Higher tau corrects harder; 0 disables. Default 1.0.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
loss_type¶
(str) - Loss used to train the classifier. For a head with two or more classes: ‘focal_loss’ (down-weights easy examples), ‘cross_entropy’, ‘label_smoothing’ (epsilon 0.1), ‘ce_weighted’ (inverse-frequency class weights), ‘logit_adjust_ce’ and ‘asl’. ‘binary_cross_entropy_with_logits’ is valid only for a single-logit head and raises otherwise. Use a weighted or focal loss for imbalanced classes. Default ‘focal_loss’. Merged Classifier starts at ‘auto’, which resolves to cross_entropy for a multi-class head and binary_cross_entropy_with_logits for a single-logit head.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
lower_percentile¶
(float) - Percentile of the non-zero pixels in each channel used as the low anchor when rescaling that channel to 0-1; the high anchor is chosen automatically between the 98th and 99.5th percentile. Raise it to crush more dim background to black, lower it to preserve faint signal. Valid 0-100, default 2.
_concatenate_and_normalize_impl_normalize_img_batch – reads itRead by _normalize_img_batch.
lower_threshold¶
annotate_filter_vision() – reads itRead by annotate_filter_vision().
magnification¶
(int) - Objective magnification, used only to derive expected object sizes: pixel diameter is 2*mag+80 for cells, 0.75*mag+45 for nuclei and mag for pathogens, with min/max area limits of diameter^2/4 and diameter^2*10. Explicit cell_diameter, nucleus_diameter or pathogen_diameter override it. Set this to the acquisition objective magnification (10, 20, 40 or 60). Default 40.
_get_object_settings – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_get_object_settings – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]_get_object_settings – reads itRead by _get_object_settings, _get_object_settings, _describe_objects.
make_adjusted_panel¶
automated_motility_assay() – reads itautomated_motility_assay() – reads itRead by automated_motility_assay().
make_mask_panel¶
automated_motility_assay() – reads itautomated_motility_assay() – reads itRead by automated_motility_assay().
manders_thresholds¶
(list) - Percentiles (0-100) used by the activation-map correlation report in spacr.deep_spacr. It no longer affects a measure run: the percentile-pair columns it drove there were removed on 2026-09-02, and measure now writes the three standards-compliant Manders coefficients, which estimate each channel’s background inside each object and take no percentile. Default [15, 50, 75].
generate_activation_map() – reads itRead by generate_activation_map(), get_default_generate_activation_map_settings().
map_name¶
convert_folder() – reads itRead by convert_folder().
mask_dims¶
load_activation_data() – reads itRead by load_activation_data().
masks¶
(bool) - Run Cellpose segmentation for every configured object channel (cell, nucleus, pathogen, organelle) and write label stacks to masks/<object>_mask_stack. False performs preprocessing only, producing normalized arrays without label masks; downstream measurement therefore requires a subsequent segmentation step. Default True.
preprocess_generate_masks() – reads itpreprocess_img_data() – reads itimport_project() – reads itanalyze_plaques() – reads itget_analyze_plaque_settings() – reads itRead by preprocess_generate_masks(), import_project(), preprocess_img_data(), get_analyze_plaque_settings(), analyze_plaques().
match_column¶
generate_ml_scores() – reads itadd_column_to_database() – reads itRead by generate_ml_scores(), add_column_to_database().
max_area¶
(int) - Upper area cutoff applied before the endodyogeny bins are built; larger objects are excluded. The cutoff is applied after um_per_px scaling and therefore uses square micrometres when a scale is set or square pixels when it is None. The default is effectively unbounded. Default 1000000000.
analyze_endodyogeny() – reads itRead by analyze_endodyogeny().
max_bins¶
(int or None) - Maximum number of area bins in the endodyogeny histogram. None derives the bin count from the data range and min_area_bin; an integer truncates the range so that the largest objects are pooled into the final bin. Set an integer when plates with different size ranges must share one axis. Default None.
analyze_endodyogeny() – reads itRead by analyze_endodyogeny().
max_buffer_bytes¶
Read by align_folder(), _work, write_stack().
max_displacement¶
(float) - Largest plausible centroid movement between consecutive frames, in pixels. A single-frame excursion followed by an immediate return is interpolated from neighbouring positions; other displacements above this value cause the complete track to be excluded. Increase the value for rapidly moving objects or sparsely sampled timelapses and decrease it to remove identity-switch artifacts. Default 50.0.
automated_motility_assay() – reads itautomated_motility_assay() – reads itapply_settings() – reads itRead by apply_settings(), automated_motility_assay().
max_failure_rate¶
(float or None) - Fraction of failed items above which the run aborts. For example, 0.2 aborts after more than 20% of items fail. The failure ledger is written to the artifact before the abort. None disables rate-based abortion; failures remain counted and reported, and incomplete artifacts are marked partial. Default None.
Read by get_measure_crop_settings().
max_iter¶
reduction_and_clustering() – reads itRead by reduction_and_clustering().
max_keypoints¶
Cap on features kept per tile after detection, which is what bounds memory on a dense field. Lowering it speeds scoring and can drop the match that would have joined two tiles. Default 4000.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
max_objects¶
filter_selection() – reads itRead by filter_selection().
max_parasite_area¶
(float or None) - Largest object area in pixels kept as a parasite. Anything bigger is several parasites merged by the mask, whose rim statistic mixes them and whose single classification then stands for all of them. None keeps everything. Default None.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_invasion(), analyze_replication(), _check_app_specific.
max_parasites_per_vacuole¶
(int) - Largest power-of-two parasite count given its own replication bucket. Counts above it remain visible in a ‘>N’ bucket and non-powers stay in the separate QC bucket; they are never clipped or rounded. Use a power of two large enough for the experiment’s duration. Default 16.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_replication_defaults, analyze_replication(), _check_app_specific.
max_ram_features¶
How many tiles’ features to hold in memory before spilling to the cache, in images. Lower it on a small machine; raising it trades memory for fewer disk reads. Default 256.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
max_shift¶
align_folder() – reads itRead by align_folder().
max_site_gap¶
How far apart two site numbers may be and still be treated as neighbours. Large enough to cover the turn at the end of a snake pattern; too small and the tiles at a row end never get compared. Default 64.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
max_train_images¶
train_cellpose() – reads itRead by train_cellpose().
max_workers¶
run_multiple_simulations() – reads itRead by run_multiple_simulations().
measure¶
Measure morphology, intensity and spatial relationships for labeled objects, and optionally export per-object image crops. Requires mask arrays produced by Make Masks or an equivalent compatible source.
import_project() – reads itRead by import_project().
measurement¶
(str) - Measurement column(s) from measurements.db used to prefilter which object crops the annotator loads, applied together with threshold and threshold_direction. Accepts a single column, a comma-separated list (each paired with the same-index threshold), or a JSON list-of-lists where an inner pair is filtered as a ratio (first divided by second). Empty (default) loads every crop unfiltered.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
measurement_object¶
import_project() – reads itRead by import_project().
measurement_table¶
import_project() – reads itRead by import_project().
measurements¶
import_project() – reads itRead by import_project().
merge_edge_pathogen_cells¶
(bool) - During measurement, reconcile pathogens straddling two host-cell masks: if 90 percent or more of the pathogen lies in one cell, its pixels in the neighbours are erased; otherwise the overlapping cell labels are fused into a single cell. Switch off to keep the raw cell segmentation when parasites legitimately touch two cells. Default True.
_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _measure_crop_core, get_measure_crop_settings().
merge_pathogens¶
(bool) - Legacy option that merged two touching pathogen labels into one when their shared boundary exceeded 66% of the smaller object’s perimeter, so a single PV split by Cellpose counted once. The current Cellpose-SAM path ignores it - use pathogen_perimeter_fraction instead. Default True.
_get_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by _get_object_settings, _get_object_settings.
merged_folder¶
generate_annotation_dataset() – reads itstream_dataset() – reads itmerged_path¶
extract_crop() – reads itreplace(...) [UNRESOLVED]Read by extract_crop().
meta_regex¶
How the well, site, channel and magnification are read OUT OF THE FILENAME. Every tile that does not match is invisible to the run, so a wrong pattern looks like missing data rather than an error. Default matches ‘10X_c1_A1_Site-1.tif’.
ops_preprocess() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), ops_preprocess(), stitch_cycle_wells().
metadata_files¶
(list) - Gene-annotation CSVs, each with a ‘Gene ID’ column, that are joined onto the regression results by gene, writing an extra results CSV per file. These are gene tables, not plate/well metadata. When toxo is True the order matters: index 0 is read as the ME49 transcription table and index 1 as the GT1 phenotype table. Default [].
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
metadata_rules¶
generate_training_dataset() – reads itRead by generate_training_dataset().
metadata_type¶
(str) - Filename convention used to parse raw images. ‘cellvoyager’ (default) and ‘cq1’ use built-in regular expressions, ‘custom’ uses custom_regex, and ‘auto’ first renames the folder to Yokogawa naming (using custom_regex when supplied, otherwise automatic detection) before parsing. An incorrect convention can misassign plate, well, field, or channel identifiers and place images in incorrect channel folders.
preprocess_generate_masks() – reads itpreprocess_img_data() – reads itrun_preflight()_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]describe_resources()_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]_check_src – reads it_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]describe_resources()_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]_check_src – reads it_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_on_measure_clicked – reads it_describe_plan_safe_inventory_scan_raw_images_candidate_patterns – reads itisinstance(...) [UNRESOLVED]get_setting_dependencies() – reads itRead by preprocess_generate_masks(), preprocess_img_data(), _on_measure_clicked, get_setting_dependencies(), _candidate_patterns, _check_src.
metadata_type_by¶
Read by _class_column.
metric¶
(str) - Distance metric used both by the reducer (UMAP or t-SNE) and by DBSCAN clustering, e.g. ‘euclidean’, ‘manhattan’, ‘cosine’ or ‘correlation’. Correlation-type metrics compare feature profiles regardless of magnitude and often separate phenotypes better than euclidean on scaled data. Default ‘euclidean’.
_compute_umap – reads itgenerate_image_umap() – reads itreducer_hyperparameter_search() – reads itRead by _compute_umap, generate_image_umap(), reducer_hyperparameter_search().
min_area_bin¶
(int) - Width of the smallest area bin in the endodyogeny histogram, and therefore the resolution at which small parasites are distinguished from one another. Expressed in the same unit as max_area, so it follows um_per_px when a scale is set. Too small a value produces sparse noisy bins; too large merges real division states. Default 500.
analyze_endodyogeny() – reads itRead by analyze_endodyogeny().
min_cells_per_well¶
(int) - Wells with fewer than this many cells are dropped. In a regression it is scored objects and the well is left out of the fit; in the machine-learning screen it is measured cells and the well is left out of the plate heatmap, whose pivot is then filled with 0, so an excluded well renders at the bottom of the colour scale rather than blank. Raising it removes noisy, sparsely imaged wells at the cost of power. Set 0 to switch it off. Default 100 for a regression, 25 for the screen.
generate_ml_scores() – reads it_preparation_key – reads it_perform_regression_perform_regression – reads itminimum_cell_simulation() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, generate_ml_scores(), minimum_cell_simulation(), _preparation_key.
min_confidence¶
Read by align_folder(), _work, build_plan().
min_control_objects¶
(int) - Minimum number of objects required from a plate’s control wells before their quantile is used as a threshold. Below this value, the plate uses the automatic per-field method and records the fallback rather than estimating a 99th percentile from an insufficient sample. Default 10.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
min_dist¶
(float) - UMAP’s minimum spacing between points in the 2-D embedding, range 0.0-1.0. Low values (0.0-0.1) let clusters pack tightly and look crisply separated; higher values spread points out and preserve more of the global layout at the cost of visible cluster structure. Ignored when reduction_method is ‘tsne’. Default 0.1.
_compute_umap – reads itgenerate_image_umap() – reads itRead by _compute_umap, generate_image_umap().
min_max¶
(str) - Color limits for the plate heatmap: ‘allq’ scales to the 2nd-98th percentile of well values so a handful of extreme wells cannot flatten the rest, ‘all’ scales to the true min and max. A two-element list is also accepted, where floats are read as quantiles and integers as absolute vmin/vmax. Default ‘allq’.
generate_ml_scores() – reads itRead by generate_ml_scores().
min_objects_for_bimodality¶
(int) - Objects required before the bimodality coefficient is computed at all; below it the coefficient is left NaN and the field or well is flagged. The statistic exceeds its cutoff on genuinely unimodal data about 45% of the time at ten objects and 15% at twenty, so computing it there would silence the check exactly where the classification is least trustworthy. Default 30, where that false-pass rate is 5%.
_invasion_field_thresholds – reads it_invasion_well_table – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_field_thresholds, _invasion_well_table, _set_analyze_invasion_defaults.
min_objects_for_threshold¶
(int) - Minimum number of objects required to derive a field-specific threshold. Below this count, the field uses its well threshold and then its plate threshold; automatic_source records the level used. Increasing the value improves statistical stability but reduces adaptation to local illumination variation. Default 10.
_invasion_field_thresholds – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_field_thresholds, _set_analyze_invasion_defaults.
min_observations_per_hit¶
(int) - Observation count a significant hit must strictly exceed to appear in results_significant_filtered.csv: gRNA hits need n_grna > min_observations_per_hit, gene hits need n_gene > min_observations_per_hit. The unfiltered hit list is still written alongside it. Raise it to drop hits resting on one or two wells. Default 0, which filters nothing.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
min_overlap_px¶
align_folder() – reads itRead by align_folder().
min_parasite_area¶
(int or float) - Smallest object area in pixels retained as a parasite. Smaller objects are treated as debris because their outside-stain statistic is estimated from too few pixels for stable thresholding. Increase it when pathogen masks are over-segmented into small fragments. Default 0, which applies no area filter.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_invasion(), analyze_replication(), _check_app_specific.
min_parasites_per_well¶
(int) - Minimum number of scored parasites required for an unflagged well-level efficiency estimate. At n=50 and p=0.5, the normal-approximation 95% interval has an approximate half-width of 13.9 percentage points. Estimates below the threshold remain in the output with a flag and their n_total value. Default 50.
_invasion_well_table – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_well_table, _set_analyze_invasion_defaults.
min_reads_per_well¶
Absolute read floor below which a well is called starved and left out of the threshold fit. 0 derives one from the run instead, as starved_read_fraction of the median well’s depth. Starved wells are always reported either way. Default 0.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
min_samples¶
(int) - Meaning depends on ‘clustering’: for DBSCAN it is how many points must fall within eps for a point to count as a core point, so raising it yields fewer, denser clusters and more noise; for KMeans this same value is reused as n_clusters, the exact number of clusters produced. Lower it (or raise eps) when no clusters are found. Default 100.
_compute_umap – reads itgenerate_image_umap() – reads itRead by _compute_umap, generate_image_umap().
min_total_intensity¶
(float or None) - Minimum mean intensity in the post-permeabilisation channel for an object to count as a parasite at all. That antibody stains every parasite, so an object dark in it is debris inside the pathogen mask rather than a dim parasite, and it would otherwise contribute a background-level outside signal and be scored invaded. None applies no filter. Default None.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
minima¶
annotate_crops() – reads itRead by annotate_crops().
mip¶
Take the maximum across z instead of a single plane. Usually right for spots, which sit at different depths across a field; off reads the plane named by z_index. Default True.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
mix¶
(str) - Plate column ID whose wells hold a mixed positive/negative population; rows with this columnID are labelled cond=’mix’ for the image UMAP, so they can be coloured separately or dropped via exclude_conditions. Any column matching none of pos, neg or mix is labelled ‘screen’. Default ‘c3’.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itRead by generate_image_umap(), reducer_hyperparameter_search().
mixed_control_wells¶
(list or str) - Wells holding a known mixture of the positive and negative controls, e.g. [‘c3’]. Removed from the regression like the other two. These provide strong validation because per-cell identities are unknown while the aggregate proportions are known from sequencing, allowing annotation methods to be scored on real rather than simulated cells. Default None.
_perform_regression_perform_regressioncontrol_block_wells() – reads itcontrol_block_wells() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by control_block_wells().
mixed_precision¶
(bool) - Enable automatic mixed precision for supported accelerator operations during training. On a modern graphics card it can make training roughly twice as fast while using less memory, although the gain depends on the model and hardware. Floating-point differences can affect exact reproducibility, so compare runs only when they use the same precision mode. Unsupported hardware disables this mode with an explicit message. Default False.
train_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads it_default_traintrain_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads ittrain_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by train_model(), get_train_test_model_settings().
mode¶
(str) - Read-pairing strategy for barcode extraction: ‘paired’ locates target_sequence in R1 and in the reverse complement of R2 and merges them base-by-base into a quality-weighted consensus; ‘single’ scans one mate alone, chosen by single_direction. Paired calls barcodes more accurately but discards any read whose anchor is missing from either mate. Default ‘paired’.
plot_region() – reads itgenerate_barecode_mapping() – reads itRead by plot_region(), generate_barecode_mapping().
model¶
config_for() – reads it_read_and_segment_sequencesegment_frame() – reads itsegment_frame() – reads itRead by config_for(), segment_frame().
model_data_layout¶
(str) - Shape handed to a fixed-effects estimator. ‘long’ preserves the historical repeated well-guide formula; ‘wide’ pivots guide or gene fractions to one row per independent well before fitting. Mixed models and Freedman-Lane permutation testing require the long representation and convert wide input back to long automatically. Default ‘long’.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
model_name¶
(str) - Cellpose model used for segmentation. Cellpose 4 provides one stock model, ‘cpsam’. Pre-SAM names (‘cyto’, ‘cyto2’, ‘cyto3’, ‘nuclei’) remain accepted for compatibility with older settings, but they are mapped to ‘cpsam’ and reported. Of the three parameters that previously distinguished models, only diameter remains operational in Cellpose 4 (eval rescales the image by 30/diameter); model_type and diam_mean are logged as ‘not used in v4.0.1+’ and omitted. Use ‘cpsam’ unless loading a custom CPSAM checkpoint. Default ‘cpsam’.
apply_settings() – reads it_seed_organelle_column(...) [UNRESOLVED]get_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads ittrain_cellpose() – reads itbuild_model_from_configuration() – reads itload_model_artifact() – reads itRead by apply_settings(), get_analyze_plaque_settings(), identify_masks_finetune(), train_cellpose(), build_model_from_configuration(), load_model_artifact().
model_path¶
(str) - Path to a trained spaCR classifier saved as a whole PyTorch object (loaded with torch.load(weights_only=False), not a state_dict). Used when applying a model to a dataset tar and when generating activation maps. deep_spacr overwrites it with the freshly trained model whenever train is True, so set it only to score with an existing model. Default ‘’.
deep_spacr() – reads itapply_model_to_tar() – reads itgenerate_activation_map() – reads itload_activation_data() – reads itapply_cellpose_model() – reads itget_default_apply_cellpose_model_settings() – reads ittest_cellpose_model() – reads itget_default_test_cellpose_model_settings() – reads itRead by apply_model_to_tar(), deep_spacr(), generate_activation_map(), load_activation_data(), get_default_apply_cellpose_model_settings(), get_default_generate_activation_map_settings(), get_default_test_cellpose_model_settings(), apply_cellpose_model(), test_cellpose_model().
model_plate_position¶
(bool) - Include plateID, rowID, and columnID terms to model plate and spatial effects. random_row_column_effects selects fixed terms or a plate-grouped mixed model with row/column variance components. Enabling random effects requires this setting. In the reference screen the layout terms were jointly significant at p = 6.7e-23, so enable them when plate, edge, row, or column effects are plausible. Default False.
_perform_regression_read_data_reconcile_random_row_column_effects – reads itsection_explainer() – reads itsection_explainer_html() – reads it_perform_regression_perform_regression – reads it_call_level_hits – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits, _perform_regression, _reconcile_random_row_column_effects, section_explainer(), section_explainer_html().
model_type¶
(str) - Backbone architecture for the single-object image classifier: any TorchVision classification model name (resnet50, maxvit_t, densenet121, …). An unrecognized name does not fail during initial validation: choose_model reports ‘Invalid model_type’ and returns None, after which training fails. The special name ‘custom’ passes validation and then raises NotImplementedError. Larger backbones require more memory and generally need more labeled crops than smaller backbones. Default ‘maxvit_t’.
_begin_flowview_run_install_classify_collectorclassify_graph() – reads itresolve_ml_model_type() – reads itresolve_ml_model_type() – reads it_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itgenerate_activation_map() – reads it_fresh_collectorclassify_graph() – reads itresolve_ml_model_type() – reads itdict(...) [UNRESOLVED]_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]load_activation_data() – reads itresolve_model_source() – reads it_describe_classifier_training – reads itRead by resolve_ml_model_type(), _cross_validate_model, _fit_one, generate_activation_map(), train_test_model(), classify_graph(), load_activation_data(), resolve_model_source(), _describe_classifier_training, get_default_generate_activation_map_settings(), get_train_test_model_settings().
model_type_ml¶
(str) - Classifier fitted by ml_analysis to separate positive- from negative-control wells and rank per-object features by permutation importance. Options are xgboost (default), lightgbm, catboost, random_forest, extra_trees, gradient_boosting, logistic_regression, svm and mlp; lightgbm and catboost require their optional packages. reg_alpha, reg_lambda and learning_rate affect only boosted models; logistic_regression provides a linear reference model.
_begin_flowview_run_install_classify_collectorresolve_ml_model_type() – reads itresolve_ml_model_type() – reads it_fresh_collectorresolve_ml_model_type() – reads itdict(...) [UNRESOLVED]run_search_for_app() – reads itgenerate_ml_scores() – reads itRead by resolve_ml_model_type(), run_search_for_app(), generate_ml_scores().
mosaic¶
Assemble the stitched tiles into one image. The same switch as write_mosaic; either turns it on. Default False.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
mosaic_csv_out¶
Explicit path for the manifest listing each tile’s position and transform in the mosaic. Empty writes it beside the well’s other outputs. The manifest is enough to rebuild the mosaic later without re-scoring. Default None.
get_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings().
mosaic_min_score¶
The lowest pair score allowed to place a tile in the mosaic. Empty uses the automatic knee of the score distribution, which adapts to the run; a fixed value is reproducible but can drop a whole well on a dim plate. Default None.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
mosaic_out¶
Explicit path for the assembled mosaic image. Empty writes it beside the well’s other outputs under its own name. Default None.
get_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings().
motility_analysis¶
(bool) - Run the automated motility assay after segmentation: it rebuilds per-object measurements from merged/*.npy, cleans tracks, computes per-track velocity and straightness, applies the infection QC, and writes motility_plots plus a well-level summary table. It only fires when timelapse is also True, and it is what reveals the Motility setting categories. Default False.
generate_cellpose_masks_sam() – reads itgenerate_cellpose_masks() – reads itRead by generate_cellpose_masks(), generate_cellpose_masks_sam().
motility_origin_xlim¶
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_make_motility_plots – reads itRead by _make_intensity_motility_panel, _make_motility_plots.
motility_origin_ylim¶
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_make_motility_plots – reads itRead by _make_intensity_motility_panel, _make_motility_plots.
motility_xlim¶
(tuple) - Spatial x-axis limits for the origin-centred track panels (infected and uninfected) of the motility figure, in plotted coordinate units - um when pixels_per_um is set, otherwise pixels - not time. The whole-field all-tracks axis next to them always autoscales from the data and ignores this setting. Set to None for autoscaling. Default (100, -100), a 200-unit window written high-to-low so the axis draws reversed.
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_make_motility_plots – reads itRead by _make_intensity_motility_panel, _make_motility_plots.
motility_ylim¶
(tuple) - Spatial y-axis limits for the origin-centred track panels (infected and uninfected) of the motility figure, in plotted coordinate units - um when pixels_per_um is set, otherwise pixels - not velocity. The whole-field all-tracks axis next to them always autoscales from the data and ignores this setting. Set to None for autoscaling. Default (100, -100), a 200-unit window written high-to-low so the axis draws reversed.
_make_intensity_motility_panel – reads it_make_intensity_motility_panel – reads it_make_motility_plots – reads itRead by _make_intensity_motility_panel, _make_motility_plots.
multilabel¶
build_model_from_configuration() – reads itRead by build_model_from_configuration().
multiple_testing_method¶
(str) - Correction applied within each outcome/support family: fdr_bh (Benjamini–Hochberg, default), fdr_by, bonferroni, holm, or none. Stricter family-wise methods generally call fewer guides.
_on_refit – reads it_on_run(...) [UNRESOLVED]_perform_regression_perform_regression – reads it_call_level_hits – reads it_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads itrecord_run() – reads itrefit_settings() – reads itRead by _call_level_hits, _perform_regression, _run_guide_permutation_analysis, _on_refit, _run_settings_row, record_run(), refit_settings().
n_components¶
_fit – reads itdict(...) [UNRESOLVED]_attribute(...) [UNRESOLVED]embed_fn() – reads it_default_umap_embed – reads itdict(...) [UNRESOLVED]UMAP(...) [UNRESOLVED]dict(...) [UNRESOLVED]umap_search() – reads itdict(...) [UNRESOLVED]Read by _default_umap_embed, _fit, embed_fn(), umap_search().
n_epochs¶
(int) - Number of training passes train_seg makes over the annotated image/mask batch. It also sets the checkpoint interval (a model is saved every n_epochs/10) and is written into the saved model filename. Raise it for a better fit on large annotation sets; lower it when a small set starts overfitting. Default 10000.
Read by _describe_cellpose_training, train_cellpose().
n_estimators¶
(int) - Number of trees or boosting rounds in the tabular ML classifier - n_estimators for RandomForest/ExtraTrees/XGBoost/LightGBM, iterations for CatBoost, max_iter for HistGradientBoosting. More rounds keep improving fit up to a plateau while training time grows linearly; boosted models can overfit past it. Default 1000.
generate_ml_scores() – reads itRead by generate_ml_scores().
n_jobs¶
(int) - CPU workers for parallel stages: measurement, mask adjustment, DataLoader loading, and the sklearn/UMAP calls where -1 means every core. Raise it to shorten CPU-bound steps until RAM or disk I/O saturates. Note the measure-and-crop pipeline overrides your value with cpu_count()-4. Defaults vary by pipeline: cpu_count()-4, -1, or None.
_compute_umap – reads itgenerate_image_umap() – reads itpreprocess_generate_masks() – reads itautomated_motility_assay() – reads itgenerate_organelle_masks_sam() – reads itrun_preflight()_describe_workload – reads itisinstance(...) [UNRESOLVED]describe_resources() – reads itisinstance(...) [UNRESOLVED]reducer_hyperparameter_search() – reads it_fit_one – reads it_inner_loader – reads itapply_model_to_tar() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itgenerate_activation_map() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itrun_search_for_app() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]measure_crop() – reads itget_measure_crop_settings() – reads itrun_preflight()_describe_workload – reads itisinstance(...) [UNRESOLVED]describe_resources() – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]generate_ml_scores() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]automated_motility_assay() – reads itapply_worker_budget() – reads it_describe_plan_safe_describe_workload – reads itisinstance(...) [UNRESOLVED]generate_barecode_mapping() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]reduction_and_clustering() – reads itRead by _compute_umap, generate_image_umap(), preprocess_generate_masks(), reducer_hyperparameter_search(), _cross_validate_model, _fit_one, _inner_loader, apply_model_to_tar(), generate_activation_map(), train_test_model(), run_search_for_app(), measure_crop(), generate_ml_scores(), interpret_vision_model(), generate_organelle_masks_sam(), apply_worker_budget(), generate_barecode_mapping(), get_default_generate_activation_map_settings(), get_measure_crop_settings(), get_train_test_model_settings(), interpret_vision_model(), automated_motility_assay(), reduction_and_clustering(), _describe_workload, describe_resources().
n_neighbors¶
(int or float) - Size of the local neighbourhood UMAP balances against global structure, and the perplexity when reduction_method is ‘tsne’. Small values (5-50) sharpen fine local structure; large values give a smoother, more global embedding. A float is read as a fraction of the number of objects, and anything below 2 is clamped to 2. Default 1000.
_compute_umap – reads itgenerate_image_umap() – reads it_fit – reads itdict(...) [UNRESOLVED]_attribute(...) [UNRESOLVED]umap_search() – reads itdict(...) [UNRESOLVED]reduction_and_clustering() – reads itRead by _compute_umap, generate_image_umap(), _fit, umap_search(), reduction_and_clustering().
n_repeats¶
(int) - Number of random shuffles per feature when computing permutation importance for the ML classifier. More repeats reduce uncertainty in the importance ranking but require an additional prediction pass per feature and repeat. Default 10; use 3-5 for a faster preliminary assessment of wide feature tables.
generate_ml_scores() – reads itRead by generate_ml_scores().
n_steps¶
_captum_attribute – reads itRead by _captum_attribute.
n_top_examples¶
(int) - Number of highest-confidence images saved per predicted class after full-dataset inference. This gives a quick visual check of class meaning and common errors. Default 20.
deep_spacr() – reads itRead by deep_spacr().
n_workers¶
How many parallel workers to use. More is faster until the disk becomes the limit; each worker holds its own tiles, so this multiplies memory. Default is the machine’s core count.
apply_worker_budget() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by apply_worker_budget(), get_preprocess_ops_settings(), stitch_cycle_wells().
n_workers_features¶
Workers for feature detection specifically. Empty follows n_workers. Lower it when feature extraction is what is exhausting memory. Default None.
apply_worker_budget() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by apply_worker_budget(), get_preprocess_ops_settings(), stitch_cycle_wells().
name¶
Read by config_for(), plot_region(), run_and_save().
need_weights¶
_ask_for_attention_weights – reads itoriginal(...) [UNRESOLVED]dict(...) [UNRESOLVED]_wrap – reads itoriginal(...) [UNRESOLVED]dict(...) [UNRESOLVED]forward() – reads itoriginal(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _ask_for_attention_weights, _wrap, forward().
neg¶
(str) - Column ID marking negative-control wells in the image UMAP. Rows whose columnID equals it are labelled cond=’neg’, so exclude_conditions can drop them; and when embedding_by_controls is True the rows whose col_to_compare equals it join pos in fitting the reducer. Default ‘c2’ (note: not ‘c1’).
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itload_search_data() – reads itRead by generate_image_umap(), reducer_hyperparameter_search(), load_search_data().
negative_control_id¶
(str) - Identifier of the negative-control class. In ML screening it is the value in location_column (e.g. ‘c1’) whose objects are labelled class 0 for training; in gRNA regression it is a gene/gRNA ID substring (e.g. ‘233460’) matched against coefficient names to tag them ‘nc’ in the results and volcano plot. Defaults ‘c1’ and ‘233460’ respectively.
load_search_data() – reads itgenerate_ml_scores() – reads it_perform_regression_perform_regression – reads it_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by load_search_data(), _perform_regression, _run_guide_permutation_analysis, generate_ml_scores().
negative_control_wells¶
(list or str) - Wells containing only the negative control, e.g. [‘c1’]. Accepts the same row, column, and well notation as positive_control_wells. These wells are excluded from the regression and define the negative endpoint for mixed-ratio calibration. Default None.
_perform_regression_perform_regressioncontrol_block_wells() – reads itcontrol_block_wells() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by control_block_wells().
negative_mean¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]validate_and_adjust_beta_params() – reads itRead by generate_parameters(), run_simulation(), validate_and_adjust_beta_params().
negative_variance¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]validate_and_adjust_beta_params() – reads itRead by generate_parameters(), run_simulation(), validate_and_adjust_beta_params().
neighbour_radius¶
Read by align_folder(), _work, build_plan().
nested_cv_inner_folds¶
(int) - Number of inner grouped folds used inside every outer CV fold. 0 (default) keeps the faster ordinary grouped CV; 2 or more trains one inner model per fold, uses inner validation for early stopping/model selection, ensembles those models, and evaluates only once on the untouched outer fold. Runtime is approximately outer_folds x inner_folds training runs, but the outer score is not reused for tuning. API: spacr.classifier_evaluation.nested_group_folds.
_cross_validate_model – reads it_default_train_cross_validate_model – reads it_cross_validate_model – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model.
nfeatures¶
How many features to look for per tile. More finds overlap in sparser fields and costs time and memory; below about 2000 a sparse field stops matching at all. Default 8000.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
non_power_of_two_warn¶
(float) - Fraction of a well’s vacuoles allowed in the non-power-of-two bucket before the well is flagged as unreliable. Three-, five-, or seven-parasite rosettes usually indicate segmentation or vacuole-linking errors, so lowering the threshold makes QC stricter without deleting any observations. Default 0.2.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_replication_defaults, analyze_replication(), _check_app_specific.
nontargeting_control_grnas¶
(list) - Non-targeting control gRNA identifiers. Their coefficients set the volcano effect-size cutoff: abs(median(control coefficients)) + threshold_multiplier × spread, with threshold_method selecting the spread estimator. A wider control distribution raises the cutoff; None disables it. Default [‘000000’] names the non-cutting control gene, which spaCR resolves to all associated guides in the loaded library. Individual guide identifiers also work, with or without the organism prefix.
_perform_regression_perform_regression – reads it_call_level_hits – reads it_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits, _perform_regression, _run_guide_permutation_analysis.
normalise¶
Read by segment_frame().
normalise_fraction¶
(bool) - Divide a gRNA’s fraction by the sum of the fractions that remain in its well after fraction_threshold, before deciding how many cells it is given. On, a gRNA’s share is measured against what survived the threshold; off, it is measured against every read the well produced, including those the threshold removed. The two differ whenever the threshold removes anything: normalising raises every surviving share, and by more the more was removed. Default True.
_perform_regression_perform_regression_calibrated_fraction_threshold_calibration_inputs – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _calibration_inputs.
normalization_percentiles¶
(list) - Two-element [low, high] percentile pair used to stretch each channel’s non-zero pixels to the full display range in plot_merged; applied only when normalize is True. Narrowing the pair (e.g. [5, 95]) boosts contrast but saturates bright objects; widening it flattens the image. Default [2, 98].
plot_merged() – reads itdisplay(...) [UNRESOLVED]Read by plot_merged().
normalize¶
(bool or list) - Control percentile normalization before display, model input, or crop export. Display and activation-map tools use True for a 2nd-to-98th-percentile stretch. Measure and External Masks start at False; Measure accepts False or a two-number [low, high] percentile pair and refuses bare True because it supplies no bounds. It affects display and exported-crop scaling, not measured source intensities. Default True in the display-oriented tools.
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]preprocess_generate_masks() – reads itpreprocess_img_data() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]crop_spec_from_settings() – reads it_fit_one – reads itapply_model_to_tar() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]generate_activation_map() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]_measure_crop_core – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]plot_merged() – reads itdisplay(...) [UNRESOLVED]_crop_choice_warnings – reads itapply_settings() – reads it_seed_organelle_column(...) [UNRESOLVED]apply_settings() – reads itcheck_cellpose_models() – reads it__init__ – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itget_default_apply_cellpose_model_settings() – reads itget_default_test_cellpose_model_settings() – reads itRead by preprocess_generate_masks(), crop_spec_from_settings(), _cross_validate_model, _fit_one, apply_model_to_tar(), generate_activation_map(), train_test_model(), _crop_shape_overrides, preprocess_img_data(), _measure_crop_core, measure_crop(), plot_merged(), _crop_choice_warnings, apply_settings(), apply_settings(), get_analyze_plaque_settings(), get_default_apply_cellpose_model_settings(), get_default_generate_activation_map_settings(), get_default_test_cellpose_model_settings(), get_measure_crop_settings(), get_train_test_model_settings(), check_cellpose_models(), identify_masks_finetune(), __init__, _check_app_specific.
normalize_by¶
(str) - Percentile source used to rescale cropped PNGs, and only active when ‘normalize’ is a [low, high] percentile pair: ‘png’ stretches each crop to its own percentiles, maximising per-object contrast; ‘fov’ uses percentiles from the whole field, keeping brightness comparable between objects. Choose ‘fov’ if crop intensities will be compared. Default ‘png’.
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]crop_spec_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by crop_spec_from_settings(), _crop_shape_overrides, _measure_crop_core, measure_crop(), get_measure_crop_settings(), _check_app_specific.
normalize_channels¶
(bool/list) - Percentile-stretch each channel before drawing, so a dim stain is visible beside a bright one. Uses ‘percentiles’. This changes only the displayed image; the stored crop and all measurements remain unchanged. Default None (off).
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
normalize_input¶
(bool) - Apply the per-channel mean 0.5 and standard deviation 0.5 used during training before generating activation maps. Match this setting to model training; otherwise inputs are out of distribution and the resulting classes and maps are invalid. This is distinct from normalize, which percentile-stretches images for display. Default True.
generate_activation_map() – reads itload_activation_data() – reads itRead by generate_activation_map(), load_activation_data(), get_default_generate_activation_map_settings().
nr¶
(int) - Number of example fields shown in a preprocessing diagnostic figure. It does not affect arrays written to disk; increasing it only adds plotting time. Use one field on large plates when a preliminary validation view is sufficient. Default 1.
preprocess_img_data() – reads it_concatenate_and_normalize_impl – reads itplot_merged() – reads itdisplay(...) [UNRESOLVED]Read by _concatenate_and_normalize_impl, preprocess_img_data(), plot_merged().
nr_classes¶
generate_training_dataset() – reads itRead by generate_training_dataset().
nr_plates¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
nt_samples¶
_captum_attribute – reads itRead by _captum_attribute.
nt_type¶
_captum_attribute – reads itRead by _captum_attribute.
nuclei_limit¶
(int, bool, or None) - Cap on nuclei per cell, applied when the per-object tables are merged. None disables the filter, True keeps only single-nucleus cells, and an integer N keeps cells with N or fewer. Cells over the cap are dropped from the merged table entirely. Do not pass False: it is interpreted as 0 and removes every cell, leaving an empty analysis rather than raising an error. Default None. Merged Classifier starts at True and Recruitment starts at 1, so both initially retain only single-nucleus cells.
generate_screen_graphs() – reads itgenerate_training_dataset() – reads itgenerate_ml_scores() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itplot_data_from_db() – reads itplot_merged() – reads itdisplay(...) [UNRESOLVED]analyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_recruitment() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itRead by generate_screen_graphs(), generate_training_dataset(), generate_ml_scores(), interpret_vision_model(), read_and_preprocess_data(), plot_data_from_db(), plot_merged(), analyze_class_proportion(), analyze_endodyogeny(), analyze_recruitment(), interpret_vision_model(), read_and_preprocess_data().
nucleus_background¶
(int) - Raw intensity value treated as background in the nucleus channel. When remove_background_nucleus is True, every pixel below it is zeroed before normalization; it is also multiplied by nucleus_signal_to_noise to set the upper-clip target. Raise it for images with high offset or autofluorescence, lower it if dim nuclei disappear. Default 100.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
nucleus_cellprob_threshold¶
(float) - Cellpose cell-probability threshold for the nucleus channel, passed straight to model.eval as cellprob_threshold. A pixel must exceed it to join a mask, so raising it shrinks masks and drops dim nuclei, while lowering it grows masks and recovers faint ones along with more debris. Useful range about -6 to 6; default 0.
generate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads itgenerate_cellpose_masks() – reads itRead by _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
nucleus_chann_dim¶
(int) - Recruitment analysis only (analyze_recruitment): the image-channel index paired with the nucleus mask when drawing outline overlays, and the switch that enables nucleus_size_range / nucleus_intensity_range filtering. Set it to None to skip nucleus filtering. It plays no part in segmentation - use nucleus_channel for that. Default 0.
analyze_recruitment() – reads itRead by analyze_recruitment().
nucleus_channel¶
(int or None) - Zero-indexed raw acquisition channel segmented into nucleus masks, and the channel that nucleus_background, nucleus_signal_to_noise and remove_background_nucleus apply to. None means no nucleus masks, hence no nucleus table, no cell-to-nucleus linking, and nothing subtracted from the cytoplasm mask. Set it whenever a DNA stain was acquired. Default None.
preprocess_generate_masks() – reads it_concatenate_and_normalize_impl_normalize_img_batch – reads itgenerate_cellpose_masks_sam() – reads itautomated_motility_assay() – reads itdense_mask_channel_positions() – reads it_segment_cellpose_sam – reads itdense_mask_channel_positions() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_get_lists_for_normalization – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_segment_cellpose – reads itgenerate_cellpose_masks() – reads itautomated_motility_assay() – reads itdense_mask_channel_positions() – reads itplot_region() – reads it_channelschannels_from_settings() – reads itapply_settings() – reads it_seed_organelle_column(...) [UNRESOLVED]apply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by preprocess_generate_masks(), channels_from_settings(), _get_lists_for_normalization, _normalize_img_batch, _segment_cellpose, _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam(), plot_region(), apply_settings(), apply_settings(), organelle_measurement_caveats(), automated_motility_assay(), dense_mask_channel_positions(), _describe_objects.
nucleus_diameter¶
(int or None) - Expected nucleus diameter in pixels, used by Cellpose 4 to rescale the image by 30/diameter before segmentation. None segments at native scale. Because nuclei are commonly the smallest segmented objects, this parameter often requires explicit configuration for low-magnification acquisitions. spacr.diameter.estimate_diameters estimates a value. Default None.
generate_cellpose_masks_sam() – reads it_get_object_settings – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_get_object_settings – reads itorganelle_morphology_now() – reads itapply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by generate_cellpose_masks_sam(), organelle_morphology_now(), apply_settings(), _get_object_settings, _describe_objects.
nucleus_flow_threshold¶
(float) - Cellpose flow_threshold for nucleus masks: the maximum allowed error between a mask’s recomputed flows and the network’s predicted flows. Lowering it discards more irregularly shaped nuclei, giving fewer but cleaner objects; raising it keeps nearly everything Cellpose proposes. Typical range 0 to 3; spaCR default 100, which keeps everything Cellpose proposes.
generate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads itgenerate_cellpose_masks() – reads itRead by _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
nucleus_intensity_merge¶
(bool) - Merge two touching nucleus labels when the mean intensity along their shared boundary reaches nucleus_intensity_threshold, which means there is no detectable dark seam between them and the split is spurious. Requires an intensity image. Set the threshold first: with none set the merge refuses and reports the boundary intensities it found. Default False.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
nucleus_intensity_range¶
(list) - Two-element [min, max] bound on mean nucleus-channel intensity used by the recruitment analysis to drop rows from the measurement table - it filters measured objects, not masks or normalization. Rows are kept only if min < mean intensity < max (raw units), and each bound is ignored unless it is an int. Default [0, 100000].
analyze_recruitment() – reads itRead by analyze_recruitment().
nucleus_intensity_split¶
(bool) - Split oversized nucleus labels by distance-transform watershed, before the merge and filter steps. Objects larger than nucleus_minimum_area_to_split are seeded at local distance maxima nucleus_min_watershed_distance apart and cut. Despite the name no intensity is used. Enable when several touching nucleuss share one label. Default False.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
nucleus_intensity_threshold¶
(float or None) - Absolute intensity, in the image’s own raw units, that the mean along a shared boundary must reach before nucleus_intensity_merge joins two touching nucleus labels. Read it off the image with a pixel inspector rather than guessing. It does NOT carry between acquisitions taken at different exposure or gain, so re-read it for a new imaging session. Leave unset and the merge refuses and reports the boundary intensities it found, which is the number to type. Ignored unless nucleus_intensity_merge is True. Default None.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
nucleus_mask_dim¶
(int) - Position along the last axis of each merged/*.npy array where the nucleus label mask sits, one plane after the cell mask. With the default four image channels (0-3) that is 5; keep a different number of channels and it shifts by the same amount. None makes measure_crop skip nucleus measurements and cell-to-nucleus linking. Default 5.
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]mask_dims_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads it_intensity_measurements – reads it_morphological_measurements – reads itmeasure_crop() – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]plot_merged() – reads itdisplay(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by mask_dims_from_settings(), _crop_shape_overrides, _intensity_measurements, _measure_crop_core, _morphological_measurements, measure_crop(), plot_merged(), get_measure_crop_settings(), organelle_measurement_caveats(), _describe_objects.
nucleus_max_area¶
(int or None) - Maximum nucleus area in pixels^2; after segmentation, labels larger than this are deleted and the remaining nuclei are renumbered. Use it to remove unsplit clumps of touching nuclei or large segmentation artifacts covering a substantial fraction of the field. 0 (the default) or None disables the filter.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
nucleus_max_size¶
(int | None) - Drop nuclei larger than this many pixels, counted the same way nucleus_min_size counts them. None – the default – disables it and preserves prior behavior. A minimum removes debris; only a maximum removes nuclei merged into a single mask, which pass every minimum and then bias downstream area and DNA-content measurements. The run prints how many objects each bound dropped.
filter_selection() – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by filter_selection(), _measure_crop_core, get_measure_crop_settings().
nucleus_method¶
run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by _describe_objects.
nucleus_min_area¶
(int) - Minimum nucleus area in pixels^2, applied twice: passed to Cellpose as min_size so small masks are never emitted, then re-applied to the label image so any surviving object below it is deleted and the rest renumbered. Raise it to drop debris and fragments. 0 (default) disables both filters.
merge_split_filter_masks() – reads it_get_object_settings – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_get_object_settings – reads itRead by _measure_crop_core, merge_split_filter_masks(), _get_object_settings, get_measure_crop_settings().
nucleus_min_size¶
(int) - (Deprecated) Minimum nucleus size in pixels^2 applied during measure_crop: labels covering fewer pixels than this are erased from the nucleus mask before any feature is measured, so those nuclei never reach the database. 0 (default) disables it. Prefer nucleus_min_area, which filters at segmentation time.
filter_selection() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_measure_crop_core – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by filter_selection(), _measure_crop_core, measure_crop(), get_measure_crop_settings(), _describe_objects.
nucleus_min_watershed_distance¶
(int) - Minimum separation in pixels between watershed seeds when dividing oversized nucleus labels; seeds are local maxima of the object’s distance transform. Set it near the radius of one nucleus: values that are too small over-segment nuclei, while values that are too large yield a single seed and prevent splitting. Default 10; used only when nucleus_intensity_split is True.
merge_split_filter_masks() – reads it_run_one – reads itRead by _run_one, merge_split_filter_masks().
nucleus_minimum_area_to_split¶
(int) - Absolute pixel area above which a nucleus label is split by nucleus_intensity_split; objects at or below it are never cut. ABSOLUTE, not a multiple of the median: the threshold no longer moves with whatever else is in the field, so the same object is treated the same way in a sparse dish and a crowded one. Raise it to protect small nucleuss from fragmentation. Default 100.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
nucleus_model_name¶
(str) - Weights used to segment nuclei. Valid values are ‘cpsam’ or a path to a custom CPSAM checkpoint produced by Train Cellpose. The legacy values ‘nuclei’ and ‘nucleus’ are accepted for compatibility and mapped to ‘cpsam’ because Cellpose 4 removed the pre-SAM models. Configure nucleus_diameter to control scale; of the three parameters that previously distinguished models, only diameter remains operational (eval rescales by 30/diameter), while model_type and diam_mean are logged as ‘not used in v4.0.1+’ and omitted. Default ‘cpsam’.
_get_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by _get_object_settings, _get_object_settings.
nucleus_morphology¶
Read by organelle_morphology_now().
nucleus_outlier_mads¶
_perform_regression_perform_regressionapply() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by apply().
nucleus_perimeter_fraction¶
(float) - Merge two touching nucleus labels when their shared boundary covers at least this fraction of the smaller object’s perimeter. Low non-zero values merge aggressively (0.1 joins barely-touching nuclei); high values only fuse objects sharing most of an edge. Range 0-1; 0 (default) disables perimeter merging. Use it when one nucleus is split into fragments.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
nucleus_perimiter_fraction¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
nucleus_remove_border_objects¶
(bool) - After segmentation, delete every nucleus label touching any of the four image edges, then renumber the rest. Enable it when measuring nucleus area or total intensity, since clipped nuclei bias those downward; leave it off for counts or positions, as it discards real objects at every field boundary. Default False.
merge_split_filter_masks() – reads it_apply_size_filter – reads itRead by merge_split_filter_masks(), _apply_size_filter.
nucleus_resample¶
_segment_cellpose_sam – reads itRead by _segment_cellpose_sam.
nucleus_restore_type¶
_get_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by _get_object_settings, _get_object_settings.
nucleus_signal_to_noise¶
(float) - Multiplied by nucleus_background to define the intensity a bright pixel must reach before normalisation stops increasing the upper clip point. spaCR evaluates the 98th through 99.5th percentiles of the non-zero nucleus channel and uses the first that reaches the threshold, with the 99.5th percentile as the fallback. A higher value raises the clip point, reduces contrast stretching and protects bright nuclei from saturation; a lower value increases contrast for dim nuclei but saturates bright nuclei sooner. Default 10.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
nucleus_size_range¶
(list) - Two-element [min, max] bound in pixels^2 on nucleus_area, used by the recruitment analysis to drop rows from the measurement table; masks are left untouched. Rows are kept only if min < area < max, and each bound is ignored unless it is an int. Default [0, 100000]; None widens it to [0, 1e100].
analyze_recruitment() – reads itRead by analyze_recruitment().
nucleus_type¶
_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]organelle_morphology_now() – reads itRead by organelle_morphology_now(), get_measure_crop_settings(), organelle_measurement_caveats().
nucleuscellpose¶
Read by preprocess_img_data().
nucleuscellpose__channel¶
generate_cellpose_masks_sam() – reads it_get_cellpose_channels – reads itgenerate_cellpose_masks() – reads it_get_cellpose_channels – reads itorganelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by generate_cellpose_masks(), generate_cellpose_masks_sam(), organelle_settings_view(), _get_cellpose_channels.
nucleusorganelle¶
organelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by organelle_settings_view().
num_classes¶
build_model_from_configuration() – reads itRead by build_model_from_configuration().
number_of_active_genes¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
number_of_control_genes¶
run_simulation() – reads itRead by run_simulation().
number_of_genes¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
number_of_organelles¶
(int) - How many organelle slots this run has, from 0 to 26. Each slot is an independent object with its own channel, its own type preset and its own copy of every detection setting, named organelle_*, organelleb_*, organellec_* and so on; raising the number generates another slot’s settings and lowering it hides the slots above the new number without deleting them. A hidden slot keeps its values, is still written to the settings file, and comes back exactly as it was when the number is raised again, so a smaller number can be tried without losing work. Default 0.
_set_organelle_defaultsorganelle_count() – reads it_set_organelle_defaultsorganelle_count() – reads itorganelle_count() – reads it_set_organelle_defaultsorganelle_count() – reads itorganelle_count() – reads it_clone_primary_organelle_valuesorganelle_count() – reads it_set_organelle_defaultsorganelle_count() – reads itorganelle_count() – reads it_clone_primary_organelle_valuesorganelle_count() – reads it_set_organelle_defaultsorganelle_count() – reads itorganelle_count() – reads it_record_organelle_caveatsorganelle_count() – reads itorganelle_count() – reads itorganelle_count() – reads itorganelle_count() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_clone_primary_organelle_valuesorganelle_count() – reads it_set_organelle_defaultsorganelle_count() – reads itorganelle_count() – reads itorganelle_count() – reads it_bulk_apply_changes_form_shapeorganelle_count() – reads itupdate(...) [UNRESOLVED]_infer_legacy_organelle_countorganelle_count() – reads itdict(...) [UNRESOLVED]_hide_the_headings_of_slots_the_run_lacksorganelle_count() – reads it_organelle_slots_worth_keepingorganelle_count() – reads itorganelle_count() – reads it_classical_organelle_mask_set_organelle_defaultsorganelle_count() – reads itorganelle_count() – reads itdict(...) [UNRESOLVED]apply_settings()organelle_count() – reads it_seed_organelle_column(...) [UNRESOLVED]apply_settings() – reads it_clone_primary_organelle_valuesorganelle_count() – reads itRead by organelle_count(), apply_settings().
object¶
annotate_crops() – reads itRead by annotate_crops().
object_array¶
(str) - On-demand crops: which object the crops are cut around - ‘cell’, ‘nucleus’, ‘pathogen’, ‘cytoplasm’ or ‘organelle’. Its mask plane in merged/*.npy is what defines each object’s extent. Default ‘cell’.
generate_annotation_dataset() – reads itfilter_selection() – reads itcrops_from_merged() – reads itvalidate() – reads itstream_dataset() – reads itRead by filter_selection(), generate_annotation_dataset(), crops_from_merged(), validate(), stream_dataset().
object_distance_intensity¶
(bool) - Include the families that need the intensity images: the local maxima above and the scalar displacement between each channel’s intensity-weighted centre of mass and the geometric centroid. False measures geometry only. Ignored when object_distances is off. Default True.
_measure_crop_core_morphological_measurements – reads it_with_distances – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _morphological_measurements, _with_distances, get_measure_crop_settings().
object_distance_maxima¶
(bool) - Also find the intensity maxima inside each object and measure where they are: how many, how spread out, and how far each is from the object’s own boundary, its centre, and the nearest surface of every other object type. The most expensive part of object_distances, and ignored when that is off. Default True.
_measure_crop_core_morphological_measurements – reads it_with_distances – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _morphological_measurements, _with_distances, get_measure_crop_settings().
object_distances¶
(bool) - Measure every distance between and within objects: centre to centre, centre to the nearest surface of each other object type (both directions), surface to surface – which is zero when two objects touch and is what ‘how far apart are they’ means – the overlap fraction, how far the centre sits from its own boundary, and how close the object is to the edge of the field. On by default: the calculation is computationally expensive on a 3-D field, but measuring again afterwards costs more. The cost is one distance transform per object type per field, not one per pair of objects.
_measure_crop_core_morphological_measurements – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _morphological_measurements, get_measure_crop_settings(), organelle_measurement_caveats().
object_size¶
(int/list) - The smallest object, in pixels, that is outlined at all. Debris below it is skipped rather than traced. Default (0, 0) — no minimum.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
object_type¶
(str) - Mask used to define an object when the pointing game scores an attribution map: ‘cell’, ‘nucleus’, ‘pathogen’ or ‘cytoplasm’. The metric checks only whether the map’s maximum-valued pixel lies inside that mask. It has low computational cost but does not evaluate the rest of the map, so a method can score 1.0 while assigning spurious attribution elsewhere. Default ‘cell’.
Read by extract_crop(), load_activation_data(), get_default_generate_activation_map_settings().
occlusion_stride¶
(int) - How far the occlusion patch moves between evaluations. Equal to occlusion_window it tiles without overlap and is fastest; half of it doubles the passes and halves the blockiness. A stride larger than the window leaves unmeasured gaps that appear as an artificial grid in the map. Default 4.
occlusion_window¶
(int) - Side length in pixels of the patch moved across the image during occlusion analysis. Larger windows reduce runtime but spatial resolution and can miss features smaller than the window; smaller windows resolve finer structure with quadratically more forward passes. Occlusion provides a gradient-independent comparison for gradient-based attribution methods. Default 8.
offset_start¶
(int) - Bases to shift from the start of the target_sequence match to the start of the extracted window; negative values move upstream to capture a barcode preceding the anchor. The start is clamped at position 0, so an over-negative value silently shifts the reading frame and the regex stops matching. Default -8.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
on_conflict¶
import_project() – reads itRead by import_project().
on_error¶
What a failed unit of work does to the run, checked at every batch boundary (field, well, plate). ‘stop’ aborts on the first failure - the default, because a run that quietly drops a third of its plates produces a number that looks exactly like a good one. ‘skip’ records the unit, the stage and the reason, then carries on; what was skipped is listed at the end and stamped into the run ledger. ‘retry’ re-attempts the unit on_error_attempts times with a doubling backoff and then behaves like ‘stop’. Default ‘stop’.
_error_policy – reads itlen(...) [UNRESOLVED]_defaults – reads itresolve_error_policy() – reads itRead by _error_policy, _defaults, resolve_error_policy().
on_error_attempts¶
Total attempts per unit when on_error=’retry’, including the first. Bounded on purpose: an unbounded retry against a dead network share is an infinite loop with a progress bar. Default 3.
_error_policy – reads itlen(...) [UNRESOLVED]_defaults – reads itresolve_error_policy() – reads itRead by _error_policy, _defaults, resolve_error_policy().
on_error_backoff¶
Seconds to wait before the second attempt when on_error=’retry’, doubled before each attempt after that and capped at 60s. Default 1.0.
_defaults – reads itresolve_error_policy() – reads itRead by _defaults, resolve_error_policy().
on_missing¶
What to do when a file named in the plan is not there: ‘error’ stops the run, ‘skip’ carries on without it and leaves a hole in the mosaic. Default ‘error’.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
opencv_threads¶
Threads OpenCV may use INSIDE each worker. Leave at 1 when running many workers: the two multiply, and oversubscribing a machine makes it slower rather than faster. Default 1.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
ops_gpu¶
Let this run use the graphics card where spaCR finds a usable one: the tile registration’s FFTs and the Cellpose outlines both have a GPU path, and both fall back to the CPU on their own if the card refuses. Turn it OFF when the card is busy with another job – a shared GPU is the common case, and an out-of-memory in the middle of a plate costs more than the time the GPU saves. Default True.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
optimizer_type¶
(str) - PyTorch optimizer used by deep_spacr.train_model: ‘adamw’, ‘adam’, ‘adamax’, ‘sgd’, ‘rmsprop’, ‘nadam’, ‘radam’, ‘adagrad’, ‘adadelta’ or ‘asgd’. AdamW is the robust fine-tuning default; SGD can generalise better but usually needs more epochs. amsgrad applies only to Adam/AdamW. API: spacr.deep_spacr.train_model(optimizer_type=…). Default ‘adamw’.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
order¶
Read by align_folder(), _v3_codec_chain, _work, build_plan().
organelle_adaptive_block_size¶
(int) - Side length in pixels of the local neighbourhood used to compute the adaptive threshold; must be odd. Small blocks track fine illumination changes but can carve holes out of large organelles; large blocks behave more like a global threshold. A few times the object diameter is a sensible starting point. Default 51.
_segment_single_image_segment_irregular – reads it_segment_network – reads it_network_ridge – reads it_segment_ring – reads it_segment_spots – reads itRead by _network_ridge, _segment_irregular, _segment_network, _segment_ring, _segment_spots.
organelle_adaptive_offset¶
(float) - Subtracted from each local mean to form the adaptive threshold, so a pixel is foreground when it exceeds local_mean minus this value. Increasing the offset lowers the threshold and produces more foreground; use a small or negative value for stricter segmentation. The value uses raw image-intensity units, so an offset tuned for 16-bit data can oversegment ridge and ring modes, which threshold a 0-1 response. Default 5.
_segment_single_image_segment_irregular – reads it_segment_network – reads it_network_ridge – reads it_segment_ring – reads it_segment_spots – reads itRead by _network_ridge, _segment_irregular, _segment_network, _segment_ring, _segment_spots.
organelle_background¶
_concatenate_and_normalize_impl_normalize_img_batch – reads itRead by _normalize_img_batch.
organelle_cellprob_threshold¶
(float) - Cellpose cellprob_threshold. Pixels whose predicted probability of belonging to an object fall below it are excluded, so raising it shrinks masks and drops faint organelles, while lowering it grows masks and recovers dim ones along with more false positives. Useful range roughly -6 to 6. Default 0.0.
generate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads it_segment_cellpose – reads itgenerate_cellpose_masks() – reads itRead by _segment_cellpose, _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
organelle_channel¶
(int) - Zero-indexed raw acquisition channel segmented into organelle masks by whichever organelle_method is chosen (otsu, adaptive, log, dog, ridge, hysteresis, cellpose, unet). Setting it to an integer adds an organelle mask plane to merged/ and unlocks the Organelle setting categories in the GUI; None skips organelle segmentation entirely. Default None.
preprocess_generate_masks() – reads it_concatenate_and_normalize_impl_normalize_img_batch – reads itgenerate_cellpose_masks_sam() – reads itgenerate_organelle_masks_sam() – reads it_segment_cellpose_sam – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_record_organelle_caveatsorganelle_measurement_caveats() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_segment_cellpose – reads itgenerate_cellpose_masks() – reads it_channelschannels_from_settings() – reads itapply_settings() – reads it_seed_organelle_column(...) [UNRESOLVED]apply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by preprocess_generate_masks(), channels_from_settings(), _normalize_img_batch, _segment_cellpose, _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam(), generate_organelle_masks_sam(), apply_settings(), apply_settings(), organelle_measurement_caveats(), _describe_objects.
organelle_clahe¶
(bool) - Rescale each image to 0-1 on its 0.5/99.5 percentiles, then run contrast-limited adaptive histogram equalisation before segmentation. Pulls dim organelles in dark corners up to the same working contrast as bright ones, at the cost of amplifying background noise and destroying absolute intensity comparability between fields. Default False.
_preprocess_batch – reads itRead by _preprocess_batch.
organelle_clahe_clip_limit¶
(float) - Contrast ceiling for CLAHE, range 0-1: each tile’s histogram is clipped at this height before equalisation, so higher values permit stronger local stretching and more noise amplification. 0.01 is gentle, 0.03-0.05 is aggressive. Only read when organelle_clahe is True. Default 0.01.
_preprocess_batch – reads itRead by _preprocess_batch.
organelle_diameter¶
(float) - Deprecated. Expected organelle diameter in pixels. The Cellpose-SAM path used for organelles calls model.eval with diameter=None, and no classical method sizes its kernels from it, so changing this value has no effect on organelle masks. Bound object size with organelle_min_area / organelle_max_area instead. Default 30.
generate_cellpose_masks_sam() – reads it_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_build_object_settings – reads it_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_morphology_of_organelle_type – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_segment_cellpose – reads it_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]organelle_morphology_now() – reads it_classical_organelle_mask_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]dict(...) [UNRESOLVED]apply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by _morphology_of_organelle_type, _build_object_settings, _segment_cellpose, generate_cellpose_masks_sam(), apply_preset(), organelle_morphology_now(), apply_settings(), _describe_objects.
organelle_dog_sigma_high¶
(float) - Largest Gaussian scale searched by Difference-of-Gaussians blob detection, in pixels. Scales are stepped up from the low sigma by a factor of 1.6 until this bound, so widening the gap costs more passes but covers a wider range of spot sizes. Raise it to catch larger spots. Default 3.0; must exceed organelle_dog_sigma_low.
_organelle_diagnostic – reads it_segment_single_image_segment_spots_spots_dog – reads itRead by _spots_dog, _organelle_diagnostic.
organelle_dog_sigma_low¶
(float) - Smallest Gaussian scale searched by Difference-of-Gaussians blob detection, in pixels; it sets the lower bound on detectable spot size (radius about sigma times sqrt(2)). Raise it to suppress fine noise, lower it to catch the smallest puncta. Default 1.0. The detection cutoff itself comes from organelle_log_threshold, not from a dog-specific key.
_organelle_diagnostic – reads it_segment_single_image_segment_spots_spots_dog – reads itRead by _spots_dog, _organelle_diagnostic.
organelle_fill_holes¶
(int) - Fill interior holes up to this area in square pixels after thresholding, preventing a darker centre from creating a ring-shaped segmentation artifact. This is applied only in irregular mode. Increase it when large organelles are incorrectly hollow; use a low value or 0 when a hollow centre is biologically expected. Default 64.
_segment_single_image_segment_irregular – reads itRead by _segment_irregular.
organelle_flow_threshold¶
(float) - Cellpose flow_threshold: maximum error allowed between a candidate mask’s flows and the network prediction. Lower values discard more irregular masks; higher values retain more irregular objects. Increase it when valid non-round organelles are being discarded. Default 0.4.
generate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads it_segment_cellpose – reads itgenerate_cellpose_masks() – reads itRead by _segment_cellpose, _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
organelle_hysteresis_high¶
(float) - Strong threshold that seeds hysteresis segmentation - only components containing a pixel above it survive at all, then they grow outward down to organelle_hysteresis_low. Values below 1.0 are read as a percentile of the smoothed image (0.6 = 60th percentile); 1.0 or above is absolute. Raise it to keep only confidently bright filaments. Default 0.6.
_organelle_diagnostic – reads it_segment_single_image_segment_network_network_hysteresis – reads itRead by _network_hysteresis, _organelle_diagnostic.
organelle_hysteresis_low¶
(float) - Weak threshold for hysteresis segmentation: pixels above it are kept only where they connect to a seed above organelle_hysteresis_high. Values below 1.0 are read as a fraction and converted to that percentile of the smoothed image (0.2 = 20th percentile); 1.0 or above is an absolute intensity. Lower it to trace filaments further into their dim tails. Default 0.2.
_organelle_diagnostic – reads it_segment_single_image_segment_network_network_hysteresis – reads itRead by _network_hysteresis, _organelle_diagnostic.
organelle_intensity_merge¶
(bool) - Merge two touching organelle labels when the mean intensity along their shared boundary reaches organelle_intensity_threshold, which means there is no detectable dark seam between them and the split is spurious. Requires an intensity image. Set the threshold first: with none set the merge refuses and reports the boundary intensities it found. Default False.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
organelle_intensity_split¶
(bool) - Split oversized organelle labels by distance-transform watershed, before the merge and filter steps. Objects larger than organelle_minimum_area_to_split are seeded at local distance maxima organelle_min_watershed_distance apart and cut. Despite the name no intensity is used. Enable when several touching organelles share one label. Default False.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
organelle_intensity_threshold¶
(float or None) - Absolute intensity, in the image’s own raw units, that the mean along a shared boundary must reach before organelle_intensity_merge joins two touching organelle labels. Read it off the image with a pixel inspector rather than guessing. It does NOT carry between acquisitions taken at different exposure or gain, so re-read it for a new imaging session. Leave unset and the merge refuses and reports the boundary intensities it found, which is the number to type. Ignored unless organelle_intensity_merge is True. Default None.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
organelle_log_max_sigma¶
(float) - Largest Gaussian scale searched by LoG blob detection, in pixels; blob radius is about sigma times sqrt(2), so 10 caps detection near a 14 px radius. Raise it to catch large puncta, at a runtime cost since the filter is evaluated once per scale. Default 10; must exceed organelle_log_min_sigma.
_organelle_diagnostic – reads it_segment_single_image_segment_ring – reads it_segment_spots_spots_log – reads itRead by _segment_ring, _spots_log, _organelle_diagnostic.
organelle_log_min_sigma¶
(float) - Smallest Gaussian scale searched by LoG blob detection, in pixels; the detected blob radius is about sigma times sqrt(2), so sigma 1 finds roughly 1.4 px radius puncta. Raise it to ignore single-pixel noise, lower it to catch the smallest spots. Default 1; must stay below organelle_log_max_sigma.
_organelle_diagnostic – reads it_segment_single_image_segment_ring – reads it_segment_spots_spots_log – reads itRead by _segment_ring, _spots_log, _organelle_diagnostic.
organelle_log_num_sigma¶
(int) - How many Gaussian scales are evaluated between organelle_log_min_sigma and organelle_log_max_sigma. More scales resolve a wider spread of spot sizes, but the filter runs once per scale so runtime grows linearly. Default 10; drop to 3-5 when spot size is uniform and you need speed.
_organelle_diagnostic – reads it_segment_single_image_segment_ring – reads it_segment_spots_spots_log – reads itRead by _segment_ring, _spots_log, _organelle_diagnostic.
organelle_log_threshold¶
(float) - Minimum LoG/DoG response a local maximum must reach to count as a blob, measured after the image is percentile-normalised to 0-1, so it behaves like a contrast fraction. Decrease it to detect fainter puncta at the cost of additional noise; increase it to retain only brighter puncta. Default 0.01. The ‘dog’ method also reads this key.
_organelle_diagnostic – reads it_segment_single_image_segment_ring – reads it_segment_spots_spots_dog – reads it_spots_log – reads itRead by _segment_ring, _spots_dog, _spots_log, _organelle_diagnostic.
organelle_mask_dim¶
(int) - Position along the last axis of each merged/*.npy array where the organelle label mask sits. Masks follow the image channels in the order cell, nucleus, pathogen, organelle, so with four channels and all three other masks present it is 7. Leave it unset/None and organelles are not measured at all. No default is applied.
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]mask_dims_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads it_morphological_measurements – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by mask_dims_from_settings(), _crop_shape_overrides, _measure_crop_core, _morphological_measurements, get_measure_crop_settings(), organelle_measurement_caveats(), _describe_objects.
organelle_mask_within_cells¶
(bool) - Zero every pixel outside the cell mask before segmenting, so organelles can only be found inside cells and extracellular debris cannot generate objects. Needs cell_mask_stack/ to already exist alongside the organelle source; if it is missing spacr prints a warning and carries on unmasked rather than failing. Default False.
generate_organelle_masks_sam() – reads itRead by generate_organelle_masks_sam().
organelle_max_area¶
(int or None) - Post-segmentation area ceiling in square pixels; larger objects are deleted rather than split. Use it to reject fused clumps, saturated debris and background merged by Otsu into one component. Values below the largest valid organelle remove biological objects without warning. One filter for both the live preview and the batch run. Default None in Mask, meaning no limit; 0 in Measure and External Masks, which also disables it.
merge_split_filter_masks() – reads itgenerate_organelle_masks_sam() – reads it_build_object_settings – reads itRead by _build_object_settings, generate_organelle_masks_sam(), merge_split_filter_masks().
organelle_max_size¶
filter_selection() – reads itRead by filter_selection().
organelle_method¶
(str) - Segmentation backend, validated against organelle_morphology: ‘otsu’ (one global threshold), ‘adaptive’ (local threshold), ‘log’/’dog’ (blob detection), ‘ridge’ (tubeness filter, network only), ‘hysteresis’ (dual threshold, network only), ‘cellpose’ (pretrained model), ‘unet’ (your own model, network only). Classical methods run on CPU across n_jobs workers; cellpose and unet run on the GPU. Default ‘otsu’.
generate_organelle_masks_sam() – reads itplot_organelle_output() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_segment_single_image – reads itpreset_for() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]get_setting_dependencies() – reads itRead by _segment_single_image, generate_organelle_masks_sam(), preset_for(), plot_organelle_output(), get_setting_dependencies(), _describe_objects.
organelle_min_area¶
(int) - Post-segmentation area floor in square pixels; smaller objects are deleted and the mask relabelled. Raise it to clear noise specks left by thresholding, lower it to keep faint puncta. One filter for both the live preview and the batch run. Default 10 in Mask; Measure and External Masks start at 0 because they consume existing labels rather than segmenting new ones.
merge_split_filter_masks() – reads itgenerate_organelle_masks_sam() – reads it_build_object_settings – reads it_segment_unet – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_segment_single_image_segment_irregular – reads it_segment_network – reads it_network_hysteresis – reads it_network_ridge – reads it_segment_ring – reads it_segment_spots – reads itRead by _measure_crop_core, _build_object_settings, _network_hysteresis, _network_ridge, _segment_irregular, _segment_network, _segment_ring, _segment_spots, _segment_unet, generate_organelle_masks_sam(), merge_split_filter_masks(), get_measure_crop_settings().
organelle_min_size¶
filter_selection() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_measure_crop_core – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by filter_selection(), _measure_crop_core, _describe_objects.
organelle_min_watershed_distance¶
(int) - Minimum separation in pixels between watershed seeds when splitting oversized organelles; distance-transform peaks closer than this collapse into a single seed. Increase it to prevent excessive fragmentation of a single organelle, or decrease it to separate tightly packed puncta. Default 10. Currently inactive because the organelle mask writer does not run the merge/split stage.
merge_split_filter_masks() – reads it_run_one – reads itRead by _run_one, merge_split_filter_masks().
organelle_minimum_area_to_split¶
(int) - Absolute pixel area above which a organelle label is split by organelle_intensity_split; objects at or below it are never cut. ABSOLUTE, not a multiple of the median: the threshold no longer moves with whatever else is in the field, so the same object is treated the same way in a sparse dish and a crowded one. Raise it to protect small organelles from fragmentation. Default 100.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
organelle_model_name¶
(str) - Cellpose model used when organelle_method=’cellpose’. Cellpose 4 provides only ‘cpsam’; the pre-SAM names are accepted and mapped to it. Change this only to point at a custom CPSAM-architecture checkpoint. Of the three parameters that used to distinguish models only diameter still acts (eval rescales by 30/diameter); model_type and diam_mean are logged ‘not used in v4.0.1+’ and dropped. Default ‘cpsam’.
_get_object_settings – reads itgenerate_organelle_masks_sam() – reads it_build_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by _build_object_settings, generate_organelle_masks_sam(), _get_object_settings, _get_object_settings.
organelle_morph_radius¶
(int) - Radius in pixels of the disk used for morphological cleanup. In irregular mode it also sets the pre-smoothing sigma (radius/2) and drives a closing then an opening, bridging gaps and erasing protrusions thinner than the disk; network modes use half this radius for closing only. Raise it to smooth ragged outlines, lower it to preserve fine detail. Default 3.
_organelle_diagnostic – reads it_segment_single_image_segment_irregular – reads it_segment_network – reads it_network_hysteresis – reads itRead by _network_hysteresis, _segment_irregular, _segment_network, _organelle_diagnostic.
organelle_morphology¶
(str) - Shape family of the target organelle; selects the segmentation pipeline and restricts valid organelle_method values. ‘spots’ denotes punctate structures such as vesicles and lipid droplets; ‘network’ denotes filamentous structures such as mitochondria and endoplasmic-reticulum tubules; ‘irregular’ denotes solid, irregular structures such as Golgi and lysosomes; and ‘ring’ denotes hollow structures such as endosomes and autophagosomes. An unsupported morphology-method pair raises before image loading. Default ‘spots’.
generate_organelle_masks_sam() – reads itplot_organelle_output() – reads it_segment_single_image – reads itorganelle_morphology_now() – reads itRead by _segment_single_image, generate_organelle_masks_sam(), plot_organelle_output(), organelle_morphology_now().
organelle_network_threshold¶
(str) - How the ridge-filter response is binarised: ‘otsu’ takes one global cut-off from the response histogram, ‘adaptive’ uses a local threshold (organelle_adaptive_block_size / _offset) and keeps faint filaments in dim regions at the cost of extra background. Only read by organelle_method=’ridge’; anything unrecognised falls back to otsu without warning. Default ‘otsu’.
_segment_single_image_segment_network_network_ridge – reads itRead by _network_ridge.
organelle_outlier_mads¶
_perform_regression_perform_regressionapply() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by apply().
organelle_perimeter_fraction¶
(float) - Merge two touching organelle labels when their shared boundary is at least this fraction of the smaller object’s perimeter. Range 0-1; increase it toward 1 to merge only nearly fully fused pairs, or decrease it to merge labels with shorter shared boundaries. Default 0 (disabled). Currently inactive because the organelle mask writer does not run the merge/split stage.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
organelle_perimiter_fraction¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
organelle_remove_border¶
(bool) - Delete organelle labels touching any image edge during final post-processing so partially imaged objects do not bias area and intensity statistics. This also removes valid objects at the field boundary, with a larger effect for larger organelles. Default False.
generate_organelle_masks_sam() – reads it_build_object_settings – reads itRead by _build_object_settings, generate_organelle_masks_sam().
organelle_remove_border_objects¶
(bool) - Delete organelle labels touching any image edge during the shared post-segmentation filter (the Qt live preview path). The batch organelle mask writer does the same job from organelle_remove_border, so set that one for a real run. Default False. Enable to keep clipped rim objects out of area and intensity statistics.
merge_split_filter_masks() – reads it_apply_size_filter – reads itRead by merge_split_filter_masks(), _apply_size_filter.
organelle_resample¶
(bool) - Deprecated. Passed to Cellpose as resample: when True the flows are recomputed at full resolution instead of on the downsampled grid, giving smoother and slightly more accurate outlines for a little extra time. Still forwarded to model.eval on the organelle path. Default True; retain the default unless reduced runtime is required.
_build_object_settings – reads it_segment_cellpose_sam – reads it_segment_cellpose – reads itRead by _build_object_settings, _segment_cellpose, _segment_cellpose_sam.
organelle_ridge_filter¶
(str) - Which vesselness filter enhances filaments before thresholding: ‘frangi’ (classic, crisp on well-separated tubules), ‘sato’ (more tolerant of varying thickness), ‘meijering’ (tuned for thin neurite-like fibres). All run with black_ridges=False, i.e. bright filaments on a dark background. Default ‘frangi’; try ‘sato’ when frangi drops faint filaments.
_organelle_diagnostic – reads it_segment_single_image_segment_network_network_ridge – reads itRead by _network_ridge, _organelle_diagnostic.
organelle_ridge_sigmas¶
(list of float) - Scales in pixels at which the vesselness filter detects tubular structures; each value should approximate the half-width of a filament, and responses are combined across scales. Add larger values to detect thick bundles and retain smaller values for fine tubules. Default [1, 2, 3]; runtime increases approximately in proportion to list length.
_organelle_diagnostic – reads it_segment_single_image_segment_network_network_ridge – reads itRead by _network_ridge, _organelle_diagnostic.
organelle_ring_fill_method¶
(str) - How detected ring walls become solid objects: ‘flood’ fills every background component that does not touch the image border - accurate, but leaks through any gap in the wall - while ‘convex’ takes the convex hull of each wall component, which tolerates broken rings but overshoots concave shapes. Default ‘flood’; switch to ‘convex’ when rings come out unfilled.
_segment_single_image_segment_ring – reads itRead by _segment_ring.
organelle_ring_min_prominence¶
(float) - Shape gate for ring mode: for each filled object spacr computes abs(mean wall intensity minus mean lumen intensity) divided by the object’s mean intensity, and deletes anything below this value. Raise it to keep only clearly hollow objects, lower it to also accept partly filled ones. 0 disables the gate. Default 0.1.
_segment_single_image_segment_ring – reads itRead by _segment_ring.
organelle_ring_sigma_inner¶
(float) - Low sigma of the Difference-of-Gaussians band-pass that highlights ring walls, in pixels; set it near the wall thickness so the wall survives the high-pass. Too small and pixel noise is retained, too large and the wall blurs into the lumen and the ring stops being detected as hollow. Default 1.0; must be below organelle_ring_sigma_outer.
_organelle_diagnostic – reads it_segment_single_image_segment_ring – reads itRead by _segment_ring, _organelle_diagnostic.
organelle_ring_sigma_outer¶
(float) - High sigma of the ring Difference-of-Gaussians band-pass, in pixels; it sets the coarse scale that gets subtracted, so keep it around the ring’s outer radius. Widen the gap from organelle_ring_sigma_inner to enhance larger rings, narrow it for tight vesicles. Default 3.0; must exceed organelle_ring_sigma_inner.
_organelle_diagnostic – reads it_segment_single_image_segment_ring – reads itRead by _segment_ring, _organelle_diagnostic.
organelle_rolling_ball¶
(bool) - Apply rolling-ball background estimation with organelle_rolling_ball_radius, subtract the estimated background, and clip negative values to zero before segmentation. This corrects uneven illumination and haze so that a single global threshold can be applied across the field of view, at an additional computational cost per image. Default False.
_preprocess_batch – reads itRead by _preprocess_batch.
organelle_rolling_ball_radius¶
(int) - Radius in pixels of the rolling-ball background estimator. It must exceed the diameter of the largest expected organelle to avoid subtracting the objects themselves; an excessively large value may not follow the illumination gradient. An initial value of several times the expected object diameter is appropriate. Default 50; runtime increases steeply with radius.
_preprocess_batch – reads itRead by _preprocess_batch.
organelle_signal_to_noise¶
_concatenate_and_normalize_impl_normalize_img_batch – reads itRead by _normalize_img_batch.
organelle_skeletonize¶
(bool) - Reduce each thresholded network to a one-pixel-wide skeleton (dilated by 1 px so it stays connected) and label that instead of the filled filaments. Measured areas then track network length rather than filament thickness. Enable for topology and length analysis, disable to measure filament mass. Default False.
_segment_unet – reads it_segment_single_image_segment_network – reads it_network_hysteresis – reads it_network_ridge – reads itRead by _network_hysteresis, _network_ridge, _segment_network, _segment_unet.
organelle_tophat_radius¶
(int) - Radius in pixels of the disk used for white top-hat filtering before Otsu or adaptive spot thresholding; it removes structures broader than the disk, reducing haze and background. Set it slightly above the largest expected spot: smaller values suppress spots, whereas larger values retain more background. Default 5. Ignored by the LoG and DoG methods.
_organelle_diagnostic – reads it_segment_single_image_segment_spots – reads itRead by _segment_spots, _organelle_diagnostic.
organelle_type¶
(str) - Organelle morphology used to populate recommended detection settings; explicitly configured values are not overwritten. Options are ‘punctate’, ‘vesicular’, ‘spherical’, ‘filamentous’, ‘tubular’, ‘reticular’, ‘cisternal’, ‘toroidal’ and ‘crescent’. Morphology alone does not determine the detector: ‘vesicular’ and ‘spherical’ also use organelle_diameter because a 200 nm vesicle appears punctate whereas a 2 µm vacuole appears annular. Default ‘custom’, which applies no recommendations.
_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]_morphology_of_organelle_type – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]organelle_morphology_now() – reads it_classical_organelle_mask_set_organelle_defaultsapply_preset() – reads itdict(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _morphology_of_organelle_type, apply_preset(), organelle_morphology_now(), get_measure_crop_settings(), organelle_measurement_caveats().
organelle_unet_model_path¶
(str or None) - Path to a serialised PyTorch model used when organelle_method=’unet’. It must be a torch.load-able whole module, not a state_dict, and take z-scored (B,1,H,W) input returning (B,1,H,W) logits; extra output channels are silently ignored except the first. A missing or invalid path raises before segmentation starts. Default None.
_load_unet_model – reads itRead by _load_unet_model.
organelle_unet_threshold¶
(float) - Probability cut-off applied to the U-Net’s sigmoid output, range 0-1. Lower it to grow the predicted network and recover faint branches at the cost of false positives; raise it to keep only confident pixels, which tends to break weak connections. Objects below organelle_min_area are still removed afterwards. Default 0.5.
_segment_unet – reads itRead by _segment_unet.
organelle_watershed_spots¶
(bool) - Split touching spots instead of labelling each connected blob once. Under otsu/adaptive it runs a distance-transform watershed with seeds at least 5 px apart; under log/dog it grows a watershed from each blob centre instead of stamping a disk whose radius comes from that blob’s own sigma (round(sigma*sqrt(2)), minimum 1 px). Turn it off when single spots are being fragmented. Default True.
_segment_single_image_segment_spots – reads itRead by _segment_spots.
organellecellpose¶
Read by preprocess_img_data().
organellecellpose__channel¶
generate_cellpose_masks_sam() – reads it_get_cellpose_channels – reads itgenerate_cellpose_masks() – reads it_get_cellpose_channels – reads itorganelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by generate_cellpose_masks(), generate_cellpose_masks_sam(), organelle_settings_view(), _get_cellpose_channels.
organelleorganelle¶
organelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by organelle_settings_view().
out_png¶
Explicit path for the downsampled preview PNG. Empty writes it beside the mosaic. The preview is for looking at; the TIFF is for measuring. Default None.
get_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings().
out_tif¶
Explicit path for a single-channel output image. Empty writes it beside the well’s other outputs. Default None.
get_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings().
outlier_detection¶
(bool) - After building the regression table, drop gRNAs whose well count falls outside 1.5x the 5th-95th percentile spread, then recompute the per-gRNA tables. This removes gRNAs present in implausibly few or many wells that would otherwise dominate coefficients; disable it if your library is deliberately uneven. Default True.
_preparation_key – reads it_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _preparation_key.
outline¶
(bool/list) - Draw the object’s own outline over the crop, as in the annotation app. The outline is computed before any channel is zeroed so it cannot trace a channel that is no longer displayed. Default None (off).
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
outline_alpha¶
How opaque the quality-control outlines are drawn, 0 to 1. Lower lets more of the image show through the line. Affects the overlay only. Default 1.0.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
outline_color¶
(str) - Three-letter code choosing the RGB colours for cell, nucleus and pathogen outlines, in that order: ‘rgb’, ‘bgr’, ‘gbr’ or ‘rbg’. The default ‘gbr’ draws cells green, nuclei blue and pathogens red. An unrecognised string falls back to ‘rbg’ without warning, so a typo changes your figure colours rather than raising. Change it when an outline clashes with a channel. Default ‘gbr’.
plot_merged() – reads itdisplay(...) [UNRESOLVED]Read by plot_merged().
outline_palette¶
(str) - Colour palette used for object outlines in overlay figures. ‘default’ assigns red to cells, blue to nuclei, green to pathogens, and yellow to organelles. ‘colourblind’ uses vermillion, sky blue, bluish green, and yellow from the Okabe-Ito palette. Under deuteranope simulation, the minimum pairwise separation is 27 of 255 for ‘default’ and 142 for ‘colourblind’. Default ‘default’ preserves the appearance of existing figures.
preprocess_generate_masks() – reads itanalyze_recruitment() – reads itoutline_sigma¶
(float) - Gaussian blur applied before the outline is found, in pixels. Larger gives a smoother, less speckled boundary at the cost of fine detail. Only read when ‘outline’ is on. Default 4.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
outline_source¶
How the foreground is found when drawing quality-control outlines: ‘otsu’ thresholds the intensity, other values use the edge detector above. Changes what the overlay traces, never the mosaic. Default ‘otsu’.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
outline_thickness¶
(int) - Width in pixels of the mask outlines on the merged overlay; the contour is drawn at this thickness and then dilated by a square of the same size, so the visible line is roughly twice the value. Raise it for large fields where a 1-2 px outline disappears. Default 3.
plot_merged() – reads itdisplay(...) [UNRESOLVED]Read by plot_merged().
outline_threshold_factor¶
(float) - How aggressively the outline is cut from the object channel. Above 1 tightens the outline onto the brightest core; below 1 loosens it outward. Only read when ‘outline’ is on. Default 1.25.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
outline_width¶
(float) - Width in points of cluster outlines and interactive selection rings. Smaller values produce thinner boundaries. Default 1.0.
generate_image_umap() – reads itRead by generate_image_umap().
outside_channel¶
(int) - Zero-indexed channel of the pre-permeabilisation antibody, which labels parasites remaining outside the host cell. Classification thresholds this channel, so an incorrect index changes the assay readout to the signal measured in another channel without raising an error. This is an image-channel index, not measure.py’s <object>_channel_<n>_outside_* columns, which quantify the ring outside an object’s mask. Default 1.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
outside_threshold¶
(float or None) - Fixed threshold for the outside-stain statistic, applied to every field and overriding both the automatic method and control wells. Use only after independent calibration: a value above the true threshold misclassifies attached parasites as invaded and inflates invasion efficiency. Control wells, when supplied, remain the reference against which quality control evaluates the fixed value. None derives the threshold per field. Default None.
analyze_invasion() – reads it_invasion_field_thresholds – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_field_thresholds, _set_analyze_invasion_defaults, analyze_invasion().
outside_threshold_method¶
(str) - Method used to derive the outside-stain threshold from each field when neither a fixed value nor control wells are supplied: ‘otsu’, ‘triangle’, ‘li’, ‘yen’, or ‘mean’. These methods identify a partition but do not test whether the distribution is bimodal; that evaluation is performed separately. ‘triangle’ is appropriate for a strongly skewed distribution with a small stained minority, whereas ‘otsu’ is appropriate for a more balanced distribution. Default ‘otsu’.
_invasion_field_thresholds – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_field_thresholds, _set_analyze_invasion_defaults.
overlap¶
Read by align_folder(), _work, build_plan().
overlay¶
(bool) - In the batch-grid figures, draw the activation map in the ‘jet’ colormap at 50 percent alpha over the source image. Turn it off and the grid tiles are left empty apart from the predicted-class label, so keep it on whenever plot is enabled. It never affects the per-object activation PNGs saved to disk, which are always the bare map. Default True.
Read by generate_activation_map(), plot_merged(), get_default_generate_activation_map_settings().
overlay_chans¶
(list) - Exactly three channel indices from the stack, mapped in order onto the red, green and blue planes of the merged overlay. Default [1, 2, 3] puts channel 1 in red, 2 in green and 3 in blue; reorder or repeat indices to change which stain reads as which colour. Indices past the stack’s channel count are ignored.
plot_merged() – reads itdisplay(...) [UNRESOLVED]Read by plot_merged().
overwrite¶
Allow the importer to replace files in an existing destination project. Leave this off to stop before previously written images, masks, or measurements can be replaced. Default False.
align_folder() – reads itconvert_folder() – reads itrun_external_masks() – reads itdict(...) [UNRESOLVED]import_project() – reads it_work – reads itwrite_stack() – reads itRead by align_folder(), convert_folder(), run_external_masks(), import_project(), _work, write_stack().
p_threshold_alpha¶
(float) - P-value threshold used to call a coefficient a hit and to draw the volcano-plot reference line. It applies to the P-value type selected by p_threshold_kind, keeping results_significant.csv and the corresponding figure consistent. Supply a fraction strictly between 0 and 1; 5 is rejected as an invalid representation of 5%. Default 0.05.
_perform_regression_read_data_reject_impossible_probabilities – reads it_perform_regression_perform_regression_call_level_hits – reads it_reject_impossible_probabilities – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits, _reject_impossible_probabilities.
p_threshold_kind¶
(str) - Whether p_threshold_alpha is applied to the multiple-testing-adjusted p-value (‘adjusted’) or the raw per-coefficient p-value (‘raw’). The analysis run and volcano plot use the same choice, so exported hits and plotted calls remain consistent. ‘raw’ generally calls more genes in screens with thousands of guides. Other values are rejected. Default ‘adjusted’.
_perform_regression_perform_regression_call_level_hits – reads it_reject_a_threshold_that_cannot_mean_what_it_says – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits, _reject_a_threshold_that_cannot_mean_what_it_says.
pair_batch_size¶
How many candidate pairs are scored per batch. Only affects peak memory and how often progress is reported; the result is identical. Default 8192.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
paired_data¶
(list of dicts) - Regression input table: each row explicitly pairs one score CSV with one count CSV. Plate identity comes from both files when they agree, from the partner when only one declares plateID, or from the row order when neither does. A conflict is refused. Legacy score_data/count_data lists are migrated positionally and logged. Default [].
_perform_regression_read_data – reads itnormalize_regression_input_pairs() – reads it_announce_the_fit_count_files_of – reads it_it_will_permute(...) [UNRESOLVED]_say_what_the_permutation_will_do(...) [UNRESOLVED]_first_count_file – reads it_perform_regression_perform_regression – reads itnormalize_regression_input_pairs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]column_run_settings() – reads itRead by _perform_regression, _perform_regression_read_data, normalize_regression_input_pairs(), _first_count_file, _count_files_of, column_run_settings().
parasite_count_column¶
(str or None) - Optional column that already stores the number of parasites represented by each segmented row. When set, the assay sums that column per vacuole instead of counting rows, which is required if one row can represent several parasites. None treats every retained row as one parasite. Default None.
analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_replication_defaults, analyze_replication().
parasite_table¶
(str) - Table in measurements/measurements.db holding one row per segmented parasite. It is read directly rather than through the usual merge, because that merge collapses pathogen rows onto their host cell and would sum several parasites’ stain intensities into a single row. Change it only if measure_crop wrote the parasite objects under a non-standard name. Default ‘pathogen’.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_invasion(), analyze_replication().
path¶
resume_queue() – reads itmovie_field_payload() – reads itRead by resume_queue(), movie_field_payload().
path_column¶
(str) - Column in a prediction CSV containing crop paths for a one-to-one object join. Change it only when the exporter used another name. Default path.
investigate_hit() – reads ithit_investigation_default_settings() – reads itrun_explain_cv() – reads itexplain_cv_default_settings() – reads itRead by hit_investigation_default_settings(), investigate_hit(), explain_cv_default_settings(), run_explain_cv().
path_method¶
reduction_and_clustering() – reads itRead by reduction_and_clustering().
path_string¶
(str) - Substring that must occur in a crop path for that crop to enter the dataset, for example ‘cell_png’ or ‘nucleus_png’. The legacy name png_type remains accepted, although the setting performs only path filtering. Default ‘cell_png’.
Read by select_crops(), generate_dataset(), generate_training_dataset().
pathogen_background¶
(int) - Assumed background intensity of the pathogen channel in raw image units. It has two jobs: when remove_background_pathogen is True every pixel below it is zeroed, and it is multiplied by pathogen_signal_to_noise to set the brightness the normalisation ceiling must reach. Raise it if dim haze is being segmented; lower it if faint parasites vanish. Default 200.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
pathogen_cellprob_threshold¶
(float) - Cellpose cellprob_threshold for the pathogen channel: a pixel is claimed by a mask only if its predicted object probability exceeds this. Lower it (toward -6) to recover dim or small parasites and grow mask boundaries; raise it (toward 6) to shrink masks and drop faint objects. Useful range about -6 to 6. Default -1.
generate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads itgenerate_cellpose_masks() – reads itRead by _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
pathogen_chann_dim¶
(int) - Recruitment analysis only (analyze_recruitment): the image-channel index paired with the pathogen mask when drawing outline overlays, and the switch that enables pathogen_size_range / pathogen_intensity_range filtering. Set it to None to skip pathogen filtering. It plays no part in segmentation - use pathogen_channel for that. Default 2.
analyze_recruitment() – reads itRead by analyze_recruitment().
pathogen_channel¶
(int or None) - Zero-indexed raw acquisition channel segmented into pathogen masks (Toxoplasma etc.), and the channel pathogen_background, pathogen_signal_to_noise and remove_background_pathogen apply to. None disables pathogen segmentation, the pathogen table, the infected-only filter (uninfected) and the adjust_cells step, which needs cell, nucleus and pathogen masks together. Default None.
preprocess_generate_masks() – reads it_concatenate_and_normalize_impl_normalize_img_batch – reads itgenerate_cellpose_masks_sam() – reads itautomated_motility_assay() – reads it_make_intensity_motility_panel – reads itdense_mask_channel_positions() – reads it_segment_cellpose_sam – reads itdense_mask_channel_positions() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]_get_lists_for_normalization – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_segment_cellpose – reads itgenerate_cellpose_masks() – reads itautomated_motility_assay() – reads it_make_intensity_motility_panel – reads itdense_mask_channel_positions() – reads itplot_region() – reads it_channelschannels_from_settings() – reads itapply_settings() – reads it_seed_organelle_column(...) [UNRESOLVED]apply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by preprocess_generate_masks(), channels_from_settings(), _get_lists_for_normalization, _normalize_img_batch, _segment_cellpose, _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam(), plot_region(), apply_settings(), apply_settings(), organelle_measurement_caveats(), _make_intensity_motility_panel, automated_motility_assay(), dense_mask_channel_positions(), _check_app_specific, _describe_objects.
pathogen_diameter¶
(int or None) - Expected pathogen diameter in pixels, used by Cellpose 4 to rescale the image by 30/diameter before segmenting. None segments at native scale. Intracellular parasites are often only a few pixels across at low magnification, where rescaling matters most. spacr.diameter.estimate_diameters proposes a value. Default None.
generate_cellpose_masks_sam() – reads it_get_object_settings – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_get_object_settings – reads itorganelle_morphology_now() – reads itapply_settings() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by generate_cellpose_masks_sam(), organelle_morphology_now(), apply_settings(), _get_object_settings, _describe_objects.
pathogen_flow_threshold¶
(float) - Cellpose flow_threshold for pathogen masks: a candidate mask is discarded when its recomputed flows disagree with the network prediction by more than this. Raise it to keep more, sometimes misshapen, parasites; lower it toward 0.4 (Cellpose’s own default) to keep only clean, well-formed objects. Typical range 0.0-3.0. Default 100.
generate_cellpose_masks_sam() – reads it_segment_cellpose_sam – reads itgenerate_cellpose_masks() – reads itRead by _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam().
pathogen_intensity_merge¶
(bool) - Merge two touching pathogen labels when the mean intensity along their shared boundary reaches pathogen_intensity_threshold, which means there is no detectable dark seam between them and the split is spurious. Requires an intensity image. Set the threshold first: with none set the merge refuses and reports the boundary intensities it found. Default False.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
pathogen_intensity_range¶
(list) - Two-element [min, max] mean-intensity filter applied to the pathogen table in analyze_recruitment; pathogens whose mean intensity in the paired mask channel falls outside the open interval are dropped before recruitment ratios are computed. Bounds must be ints - floats are silently ignored. Default [0, 100000]. Use it to exclude dead or saturated parasites.
analyze_recruitment() – reads itRead by analyze_recruitment().
pathogen_intensity_split¶
(bool) - Split oversized pathogen labels by distance-transform watershed, before the merge and filter steps. Objects larger than pathogen_minimum_area_to_split are seeded at local distance maxima pathogen_min_watershed_distance apart and cut. Despite the name no intensity is used. Enable when several touching pathogens share one label. Default False.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
pathogen_intensity_threshold¶
(float or None) - Absolute intensity, in the image’s own raw units, that the mean along a shared boundary must reach before pathogen_intensity_merge joins two touching pathogen labels. Read it off the image with a pixel inspector rather than guessing. It does NOT carry between acquisitions taken at different exposure or gain, so re-read it for a new imaging session. Leave unset and the merge refuses and reports the boundary intensities it found, which is the number to type. Ignored unless pathogen_intensity_merge is True. Default None.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
pathogen_limit¶
(int, bool, or None) - Maximum pathogens per cell. True or 1 = single pathogen only; None or False = no limit; int = custom limit. Default varies by module (1, 3, 10 or 1000 depending on the factory that fills it), so check the module’s own settings rather than assuming one value.
generate_screen_graphs() – reads itgenerate_training_dataset() – reads itgenerate_ml_scores() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itplot_data_from_db() – reads itplot_merged() – reads itdisplay(...) [UNRESOLVED]analyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_recruitment() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itRead by generate_screen_graphs(), generate_training_dataset(), generate_ml_scores(), interpret_vision_model(), read_and_preprocess_data(), plot_data_from_db(), plot_merged(), analyze_class_proportion(), analyze_endodyogeny(), analyze_recruitment(), interpret_vision_model(), read_and_preprocess_data().
pathogen_loc¶
(list of lists) - Well locations of each pathogen condition, one inner list per name in pathogens, read by annotate_filter_vision when labelling vision-model score CSVs. Every entry must be a row or column ID string such as ‘c1’ or ‘r3’; ranges are not expanded and unmatched entries leave those wells NaN. Set it alongside pathogens, or leave both None. Default None.
annotate_filter_vision() – reads itRead by annotate_filter_vision().
pathogen_mask_dim¶
(int) - Position along the last axis of each merged/*.npy array where the pathogen label mask sits, one plane after the nucleus mask. With the default four image channels (0-3) that is 6; shift it if you keep a different number of channels. None makes measure_crop skip pathogen measurements, so infection status cannot be scored. Default 6.
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]mask_dims_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads it_intensity_measurements – reads it_morphological_measurements – reads itmeasure_crop() – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]plot_merged() – reads itdisplay(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by mask_dims_from_settings(), _crop_shape_overrides, _intensity_measurements, _measure_crop_core, _morphological_measurements, measure_crop(), plot_merged(), get_measure_crop_settings(), organelle_measurement_caveats(), _describe_objects.
pathogen_max_area¶
(int or None) - Maximum pathogen area in pixels squared; labels larger than this are deleted after segmentation. 0, the default, or None disables the filter. Use it to remove fused clumps and large segmentation artifacts that would otherwise dominate per-object statistics; use pathogen_intensity_split instead when clumps should be separated rather than discarded.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
pathogen_max_size¶
(int | None) - Drop pathogens larger than this many pixels, counted the same way pathogen_min_size counts them. None – the default – disables it and preserves prior behavior. This bound removes a vacuole of tightly packed parasites segmented as one object: it passes every minimum size and inflates both the per-cell burden and the mean parasite area. The run prints how many objects each bound dropped.
filter_selection() – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by filter_selection(), _measure_crop_core, get_measure_crop_settings().
pathogen_method¶
run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by _describe_objects.
pathogen_min_area¶
(int) - Minimum pathogen area in pixels squared. Passed to Cellpose as min_size so undersized masks never leave segmentation, then re-applied in the merge/split/filter pass. 0, the default, disables it. Raise it to clear speckle and debris; set it too high and small or newly divided parasites disappear.
merge_split_filter_masks() – reads it_get_object_settings – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_get_object_settings – reads itRead by _measure_crop_core, merge_split_filter_masks(), _get_object_settings, get_measure_crop_settings().
pathogen_min_size¶
(int) - (Deprecated) Minimum pathogen object area in pixels squared, applied during measurement: any label with fewer pixels than this is erased from the pathogen mask before features are extracted. 0, the default, disables it. Superseded by pathogen_min_area, which filters at segmentation time instead.
filter_selection() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_measure_crop_core – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by filter_selection(), _measure_crop_core, measure_crop(), get_measure_crop_settings(), _describe_objects.
pathogen_min_watershed_distance¶
(int) - Minimum separation in pixels between watershed seeds when splitting oversized pathogen labels; seeds are local maxima of the distance transform. Raise it for fewer, larger fragments (or none, leaving the object intact); lower it to cut clumps into more pieces. Used only when pathogen_intensity_split is True. Default 10.
merge_split_filter_masks() – reads it_run_one – reads itRead by _run_one, merge_split_filter_masks().
pathogen_minimum_area_to_split¶
(int) - Absolute pixel area above which a pathogen label is split by pathogen_intensity_split; objects at or below it are never cut. ABSOLUTE, not a multiple of the median: the threshold no longer moves with whatever else is in the field, so the same object is treated the same way in a sparse dish and a crowded one. Raise it to protect small pathogens from fragmentation. Default 100.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
pathogen_model¶
(str or None) - Path to a custom Cellpose checkpoint used to detect pathogen objects, overriding pathogen_model_name when set. It must be a CPSAM-architecture checkpoint (one your own Train Cellpose run produced); a Cellpose-3 CPnet file cannot load into Cellpose 4. A path that does not exist stops the run rather than falling back to the stock weights silently. Default None.
generate_cellpose_masks_sam() – reads itrun_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_describe_objects – reads itisinstance(...) [UNRESOLVED]_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]generate_cellpose_masks() – reads it_describe_plan_safe_describe_objects – reads itisinstance(...) [UNRESOLVED]Read by generate_cellpose_masks(), generate_cellpose_masks_sam(), _check_app_specific, _describe_objects.
pathogen_model_name¶
(str) - Which weights segment pathogens. ‘cpsam’ or a path to your own Train Cellpose checkpoint. The bundled toxo_pv_lumen / toxo_cyto checkpoints were Cellpose-3 CPnet and cannot load into CPSAM’s transformer, so they are mapped to ‘cpsam’ and reported. The older ‘pathogen_model’ key still overrides this one when set. Of the three parameters that used to distinguish models only diameter still acts (eval rescales by 30/diameter); model_type and diam_mean are logged ‘not used in v4.0.1+’ and dropped. Default ‘cpsam’.
_get_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by _get_object_settings, _get_object_settings.
pathogen_morphology¶
Read by organelle_morphology_now().
pathogen_outlier_mads¶
_perform_regression_perform_regressionapply() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by apply().
pathogen_perimeter_fraction¶
(float) - Fraction, from 0 to 1, of the smaller label’s perimeter that two touching pathogen objects must share before they are merged. The default of 0 disables perimeter-based merging. Values near 0.1 merge most touching objects, whereas values from 0.5 to 0.8 merge only objects with a long shared boundary. Use this setting to join vacuoles that Cellpose divided into multiple labels.
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
pathogen_perimiter_fraction¶
merge_split_filter_masks() – reads itRead by merge_split_filter_masks().
pathogen_plate_metadata¶
(list of lists) - Well locations of each pathogen condition, one inner list per entry in pathogen_types. Every item must be a row or column ID string such as ‘c1’ or ‘r3’; anything else is silently ignored and those wells stay unannotated. Ranges like ‘c2-c11’ are not expanded - list each row/column. Do not leave it None while pathogen_types is set: annotation is not skipped, every row is labelled with the first pathogen_types entry. Defaults: None in the plot-from-db settings, [[‘c1’,’c2’,’c3’],[‘c4’,’c5’,’c6’]] for recruitment analysis.
plot_data_from_db() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_recruitment() – reads itanalyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by plot_data_from_db(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_recruitment(), analyze_replication().
pathogen_remove_border_objects¶
(bool) - Delete any pathogen label touching the first or last row or column of the image. Enable it so partially imaged parasites do not enter area and intensity statistics with truncated values; leave it off when parasites are sparse and losing edge objects costs too much data. Default False.
merge_split_filter_masks() – reads it_apply_size_filter – reads itRead by merge_split_filter_masks(), _apply_size_filter.
pathogen_resample¶
_segment_cellpose_sam – reads itRead by _segment_cellpose_sam.
pathogen_restore_type¶
_get_object_settings – reads it_get_object_settings – reads it_get_object_settings – reads itRead by _get_object_settings, _get_object_settings.
pathogen_signal_to_noise¶
(int) - Expected foreground-to-background ratio of the pathogen channel. Multiplied by pathogen_background, it defines the minimum intensity for the normalisation ceiling. spaCR evaluates percentiles 98 through 99.5 and uses the first that reaches the threshold, with the 99.5th percentile as the fallback. Increase it for a higher ceiling with less clipping; decrease it for greater contrast. Default 20.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
pathogen_size_range¶
(list) - Two-element [min, max] area filter in pixels squared applied to the pathogen table in analyze_recruitment, well after segmentation: rows with pathogen_area outside the open interval are dropped. Bounds must be ints - floats are silently ignored. None widens it to effectively unlimited. Default [0, 100000]. Use it to discard debris and merged clumps.
analyze_recruitment() – reads itRead by analyze_recruitment().
pathogen_type¶
_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]organelle_morphology_now() – reads itRead by organelle_morphology_now(), get_measure_crop_settings(), organelle_measurement_caveats().
pathogen_types¶
(list) - Names given to each pathogen condition on the plate, e.g. [‘wt’,’ku80’]. Element i is written into the pathogen column for every well listed in pathogen_plate_metadata[i] and folded into the combined condition label used for grouping and plotting. Must match pathogen_plate_metadata in length and order; None skips pathogen annotation. Default [‘pathogen_1’, ‘pathogen_2’] for the dataset builders, [‘pc’] for the control-based paths, None where types are not used.
plot_data_from_db() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_recruitment() – reads itanalyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by plot_data_from_db(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_recruitment(), analyze_replication().
pathogencellpose¶
Read by preprocess_img_data().
pathogencellpose__channel¶
generate_cellpose_masks_sam() – reads it_get_cellpose_channels – reads itgenerate_cellpose_masks() – reads it_get_cellpose_channels – reads itorganelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by generate_cellpose_masks(), generate_cellpose_masks_sam(), organelle_settings_view(), _get_cellpose_channels.
pathogenorganelle¶
organelle_settings_view() – reads itdict(...) [UNRESOLVED]Read by organelle_settings_view().
pathogens¶
(list) - Names of the pathogen conditions scored by annotate_filter_vision, e.g. [‘wt’,’mutant’]. Element i is written into the pathogen column for every well in pathogen_loc[i] and folded into the combined condition label. Must match pathogen_loc element for element; if pathogen_loc is None, only the first name is applied to every row. Default None.
annotate_filter_vision() – reads itRead by annotate_filter_vision().
pca_svd_solver¶
(str) - PCA decomposition algorithm: auto chooses from the data shape, full is exact, randomized is faster on large matrices, and covariance_eigh suits many rows with relatively few features. Used only by PCA. Default ‘auto’.
generate_image_umap() – reads itRead by generate_image_umap().
pca_whiten¶
(bool) - Rescale PCA components to unit variance after projection. This can help distance-based clustering but discards relative component magnitude. Used only by PCA. Default False.
generate_image_umap() – reads itRead by generate_image_umap().
pen¶
_pen_of – reads itRead by _pen_of.
percentiles¶
(list) - Two percentiles [low, high] used to rescale each channel of each image to 0-1 before segmentation, e.g. [2, 98]. Narrowing the window boosts contrast on dim objects but clips bright ones. Set None to derive them automatically: low fixed at 2, high the first of 98/99/99.9/99.99/99.999 exceeding background * Signal_to_noise. Default None in the Cellpose steps.
plot_region() – reads itset_annotate_default_settings() – reads itcheck_cellpose_models() – reads it__init__ – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itget_default_apply_cellpose_model_settings() – reads itget_default_test_cellpose_model_settings() – reads itRead by plot_region(), get_analyze_plaque_settings(), get_default_apply_cellpose_model_settings(), get_default_test_cellpose_model_settings(), set_annotate_default_settings(), check_cellpose_models(), identify_masks_finetune(), __init__.
permutation_importance¶
(bool) - Re-score the fitted forest after shuffling each feature in turn for ten repeats, then rank features by the resulting score decrease. This is slower than feature_importance but avoids its impurity-based bias. Enable it when the feature ranking will support a substantive conclusion. Default False.
interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]perplexity¶
reduction_and_clustering() – reads itRead by reduction_and_clustering().
phenotype_source¶
The folder holding the high-magnification acquisition that carries the morphology. These images are PLACED onto the stitched genotype mosaic, not stitched themselves. Default None.
ops_preprocess() – reads itget_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]photometric¶
write_tiff() – reads itRead by write_tiff().
pin_memory¶
(bool) - Decode and hold the entire train/test image set in RAM up front (loaded in parallel across all cores) and hand batches to the GPU from page-locked memory. Enable when the dataset fits comfortably in RAM and disk I/O is the bottleneck; disable for large datasets or it will exhaust memory before the first epoch even starts. Default False.
_inner_loader – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]__init__ – reads itRead by _cross_validate_model, _inner_loader, train_test_model(), __init__, get_train_test_model_settings().
pipeline_style¶
(str) - Which mask pipeline runs. ‘v1’ is the disk-based chain (rename, per-channel folders, npy, npz, mask npy, merged/) that measure, annotate and every downstream tool expect, and is the fully tested path. ‘v2’ streams from the originals and writes one npy per field with masks appended in place, using roughly 60-80% less disk but producing no .npz. Default ‘v1’.
Read by preprocess_generate_masks().
pixels_per_um¶
(float) - Image scale in pixels per micrometre. Track coordinates are divided by it, so plots switch from px to um, and together with seconds_per_frame it converts velocity from px/frame to um/min. Take it from the objective and camera pixel size rather than tuning it - it rescales every reported velocity. Default 1.78.
automated_motility_assay() – reads itautomated_motility_assay() – reads itapply_settings() – reads itRead by apply_settings(), automated_motility_assay().
planarconfig¶
write_tiff() – reads itRead by write_tiff().
plaque_model¶
(str) - Cellpose checkpoint used to segment plaques: ‘bundled’ keeps the historical packaged model, a model-zoo key downloads its checksum-verified replacement on first use, and a filesystem path selects a custom checkpoint. Changing it can change every plaque count, so recorded runs should keep the chosen value. Default ‘bundled’.
Read by get_analyze_plaque_settings(), _resolve_plaque_model.
plate¶
(str) - Legacy setting that is not read by the regression path. Use plateID instead; perform_regression passes plateID to process_scores and process_reads, which apply it to count and score rows lacking a plate identifier. Default None.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
plateID¶
(str) - Plate name stamped onto count and score rows that carry no plate of their own, and used as the first field of the plate_row_column key that joins the two tables. It is ignored with a warning when the input already contains more than one distinct plate, so it matters only for single-plate inputs. Default ‘plate1’.
Read by _preparation_key, generate_score_heatmap().
plate_format¶
(str or None) - Standard culture-plate format used as the physical ruler for detected wells: ‘6-well’, ‘12-well’, ‘24-well’, ‘48-well’ or ‘96-well’. It converts plaque areas from pixels to square millimetres; None leaves physical-area columns empty unless well_diameter_mm is supplied. Default None.
Read by get_analyze_plaque_settings(), _plaque_scale_for.
plate_id¶
Read by stitch_cycle_wells().
plate_naming¶
How imported plates are named when the source does not provide one. ‘index’ assigns stable plate numbers in discovered input order. Default ‘index’.
Read by convert_folder(), plan_external_masks(), import_project().
plot¶
(bool) - Render and save quality-control figures during the pipeline, including channel montages, Cellpose mask overlays, filtration comparisons, and crop grids. Figure generation increases runtime and memory use, particularly for complete plates. test_mode enables this setting automatically. Default False. Merged Classifier and Recruitment both start with plotting enabled so their diagnostic figures are produced on the first run.
preprocess_generate_masks() – reads itpreprocess_img_data() – reads it_concatenate_and_normalize_impl – reads itgenerate_cellpose_masks_sam() – reads itgenerate_organelle_masks_sam() – reads it_segment_cellpose_sam – reads itsave_settings() – reads it_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itsave_settings() – reads itsave_settings() – reads itsave_settings() – reads itsave_settings() – reads itgenerate_activation_map() – reads itsave_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itsave_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itsave_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]_measure_crop_core – reads itget_measure_crop_settings() – reads itsave_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itread_and_preprocess_data(...) [UNRESOLVED]_segment_cellpose – reads itgenerate_cellpose_masks() – reads itsave_settings() – reads itsave_settings() – reads it_perform_regression_perform_regression_show_response_distribution – reads itsave_settings() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]refit_settings() – reads itsave_settings() – reads itbarcode_qc() – reads itbarcode_qc_defaults() – reads itrun_and_save() – reads itcheck_cellpose_models() – reads itsave_settings() – reads itsave_settings() – reads itsave_settings() – reads itapply_defaults(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itsave_settings() – reads itanalyze_recruitment() – reads itsave_settings() – reads itsave_settings() – reads itapply_defaults(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itsave_settings() – reads itRead by preprocess_generate_masks(), _cross_validate_model, _fit_one, generate_activation_map(), train_test_model(), _concatenate_and_normalize_impl, preprocess_img_data(), _measure_crop_core, _show_response_distribution, _segment_cellpose, _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam(), generate_organelle_masks_sam(), refit_settings(), barcode_qc(), barcode_qc_defaults(), get_default_generate_activation_map_settings(), get_measure_crop_settings(), get_train_test_model_settings(), run_and_save(), check_cellpose_models(), analyze_recruitment(), save_settings().
plot_by_cluster¶
(bool) - Chooses which thumbnails get overlaid on the embedding: when True, up to image_nr crops are sampled from each cluster (DBSCAN noise excluded) so every cluster is represented; when False, image_nr crops are sampled at random across the whole map. Keep True to compare cluster morphologies, False for an unbiased sample. Default True.
generate_image_umap() – reads itRead by generate_image_umap().
plot_cluster_grids¶
(bool) - Render a second figure with one colour-bordered panel per cluster, each containing up to image_nr example crops, and save it as <METHOD>_grid.pdf when save_figure is enabled. The cluster grid is emitted after the embedding and therefore becomes the final displayed figure. Ignored unless plot_images is True. Default False.
generate_image_umap() – reads itRead by generate_image_umap().
plot_control¶
(bool) - Before the recruitment plots, draw a control panel of per-compartment mean intensities (cell, nucleus, pathogen, cytoplasm) for every channel, split by condition. Use it to confirm channel assignment and that positive/negative control wells separate as expected before trusting the recruitment numbers. Turn it off to shorten the run. Default True.
analyze_recruitment() – reads itRead by analyze_recruitment().
plot_images¶
(bool) - Paste the actual object crops onto the embedding scatter instead of showing bare points. Turn it off for a fast, plain scatter on large datasets - doing so also forces black_background to False and skips the cluster grid figure entirely. Default True.
generate_image_umap() – reads itRead by generate_image_umap().
plot_nr¶
(int) - Number of merged image stacks from the start of the folder displayed with cell, nucleus, and pathogen outlines before recruitment analysis. The implementation checks index <= plot_nr, so plot_nr + 1 images are displayed and 0 displays one image. Increase the value to inspect segmentation across more fields. Default 3.
analyze_recruitment() – reads itRead by analyze_recruitment().
plot_outlines¶
(bool) - Draw a boundary around each cluster in the embedding - a smoothed hull when smooth_lines is True, otherwise the raw convex hull edges. Helps show cluster extent and overlap but clutters dense maps; clusters with fewer than three points are skipped. Forced off when color_by is set. Default True.
generate_image_umap() – reads itRead by generate_image_umap().
plot_points¶
(bool) - Show the scatter marker for each object in the embedding. When False the markers are still drawn but at alpha 0, so cluster colors and the legend survive while only the outlines and overlaid thumbnails stay visible - handy for image-only UMAP figures. Marker size comes from dot_size. Default True.
generate_image_umap() – reads itRead by generate_image_umap().
png_channel_mapping¶
(dict) - Which source channel goes in each colour of the saved PNG, e.g. {‘r’: 2, ‘g’: 1, ‘b’: 0}: channel 2 is red, 1 is green, 0 is blue. Says outright what png_dims only implied. Channels not named are absent from the crops (measurements are unaffected); a colour left blank is an empty plane. Naming the same channel for all three writes a greyscale PNG. Default {‘r’: 2, ‘g’: 1, ‘b’: 0}, which for a standard 405/488/555 stack puts the nuclear stain in blue.
Read by get_measure_crop_settings().
png_dims¶
(list of int) - Deprecated in favor of png_channel_mapping and retained for compatibility with older settings files. Under the legacy mapping, entry 0 becomes blue, entry 1 green and entry 2 red, matching the wavelength order 0=405, 1=488 and 2=555. Ignored when png_channel_mapping is set. Default [].
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_write_crop_tar – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_settings – reads itRead by _crop_shape_overrides, _write_crop_tar, _crop_settings.
png_size¶
(list of int) - Output crop size as [width, height] in pixels, centred on the object centroid; larger keeps more surroundings, smaller clips large objects. Should match the classifier input size (default [224,224]). With several crop_mode entries pass a list of lists, one size per mode, or a single size is reused for all.
generate_annotation_dataset() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]run_preflight()describe_plan() – reads itisinstance(...) [UNRESOLVED]crop_spec_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads itget_measure_crop_settings() – reads itrun_preflight()describe_plan() – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safedescribe_plan() – reads itisinstance(...) [UNRESOLVED]Read by generate_annotation_dataset(), crop_spec_from_settings(), _crop_shape_overrides, _measure_crop_core, get_measure_crop_settings(), describe_plan().
png_type¶
(str) - Object crop type selected from the png_list table when building the training dataset; a row is retained only if its PNG path contains this substring. Use ‘cell_png’, ‘nucleus_png’, ‘pathogen_png’, ‘cytoplasm_png’ or ‘organelle_png’ to train on whole cells, nuclei, parasites, cytoplasm or organelles. It must match a crop_mode saved by measure_crop. Default ‘cell_png’.
Read by select_crops(), generate_dataset(), generate_training_dataset().
point_alpha¶
(float) - Opacity of UMAP points from 0 (invisible) to 1 (opaque), used by both static and interactive plots. Default 0.65.
generate_image_umap() – reads itRead by generate_image_umap().
point_color¶
(str) - Point color for static and interactive UMAP plots. Use ‘cluster’ or ‘viridis’ for cluster-based Viridis colors, or any Matplotlib color such as ‘#4cc9f0’, ‘orange’, or ‘white’ for one fixed color. Default ‘cluster’.
generate_image_umap() – reads itRead by generate_image_umap().
pos¶
(str) - Column ID marking positive-control wells in the image UMAP. Rows whose columnID equals it are labelled cond=’pos’, so exclude_conditions can drop them; and when embedding_by_controls is True the rows whose col_to_compare equals it help fit the reducer. Default ‘c1’ (note: not ‘c2’).
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itRead by generate_image_umap(), reducer_hyperparameter_search().
position_effect_ratio¶
Fold-change from its plate’s median read depth at which a plate row or column is flagged as a position effect. 2.0 flags a row at half or double the plate. Must be above 1. Default 2.0.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
positive_control_id¶
(str) - Identifier of the positive-control class. In ML screening it is the value in location_column (e.g. ‘c2’) whose objects are labelled class 1 for training; in gRNA regression it is a gene/gRNA ID substring (e.g. ‘239740’) matched against coefficient names to tag them ‘pc’ in the results and volcano plot. Defaults ‘c2’ and ‘239740’ respectively.
load_search_data() – reads itgenerate_ml_scores() – reads it_perform_regression_perform_regression – reads it_calibrated_fraction_threshold_calibration_inputs – reads it_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by load_search_data(), _calibration_inputs, _perform_regression, _run_guide_permutation_analysis, generate_ml_scores().
positive_control_wells¶
(list or str) - Wells containing only the positive control, e.g. [‘c2’]. Accepts rows (r1), columns (c1), or individual wells (A01); the Plate button selects them from a map. Pure controls are calibration references rather than screen observations, so they are excluded from the regression. They define the positive endpoint for mixed-ratio calibration and should be identified from the plate design. Default None.
_perform_regression_perform_regressioncontrol_block_wells() – reads itcontrol_block_wells() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by control_block_wells().
positive_mean¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]validate_and_adjust_beta_params() – reads itRead by generate_parameters(), run_simulation(), validate_and_adjust_beta_params().
positive_variance¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]validate_and_adjust_beta_params() – reads itRead by generate_parameters(), run_simulation(), validate_and_adjust_beta_params().
power_backend¶
Inference backend: ‘torch’ uses mean-field ADVI; ‘numpyro’ and ‘pymc’ use optional NUTS; ‘auto’ prefers numpyro, then pymc, then torch. An unavailable named backend raises. AUROC uses coefficient ordering, but ADVI can reach a local optimum and its intervals are not calibrated. Default ‘torch’.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_background_positive_rate¶
Mean probability that a non-hit cell is called positive. Rates vary across library-unit/well observations according to the held class_neg_var. Default 0.12.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_cells_per_well¶
Mean cells imaged per well. The real screen averaged 123. This is the parameter you buy with microscope time, and the first curve sweeps it. Default 123.0.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_constructs_per_well¶
Target mean distinct library units present per well (genes in gene mode, guides in guide mode), passed as well_abundance_factor_mu. Probability clipping can make the realised mean lower. Default 4.6.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_detection_auroc¶
The AUROC a simulated screen must reach to count as a detection. There is no p-value here - the model ranks genes, so the bar is a ranking quality and you choose it. Default 0.8.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_effect_fold¶
Requested fold multiplier on the mean background positive-call rate. The simulator uses min(0.999, background rate × fold); the screen rejects folds below 1 and classifier mean/variance combinations that cannot define a beta distribution. Default 6.667, yielding 0.80004 at the default background.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_hit_rate¶
Independent probability that each simulated library unit is a true hit; the realised hit fraction varies between replicates. In guide mode, guide units are assigned independently because no gene-grouping layer is modelled. Default 0.025.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_n_genes¶
Number of genes in the design. In gene mode this is the number of simulated library units; in guide mode the simulator uses genes × guides-per-gene independent units. Larger libraries generally make recovery harder, but the simulator evaluates the resulting design directly rather than assuming a fixed scaling law. Default 452.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_n_grnas_per_gene¶
Guides per gene. Only reaches the simulation when power_score_per is ‘guide’; there is no guide-efficiency layer in the port, so scoring per gene it changes no number. Default 4.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_n_plates¶
Number of plates used to calculate total simulated wells: wells-per-plate × plates. The simulator and fitted model receive only that total and do not model plate identity or plate-to-plate variance. Default 4.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_n_replicates¶
Simulated screens per grid point. One screen at one setting is a single draw from a noisy process. Default 3.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_reads_per_well¶
Target mean sequencing depth per well. Per-well targets vary according to the held read_depth_cv, and realised reads cannot exceed the amplified barcode pool. Default 30000.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_score_per¶
‘gene’ simulates one aggregate library unit and coefficient per gene; ‘guide’ simulates genes × guides-per-gene independent guide-level units, each with its own coefficient and reads. The simulator has no guide-efficiency or within-gene grouping layer. Choices: ‘gene’ or ‘guide’. Default ‘gene’.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_seed¶
Master seed used to derive each grid-point replicate seed. Reproduction requires the complete DesignSpec, the same sweep grid and order, resolved backend, and software stack. Default 0.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
power_wells_per_plate¶
Wells per plate. The GUI offers 96, 384 and 1536; programmatic designs are not restricted to those formats. This is multiplied by power_n_plates and only the resulting total well count reaches the simulator; no plate identity is modelled. Default 384.
spec_from_settings() – reads itpower_default_settings() – reads itRead by power_default_settings(), spec_from_settings().
prediction_column¶
Column containing the CV class the surrogate must reproduce. Selecting a score instead changes the learning target and meaning of fidelity. Default pred.
run_explain_cv() – reads itexplain_cv_default_settings() – reads itpredictions_file¶
(str) - Existing per-object prediction CSV joined to measured objects. Analysis modules read this exact output and do not silently rerun a model or substitute another run. Default ‘’ requires selection.
Read by investigate_hit(), explain_cv_default_settings(), run_explain_cv().
preprocess¶
(bool) - Run image preparation before segmentation: group raw files into per-field channel stacks, optionally subtract background, and percentile-normalize each channel into floating-point arrays. Keep True for unprocessed input; set False only when the normalized arrays already exist, because segmentation requires those arrays. Default True.
Read by preprocess_generate_masks().
preview_downsample¶
How much to shrink the preview PNG, as a divisor. The mosaic itself is unaffected. Larger is a smaller file that hides fine seams. Default 8.
get_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings().
preview_only¶
Build and print the complete input-to-mask assignment plan without writing a project or running Measure. Use this to review automatic role and object-type detection first. Default False.
align_folder() – reads itconvert_folder() – reads itrun_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]prepare_external_masks() – reads itimport_project() – reads itrun_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by align_folder(), convert_folder(), prepare_external_masks(), import_project(), _check_required_paths.
preview_rows¶
convert_folder() – reads itRead by convert_folder().
print_object_number¶
plot_merged() – reads itdisplay(...) [UNRESOLVED]Read by plot_merged().
project¶
register() – reads itRead by register().
prune_features¶
(bool) - Before training, keep only the top_features columns with the highest ANOVA F-score against the control labels (sklearn SelectKBest with f_classif). Speeds up fitting and can curb overfitting on small control sets, but discards features the model might have used and scores each feature in isolation, ignoring interactions. Default False.
generate_ml_scores() – reads itRead by generate_ml_scores().
qc_data¶
Path(s) to the qc.csv a barcode-mapping run wrote beside its count table. Supplies the unmapped-read panel: how many reads reached barcode lookup and how many of them matched no entry in each reference. Leave empty to skip that panel; every other panel works from count_data alone. Default ‘’.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
qc_plot_max_panels¶
(int) - Largest number of wells drawn in the threshold-diagnostic figure, taken in sorted well order. It exists so a 384-well plate does not produce a 384-panel figure; the CSVs always carry every well regardless. Default 12.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
quantile¶
(float) - Which quantile of the response quantile regression fits, strictly inside 0 and 1: 0.5 is the median (robust to outlier wells), 0.9 asks which gRNAs move the top of the distribution rather than its centre. Aggregation is turned off automatically so the quantile is taken over cells, not over well means. Read only by regression_type ‘quantile’; it replaced the old overload of alpha. Default 0.5.
_perform_regression_read_data_reconcile_random_row_column_effects – reads it_perform_regression_perform_regression – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _reconcile_random_row_column_effects.
queue_by_uncertainty¶
(bool) - Order the Annotate grid by increasing classifier confidence rather than database order. Samples near the decision boundary generally provide more information for active learning than samples assigned 99% confidence. Requires model scores in png_list; when none are available, the grid uses page order and reports the fallback. Previously annotated crops are excluded. Default False.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
queue_diversity¶
(str) - Metadata level across which the annotation queue is distributed. Ranking solely by uncertainty can concentrate the highest-ranked crops in one or two wells, repeatedly sampling the same source of ambiguity. Distribution across wells or plates increases experimental coverage at a modest cost in per-item uncertainty. Default ‘well’.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
queue_limit¶
(int) - Maximum number of crops in the annotation queue. 0 queues the complete unlabelled pool. When the limit is smaller than the number of wells, queue_diversity selects approximately one crop from each represented well rather than the globally highest-uncertainty crops. Default 0.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
queue_measure¶
(str) - Method used to score uncertainty for the annotation queue. ‘entropy’ incorporates all classes and is the preferred default for three or more classes; ‘least_confidence’ ranks samples by the highest class score; ‘margin’ ranks them by the difference between the two highest scores. With exactly two classes, margin and least_confidence produce identical rankings, including ties, and diverge only with three or more classes. These values are uncertainty scores rather than calibrated probabilities. Default ‘entropy’.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
radial_dist¶
(bool) - Measure how each channel’s intensity varies with distance from the nucleus, pathogen and organelle boundaries inside each cell, binned into 6 shells and saved as <object>_rad_dist_channel_<c>_bin_0-5. Keep it on to quantify recruitment or intensity gradients toward an object; turn it off to shrink the feature table and speed up measurement. Default True.
_measure_crop_core_intensity_measurements – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _intensity_measurements, get_measure_crop_settings().
random_row_column_effects¶
(bool) - Fit plate, row and column as random effects instead of fixed ones: True overrides regression_type to ‘mixed’ and fits a MixedLM grouped by plateID with rowID and columnID variance components, dropping them from the fixed-effect formula. Use it when edge or row artefacts differ between plates; it is slower and may fail to converge. Default False.
_perform_regression_read_data_reconcile_random_row_column_effects – reads itsection_explainer() – reads itsection_explainer_html() – reads it_perform_regression_perform_regression – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]refit_settings() – reads it_level_is_read – reads it_is_nonparametric(...) [UNRESOLVED]_level_reason – reads itget_setting_dependencies() – reads itRead by _perform_regression, _reconcile_random_row_column_effects, section_explainer(), section_explainer_html(), refit_settings(), _level_is_read, _level_reason, get_setting_dependencies().
random_seed¶
(int) - Reproducibility seed shared by labelled train/test splitting, train/validation splitting, and grouped cross-validation folds. Keep it fixed to reproduce a run; vary it to evaluate sensitivity to the sampled partition. Default 42.
generate_annotation_dataset() – reads itgenerate_image_umap() – reads itresolve_seed() – reads ittrain_test_model() – reads it_cross_validate_model – reads itresolve_seed() – reads itgenerate_training_dataset() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itresolve_seed() – reads ittrain_test_model() – reads it_cross_validate_model – reads itresolve_seed() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]resolve_seed() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_defaults – reads itresolve_seed() – reads itrun_and_save() – reads itstream_dataset() – reads itRead by generate_annotation_dataset(), generate_image_umap(), _cross_validate_model, train_test_model(), generate_training_dataset(), _defaults, resolve_seed(), run_and_save(), stream_dataset().
random_state¶
embed_fn() – reads it_default_umap_embed – reads itdict(...) [UNRESOLVED]UMAP(...) [UNRESOLVED]dict(...) [UNRESOLVED]umap_search() – reads itdict(...) [UNRESOLVED]_call_simulator – reads itdict(...) [UNRESOLVED]simulate_fn(...) [UNRESOLVED]Read by _default_umap_embed, embed_fn(), umap_search(), _call_simulator.
random_test¶
(bool) - Seed the random draw of test-mode image sets with a fixed value (42), so every test run picks the same subset and results stay comparable. The selection is shuffled either way; set False when you want a different random subset each run to check that behaviour is not subset-specific. Default True.
Read by preprocess_img_data().
randomize¶
(bool) - Shuffle the order of the per-field arrays before they are grouped into normalization batches, so each batch spans plates and wells instead of one acquisition block - this matters because normalization percentiles are computed per batch. Forced to False for timelapse runs to keep frames in sequence. Default True.
preprocess_generate_masks() – reads it_concatenate_and_normalize_impl – reads itRead by preprocess_generate_masks(), _concatenate_and_normalize_impl.
ransac_thresh_px¶
How far a matched feature may sit, in pixels, from where the fitted transform predicts and still count as agreeing with it. Larger accepts looser fits and more of them; smaller rejects real matches on a distorted field. Default 3.0.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
recursive¶
Search sub-folders as well as the source folder. Turn it off when a plate’s wells are already separated and you want only this level. Default True.
align_folder() – reads itplan_external_masks() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by align_folder(), plan_external_masks(), get_preprocess_ops_settings(), stitch_cycle_wells().
reduction_method¶
(str) - Dimensionality reduction run before clustering and plotting: ‘umap’ preserves more global structure and can be fitted on controls then applied to all data, ‘tsne’ emphasises local neighbourhoods and cannot reuse a fitted model. With ‘tsne’, min_dist is ignored and n_neighbors is used as perplexity. Anything else raises ValueError. Default ‘umap’.
_compute_umap – reads itgenerate_image_umap() – reads itreducer_hyperparameter_search() – reads itRead by _compute_umap, generate_image_umap(), reducer_hyperparameter_search().
reference_channel¶
Read by align_folder(), _work, build_plan().
reg_alpha¶
(float) - L1 penalty on leaf weights for the gradient-boosted classifier (XGBoost and LightGBM; ignored by the other model_type_ml choices). Raising it drives more leaf weights to exactly zero, shrinking the model and its effective feature set - raise it when training accuracy far exceeds test accuracy. Any value >= 0. Default 0.1.
generate_ml_scores() – reads itRead by generate_ml_scores().
reg_lambda¶
(float) - L2 penalty on leaf weights for the gradient-boosted classifier (XGBoost, LightGBM, and CatBoost’s l2_leaf_reg). Raising it shrinks all weights smoothly rather than zeroing them, damping the influence of any single feature and curbing overfitting, at the risk of underfitting if pushed too far. Any value >= 0. Default 1.0.
generate_ml_scores() – reads itRead by generate_ml_scores().
regex¶
(str) - Regex applied with re.match to each extracted read window; it must define the named groups columnID, grna and rowID, whose captured sequences are looked up in the three barcode CSVs. Non-matching reads are silently dropped, so a wrong group name or barcode orientation yields zero counts. The default captures an 8 bp column, 20-21 bp gRNA and 8 bp row barcode.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
regression_backend¶
(str) - Selects the library and device that fit the chosen regression_type. ‘statsmodels (CPU)’ preserves the established results and is the default. ‘torch (GPU)’ accelerates mixed models, ‘pyfixest (CPU)’ accelerates OLS/WLS with absorbed plate-position effects, and ‘glum (CPU)’ accelerates wide GLMs. The selector describes measured costs and numerical differences for each option; unavailable or incompatible backends are greyed out with the reason. Default ‘statsmodels (CPU)’.
_announce_the_fit – reads it_it_will_permute(...) [UNRESOLVED]_say_what_the_permutation_will_do(...) [UNRESOLVED]_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads itrecord_run() – reads itRead by _perform_regression, _announce_the_fit, _run_settings_row, record_run().
regression_panel_manifest¶
(dict, str, or None) - Publication panels built after the regression CSVs are written. None skips them. Supply a mapping, or a path to JSON/YAML, declaring each panel’s result source, phenotype, gene/gRNA level and plot kind; the packages land under publication_panels/. Structured rather than a one-line field, so it is set from a script or a saved settings file rather than in the GUI. Default None.
Read by _write_regression_panel_packages.
regression_qc¶
(bool) - Write variance-homogeneity, residual, design, influence, and calibration diagnostics to <res_folder>/regression_qc/ as figures, a combined PDF, and a text report. One fit requires approximately 5.8 seconds and writes 19 files, so this is enabled for individual analyses but disabled automatically during parameter sweeps to avoid producing thousands of diagnostic files. Reopen a selected trial to generate its diagnostics. Applies to every regression_type. Default True.
_trial_settings – reads it_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _trial_settings.
regression_type¶
(str) - Model family. Default ‘mixed’ nests guides inside genes as random effects, so guides disagreeing widens that gene’s interval; it answers both levels at once, which is why ‘level’ greys out. Otherwise: ‘ols’, ‘wls’, ‘rlm’, ‘huber’ or ‘quantile’ for a continuous response; ‘logit’, ‘probit’, ‘beta’ or ‘quasi_binomial’ for fractions; ‘poisson’ for counts; ‘lasso’, ‘ridge’, ‘elasticnet’ or ‘horseshoe’ when the predictors outnumber the wells. Note regression_type ‘beta’ fits a beta GLM, while transform ‘beta’ only transforms the response.
_perform_regression_read_data – reads it_reconcile_random_row_column_effects – reads it_reject_unused_run_settings – reads it_announce_the_fit – reads it_it_will_permute(...) [UNRESOLVED]_say_what_the_permutation_will_do(...) [UNRESOLVED]_on_refit – reads it_on_run(...) [UNRESOLVED]section_explainer() – reads itsection_explainer_html() – reads it_perform_regression_perform_regression – reads it_perform_regression_set_pathsresults_folder_kind() – reads it_reconcile_random_row_column_effects – reads it_reject_unused_run_settings – reads it_run_guide_permutation_analysis – reads it_warn_if_penalised_no_hits – reads it_write_regression_diagnostics – reads itbuild_run_summary() – reads itisinstance(...) [UNRESOLVED]_resolve_regression_analysis_choices – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads itrecord_run() – reads itdestination() – reads itrefit_settings() – reads itrecommend() – reads it_level_is_read – reads it_is_nonparametric(...) [UNRESOLVED]_level_reason – reads itget_setting_dependencies() – reads it_stale_against – reads itrefusals() – reads itRead by _perform_regression, _perform_regression_read_data, _reconcile_random_row_column_effects, _reject_unused_run_settings, _run_guide_permutation_analysis, _warn_if_penalised_no_hits, _write_regression_diagnostics, results_folder_kind(), _announce_the_fit, _on_refit, section_explainer(), section_explainer_html(), _run_settings_row, record_run(), destination(), refit_settings(), build_run_summary(), recommend(), _level_is_read, _level_reason, _resolve_regression_analysis_choices, get_setting_dependencies(), _stale_against, refusals().
relative_scale¶
How much bigger the phenotype magnification is than the genotype one – 2.0 for 20x onto 10x. Wrong here and the alignment cannot converge, because it is solving for a scale it has been told is different. Default 2.0.
ops_preprocess() – reads itget_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]remove_background¶
(bool) - Hard-clip every pixel below the ‘background’ value to zero before normalization and segmentation. Use it when a channel carries a bright, even haze that inflates the normalization floor; leave it off for dim or already flat-fielded data, since the clip silently deletes faint real signal. Default False.
_concatenate_and_normalize_impl_normalize_img_batch – reads itplot_merged() – reads itdisplay(...) [UNRESOLVED]check_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by _normalize_img_batch, plot_merged(), get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
remove_background_cell¶
(bool) - Before normalisation, zero every pixel in the cell channel below cell_background. This flattens haze so the percentile stretch is driven by real signal, but it also erases genuinely dim cell edges and can shrink masks. Enable only once cell_background is set from an actual empty region. Default False.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
remove_background_nucleus¶
(bool) - Before normalizing the nucleus channel, zero every pixel below nucleus_background and exclude those pixels from the percentile calculation. Enabling it raises contrast on real nuclei and suppresses haze, but clips genuinely dim nuclei to zero so they may become unsegmentable. Default False; check nucleus_background against raw images first.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
remove_background_organelle¶
_concatenate_and_normalize_impl_normalize_img_batch – reads itRead by _normalize_img_batch.
remove_background_pathogen¶
(bool) - Before normalising the pathogen channel, hard-zero every pixel whose raw intensity is below pathogen_background. Enable it when diffuse autofluorescence inflates the low percentile and Cellpose starts segmenting haze; leave it off for dim parasites, since the clipping erases real signal and biases downstream intensity measurements. Default True.
_concatenate_and_normalize_impl_normalize_img_batch – reads it_get_lists_for_normalization – reads itRead by _get_lists_for_normalization, _normalize_img_batch.
remove_cluster_noise¶
(bool) - Remove points that DBSCAN labels as noise (-1) before plotting the embedding, so the figure contains only clustered points. Disable it to retain all embedded points, including diffuse background. It has no effect with kmeans, which never emits -1, and is disabled automatically when color_by is set. Default True.
generate_image_umap() – reads itRead by generate_image_umap().
remove_image_canvas¶
(bool) - When object thumbnails are overlaid on the embedding plot, make zero-valued background pixels transparent so only the segmented object is visible. Enable it to remove black thumbnail backgrounds, especially with black_background. Only L, I and RGB crops are supported; other PIL modes raise an error. Default False.
generate_image_umap() – reads itRead by generate_image_umap().
remove_low_variance_features¶
(bool) - Drop numeric features whose variance across objects falls below 0.01 before model fitting – near-constant columns that carry no discriminative signal but still cost time and dilute importance rankings. Turn it off only when your features live on a very small numeric scale, where genuine signal can fall under that fixed cut-off. Default True.
generate_ml_scores() – reads itRead by generate_ml_scores().
remove_outliers¶
plot_data_from_csv() – reads itRead by plot_data_from_csv().
remove_train¶
annotate_filter_vision() – reads itRead by annotate_filter_vision().
replicates¶
Read by generate_parameters().
representation¶
Read by generate_screen_graphs(), plot_data_from_csv(), plot_data_from_db().
require_host_cell¶
(bool) - Drop parasite rows with no valid host-cell link before constructing vacuoles. This prevents extracellular debris and attached parasites from entering a replication readout, but it will also remove real infected cells when cell segmentation or parent assignment failed. The number removed is reported. Default True.
analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_replication_defaults, analyze_replication().
resample¶
(bool) - Passed to Cellpose model.eval: run the mask-tracking dynamics at full image resolution instead of on the downsampled network grid. Enabling it gives smoother, better-fitting object outlines at the cost of time and memory, and helps most when objects differ a lot from the model’s training diameter. Default False; the object pipeline sets True for cell/nucleus and False for pathogen.
Read by get_analyze_plaque_settings(), identify_masks_finetune().
rescale¶
(bool) - Let Cellpose rescale each image by 30/diameter before segmenting, so objects arrive at the size the model expects. Turn off only when the diameter is already correct for the model. Default False.
Read by get_analyze_plaque_settings(), identify_masks_finetune().
resize¶
(bool or float) - Resize every image to target_height x target_width before running Cellpose, then scale the returned mask back to the original dimensions with nearest-neighbour interpolation so measurements remain in original pixels. Enable this setting to match oversized fields to the model’s training scale or reduce GPU memory use. Requires target_height and target_width. Default False (True for plaque analysis).
check_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
resnet_features¶
(bool) - Placeholder for embedding raw crops with ResNet features instead of the measured feature table. The branch in generate_image_umap is an empty pass, so enabling it skips the embedding step entirely and the run then fails on an unbound ‘embedding’ variable. Leave it False. Default False.
generate_image_umap() – reads itRead by generate_image_umap().
results_folder¶
Exact regression output folder that produced the selected hit. Its CSV and JSON bytes are hashed so later edits create different provenance. Default ‘’ requires selection.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]investigate_hit() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by investigate_hit(), _check_app_specific.
resume¶
(bool) - Continue an interrupted run from its last validated boundary. Mask revalidates existing mask and merged arrays; Measure accepts only fields complete in every owned table and clears partial rows before retrying; and Format Converter reopens each checkpointed TIFF. These validations reduce the risk of reusing partial output and require additional reads during resumption. Default False.
convert_folder() – reads itpreprocess_generate_masks() – reads it_concatenate_and_normalize_impl – reads itgenerate_cellpose_masks_sam() – reads itgenerate_organelle_masks_sam() – reads it_segment_cellpose_sam – reads itresume_enabled() – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]get_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_segment_cellpose – reads itgenerate_cellpose_masks() – reads itRead by convert_folder(), preprocess_generate_masks(), _concatenate_and_normalize_impl, _segment_cellpose, _segment_cellpose_sam, generate_cellpose_masks(), generate_cellpose_masks_sam(), generate_organelle_masks_sam(), resume_enabled(), get_measure_crop_settings().
resume_checkpoint¶
(str) - Path to a spaCR training artifact to continue exactly: restores model, optimizer, scheduler, epoch, best score and random-generator state. Use custom_model_path instead when only the weights should be reused. Default ‘’.
train_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, train_test_model(), get_train_test_model_settings().
reuse_existing_measurements¶
(bool) - If measurements.db already holds the table named by db_table_name, load it instead of re-extracting regionprops from merged/*.npy. Saves most of the runtime when re-running only the infection QC or the plots, but it also skips track smoothing, so changes to max_displacement or zscore_thresh only take effect with this set to False. Default True.
automated_motility_assay() – reads itautomated_motility_assay() – reads itRead by automated_motility_assay().
rng¶
_call_simulator – reads itdict(...) [UNRESOLVED]simulate_fn(...) [UNRESOLVED]Read by _call_simulator.
row_csv¶
(path) - CSV mapping row barcodes to well names; it must have ‘sequence’ and ‘name’ columns. Reads are matched verbatim with no reverse-complementing, so the sequences must be in the same orientation as the reads - use barecodes_reverse_complement to flip the file if needed. Unmatched reads get NA for rowID. Default: the bundled spacr/resources/data/barcodes_row.csv.
Read by _run_barcode_qc, generate_barecode_mapping(), barcode_qc_defaults().
row_limit¶
(int) - Randomly subsample the joined measurement table down to this many objects (fixed seed 42) before dimensionality reduction, keeping UMAP and clustering tractable. Raise it for a more faithful map at higher memory and runtime cost, or set to None to use every row. Must not exceed the available row count. Default 1000.
generate_image_umap() – reads itreducer_hyperparameter_search() – reads itload_search_data() – reads itRead by generate_image_umap(), reducer_hyperparameter_search(), load_search_data().
rra_alpha¶
(float) - The top fraction of the ranked guide list robust rank aggregation scores against: 0.25 asks whether a gene’s guides cluster in the best quarter of the ranking more than chance allows, ignoring the rest. Smaller is stricter and returns fewer, better-supported genes. Above 0 and at most 1; 25 for ‘25%’ is refused. Default 0.25.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
rra_permutations¶
(int) - How many permuted rankings the robust rank aggregation null is built from. The smallest P value it can report is about 1/rra_permutations, so 10000 resolves the tail to 1e-4; raise it when many genes pile up at that floor and lower it while exploring, since the cost is linear in this number. Default 10000.
_perform_regression_perform_regression – reads it_reject_a_threshold_that_cannot_mean_what_it_says – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _reject_a_threshold_that_cannot_mean_what_it_says.
sample¶
(int, list or None) - Randomly draw this many PNG crops from the database when building the dataset tar instead of using all of them; a list uses its first element, and values above the total are clamped. Use it to build a quick trial dataset or to cap a huge screen. None uses every crop, shuffled. Default None.
generate_dataset() – reads itRead by generate_dataset().
sampling¶
interior_distance_transform() – reads itsurface_distance_transform() – reads itRead by interior_distance_transform(), surface_distance_transform().
sanity_check¶
(bool) - Randomize the model’s weights layer by layer, recompute attribution and report the similarity between maps. A method that produces nearly the same map for a randomized model is responding to image structure rather than the trained decision function. On a small CNN, the CAM family, including spaCR’s default Grad-CAM, fails this test while saliency and integrated gradients pass. The resulting similarity is reported for the selected model rather than inferred from benchmark behavior. This costs one additional attribution per randomized layer. Default True.
run_search_for_app() – reads itRead by run_search_for_app(), get_default_generate_activation_map_settings().
save¶
(bool or list of bool) - Controls whether the current module writes its optional disk artifacts, such as masks, figures or result tables. Mask accepts a three-item list for [cell, nucleus, pathogen] independently; other modules use one boolean. Default varies by module.
preprocess_generate_masks() – reads itgenerate_cellpose_masks_sam() – reads itgenerate_organelle_masks_sam() – reads itgenerate_activation_map() – reads itcreate_extended_radar_plot() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]generate_cellpose_masks() – reads itgraph_importance() – reads itplot_data_from_csv() – reads itplot_data_from_db() – reads itbarcode_qc() – reads itbarcode_qc_defaults() – reads itcheck_cellpose_models() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]get_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itanalyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]apply_cellpose_model() – reads itget_default_apply_cellpose_model_settings() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]test_cellpose_model() – reads itget_default_test_cellpose_model_settings() – reads itRead by preprocess_generate_masks(), generate_activation_map(), create_extended_radar_plot(), interpret_vision_model(), generate_cellpose_masks(), generate_cellpose_masks_sam(), generate_organelle_masks_sam(), graph_importance(), plot_data_from_csv(), plot_data_from_db(), barcode_qc(), barcode_qc_defaults(), get_analyze_plaque_settings(), get_default_apply_cellpose_model_settings(), get_default_generate_activation_map_settings(), get_default_test_cellpose_model_settings(), check_cellpose_models(), identify_masks_finetune(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_replication(), apply_cellpose_model(), interpret_vision_model(), test_cellpose_model().
save_arrays¶
(bool) - Also save each object as a raw .npy array - all channels, cropped to its bounding box, unnormalised - under a region_array/ folder. Enable when you need full bit depth or channels beyond png_dims for custom analysis; it uses far more disk than PNGs. Requires save_png to be True as well. Default False.
_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _measure_crop_core, get_measure_crop_settings().
save_figure¶
(bool) - Write the embedding, plus the cluster grid when plot_cluster_grids is on, as vector PDFs to <src>/results/<METHOD>_embedding.pdf and <METHOD>_grid.pdf. Enable it when you want the figure for a paper or a record of the run; either way the plots are still displayed on screen. Default False.
generate_image_umap() – reads itRead by generate_image_umap().
save_h5¶
(bool) - Also write every annotated read (consensus sequence plus its parsed row/column/gRNA barcodes and IDs) to annotated_reads.h5. The per-well counts in unique_combinations.csv and qc.csv are written either way, so set it False unless you need read-level data; True produces a very large file and compression can dominate runtime. Default True.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
save_measurements¶
(bool) - Master switch for the measurement half of measure_crop: compute morphology and intensity features for every cell, nucleus, pathogen, organelle and cytoplasm object and write them to the plate’s SQLite database. Set it False when you only want cropped PNGs or filtered masks – segmentation and cropping still run, but no measurement tables are written. Default True.
run_import() – reads it_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by run_import(), _measure_crop_core, get_measure_crop_settings().
save_original_images¶
(bool) - After each batch is MIP-projected and merged into stack/, either move the raw input images into src/orig/ (True) or delete them so the pixels live only in stack/ (False). Set False on large screens where the duplicate raw copy will not fit on disk; the deletion is not reversible. Default True.
Read by preprocess_img_data().
save_png¶
(bool) - Write one PNG crop per segmented object into <crop_mode>_png/ and register each path in the png_list table of measurements.db. Required for training or applying a classifier, for the Annotate app and for the UMAP image plots. Turn off to only compute measurements and save time and disk. Default True.
run_preflight()describe_plan() – reads it_describe_outputs – reads itisinstance(...) [UNRESOLVED]describe_resources() – reads itisinstance(...) [UNRESOLVED]_measure_crop_core – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itrun_preflight()describe_plan() – reads it_describe_outputs – reads itisinstance(...) [UNRESOLVED]describe_resources() – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_describe_plan_safedescribe_plan() – reads it_describe_outputs – reads itisinstance(...) [UNRESOLVED]Read by _measure_crop_core, measure_crop(), get_measure_crop_settings(), _describe_outputs, describe_plan(), describe_resources().
save_qc¶
Write overlay images showing where each tile was placed. The cheapest way to see that a stitch is right; costs one image per well. Default False.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
save_stack¶
align_folder() – reads itRead by align_folder().
save_stitched_default¶
Also write each stitched PAIR, not only the whole-well mosaic. A great many files; useful when diagnosing one bad seam and wasteful otherwise. Default False.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
save_workspace¶
mode_from_settings() – reads itisinstance(...) [UNRESOLVED]Read by mode_from_settings().
schedule¶
(str) - Learning-rate scheduler used by spacr.deep_spacr.train_model: ‘cosine’, ‘cosine_warm_restarts’, ‘reduce_lr_on_plateau’, ‘step_lr’, ‘exponential’, ‘linear’, or ‘none’. Plateau reacts to validation loss; cosine and linear use the epoch budget; warm restarts periodically raise the rate to escape a narrow minimum. API: train_model(schedule=…). Default ‘cosine’.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
score_column¶
(str) - Which column of the prediction CSV holds the CNN score that Explain CV and the hit-investigation montages read. The regression module no longer has this setting: it fits dependent_variable and simulates the minimum cell count on that same column, so one measurement cannot be named two ways there. Default ‘cv_predictions’. Investigate Hit starts blank because no score field can be inferred universally; select the prediction column before building its montages.
investigate_hit() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itRead by investigate_hit(), interpret_vision_model(), read_and_preprocess_data(), interpret_vision_model(), read_and_preprocess_data().
score_data¶
(str or list) - CSV(s) of per-object or per-well phenotype scores, typically from generate_ml_scores. Each must contain dependent_variable. Pass one path per plate, position-aligned with plates_score. The score filename does not name the output folder: runs go under src/results, named for the inference or regression kind and then suffixed _1, _2, and so on. When src is unset, results is created beside the first count_data file. Default ‘list of paths’.
_perform_regression_read_data – reads itnormalize_regression_input_pairs() – reads it_preparation_key – reads it_perform_regression_perform_regression – reads it_calibrated_fraction_threshold_calibration_inputs – reads itminimum_cell_simulation() – reads itnormalize_regression_input_pairs() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]column_run_settings() – reads itRead by _calibration_inputs, _perform_regression, _perform_regression_read_data, minimum_cell_simulation(), normalize_regression_input_pairs(), _preparation_key, column_run_settings().
score_threshold¶
(float) - Probability cutoff (0-1) applied to the model’s positive-class score when deriving the binary cv_predictions column: pred >= threshold becomes 1. The raw probability is always saved alongside it, so this only changes the hard call, not the score. Lower it to catch more positives at the cost of false positives; raise it for precision. Default 0.5.
apply_model_to_tar() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by apply_model_to_tar(), get_preprocess_ops_settings(), stitch_cycle_wells().
scores¶
(str, path) - CSV of per-object model scores to interpret, joined to measurements on plateID, rowID, columnID, fieldID and object_label. This must be the output of the classification run under interpretation. A CSV from a different model or plate may still satisfy the join keys and produce attribution results for mismatched predictions without raising an error. Default None.
interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itRead by interpret_vision_model(), read_and_preprocess_data(), interpret_vision_model(), read_and_preprocess_data().
sd_cells_per_well¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
sd_genes_per_well¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
sd_reads_per_gene¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
seconds_per_frame¶
(int) - Interval between consecutive timelapse frames, in seconds. Used with pixels_per_um to convert mean per-frame displacement into um/min; if either is missing, velocities stay in px/frame. It is also printed in the motility plot legend box. A wrong value rescales every reported velocity linearly. Default 60.
_t_stack_planplan_4d_from_settings() – reads itautomated_motility_assay() – reads itautomated_motility_assay() – reads itapply_settings() – reads itRead by apply_settings(), automated_motility_assay(), plan_4d_from_settings().
seed¶
resolve_seed() – reads itresolve_seed() – reads it_default_trainresolve_seed() – reads itresolve_seed() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]resolve_seed() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_call_simulator – reads itdict(...) [UNRESOLVED]simulate_fn(...) [UNRESOLVED]resolve_seed() – reads itRead by _call_simulator, resolve_seed().
seed_wells_from_cells¶
(bool) - Read the cell table as well, so a well holding host cells but no parasites appears in the results with a zero denominator instead of vanishing from the plate entirely. Switch it off only when the database has no cell table. Default True.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_invasion(), analyze_replication().
seg_qc¶
(str) - Segmentation quality control performed when masks are written, before measurement. ‘off’ skips scoring; ‘report’ scores every field, writes qc/segmentation_qc_<object>.csv, and displays detected quality issues; ‘flag’ also writes per-field JSON for downstream processing; ‘stop’ raises when the plate verdict is ‘fail’, after writing the scorecard. No mode deletes or omits a field, and ‘stop’ does not raise for a ‘warn’ verdict. Default ‘report’.
_score_v2_masksqc_mode() – reads it_run_seg_qcqc_mode() – reads it_run_seg_qcqc_mode() – reads it_run_seg_qcqc_mode() – reads itRead by qc_mode().
seg_qc_border_fraction¶
(float) - Fraction of a field’s objects allowed to touch the image edge before the field is flagged. Edge objects are truncated, so their crop dimensions and measured areas are biased downward. Geometry alone places approximately two object diameters on the border, corresponding to about 8% for 60 px cells in a 1400 px field; the default is therefore above the fraction expected in a valid field. Default 0.3.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_count_ratio¶
(float) - Permitted ratio between a field’s object count and the plate median before the field is flagged. A value of 0.25 flags counts below one quarter of the median or above its reciprocal, four times the median. Calibrate this threshold with representative control plates when expected object density varies by assay. Default 0.25.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_flags¶
_score_v2_masks – reads it_run_seg_qc – reads it_run_seg_qc – reads it_run_seg_qc – reads itRead by _score_v2_masks, _run_seg_qc.
seg_qc_foreground_fraction¶
(float) - Foreground coverage at or above which a field is classified as confluent. The distance-transform fusion check runs only above this threshold. Increasing it reduces computation but decreases sensitivity to fusion in moderately dense fields; decreasing it evaluates more sparse fields and increases runtime. It matches the fused_fraction used by the diameter estimator. Default 0.35.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_max_object_fraction¶
(float) - Fraction of the field that a single label may cover before it is classified as evidence of fusion rather than a valid object. A component covering one quarter of a field commonly represents a confluent monolayer merged into one mask; the diameter estimator excludes such components for the same reason. Lower it for small objects on large fields; raise it only when a single large object per field is expected. Default 0.25.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_min_diameter¶
(float) - Equivalent diameter in pixels below which an object is treated as a fragment; it drives the over-segmentation check and sets the seed floor of the fusion cross-check. Lower it to two or three for punctate organelles, where five-pixel objects may represent valid signal rather than debris, and raise it for large cells where components of that size are likely segmentation fragments. Default 5.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_min_objects¶
(int) - Fields with fewer objects than this are classified as near-empty, and robust per-field size statistics are suppressed because the median absolute deviation is unstable for very small samples. Increase the value for confluent cell plates expected to contain hundreds of objects per field; reduce it to 3-5 for low-multiplicity pathogen assays in which few objects per field are expected. Default 10.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_outlier_fraction¶
(float) - Fraction of a field’s objects that must fall outside the robust size range before the field is reported as containing multiple size populations. Such objects commonly represent debris, fused pairs, or fragments. Decrease the value to increase sensitivity to mixed fields, at the cost of more flags. Default 0.15.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_outlier_mad¶
(float) - Number of robust standard deviations, each defined as 1.4826 times the median absolute deviation, that an object’s diameter may differ from the field median before it is classified as a size outlier. Median and MAD limit the influence of debris. The default of five accommodates heavier-tailed biological size distributions; a threshold of three can flag valid objects. Default 5.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_plate_fail_fraction¶
(float) - Fraction of failing fields at which the plate-level scorecard changes from warn to fail. The default 0.1 corresponds approximately to one column of a 96-well plate. This setting changes only the reported verdict and does not determine which fields are processed. Default 0.1.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_size_ratio¶
(float) - Fold change in a field’s median object diameter, measured against the plate median, that marks it as fused or fragmented when its object count has moved in the opposite direction. Merging two equal objects into one increases equivalent diameter by a factor of approximately 1.41, while dividing one object into two produces the reciprocal change; the default therefore reflects the expected geometric ratio. Default 1.4.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_split_ratio¶
(float) - Minimum ratio of distance-transform maxima to mask objects required to flag fusion in a field already classified as confluent. A value of 2 requires at least two resolved maxima per mask object on average. Increasing the value reduces sensitivity to fused masks. Default 2.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
seg_qc_tiny_fraction¶
(float) - Fraction of a field’s objects that may be smaller than seg_qc_min_diameter before the field is classified as over-segmented. Dividing one cell into multiple fragments increases this fraction, whereas a valid field containing limited debris should remain below the default threshold. Default 0.3.
_score_v2_masksthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads it_run_seg_qcthresholds_from_settings() – reads itRead by thresholds_from_settings().
sep¶
read_table() – reads itwrite_table() – reads itRead by read_table(), write_table().
sequencing_error¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
settings¶
_flowview_pipeline – reads itdecorate() – reads itobserved() – reads it_flowview_pipeline – reads itdecorate() – reads itobserved() – reads itRead by _flowview_pipeline, decorate(), observed(), _flowview_pipeline, decorate(), observed().
shap¶
(bool) - Compute SHAP values, which attribute each individual prediction to each feature instead of ranking features overall. It is the only one of the three that can explain a single object, and by far the slowest - see shap_sample before enabling it on a full plate. Default False.
interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]shap_sample¶
(bool) - Run SHAP on a subsample rather than every object. Runtime and memory use increase with row count. Subsampling reduces computational cost but omits local explanations for excluded objects. Disable when attribution is required for every object. Default True.
interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]show_progress¶
_captum_attribute – reads itRead by _captum_attribute.
shuffle¶
(bool) - Shuffle the tar dataset in the DataLoader when generating activation maps, so each batch-grid PDF contains a mixed sample rather than consecutive files from one plate or class. False preserves deterministic file order and permits direct alignment with the dataset listing. Default True.
generate_activation_map() – reads itRead by generate_activation_map(), get_default_generate_activation_map_settings().
sim_time¶
run_and_save() – reads itRead by run_and_save().
single_direction¶
(str) - Which mate to scan when mode is ‘single’: ‘R1’ or ‘R2’. The chosen file is read as-is with no reverse-complementing, so selecting ‘R2’ means target_sequence and regex must be written in R2 orientation or nothing will match. Ignored when mode is ‘paired’. Default ‘R1’.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
sliding_window_shapes¶
_captum_attribute – reads itRead by _captum_attribute.
smooth_lines¶
(bool) - Draw cluster outlines as a smoothed spline through the convex hull (2 pt wide) rather than the raw straight hull segments (4 pt). Purely cosmetic - it does not change clustering; switch it off if smoothing distorts the true cluster boundary. No effect unless plot_outlines is on, and forced off when color_by is set. Default True.
generate_image_umap() – reads itRead by generate_image_umap().
smoothgrad_samples¶
(int) - Number of noise-perturbed image copies averaged into one attribution map. Using 8-50 samples reduces local gradient variability and improves between-image comparability. A value of 0, the default, evaluates the method once and minimizes computation during method selection. Applies to every method, including the CAM family, where maps are averaged explicitly rather than through Captum.
generate_activation_map() – reads itRead by generate_activation_map(), get_default_generate_activation_map_settings().
smoothgrad_sigma¶
(float) - Standard deviation of the noise added by SmoothGrad, expressed as a fraction of the image intensity range. Values that are too small produce nearly identical samples and little averaging effect; values that are too large move samples outside the training distribution, causing the average to characterize responses to noise rather than the experimental images. Values of 0.1-0.2 are typical. Ignored when smoothgrad_samples is 0. Default 0.15.
generate_activation_map() – reads itRead by generate_activation_map(), get_default_generate_activation_map_settings().
sort_keys¶
to_json() – reads itRead by to_json().
spatial_measurements¶
(bool) - Measure each object’s neighbourhood: the number of neighbours within a radius, first and second nearest-neighbour distances, and the fraction of its border contacting another object. These measurements can be used to model density-associated variation in morphology and intensity. They are not produced for cytoplasm, which is defined as one object per cell. Computation requires one KD-tree and one boundary pass per field. Default True.
_measure_crop_core_morphological_measurements – reads it_record_organelle_caveatsorganelle_measurement_caveats() – reads itget_measure_crop_settings() – reads itorganelle_measurement_caveats() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _morphological_measurements, get_measure_crop_settings(), organelle_measurement_caveats().
spatial_neighbor_radius¶
(int) - Radius used by spatial_measurements when counting neighbouring objects. The value is expressed in the units recorded for the measurement table: pixels for two-dimensional data and micrometres for calibrated three-dimensional data. The radius is included in the output column name, so use one value consistently across plates that will be combined. Ignored unless spatial_measurements is enabled. Default 50.
_measure_crop_core_morphological_measurements – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _morphological_measurements, get_measure_crop_settings().
spectral_affinity¶
(str) - Graph construction for Spectral Embedding: nearest_neighbors builds a sparse local graph; rbf builds a dense radial-basis affinity. Used only by Spectral Embedding. Default ‘nearest_neighbors’.
generate_image_umap() – reads itRead by generate_image_umap().
spectral_n_neighbors¶
(int) - Neighbor count for Spectral Embedding when affinity is nearest_neighbors. Ignored for rbf affinity. Default 15.
generate_image_umap() – reads itRead by generate_image_umap().
spline_degree¶
(int) - The polynomial degree of each covariate’s spline basis. 3 is a cubic spline, the usual choice; 1 is piecewise linear. Read only by regression_type ‘spline’. Default 3.
_perform_regression_read_data_reconcile_random_row_column_effects – reads it_perform_regression_perform_regression – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _reconcile_random_row_column_effects.
spline_knots¶
(int) - How many knots the spline basis gets for each CONTINUOUS covariate. More knots let the covariate bend more freely and spend more degrees of freedom; a covariate with fewer distinct values than the degree is left linear rather than given a basis made out of nothing. The guide columns are never given a basis, so the fit still returns one coefficient and one p-value per guide. Read only by regression_type ‘spline’. Default 4.
_perform_regression_read_data_reconcile_random_row_column_effects – reads it_perform_regression_perform_regression – reads it_reconcile_random_row_column_effects – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, _reconcile_random_row_column_effects.
squeeze_singleton¶
Drop axes of length one when reading a file. Off keeps a (1, Y, X) file three-dimensional, which matters when downstream code counts dimensions. Default True.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
src¶
(str, path) - Folder the current step reads from and writes into: raw images for mask generation, the merged/ folder of .npy stacks for measure, the plate root for dataset/regression steps, or the folder of .fastq.gz reads for sequencing. Outputs (stack/, masks/, measurements/measurements.db, datasets/, results/) are created inside it. A list of paths, or a “[‘a’,’b’]” string, processes several plates in one run. No usable default: the settings factories fill a placeholder (‘path’ or ‘/path/to/src’), so this must be supplied.
align_folder() – reads itrun_anndata_export() – reads itanndata_export_settings() – reads itgenerate_annotation_dataset() – reads it_collect_run_status_status_artifacts_src_values – reads it_deferrable_src_values – reads it_status_snapshot_status_artifacts_src_values – reads itdefault_label() – reads it_begin_flowview_run_install_classify_collector_source_value – reads it_call_entry – reads itfunc(...) [UNRESOLVED]convert_folder() – reads itgenerate_image_umap() – reads itopen_crop_source() – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]generate_screen_graphs() – reads itpreprocess_generate_masks() – reads itpreprocess_img_data() – reads itprepare_segmentation_illumination() – reads itprepare_illumination_model() – reads itgenerate_cellpose_masks_sam() – reads itautomated_motility_assay() – reads itsave_settings() – reads itrun_preflight()_describe_outputs – reads itisinstance(...) [UNRESOLVED]reducer_hyperparameter_search() – reads itresolve_crop_source() – reads itannotate_filter_vision() – reads itdeep_spacr() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itsave_settings() – reads itgenerate_dataset() – reads itopen_crop_source() – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]save_settings() – reads itgenerate_training_dataset() – reads itopen_crop_source() – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itgenerate_activation_map() – reads itsave_settings() – reads it_fresh_collector_source_value – reads itdict(...) [UNRESOLVED]run_import() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itsave_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itsave_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]load_activation_data() – reads itload_search_data() – reads itumap_checkpoint_path() – reads itillumination_settings() – reads it_project_root – reads itisinstance(...) [UNRESOLVED]_measure_crop_core – reads it_write_intensity_rescale_record – reads itmeasure_crop() – reads itprepare_illumination_model() – reads itvalidate_measurement_illumination_inputs() – reads it_save_settings_to_db – reads itprocess_measure_crop_results() – reads itplan_measure_resume() – reads itmeasurements_db_path() – reads itdict(...) [UNRESOLVED]get_measure_crop_settings() – reads itmeasure_test_mode() – reads itsave_settings() – reads itrun_preflight()_describe_outputs – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]process_measure_crop_results() – reads itcreate_extended_radar_plot() – reads itgenerate_ml_scores() – reads itsave_settings() – reads itinterpret_vision_model() – reads itsave_settings() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads it_describe_cellpose_training – reads it_describe_classifier_training – reads itgenerate_cellpose_masks() – reads itautomated_motility_assay() – reads it_trial_settings – reads itrerun_trial() – reads itrun_trial_contained() – reads itdict(...) [UNRESOLVED]settings_for_trial() – reads itgraph_importance() – reads itsave_settings() – reads itplot_data_from_csv() – reads itplot_data_from_db() – reads itsave_settings() – reads itplot_region() – reads itapply() – reads itbuild() – reads itdict(...) [UNRESOLVED]import_plates_from_csv() – reads it_load_regression_results – reads it_measurements_destination – reads it_on_run – reads it_search_figure_dir – reads it_make_screen – reads it_trial_figures_ready – reads itapply_settings() – reads itisinstance(...) [UNRESOLVED]apply_settings(...) [UNRESOLVED]_on_click – reads itwire_add_current() – reads itresolve_default_settings() – reads itsettings_from_pack() – reads itdict(...) [UNRESOLVED]apply_settings() – reads it_perform_regression_perform_regression_annotation_cache – reads it_perform_regression_set_paths – reads it_show_response_distribution – reads itsave_settings() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]destination() – reads it_describe_plan_safe_describe_outputs – reads itisinstance(...) [UNRESOLVED]_settings_point_at – reads itisinstance(...) [UNRESOLVED]plates_of() – reads itgenerate_barecode_mapping() – reads itsave_settings() – reads itset_annotate_default_settings() – reads itrun_and_save() – reads itcheck_cellpose_models() – reads itops_preprocess() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]stream_dataset() – reads itanalyze_class_proportion() – reads itsave_settings() – reads itanalyze_endodyogeny() – reads itsave_settings() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itsave_settings() – reads itapply_defaults(...) [UNRESOLVED]analyze_percent_positive() – reads itdefault_settings_analyze_percent_positive() – reads itsave_settings() – reads itanalyze_plaques() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itsave_settings() – reads itsplit_wells() – reads itsave_settings() – reads itanalyze_recruitment() – reads itsave_settings() – reads itanalyze_replication() – reads it_set_analyze_replication_defaults – reads itsave_settings() – reads itapply_defaults(...) [UNRESOLVED]apply_cellpose_model() – reads itget_default_apply_cellpose_model_settings() – reads itsave_settings() – reads itcount_phenotypes() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads ittest_cellpose_model() – reads itget_default_test_cellpose_model_settings() – reads itsave_settings() – reads ittrain_cellpose() – reads itsave_settings() – reads itmain() – reads itdelete_intermedeate_files() – reads itRead by align_folder(), anndata_export_settings(), run_anndata_export(), generate_annotation_dataset(), _src_values, default_label(), _call_entry, convert_folder(), generate_image_umap(), generate_screen_graphs(), preprocess_generate_masks(), reducer_hyperparameter_search(), resolve_crop_source(), _cross_validate_model, annotate_filter_vision(), deep_spacr(), generate_activation_map(), train_test_model(), _source_value, run_import(), load_activation_data(), load_search_data(), umap_checkpoint_path(), illumination_settings(), prepare_illumination_model(), prepare_segmentation_illumination(), validate_measurement_illumination_inputs(), _save_settings_to_db, generate_dataset(), generate_training_dataset(), open_crop_source(), preprocess_img_data(), _project_root, _measure_crop_core, _write_intensity_rescale_record, measure_crop(), process_measure_crop_results(), _annotation_cache, _perform_regression_set_paths, _show_response_distribution, create_extended_radar_plot(), generate_ml_scores(), interpret_vision_model(), read_and_preprocess_data(), _describe_cellpose_training, _describe_classifier_training, generate_cellpose_masks(), generate_cellpose_masks_sam(), _trial_settings, rerun_trial(), run_trial_contained(), settings_for_trial(), graph_importance(), plot_data_from_csv(), plot_data_from_db(), plot_region(), apply(), build(), import_plates_from_csv(), _load_regression_results, _measurements_destination, _on_run, _search_figure_dir, _make_screen, _trial_figures_ready, apply_settings(), _on_click, wire_add_current(), resolve_default_settings(), settings_from_pack(), apply_settings(), destination(), _settings_point_at, measurements_db_path(), plan_measure_resume(), plates_of(), generate_barecode_mapping(), default_settings_analyze_percent_positive(), get_analyze_plaque_settings(), get_default_apply_cellpose_model_settings(), get_default_test_cellpose_model_settings(), get_measure_crop_settings(), get_train_test_model_settings(), set_annotate_default_settings(), run_and_save(), check_cellpose_models(), identify_masks_finetune(), get_preprocess_ops_settings(), ops_preprocess(), stitch_cycle_wells(), stream_dataset(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_percent_positive(), analyze_plaques(), analyze_recruitment(), analyze_replication(), apply_cellpose_model(), count_phenotypes(), interpret_vision_model(), read_and_preprocess_data(), split_wells(), test_cellpose_model(), train_cellpose(), main(), automated_motility_assay(), delete_intermedeate_files(), measure_test_mode(), save_settings(), _describe_outputs.
stage¶
register_declared() – reads itRead by register_declared().
stain_baseline_wells¶
(list or None) - These wells set the empirical negative distribution for the pre-permeabilisation stain and are excluded from efficiency calculations. Set a column (‘c12’), row (‘r1’), well (‘r1_c12’), or complete plate key whose parasites received no stain; None uses the automatic per-field method. Default None.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
start_time¶
run_and_save() – reads itrun_multiple_simulations() – reads itRead by run_and_save(), run_multiple_simulations().
starved_read_fraction¶
Share of the median well’s read total used as the starvation cut when min_reads_per_well is 0. A well at a tenth of typical depth turns single stray reads into 10% abundances, which is why 0.1 is the default. Ignored when min_reads_per_well is set.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
stdevs¶
_captum_attribute – reads itRead by _captum_attribute.
stitch¶
Run the pairwise stitch. Off scores no pairs and only organises the plate into per-well folders, which is what you want when the images are already stitched. Default False.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
stitch_threshold¶
(float) - Minimum overlap, as an intersection-over-union between 0 and 1, for a label in one plane to be treated as the same object as a label in the plane below when z_segmentation_mode is ‘stitch’. Raising it splits objects that drift or change shape between planes into several shorter ones; lowering it fuses neighbouring objects that merely overlap in projection. Matching is one-to-one, so when two objects both overlap the same object below only the better match inherits its label and the other starts a new one. Ignored by the other two modes. Default 0.25.
Read by plan_4d_from_settings(), plan_from_settings().
straightness_filter¶
(bool) - Apply the straightness threshold. False reports how many tracks exceed straightness_threshold without changing the data; True removes those tracks from the velocity table, per-well summary and plots. Enable it when stage drift or identity swaps produce implausibly straight trajectories. Default False.
_compute_velocities_and_well_summary – reads it_compute_velocities_and_well_summary – reads itapply_settings() – reads itRead by apply_settings(), _compute_velocities_and_well_summary.
straightness_threshold¶
(float) - Straightness cut-off, where straightness = net displacement / total path length (0 = returns to start, 1 = perfectly straight). When straightness_filter is True, tracks at or above this value are dropped as drift or tracking artifacts, so lowering it discards more tracks. The count is always logged. Default 0.95.
_compute_velocities_and_well_summary – reads it_compute_velocities_and_well_summary – reads itapply_settings() – reads itRead by apply_settings(), _compute_velocities_and_well_summary.
stream_csv¶
Write each result to the report as it is produced rather than at the end, so a long run can be watched and an interrupted one keeps what it had. Off is marginally faster and loses everything on a crash. Default True.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
stream_method¶
(str) - Method used to locate objects for streamed crops. ‘column’ uses coordinates stored in the object table and requires object_array and channel_arrays. ‘array’ uses labelled objects in the same object_array plane and additionally requires bounding_box. Settings that do not apply to the selected method are ignored. Default ‘column’.
get_setting_dependencies() – reads itRead by get_setting_dependencies().
stream_source¶
generate_annotation_dataset() – reads itRead by generate_annotation_dataset().
strict¶
register_outputs() – reads itRead by register_outputs().
strict_errors¶
(bool or None) - Error-handling policy for recoverable steps. Off records failures in the run ledger and final summary while continuing with successful items. On raises immediately for setup or configuration errors such as unreadable paths, missing columns or inaccessible databases, preventing partial batch results from invalid inputs. Per-item failures such as one corrupt image remain recoverable under either policy. None defers to $SPACR_STRICT_ERRORS. Default None.
strict_errors() – reads itisinstance(...) [UNRESOLVED]_measure_crop_corestrict_errors() – reads itisinstance(...) [UNRESOLVED]strict_errors() – reads itisinstance(...) [UNRESOLVED]get_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]strict_errors() – reads itisinstance(...) [UNRESOLVED]strict_errors() – reads itisinstance(...) [UNRESOLVED]Read by strict_errors(), get_measure_crop_settings().
strides¶
_captum_attribute – reads itRead by _captum_attribute.
subpixel¶
align_folder() – reads itRead by align_folder().
summarize_organelles_by¶
(str, list or None) - Parent compartments to roll every enabled organelle slot into. Accepts ‘cell’, ‘nucleus’, ‘pathogen’ and ‘cytoplasm’; each writes one <parent>_organelle_summary row per parent with a separate organelle_summary_<slot>_* column family. Raw per-organelle tables are always written when their mask dim is enabled. Default ‘cell’; None disables only these rollups.
_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by _measure_crop_core, get_measure_crop_settings().
summary_func¶
generate_screen_graphs() – reads itRead by generate_screen_graphs().
surrogate_correlation_threshold¶
Absolute Spearman correlation above which a held-out feature pair is disclosed. Lower values reveal more redundancy and produce a larger audit table. Default 0.9.
run_explain_cv() – reads itexplain_cv_default_settings() – reads itsurrogate_exclude¶
Additional measured features barred from the explanatory matrix. Use it for known artifacts or post-treatment annotations; model outputs, classes and identifiers remain excluded automatically. Default [].
run_explain_cv() – reads itexplain_cv_default_settings() – reads itsurrogate_min_fidelity_improvement¶
Accuracy improvement over the majority-class baseline required before feature importances are presented as explanations. Raising it withholds more weak surrogates. Default 0.05.
run_explain_cv() – reads itexplain_cv_default_settings() – reads itsurrogate_model¶
Estimator family used to reproduce CV decisions: Random Forest, histogram gradient boosting, or optional XGBoost. Missing XGBoost is refused rather than silently substituted. Default random_forest.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]run_explain_cv() – reads itexplain_cv_default_settings() – reads itRead by explain_cv_default_settings(), run_explain_cv(), _check_app_specific.
surrogate_n_estimators¶
Number of trees or boosting iterations in the selected surrogate. Raising it can improve fidelity but increases runtime and may overfit small training sets. Default 300.
run_explain_cv() – reads itexplain_cv_default_settings() – reads itsurrogate_n_repeats¶
Repeated held-out shuffles per feature for permutation importance. More repeats stabilize rankings but multiply scoring time without changing the fitted model. Default 5.
run_explain_cv() – reads itexplain_cv_default_settings() – reads itsurrogate_random_seed¶
Seed shared by grouped splitting, estimator fitting and importance calculations. Keep it fixed for exact reproduction and change it only for sensitivity checks. Default 0.
run_explain_cv() – reads itexplain_cv_default_settings() – reads itsurrogate_shap_max_samples¶
Maximum held-out objects receiving signed SHAP values and dependence plots. Raising it improves coverage at substantial computational cost; sampled IDs are recorded. Default 500.
run_explain_cv() – reads itexplain_cv_default_settings() – reads itsurrogate_split_by¶
Independent acquisition unit kept intact between fitting and fidelity evaluation. Use well by default, or plate for a harder batch-generalization test.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]run_explain_cv() – reads itexplain_cv_default_settings() – reads itRead by explain_cv_default_settings(), run_explain_cv(), _check_app_specific.
surrogate_test_size¶
Fraction of independent well or plate groups held out for fidelity and permutation importance. Larger values strengthen evaluation but leave fewer groups for fitting. Default 0.3.
run_explain_cv() – reads itexplain_cv_default_settings() – reads itsvd_solver¶
reduction_and_clustering() – reads itRead by reduction_and_clustering().
sweep_points¶
Log-spaced points on the sweep, before the derived threshold is added to them. Must be at least 3. Default 25.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
sweep_span¶
How far either side of the derived threshold the sweep runs, as a multiplicative factor. 4.0 sweeps a quarter to four times the derived value. Must be above 1. Default 4.0.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
t_axis¶
(int or None) - Index of the time axis in the incoming array, as an alternative to spelling out the whole order in t_axis_order; the z axis is then taken to be the other of the two leading axes, or whatever z_axis says. Use it for an acquisition whose axes are not in either of the two standard orders. When both this and t_axis_order are set they must agree, and spaCR stops if they do not rather than silently preferring one. Default None.
_t_stack_planplan_4d_from_settings() – reads itRead by plan_4d_from_settings().
t_axis_order¶
(str or None) - Which of the two leading axes is time and which is z: ‘TZYX’ for a stack per timepoint, ‘ZTYX’ for a time series per plane. Real microscopes write both and the array shape cannot distinguish them, so spaCR raises an error until the axis order is specified. An incorrect value does not raise an exception; it links objects at corresponding lateral positions in adjacent z planes and interprets those displacements as temporal motion, producing invalid velocity estimates. Verify the setting against the acquisition axis order. Default None.
_t_stack_planplan_4d_from_settings() – reads itRead by plan_4d_from_settings().
t_index¶
Which timepoint to take from a time series, zero-indexed. Only read when the file has a time axis. Default 0.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
t_link_threshold¶
(float) - Minimum intersection-over-union, between 0 and 1, required to assign an object at one timepoint to the same track at the next when t_track_backend is ‘iou’. Increasing it can divide moving or growing objects into short tracks; decreasing it can join neighbouring objects whose volumes overlap. This parameter is separate from stitch_threshold because overlap distributions differ between consecutive z planes and consecutive timepoints. Matching is one-to-one. Default 0.25.
_t_stack_planplan_4d_from_settings() – reads itRead by plan_4d_from_settings().
t_max_displacement_px¶
(float or None) - How far an object may move between consecutive timepoints and still count as the same object, in image pixels, for the distance-based backends. The z component is multiplied by anisotropy first, so a one-plane move on a stack with a 5x z step costs 5 px of the budget rather than 1. Too small breaks tracks at every fast frame; too large joins neighbours into one. Default None.
_t_stack_planplan_4d_from_settings() – reads itRead by plan_4d_from_settings().
t_max_displacement_um¶
(float or None) - Maximum between-frame movement, equivalent to t_max_displacement_px but expressed in micrometres. Conversion requires voxel_size_z_um and voxel_size_xy_um; with both values defined, anisotropy is incorporated into the physical coordinates. Set either this value or t_max_displacement_px, not both, because they define the same displacement threshold in different units. Default None.
_t_stack_planplan_4d_from_settings() – reads itRead by plan_4d_from_settings().
t_project_for_tracking¶
(bool) - Collapse each timepoint’s z-stack to one plane before linking, so tracking uses the projection while segmentation uses the volume. Enable this setting when volumetric linking is too slow or anisotropy is uncertain. Objects at the same lateral position but different z positions then merge in the projection and cannot be distinguished downstream. This setting does not enable backends that do not support volumetric data. Default False.
_t_stack_planplan_4d_from_settings() – reads itRead by plan_4d_from_settings().
t_stack¶
(bool) - When enabled, spaCR requires each field to be a (T, Z, Y, X) volume. Standard image ingestion collapses z by maximum-intensity projection, so a run using that path stops with an error instead of pretending the projected data are 4-D. Enable this setting only when passing volumes to spacr.zstack.segment_4d through the Python API, and specify t_axis_order because shape alone cannot identify the time axis. When disabled, no 4-D processing occurs. Default False.
_t_stack_planplan_4d_from_settings() – reads it_refuse_t_stack – reads it_refuse_t_stack – reads itRead by _refuse_t_stack, plan_4d_from_settings().
t_track_backend¶
(str) - Linker used to join objects between consecutive timepoints. ‘iou’ compares complete object volumes and requires no distance or anisotropy parameter, but cannot link an object that moves farther than its own width between frames. ‘centroid’ links nearest centroids within the displacement limit and supports faster movement, but requires an appropriate limit. Use ‘iou’ for crowded fields with slow motion and ‘centroid’ for sparse fields with rapid motion. Default ‘iou’.
_t_stack_planplan_4d_from_settings() – reads itRead by plan_4d_from_settings().
table¶
generate_annotation_dataset() – reads itRead by generate_annotation_dataset().
table_name¶
generate_ml_scores() – reads itadd_column_to_database() – reads itRead by generate_ml_scores(), add_column_to_database().
table_names¶
plot_data_from_db() – reads itRead by plot_data_from_db().
tables¶
(list) - Measurement tables read from each plate’s database and merged into one analysis frame. Only ‘cell’, ‘nucleus’, ‘pathogen’, ‘cytoplasm’, and ‘png_list’ are merged. Any other table, including ‘organelle’, is loaded but omitted from the merged result without a warning. Default [‘cell’, ‘nucleus’, ‘pathogen’, ‘cytoplasm’].
generate_image_umap() – reads itgenerate_screen_graphs() – reads itreducer_hyperparameter_search() – reads itgenerate_training_dataset() – reads itload_search_data() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_percent_positive() – reads itdefault_settings_analyze_percent_positive() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]read_and_preprocess_data() – reads itRead by generate_image_umap(), generate_screen_graphs(), reducer_hyperparameter_search(), load_search_data(), generate_training_dataset(), interpret_vision_model(), read_and_preprocess_data(), default_settings_analyze_percent_positive(), analyze_class_proportion(), analyze_endodyogeny(), analyze_percent_positive(), interpret_vision_model(), read_and_preprocess_data().
tar_path¶
(str) - Existing full-dataset tar to reuse for inference. Leave blank to generate one beneath the first plate’s datasets folder. Multiple selected plates are combined into one tar. API: spacr.deep_spacr.apply_model_to_tar. Default empty.
deep_spacr() – reads itapply_model_to_tar() – reads itRead by apply_model_to_tar(), deep_spacr().
target¶
(str) - Free-text label for the protein or marker imaged in channel_of_interest, e.g. ‘GRA1’. The recruitment run prints it in its banner (‘channel:3 = protein’) to record what the recruitment ratio is measuring; it feeds no computation, so changing it alters nothing but that log line. Default ‘protein’.
analyze_recruitment() – reads itRead by analyze_recruitment().
target_gene¶
Gene identifier carried from the selected Hit List row. It labels the attribution run and output folder without being inferred again. Default ‘’ requires selection.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]investigate_hit() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by investigate_hit(), _check_app_specific.
target_grnas_per_well¶
How many gRNAs a well is meant to carry. This is the biological target that replaces picking an abundance cutoff by eye: spaCR solves for the read-fraction threshold that delivers it in THIS run’s data and prints the number it derived, then sweeps around it so the trade-off is visible. Raise it for statistical power (more guides per well, more wells kept), lower it for attributability (a phenotype traceable to fewer guides). A well holding more than this is counted as a collision. Default 5.
Read by _run_barcode_qc, barcode_qc(), barcode_qc_defaults().
target_guides¶
Exact guides supporting the selected gene in the source result. Each remains separate evidence so discordance cannot be averaged away. Default [].
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]investigate_hit() – reads ithit_investigation_default_settings() – reads itrun_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by hit_investigation_default_settings(), investigate_hit(), _check_app_specific.
target_height¶
(int) - Height in pixels that images are resized to before segmentation; masks are scaled back to the original dimensions afterwards. Only applied when both target_height and target_width are set (and, on the non-normalized path, when resize is True). Use it to match the field size the model was trained at. Default None, which disables resizing; 1120 for plaque analysis.
check_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
target_intensity_min¶
(float) - Recruitment-analysis cutoff on the 95th-percentile intensity of channel_of_interest inside each cell: cells at or below it are discarded before recruitment ratios are computed. Raise it to keep only strongly expressing cells; set 0 or None to disable the filter entirely. Raw intensity units, default 1.
analyze_recruitment() – reads itRead by analyze_recruitment().
target_layer¶
(str) - Dotted attribute path to the convolutional layer whose activations and gradients Grad-CAM hooks, e.g. ‘base_model.blocks.3.layers.1.layers.MBconv.layers.conv_b’; utils.recommend_target_layers(model) lists valid names. Later layers give class-specific but coarse maps, earlier ones finer detail. Required for ‘gradcam’/’gradcam_pp’ - it is auto-filled only when model_type is exactly ‘maxvit’, and left None it raises. Default None.
generate_activation_map() – reads it_attribute_activation_params – reads itdict(...) [UNRESOLVED]update(...) [UNRESOLVED]dict(...) [UNRESOLVED]_fit_activation_params – reads itdict(...) [UNRESOLVED]update(...) [UNRESOLVED]dict(...) [UNRESOLVED]_attribute(...) [UNRESOLVED]Read by generate_activation_map(), _activation_params, get_default_generate_activation_map_settings().
target_sequence¶
(str) - Constant vector sequence used as the anchor: every read is scanned for an exact match and the barcode window is then sliced relative to that hit using offset_start and window_length. Reads without an exact match are skipped entirely, so it must be error-free and given in the orientation of the read being scanned. Default ‘TGCTGTTTCCAGCATAGCTCTTAAAC’.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
target_size¶
(int) - Edge length in pixels to which training images and masks are resized before Cellpose fine-tuning, applied to both axes to produce square input. Larger values preserve finer boundary detail while increasing VRAM use and computation approximately quadratically; smaller values reduce computation but may blur segmentation boundaries. Default 1000.
_describe_cellpose_training – reads it__init__ – reads itget_default_apply_cellpose_model_settings() – reads itget_default_test_cellpose_model_settings() – reads ittrain_cellpose() – reads itRead by _describe_cellpose_training, get_default_apply_cellpose_model_settings(), get_default_test_cellpose_model_settings(), __init__, train_cellpose().
target_statistic¶
Whether target_grnas_per_well is a ‘median’ or a ‘mean’ over wells. Median is the default because a handful of wells that soaked up the whole library drag a mean far off the typical well. Default ‘median’.
barcode_qc() – reads itbarcode_qc_defaults() – reads itRead by barcode_qc(), barcode_qc_defaults().
target_unique_count¶
(int) - Desired mean number of distinct gRNAs per well. spaCR evaluates 1000 read-fraction thresholds, selects the threshold whose per-well mean unique-gRNA count has the smallest absolute difference from this value, and discards every gRNA call below that fraction. Decrease it for a stricter well assignment or increase it to retain more gRNAs per well. Default 5.
_preparation_key – reads it_perform_regression_perform_regression_draw_the_threshold_sweep_graph_sequencing_statsgraph_sequencing_stats() – reads it_graph_sequencing_statsgraph_sequencing_stats() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _preparation_key, graph_sequencing_stats().
target_width¶
(int) - Width in pixels that images are resized to before segmentation; masks are scaled back to the original dimensions afterwards. Only applied when both target_width and target_height are set (and, on the non-normalized path, when resize is True). Use it to match the field size the model was trained at. Default None, which disables resizing; 1120 for plaque analysis.
check_cellpose_models() – reads itget_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itRead by get_analyze_plaque_settings(), check_cellpose_models(), identify_masks_finetune().
tensorboard¶
(bool) - Write PyTorch loss, accuracy, macro-F1, and learning-rate events to dst/tensorboard while the vision model trains. Run tensorboard –logdir <path> with that directory to open an interactive dashboard and compare runs. The in-app loss and accuracy monitor is controlled separately by plot. Default True.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings().
test¶
(bool) - In classifier training, run the held-out evaluation pass, either with train or alone to score an existing model. In the sequencing barcode mapper, process only the first read chunk and print a preview so the regex and barcode CSVs can be validated quickly. Default False.
run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]deep_spacr() – reads ittrain_test_model() – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]generate_barecode_mapping() – reads itRead by deep_spacr(), train_test_model(), generate_barecode_mapping(), get_train_test_model_settings(), _check_required_paths.
test_images¶
(int) - How many plate/well/field image sets are copied into a test/ folder when test_mode is on; every channel file belonging to a chosen set is copied together. Raise it for a broader smoke test, lower it for a faster one. Forced to 1 for timelapse runs so a full sequence stays intact. Default 10.
Read by preprocess_img_data().
test_mode¶
(bool) - Run the pipeline on a small random subset instead of the whole folder. Mask generation copies test_images (default 10) complete image sets into <src>/test and works there; measure_crop copies test_nr (default 10) merged arrays into test/merged. Both also force verbose and plot on. Use it to check channel assignment, diameters and thresholds before committing to a full plate. Default False.
preprocess_generate_masks() – reads itpreprocess_img_data() – reads itsave_settings() – reads itrun_preflight()describe_plan() – reads itisinstance(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itsave_settings() – reads itsave_settings() – reads itsave_settings() – reads it_default_trainsave_settings() – reads itsave_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]get_measure_crop_settings() – reads itmeasure_test_mode() – reads itsave_settings() – reads itrun_preflight()describe_plan() – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itread_and_preprocess_data(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads it_perform_regression_perform_regressionsave_settings() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]refit_settings() – reads it_describe_plan_safedescribe_plan() – reads itisinstance(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itsave_settings() – reads itsave_settings() – reads itapply_defaults(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itsave_settings() – reads itsave_settings() – reads itsave_settings() – reads itapply_defaults(...) [UNRESOLVED]save_settings() – reads itsave_settings() – reads itsave_settings() – reads itRead by preprocess_generate_masks(), preprocess_img_data(), refit_settings(), get_measure_crop_settings(), measure_test_mode(), save_settings(), describe_plan().
test_nr¶
(int) - How many files are sampled at random from merged/ into test/merged when test_mode is on in the measure-and-crop pipeline, so measurement runs on a small subset. Raise it if a handful of fields is not representative; each extra file costs a full measurement pass. Default 10.
get_measure_crop_settings() – reads itmeasure_test_mode() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]test_size¶
(float) - Fraction of labelled single-object rows reserved as the test split for the tabular machine-learning classifier; the remainder trains the model. Increasing the value reduces uncertainty in the accuracy estimate but leaves fewer labelled rows for training. Valid range 0-1; default 0.2 (20% test).
generate_ml_scores() – reads itRead by generate_ml_scores().
test_split¶
(float) - Fraction of the generated crops held out as the test set, between 0 and 1. The split respects the grouping level chosen elsewhere, so crops from one well do not straddle it and the score is not inflated by the model recognising the well. Raising it buys a steadier estimate and costs training data. Default 0.1.
generate_annotation_dataset() – reads it_begin_flowview_run_install_classify_collectorclassify_graph() – reads itgenerate_training_dataset() – reads it_fresh_collectorclassify_graph() – reads itdict(...) [UNRESOLVED]stream_dataset() – reads itRead by generate_annotation_dataset(), classify_graph(), generate_training_dataset(), stream_dataset().
theme¶
generate_screen_graphs() – reads itplot_data_from_csv() – reads itplot_data_from_db() – reads itinstall_dna_rain() – reads itRead by generate_screen_graphs(), plot_data_from_csv(), plot_data_from_db(), install_dna_rain().
threshold¶
(list, list-of-lists, int or None) - Cut-off applied to ‘measurement’ before the annotation grid loads, so you only label the objects you care about. Accepts a number or a quantile code ‘q1’-‘q9’ (q3 = the 30th percentile of that column), or one entry per measurement when measurement is a list. Empty or None loads every object unfiltered. Default 2000 where a numeric cutoff is used; empty where the setting is optional.
set_annotate_default_settings() – reads itanalyze_percent_positive() – reads itdefault_settings_analyze_percent_positive() – reads itRead by default_settings_analyze_percent_positive(), set_annotate_default_settings(), analyze_percent_positive().
threshold_agreement_tolerance¶
(float) - Relative distance a threshold may sit from its reference before the field and well are flagged; the reference is the control-derived cut when controls exist, otherwise the field’s own automatic cut. 0.5 means a factor of two. Lower it to catch smaller drifts between a fixed threshold and what the data would have chosen. Default 0.5.
_invasion_field_thresholds – reads it_invasion_well_table – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_field_thresholds, _invasion_well_table, _set_analyze_invasion_defaults.
threshold_direction¶
(list, list-of-lists, int or None) - Which side of ‘threshold’ to keep when prefiltering objects for annotation: ‘higher’ keeps rows whose measurement is >= the threshold, ‘lower’ keeps rows <= it. Give one value, or one per entry in ‘measurement’ (a single string is broadcast to the whole list). Default ‘higher’.
set_annotate_default_settings() – reads itRead by set_annotate_default_settings().
threshold_method¶
(str) - Select the spread estimator for the control-based effect-size cutoff: ‘std’, legacy ‘var’ (squared units), ‘mad’, ‘iqr’, ‘percentile’ (the 95th percentile of absolute coefficients), or ‘range’. ‘none’ disables the effect-size cutoff. Historical aliases such as ‘standard_deveation’, ‘variance’, and ‘quantile’ are accepted. Used only when controls are set. Default ‘std’.
_perform_regression_perform_regression_call_level_hits – reads it_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits, _run_guide_permutation_analysis.
threshold_multiplier¶
(float) - Set how many control-distribution spreads are required for a hit. The cutoff is abs(median(control coefficients)) + threshold_multiplier × spread, using threshold_method for the spread. Larger values demand a larger effect; threshold_method=’none’ disables the cutoff. Used only when controls are set. Default 3.
_perform_regression_perform_regression_call_level_hits – reads it_run_guide_permutation_analysis – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _call_level_hits, _run_guide_permutation_analysis.
threshold_sensitivity¶
(float) - Fractional amount the threshold is moved up and down to produce the invasion_efficiency_low_threshold and _high_threshold bracket, which shows how much of a well’s answer is the threshold rather than the biology. Widening it widens the bracket and makes the inflation flag more eager. Default 0.25.
_invasion_field_thresholds – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _invasion_field_thresholds, _set_analyze_invasion_defaults.
timelapse¶
(bool) - Treat each well/field as a time series instead of independent images: files are grouped into time stacks, randomization is switched off, per-channel movies are written, objects in timelapse_objects are tracked across frames, a timeID column is added to the measurement tables, and measure_crop stops writing single-object PNGs. Only enable when filenames carry a time index. Default False.
preprocess_generate_masks_timelapse() – reads itpreprocess_generate_masks() – reads itpreprocess_img_data() – reads it_concatenate_and_normalize_impl – reads itgenerate_cellpose_masks_sam() – reads itrun_preflight()describe_resources()_plate_id – reads itisinstance(...) [UNRESOLVED]get_timelapse_settings() – reads it_measure_crop_core – reads it_plate_id – reads it_plate_id – reads it_write_intensity_rescale_record – reads itfilepaths_to_database() – reads itmeasure_crop() – reads it_plate_id – reads itplan_measure_resume() – reads itdict(...) [UNRESOLVED]get_measure_crop_settings() – reads itrun_preflight()describe_resources()_plate_id – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]generate_cellpose_masks() – reads itgenerate_timelapse_demo() – reads itRead by preprocess_generate_masks(), preprocess_generate_masks_timelapse(), _plate_id, _concatenate_and_normalize_impl, preprocess_img_data(), _measure_crop_core, _write_intensity_rescale_record, measure_crop(), generate_cellpose_masks(), generate_cellpose_masks_sam(), generate_timelapse_demo(), plan_measure_resume(), get_measure_crop_settings(), get_timelapse_settings(), filepaths_to_database().
timelapse_batch_size¶
generate_cellpose_masks_sam() – reads itgenerate_cellpose_masks() – reads itRead by generate_cellpose_masks(), generate_cellpose_masks_sam().
timelapse_displacement¶
(int or None) - Maximum distance in pixels an object may travel between consecutive frames when linking: trackpy’s search_range, or btrack’s max search radius. Too small fragments tracks, too large causes identity swaps and SubnetOversize failures. None auto-searches downward from 500 for trackpy and falls back to 100 for btrack. Default None.
generate_cellpose_masks_sam() – reads itgenerate_cellpose_masks() – reads itapply_settings() – reads itRead by generate_cellpose_masks(), generate_cellpose_masks_sam(), apply_settings().
timelapse_frame_limits¶
(list) - Slice of frame indices [start, end] kept from each batch before tracking, e.g. [0,10] to work on the first ten frames while tuning settings. The list is ignored unless it has at least two elements, which is why the shipped default [5,] has no effect. Default [5,].
generate_cellpose_masks_sam() – reads itgenerate_cellpose_masks() – reads itgenerate_timelapse_demo() – reads itRead by generate_cellpose_masks(), generate_cellpose_masks_sam(), generate_timelapse_demo().
timelapse_memory¶
(int) - Number of consecutive frames an object may vanish (e.g. missed by segmentation) and still be re-linked to the same track by trackpy. Raise it when tracks fragment because objects blink out; too high risks merging two different objects into one track. Not used by the btrack mode. Default 3.
generate_cellpose_masks_sam() – reads itgenerate_cellpose_masks() – reads itapply_settings() – reads itRead by generate_cellpose_masks(), generate_cellpose_masks_sam(), apply_settings().
timelapse_mode¶
(str) - Tracking backend used to link objects between frames. ‘trackastra’ is a pretrained transformer with division-aware linking; ‘ultrack’ jointly optimizes segmentation and linking and supports dense or three-dimensional data at increased computational cost; ‘trackpy’ uses a configurable search radius and frame memory; ‘btrack’ uses a motion model; and ‘iou’ links masks by overlap and may fail when inter-frame displacement is large. Default ‘trackastra’.
generate_cellpose_masks_sam() – reads itgenerate_cellpose_masks() – reads itapply_settings() – reads itRead by generate_cellpose_masks(), generate_cellpose_masks_sam(), apply_settings().
timelapse_objects¶
(list) - Which segmented objects are tracked across frames and relabelled with track IDs: any subset of [‘cell’, ‘nucleus’, ‘pathogen’]; any other value aborts the run with a message. Each extra entry costs a full additional tracking pass. Tracking nuclei is often more stable than cells when cells touch. Default [‘cell’].
generate_cellpose_masks_sam() – reads it_measure_crop_core – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]generate_cellpose_masks() – reads itapply_settings() – reads itRead by _measure_crop_core, measure_crop(), generate_cellpose_masks(), generate_cellpose_masks_sam(), apply_settings(), get_measure_crop_settings().
timelapse_remove_transient¶
(bool) - After linking, drop every track not present in all frames (trackpy filter_stubs over the full stack length), keeping only objects tracked from first frame to last. Enable for clean per-object time courses; expect to lose cells that divide, enter or leave the field, so object counts fall. Default False.
generate_cellpose_masks_sam() – reads itgenerate_cellpose_masks() – reads itapply_settings() – reads itRead by generate_cellpose_masks(), generate_cellpose_masks_sam(), apply_settings().
tmp_dir¶
Scratch space for intermediate files and the mosaic’s memory map. Empty uses the system temporary folder, which may be too small for a large plate. Default None.
get_preprocess_ops_settings() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings().
tolerance¶
(int or float) - How close a subsampled well mean has to be to the full-well mean before minimum_cell_simulation calls that sample size sufficient, which is what sets min_cells_per_well when you leave it None. An int is read as a percentage (2 means 2%), a float as a fraction (0.02 means the same); anything else raises ValueError. Tighten it toward 0.01 to demand more cells per well and drop more wells, loosen it to 0.05 to keep sparse wells at the cost of noisier per-well scores. Default 0.02.
_perform_regression_perform_regression – reads itminimum_cell_simulation() – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression, minimum_cell_simulation().
top_features¶
(int) - Feature cap in the ML screen analysis: how many rows the feature-importance and permutation-importance bar plots show, and how many top-ranked features the SHAP refit and its summary plot use. It is also the k of the SelectKBest pruning applied before the model is fitted, but only when prune_features is True - with prune_features at its default False the classifier trains on every feature and this is reporting/SHAP scope only. Raise for a fuller picture, lower for readable plots. Default 30.
generate_ml_scores() – reads itinterpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]interpret_vision_model() – reads itread_and_preprocess_data(...) [UNRESOLVED]Read by generate_ml_scores(), interpret_vision_model(), interpret_vision_model().
total_channel¶
(int or None) - Zero-indexed channel of the post-permeabilisation antibody that stains every parasite. Nothing is classified from it; it only supplies the intensity that min_total_intensity filters on, so an incorrect value costs nothing until that filter is switched on. Default 0.
analyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_invasion_defaults, analyze_invasion().
trackastra_linking¶
(str) - How Trackastra turns predicted association scores into tracks: ‘greedy’ takes the best match per object and is fast, ‘ilp’ solves the assignment globally and is more accurate on crowded or dividing populations but needs the trackastra ilp extra and considerably more time. Default ‘greedy’.
generate_cellpose_masks_sam() – reads itRead by generate_cellpose_masks_sam().
trackastra_model¶
(str) - Pretrained Trackastra checkpoint used for frame linking. ‘general_2d’ is the general-purpose two-dimensional model for live-cell data. This setting is used only when timelapse_mode=’trackastra’. Select a different checkpoint only when it was trained for substantially different image characteristics. Default ‘general_2d’.
generate_cellpose_masks_sam() – reads itRead by generate_cellpose_masks_sam().
tracked_object¶
(str) - Which object’s feature block ({object}_* columns) the XGBoost infection classifier trains on: ‘cell’, ‘nucleus’ or ‘pathogen’; anything else falls back to ‘cell’. It does not change what is tracked - track geometry and velocity always come from the cell centroids. Default ‘cell’.
apply_settings() – reads it_infection_qc_xgboost – reads itRead by apply_settings(), _infection_qc_xgboost.
train¶
(bool) - Run the training stage. Disable this setting to apply an existing model_path to a dataset without retraining, as when scoring a new plate with a previously trained model. Default True.
run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]deep_spacr() – reads ittrain_test_model() – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]run_preflight()_check_required_paths – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by deep_spacr(), train_test_model(), get_train_test_model_settings(), _check_required_paths.
train_channels¶
(list) - Which colour planes of each object crop the classifier sees, chosen from ‘r’, ‘g’ and ‘b’. Fewer channels means a smaller input tensor and a model that cannot use the dropped stain, so drop a channel only when it carries no signal for your phenotype. The joined letters also become part of the saved model’s filename. Default [‘r’, ‘g’, ‘b’].
_fit_one – reads it_inner_loader – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by _cross_validate_model, _fit_one, _inner_loader, train_test_model(), get_train_test_model_settings().
train_test_leakage_audit_path¶
train_test_model() – reads it_default_traintrain_test_model() – reads ittrain_test_model() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by train_test_model().
train_validation_leakage_audit_path¶
train_test_model() – reads it_default_traintrain_test_model() – reads ittrain_test_model() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by train_test_model().
transform¶
(str or None) - Optional transform applied to the aggregated per-well response before fitting: ‘log’ (log1p), ‘sqrt’, ‘square’, ‘beta’ (logit for a proportional response, with endpoints moved away from 0 and 1 and reported in the summary), or None. Use a transform when the response is skewed and fails the normality check. The fit then reports coefficients for ‘<transform>_<dependent_variable>’. Default None. Regression starts at ‘log’, so its first fit applies log1p unless this is changed.
_preparation_key – reads it_perform_regression_perform_regression – reads it_show_response_distribution – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]_run_settings_row – reads itrecord_run() – reads itget_setting_dependencies() – reads itRead by _perform_regression, _show_response_distribution, _preparation_key, _run_settings_row, record_run(), get_setting_dependencies().
treatment_loc¶
(list of lists) - Plate wells that received each entry of treatments, one inner list per treatment in the same order, e.g. [[‘r1’,’r2’],[‘r3’]]. Identifiers must start with ‘r’ (row) or ‘c’ (column); wells you do not list get no treatment label. Used by the vision-score annotation step. No default - supply it alongside treatments.
annotate_filter_vision() – reads itRead by annotate_filter_vision().
treatment_plate_metadata¶
(list of lists) - Wells that received each treatment, with one inner list per treatment in the same order, for example [[‘r1’,’r2’,’r3’],[‘r4’,’r5’,’r6’]]. Entries must start with ‘r’ (row) or ‘c’ (column); other entries are ignored and receive no treatment label. Unlisted wells remain in the output, and their condition values contain only the available cell, pathogen, or treatment labels. Default None. Recruitment starts with [[‘r1’, ‘r2’, ‘r3’], [‘r4’, ‘r5’, ‘r6’]], positionally paired with its two initial treatment names.
plot_data_from_db() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_recruitment() – reads itanalyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by plot_data_from_db(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_recruitment(), analyze_replication().
treatments¶
(list or None) - Names of the drug or treatment conditions in the experiment, e.g. [‘dmso’,’lovastatin’]. Each name is written into the treatment column and folded into the combined condition label used for grouping and plotting; positionally paired with treatment_plate_metadata (or treatment_loc), which lists the wells for each. Default [‘cm’,’lovastatin’]. Invasion and Replication start at None, so they add no treatment condition until names and matching well groups are supplied.
annotate_filter_vision() – reads itplot_data_from_db() – reads itanalyze_class_proportion() – reads itanalyze_endodyogeny() – reads itanalyze_invasion() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]analyze_recruitment() – reads itanalyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by annotate_filter_vision(), plot_data_from_db(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_recruitment(), analyze_replication().
tsne_early_exaggeration¶
(float) - t-SNE’s initial attraction multiplier, controlling how much space forms between natural groups early in optimization. Used only by t-SNE. Default 12.
generate_image_umap() – reads itRead by generate_image_umap().
tsne_learning_rate¶
(float) - t-SNE optimization step size. Too small crowds points into a dense ball; too large can scatter them. Used only by t-SNE. Default 200.
generate_image_umap() – reads itRead by generate_image_umap().
tsne_max_iter¶
(int) - Maximum t-SNE optimization iterations. Increase it when optimization has not stabilized; every increase costs runtime. Used only by t-SNE. Default 1000.
generate_image_umap() – reads itRead by generate_image_umap().
tsne_perplexity¶
(float) - t-SNE neighborhood scale. It must be smaller than the number of rows; values around 5-50 are typical. Low values emphasize very local structure and can fragment populations; high values smooth them together. Used only by t-SNE. Default 30.
generate_image_umap() – reads itRead by generate_image_umap().
ultrack_contour_sigma¶
(float) - Standard deviation of the Gaussian blur applied while turning the segmentation labels into the contour map Ultrack builds its candidate objects from. Zero keeps the boundaries exactly as Cellpose drew them; one to four softens them so the joint solver is free to redraw boundaries between objects that were merged or split. Only consulted when timelapse_mode=’ultrack’. Default 0.0.
generate_cellpose_masks_sam() – reads itRead by generate_cellpose_masks_sam().
ultrack_division_weight¶
(float) - Cost the Ultrack solver pays to split one track into two daughters; the value is negative and the more negative it is the more readily divisions are accepted. Make it less negative when a replication assay over-calls divisions on touching cells, more negative when real division events are being missed. Only consulted when timelapse_mode=’ultrack’. Default -0.1.
generate_cellpose_masks_sam() – reads itRead by generate_cellpose_masks_sam().
ultrack_max_distance¶
(float) - The largest jump in pixels Ultrack will consider when linking an object in one frame to a candidate in the next; anything further apart is never joined, so the track breaks instead. Raise it for fast-moving or sparsely sampled cells, lower it on crowded fields where a generous radius invites identity swaps. Only consulted when timelapse_mode=’ultrack’. Default 25.0.
generate_cellpose_masks_sam() – reads itRead by generate_cellpose_masks_sam().
ultrack_n_workers¶
(int) - How many worker processes Ultrack runs during its candidate-segmentation and linking passes; they all write into the same temporary sqlite store, so extra workers cut wall-clock on long movies but add database contention and memory. Leave it at one for short batches or a busy machine. Only consulted when timelapse_mode=’ultrack’. Default 1.
generate_cellpose_masks_sam() – reads itapply_worker_budget() – reads itRead by generate_cellpose_masks_sam(), apply_worker_budget().
um_per_px¶
(float or None) - Physical size of one pixel, used to convert the endodyogeny area column into square microns before binning. Set it and max_area, min_area_bin and every reported area are in microns; leave it None and they stay in pixels, which makes numbers from objectives of different magnification incomparable. Default 0.1.
import_project() – reads itanalyze_endodyogeny() – reads itRead by import_project(), analyze_endodyogeny().
umap_canvas_width¶
(int) - Initial interactive UMAP chart width in pixels. The chart/sidebar divider can also be dragged while exploring. Default 900.
generate_image_umap() – reads itRead by generate_image_umap().
uninfected¶
(bool) - Decides which cells survive the consistency filter in measure_crop. True keeps any cell that has both a nucleus and a cytoplasm; False also demands at least one pathogen, dropping uninfected cells from every table. Either way, nucleus/pathogen/cytoplasm labels outside the surviving cells are zeroed. Only applied when cell, nucleus and pathogen masks all exist; forced True otherwise. Default True.
_measure_crop_core – reads itmeasure_crop() – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]annotate_crops() – reads itRead by _measure_crop_core, measure_crop(), annotate_crops(), get_measure_crop_settings().
update_column¶
generate_ml_scores() – reads itadd_column_to_database() – reads itRead by generate_ml_scores(), add_column_to_database().
upper_threshold¶
annotate_filter_vision() – reads itRead by annotate_filter_vision().
upsample¶
align_folder() – reads itRead by align_folder().
use_bfloat16¶
cellpose_kwargs() – reads itRead by cellpose_kwargs().
use_bounding_box¶
(bool) - Crop the object’s rectangular bounding box padded by 10 px instead of its mask, so neighbouring cells and background inside the box are kept rather than zeroed out. Enable when the classifier should see local context; leave off to isolate a single object on a black background. Default False.
_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]crop_spec_from_settings() – reads it_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_crop_shape_overrides – reads itisinstance(...) [UNRESOLVED]len(...) [UNRESOLVED]_measure_crop_core – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_crop_choice_warnings – reads itRead by crop_spec_from_settings(), _crop_shape_overrides, _measure_crop_core, _crop_choice_warnings, get_measure_crop_settings().
use_checkpoint¶
(bool) - Run the backbone’s forward pass through torch.utils.checkpoint: intermediate activations are discarded and recomputed during the backward pass, trading extra compute for a large drop in activation memory. Enable when a bigger batch_size or image_size gives CUDA out-of-memory; disable for the fastest epochs when VRAM is not the constraint. Default True.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]build_model_from_configuration() – reads itRead by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings(), build_model_from_configuration().
vacuole_key¶
(str) - Rule used to group individually segmented parasites into vacuoles. ‘auto’ prefers an explicit vacuole-ID column, otherwise spatially clusters centroids, then falls back to host cell or one parasite per vacuole with a warning. Set ‘spatial’, ‘cell_id’, ‘object’, or an explicit column name to make that biological assumption reproducible. Default ‘auto’.
analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_replication_defaults, analyze_replication().
vacuole_link_distance¶
(float or None) - Maximum centroid-to-centroid distance in pixels for spatially linking parasites into the same vacuole. None derives the distance from median parasite diameter times vacuole_link_factor; set a calibrated value when magnification or segmentation scale varies between plates. Too large merges separate vacuoles and too small splits one rosette. Default None.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_replication_defaults, analyze_replication(), _check_app_specific.
vacuole_link_factor¶
(float) - Multiplier applied to the median segmented-parasite diameter when vacuole_link_distance is derived automatically. Increasing it joins wider rosettes but also raises the risk of merging nearby vacuoles; decreasing it does the reverse. It is ignored when an explicit link distance or vacuole-ID column is used. Default 1.5.
run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]run_preflight()_check_app_specific – reads itisinstance(...) [UNRESOLVED]dict(...) [UNRESOLVED]dict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]analyze_replication() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]Read by _set_analyze_replication_defaults, analyze_replication(), _check_app_specific.
val_split¶
(float) - Fraction of src/train randomly held out as a validation set in each run (0.1 = 10 percent). The validation score controls checkpoint selection, early stopping and live training curves; at 0 there is no validation loader, so checkpointing uses training accuracy and may favour memorisation. Increase it on small datasets for a less variable estimate. When a grouping level is set, complete groups are held out, so the realised fraction is quantised and may differ substantially from the requested value; both values are reported. Default 0.1.
train_test_model() – reads ittrain_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads it_default_traintrain_test_model() – reads ittrain_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads ittrain_test_model() – reads ittrain_model() – reads itisinstance(...) [UNRESOLVED]get_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]Read by train_model(), train_test_model(), get_train_test_model_settings().
value_col¶
(str) - Measurement column compared with threshold to classify each object as positive. Objects strictly above the threshold are annotated ‘above’; the remainder are annotated ‘below’, and the reported percentage per well is the fraction above. Select the column representing the phenotype, such as a recruitment ratio or mean intensity. Results from different value_col settings are not directly comparable. Default None; must be supplied.
analyze_percent_positive() – reads itdefault_settings_analyze_percent_positive() – reads itRead by default_settings_analyze_percent_positive(), analyze_percent_positive().
var_weights¶
Read by _glm_auto, _glm_binomial, regression_model().
variable¶
run_and_save() – reads itRead by run_and_save().
verbose¶
(bool) - Print the resolved settings table, channel and model choices per object type, row counts per table, and object counts after each filter. It only adds console output; enable it to identify which stage produced an unexpected object count. The default is True for mask, UMAP, screen analysis, barcode mapping and Cellpose training, and False for measure, plotting helpers and regression. Invasion and Replication also start with console detail disabled.
generate_image_umap() – reads itpreprocess_generate_masks() – reads it_score_v2_masks – reads itload_segmentation_illumination_resume() – reads itpreprocess_img_data() – reads itload_segmentation_illumination_resume() – reads itprepare_illumination_model() – reads itgenerate_cellpose_masks_sam() – reads it_run_seg_qc – reads it_get_object_settings – reads itgenerate_organelle_masks_sam() – reads it_run_seg_qc – reads itreducer_hyperparameter_search() – reads itapply_model_to_tar() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itinvestigate_hit() – reads ithit_investigation_default_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]_measure_crop_core – reads itprepare_illumination_correction() – reads itprepare_illumination_model() – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]generate_ml_scores() – reads itgenerate_cellpose_masks() – reads it_run_seg_qc – reads it_get_object_settings – reads it_trial_settings – reads itrerun_trial() – reads itplot_data_from_csv() – reads itplot_data_from_db() – reads itplot_merged() – reads itdisplay(...) [UNRESOLVED]_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]barcode_qc() – reads itbarcode_qc_defaults() – reads itcheck_cellpose_models() – reads itget_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]analyze_class_proportion() – reads itplot_proportion_stacked_bars() – reads itanalyze_endodyogeny() – reads itplot_proportion_stacked_bars() – reads itanalyze_invasion() – reads it_invasion_stacked_barsplot_proportion_stacked_bars() – reads it_set_analyze_invasion_defaults – reads itapply_defaults(...) [UNRESOLVED]get_analyze_plaque_settings() – reads itidentify_masks_finetune() – reads itanalyze_replication() – reads it_replication_stacked_barsplot_proportion_stacked_bars() – reads it_set_analyze_replication_defaults – reads itapply_defaults(...) [UNRESOLVED]run_explain_cv() – reads itexplain_cv_default_settings() – reads it_get_object_settings – reads itRead by _score_v2_masks, generate_image_umap(), preprocess_generate_masks(), reducer_hyperparameter_search(), _cross_validate_model, apply_model_to_tar(), train_test_model(), hit_investigation_default_settings(), investigate_hit(), load_segmentation_illumination_resume(), prepare_illumination_correction(), prepare_illumination_model(), preprocess_img_data(), _measure_crop_core, _perform_regression, generate_ml_scores(), _run_seg_qc, generate_cellpose_masks(), generate_cellpose_masks_sam(), generate_organelle_masks_sam(), _trial_settings, rerun_trial(), plot_data_from_csv(), plot_data_from_db(), plot_merged(), plot_proportion_stacked_bars(), barcode_qc(), barcode_qc_defaults(), _get_object_settings, get_analyze_plaque_settings(), get_measure_crop_settings(), get_train_test_model_settings(), check_cellpose_models(), identify_masks_finetune(), get_preprocess_ops_settings(), stitch_cycle_wells(), _set_analyze_invasion_defaults, _set_analyze_replication_defaults, analyze_class_proportion(), analyze_endodyogeny(), analyze_invasion(), analyze_replication(), explain_cv_default_settings(), run_explain_cv(), _get_object_settings.
voxel_size_xy_um¶
(float or None) - Width of one pixel in micrometres in the image plane, assumed square. Used with voxel_size_z_um to derive anisotropy and to turn voxel counts into physical volumes and surface areas. Note this is a different setting from um_per_pixel, which only sizes the scale bar drawn on figures and never reaches a measurement. This one does reach measurements, but only on a 3-D run: a 2-D run never applies it, because doing so would turn every *_area from px2 into um2 under an unchanged column name. Default None.
_t_stack_planplan_4d_from_settings() – reads it_z_stack_planplan_from_settings() – reads it_measure_crop_core_intensity_measurements_measure_intensity_distanceresolve_measurement_spacing() – reads itresolve_measurement_spacing() – reads it_morphological_measurementsresolve_measurement_spacing() – reads itresolve_measurement_spacing() – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]_plane_spacing – reads itRead by resolve_measurement_spacing(), _plane_spacing, get_measure_crop_settings(), plan_4d_from_settings(), plan_from_settings().
voxel_size_z_um¶
(float or None) - Spacing between consecutive z planes in micrometres, obtained from the acquisition metadata. Together with voxel_size_xy_um it determines anisotropy and converts object volumes from voxel counts into cubic micrometres. Changing it rescales every physical z quantity and the anisotropy used for segmentation; it has no effect on a ‘project’ run. Measure uses the pair to report 3-D morphology in physical units and records the units in measurement_units. Default None.
_t_stack_planplan_4d_from_settings() – reads it_z_stack_planplan_from_settings() – reads it_measure_crop_core_intensity_measurements_measure_intensity_distanceresolve_measurement_spacing() – reads itresolve_measurement_spacing() – reads it_morphological_measurementsresolve_measurement_spacing() – reads itresolve_measurement_spacing() – reads itget_measure_crop_settings() – reads itdict(...) [UNRESOLVED]register_outputs(...) [UNRESOLVED]Read by resolve_measurement_spacing(), get_measure_crop_settings(), plan_4d_from_settings(), plan_from_settings().
weight_decay¶
(float) - L2 penalty applied to the weights on every optimizer step (AdamW applies it decoupled from the gradient). Raise it, toward 1e-3 to 1e-2, when validation loss climbs while training loss keeps falling; lower it toward 0 when the model cannot fit the training set at all. Every supported optimizer honours it. Default 0.00001.
_fit_one – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads it_default_traintrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads ittrain_test_model() – reads it_cross_validate_model – reads itget_train_test_model_settings() – reads itdict(...) [UNRESOLVED]trainer(...) [UNRESOLVED]train_cellpose() – reads itRead by _cross_validate_model, _fit_one, train_test_model(), get_train_test_model_settings(), train_cellpose().
well_confidence¶
(float) - Minimum YOLO confidence, from 0 to 1, for keeping a detected well when well_detection is enabled. Raising it removes uncertain boxes but can lose an entire condition; lowering it retains more candidates and can create spurious well crops. Default 0.25.
Read by get_analyze_plaque_settings(), split_wells().
well_detection¶
(str or bool) - Split a plate image into detected wells before plaque segmentation. False passes each source image through whole; True selects the default YOLO detector, while a model-zoo key or checkpoint path selects another detector. Enabling it changes result rows from one per image to one per detected well. Default False.
analyze_plaques() – reads itget_analyze_plaque_settings() – reads it_resolve_well_detector – reads itRead by get_analyze_plaque_settings(), _resolve_well_detector, analyze_plaques().
well_diameter_mm¶
(float, int or None) - Known interior diameter of a detected well in millimetres, overriding plate_format when both are set. It converts the detected pixel diameter into pixels per millimetre and therefore rescales every physical plaque area; use None when the diameter is unknown. Default None.
Read by get_analyze_plaque_settings(), _plaque_scale_for.
well_group¶
Which named group in meta_regex holds the well identifier. Change it when your filenames name the well under a different group; getting it wrong groups every tile into one well. Default ‘well’.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
well_ineq_coeff¶
run_simulation() – reads itgenerate_parameters() – reads itdeepcopy(...) [UNRESOLVED]append(...) [UNRESOLVED]Read by generate_parameters(), run_simulation().
well_pad¶
(int) - Extra image pixels retained on every side of a detected well crop, clipped at the source-image boundary. Increase it when the detector box trims the well edge; excessive padding can include neighbouring wells or background. Default 0.
Read by get_analyze_plaque_settings(), split_wells().
wells¶
filter_selection() – reads itRead by filter_selection().
whiten¶
reduction_and_clustering() – reads itRead by reduction_and_clustering().
wide_predictor_columns¶
(list) - Guide columns in a wide independent-variable table. Leave empty to use all numeric columns other than plate/well metadata; list them explicitly when the table contains additional numeric metadata. Ignored for long input. Default [].
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
width_height¶
(list of int) - Legacy Cellpose checkpoint metadata retained for older settings files. Current training resizes every image-mask pair with the scalar target_size and does not read this list, so changing it does not change training. Default [1000, 1000].
_describe_cellpose_training – reads itRead by _describe_cellpose_training.
window_length¶
(int) - Number of bases sliced out of each read starting at offset_start relative to the target_sequence hit; this window is what the regex is matched against. It must span the whole barcode block (column + gRNA + row) or the regex stops matching and reads are dropped; shorter reads are padded with ‘N’. Default 89.
generate_barecode_mapping() – reads itRead by generate_barecode_mapping().
workspace_copy_limit_mb¶
copy_limit_from_settings() – reads itisinstance(...) [UNRESOLVED]Read by copy_limit_from_settings().
write_mosaic¶
Write the assembled mosaic to disk. Off still produces the pairwise report and the manifest, which is enough to assemble it later without re-scoring. Default False.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
write_random_annotation_column¶
(bool) - In annotation mode, persist an automatically selected unannotated comparison group into png_list as <column>_random. This makes an automatically generated control class reproducible and auditable. Default False.
Read by _rules_from_annotation, generate_training_dataset().
writer¶
align_folder() – reads itRead by align_folder().
x_lim¶
(list) - Two-element [min, max] limits on the coefficient (x) axis of the Toxoplasma volcano plot produced by the regression pipeline when toxo mode is on. Narrow it to zoom in on hits clustered near zero, widen it to keep large-effect genes on the plot. Leaving it None falls back to [-0.5, 0.5], not auto-scaling. Default None.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
xtick.color¶
rc_params() – reads itRead by rc_params().
y_axis_start¶
generate_screen_graphs() – reads itRead by generate_screen_graphs().
y_lim¶
plot_data_from_csv() – reads itplot_data_from_db() – reads itRead by plot_data_from_csv(), plot_data_from_db().
y_lims¶
(list or None) - Limits of the -log10(p) axis of the Toxoplasma volcano plot. None auto-scales to the data; [low, high] fixes the axis so several plates can be compared at the same scale; [[low1, high1], [low2, high2]] draws a broken axis with the gap between the two ranges removed, which keeps a handful of extremely significant genes on the plot without flattening everything else. Any other shape raises ValueError. Default None.
_perform_regression_perform_regression – reads it_perform_regression_read_data(...) [UNRESOLVED]dict(...) [UNRESOLVED]Read by _perform_regression.
ytick.color¶
rc_params() – reads itRead by rc_params().
z_axis¶
(int or None) - Axis of the incoming array that holds z, specified as 0, 1 or 2. None infers it from shape only when one axis is clearly shorter than the other two, such as a 21x512x512 or 512x512x21 stack. An ambiguous shape such as 64x64x64 raises an error because an incorrect axis segments a transposed volume and produces invalid masks. Set this explicitly whenever the acquisition shape is ambiguous. Default None.
Read by plan_4d_from_settings(), plan_from_settings().
z_handling¶
How multiple Z planes become a 2-D Measure input. ‘max’ takes a maximum-intensity projection and ‘first’ keeps only the first plane; inputs that still contain separate planes are rejected. Default ‘max’.
Read by convert_folder(), plan_external_masks(), import_project().
z_index¶
Which z-plane to take when not projecting, zero-indexed. Ignored when mip is on. Default 0.
get_preprocess_ops_settings() – reads itstitch_cycle_wells() – reads itget_preprocess_ops_settings() – reads itdict(...) [UNRESOLVED]Read by get_preprocess_ops_settings(), stitch_cycle_wells().
z_projection¶
(str or None) - Method used to collapse z when z_segmentation_mode is ‘project’. ‘max’ retains the brightest value along the stack and is appropriate for sparse fluorescent objects; ‘mean’ suppresses noise but dilutes signal present in few planes; ‘sum’ preserves total signal; and ‘best_focus’ retains only the sharpest plane, which is preferable when one plane is in focus and a maximum-intensity projection would include substantial out-of-focus signal. Ignored by the other modes. Default ‘max’.
Read by plan_4d_from_settings(), plan_from_settings().
z_segmentation_mode¶
(str) - How the z dimension is handled. The three modes answer different questions and their masks are not comparable, so the choice is recorded alongside them. ‘project’ collapses the stack with z_projection and segments one plane; it is the only mode the Measure module can consume. ‘stitch’ segments each plane in 2-D and links labels through the stack. ‘volumetric’ segments the 3-D volume directly and requires anisotropy or voxel sizes. Default ‘project’.
Read by plan_4d_from_settings(), plan_from_settings().
z_stack¶
(bool) - When True, spaCR requires the array to contain an explicit z dimension and raises an error instead of inferring the axis; this enables z_segmentation_mode, anisotropy and stitch_threshold. Standard ingestion collapses z by maximum-intensity projection while organising raw files, so its output has no z axis to segment; supply volumetric arrays directly to spacr.zstack instead. When False, no z-stack code runs and masks match a two-dimensional run. Default False.
_z_stack_planplan_from_settings() – reads itRead by plan_from_settings().
zscore_thresh¶
(float) - Outlier sensitivity when smoothing scalar features within a track (area, bbox area, equivalent diameter, perimeter, solidity, mean/max/min intensity). A frame more than this many standard deviations from its own track mean, whose two neighbours are both within half that, is replaced by their average. Lower smooths more; nothing is deleted. Default 3.0.
automated_motility_assay() – reads itautomated_motility_assay() – reads itRead by automated_motility_assay().