API Reference
This page groups the main public API surfaces. It is an index, not a replacement
for detailed function pages.
Core Loading and Saving
| Object |
Import |
Purpose |
create_batch |
from PyFLASH import create_batch |
Create or load a processed Batch. |
from_dataframe |
from PyFLASH import from_dataframe |
Wrap already-tabular data in a PyFLASH-compatible object. |
DataFrameExperiment |
from PyFLASH import DataFrameExperiment |
The object returned by from_dataframe. |
save_state |
from PyFLASH import save_state |
Save a Batch or Experiment to pickle. |
load_state |
from PyFLASH import load_state |
Load a saved pickle. |
normalize_paths |
from PyFLASH import normalize_paths |
Rebase saved paths after moving data between machines. |
Detailed pages:
Several summary-first plotting and pipeline functions also accept a raw
pandas.DataFrame directly with group_col=..., group_cols=..., and
subject_col=.... They call from_dataframe internally. Legacy aliases such
as condition_col, factor_cols, and animal_col still work.
Import Utilities
| Object |
Import |
Purpose |
resolve_experiment_paths |
from PyFLASH.experiment import resolve_experiment_paths |
Resolve an experiment folder to its importable data folder and root. |
resolve_experiment_data_path |
from PyFLASH.experiment import resolve_experiment_data_path |
Return the data folder for an experiment path. |
is_experiment_folder |
from PyFLASH.experiment import is_experiment_folder |
Check whether a folder looks like an importable experiment. |
Data Objects
| Object |
Import |
Purpose |
Batch |
from PyFLASH import Batch |
Collection of experiments with a shared group list. |
Experiment |
from PyFLASH import Experiment |
Full experiment folder import object. |
MiniExperiment |
from PyFLASH import MiniExperiment |
Lightweight table-based experiment object. |
DataFrameExperiment |
from PyFLASH import DataFrameExperiment |
In-memory object backed by user-supplied DataFrames. |
Antibody |
from PyFLASH import Antibody |
Marker data class for antibody channels. |
cellMarker |
from PyFLASH import cellMarker |
Marker data class for cell/object counters. |
objectMarker |
from PyFLASH import objectMarker |
Marker data class for object-level measurements. |
Attribute |
from PyFLASH import Attribute |
Generic attribute data class. |
See also: Object model.
Batch Export Methods
These are methods on a Batch object rather than top-level functions.
| Method |
Purpose |
batch.export_all_excel() |
Write the standard formatted Excel outputs. |
batch.export_IF_summary_excel() |
Export the immunofluorescence summary workbook. |
batch.export_extended_data_excel() |
Export the extended immunofluorescence workbook. |
batch.export_behavior_summary_excel() |
Export behavior data when present. |
batch.export_extra_summary_excel() |
Export extra summary tables when present. |
Groups
| Object |
Import |
Purpose |
GroupBuilder |
from PyFLASH import GroupBuilder |
Preferred fluent builder for group lists. Classic alias: ConditionBuilder. |
group |
from PyFLASH import group |
Single group object. Classic alias: condition. |
multiGroup |
from PyFLASH import multiGroup |
Crossed group object. Classic alias: multiCondition. |
groupList |
from PyFLASH import groupList |
Ordered group collection. Classic alias: conditionList. |
zipGroups |
from PyFLASH import zipGroups |
Create groups from parallel lists. Classic alias: zipConditions. |
zipGroupLists |
from PyFLASH import zipGroupLists |
Cross two group lists. Classic alias: zipConditionLists. |
The classic ConditionBuilder, condition, multiCondition, conditionList,
zipConditions, and zipConditionLists names remain supported.
Pipeline Functions
| Function |
Import |
Purpose |
correlation |
from PyFLASH import correlation |
Correlation discovery, significance gates, and regression plots. |
adjusted_correlation |
from PyFLASH import adjusted_correlation |
Correlation analysis adjusted for covariates. |
data_overview |
from PyFLASH import data_overview |
One-call descriptive overview and quality-control report. |
group_comparison |
from PyFLASH import group_comparison |
Per-marker group comparisons with effect sizes and figures. |
linear_model |
from PyFLASH import linear_model |
Adjusted linear-model pipeline with coefficients and adjusted means. |
rhythm |
from PyFLASH import rhythm |
Cosinor and circular rhythm analysis. |
Detailed pages:
Modelling Functions
| Function |
Import |
Purpose |
iterative_best_fit |
from PyFLASH import iterative_best_fit |
Leave-one-out feature-subset search for linear regression. |
iterative_model_sweep |
from PyFLASH import iterative_model_sweep |
Classifier and feature-subset sweep for categorical targets. |
run_linear_model_pipeline |
from PyFLASH import run_linear_model_pipeline |
Compatibility wrapper for the original table-only linear-model workflow. |
Detailed pages:
Plot Utilities
| Function |
Import |
Purpose |
get_display_name |
from PyFLASH.plotting import get_display_name |
Convert raw summary column names to readable labels. |
set_display_name |
from PyFLASH.plotting import set_display_name |
Apply readable labels to Matplotlib axes. |
set_axis_limits |
from PyFLASH import set_axis_limits |
Store manual axis limits on an experiment or batch. |
clear_axis_limits |
from PyFLASH import clear_axis_limits |
Remove stored axis limits. |
lock_axis_limits |
from PyFLASH import lock_axis_limits |
Derive and store reusable axis limits from data. |
cheat_sheet |
from PyFLASH import cheat_sheet |
Print plot parameter help from the live plotting signatures. |
Plotting Registry
These names are available through PyFLASH.spec.PLOT_REGISTRY, the Streamlit UI
plot launcher, and plot spec files.
| Registry name |
Callable |
Purpose |
mean_bars |
plot_mean_bars |
Bar charts with individual points and statistical comparisons. |
matrices |
plot_matrices |
Correlation matrices. |
rect_matrices |
plot_rect_matrices |
Rectangular correlation heatmaps. |
matrix_differences |
plot_matrix_differences |
Compare correlation matrices between groups. |
regressions |
plot_regressions |
Regression plots. |
multivariable_regression_matrix |
plot_multivariable_regression_matrix |
Joint regression matrix heatmaps. |
volcano |
plot_volcano |
Effect-vs-significance plot. |
radar |
plot_radar |
Radar/spider plot over selected summary columns. |
histograms |
plot_histograms |
Marker-level histogram plots. |
ridgeline |
plot_ridgeline |
Marker-level ridgeline density plots. |
ecdf |
plot_ecdf |
Empirical cumulative distribution plots. |
pie_charts |
plot_pie_charts |
Category distribution pies or related formats. |
combo_pies |
plot_combo_pies |
Combo-family distribution plots. |
locations |
plot_locations |
Spatial object location plots. |
images |
plot_images |
Image grids. |
representative_images |
plot_representative_images |
Representative image panels. |
scatter_3d |
plot_scatter_3d |
3D scatter plots. |
condition_key |
plot_condition_key |
Standalone condition legend/key. |
coloc_upset |
plot_coloc_upset |
Colocalisation intersection plots. |
coloc_sankey |
plot_coloc_sankey |
Conditional branching/alluvial colocalisation plots. |
power_curve |
plot_power_curve |
Statistical power vs sample size. |
marker_pca |
plot_marker_pca |
PCA biplot of marker profiles. |
timecourse |
plot_timecourse |
Timecourse or growth-curve plot. |
superplot |
plot_superplot |
ROI-level points grouped by subject/sample. |
effect_forest |
plot_effect_forest |
Effect-size forest plot. |
group_matrix |
plot_group_matrix |
Group-vs-control effect matrix. |
cosinor |
plot_cosinor |
Cosinor rhythm fit plot. |
acrophase_clock |
plot_acrophase_clock |
Circular acrophase clock plot. |
correlation_pipeline |
PyFLASH.pipeline.correlation |
Pipeline callable exposed through the registry. |
adjusted_correlation_pipeline |
PyFLASH.pipeline.adjusted_correlation |
Adjusted correlation pipeline. |
data_overview_pipeline |
PyFLASH.pipeline.data_overview |
Overview/QC pipeline. |
group_comparison_pipeline |
PyFLASH.pipeline.group_comparison |
Group comparison pipeline. |
linear_model_pipeline |
PyFLASH.pipeline.linear_model |
Adjusted linear-model pipeline. |
rhythm_pipeline |
PyFLASH.pipeline.rhythm |
Rhythm pipeline. |
iterative_model_sweep |
PyFLASH.modelling.iterative_model_sweep |
Classifier model sweep. |
Detailed pages:
Declarative Specs
| Object |
Import |
Purpose |
PLOT_REGISTRY |
from PyFLASH.spec import PLOT_REGISTRY |
Short-name registry that maps spec/UI plot names to callables. |
load_spec |
from PyFLASH.spec import load_spec |
Load a YAML, TOML, or JSON plot specification. |
validate_spec |
from PyFLASH.spec import validate_spec |
Validate a plot spec before running it. |
run_spec |
from PyFLASH import run_spec |
Load, validate, and execute a plot spec. |
describe_status |
from PyFLASH.spec import describe_status |
Report the documentation/status metadata for a registry name. |
Exclusion Helpers
| Function |
Import |
Purpose |
exclude_outliers |
from PyFLASH import exclude_outliers |
Detect outliers and return a cleaned copy for analysis. |
mark_outliers |
from PyFLASH import mark_outliers |
Record detected outliers without changing data. |
apply_exclusions |
from PyFLASH import apply_exclusions |
Apply manual exclusion rules to cells, subjects, or columns. |
mark_exclusions |
from PyFLASH import mark_exclusions |
Record manual exclusions without changing data. |
exclude_subjects |
from PyFLASH import exclude_subjects |
Manually exclude whole subjects/samples. |
mark_subjects |
from PyFLASH import mark_subjects |
Record whole-subject exclusions without changing data. |
exclude_animals |
from PyFLASH import exclude_animals |
Legacy alias for subject exclusion. |
mark_animals |
from PyFLASH import mark_animals |
Legacy alias for subject exclusion marking. |
Configuration and Output Control
| Object |
Import |
Purpose |
Config |
from PyFLASH import Config |
Global thresholds, color maps, aliases, and project settings. |
Verbosity |
from PyFLASH import Verbosity |
Output verbosity levels. |
set_verbosity |
from PyFLASH import set_verbosity |
Set package output verbosity. |
silent |
from PyFLASH import silent |
Context manager that suppresses package output. |
verbose |
from PyFLASH import verbose |
Context manager that temporarily increases output detail. |
format_summary_for_display |
from PyFLASH import format_summary_for_display |
Return a display-only summary table with readable labels. |
Streamlit-Free UI Services
These live in PyFLASH.ui.services. They are intended for the Streamlit app and
tests, but are also useful for scripts.
| Function |
Purpose |
open_pickle / save_pickle |
Load or save PyFLASH pickle files. |
package_info |
Return import and package smoke-test information. |
summary_table |
Prepare a summary table for display or download. |
batch_overview |
Return a structured overview of a loaded batch. |
validate_experiment_folder |
Check whether a folder looks importable. |
discover_experiments |
Validate immediate subfolders as experiments. |
build_conditions / preview_conditions |
Build and preview group lists from JSON-style specs. |
run_create_batch |
Run batch creation while capturing progress output. |
available_plots |
Return registered plot names. |
run_plot_spec |
Validate and execute a plot spec file. |
run_quick_plot |
Run one registered plot from form parameters. |
run_image_grid / run_representative_panels / run_locations |
UI adapters for image and spatial plotting. |
| ## Detailed Function Pages |
|