Core, Publication, And Utility Reference

Core I/O, documents, LLM helpers, reporting, audit manifests, console utilities, and banner helpers.

For workflow context, see Core, Publication, And Utility module guide.

Functions And Classes

fyron.audit.manifest.analysis_run_manifest
fyron.audit.manifest.create_provenance_manifest
fyron.audit.manifest.hash_file
fyron.audit.manifest.write_manifest
fyron.banner.get_banner
fyron.banner.get_run_footer
fyron.banner.get_run_header
fyron.banner.print_banner
fyron.clinical_codes.charlson.add_charlson_comorbidity_columns
fyron.clinical_codes.charlson.charlson_comorbidity_index
fyron.clinical_codes.charlson.classify_charlson_comorbidities
fyron.clinical_codes.charlson.derive_charlson_category
fyron.clinical_codes.encounter.add_encounter_type_features
fyron.clinical_codes.encounter.classify_elective_status
fyron.clinical_codes.icd10.add_icd10_disease_groups
fyron.clinical_codes.icd10.classify_icd10
fyron.clinical_codes.icd10.classify_icd10_string
fyron.clinical_codes.icd10.icd10_group_prevalence
fyron.clinical_codes.ops.add_mdc_partition
fyron.clinical_codes.ops.add_ops_categories
fyron.clinical_codes.ops.aggregate_ops_by_encounter
fyron.clinical_codes.ops.classify_mdc_partition
fyron.clinical_codes.ops.classify_ops
fyron.clinical_codes.ops.classify_ops_string
fyron.clinical_codes.ops.get_ops_chapter
fyron.clinical_codes.ops.normalize_ops_codes
fyron.clinical_codes.ops.ops_category_prevalence
fyron.clinical_codes.utils.aggregate_code_rows
fyron.clinical_codes.utils.explode_code_column
fyron.clinical_codes.utils.normalize_code
fyron.clinical_codes.utils.split_code_string
fyron.console.configure_logging
fyron.console.console_message
fyron.console.error
fyron.console.format_count
fyron.console.format_duration
fyron.console.format_path
fyron.console.get_logger
fyron.console.get_timer
fyron.console.info
fyron.console.success
fyron.console.supports_color
fyron.console.warning
fyron.core.env.load_env
fyron.core.io.DataIO
fyron.core.io.DataIO.read_csv
fyron.core.io.DataIO.write_csv
fyron.core.io.DataIO.read_excel
fyron.core.io.DataIO.write_excel
fyron.core.io.DataIO.read_json
fyron.core.io.DataIO.write_json
fyron.core.io.DataIO.read_table
fyron.core.io.DataIO.write_table
fyron.core.io.DataIO.summarize_table
fyron.core.io.DataIO.describe_file
fyron.core.io.TeableClient
fyron.core.io.TeableClient.list_records
fyron.core.io.TeableClient.list_spaces
fyron.core.io.TeableClient.list_bases
fyron.core.io.TeableClient.create_base
fyron.core.io.TeableClient.get_or_create_base
fyron.core.io.TeableClient.list_tables
fyron.core.io.TeableClient.create_table
fyron.core.io.TeableClient.get_or_create_table
fyron.core.io.TeableClient.read_table
fyron.core.io.TeableClient.create_records
fyron.core.io.TeableClient.delete_records
fyron.core.io.TeableClient.clear_table
fyron.core.io.TeableClient.write_dataframe
fyron.core.io.TeableClient.overwrite_table
fyron.core.io.TeableClient.read_cohort
fyron.core.io.TeableClient.write_research_cohort
fyron.core.io.TeableClient.create_research_cohort_table
fyron.core.io.TeableClient.summarize_teable_table
fyron.core.io.TeableConfig
fyron.core.io.describe_file
fyron.core.io.ensure_parent_dir
fyron.core.io.read_csv
fyron.core.io.read_excel
fyron.core.io.read_json
fyron.core.io.read_table
fyron.core.io.summarize_table
fyron.core.io.write_csv
fyron.core.io.write_excel
fyron.core.io.write_json
fyron.core.io.write_table
fyron.core.s3.S3Storage
fyron.core.s3.S3Storage.key
fyron.core.s3.S3Storage.exists
fyron.core.s3.S3Storage.list
fyron.core.s3.S3Storage.read_bytes
fyron.core.s3.S3Storage.write_bytes
fyron.core.s3.S3Storage.read_json
fyron.core.s3.S3Storage.write_json
fyron.core.s3.S3Storage.read_table
fyron.core.s3.S3Storage.write_table
fyron.core.s3.S3Storage.download_file
fyron.core.s3.S3Storage.upload_file
fyron.core.s3.parse_s3_uri
fyron.core.s3.read_s3_table
fyron.core.s3.write_s3_table
fyron.curate.build_compose_command
fyron.curate.default_app_dir
fyron.curate.start_curator
fyron.datasets.make_boa_like_folder
fyron.datasets.make_classification_cohort
fyron.datasets.make_clinical_cohort
fyron.datasets.make_dicom_series
fyron.datasets.make_nifti_pair
fyron.datasets.make_survival_cohort
fyron.datasets.preparation.prepare_nnunetv2_dataset
fyron.datasets.preparation.prepare_yolo_classification_dataset
fyron.datasets.preparation.prepare_yolo_detection_dataset
fyron.datasets.preparation.validate_nnunetv2_dataset
fyron.datasets.preparation.validate_yolo_dataset
fyron.datasets.preparation.write_dataset_manifest
fyron.documents.client.DocumentDownloader
fyron.documents.client.DocumentDownloader.close
fyron.documents.client.DocumentDownloader.download_id
fyron.documents.client.DocumentDownloader.download_url
fyron.documents.client.DocumentDownloader.download_urls
fyron.documents.client.DocumentDownloader.download_from_dataframe
fyron.documents.client.DocumentDownloader.download_from_df
fyron.documents.client.DocumentResult
fyron.get_banner
fyron.get_run_footer
fyron.get_run_header
fyron.llm.agent.LLMAgent
fyron.llm.agent.LLMAgent.prompt
fyron.llm.agent.LLMAgent.chat
fyron.llm.agent.LLMAgent.run_on_dataframe
fyron.llm.agent.LLMAgent.run_on_documents
fyron.llm.agent.LLMAgent.prompt_documents
fyron.llm.agent.LLMAgent.prompt_dataframe
fyron.llm.agent.LLMAgent.prompt_df
fyron.llm.agent.LLMAgent.run_prompt_on_dataframe
fyron.llm.agent.LLMAgent.describe_python_file
fyron.llm.agent.LLMAgent.run_on_directory
fyron.llm.agent.LLMAgent.prompt_directory
fyron.llm.agent.LLMConfig
fyron.plotting_palettes.PaletteSpec
fyron.plotting_palettes.get_colormap
fyron.plotting_palettes.get_palette
fyron.plotting_palettes.list_palettes
fyron.plotting_palettes.palette_grayscale_contrast
fyron.plotting_palettes.palette_luminance_table
fyron.plotting_palettes.preview_palette
fyron.plotting_palettes.resolve_colormap
fyron.plotting_palettes.resolve_palette
fyron.plotting_palettes.set_plot_style
fyron.print_banner
fyron.reporting.tables.cohort_flow_table
fyron.reporting.tables.compare_models_table
fyron.reporting.tables.dataframe_to_markdown
fyron.reporting.tables.format_estimate_ci
fyron.reporting.tables.metrics_table
fyron.reporting.tables.summarize_baseline_table

`fyron.audit.manifest.analysis_run_manifest`

Create a provenance manifest for one clinical analysis run.

Import path: fyron.audit.manifest.analysis_run_manifest

python

analysis_run_manifest(*, title: str, inputs: Sequence[str | Path] | None = None, outputs: Sequence[str | Path] | None = None, parameters: Mapping[str, Any] | None = None, random_seed: int | None = None, analysis_type: str | None = None, cohort_id: str | None = None, notes: str | None = None) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`title`	yes	`str`		Human-readable run title.
`inputs`	no	`Sequence[str or Path] or None`	`None`	See signature.
`outputs`	no	`Sequence[str or Path] or None`	`None`	Input and output artifacts. Existing input files are hashed.
`parameters`	no	`Mapping[str, Any] or None`	`None`	See signature.
`random_seed`	no	`int or None`	`None`	Optional seed used for splitting, bootstrapping, or model fitting.
`analysis_type`	no	`str or None`	`None`	Optional label such as ``"survival",` `"classification", or` `"boa_features"``.
`cohort_id`	no	`str or None`	`None`	Optional cohort identifier or version.
`notes`	no	`str or None`	`None`	Free-text provenance notes.

Returns

dict - JSON-serializable manifest with Fyron/runtime metadata and analysis context.

`fyron.audit.manifest.create_provenance_manifest`

Create a JSON-serializable provenance manifest.

Import path: fyron.audit.manifest.create_provenance_manifest

python

create_provenance_manifest(*, title: str | None = None, inputs: Sequence[str | Path] | None = None, outputs: Sequence[str | Path] | None = None, parameters: Mapping[str, Any] | None = None, notes: str | None = None) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`title`	no	`str or None`	`None`	See signature.
`inputs`	no	`Sequence[str or Path] or None`	`None`	See signature.
`outputs`	no	`Sequence[str or Path] or None`	`None`	See signature.
`parameters`	no	`Mapping[str, Any] or None`	`None`	See signature.
`notes`	no	`str or None`	`None`	See signature.

Returns

dict[str, Any]

`fyron.audit.manifest.hash_file`

Hash a file for provenance tracking.

Import path: fyron.audit.manifest.hash_file

python

hash_file(path: str | Path, *, algorithm: str = 'sha256', chunk_size: int = 1024 * 1024) -> str

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.
`algorithm`	no	`str`	`'sha256'`	See signature.
`chunk_size`	no	`int`	`1024 * 1024`	See signature.

Returns

str

`fyron.audit.manifest.write_manifest`

Write a provenance manifest as pretty JSON.

Import path: fyron.audit.manifest.write_manifest

python

write_manifest(manifest: Mapping[str, Any], path: str | Path) -> Path

Parameters

Parameter	Required	Type	Default	Description
`manifest`	yes	`Mapping[str, Any]`		See signature.
`path`	yes	`str or Path`		See signature.

Returns

Path

`fyron.banner.get_banner`

Return the Bits & Flames Fyron terminal banner.

Import path: fyron.banner.get_banner

python

get_banner(version: str | None = None, *, include_version: bool = True, color: bool = True, theme: str = 'bf') -> str

Parameters

Parameter	Required	Type	Default	Description
`version`	no	`str or None`	`None`	Optional version label. If omitted, the installed package version is used.
`include_version`	no	`bool`	`True`	Append a version line below the banner when ``True``.
`color`	no	`bool`	`True`	Render the pixel logo with ANSI colors when ``True``.
`theme`	no	`str`	`'bf'`	Terminal theme: ``"bf"` `for the colored Bits & Flames wordmark,` `"plain"` `for ASCII logs, or` `"mono"`` for minimal monochrome output.

Returns

str

`fyron.banner.get_run_footer`

Return a compact Fyron run footer.

Import path: fyron.banner.get_run_footer

python

get_run_footer(status: str = 'success', *, duration: float | None = None, outputs: list[str | Path] | tuple[str | Path, ...] | None = None, color: bool = True) -> str

Parameters

Parameter	Required	Type	Default	Description
`status`	no	`str`	`'success'`	See signature.
`duration`	no	`float or None`	`None`	See signature.
`outputs`	no	`list[str or Path] or tuple[str or Path, ...] or None`	`None`	See signature.
`color`	no	`bool`	`True`	See signature.

Returns

str

`fyron.banner.get_run_header`

Return a compact Fyron run header for scripts and CLI jobs.

Import path: fyron.banner.get_run_header

python

get_run_header(command: str | None = None, *, version: bool = True, timestamp: bool = True, color: bool = True) -> str

Parameters

Parameter	Required	Type	Default	Description
`command`	no	`str or None`	`None`	See signature.
`version`	no	`bool`	`True`	See signature.
`timestamp`	no	`bool`	`True`	See signature.
`color`	no	`bool`	`True`	See signature.

Returns

str

`fyron.banner.print_banner`

Print the Bits & Flames Fyron terminal banner to a text stream.

Import path: fyron.banner.print_banner

python

print_banner(version: str | None = None, *, include_version: bool = True, color: bool = True, theme: str = 'bf', file: TextIO | None = None) -> None

Parameters

Parameter	Required	Type	Default	Description
`version`	no	`str or None`	`None`	See signature.
`include_version`	no	`bool`	`True`	See signature.
`color`	no	`bool`	`True`	See signature.
`theme`	no	`str`	`'bf'`	See signature.
`file`	no	`TextIO or None`	`None`	See signature.

Returns

None

`fyron.clinical_codes.charlson.add_charlson_comorbidity_columns`

Add Charlson Comorbidity Index columns from ICD-10 code strings.

Import path: fyron.clinical_codes.charlson.add_charlson_comorbidity_columns

python

add_charlson_comorbidity_columns(df: pd.DataFrame, icd_column: str = 'pni_icd', id_column: str | None = None) -> tuple[pd.DataFrame, str | None]

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame containing ICD-10 code strings.
`icd_column`	no	`str`	`'pni_icd'`	Column containing one or more semicolon-separated ICD-10 codes.
`id_column`	no	`str or None`	`None`	Optional encounter or patient identifier column. Repeated IDs are scored across all ICD-10 codes observed for that ID.

Returns

tuple[pandas.DataFrame, str or None] - DataFrame copy with `charlson_comorbidity_index, charlson_comorbidity_category, and binary charlson_* comorbidity indicators. skipped_reason` is None when scoring succeeds.

`fyron.clinical_codes.charlson.charlson_comorbidity_index`

Calculate a Charlson Comorbidity Index score from ICD-10 codes.

Import path: fyron.clinical_codes.charlson.charlson_comorbidity_index

python

charlson_comorbidity_index(icd_codes: Any) -> int | None

Parameters

Parameter	Required	Type	Default	Description
`icd_codes`	yes	`Any`		Missing value, semicolon-separated ICD-10 string, or iterable of ICD-10 codes.

Returns

int or None - Charlson score, or None when no parsable ICD-10 codes are present.

`fyron.clinical_codes.charlson.classify_charlson_comorbidities`

Classify ICD-10 codes into Charlson comorbidity indicators.

Import path: fyron.clinical_codes.charlson.classify_charlson_comorbidities

python

classify_charlson_comorbidities(icd_codes: Any) -> dict[str, int]

Parameters

Parameter	Required	Type	Default	Description
`icd_codes`	yes	`Any`		Missing value, semicolon-separated ICD-10 string, or iterable of ICD-10 codes.

Returns

dict[str, int] - Binary Charlson comorbidity indicators using explicit ICD-10 ranges.

`fyron.clinical_codes.charlson.derive_charlson_category`

Bin Charlson scores into ordered clinical categories.

Import path: fyron.clinical_codes.charlson.derive_charlson_category

python

derive_charlson_category(score: pd.Series) -> pd.Categorical

Parameters

Parameter	Required	Type	Default	Description
`score`	yes	`pd.Series`		Numeric Charlson Comorbidity Index values.

Returns

pandas.Categorical - Ordered categories `"0", "1-2", "3-4", and ">=5"`.

`fyron.clinical_codes.encounter.add_encounter_type_features`

Add normalized encounter type and elective-status columns.

Import path: fyron.clinical_codes.encounter.add_encounter_type_features

python

add_encounter_type_features(df: pd.DataFrame, encounter_type_column: str = 'pni_encounter_type') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame with an encounter type column.
`encounter_type_column`	no	`str`	`'pni_encounter_type'`	Source column containing encounter type codes.

Returns

pandas.DataFrame - Copy of `df with pni_encounter_type_normalized and pni_encounter_elective_status`.

`fyron.clinical_codes.encounter.classify_elective_status`

Classify elective status from an encounter type code.

Import path: fyron.clinical_codes.encounter.classify_elective_status

python

classify_elective_status(encounter_type: Any) -> str

Parameters

Parameter	Required	Type	Default	Description
`encounter_type`	yes	`Any`		Encounter type such as ``"AMB",` `"IMP", or` `"EMER"``.

Returns

str - `"elective", "non_elective", or "unknown"`.

`fyron.clinical_codes.icd10.add_icd10_disease_groups`

Add broad ICD-10 disease-group indicator columns.

Import path: fyron.clinical_codes.icd10.add_icd10_disease_groups

python

add_icd10_disease_groups(df: pd.DataFrame, icd_col: str, *, sep: str = ';', prefix: str = 'icd10_group_', summary_col: str = 'icd10_groups', count_col: str = 'n_icd10_groups', groups: Sequence[str] | None = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame containing an ICD-10 code column.
`icd_col`	yes	`str`		Column with one or more ICD-10 codes per row.
`sep`	no	`str`	`';'`	Delimiter between multiple codes in one cell.
`prefix`	no	`str`	`'icd10_group_'`	Prefix for generated binary indicator columns.
`summary_col`	no	`str`	`'icd10_groups'`	Output column containing human-readable disease groups.
`count_col`	no	`str`	`'n_icd10_groups'`	Output column containing the number of disease groups per row.
`groups`	no	`Sequence[str] or None`	`None`	Optional explicit disease-group order. Defaults to Fyron's built-in broad ICD-10 groups.

Returns

pandas.DataFrame - Copy of `df` with binary disease-group indicators and summary columns.

`fyron.clinical_codes.icd10.classify_icd10`

Classify one ICD-10 code into a broad disease group.

Import path: fyron.clinical_codes.icd10.classify_icd10

python

classify_icd10(code: Any) -> str | None

Parameters

Parameter	Required	Type	Default	Description
`code`	yes	`Any`		ICD-10 code such as ``"C34",` `"I50.1", or` `"Z51"``.

Returns

str or None - Broad disease group name, `"Other"` for malformed non-empty codes, or None for missing values.

`fyron.clinical_codes.icd10.classify_icd10_string`

Classify a delimited ICD-10 code string into disease groups.

Import path: fyron.clinical_codes.icd10.classify_icd10_string

python

classify_icd10_string(value: Any, *, sep: str = ';') -> set[str]

Parameters

Parameter	Required	Type	Default	Description
`value`	yes	`Any`		Scalar cell containing one or more ICD-10 codes.
`sep`	no	`str`	`';'`	Delimiter between codes.

Returns

set[str] - Disease groups present in the code string.

`fyron.clinical_codes.icd10.icd10_group_prevalence`

Summarize ICD-10 disease-group indicator prevalence.

Import path: fyron.clinical_codes.icd10.icd10_group_prevalence

python

icd10_group_prevalence(df: pd.DataFrame, *, group_cols: Sequence[str] | None = None, prefix: str = 'icd10_group_') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame with ICD-10 group indicator columns.
`group_cols`	no	`Sequence[str] or None`	`None`	Optional explicit indicator columns. If omitted, columns with ``prefix`` are used.
`prefix`	no	`str`	`'icd10_group_'`	Prefix used to discover indicator columns.

Returns

pandas.DataFrame - Prevalence table with group, column, patient count, and percent.

`fyron.clinical_codes.ops.add_mdc_partition`

Add MDC partition labels from an OPS code column.

Import path: fyron.clinical_codes.ops.add_mdc_partition

python

add_mdc_partition(df: pd.DataFrame, ops_column: str | None = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame containing OPS code values.
`ops_column`	no	`str or None`	`None`	Explicit source column. If omitted, common OPS column names are tried.

Returns

pandas.DataFrame - Copy of `df with mdc_partition and mdc_partition_source`.

`fyron.clinical_codes.ops.add_ops_categories`

Add broad German OPS procedure-category indicator columns.

Import path: fyron.clinical_codes.ops.add_ops_categories

python

add_ops_categories(df: pd.DataFrame, ops_col: str, *, sep: str = ';', prefix: str = 'ops_category_', summary_col: str = 'ops_categories', count_col: str = 'n_ops_categories', categories: Sequence[str] | None = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame containing an OPS code column.
`ops_col`	yes	`str`		Column with one or more OPS codes per row.
`sep`	no	`str`	`';'`	Delimiter between multiple codes in one cell.
`prefix`	no	`str`	`'ops_category_'`	Prefix for generated binary indicator columns.
`summary_col`	no	`str`	`'ops_categories'`	Output column containing human-readable OPS categories.
`count_col`	no	`str`	`'n_ops_categories'`	Output column containing the number of OPS categories per row.
`categories`	no	`Sequence[str] or None`	`None`	Optional explicit category order. Defaults to Fyron's broad OPS groups.

Returns

pandas.DataFrame - Copy of `df` with binary OPS category indicators and summary columns.

`fyron.clinical_codes.ops.aggregate_ops_by_encounter`

Aggregate one-OPS-procedure-per-row data to encounter-level lists.

Import path: fyron.clinical_codes.ops.aggregate_ops_by_encounter

python

aggregate_ops_by_encounter(procedures: pd.DataFrame, *, patient_col: str = 'patient_id', encounter_col: str = 'encounter_id', date_col: str = 'performed_date', ops_code_col: str = 'ops_code', ops_display_col: str | None = 'ops_display', complete_encounters: pd.DataFrame | None = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`procedures`	yes	`pd.DataFrame`		Long procedure DataFrame with one OPS code per row.
`patient_col`	no	`str`	`'patient_id'`	Patient identifier column.
`encounter_col`	no	`str`	`'encounter_id'`	Encounter identifier column.
`date_col`	no	`str`	`'performed_date'`	Procedure date column used for first/last performed dates.
`ops_code_col`	no	`str`	`'ops_code'`	OPS code column.
`ops_display_col`	no	`str or None`	`'ops_display'`	Optional OPS display text column.
`complete_encounters`	no	`pd.DataFrame or None`	`None`	Optional patient/encounter table used to retain encounters without OPS codes.

Returns

pandas.DataFrame - One row per patient encounter with `ops_code_list, ops_display_list, first/last performed dates, and n_ops_codes`.

`fyron.clinical_codes.ops.classify_mdc_partition`

Classify a stay into a broad MDC partition from OPS codes.

Import path: fyron.clinical_codes.ops.classify_mdc_partition

python

classify_mdc_partition(ops_codes: Any) -> str

Parameters

Parameter	Required	Type	Default	Description
`ops_codes`	yes	`Any`		Missing value, scalar code, delimited string, Python-style list string, or iterable of OPS codes.

Returns

str - `"surgical" when any chapter-5 OPS code is present, "medical" when any other valid OPS chapter is present, otherwise "other"`.

`fyron.clinical_codes.ops.classify_ops`

Classify one German OPS code into a broad procedure category.

Import path: fyron.clinical_codes.ops.classify_ops

python

classify_ops(code: Any) -> str | None

Parameters

Parameter	Required	Type	Default	Description
`code`	yes	`Any`		OPS code such as ``"1-620",` `"3-20",` `"5-541", or` `"8-980"``.

Returns

str or None - Broad OPS category, `"Other"` for malformed non-empty codes, or None for missing values.

`fyron.clinical_codes.ops.classify_ops_string`

Classify a delimited OPS code string into broad procedure categories.

Import path: fyron.clinical_codes.ops.classify_ops_string

python

classify_ops_string(value: Any, *, sep: str = ';') -> set[str]

Parameters

Parameter	Required	Type	Default	Description
`value`	yes	`Any`		Scalar cell containing one or more OPS codes.
`sep`	no	`str`	`';'`	Delimiter between codes.

Returns

set[str] - OPS categories present in the code string.

`fyron.clinical_codes.ops.get_ops_chapter`

Extract the one-digit OPS chapter from an OPS code.

Import path: fyron.clinical_codes.ops.get_ops_chapter

python

get_ops_chapter(ops_code: Any) -> str | None

Parameters

Parameter	Required	Type	Default	Description
`ops_code`	yes	`Any`		OPS code such as ``"5-470.0",` `"8-930", or` `"1-632"``.

Returns

str or None - One-digit chapter, or None for missing/malformed values.

`fyron.clinical_codes.ops.normalize_ops_codes`

Convert different OPS column formats into a clean list of OPS codes.

Import path: fyron.clinical_codes.ops.normalize_ops_codes

python

normalize_ops_codes(ops_codes: Any) -> list[str]

Parameters

Parameter	Required	Type	Default	Description
`ops_codes`	yes	`Any`		Missing value, scalar code, delimited string, Python-style list string, or iterable of OPS codes.

Returns

list[str] - Normalized OPS code strings.

`fyron.clinical_codes.ops.ops_category_prevalence`

Summarize OPS procedure-category indicator prevalence.

Import path: fyron.clinical_codes.ops.ops_category_prevalence

python

ops_category_prevalence(df: pd.DataFrame, *, category_cols: Sequence[str] | None = None, prefix: str = 'ops_category_') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame with OPS category indicator columns.
`category_cols`	no	`Sequence[str] or None`	`None`	Optional explicit indicator columns. If omitted, columns with ``prefix`` are used.
`prefix`	no	`str`	`'ops_category_'`	Prefix used to discover indicator columns.

Returns

pandas.DataFrame - Prevalence table with category, column, patient count, and percent.

`fyron.clinical_codes.utils.aggregate_code_rows`

Aggregate one-code-per-row data into list-valued group rows.

Import path: fyron.clinical_codes.utils.aggregate_code_rows

python

aggregate_code_rows(df: pd.DataFrame, group_cols: str | Sequence[str], code_col: str, *, display_col: str | None = None, date_col: str | None = None, code_list_col: str | None = None, display_list_col: str | None = None, first_date_col: str | None = None, last_date_col: str | None = None, count_col: str | None = None, complete_groups: pd.DataFrame | None = None, sort_items: bool = True, unique: bool = True, strip_strings: bool = True) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		Long DataFrame with one clinical code per row.
`group_cols`	yes	`str or Sequence[str]`		Column or columns defining the output row, such as patient and encounter identifiers.
`code_col`	yes	`str`		Source column containing the clinical code.
`display_col`	no	`str or None`	`None`	Optional source column containing code display text.
`date_col`	no	`str or None`	`None`	Optional date column used for first/last date outputs and sorting.
`code_list_col`	no	`str or None`	`None`	Output column for code lists. Defaults to ``f"{code_col}_list"``.
`display_list_col`	no	`str or None`	`None`	Output column for display text lists. Defaults to ``f"{display_col}_list"` `when` `display_col`` is provided.
`first_date_col`	no	`str or None`	`None`	Output column for the earliest date. Defaults to ``f"{date_col}_first"` `when` `date_col`` is provided.
`last_date_col`	no	`str or None`	`None`	Output column for the latest date. Defaults to ``f"{date_col}_last"` `when` `date_col`` is provided.
`count_col`	no	`str or None`	`None`	Output column for the number of distinct codes. Defaults to ``f"n_{code_col}s"``.
`complete_groups`	no	`pd.DataFrame or None`	`None`	Optional DataFrame with all desired output groups. Groups with no codes are retained with empty lists, missing dates, and a zero count.
`sort_items`	no	`bool`	`True`	If True, list outputs are sorted for stable CSV/JSON artifacts.
`unique`	no	`bool`	`True`	If True, each code/display appears once per output group.
`strip_strings`	no	`bool`	`True`	If True, trim whitespace from string-like group, code, and display columns before aggregation.

Returns

pandas.DataFrame - One row per group with list-valued code/display columns, optional first/last dates, and a code count.

`fyron.clinical_codes.utils.explode_code_column`

Explode a delimited clinical code column into one row per code.

Import path: fyron.clinical_codes.utils.explode_code_column

python

explode_code_column(df: pd.DataFrame, code_col: str, *, sep: str = ';', output_col: str = 'code', keep_empty: bool = False) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame containing a delimited code column.
`code_col`	yes	`str`		Column with one or more codes per row.
`sep`	no	`str`	`';'`	Delimiter between codes.
`output_col`	no	`str`	`'code'`	Name of the exploded output code column.
`keep_empty`	no	`bool`	`False`	If True, keep rows without codes and set ``output_col`` to missing.

Returns

pandas.DataFrame - Exploded DataFrame with one row per parsed code.

`fyron.clinical_codes.utils.normalize_code`

Normalize one clinical code value for matching.

Import path: fyron.clinical_codes.utils.normalize_code

python

normalize_code(value: Any, *, uppercase: bool = True, remove_spaces: bool = True, remove_dots: bool = False) -> str | None

Parameters

Parameter	Required	Type	Default	Description
`value`	yes	`Any`		Raw code value such as an ICD-10, OPS, medication, or local registry code.
`uppercase`	no	`bool`	`True`	If True, convert the returned code to uppercase.
`remove_spaces`	no	`bool`	`True`	If True, remove whitespace within the code.
`remove_dots`	no	`bool`	`False`	If True, remove dots from the code.

Returns

str or None - Normalized code string, or None for missing/empty values.

`fyron.clinical_codes.utils.split_code_string`

Split one cell containing one or more clinical codes.

Import path: fyron.clinical_codes.utils.split_code_string

python

split_code_string(value: Any, *, sep: str = ';', normalize: bool = True) -> list[str]

Parameters

Parameter	Required	Type	Default	Description
`value`	yes	`Any`		Scalar value containing one or more codes, usually separated by semicolons.
`sep`	no	`str`	`';'`	Delimiter used between codes.
`normalize`	no	`bool`	`True`	If True, normalize each code with :func:`normalize_code`.

Returns

list[str] - Ordered list of non-empty codes.

`fyron.console.configure_logging`

Configure root Fyron logging and return the `fyron` logger.

Import path: fyron.console.configure_logging

python

configure_logging(level: str | int = 'INFO', *, color: bool = True, json: bool = False, stream: TextIO | None = None) -> logging.Logger

Parameters

Parameter	Required	Type	Default	Description
`level`	no	`str or int`	`'INFO'`	See signature.
`color`	no	`bool`	`True`	See signature.
`json`	no	`bool`	`False`	See signature.
`stream`	no	`TextIO or None`	`None`	See signature.

Returns

logging.Logger

`fyron.console.console_message`

Print and return a styled terminal message.

Import path: fyron.console.console_message

python

console_message(message: str, *, level: str = 'info', color: bool = True, prefix: str = 'fyron', stream: TextIO | None = None, quiet: bool = False) -> str

Parameters

Parameter	Required	Type	Default	Description
`message`	yes	`str`		See signature.
`level`	no	`str`	`'info'`	See signature.
`color`	no	`bool`	`True`	See signature.
`prefix`	no	`str`	`'fyron'`	See signature.
`stream`	no	`TextIO or None`	`None`	See signature.
`quiet`	no	`bool`	`False`	See signature.

Returns

str

`fyron.console.error`

See the signature and linked module guide for context.

Import path: fyron.console.error

python

error(message: str, **kwargs: Any) -> str

Parameters

Parameter	Required	Type	Default	Description
`message`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

str

`fyron.console.format_count`

See the signature and linked module guide for context.

Import path: fyron.console.format_count

python

format_count(label: str, value: int | float) -> str

Parameters

Parameter	Required	Type	Default	Description
`label`	yes	`str`		See signature.
`value`	yes	`int or float`		See signature.

Returns

str

`fyron.console.format_duration`

See the signature and linked module guide for context.

Import path: fyron.console.format_duration

python

format_duration(seconds: float) -> str

Parameters

Parameter	Required	Type	Default	Description
`seconds`	yes	`float`		See signature.

Returns

str

`fyron.console.format_path`

See the signature and linked module guide for context.

Import path: fyron.console.format_path

python

format_path(path: str | Path) -> str

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.

Returns

str

`fyron.console.get_logger`

Return a logger below the Fyron namespace.

Import path: fyron.console.get_logger

python

get_logger(name: str | None = None) -> logging.Logger

Parameters

Parameter	Required	Type	Default	Description
`name`	no	`str or None`	`None`	See signature.

Returns

logging.Logger

`fyron.console.get_timer`

See the signature and linked module guide for context.

Import path: fyron.console.get_timer

python

get_timer() -> float

Parameters

No parameters documented.

Returns

float

`fyron.console.info`

See the signature and linked module guide for context.

Import path: fyron.console.info

python

info(message: str, **kwargs: Any) -> str

Parameters

Parameter	Required	Type	Default	Description
`message`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

str

`fyron.console.success`

See the signature and linked module guide for context.

Import path: fyron.console.success

python

success(message: str, **kwargs: Any) -> str

Parameters

Parameter	Required	Type	Default	Description
`message`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

str

`fyron.console.supports_color`

Return whether ANSI colors should be emitted for a stream.

Import path: fyron.console.supports_color

python

supports_color(stream: TextIO | None = None) -> bool

Parameters

Parameter	Required	Type	Default	Description
`stream`	no	`TextIO or None`	`None`	See signature.

Returns

bool

`fyron.console.warning`

See the signature and linked module guide for context.

Import path: fyron.console.warning

python

warning(message: str, **kwargs: Any) -> str

Parameters

Parameter	Required	Type	Default	Description
`message`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

str

`fyron.core.env.load_env`

Load environment variables from a .env file.

Import path: fyron.core.env.load_env

python

load_env(path: Optional[str] = None, *, override: bool = False, warn_if_missing: bool = True) -> Optional[str]

Parameters

Parameter	Required	Type	Default	Description
`path`	no	`Optional[str]`	`None`	Optional path to a .env file. If None, searches from the current working directory.
`override`	no	`bool`	`False`	Whether to override existing environment variables.
`warn_if_missing`	no	`bool`	`True`	If True, logs a warning when no .env file is found.

Returns

Optional[str]

`fyron.core.io.DataIO`

Convenience wrappers for local table and JSON IO.

Import path: fyron.core.io.DataIO

python

DataIO()

Parameters

No parameters documented.

Returns

Class constructor.

Public Methods

`fyron.core.io.DataIO.read_csv`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.read_csv

python

DataIO.read_csv(path: str, **kwargs: Any) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

pd.DataFrame

`fyron.core.io.DataIO.write_csv`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.write_csv

python

DataIO.write_csv(df: pd.DataFrame, path: str, **kwargs: Any) -> None

Parameters

Parameter	Required	Type	Description
`df`	yes	`pd.DataFrame`	See signature.
`path`	yes	`str`	See signature.
`kwargs`	yes	`Any`	See signature.

Returns

None

`fyron.core.io.DataIO.read_excel`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.read_excel

python

DataIO.read_excel(path: str, **kwargs: Any) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

pd.DataFrame

`fyron.core.io.DataIO.write_excel`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.write_excel

python

DataIO.write_excel(df: pd.DataFrame, path: str, **kwargs: Any) -> None

Parameters

Parameter	Required	Type	Description
`df`	yes	`pd.DataFrame`	See signature.
`path`	yes	`str`	See signature.
`kwargs`	yes	`Any`	See signature.

Returns

None

`fyron.core.io.DataIO.read_json`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.read_json

python

DataIO.read_json(path: str | Path, **kwargs: Any) -> Any

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

Any

`fyron.core.io.DataIO.write_json`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.write_json

python

DataIO.write_json(payload: Any, path: str | Path, *, indent: int | None = 2, create_parent: bool = True, **kwargs: Any) -> Path

Parameters

Parameter	Required	Type	Default	Description
`payload`	yes	`Any`		See signature.
`path`	yes	`str or Path`		See signature.
`indent`	no	`int or None`	`2`	See signature.
`create_parent`	no	`bool`	`True`	See signature.
`kwargs`	yes	`Any`		See signature.

Returns

Path

`fyron.core.io.DataIO.read_table`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.read_table

python

DataIO.read_table(path: str | Path, *, file_format: str | None = None, **kwargs: Any) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.
`file_format`	no	`str or None`	`None`	See signature.
`kwargs`	yes	`Any`		See signature.

Returns

pd.DataFrame

`fyron.core.io.DataIO.write_table`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.write_table

python

DataIO.write_table(df: pd.DataFrame, path: str | Path, *, file_format: str | None = None, create_parent: bool = True, index: bool = False, **kwargs: Any) -> Path

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`path`	yes	`str or Path`		See signature.
`file_format`	no	`str or None`	`None`	See signature.
`create_parent`	no	`bool`	`True`	See signature.
`index`	no	`bool`	`False`	See signature.
`kwargs`	yes	`Any`		See signature.

Returns

Path

`fyron.core.io.DataIO.summarize_table`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.summarize_table

python

DataIO.summarize_table(df: pd.DataFrame, *, name: str | None = None) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`name`	no	`str or None`	`None`	See signature.

Returns

dict[str, Any]

`fyron.core.io.DataIO.describe_file`

See the signature and linked module guide for context.

Import path: fyron.core.io.DataIO.describe_file

python

DataIO.describe_file(path: str | Path) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.

Returns

dict[str, Any]

`fyron.core.io.TeableClient`

Minimal Teable table client for reading/writing DataFrames.

Import path: fyron.core.io.TeableClient

python

TeableClient(self, base_url: Optional[str] = None, token: Optional[str] = None, timeout: int = 30)

Parameters

Parameter	Required	Type	Default	Description
`base_url`	no	`Optional[str]`	`None`	See signature.
`token`	no	`Optional[str]`	`None`	See signature.
`timeout`	no	`int`	`30`	See signature.

Returns

Class constructor.

Public Methods

`fyron.core.io.TeableClient.list_records`

See the signature and linked module guide for context.

Import path: fyron.core.io.TeableClient.list_records

python

TeableClient.list_records(self, table_id: str, *, view_id: Optional[str] = None, take: int = 1000, skip: int = 0, field_key_type: str = 'name', cell_format: str = 'json', projection: Optional[List[str]] = None, **params: Any) -> Dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`view_id`	no	`Optional[str]`	`None`	See signature.
`take`	no	`int`	`1000`	See signature.
`skip`	no	`int`	`0`	See signature.
`field_key_type`	no	`str`	`'name'`	See signature.
`cell_format`	no	`str`	`'json'`	See signature.
`projection`	no	`Optional[List[str]]`	`None`	See signature.
`params`	yes	`Any`		See signature.

Returns

Dict[str, Any]

`fyron.core.io.TeableClient.list_spaces`

List spaces available to the current token.

Import path: fyron.core.io.TeableClient.list_spaces

python

TeableClient.list_spaces(self) -> List[Dict[str, Any]]

Parameters

No parameters documented.

Returns

List[Dict[str, Any]]

`fyron.core.io.TeableClient.list_bases`

List bases for a space or all accessible bases.

Import path: fyron.core.io.TeableClient.list_bases

python

TeableClient.list_bases(self, *, space_id: Optional[str] = None) -> List[Dict[str, Any]]

Parameters

Parameter	Required	Type	Default	Description
`space_id`	no	`Optional[str]`	`None`	See signature.

Returns

List[Dict[str, Any]]

`fyron.core.io.TeableClient.create_base`

Create a base in a space.

Import path: fyron.core.io.TeableClient.create_base

python

TeableClient.create_base(self, *, space_id: str, name: str, icon: Optional[str] = None) -> Dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`space_id`	yes	`str`		See signature.
`name`	yes	`str`		See signature.
`icon`	no	`Optional[str]`	`None`	See signature.

Returns

Dict[str, Any]

`fyron.core.io.TeableClient.get_or_create_base`

Return a base by name, creating it if it doesn't exist.

Import path: fyron.core.io.TeableClient.get_or_create_base

python

TeableClient.get_or_create_base(self, *, name: str, space_id: Optional[str] = None, space_name: Optional[str] = None, icon: Optional[str] = None) -> Dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`name`	yes	`str`		See signature.
`space_id`	no	`Optional[str]`	`None`	See signature.
`space_name`	no	`Optional[str]`	`None`	See signature.
`icon`	no	`Optional[str]`	`None`	See signature.

Returns

Dict[str, Any]

`fyron.core.io.TeableClient.list_tables`

List tables in a base.

Import path: fyron.core.io.TeableClient.list_tables

python

TeableClient.list_tables(self, base_id: str) -> List[Dict[str, Any]]

Parameters

Parameter	Required	Type	Default	Description
`base_id`	yes	`str`		See signature.

Returns

List[Dict[str, Any]]

`fyron.core.io.TeableClient.create_table`

Create a table in a base.

Import path: fyron.core.io.TeableClient.create_table

python

TeableClient.create_table(self, *, base_id: str, name: str, db_table_name: Optional[str] = None, description: Optional[str] = None, icon: Optional[str] = None, fields: Optional[List[Dict[str, Any]]] = None, views: Optional[List[Dict[str, Any]]] = None, records: Optional[List[Dict[str, Any]]] = None, order: Optional[int] = None, field_key_type: str = 'name') -> Dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`base_id`	yes	`str`		See signature.
`name`	yes	`str`		See signature.
`db_table_name`	no	`Optional[str]`	`None`	See signature.
`description`	no	`Optional[str]`	`None`	See signature.
`icon`	no	`Optional[str]`	`None`	See signature.
`fields`	no	`Optional[List[Dict[str, Any]]]`	`None`	See signature.
`views`	no	`Optional[List[Dict[str, Any]]]`	`None`	See signature.
`records`	no	`Optional[List[Dict[str, Any]]]`	`None`	See signature.
`order`	no	`Optional[int]`	`None`	See signature.
`field_key_type`	no	`str`	`'name'`	See signature.

Returns

Dict[str, Any]

`fyron.core.io.TeableClient.get_or_create_table`

Return a table by name (or db_table_name), creating it if missing.

Import path: fyron.core.io.TeableClient.get_or_create_table

python

TeableClient.get_or_create_table(self, *, base_id: str, name: str, db_table_name: Optional[str] = None, description: Optional[str] = None, icon: Optional[str] = None, fields: Optional[List[Dict[str, Any]]] = None, views: Optional[List[Dict[str, Any]]] = None, records: Optional[List[Dict[str, Any]]] = None, order: Optional[int] = None, field_key_type: str = 'name') -> Dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`base_id`	yes	`str`		See signature.
`name`	yes	`str`		See signature.
`db_table_name`	no	`Optional[str]`	`None`	See signature.
`description`	no	`Optional[str]`	`None`	See signature.
`icon`	no	`Optional[str]`	`None`	See signature.
`fields`	no	`Optional[List[Dict[str, Any]]]`	`None`	See signature.
`views`	no	`Optional[List[Dict[str, Any]]]`	`None`	See signature.
`records`	no	`Optional[List[Dict[str, Any]]]`	`None`	See signature.
`order`	no	`Optional[int]`	`None`	See signature.
`field_key_type`	no	`str`	`'name'`	See signature.

Returns

Dict[str, Any]

`fyron.core.io.TeableClient.read_table`

See the signature and linked module guide for context.

Import path: fyron.core.io.TeableClient.read_table

python

TeableClient.read_table(self, table_id: str, *, view_id: Optional[str] = None, field_key_type: str = 'name', cell_format: str = 'json', projection: Optional[List[str]] = None, take: int = 1000, max_records: Optional[int] = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`view_id`	no	`Optional[str]`	`None`	See signature.
`field_key_type`	no	`str`	`'name'`	See signature.
`cell_format`	no	`str`	`'json'`	See signature.
`projection`	no	`Optional[List[str]]`	`None`	See signature.
`take`	no	`int`	`1000`	See signature.
`max_records`	no	`Optional[int]`	`None`	See signature.

Returns

pd.DataFrame

`fyron.core.io.TeableClient.create_records`

See the signature and linked module guide for context.

Import path: fyron.core.io.TeableClient.create_records

python

TeableClient.create_records(self, table_id: str, records: Iterable[Dict[str, Any]], *, field_key_type: str = 'name', typecast: bool = True, order: Optional[Dict[str, Any]] = None) -> Dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`records`	yes	`Iterable[Dict[str, Any]]`		See signature.
`field_key_type`	no	`str`	`'name'`	See signature.
`typecast`	no	`bool`	`True`	See signature.
`order`	no	`Optional[Dict[str, Any]]`	`None`	See signature.

Returns

Dict[str, Any]

`fyron.core.io.TeableClient.delete_records`

Delete records by ID.

Import path: fyron.core.io.TeableClient.delete_records

python

TeableClient.delete_records(self, table_id: str, record_ids: List[str]) -> None

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`record_ids`	yes	`List[str]`		See signature.

Returns

None

`fyron.core.io.TeableClient.clear_table`

Delete all records in a table and return deleted count.

Import path: fyron.core.io.TeableClient.clear_table

python

TeableClient.clear_table(self, table_id: str, *, batch_size: int = 200, take: int = 1000) -> int

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`batch_size`	no	`int`	`200`	See signature.
`take`	no	`int`	`1000`	See signature.

Returns

int

`fyron.core.io.TeableClient.write_dataframe`

See the signature and linked module guide for context.

Import path: fyron.core.io.TeableClient.write_dataframe

python

TeableClient.write_dataframe(self, table_id: str, df: pd.DataFrame, *, field_key_type: str = 'name', typecast: bool = True, chunk_size: int = 200) -> List[str]

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`df`	yes	`pd.DataFrame`		See signature.
`field_key_type`	no	`str`	`'name'`	See signature.
`typecast`	no	`bool`	`True`	See signature.
`chunk_size`	no	`int`	`200`	See signature.

Returns

List[str]

`fyron.core.io.TeableClient.overwrite_table`

Delete all records in a table, then write the DataFrame.

Import path: fyron.core.io.TeableClient.overwrite_table

python

TeableClient.overwrite_table(self, table_id: str, df: pd.DataFrame, *, delete_batch_size: int = 200, create_chunk_size: int = 200, field_key_type: str = 'name', typecast: bool = True) -> List[str]

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`df`	yes	`pd.DataFrame`		See signature.
`delete_batch_size`	no	`int`	`200`	See signature.
`create_chunk_size`	no	`int`	`200`	See signature.
`field_key_type`	no	`str`	`'name'`	See signature.
`typecast`	no	`bool`	`True`	See signature.

Returns

List[str]

`fyron.core.io.TeableClient.read_cohort`

Read a reviewed Teable cohort and validate basic cohort shape.

Import path: fyron.core.io.TeableClient.read_cohort

python

TeableClient.read_cohort(self, table_id: str, *, id_col: str | None = None, required_columns: list[str] | None = None, view_id: str | None = None, max_records: int | None = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`id_col`	no	`str or None`	`None`	See signature.
`required_columns`	no	`list[str] or None`	`None`	See signature.
`view_id`	no	`str or None`	`None`	See signature.
`max_records`	no	`int or None`	`None`	See signature.

Returns

pd.DataFrame

`fyron.core.io.TeableClient.write_research_cohort`

Write a finished research cohort to Teable and return an audit summary.

Import path: fyron.core.io.TeableClient.write_research_cohort

python

TeableClient.write_research_cohort(self, table_id: str, df: pd.DataFrame, *, mode: str = 'append', cohort_name: str | None = None, version: str | None = None, metadata: dict[str, Any] | None = None, id_col: str | None = None, field_key_type: str = 'name', typecast: bool = True, chunk_size: int = 200) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`df`	yes	`pd.DataFrame`		See signature.
`mode`	no	`str`	`'append'`	See signature.
`cohort_name`	no	`str or None`	`None`	See signature.
`version`	no	`str or None`	`None`	See signature.
`metadata`	no	`dict[str, Any] or None`	`None`	See signature.
`id_col`	no	`str or None`	`None`	See signature.
`field_key_type`	no	`str`	`'name'`	See signature.
`typecast`	no	`bool`	`True`	See signature.
`chunk_size`	no	`int`	`200`	See signature.

Returns

dict[str, Any]

`fyron.core.io.TeableClient.create_research_cohort_table`

Create a Teable table for a research cohort.

Import path: fyron.core.io.TeableClient.create_research_cohort_table

python

TeableClient.create_research_cohort_table(self, *, base_id: str, name: str, df: pd.DataFrame | None = None, description: str | None = None, fields: list[dict[str, Any]] | None = None, metadata: dict[str, Any] | None = None) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`base_id`	yes	`str`		See signature.
`name`	yes	`str`		See signature.
`df`	no	`pd.DataFrame or None`	`None`	See signature.
`description`	no	`str or None`	`None`	See signature.
`fields`	no	`list[dict[str, Any]] or None`	`None`	See signature.
`metadata`	no	`dict[str, Any] or None`	`None`	See signature.

Returns

dict[str, Any]

`fyron.core.io.TeableClient.summarize_teable_table`

Return a compact summary for a Teable table.

Import path: fyron.core.io.TeableClient.summarize_teable_table

python

TeableClient.summarize_teable_table(self, table_id: str, *, view_id: str | None = None, projection: list[str] | None = None, max_records: int | None = None) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`table_id`	yes	`str`		See signature.
`view_id`	no	`str or None`	`None`	See signature.
`projection`	no	`list[str] or None`	`None`	See signature.
`max_records`	no	`int or None`	`None`	See signature.

Returns

dict[str, Any]

`fyron.core.io.TeableConfig`

See the signature and linked module guide for context.

Import path: fyron.core.io.TeableConfig

python

TeableConfig()

Parameters

No parameters documented.

Returns

Class constructor.

`fyron.core.io.describe_file`

Return file metadata without reading the payload.

Import path: fyron.core.io.describe_file

python

describe_file(path: str | Path) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.

Returns

dict[str, Any]

`fyron.core.io.ensure_parent_dir`

Create the parent directory for a file path and return the path.

Import path: fyron.core.io.ensure_parent_dir

python

ensure_parent_dir(path: str | Path) -> Path

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.

Returns

Path

`fyron.core.io.read_csv`

Read a CSV file (shortcut for DataIO.read_csv).

Import path: fyron.core.io.read_csv

python

read_csv(path: str, **kwargs: Any) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

pd.DataFrame

`fyron.core.io.read_excel`

Read an Excel file (shortcut for DataIO.read_excel).

Import path: fyron.core.io.read_excel

python

read_excel(path: str, **kwargs: Any) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

pd.DataFrame

`fyron.core.io.read_json`

Read a UTF-8 JSON file.

Import path: fyron.core.io.read_json

python

read_json(path: str | Path, **kwargs: Any) -> Any

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

Any

`fyron.core.io.read_table`

Read a table from CSV, TSV, Excel, JSON, or JSONL.

Import path: fyron.core.io.read_table

python

read_table(path: str | Path, *, file_format: str | None = None, **kwargs: Any) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`path`	yes	`str or Path`		See signature.
`file_format`	no	`str or None`	`None`	See signature.
`kwargs`	yes	`Any`		See signature.

Returns

pd.DataFrame

`fyron.core.io.summarize_table`

Return compact metadata for a DataFrame.

Import path: fyron.core.io.summarize_table

python

summarize_table(df: pd.DataFrame, *, name: str | None = None) -> dict[str, Any]

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`name`	no	`str or None`	`None`	See signature.

Returns

dict[str, Any]

`fyron.core.io.write_csv`

Write a DataFrame to CSV (shortcut for DataIO.write_csv).

Import path: fyron.core.io.write_csv

python

write_csv(df: pd.DataFrame, path: str, **kwargs: Any) -> None

Parameters

Parameter	Required	Type	Description
`df`	yes	`pd.DataFrame`	See signature.
`path`	yes	`str`	See signature.
`kwargs`	yes	`Any`	See signature.

Returns

None

`fyron.core.io.write_excel`

Write a DataFrame to Excel (shortcut for DataIO.write_excel).

Import path: fyron.core.io.write_excel

python

write_excel(df: pd.DataFrame, path: str, **kwargs: Any) -> None

Parameters

Parameter	Required	Type	Description
`df`	yes	`pd.DataFrame`	See signature.
`path`	yes	`str`	See signature.
`kwargs`	yes	`Any`	See signature.

Returns

None

`fyron.core.io.write_json`

Write a UTF-8 JSON file and return the output path.

Import path: fyron.core.io.write_json

python

write_json(payload: Any, path: str | Path, *, indent: int | None = 2, create_parent: bool = True, **kwargs: Any) -> Path

Parameters

Parameter	Required	Type	Default	Description
`payload`	yes	`Any`		See signature.
`path`	yes	`str or Path`		See signature.
`indent`	no	`int or None`	`2`	See signature.
`create_parent`	no	`bool`	`True`	See signature.
`kwargs`	yes	`Any`		See signature.

Returns

Path

`fyron.core.io.write_table`

Write a table to CSV, TSV, Excel, JSON, or JSONL and return the path.

Import path: fyron.core.io.write_table

python

write_table(df: pd.DataFrame, path: str | Path, *, file_format: str | None = None, create_parent: bool = True, index: bool = False, **kwargs: Any) -> Path

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`path`	yes	`str or Path`		See signature.
`file_format`	no	`str or None`	`None`	See signature.
`create_parent`	no	`bool`	`True`	See signature.
`index`	no	`bool`	`False`	See signature.
`kwargs`	yes	`Any`		See signature.

Returns

Path

`fyron.core.s3.S3Storage`

Small S3 wrapper for clinical data science tables and sidecar files.

Import path: fyron.core.s3.S3Storage

python

S3Storage(self, bucket: str | None = None, *, prefix: str = '', endpoint_url: str | None = None, region_name: str | None = None, profile_name: str | None = None, access_key_id: str | None = None, secret_access_key: str | None = None, session_token: str | None = None)

Parameters

Parameter	Required	Type	Default	Description
`bucket`	no	`str or None`	`None`	Bucket name. Defaults to ``S3_BUCKET``.
`prefix`	no	`str`	`''`	Optional key prefix prepended to reads and writes. Defaults to ``S3_PREFIX`` when omitted.
`endpoint_url`	no	`str or None`	`None`	Optional S3-compatible endpoint URL such as MinIO. Defaults to ``S3_ENDPOINT_URL``.
`region_name`	no	`str or None`	`None`	AWS region name. Defaults to ``AWS_REGION` `or` `AWS_DEFAULT_REGION``.
`profile_name`	no	`str or None`	`None`	Optional boto3 profile name for local credential resolution.
`access_key_id`	no	`str or None`	`None`	Optional explicit access key ID for S3-compatible deployments.
`secret_access_key`	no	`str or None`	`None`	Optional explicit secret access key. Prefer environment variables or a profile for shared research environments.
`session_token`	no	`str or None`	`None`	Optional temporary session token.

Returns

Class constructor.

Public Methods

`fyron.core.s3.S3Storage.key`

Return a key with the configured prefix applied.

Import path: fyron.core.s3.S3Storage.key

python

S3Storage.key(self, key: str) -> str

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.

Returns

str

`fyron.core.s3.S3Storage.exists`

Return whether an object exists.

Import path: fyron.core.s3.S3Storage.exists

python

S3Storage.exists(self, key: str) -> bool

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.

Returns

bool

`fyron.core.s3.S3Storage.list`

List objects under a prefix.

Import path: fyron.core.s3.S3Storage.list

python

S3Storage.list(self, prefix: str | None = None, *, suffix: str | None = None, max_keys: int | None = None) -> list[dict[str, Any]]

Parameters

Parameter	Required	Type	Default	Description
`prefix`	no	`str or None`	`None`	See signature.
`suffix`	no	`str or None`	`None`	See signature.
`max_keys`	no	`int or None`	`None`	See signature.

Returns

list[dict[str, Any]]

`fyron.core.s3.S3Storage.read_bytes`

Read an object as bytes.

Import path: fyron.core.s3.S3Storage.read_bytes

python

S3Storage.read_bytes(self, key: str) -> bytes

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.

Returns

bytes

`fyron.core.s3.S3Storage.write_bytes`

Write bytes to S3 and return the s3 URI.

Import path: fyron.core.s3.S3Storage.write_bytes

python

S3Storage.write_bytes(self, key: str, data: bytes, *, content_type: str | None = None) -> str

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.
`data`	yes	`bytes`		See signature.
`content_type`	no	`str or None`	`None`	See signature.

Returns

str

`fyron.core.s3.S3Storage.read_json`

Read a JSON object.

Import path: fyron.core.s3.S3Storage.read_json

python

S3Storage.read_json(self, key: str) -> Any

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.

Returns

Any

`fyron.core.s3.S3Storage.write_json`

Write a JSON object.

Import path: fyron.core.s3.S3Storage.write_json

python

S3Storage.write_json(self, key: str, payload: Any, *, indent: int | None = 2) -> str

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.
`payload`	yes	`Any`		See signature.
`indent`	no	`int or None`	`2`	See signature.

Returns

str

`fyron.core.s3.S3Storage.read_table`

Read a table from S3.

Import path: fyron.core.s3.S3Storage.read_table

python

S3Storage.read_table(self, key: str, *, file_format: str | None = None, **kwargs: Any) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.
`file_format`	no	`str or None`	`None`	See signature.
`kwargs`	yes	`Any`		See signature.

Returns

pd.DataFrame

`fyron.core.s3.S3Storage.write_table`

Write a table to S3.

Import path: fyron.core.s3.S3Storage.write_table

python

S3Storage.write_table(self, key: str, df: pd.DataFrame, *, file_format: str | None = None, index: bool = False, **kwargs: Any) -> str

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.
`df`	yes	`pd.DataFrame`		See signature.
`file_format`	no	`str or None`	`None`	See signature.
`index`	no	`bool`	`False`	See signature.
`kwargs`	yes	`Any`		See signature.

Returns

str

`fyron.core.s3.S3Storage.download_file`

Download an object to a local file.

Import path: fyron.core.s3.S3Storage.download_file

python

S3Storage.download_file(self, key: str, local_path: str | Path, *, create_parent: bool = True) -> Path

Parameters

Parameter	Required	Type	Default	Description
`key`	yes	`str`		See signature.
`local_path`	yes	`str or Path`		See signature.
`create_parent`	no	`bool`	`True`	See signature.

Returns

Path

`fyron.core.s3.S3Storage.upload_file`

Upload a local file and return the s3 URI.

Import path: fyron.core.s3.S3Storage.upload_file

python

S3Storage.upload_file(self, local_path: str | Path, key: str | None = None, *, content_type: str | None = None) -> str

Parameters

Parameter	Required	Type	Default	Description
`local_path`	yes	`str or Path`		See signature.
`key`	no	`str or None`	`None`	See signature.
`content_type`	no	`str or None`	`None`	See signature.

Returns

str

`fyron.core.s3.parse_s3_uri`

Parse an s3://bucket/key URI.

Import path: fyron.core.s3.parse_s3_uri

python

parse_s3_uri(uri: str) -> dict[str, str]

Parameters

Parameter	Required	Type	Default	Description
`uri`	yes	`str`		See signature.

Returns

dict[str, str]

`fyron.core.s3.read_s3_table`

Read a table from an s3:// URI.

Import path: fyron.core.s3.read_s3_table

python

read_s3_table(uri: str, **kwargs: Any) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`uri`	yes	`str`		See signature.
`kwargs`	yes	`Any`		See signature.

Returns

pd.DataFrame

`fyron.core.s3.write_s3_table`

Write a DataFrame to an s3:// URI.

Import path: fyron.core.s3.write_s3_table

python

write_s3_table(df: pd.DataFrame, uri: str, **kwargs: Any) -> str

Parameters

Parameter	Required	Type	Description
`df`	yes	`pd.DataFrame`	See signature.
`uri`	yes	`str`	See signature.
`kwargs`	yes	`Any`	See signature.

Returns

str

`fyron.curate.build_compose_command`

Build the Docker Compose command used to run the curator stack.

Import path: fyron.curate.build_compose_command

python

build_compose_command(*, build: bool = True, detached: bool = True) -> list[str]

Parameters

Parameter	Required	Type	Default	Description
`build`	no	`bool`	`True`	See signature.
`detached`	no	`bool`	`True`	See signature.

Returns

list[str]

`fyron.curate.default_app_dir`

Return the best available path for the bundled image curator app.

Import path: fyron.curate.default_app_dir

python

default_app_dir() -> Path

Parameters

No parameters documented.

Returns

Path

`fyron.curate.start_curator`

Start the local Fyron image curator Docker Compose app.

Import path: fyron.curate.start_curator

python

start_curator(*, app_dir: str | Path | None = None, build: bool = True, detached: bool = True, open_browser: bool = False, color: bool = True, quiet: bool = False) -> int

Parameters

Parameter	Required	Type	Default	Description
`app_dir`	no	`str or Path or None`	`None`	See signature.
`build`	no	`bool`	`True`	See signature.
`detached`	no	`bool`	`True`	See signature.
`open_browser`	no	`bool`	`False`	See signature.
`color`	no	`bool`	`True`	See signature.
`quiet`	no	`bool`	`False`	See signature.

Returns

int

`fyron.datasets.make_boa_like_folder`

Create a minimal BOA-like cohort folder with one synthetic case.

Import path: fyron.datasets.make_boa_like_folder

python

make_boa_like_folder(output_dir: str | Path) -> Path

Parameters

Parameter	Required	Type	Default	Description
`output_dir`	yes	`str or Path`		See signature.

Returns

Path

`fyron.datasets.make_classification_cohort`

Create a tiny tabular classification dataset.

Import path: fyron.datasets.make_classification_cohort

python

make_classification_cohort(n: int = 80, *, random_state: int = 42) -> tuple[pd.DataFrame, pd.Series]

Parameters

Parameter	Required	Type	Default	Description
`n`	no	`int`	`80`	See signature.
`random_state`	no	`int`	`42`	See signature.

Returns

tuple[pd.DataFrame, pd.Series]

`fyron.datasets.make_clinical_cohort`

Create a deterministic synthetic clinical cohort.

Import path: fyron.datasets.make_clinical_cohort

python

make_clinical_cohort(n: int = 40, *, random_state: int = 42) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`n`	no	`int`	`40`	See signature.
`random_state`	no	`int`	`42`	See signature.

Returns

pd.DataFrame

`fyron.datasets.make_dicom_series`

Write a tiny synthetic CT DICOM series.

Import path: fyron.datasets.make_dicom_series

python

make_dicom_series(output_dir: str | Path, *, shape: tuple[int, int, int] = (3, 8, 8), patient_id: str = 'SYNTHETIC') -> list[Path]

Parameters

Parameter	Required	Type	Default	Description
`output_dir`	yes	`str or Path`		See signature.
`shape`	no	`tuple[int, int, int]`	`(3, 8, 8)`	See signature.
`patient_id`	no	`str`	`'SYNTHETIC'`	See signature.

Returns

list[Path]

`fyron.datasets.make_nifti_pair`

Write a tiny synthetic image/mask NIfTI pair.

Import path: fyron.datasets.make_nifti_pair

python

make_nifti_pair(output_dir: str | Path, *, shape: tuple[int, int, int] = (4, 8, 8)) -> dict[str, Path]

Parameters

Parameter	Required	Type	Default	Description
`output_dir`	yes	`str or Path`		See signature.
`shape`	no	`tuple[int, int, int]`	`(4, 8, 8)`	See signature.

Returns

dict[str, Path]

`fyron.datasets.make_survival_cohort`

Create a deterministic survival-ready cohort with time and event.

Import path: fyron.datasets.make_survival_cohort

python

make_survival_cohort(n: int = 40, *, random_state: int = 42) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`n`	no	`int`	`40`	See signature.
`random_state`	no	`int`	`42`	See signature.

Returns

pd.DataFrame

`fyron.datasets.preparation.prepare_nnunetv2_dataset`

Prepare an nnU-Net v2 segmentation dataset from a tabular manifest.

Import path: fyron.datasets.preparation.prepare_nnunetv2_dataset

python

prepare_nnunetv2_dataset(manifest: pd.DataFrame | str | Path, output_dir: str | Path, dataset_id: int, dataset_name: str, image_col: str = 'image_path', label_col: str = 'label_path', case_id_col: str = 'case_id', split_col: str = 'split', channel_names: dict[int | str, str] | Sequence[str] | None = None, labels: dict[int | str, str] | None = None, copy_mode: CopyMode = 'copy', overwrite: bool = False, validate: bool = True) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`manifest`	yes	`pd.DataFrame or str or Path`		See signature.
`output_dir`	yes	`str or Path`		See signature.
`dataset_id`	yes	`int`		See signature.
`dataset_name`	yes	`str`		See signature.
`image_col`	no	`str`	`'image_path'`	See signature.
`label_col`	no	`str`	`'label_path'`	See signature.
`case_id_col`	no	`str`	`'case_id'`	See signature.
`split_col`	no	`str`	`'split'`	See signature.
`channel_names`	no	`dict[int or str, str] or Sequence[str] or None`	`None`	See signature.
`labels`	no	`dict[int or str, str] or None`	`None`	See signature.
`copy_mode`	no	`CopyMode`	`'copy'`	See signature.
`overwrite`	no	`bool`	`False`	See signature.
`validate`	no	`bool`	`True`	See signature.

Returns

pd.DataFrame

`fyron.datasets.preparation.prepare_yolo_classification_dataset`

Prepare a YOLO classification dataset from images, DICOM, or NIfTI.

Import path: fyron.datasets.preparation.prepare_yolo_classification_dataset

python

prepare_yolo_classification_dataset(manifest: pd.DataFrame | str | Path, output_dir: str | Path, image_col: str = 'image_path', class_col: str = 'class', split_col: str = 'split', sample_id_col: str | None = None, image_format: str = 'png', copy_mode: CopyMode = 'copy', dicom_window: tuple[float, float] | None = None, nifti_slice: int | Literal['middle', 'all'] = 'middle', overwrite: bool = False, validate: bool = True) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`manifest`	yes	`pd.DataFrame or str or Path`		See signature.
`output_dir`	yes	`str or Path`		See signature.
`image_col`	no	`str`	`'image_path'`	See signature.
`class_col`	no	`str`	`'class'`	See signature.
`split_col`	no	`str`	`'split'`	See signature.
`sample_id_col`	no	`str or None`	`None`	See signature.
`image_format`	no	`str`	`'png'`	See signature.
`copy_mode`	no	`CopyMode`	`'copy'`	See signature.
`dicom_window`	no	`tuple[float, float] or None`	`None`	See signature.
`nifti_slice`	no	`int or Literal['middle', 'all']`	`'middle'`	See signature.
`overwrite`	no	`bool`	`False`	See signature.
`validate`	no	`bool`	`True`	See signature.

Returns

pd.DataFrame

`fyron.datasets.preparation.prepare_yolo_detection_dataset`

Prepare an Ultralytics-compatible YOLO object-detection dataset.

Import path: fyron.datasets.preparation.prepare_yolo_detection_dataset

python

prepare_yolo_detection_dataset(manifest: pd.DataFrame | str | Path, output_dir: str | Path, image_col: str = 'image_path', split_col: str = 'split', class_col: str = 'class', bbox_cols: Sequence[str] = ('x_min', 'y_min', 'x_max', 'y_max'), image_width_col: str | None = None, image_height_col: str | None = None, coord_format: Literal['xyxy_pixels'] = 'xyxy_pixels', sample_id_col: str | None = None, image_format: str = 'png', copy_mode: CopyMode = 'copy', dicom_window: tuple[float, float] | None = None, nifti_slice: int | Literal['middle', 'all'] = 'middle', overwrite: bool = False, validate: bool = True) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`manifest`	yes	`pd.DataFrame or str or Path`		See signature.
`output_dir`	yes	`str or Path`		See signature.
`image_col`	no	`str`	`'image_path'`	See signature.
`split_col`	no	`str`	`'split'`	See signature.
`class_col`	no	`str`	`'class'`	See signature.
`bbox_cols`	no	`Sequence[str]`	`('x_min', 'y_min', 'x_max', 'y_max')`	See signature.
`image_width_col`	no	`str or None`	`None`	See signature.
`image_height_col`	no	`str or None`	`None`	See signature.
`coord_format`	no	`Literal['xyxy_pixels']`	`'xyxy_pixels'`	See signature.
`sample_id_col`	no	`str or None`	`None`	See signature.
`image_format`	no	`str`	`'png'`	See signature.
`copy_mode`	no	`CopyMode`	`'copy'`	See signature.
`dicom_window`	no	`tuple[float, float] or None`	`None`	See signature.
`nifti_slice`	no	`int or Literal['middle', 'all']`	`'middle'`	See signature.
`overwrite`	no	`bool`	`False`	See signature.
`validate`	no	`bool`	`True`	See signature.

Returns

pd.DataFrame

`fyron.datasets.preparation.validate_nnunetv2_dataset`

Validate the expected files and names in an nnU-Net v2 dataset folder.

Import path: fyron.datasets.preparation.validate_nnunetv2_dataset

python

validate_nnunetv2_dataset(dataset_dir: str | Path) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`dataset_dir`	yes	`str or Path`		See signature.

Returns

pd.DataFrame

`fyron.datasets.preparation.validate_yolo_dataset`

Validate a prepared YOLO classification or detection dataset.

Import path: fyron.datasets.preparation.validate_yolo_dataset

python

validate_yolo_dataset(dataset_dir: str | Path, task: Literal['detect', 'classify'] = 'detect') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`dataset_dir`	yes	`str or Path`		See signature.
`task`	no	`Literal['detect', 'classify']`	`'detect'`	See signature.

Returns

pd.DataFrame

`fyron.datasets.preparation.write_dataset_manifest`

Write a dataset preparation manifest as CSV or JSON.

Import path: fyron.datasets.preparation.write_dataset_manifest

python

write_dataset_manifest(df: pd.DataFrame, output_path: str | Path) -> Path

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`output_path`	yes	`str or Path`		See signature.

Returns

Path

`fyron.documents.client.DocumentDownloader`

Download documents from URLs into a deterministic folder structure.

Import path: fyron.documents.client.DocumentDownloader

python

DocumentDownloader(self, auth: Optional[Union[Auth, requests.Session]] = None, base_url: Optional[str] = None, output_dir: Union[str, pathlib.Path] = 'documents_out', timeout: int = 30, skip_existing: bool = True, log_downloads: bool = False, max_workers: int = 4, retries: Optional[Retry] = None, save_mode: str = 'auto', force_extension: Optional[str] = None, basic_auth: Optional[Tuple[str, str]] = None)

Parameters

Parameter	Required	Type	Default	Description
`auth`	no	`Optional[Union[Auth, requests.Session]]`	`None`	Optional Auth or requests.Session to reuse.
`base_url`	no	`Optional[str]`	`None`	Optional base URL to prefix relative paths.
`output_dir`	no	`Union[str, pathlib.Path]`	`'documents_out'`	Parent folder where documents are stored.
`timeout`	no	`int`	`30`	Request timeout in seconds.
`skip_existing`	no	`bool`	`True`	Skip downloads if the target file already exists.
`log_downloads`	no	`bool`	`False`	If True, logs each download.
`max_workers`	no	`int`	`4`	See signature.
`retries`	no	`Optional[Retry]`	`None`	See signature.
`save_mode`	no	`str`	`'auto'`	See signature.
`force_extension`	no	`Optional[str]`	`None`	See signature.
`basic_auth`	no	`Optional[Tuple[str, str]]`	`None`	Optional (user, password) tuple for basic auth (standalone).

Returns

Class constructor.

Public Methods

`fyron.documents.client.DocumentDownloader.close`

Close the underlying session if owned.

Import path: fyron.documents.client.DocumentDownloader.close

python

DocumentDownloader.close(self) -> None

Parameters

No parameters documented.

Returns

None

`fyron.documents.client.DocumentDownloader.download_id`

Return a deterministic download ID for a URL.

Import path: fyron.documents.client.DocumentDownloader.download_id

python

DocumentDownloader.download_id(url: str) -> str

Parameters

Parameter	Required	Type	Default	Description
`url`	yes	`str`		See signature.

Returns

str

`fyron.documents.client.DocumentDownloader.download_url`

Download a single URL into its deterministic folder.

Import path: fyron.documents.client.DocumentDownloader.download_url

python

DocumentDownloader.download_url(self, url: str) -> DocumentResult

Parameters

Parameter	Required	Type	Default	Description
`url`	yes	`str`		See signature.

Returns

DocumentResult

`fyron.documents.client.DocumentDownloader.download_urls`

Download a list of URLs and return results as a DataFrame.

Import path: fyron.documents.client.DocumentDownloader.download_urls

python

DocumentDownloader.download_urls(self, urls: Iterable[str], results_csv: Optional[Union[str, pathlib.Path]] = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`urls`	yes	`Iterable[str]`		See signature.
`results_csv`	no	`Optional[Union[str, pathlib.Path]]`	`None`	See signature.

Returns

pd.DataFrame

`fyron.documents.client.DocumentDownloader.download_from_dataframe`

Download all URLs from a DataFrame column.

Import path: fyron.documents.client.DocumentDownloader.download_from_dataframe

python

DocumentDownloader.download_from_dataframe(self, df: pd.DataFrame, url_col: str = 'document_url', results_csv: Optional[Union[str, pathlib.Path]] = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`url_col`	no	`str`	`'document_url'`	See signature.
`results_csv`	no	`Optional[Union[str, pathlib.Path]]`	`None`	See signature.

Returns

pd.DataFrame

`fyron.documents.client.DocumentDownloader.download_from_df`

Short alias for download_from_dataframe.

Import path: fyron.documents.client.DocumentDownloader.download_from_df

python

DocumentDownloader.download_from_df(self, df: pd.DataFrame, url_col: str = 'document_url', results_csv: Optional[Union[str, pathlib.Path]] = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`url_col`	no	`str`	`'document_url'`	See signature.
`results_csv`	no	`Optional[Union[str, pathlib.Path]]`	`None`	See signature.

Returns

pd.DataFrame

`fyron.documents.client.DocumentResult`

See the signature and linked module guide for context.

Import path: fyron.documents.client.DocumentResult

python

DocumentResult()

Parameters

No parameters documented.

Returns

Class constructor.

`fyron.get_banner`

See the signature and linked module guide for context.

Import path: fyron.get_banner

python

get_banner(version: str | None = None, *, include_version: bool = True, color: bool = True, theme: str = 'bf') -> str

Parameters

Parameter	Required	Type	Default	Description
`version`	no	`str or None`	`None`	See signature.
`include_version`	no	`bool`	`True`	See signature.
`color`	no	`bool`	`True`	See signature.
`theme`	no	`str`	`'bf'`	See signature.

Returns

str

`fyron.get_run_footer`

See the signature and linked module guide for context.

Import path: fyron.get_run_footer

python

get_run_footer(*args, **kwargs) -> str

Parameters

Parameter	Required	Type	Default	Description
`args`	yes			See signature.
`kwargs`	yes			See signature.

Returns

str

`fyron.get_run_header`

See the signature and linked module guide for context.

Import path: fyron.get_run_header

python

get_run_header(*args, **kwargs) -> str

Parameters

Parameter	Required	Type	Default	Description
`args`	yes			See signature.
`kwargs`	yes			See signature.

Returns

str

`fyron.llm.agent.LLMAgent`

Simple LLM agent for DataFrame and document directory workflows.

Import path: fyron.llm.agent.LLMAgent

python

LLMAgent(self, provider: Optional[str] = None, base_url: Optional[str] = None, api_key: Optional[str] = None, model: Optional[str] = None, timeout: Optional[int] = None, extra_headers: Optional[Dict[str, str]] = None, auth_required: Optional[bool] = None, verify_ssl: bool = True)

Parameters

Parameter	Required	Type	Default	Description
`provider`	no	`Optional[str]`	`None`	Provider mode: ``openai,` `anyllm, or` `custom. Defaults to` `LLM_PROVIDER` `or` `openai``.
`base_url`	no	`Optional[str]`	`None`	Provider base URL or custom endpoint. Defaults to ``LLM_BASE_URL``.
`api_key`	no	`Optional[str]`	`None`	Optional API key. Defaults to ``LLM_API_KEY`` and is sent as a bearer token when present.
`model`	no	`Optional[str]`	`None`	Model name for the provider. Defaults to ``LLM_MODEL``.
`timeout`	no	`Optional[int]`	`None`	Request timeout in seconds. Defaults to ``LLM_TIMEOUT`` or 30.
`extra_headers`	no	`Optional[Dict[str, str]]`	`None`	Additional HTTP headers merged into the session.
`auth_required`	no	`Optional[bool]`	`None`	Whether an API key is required. Defaults to true for ``openai` `and` `anyllm` `and false for` `custom``.
`verify_ssl`	no	`bool`	`True`	Whether HTTPS certificate verification is enabled for requests.

Returns

Class constructor.

Public Methods

`fyron.llm.agent.LLMAgent.prompt`

Send a single prompt and return the model response.

Import path: fyron.llm.agent.LLMAgent.prompt

python

LLMAgent.prompt(self, user_prompt: str, *, image_path: Optional[str] = None, file_path: Optional[str] = None, mode: str = 'text') -> str

Parameters

Parameter	Required	Type	Default	Description
`user_prompt`	yes	`str`		See signature.
`image_path`	no	`Optional[str]`	`None`	See signature.
`file_path`	no	`Optional[str]`	`None`	See signature.
`mode`	no	`str`	`'text'`	See signature.

Returns

str

`fyron.llm.agent.LLMAgent.chat`

Alias for prompt (kept for convenience).

Import path: fyron.llm.agent.LLMAgent.chat

python

LLMAgent.chat(self, user_prompt: str, *, image_path: Optional[str] = None, file_path: Optional[str] = None, mode: str = 'text') -> str

Parameters

Parameter	Required	Type	Default	Description
`user_prompt`	yes	`str`		See signature.
`image_path`	no	`Optional[str]`	`None`	See signature.
`file_path`	no	`Optional[str]`	`None`	See signature.
`mode`	no	`str`	`'text'`	See signature.

Returns

str

`fyron.llm.agent.LLMAgent.run_on_dataframe`

Run a prompt over a DataFrame column and append results.

Import path: fyron.llm.agent.LLMAgent.run_on_dataframe

python

LLMAgent.run_on_dataframe(self, df: pd.DataFrame, text_col: str, prompt: str, output_col: str = 'llm_output', format_func: Optional[Callable[[str, Dict[str, Any]], str]] = None, image_col: Optional[str] = None, file_col: Optional[str] = None, mode: str = 'text') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		Input DataFrame.
`text_col`	yes	`str`		Column containing text to analyze.
`prompt`	yes	`str`		Prompt template. If format_func is not provided, text is appended.
`output_col`	no	`str`	`'llm_output'`	Name of output column.
`format_func`	no	`Optional[Callable[[str, Dict[str, Any]], str]]`	`None`	Optional function (text, row_dict) -> prompt string.
`image_col`	no	`Optional[str]`	`None`	Optional column with image file paths or URLs.
`file_col`	no	`Optional[str]`	`None`	Optional column with PDF file paths (OpenAI Responses only).
`mode`	no	`str`	`'text'`	"text", "image", "pdf", or "auto".

Returns

pd.DataFrame

`fyron.llm.agent.LLMAgent.run_on_documents`

Run a prompt over a list of documents (paths or raw strings).

Import path: fyron.llm.agent.LLMAgent.run_on_documents

python

LLMAgent.run_on_documents(self, documents: List[str], prompt: str, output_csv: Optional[str] = None, mode: str = 'text') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`documents`	yes	`List[str]`		List of file paths or raw text strings.
`prompt`	yes	`str`		Prompt to apply to each document.
`output_csv`	no	`Optional[str]`	`None`	Optional path to write results as CSV.
`mode`	no	`str`	`'text'`	"text", "image", "pdf", or "auto".

Returns

pd.DataFrame

`fyron.llm.agent.LLMAgent.prompt_documents`

Short alias for run_on_documents.

Import path: fyron.llm.agent.LLMAgent.prompt_documents

python

LLMAgent.prompt_documents(self, documents: List[str], prompt: str, output_csv: Optional[str] = None, mode: str = 'text') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`documents`	yes	`List[str]`		See signature.
`prompt`	yes	`str`		See signature.
`output_csv`	no	`Optional[str]`	`None`	See signature.
`mode`	no	`str`	`'text'`	See signature.

Returns

pd.DataFrame

`fyron.llm.agent.LLMAgent.prompt_dataframe`

Run a prompt over a DataFrame column and optionally save results.

Import path: fyron.llm.agent.LLMAgent.prompt_dataframe

python

LLMAgent.prompt_dataframe(self, df: pd.DataFrame, text_col: str, prompt: str, output_col: str = 'llm_output', output_csv: Optional[str] = None, format_func: Optional[Callable[[str, Dict[str, Any]], str]] = None, image_col: Optional[str] = None, file_col: Optional[str] = None, mode: str = 'text') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`text_col`	yes	`str`		See signature.
`prompt`	yes	`str`		See signature.
`output_col`	no	`str`	`'llm_output'`	See signature.
`output_csv`	no	`Optional[str]`	`None`	See signature.
`format_func`	no	`Optional[Callable[[str, Dict[str, Any]], str]]`	`None`	See signature.
`image_col`	no	`Optional[str]`	`None`	See signature.
`file_col`	no	`Optional[str]`	`None`	See signature.
`mode`	no	`str`	`'text'`	See signature.

Returns

pd.DataFrame

`fyron.llm.agent.LLMAgent.prompt_df`

Short alias for prompt_dataframe.

Import path: fyron.llm.agent.LLMAgent.prompt_df

python

LLMAgent.prompt_df(self, df: pd.DataFrame, text_col: str, prompt: str, output_col: str = 'llm_output', output_csv: Optional[str] = None, format_func: Optional[Callable[[str, Dict[str, Any]], str]] = None, image_col: Optional[str] = None, file_col: Optional[str] = None, mode: str = 'text') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`text_col`	yes	`str`		See signature.
`prompt`	yes	`str`		See signature.
`output_col`	no	`str`	`'llm_output'`	See signature.
`output_csv`	no	`Optional[str]`	`None`	See signature.
`format_func`	no	`Optional[Callable[[str, Dict[str, Any]], str]]`	`None`	See signature.
`image_col`	no	`Optional[str]`	`None`	See signature.
`file_col`	no	`Optional[str]`	`None`	See signature.
`mode`	no	`str`	`'text'`	See signature.

Returns

pd.DataFrame

`fyron.llm.agent.LLMAgent.run_prompt_on_dataframe`

Backward-compatible alias for prompt_dataframe.

Import path: fyron.llm.agent.LLMAgent.run_prompt_on_dataframe

python

LLMAgent.run_prompt_on_dataframe(self, df: pd.DataFrame, text_col: str, prompt: str, output_col: str = 'llm_output', output_csv: Optional[str] = None, format_func: Optional[Callable[[str, Dict[str, Any]], str]] = None, image_col: Optional[str] = None, file_col: Optional[str] = None, mode: str = 'text') -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		See signature.
`text_col`	yes	`str`		See signature.
`prompt`	yes	`str`		See signature.
`output_col`	no	`str`	`'llm_output'`	See signature.
`output_csv`	no	`Optional[str]`	`None`	See signature.
`format_func`	no	`Optional[Callable[[str, Dict[str, Any]], str]]`	`None`	See signature.
`image_col`	no	`Optional[str]`	`None`	See signature.
`file_col`	no	`Optional[str]`	`None`	See signature.
`mode`	no	`str`	`'text'`	See signature.

Returns

pd.DataFrame

`fyron.llm.agent.LLMAgent.describe_python_file`

Generate a scientific-methods style description of a Python file.

Import path: fyron.llm.agent.LLMAgent.describe_python_file

python

LLMAgent.describe_python_file(self, file_path: str | pathlib.Path, output_md: str | pathlib.Path, prompt: Optional[str] = None) -> str

Parameters

Parameter	Required	Type	Default	Description
`file_path`	yes	`str or pathlib.Path`		Path to the Python file to analyze.
`output_md`	yes	`str or pathlib.Path`		Path to write the markdown summary.
`prompt`	no	`Optional[str]`	`None`	Optional prompt override.

Returns

str

`fyron.llm.agent.LLMAgent.run_on_directory`

Run a prompt over all files in a directory.

Import path: fyron.llm.agent.LLMAgent.run_on_directory

python

LLMAgent.run_on_directory(self, input_dir: str | pathlib.Path, prompt: str, glob: str = '*.txt', mode: str = 'text') -> Dict[str, str]

Parameters

Parameter	Required	Type	Default	Description
`input_dir`	yes	`str or pathlib.Path`		See signature.
`prompt`	yes	`str`		See signature.
`glob`	no	`str`	`'*.txt'`	See signature.
`mode`	no	`str`	`'text'`	See signature.

Returns

Dict[str, str]

`fyron.llm.agent.LLMAgent.prompt_directory`

Short alias for run_on_directory.

Import path: fyron.llm.agent.LLMAgent.prompt_directory

python

LLMAgent.prompt_directory(self, input_dir: str | pathlib.Path, prompt: str, glob: str = '*.txt', mode: str = 'text') -> Dict[str, str]

Parameters

Parameter	Required	Type	Default	Description
`input_dir`	yes	`str or pathlib.Path`		See signature.
`prompt`	yes	`str`		See signature.
`glob`	no	`str`	`'*.txt'`	See signature.
`mode`	no	`str`	`'text'`	See signature.

Returns

Dict[str, str]

`fyron.llm.agent.LLMConfig`

See the signature and linked module guide for context.

Import path: fyron.llm.agent.LLMConfig

python

LLMConfig()

Parameters

No parameters documented.

Returns

Class constructor.

`fyron.plotting_palettes.PaletteSpec`

Metadata for one named scientific color palette.

Import path: fyron.plotting_palettes.PaletteSpec

python

PaletteSpec()

Parameters

No parameters documented.

Returns

Class constructor.

`fyron.plotting_palettes.get_colormap`

Return a Matplotlib colormap from a named sequential/diverging palette.

Import path: fyron.plotting_palettes.get_colormap

python

get_colormap(name: str = 'fyron_sequential_blue', *, reverse: bool = False) -> LinearSegmentedColormap

Parameters

Parameter	Required	Type	Default	Description
`name`	no	`str`	`'fyron_sequential_blue'`	See signature.
`reverse`	no	`bool`	`False`	See signature.

Returns

LinearSegmentedColormap

`fyron.plotting_palettes.get_palette`

Return a named scientific palette as hex colors.

Import path: fyron.plotting_palettes.get_palette

python

get_palette(name: str = 'okabe_ito', n: int | None = None, *, reverse: bool = False) -> list[str]

Parameters

Parameter	Required	Type	Default	Description
`name`	no	`str`	`'okabe_ito'`	See signature.
`n`	no	`int or None`	`None`	See signature.
`reverse`	no	`bool`	`False`	See signature.

Returns

list[str]

`fyron.plotting_palettes.list_palettes`

List available Fyron scientific palettes.

Import path: fyron.plotting_palettes.list_palettes

python

list_palettes(kind: str | None = None) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`kind`	no	`str or None`	`None`	See signature.

Returns

pd.DataFrame

`fyron.plotting_palettes.palette_grayscale_contrast`

Return adjacent luminance differences for grayscale readability checks.

Import path: fyron.plotting_palettes.palette_grayscale_contrast

python

palette_grayscale_contrast(name_or_colors: str | Sequence[str]) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`name_or_colors`	yes	`str or Sequence[str]`		See signature.

Returns

pd.DataFrame

`fyron.plotting_palettes.palette_luminance_table`

Return luminance and text-color guidance for a palette.

Import path: fyron.plotting_palettes.palette_luminance_table

python

palette_luminance_table(name_or_colors: str | Sequence[str]) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`name_or_colors`	yes	`str or Sequence[str]`		See signature.

Returns

pd.DataFrame

`fyron.plotting_palettes.preview_palette`

Preview a named palette as labeled swatches.

Import path: fyron.plotting_palettes.preview_palette

python

preview_palette(name: str, n: int | None = None, *, save_path: str | Path | None = None) -> tuple[Any, Any]

Parameters

Parameter	Required	Type	Default	Description
`name`	yes	`str`		See signature.
`n`	no	`int or None`	`None`	See signature.
`save_path`	no	`str or Path or None`	`None`	See signature.

Returns

tuple[Any, Any]

`fyron.plotting_palettes.resolve_colormap`

Resolve a Fyron colormap name or pass through any Matplotlib colormap.

Import path: fyron.plotting_palettes.resolve_colormap

python

resolve_colormap(cmap: str | Any) -> Any

Parameters

Parameter	Required	Type	Default	Description
`cmap`	yes	`str or Any`		See signature.

Returns

Any

`fyron.plotting_palettes.resolve_palette`

Resolve a named or explicit palette to `n` colors.

Import path: fyron.plotting_palettes.resolve_palette

python

resolve_palette(palette: str | Sequence[str] | None, n: int, *, default: str = 'okabe_ito') -> list[str]

Parameters

Parameter	Required	Type	Default	Description
`palette`	yes	`str or Sequence[str] or None`		See signature.
`n`	yes	`int`		See signature.
`default`	no	`str`	`'okabe_ito'`	See signature.

Returns

list[str]

`fyron.plotting_palettes.set_plot_style`

Set Fyron Matplotlib defaults for publication figures.

Import path: fyron.plotting_palettes.set_plot_style

python

set_plot_style(style: str = 'paper', palette: str = 'okabe_ito') -> None

Parameters

Parameter	Required	Type	Default	Description
`style`	no	`str`	`'paper'`	See signature.
`palette`	no	`str`	`'okabe_ito'`	See signature.

Returns

None

`fyron.print_banner`

See the signature and linked module guide for context.

Import path: fyron.print_banner

python

print_banner(version: str | None = None, *, include_version: bool = True, color: bool = True, theme: str = 'bf', file: TextIO | None = None) -> None

Parameters

Parameter	Required	Type	Default	Description
`version`	no	`str or None`	`None`	See signature.
`include_version`	no	`bool`	`True`	See signature.
`color`	no	`bool`	`True`	See signature.
`theme`	no	`str`	`'bf'`	See signature.
`file`	no	`TextIO or None`	`None`	See signature.

Returns

None

`fyron.reporting.tables.cohort_flow_table`

Create a simple cohort flow table from named analysis steps.

Import path: fyron.reporting.tables.cohort_flow_table

python

cohort_flow_table(steps: Sequence[Mapping[str, Any]] | pd.DataFrame) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`steps`	yes	`Sequence[Mapping[str, Any]] or pd.DataFrame`		Ordered step records with at least ``step` `or` `label` `and` `n. Optional` `excluded` `and` `reason`` values are preserved or derived.

Returns

pandas.DataFrame - Ordered flow table with step number, label, count, excluded count, and exclusion reason.

`fyron.reporting.tables.compare_models_table`

Combine multiple model result objects into one paper-ready metrics table.

Import path: fyron.reporting.tables.compare_models_table

python

compare_models_table(results: Mapping[str, Any] | Sequence[Mapping[str, Any]], *, model_col: str = 'model', metrics_key: str = 'metrics', digits: int = 3) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`results`	yes	`Mapping[str, Any] or Sequence[Mapping[str, Any]]`		Either ``{"model name": result}` `or a sequence of result dictionaries. Each result may contain a` `metrics`` dictionary or metric keys at top level.
`model_col`	no	`str`	`'model'`	Name of the output model column.
`metrics_key`	no	`str`	`'metrics'`	Key containing the metric dictionary when present.
`digits`	no	`int`	`3`	Decimal places for numeric metric columns.

Returns

pandas.DataFrame - One row per model/result with rounded metric columns.

`fyron.reporting.tables.dataframe_to_markdown`

Render a DataFrame as a plain Markdown table.

Import path: fyron.reporting.tables.dataframe_to_markdown

python

dataframe_to_markdown(df: pd.DataFrame, *, index: bool = False) -> str

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		DataFrame to render.
`index`	no	`bool`	`False`	If True, include the DataFrame index as the first column.

Returns

str - Markdown table string suitable for notes, reports, documentation, or pull-request review.

`fyron.reporting.tables.format_estimate_ci`

Format an estimate and confidence interval for manuscript tables.

Import path: fyron.reporting.tables.format_estimate_ci

python

format_estimate_ci(estimate: float, lower: float, upper: float, *, digits: int = 2, separator: str = ' to ') -> str

Parameters

Parameter	Required	Type	Default	Description
`estimate`	yes	`float`		Point estimate, such as beta, odds ratio, hazard ratio, or metric value.
`lower`	yes	`float`		Lower confidence interval bound.
`upper`	yes	`float`		Upper confidence interval bound.
`digits`	no	`int`	`2`	Decimal places used for all numeric values.
`separator`	no	`str`	`' to '`	Text placed between lower and upper confidence interval bounds.

Returns

str - Formatted string such as `"1.24 (1.02 to 1.51)"`.

`fyron.reporting.tables.metrics_table`

Normalize model metrics into a rounded report table.

Import path: fyron.reporting.tables.metrics_table

python

metrics_table(metrics: Mapping[str, Mapping[str, Any]] | pd.DataFrame, *, model_col: str = 'model', digits: int = 3) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`metrics`	yes	`Mapping[str, Mapping[str, Any]] or pd.DataFrame`		Either a DataFrame that already contains metric columns, or a nested mapping such as ``{"RF": {"auc": 0.82}}``.
`model_col`	no	`str`	`'model'`	Name of the output column containing model names.
`digits`	no	`int`	`3`	Decimal places used for numeric metric columns.

Returns

pandas.DataFrame - One row per model or metric source with rounded numeric metric columns.

`fyron.reporting.tables.summarize_baseline_table`

Create a compact baseline characteristics table.

Import path: fyron.reporting.tables.summarize_baseline_table

python

summarize_baseline_table(df: pd.DataFrame, *, group_col: str | None = None, continuous: Sequence[str] | None = None, categorical: Sequence[str] | None = None, digits: int = 1) -> pd.DataFrame

Parameters

Parameter	Required	Type	Default	Description
`df`	yes	`pd.DataFrame`		Patient-level cohort or analysis table.
`group_col`	no	`str or None`	`None`	Optional column used to stratify summaries, such as treatment arm, risk group, cohort, or center.
`continuous`	no	`Sequence[str] or None`	`None`	Continuous variables to summarize as mean and standard deviation. If omitted, numeric columns are selected automatically.
`categorical`	no	`Sequence[str] or None`	`None`	Categorical variables to summarize as count and percent. If omitted, non-continuous columns are selected automatically.
`digits`	no	`int`	`1`	Decimal places used in formatted summary strings.

Returns

pandas.DataFrame - Long baseline table with variable, level, summary type, and one column per group or an overall column.