dae.pheno package

Subpackages

• dae.pheno.prepare package
  • Submodules
  • dae.pheno.prepare.individuals2ped module
  • dae.pheno.prepare.measure_classifier module
    • ClassifierReport
      • ClassifierReport.DISTRIBUTION_CUTOFF
      • ClassifierReport.MAX_CHARS
      • ClassifierReport.calc_distribution_report()
      • ClassifierReport.header_line()
      • ClassifierReport.log_line()
      • ClassifierReport.set_measure()
      • ClassifierReport.short_attributes()
      • ClassifierReport.short_header_line()
    • Convertible
      • Convertible.nan
      • Convertible.non_numeric
      • Convertible.numeric
    • MeasureClassifier
      • MeasureClassifier.classify()
      • MeasureClassifier.convert_to_numeric()
      • MeasureClassifier.convert_to_string()
      • MeasureClassifier.meta_measures()
    • convert_to_numeric()
    • convert_to_string()
    • is_convertible_to_numeric()
    • is_nan()
  • dae.pheno.prepare.ped2individuals module
  • dae.pheno.prepare.pheno_prepare module
    • ClassifyMeasureTask
      • ClassifyMeasureTask.build_meta_measure()
      • ClassifyMeasureTask.create_default_measure()
      • ClassifyMeasureTask.done()
      • ClassifyMeasureTask.run()
    • MeasureValuesTask
      • MeasureValuesTask.done()
      • MeasureValuesTask.run()
    • PrepareBase
      • PrepareBase.get_persons()
    • PrepareCommon
      • PrepareCommon.PED_COLUMNS_REQUIRED
      • PrepareCommon.PERSON_ID
      • PrepareCommon.PID_COLUMN
    • PreparePersons
      • PreparePersons.build_pedigree()
      • PreparePersons.save_pedigree()
    • PrepareVariables
      • PrepareVariables.build_instrument()
      • PrepareVariables.build_pheno_common()
      • PrepareVariables.build_variables()
      • PrepareVariables.create_default_measure()
      • PrepareVariables.load_descriptions()
      • PrepareVariables.load_instrument()
      • PrepareVariables.log_filename
      • PrepareVariables.log_header()
      • PrepareVariables.log_measure()
    • Task
      • Task.done()
      • Task.run()
    • TaskQueue
      • TaskQueue.empty()
      • TaskQueue.get()
      • TaskQueue.put()
  • dae.pheno.prepare.ssc_prepare module
  • Module contents
• dae.pheno.utils package
  • Submodules
  • dae.pheno.utils.commons module
    • remove_annoying_characters()
  • dae.pheno.utils.lin_regress module
    • LinearRegression
      • LinearRegression.fit()
      • LinearRegression.pvalues
      • LinearRegression.tvalues
  • Module contents

Submodules

dae.pheno.common module

class dae.pheno.common.MeasureType(value)

Bases: Enum

Definition of measure types.

categorical = 3

continuous = 1

static from_str(measure_type: str) → MeasureType

static is_numeric(measure_type: MeasureType) → bool

static is_text(measure_type: MeasureType) → bool

ordinal = 2

other = 100

raw = 5

skipped = 1000

text = 4

dae.pheno.common.check_phenotype_data_config(config: Box) → bool

Check phenotype database preparation config for consistency.

dae.pheno.common.default_config() → Box

Construct phenotype database preparation configuration.

dae.pheno.common.dump_config(config: Box) → None

Print phenotype database preparation configuration.

dae.pheno.db module

class dae.pheno.db.PhenoDb(dbfile: str, read_only: bool = True)

Bases: object

Class that manages access to phenotype databases.

STREAMING_CHUNK_SIZE = 25

build(create: bool = False) → None

Construct all needed table connections.

build_browser() → None

build_instrument_values_tables() → None

Create instrument values tables.

Each row is basically a list of every measure value in the instrument for a certain person.

build_instruments_and_measures_table() → None

Create tables for instruments and measures.

clear_instrument_values_tables(drop: bool = False) → None

Clear all instrument values tables.

clear_instruments_table(drop: bool = False) → None

Clear the instruments table.

clear_measures_table(drop: bool = False) → None

Clear the measures table.

create_all_tables() → None

get_browser_measure(measure_id: str) → dict | None

Get measrue description from phenotype browser database.

get_families() → dict

Return families in the phenotype database.

get_instrument_column_names() → dict[str, list[str]]

Return a map of instruments and their measure column names.

get_measure_column_names(measure_ids: list[str] | None = None) → dict[str, str]

Return measure column names mapped to their measure IDs.

get_measure_column_names_reverse(measure_ids: list[str] | None = None) → dict[str, str]

Return measure column names mapped to their measure IDs.

get_measures() → dict

Return measures in the phenotype database.

get_persons() → dict

Return individuals in the phenotype database.

get_regression(regression_id: str) → Any

Return regressions.

get_regression_values(measure_id: str) → list[box.box.Box]

property has_descriptions: bool

Check if the database has a description data.

property regression_display_names: Dict[str, str]

Return regressions display name.

property regression_display_names_with_ids: dict[str, Any]

Return regression display names with measure IDs.

property regression_ids: list[str]

save(v: Dict[str, str | None]) → None

Save measure values into the database.

save_regression(reg: Dict[str, str]) → None

Save regressions into the database.

save_regression_values(reg: Dict[str, str]) → None

Save regression values into the databases.

search_measures(instrument_name: str | None = None, keyword: str | None = None) → Iterator[dict[str, Any]]

Find measert by keyword search.

search_measures_df(instrument_name: str | None = None, keyword: str | None = None) → DataFrame

Find measures and return a dataframe with values.

static verify_pheno_folder(folder: Path) → None

Verify integrity of a pheno db folder.

dae.pheno.db.generate_instrument_table_name(instrument_name: str) → str

dae.pheno.db.safe_db_name(name: str) → str

dae.pheno.graphs module

class dae.pheno.graphs.GraphColumn(name, roles, status, df)

Bases: object

Build a colum to produce a graph from it.

all_count()

static build(df, role_name, role_subroles, status)

Construct a graph column object.

females_count()

property label

males_count()

dae.pheno.graphs.column_counts(column)

Collect counts for a graph column.

dae.pheno.graphs.draw_categorical_violin_distribution(df, measure_id, roles_definition=None, ax=None, numerical_categories=False, max_categories=12)

Draw violin distribution for categorical measures.

dae.pheno.graphs.draw_distribution(df, measure_id, ax=None)

Draw measure distribution.

dae.pheno.graphs.draw_linregres(df, col1, col2, jitter: int | None = None, ax=None)

Draw a graph display linear regression between two columns.

dae.pheno.graphs.draw_measure_violinplot(df, measure_id, roles_definition=None, ax=None)

Draw a violin plot for a measure.

dae.pheno.graphs.draw_ordinal_violin_distribution(df, measure_id, ax=None)

dae.pheno.graphs.gender_palette()

dae.pheno.graphs.gender_palette_light()

dae.pheno.graphs.get_columns_to_draw(roles, df)

Collect columns needed for graphs.

dae.pheno.graphs.male_female_colors()

dae.pheno.graphs.male_female_legend(color_male, color_female, ax=None)

Consturct a legend for female graph.

dae.pheno.graphs.names(col1, col2)

dae.pheno.graphs.role_labels(ordered_columns)

dae.pheno.graphs.set_figure_size(figure, x_count)

dae.pheno.husl module

dae.pheno.palletes module

dae.pheno.pheno_db module

dae.pheno.plots module

dae.pheno.prepare_data module

class dae.pheno.prepare_data.PreparePhenoBrowserBase(pheno_name: str, phenotype_data: PhenotypeStudy, output_dir: str, pheno_regressions: Box | None = None, images_dir: str | None = None)

Bases: object

Prepares phenotype data for the phenotype browser.

LARGE_DPI = 150

SMALL_DPI = 16

browsable_figure_path(measure: Measure, suffix: str) → str

Construct file path for storing a measure figures.

build_regression(dependent_measure: Measure, independent_measure: Measure, jitter: float) → dict[str, Union[str, float]]

Build measure regressiongs.

build_values_categorical_distribution(measure: Measure) → dict[str, Any]

Build a categorical value distribution fiugre.

build_values_ordinal_distribution(measure: Measure) → dict[str, Any]

Build an ordinal value distribution figure.

build_values_other_distribution(measure: Measure) → dict[str, Any]

Build an other value distribution figure.

build_values_violinplot(measure: Measure) → dict[str, Any]

Build a violin plot figure for the measure.

dump_browser_variable(var: dict[str, Any]) → None

Print browser measure description.

figure_filepath(measure: Measure, suffix: str) → str

Construct file path for storing a measure figures.

handle_measure(measure: Measure) → dict[str, Any]

Build appropriate figures for a measure.

handle_regressions(measure: Measure) → Iterator[dict[str, Any]]

Build appropriate regressions and regression figures.

load_measure(measure: Measure) → DataFrame

run() → None

Run browser preparations for all measures in a phenotype data.

save_fig(measure: Measure, suffix: str) → tuple[Optional[str], Optional[str]]

Save measure figures.

Module contents