Source code for phenotypic.schema._log_growth_model

"""Fitted parameters and bounds for the logistic growth model."""

from ._measurement_info import Entry
from ._tiers import DerivedMeasure


[docs] class LOG_GROWTH_MODEL(DerivedMeasure): """Fitted parameters and bounds of the logistic growth model. Output columns are **metric-qualified**: each header is ``LogGrowthModel_<metric>_<parameter>``, where ``<metric>`` records the measurement the model was fit on (``self.on`` with its category prefix stripped, e.g. ``Shape_Area`` → ``Area``). For example, fitting on ``Shape_Area`` emits ``LogGrowthModel_Area_r`` (intrinsic growth rate) and ``LogGrowthModel_Area_µmax`` (maximum specific growth rate). The labels below are the ``<parameter>`` segment; the ``<metric>`` infix is filled in at fit time. """
[docs] @classmethod def category(cls) -> str: return "LogGrowthModel"
[docs] @classmethod def header_scheme(cls) -> str: return "metric_qualified"
R_FIT = Entry("r", "The intrinsic growth rate", tier=1, derivation_type="parameterization", derives_from="SIZE") K_FIT = Entry("K", "The carrying capacity", tier=1, derivation_type="parameterization", derives_from="SIZE") N0_FIT = Entry("N0", "The initial number of the colony size metric being fitted", tier=1, derivation_type="parameterization", derives_from="SIZE") LAM = Entry( "lambda", "The regularization factor applied to the max specific growth rate " "and initial population size", derivation_type="diagnostic", ) BETA = Entry( "beta", ( "The penalty factor applied to relative difference of " "the carrying capacity from the largest measurement" ), derivation_type="diagnostic", ) GROWTH_RATE = Entry("µmax", "The growth rate of the colony calculated as (K*r)/4", tier=1, derivation_type="parameterization", derives_from="SIZE") K_MAX = Entry("Kmax", "The upper bound of the carrying capacity for model fitting", derivation_type="diagnostic")