# GillesPy2 is a modeling toolkit for biochemical simulation.
# Copyright (C) 2019-2023 GillesPy2 developers.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from gillespy2.core.jsonify import Jsonify
from gillespy2.core.sortableobject import SortableObject
from gillespy2.core.gillespyError import SpeciesError
[docs]class Species(SortableObject, Jsonify):
"""
Chemical species. Can be added to Model object to interact with other
species or time.
:param name: The name by which this species will be called in reactions and within the model.
:type name: str
:param initial_value: Initial population (discrete) or concentration (continuous) of this species.
:type initial_value: int | float
:param constant: If true, the value of the species cannot be changed (currently TauHybridSolver only)
:type constant: bool
:param boundary_condition: If true, species can be changed by events and rate rules, but not by reactions.
(TauHybridSolver only)
:type boundary_condition: bool
:param mode: ***FOR USE WITH TauHybridSolver ONLY***
Sets the mode of representation of this species for the TauHybridSolver,
can be discrete, continuous, or dynamic.
mode='dynamic' - Allows a species to be represented as either discrete or continuous
mode='continuous' - Species will only be represented as continuous
mode='discrete' - Species will only be represented as discrete
:type mode: str
:param allow_negative_populations: If true, population can be reduced below 0.
:type allow_negative_populations: bool
:param switch_tol: ***FOR USE WITH TauHybridSolver ONLY***
Tolerance level for considering a dynamic species deterministically, value is compared to an estimated sd/mean
population of a species after a given time step. This value will be used if a switch_min is not provided.
The default value is 0.03
:type switch_tol: float
:param switch_min: ***FOR USE WITH TauHybridSolver ONLY***
Minimum population value at which species will be represented as continuous.
If a value is given, switch_min will be used instead of switch_tol
:type switch_min: float
:raises SpeciesError: Arg is of invalid type. Required arg set to None. Arg value is outside of accepted bounds.
"""
def __init__(self, name=None, initial_value=0, constant=False, boundary_condition=False, mode=None,
allow_negative_populations=False, switch_min=0, switch_tol=0.03):
# A species has a name (string) and an initial value (positive integer)
self.name = name
self.constant = constant
self.boundary_condition = boundary_condition
self.mode = mode
self.allow_negative_populations = allow_negative_populations
self.switch_min = switch_min
self.switch_tol = switch_tol
if initial_value is None:
raise SpeciesError("initial_value can't be None type.")
if isinstance(initial_value, str):
try:
initial_value = float(initial_value)
except ValueError:
pass
self.validate(initial_value=initial_value)
self.initial_value = float(initial_value) if self.mode == "continuous" else int(initial_value)
def __str__(self):
print_string = self.name
print_string += ': ' + str(self.initial_value)
return print_string
[docs] def set_initial_value(self, initial_value):
"""
Setter method for initial_value of a population
:param initial_value: Initial population (discrete) or concentration (continuous) of this species.
:type initial_value: int | float
:raises SpeciesError: initial_value is of invalid type. initial_value set to None. \
initial_value is a float when mode != 'continuous'. \
initial_value is negative when allow_negative_populations=False.
"""
if initial_value is None:
raise SpeciesError("initial_value can't be None type.")
if isinstance(initial_value, str):
try:
initial_value = float(initial_value)
except ValueError:
pass
self.validate(initial_value=initial_value, coverage="initial_value")
self.initial_value = float(initial_value) if self.mode == "continuous" else int(initial_value)
[docs] def validate(self, initial_value=None, coverage="all"):
"""
Validate the species.
:param initial_value: Initial population (discrete) or concentration (continuous) of this species.
:type initial_value: int | float
:param coverage: The scope of attributes to validate. Set to an attribute name to restrict validation \
to a specific attribute.
:type coverage: str
:raises SpeciesError: Attribute is of invalid type. Required attribute set to None. \
Attribute is value outside of accepted bounds.
"""
# Check name
if coverage in ("all", "name"):
if self.name is None:
raise SpeciesError("name can't be None type.")
if not isinstance(self.name, str):
raise SpeciesError(f"name must be of type str not {type(self.name)}.")
if self.name == "":
raise SpeciesError("name can't be an empty string.")
# Check initial_value
if coverage in ("all", "initial_value"):
if initial_value is None:
initial_value = self.initial_value
if initial_value is None:
raise SpeciesError("initial_value can't be None type.")
if not isinstance(initial_value, (float, int)):
raise SpeciesError(f"initial_value must be of type float or int not {type(initial_value)}.")
if self.mode != "continuous" and int(initial_value) != initial_value:
raise SpeciesError(
"""
initial_value with mode='discrete' must be an integer value.
Change to mode='continuous' to use floating point values.
"""
)
if not self.allow_negative_populations and initial_value < 0:
raise SpeciesError(
'A species initial value must be non-negative unless allow_negative_populations=True'
)
# Check constant
if coverage in ("all", "constant"):
if not isinstance(self.constant, bool):
errmsg = f"constant must be of type bool not {type(self.constant)}."
raise SpeciesError(errmsg)
# Check boundary_condition
if coverage in ("all", "boundary_condition"):
if not isinstance(self.boundary_condition, bool):
errmsg = f"boundary_condition must be of type bool not {type(self.boundary_condition)}."
raise SpeciesError(errmsg)
# Check mode
if coverage in ("all", "mode"):
mode_list = ['continuous', 'dynamic', 'discrete', None]
if self.mode not in mode_list:
raise SpeciesError(
f"""
mode must be 'continuous', 'dynamic', 'discrete', or
unspecified (defaults to 'dynamic' for TauHybridSolver) not {self.mode}.
"""
)
# Check allow_negative_populations
if coverage in ("all", "allow_negative_populations"):
if not isinstance(self.allow_negative_populations, bool):
errmsg = f"allow_negative_populations must be of type bool not {type(self.allow_negative_populations)}."
raise SpeciesError(errmsg)
# Check switch_tol
if coverage in ("all", "switch_tol"):
if self.switch_tol is None:
raise SpeciesError("switch_tol can't be None type.")
if not isinstance(self.switch_tol, (float, int)):
raise SpeciesError(f"switch_tol must of type float or int not {type(self.switch_tol)}")
if self.switch_tol < 0:
raise SpeciesError(f"switch_tol must be a positive value not {self.switch_tol}")
# Check switch_min
if coverage in ("all", "switch_min"):
if self.switch_min is None:
raise SpeciesError("switch_min can't be None type.")
if not isinstance(self.switch_min, (float, int)):
raise SpeciesError(f"switch_min must of type float or int not {type(self.switch_min)}")
if self.switch_min < 0:
raise SpeciesError(f"switch_min must be a positive value not {self.switch_min}")
