6.1.1.4.1.8. `pytfa.optim.variables`¶

Variable declarations

6.1.1.4.1.8.1. Module Contents¶

6.1.1.4.1.8.1.1. Classes¶

`GenericVariable`	Class to represent a generic variable. The purpose is that the interface
`ModelVariable`	Class to represent a variable attached to the model
`GeneVariable`	Class to represent a gene variable
`BinaryVariable`	Class to represent a generic binary variable
`ReactionVariable`	Class to represent a variable attached to a reaction
`MetaboliteVariable`	Class to represent a variable attached to a enzyme
`ForwardUseVariable`	Class to represent a forward use variable, a type of binary variable used to
`BackwardUseVariable`	Class to represent a backward use variable, a type of binary variable used
`ForwardBackwardUseVariable`	Class to represent a type of binary variable used to tell whether the
`LogConcentration`	Class to represent a log concentration of a enzyme
`DeltaGErr`	Class to represent a DeltaGErr
`DeltaG`	Class to represent a DeltaG
`DeltaGstd`	Class to represent a DeltaG^o (naught) - standard conditions
`ThermoDisplacement`	Class to represent the thermodynamic displacement of a reaction
`PosSlackVariable`	Class to represent a positive slack variable for relaxation problems
`NegSlackVariable`	Class to represent a negative slack variable for relaxation problems
`PosSlackLC`	Class to represent a variable attached to a enzyme
`NegSlackLC`	Class to represent a variable attached to a enzyme
`LinearizationVariable`	Class to represent a product A*B when performing linearization of the

6.1.1.4.1.8.1.2. Functions¶

get_binary_type()

FIX : We enforce type to be integer instead of binary, else optlang does

6.1.1.4.1.8.1.3. Attributes¶

op_replace_dict

pytfa.op_replace_dict¶

class pytfa.GenericVariable(id_='', model=None, hook=None, queue=False, scaling_factor=1, **kwargs)¶

Class to represent a generic variable. The purpose is that the interface is instantiated on initialization, to follow the type of interface used by the problem, and avoid incompatibilities in optlang

Attributes:

id

Used for DictList comprehension. Usually points back at a

enzyme or reaction id for ease of linking. Should be unique given a variable type. :name: Should be a concatenation of the id and a prefix that is specific to the variable type. will be used to address the variable at the solver level, and hence should be unique in the whole cobra_model :cobra_model: the cobra_model hook. :variable: links directly to the cobra_model representation of tbe variable

prefix¶

property __attrname__(self)¶: Name the attribute the instances will have Example: GenericVariable -> generic_variable :return:

get_interface(self, queue)¶

Called upon completion of __init__, initializes the value of self.var, which is returned upon call, and stores the actual interfaced variable.

Returns: instance of Variable from the problem

make_name(self)¶

Needs to be overridden by the subclass, concats the id with a: prefix

Returns: None

property name(self)¶

property id(self)¶: for cobra.thermo.DictList compatibility :return:

property variable(self)¶

property scaling_factor(self)¶

property unscaled(self)¶

If the scaling factor of quantity X is a, it is represented by the variable X_hat = X/a. This returns X = a.X_hat Useful for nondimensionalisation of variables and constraints

Returns: The variable divided by its scaling factor

property value(self)¶

property unscaled_value(self)¶

property model(self)¶

property type(self)¶

test_consistency(self, other)¶

Tests whether a candidate to an operation is of the right type and is from the same problem

Parameters: other – an object
Returns: None

get_operand(self, other)¶

For operations, choose if the operand is a GenericVariable, in which we return its optlang variable, or something else (presumably a numeric) and we let optlang decide what to do

Parameters: other –
Returns

__add__(self, other)¶: Adding either two variables together or a variable and a numeric results in a new variable :param other: :return: a new Generic Variable

__radd__(self, other)¶: Take priority on symmetric arithmetic operation :param other: :return:

__sub__(self, other)¶: Substracting either two variables together or a variable and a numeric results in a new variable :param other: :return: a new Generic Variable

__rsub__(self, other)¶: Take priority on symmetric arithmetic operation :param other: :return:

__mul__(self, other)¶: Multiplying either two variables together or a variable and a numeric results in a new variable :param other: :return: a new Generic Variable

__rmul__(self, other)¶: Take priority on symmetric arithmetic operation :param other: :return:

__truediv__(self, other)¶: Dividing either two variables together or a variable and a numeric results in a new variable :param other: :return: a new Generic Variable

__rtruediv__(self, other)¶: Take priority on symmetric arithmetic operation :param other: :return:

make_result(self, new_variable)¶: Returns a Sympy expression :param new_variable: :return:

__repr__(self)¶: Return repr(self).

pytfa.get_binary_type()¶: FIX : We enforce type to be integer instead of binary, else optlang does not allow to set the binary variable bounds to anything other than (0,1) You might want to set it at (0,0) to enforce directionality for example

class pytfa.ModelVariable(model, id_, **kwargs)¶