6.1.1.4. pytfa.optim

6.1.1.4.1. Submodules

• 6.1.1.4.1.1. pytfa.optim.config
• 6.1.1.4.1.2. pytfa.optim.constraints
• 6.1.1.4.1.3. pytfa.optim.debugging
• 6.1.1.4.1.4. pytfa.optim.meta
• 6.1.1.4.1.5. pytfa.optim.reformulation
• 6.1.1.4.1.6. pytfa.optim.relaxation
• 6.1.1.4.1.7. pytfa.optim.utils
• 6.1.1.4.1.8. pytfa.optim.variables

6.1.1.4.2. Package Contents

6.1.1.4.2.1. Classes

GenericConstraint	Class to represent a generic constraint. The purpose is that the interface
ModelConstraint	Class to represent a variable attached to the model
GeneConstraint	Class to represent a variable attached to a enzyme
ReactionConstraint	Class to represent a variable attached to a reaction
MetaboliteConstraint	Class to represent a variable attached to a enzyme
NegativeDeltaG	Class to represent thermodynamics constraints.
ForwardDeltaGCoupling	Class to represent thermodynamics coupling: DeltaG of reactions has to be
BackwardDeltaGCoupling	Class to represent thermodynamics coupling: DeltaG of reactions has to be
ForwardDirectionCoupling	Class to represent a forward directionality coupling with thermodynamics on
BackwardDirectionCoupling	Class to represent a backward directionality coupling with thermodynamics on
SimultaneousUse	Class to represent a simultaneous use constraint on reaction variables
DisplacementCoupling	Class to represent the coupling to the thermodynamic displacement
ForbiddenProfile	Class to represent a forbidden net flux directionality profile
LinearizationConstraint	Class to represent a variable attached to a reaction
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
NegativeDeltaG	Class to represent thermodynamics constraints.
PosSlackVariable	Class to represent a positive slack variable for relaxation problems
NegSlackVariable	Class to represent a negative slack variable for relaxation problems
DeltaGstd	Class to represent a DeltaG^o (naught) - standard conditions
LogConcentration	Class to represent a log concentration of a enzyme
NegSlackLC	Class to represent a variable attached to a enzyme
PosSlackLC	Class to represent a variable attached to a enzyme
GenericConstraint	Class to represent a generic constraint. The purpose is that the interface
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
GenericVariable	Class to represent a generic variable. The purpose is that the interface

6.1.1.4.2.2. Functions

camel2underscores(name)
camel2underscores(name)
get_binary_type()	FIX : We enforce type to be integer instead of binary, else optlang does
dg_relax_config(model)	param model
get_solution_value_for_variables(solution, these_vars, index_by_reaction=False)
chunk_sum(variables)	This functions handles the sum of many sympy variables by chunks, which
symbol_sum(variables)	``` python
relax_dgo_gurobi(model, relax_obj_type=0)
relax_dgo(tmodel, reactions_to_ignore=(), solver=None, in_place=False)	param t_tmodel
relax_lc(tmodel, metabolites_to_ignore=(), solver=None)	param metabolites_to_ignore
get_all_subclasses(cls)	Given a variable or constraint class, get all the subclassses
chunk_sum(variables)	This functions handles the sum of many sympy variables by chunks, which
symbol_sum(variables)	``` python
get_solution_value_for_variables(solution, these_vars, index_by_reaction=False)
compare_solutions(models)	returns the solution dictionnary for each cobra_model
evaluate_constraint_at_solution(constraint, solution)	param expression
get_active_use_variables(tmodel, solution)	Returns the active use variables in a solution. Use Variables are binary
get_direction_use_variables(tmodel, solution)	Returns the active use variables in a solution. Use Variables are binary
get_primal(tmodel, vartype, index_by_reactions=False)	Returns the primal value of the cobra_model for variables of a given type
strip_from_integer_variables(tmodel)	Removes all integer and binary variables of a cobra_model, to make it sample-able
copy_solver_configuration(source, target)	Copies the solver configuration from a source model to a target model

6.1.1.4.2.3. Attributes

ABCRequirePrefixMeta
ABCRequirePrefixMeta
op_replace_dict
BIGM
BIGM_THERMO
BIGM_DG
BIGM_P
EPSILON
SYMPY_ADD_CHUNKSIZE
INTEGER_VARIABLE_TYPES

pytfa.optim.camel2underscores(name)

pytfa.optim.ABCRequirePrefixMeta

class pytfa.optim.GenericConstraint(expr, id_='', model=None, hook=None, queue=False, **kwargs)

Class to represent a generic constraint. 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 constraint 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 constraint at the solver level, and hence should be unique in the whole cobra_model :expr: the expression of the constraint (sympy.Expression subtype) :cobra_model: the cobra_model hook. :constraint: links directly to the cobra_model representation of tbe constraint

prefix

property __attrname__(self)

Name the attribute the instances will have Example: GenericConstraint -> generic_constraint :return:

get_interface(self, expr, 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

change_expr(self, new_expr, sloppy=False)

property expr(self)

property name(self)

property id(self)

for cobra.thermo.DictList compatibility :return:

property constraint(self)

property model(self)

__repr__(self)

Return repr(self).

class pytfa.optim.ModelConstraint(model, expr, id_, **kwargs)

Bases: GenericConstraint

Class to represent a variable attached to the model

prefix = MODC_

class pytfa.optim.GeneConstraint(gene, expr, **kwargs)

Bases: GenericConstraint

Class to represent a variable attached to a enzyme

prefix = GC_

property gene(self)

property id(self)

for cobra.thermo.DictList compatibility :return:

property model(self)

class pytfa.optim.ReactionConstraint(reaction, expr, **kwargs)

Bases: GenericConstraint

Class to represent a variable attached to a reaction

prefix = RC_

property reaction(self)

property id(self)

for cobra.thermo.DictList compatibility :return:

property model(self)

class pytfa.optim.MetaboliteConstraint(metabolite, expr, **kwargs)

Bases: GenericConstraint

Class to represent a variable attached to a enzyme

prefix = MC_

property metabolite(self)

property id(self)

for cobra.thermo.DictList compatibility :return:

property model(self)

class pytfa.optim.NegativeDeltaG(reaction, expr, **kwargs)

Bases: ReactionConstraint

Class to represent thermodynamics constraints.

G: - DGR_rxn + DGoRerr_Rxn + RT * StoichCoefProd1 * LC_prod1

• RT * StoichCoefProd2 * LC_prod2

• RT * StoichCoefSub1 * LC_subs1

• RT * StoichCoefSub2 * LC_subs2

• …

= 0

prefix = G_

class pytfa.optim.ForwardDeltaGCoupling(reaction, expr, **kwargs)

Bases: ReactionConstraint

Class to represent thermodynamics coupling: DeltaG of reactions has to be DGR < 0 for the reaction to proceed forwards Looks like: FU_rxn: 1000 FU_rxn + DGR_rxn < 1000

prefix = FU_

class pytfa.optim.BackwardDeltaGCoupling(reaction, expr, **kwargs)

Bases: ReactionConstraint

Class to represent thermodynamics coupling: DeltaG of reactions has to be DGR > 0 for the reaction to proceed backwards Looks like: BU_rxn: 1000 BU_rxn - DGR_rxn < 1000

prefix = BU_

class pytfa.optim.ForwardDirectionCoupling(reaction, expr, **kwargs)

Bases: ReactionConstraint

Class to represent a forward directionality coupling with thermodynamics on reaction variables Looks like : UF_rxn: F_rxn - M FU_rxn < 0

prefix = UF_

class pytfa.optim.BackwardDirectionCoupling(reaction, expr, **kwargs)

Bases: ReactionConstraint

Class to represent a backward directionality coupling with thermodynamics on reaction variables Looks like : UR_rxn: R_rxn - M RU_rxn < 0

prefix = UR_

class pytfa.optim.SimultaneousUse(reaction, expr, **kwargs)

Bases: ReactionConstraint

Class to represent a simultaneous use constraint on reaction variables Looks like: SU_rxn: FU_rxn + BU_rxn <= 1

prefix = SU_

class pytfa.optim.DisplacementCoupling(reaction, expr, **kwargs)

Bases: ReactionConstraint

Class to represent the coupling to the thermodynamic displacement Looks like: Ln(Gamma) - (1/RT)*DGR_rxn = 0

prefix = DC_

class pytfa.optim.ForbiddenProfile(model, expr, id_, **kwargs)

Bases: GenericConstraint

Class to represent a forbidden net flux directionality profile Looks like: FU_rxn_1 + BU_rxn_2 + … + FU_rxn_n <= n-1

prefix = FP_

class pytfa.optim.LinearizationConstraint(model, expr, id_, **kwargs)

Bases: ModelConstraint

Class to represent a variable attached to a reaction

prefix = LC_

static from_constraints(cons, model)

pytfa.optim.camel2underscores(name)

pytfa.optim.ABCRequirePrefixMeta

pytfa.optim.op_replace_dict

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

Bases: GenericVariable

Class to represent a variable attached to the model

prefix = MODV_

class pytfa.optim.GeneVariable(gene, **kwargs)

Bases: GenericVariable

Class to represent a gene variable

prefix = GV_

property gene(self)

property id(self)

for cobra.thermo.DictList compatibility :return:

property model(self)

class pytfa.optim.BinaryVariable(id_, model, **kwargs)

Bases: GenericVariable

Class to represent a generic binary variable

prefix = B_

class pytfa.optim.ReactionVariable(reaction, **kwargs)

Bases: GenericVariable

Class to represent a variable attached to a reaction

prefix = RV_

property reaction(self)

property id(self)

for cobra.thermo.DictList compatibility :return:

property model(self)

class pytfa.optim.MetaboliteVariable(metabolite, **kwargs)

Bases: GenericVariable

Class to represent a variable attached to a enzyme

prefix = MV_

property metabolite(self)

property id(self)

for cobra.thermo.DictList compatibility :return:

property model(self)

class pytfa.optim.ForwardUseVariable(reaction, **kwargs)

Bases: ReactionVariable, BinaryVariable

Class to represent a forward use variable, a type of binary variable used to enforce forward directionality in reaction net fluxes

prefix = FU_

class pytfa.optim.BackwardUseVariable(reaction, **kwargs)

Bases: ReactionVariable, BinaryVariable

Class to represent a backward use variable, a type of binary variable used to enforce backward directionality in reaction net fluxes

prefix = BU_

class pytfa.optim.ForwardBackwardUseVariable(reaction, **kwargs)

Bases: ReactionVariable, BinaryVariable

Class to represent a type of binary variable used to tell whether the reaction is active or not such that:

FU + BU + BFUSE = 1

prefix = BFUSE_

class pytfa.optim.LogConcentration(metabolite, **kwargs)

Bases: MetaboliteVariable

Class to represent a log concentration of a enzyme

prefix = LC_

class pytfa.optim.DeltaGErr(reaction, **kwargs)

Bases: ReactionVariable

Class to represent a DeltaGErr

prefix = DGE_

class pytfa.optim.DeltaG(reaction, **kwargs)

Bases: ReactionVariable

Class to represent a DeltaG

prefix = DG_

class pytfa.optim.DeltaGstd(reaction, **kwargs)

Bases: ReactionVariable

Class to represent a DeltaG^o (naught) - standard conditions

prefix = DGo_

class pytfa.optim.ThermoDisplacement(reaction, **kwargs)

Bases: ReactionVariable

Class to represent the thermodynamic displacement of a reaction Gamma = -DeltaG/RT

prefix = LnGamma_

class pytfa.optim.PosSlackVariable(reaction, **kwargs)

Bases: ReactionVariable

Class to represent a positive slack variable for relaxation problems

prefix = PosSlack_

class pytfa.optim.NegSlackVariable(reaction, **kwargs)

Bases: ReactionVariable

Class to represent a negative slack variable for relaxation problems

prefix = NegSlack_

class pytfa.optim.PosSlackLC(metabolite, **kwargs)

Bases: MetaboliteVariable

Class to represent a variable attached to a enzyme

prefix = PosSlackLC_

class pytfa.optim.NegSlackLC(metabolite, **kwargs)

Bases: MetaboliteVariable

Class to represent a variable attached to a enzyme

prefix = NegSlackLC_

class pytfa.optim.LinearizationVariable(model, id_, **kwargs)

Bases: ModelVariable

Class to represent a product A*B when performing linearization of the model

prefix = LZ_

class pytfa.optim.NegativeDeltaG(reaction, expr, **kwargs)

Bases: ReactionConstraint

Class to represent thermodynamics constraints.

G: - DGR_rxn + DGoRerr_Rxn + RT * StoichCoefProd1 * LC_prod1

• RT * StoichCoefProd2 * LC_prod2

• RT * StoichCoefSub1 * LC_subs1

• RT * StoichCoefSub2 * LC_subs2

• …

= 0

prefix = G_

pytfa.optim.dg_relax_config(model)

Parameters

model –

Returns

pytfa.optim.get_solution_value_for_variables(solution, these_vars, index_by_reaction=False)

pytfa.optim.chunk_sum(variables)

This functions handles the sum of many sympy variables by chunks, which somehow increases the speed of the computation

You can test it in IPython: ```python a = sympy.symbols(‘a0:100’) %timeit (sum(a)) # >>> 198 µs ± 11.4 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

b = sympy.symbols(‘b0:1000’) %timeit (sum(b)) # >>> 1.85 ms ± 356 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

c = sympy.symbols(‘c0:3000’) %timeit (sum(c)) # >>> 5min 7s ± 2.57 s per loop (mean ± std. dev. of 7 runs, 1 loop each) ```

See the github thread

Parameters

variables –

Returns

pytfa.optim.symbol_sum(variables)

``` python a = symbols(‘a0:100’)

%timeit Add(*a) # >>> 10000 loops, best of 3: 34.1 µs per loop

b = symbols(‘b0:1000’)

%timeit Add(*b) # >>> 1000 loops, best of 3: 343 µs per loop

c = symbols(‘c0:3000’)

%timeit Add(*c) # >>> 1 loops, best of 3: 1.03 ms per loop ```

See the github thread :param variables: :return:

class pytfa.optim.PosSlackVariable(reaction, **kwargs)

Bases: ReactionVariable

Class to represent a positive slack variable for relaxation problems

prefix = PosSlack_

class pytfa.optim.NegSlackVariable(reaction, **kwargs)

Bases: ReactionVariable

Class to represent a negative slack variable for relaxation problems

prefix = NegSlack_

class pytfa.optim.DeltaGstd(reaction, **kwargs)

Bases: ReactionVariable

Class to represent a DeltaG^o (naught) - standard conditions

prefix = DGo_

class pytfa.optim.LogConcentration(metabolite, **kwargs)

Bases: MetaboliteVariable

Class to represent a log concentration of a enzyme

prefix = LC_

class pytfa.optim.NegSlackLC(metabolite, **kwargs)

Bases: MetaboliteVariable

Class to represent a variable attached to a enzyme

prefix = NegSlackLC_

class pytfa.optim.PosSlackLC(metabolite, **kwargs)

Bases: MetaboliteVariable

Class to represent a variable attached to a enzyme

prefix = PosSlackLC_

pytfa.optim.BIGM

pytfa.optim.BIGM_THERMO

pytfa.optim.BIGM_DG

pytfa.optim.BIGM_P

pytfa.optim.EPSILON

pytfa.optim.relax_dgo_gurobi(model, relax_obj_type=0)

pytfa.optim.relax_dgo(tmodel, reactions_to_ignore=(), solver=None, in_place=False)

Parameters

• t_tmodel (pytfa.thermo.ThermoModel:) –

• reactions_to_ignore – Iterable of reactions that should not be relaxed

• solver – solver to use (e.g. ‘optlang-glpk’, ‘optlang-cplex’, ‘optlang-gurobi’

Returns

a cobra_model with relaxed bounds on standard Gibbs free energy

pytfa.optim.relax_lc(tmodel, metabolites_to_ignore=(), solver=None)

Parameters

• metabolites_to_ignore –

• in_tmodel (pytfa.thermo.ThermoModel:) –

• min_objective_value –

Returns

class pytfa.optim.GenericConstraint(expr, id_='', model=None, hook=None, queue=False, **kwargs)

Class to represent a generic constraint. 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 constraint 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 constraint at the solver level, and hence should be unique in the whole cobra_model :expr: the expression of the constraint (sympy.Expression subtype) :cobra_model: the cobra_model hook. :constraint: links directly to the cobra_model representation of tbe constraint

prefix

property __attrname__(self)

Name the attribute the instances will have Example: GenericConstraint -> generic_constraint :return:

get_interface(self, expr, 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

change_expr(self, new_expr, sloppy=False)

property expr(self)

property name(self)

property id(self)

for cobra.thermo.DictList compatibility :return:

property constraint(self)

property model(self)

__repr__(self)

Return repr(self).

class pytfa.optim.ForwardUseVariable(reaction, **kwargs)

Bases: ReactionVariable, BinaryVariable

Class to represent a forward use variable, a type of binary variable used to enforce forward directionality in reaction net fluxes

prefix = FU_

class pytfa.optim.BackwardUseVariable(reaction, **kwargs)

Bases: ReactionVariable, BinaryVariable

Class to represent a backward use variable, a type of binary variable used to enforce backward directionality in reaction net fluxes

prefix = BU_

class pytfa.optim.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.optim.SYMPY_ADD_CHUNKSIZE = 100

pytfa.optim.INTEGER_VARIABLE_TYPES = ['binary', 'integer']

pytfa.optim.get_all_subclasses(cls)

Given a variable or constraint class, get all the subclassses that inherit from it

Parameters

cls –

Returns

pytfa.optim.chunk_sum(variables)

This functions handles the sum of many sympy variables by chunks, which somehow increases the speed of the computation

You can test it in IPython: ```python a = sympy.symbols(‘a0:100’) %timeit (sum(a)) # >>> 198 µs ± 11.4 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

b = sympy.symbols(‘b0:1000’) %timeit (sum(b)) # >>> 1.85 ms ± 356 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

c = sympy.symbols(‘c0:3000’) %timeit (sum(c)) # >>> 5min 7s ± 2.57 s per loop (mean ± std. dev. of 7 runs, 1 loop each) ```

See the github thread

Parameters

variables –

Returns

pytfa.optim.symbol_sum(variables)

``` python a = symbols(‘a0:100’)

%timeit Add(*a) # >>> 10000 loops, best of 3: 34.1 µs per loop

b = symbols(‘b0:1000’)

%timeit Add(*b) # >>> 1000 loops, best of 3: 343 µs per loop

c = symbols(‘c0:3000’)

%timeit Add(*c) # >>> 1 loops, best of 3: 1.03 ms per loop ```

See the github thread :param variables: :return:

pytfa.optim.get_solution_value_for_variables(solution, these_vars, index_by_reaction=False)

pytfa.optim.compare_solutions(models)

returns the solution dictionnary for each cobra_model :param (iterable (pytfa.thermo.ThermoModel)) models: :return:

pytfa.optim.evaluate_constraint_at_solution(constraint, solution)

Parameters

• expression –

• solution – pandas.DataFrame, with index as variable names

Returns

pytfa.optim.get_active_use_variables(tmodel, solution)

Returns the active use variables in a solution. Use Variables are binary variables that control the directionality of the reaction

ex: FU_ACALDt BU_PFK

Parameters

• tmodel (pytfa.core.ThermoModel) –

• solution –

Returns

pytfa.optim.get_direction_use_variables(tmodel, solution)

Returns the active use variables in a solution. Use Variables are binary variables that control the directionality of the reaction The difference with get_active_use_variables is that variables with both UseVariables at 0 will return as going forwards. This is to ensure that the output size of the function is equal to the number of FDPs

ex: FU_ACALDt BU_PFK

Parameters

• tmodel (pytfa.core.ThermoModel) –

• solution –

Returns

pytfa.optim.get_primal(tmodel, vartype, index_by_reactions=False)

Returns the primal value of the cobra_model for variables of a given type :param tmodel: :param vartype: Class of variable. Ex: pytfa.optim.variables.ThermoDisplacement :param index_by_reactions: Set to true to get reaction names as index instead of

variables. Useful for Escher export

Returns

pytfa.optim.strip_from_integer_variables(tmodel)

Removes all integer and binary variables of a cobra_model, to make it sample-able :param tmodel: :return:

pytfa.optim.copy_solver_configuration(source, target)

Copies the solver configuration from a source model to a target model :param source: :param target: :return: