6.1.1.4.1.7. pytfa.optim.utils¶
Relaxation of models with constraint too tight
6.1.1.4.1.7.1. Module Contents¶
6.1.1.4.1.7.1.1. Functions¶
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Given a variable or constraint class, get all the subclassses |
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This functions handles the sum of many sympy variables by chunks, which |
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returns the solution dictionnary for each cobra_model |
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Returns the active use variables in a solution. Use Variables are binary |
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Returns the active use variables in a solution. Use Variables are binary |
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Returns the primal value of the cobra_model for variables of a given type |
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Removes all integer and binary variables of a cobra_model, to make it sample-able |
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Copies the solver configuration from a source model to a target model |
6.1.1.4.1.7.1.2. Attributes¶
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- pytfa.SYMPY_ADD_CHUNKSIZE = 100¶
- pytfa.INTEGER_VARIABLE_TYPES = ['binary', 'integer']¶
- pytfa.get_all_subclasses(cls)¶
Given a variable or constraint class, get all the subclassses that inherit from it
- Parameters
cls –
- Returns
- pytfa.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.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.get_solution_value_for_variables(solution, these_vars, index_by_reaction=False)¶
- pytfa.compare_solutions(models)¶
returns the solution dictionnary for each cobra_model :param (iterable (pytfa.thermo.ThermoModel)) models: :return:
- pytfa.evaluate_constraint_at_solution(constraint, solution)¶
- Parameters
expression –
solution – pandas.DataFrame, with index as variable names
- Returns
- pytfa.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.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.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.strip_from_integer_variables(tmodel)¶
Removes all integer and binary variables of a cobra_model, to make it sample-able :param tmodel: :return:
- pytfa.copy_solver_configuration(source, target)¶
Copies the solver configuration from a source model to a target model :param source: :param target: :return: