For each model variable and constraint combination, generate a function evaluated by all constant parameter values, but not by the varied values of the x_vals table. This results in a DataFrame attribute df_lam_func, which is subsequently used (symenergy.evaluator.evaluator.Evaluator.expand_to_x_vals_parallel()) to generate the numerical values for variables and multipliers.

Continuing the example from symenergy.evaluator.evaluator.Evaluator:

>>> import inspect
>>> ev.cache_lambd.delete()
>>> ev.get_evaluated_lambdas_parallel()
>>> func = (ev.df_lam_func.set_index(['idx', 'func'])
                          .loc[(4, 'g_p_day')]
                          .lambd_func)
>>> print(inspect.getsource(func))

def _ef0ba06865119477ac40bc0b40038a25(vre_scale_none,C_n_none):
    return(-C_n_none - 4500*vre_scale_none + 4500)

In the example above the generated example function thus depends on the parameters with names vre_scale_none and C_n_none (the model VRE scale and the power plant capacity, as specified through the x_vals attribute).

Note: The DataFrame ev.df_lam_func is typically only used internally. Instead, access the tables

• Model.df_comb to obtain the result expressions for the model variables
• Evaluator.df_exp to obtain the fully evaluated numerical solutions

Generates generates a table indexed by:

• model variable/multiplier (column func)
• constraint combination (columns idx)
• varied parameters (columns specified by the list Evaluator.x_name)

with all numerically evaluated values of functions and multipliers.

Other key columns are:

• lambd numerical value
• is_optimum: boolean; if Evaluator was initialized with drop_non_optimum=True, all non-optimal rows are dropped
• mask_valid: indicates whether the constraint combination yields valid results under the corresponding parameter values; see documentation section Theoretical discussion of the minimal example for explanations on infeasible constraint combinations by parameter values.

Continuing the example from symenergy.evaluator.evaluator.Evaluator.get_evaluated_lambdas_parallel():

>>> ev.expand_to_x_vals_parallel()
>>> (ev.df_exp.query('is_optimum')
       .set_index(['func', 'idx'] + ev.x_name)[
               ['lambd', 'is_optimum', 'mask_valid']]).head()

                                           lambd  is_optimum  mask_valid
func          idx vre_scale_none C_n_none
curt_p_day    1   1.0            0           0.0        True        True
g_p_day       1   1.0            0           0.0        True        True
n_p_day       1   1.0            0           0.0        True        True
pi_supply_day 1   1.0            0           0.0        True        True
curt_p_day    2   0.0            0           0.0        True        True
...

Note: Under some circumstances the serial evaluation is overall faster than the parallel approach. Serial evaluation is obtained by setting the SymEnergy multiprocessing nworkers parameter to None:

>>> from symenergy.auxiliary.parallelization import multiproc_params
>>> multiproc_params('nworkers') = None