Removing libE_info from simf and genf that don't use them

libensemble/gen_funcs/aposmm_logic.py

@@ -14,7 +14,7 @@
 from petsc4py import PETSc
 import nlopt
 
-def aposmm_logic(H,persis_info,gen_specs,libE_info):
+def aposmm_logic(H,persis_info,gen_specs,_):
     """
     Receives the following data from H:
 
@@ -58,8 +58,6 @@ def aposmm_logic(H,persis_info,gen_specs,libE_info):
     
     """
 
-    # del libE_info # Ignored parameter
-
     n, n_s, c_flag, O, rk_const, lhs_divisions, mu, nu = initialize_APOSMM(H, gen_specs)
 
     # np.savez('H'+str(len(H)),H=H,gen_specs=gen_specs,persis_info=persis_info)

libensemble/gen_funcs/uniform_sampling.py

@@ -3,15 +3,12 @@
 
 import numpy as np
 
-def uniform_random_sample_with_different_nodes_and_ranks(H,persis_info,gen_specs,libE_info):
+def uniform_random_sample_with_different_nodes_and_ranks(H,persis_info,gen_specs,_):
     """
     Generates points uniformly over the domain defined by gen_specs['ub'] and
     gen_specs['lb']. Also randomly requests a different number of nodes to be
     used in the evaluation of the generated point.
     """
-
-    del libE_info # Ignored parameter
-
     ub = gen_specs['ub']
     lb = gen_specs['lb']
     n = len(lb)
@@ -38,13 +35,11 @@ def uniform_random_sample_with_different_nodes_and_ranks(H,persis_info,gen_specs
     return O, persis_info
 
 
-def uniform_random_sample_obj_components(H,persis_info,gen_specs,libE_info):
+def uniform_random_sample_obj_components(H,persis_info,gen_specs,_):
     """
     Generates points uniformly over the domain defined by gen_specs['ub'] and
     gen_specs['lb'] but requests each component be evaluated separately.
     """
-    del libE_info # Ignored parameter
-
     ub = gen_specs['ub']
     lb = gen_specs['lb']
 
@@ -66,13 +61,11 @@ def uniform_random_sample_obj_components(H,persis_info,gen_specs,libE_info):
 
     return O, persis_info
 
-def uniform_random_sample(H,persis_info,gen_specs,libE_info):
+def uniform_random_sample(H,persis_info,gen_specs,_):
     """
     Generates points uniformly over the domain defined by gen_specs['ub'] and
     gen_specs['lb'].
     """
-    del libE_info # Ignored parameter
-
     ub = gen_specs['ub']
     lb = gen_specs['lb']
 

libensemble/sim_funcs/chwirut1.py

@@ -250,13 +250,11 @@ def EvaluateJacobian(x):
 
     return j
 
-def libE_func_wrapper(H,persis_info,sim_specs,libE_info):
+def libE_func_wrapper(H,persis_info,sim_specs,_):
     """
     libEnsemble wrapper around EvaluateFunction
     """
 
-    del libE_info # Ignored parameter
-
     batch = len(H['x'])
     O = np.zeros(batch,dtype=sim_specs['out'])
 

libensemble/sim_funcs/comms_testing.py

@@ -5,7 +5,7 @@
 from libensemble.controller import JobController
 
 
-def float_x1000(H, persis_info, sim_specs, libE_info):
+def float_x1000(H, persis_info, sim_specs, _):
     """
     Multiplies worker ID by 1000 and sends back values
     Input (X) is ignored in this case
@@ -24,7 +24,7 @@ def float_x1000(H, persis_info, sim_specs, libE_info):
     return output, persis_info
 
 
-#def input_double(H, persis_info, sim_specs, libE_info):
+#def input_double(H, persis_info, sim_specs, _):
     #"""
     #Not yet implemented
     #Multiplies input X values by 2 and sends back values

libensemble/sim_funcs/job_control_hworld.py

@@ -60,7 +60,7 @@ def polling_loop(jobctl, job, timeout_sec=6.0, delay=1.0):
     return job, calc_status
 
 
-def job_control_hworld(H, persis_info, sim_specs, libE_info):
+def job_control_hworld(H, persis_info, sim_specs, _):
     """ Test of launching and polling job and exiting on job finish"""
     jobctl = JobController.controller
     cores = sim_specs['cores']

libensemble/sim_funcs/six_hump_camel.py

@@ -46,11 +46,10 @@ def six_hump_camel_with_different_ranks_and_nodes(H, persis_info, sim_specs, lib
     return O, persis_info
 
 
-def six_hump_camel(H, persis_info, sim_specs, libE_info):
+def six_hump_camel(H, persis_info, sim_specs, _):
     """
     Evaluates the six_hump_camel_func and possible six_hump_camel_grad
     """
-    del libE_info # Ignored parameter
 
     batch = len(H['x'])
     O = np.zeros(batch,dtype=sim_specs['out'])
@@ -66,11 +65,10 @@ def six_hump_camel(H, persis_info, sim_specs, libE_info):
 
     return O, persis_info
 
-def six_hump_camel_simple(x, persis_info, sim_specs, libE_info):
+def six_hump_camel_simple(x, persis_info, sim_specs, _):
     """
     Evaluates the six_hump_camel_func and possible six_hump_camel_grad
     """
-    del libE_info # Ignored parameter
 
     O = np.zeros(1,dtype=sim_specs['out'])