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import pprintIf a parameter is a list, that is mutable, or a dict. Default values for the mutable parameter should be None, with the actual default value provided inside the function.
# Mutable dict as parameters
def ffi_tab_txt2tex(dc_fmd_sym_sig=None, dc_tex_sig_sym=None):
if dc_fmd_sym_sig is None:
# defaults
dc_fmd_sym_sig = {'***': 1e-2, '**': 5e-2, '*': 1e-1}
if dc_tex_sig_sym is None:
# defaults
dc_tex_sig_sym = {'1e-3': '\\sym{***}',
'1e-2': '\\sym{**}',
'5e-2': '\\sym{*}',
'1e-1': '\\dagger'}
# print
print(f'{dc_fmd_sym_sig=}')
print(f'{dc_tex_sig_sym=}')
# Call function with default
ffi_tab_txt2tex()
# Call function update the first dict ## dc_fmd_sym_sig={'***': 0.01, '**': 0.05, '*': 0.1}
## dc_tex_sig_sym={'1e-3': '\\sym{***}', '1e-2': '\\sym{**}', '5e-2': '\\sym{*}', '1e-1': '\\dagger'}ffi_tab_txt2tex(dc_fmd_sym_sig = {'***': 1e-3, '**': 1e-2, '*': 5e-2})## dc_fmd_sym_sig={'***': 0.001, '**': 0.01, '*': 0.05}
## dc_tex_sig_sym={'1e-3': '\\sym{***}', '1e-2': '\\sym{**}', '5e-2': '\\sym{*}', '1e-1': '\\dagger'}There is a python function that outputs a dictionary with key and value pairs that specify key aspects of how a model should be solved. For example, one of the parameters could specify the vcpu requirement. This vcpu requirement might change, and so it should be easy to update this key with alternative values.
These are accomplished in the following manner. Define the full key-value pair list, with default values for several dictionaries, with model simulation, support, and compute parameters for example. These lists could be updated with some default alternative combinations, or alternatively, it could be updated with externally provided dictionary with both updated values for existing keys, or even additional key value pairs.
First, we create a function that processes and outputs default parameters, it has two inputs, it_default_group to specify pre-fixed adjustments from defaults, and kwargs that allows for arbitrarily modifications and additions to parameter dictionary.
def gen_compesti_spec(it_default_group=None, **kwargs):
# A. Define the default parameter keys and values
esti_specs = {'esti_method': 'MomentsSimuStates',
'momsets_type': ['a', '20180805a'],
'esti_param_vec_count': 1,
'esti_max_func_eval': 10,
'graph_frequncy': 20}
compute_specs = {'cpu': str(1024 * 1),
'memory': str(517), # only need about 160 mb in reality
'workers': 1,
'aws_fargate': False}
# B. For different
compesti_specs = {**compute_specs, **esti_specs}
# C. Update dictionaries with parameter group values
if it_default_group == 1:
compesti_specs_updates = {'memory': str(1024 * 55),
'compute_param_vec_count': 6,
'esti_param_vec_count': 640}
compesti_specs.update(compesti_specs_updates)
# D. Update with kward, could append new
compesti_specs.update(kwargs)
return compesti_specsSecond, we test the defaults:
compesti_specs = gen_compesti_spec()
pprint.pprint(compesti_specs, width=1)## {'aws_fargate': False,
## 'cpu': '1024',
## 'esti_max_func_eval': 10,
## 'esti_method': 'MomentsSimuStates',
## 'esti_param_vec_count': 1,
## 'graph_frequncy': 20,
## 'memory': '517',
## 'momsets_type': ['a',
## '20180805a'],
## 'workers': 1}Third, we test using default group 1, pre-fixed changes to defaults:
compesti_specs = gen_compesti_spec(it_default_group=1)
pprint.pprint(compesti_specs, width=1)## {'aws_fargate': False,
## 'compute_param_vec_count': 6,
## 'cpu': '1024',
## 'esti_max_func_eval': 10,
## 'esti_method': 'MomentsSimuStates',
## 'esti_param_vec_count': 640,
## 'graph_frequncy': 20,
## 'memory': '56320',
## 'momsets_type': ['a',
## '20180805a'],
## 'workers': 1}Fourth, we use kwargs to feed in arbitrary dictionary to update and append to existing parameter dictionary:
compesti_specs_updates = {'esti_method': 'MomentsSimuStateszzz',
'moments_type': ['a', '20180805azzz'],
'momsets_type': ['a', '20180805azzz'],
'momsets_type_uuu': ['a', '20180805azzz']}
compesti_specs = gen_compesti_spec(it_default_group=None, **compesti_specs_updates)
pprint.pprint(compesti_specs, width=1)## {'aws_fargate': False,
## 'cpu': '1024',
## 'esti_max_func_eval': 10,
## 'esti_method': 'MomentsSimuStateszzz',
## 'esti_param_vec_count': 1,
## 'graph_frequncy': 20,
## 'memory': '517',
## 'moments_type': ['a',
## '20180805azzz'],
## 'momsets_type': ['a',
## '20180805azzz'],
## 'momsets_type_uuu': ['a',
## '20180805azzz'],
## 'workers': 1}Define a function with named and unnamed arguments:
def gen_compesti_spec_named(it_default_group, esti_method, memory=123, graph_frequncy=10):
# A. Define the default parameter keys and values
esti_specs = {'esti_method': 'MomentsSimuStates',
'momsets_type': ['a', '20180805a'],
'it_default_group': it_default_group,
'esti_param_vec_count': 1,
'esti_max_func_eval': 10,
'graph_frequncy': graph_frequncy}
compute_specs = {'cpu': str(1024 * 1),
'memory': str(memory), # only need about 160 mb in reality
'workers': 1,
'aws_fargate': False}
# B. For different
compesti_specs = {**compute_specs, **esti_specs}
return compesti_specsProvide inputs for the first two unnamed parameters explicitly. Then provided the two named parameters via a dictionary:
dc_inputs = {'memory':12345, 'graph_frequncy':2}
compesti_specs = gen_compesti_spec_named(None, 'MomentsSimuStates', **dc_inputs)
pprint.pprint(compesti_specs, width=1)## {'aws_fargate': False,
## 'cpu': '1024',
## 'esti_max_func_eval': 10,
## 'esti_method': 'MomentsSimuStates',
## 'esti_param_vec_count': 1,
## 'graph_frequncy': 2,
## 'it_default_group': None,
## 'memory': '12345',
## 'momsets_type': ['a',
## '20180805a'],
## 'workers': 1}