def _write_table(self, var_type, var_data, hierarchical, print_arrays,
out_stream):
"""
Write table of variable names, values, residuals, and metadata to out_stream.
Parameters
----------
var_type : 'input', 'explicit' or 'implicit'
Indicates type of variables, input or explicit/implicit output.
var_data : list
List of (name, dict of vals and metadata) tuples.
hierarchical : bool
When True, human readable output shows variables in hierarchical format.
print_arrays : bool
When False, in the columnar display, just display norm of any ndarrays with size > 1.
The norm is surrounded by vertical bars to indicate that it is a norm.
When True, also display full values of the ndarray below the row. Format is affected
by the values set with numpy.set_printoptions
Default is False.
out_stream : file-like object
Where to send human readable output.
Set to None to suppress.
"""
if out_stream is None:
return
# Make a dict of variables. Makes it easier to work with in this method
var_dict = OrderedDict()
for name, vals in var_data:
var_dict[name] = vals
# determine pathname of the system
if self.source in ('root', 'driver', 'problem',
'root.nonlinear_solver'):
pathname = ''
elif '|' in self.source:
pathname = get_source_system(self.source)
else:
pathname = self.source.replace('root.', '')
if pathname.endswith('.nonlinear_solver'):
pathname = pathname[:-17] # len('.nonlinear_solver') == 17
# vars should be in execution order
if 'execution_order' in self._var_info:
var_order = self._var_info['execution_order']
var_list = [
var_name for var_name in var_order if var_name in var_dict
]
else:
# don't have execution order, just sort for determinism
var_list = sorted(var_dict.keys())
write_var_table(pathname,
var_list,
var_type,
var_dict,
hierarchical=hierarchical,
top_name='model',
print_arrays=print_arrays,
out_stream=out_stream)
def _write_table(self, var_type, var_data, hierarchical, print_arrays, out_stream):
"""
Write table of variable names, values, residuals, and metadata to out_stream.
The output values could actually represent input variables.
In this context, outputs refers to the data that is being logged to an output stream.
Parameters
----------
var_type : 'input', 'explicit' or 'implicit'
Indicates type of variables, input or explicit/implicit output.
var_data : list
List of (name, dict of vals and metadata) tuples.
hierarchical : bool
When True, human readable output shows variables in hierarchical format.
print_arrays : bool
When False, in the columnar display, just display norm of any ndarrays with size > 1.
The norm is surrounded by vertical bars to indicate that it is a norm.
When True, also display full values of the ndarray below the row. Format is affected
by the values set with numpy.set_printoptions
Default is False.
out_stream : file-like object
Where to send human readable output.
Set to None to suppress.
"""
if out_stream is None:
return
# Make a dict of outputs. Makes it easier to work with in this method
var_dict = OrderedDict()
for name, vals in var_data:
var_dict[name] = vals
write_var_table('model', var_dict.keys(), var_type, var_dict,
hierarchical, print_arrays, out_stream)