"""
Utility functions and constants related to writing a table of variable metadata.
"""
import sys
import pprint
from io import TextIOBase
import numpy as np
from openmdao.core.constants import _DEFAULT_OUT_STREAM
from openmdao.utils.notebook_utils import notebook, display, HTML
from openmdao.visualization.tables.table_builder import generate_table
column_widths = {
'val': 20,
'resids': 20,
'units': 10,
'shape': 10,
'lower': 20,
'upper': 20,
'ref': 20,
'ref0': 20,
'res_ref': 20,
}
align = ''
column_spacing = 2
indent_inc = 2
[docs]def write_var_table(pathname, var_list, var_type, var_dict,
hierarchical=True, top_name='model', print_arrays=False,
out_stream=_DEFAULT_OUT_STREAM):
"""
Write table of variable names, values, residuals, and metadata to out_stream.
Parameters
----------
pathname : str
Pathname to be printed. If None, defaults to 'model'.
var_list : list of str
List of variable names in the order they are to be written.
var_type : 'input', 'explicit' or 'implicit'
Indicates type of variables, input or explicit/implicit output.
var_dict : dict
Dict storing vals and metadata for each var name.
hierarchical : bool
When True, human readable output shows variables in hierarchical format.
top_name : str
The name of the top level group when using 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.
out_stream : file-like object
Where to send human readable output.
"""
if out_stream is None:
return
if notebook and not hierarchical and out_stream is _DEFAULT_OUT_STREAM:
use_html = True
else:
use_html = False
if out_stream is _DEFAULT_OUT_STREAM:
out_stream = sys.stdout
elif not isinstance(out_stream, TextIOBase):
raise TypeError("Invalid output stream specified for 'out_stream'.")
count = len(var_dict)
# Write header
rel_idx = len(pathname) + 1 if pathname else 0
pathname = pathname if pathname else 'model'
if var_type == 'input':
header = "%d Input(s) in '%s'" % (count, pathname)
else:
header = "%d %s Output(s) in '%s'" % (count, var_type.capitalize(), pathname)
out_stream.write(header + '\n')
if not count:
out_stream.write('\n\n')
return
# Need an ordered list of possible output values for the two cases: inputs and outputs
# so that we do the column output in the correct order
if var_type == 'input':
out_types = ('val', 'units', 'shape', 'global_shape', 'prom_name', 'desc', 'min', 'max')
else:
out_types = ('val', 'resids', 'units', 'shape', 'global_shape', 'lower', 'upper',
'ref', 'ref0', 'res_ref', 'prom_name', 'desc', 'min', 'max')
# Figure out which columns will be displayed
# Look at any one of the outputs, they should all be the same, so just look at first one
for outputs in var_dict.values():
column_names = [out_type for out_type in out_types if out_type in outputs]
break
if use_html and var_list:
rows = []
for name in var_list:
rows.append([name] + [var_dict[name][field] for field in column_names])
hdrs = ['varname'] + column_names
display(HTML(str(generate_table(rows, headers=hdrs, tablefmt='html'))))
return
# Find with width of the first column in the table
# Need to look through all the possible varnames to find the max width
max_varname_len = len('varname')
if hierarchical:
for name in var_dict:
for i, name_part in enumerate(name[rel_idx:].split('.')):
total_len = i * indent_inc + len(name_part)
max_varname_len = max(max_varname_len, total_len)
else:
for name in var_dict:
max_varname_len = max(max_varname_len, len(name[rel_idx:]))
# Determine the column widths of the data fields by finding the max width for all rows
for column_name in column_names:
column_widths[column_name] = len(column_name) # has to be able to display name!
for name in var_list:
for column_name in column_names:
column_value = var_dict[name][column_name]
if isinstance(column_value, np.ndarray) and column_value.size > 1:
out = '|{}|'.format(str(np.linalg.norm(column_value)))
else:
out = str(column_value)
column_widths[column_name] = max(column_widths[column_name], len(str(out)))
# Write out the column headers
column_header = '{:{align}{width}}'.format('varname', align=align,
width=max_varname_len)
column_dashes = max_varname_len * '-'
for column_name in column_names:
column_header += column_spacing * ' '
column_header += '{:{align}{width}}'.format(column_name, align=align,
width=column_widths[column_name])
column_dashes += column_spacing * ' ' + column_widths[column_name] * '-'
out_stream.write('\n')
out_stream.write(column_header + '\n')
out_stream.write(column_dashes + '\n')
# Write out the variable names and optional values and metadata
if hierarchical:
cur_sys_names = []
for abs_name in var_list:
rel_name = abs_name[rel_idx:]
# For hierarchical, need to display system levels in the rows above the
# actual row containing the var name and values. Want to make use
# of the hierarchies that have been written about this.
existing_sys_names = []
sys_names = rel_name.split('.')[:-1]
for i, sys_name in enumerate(sys_names):
if sys_names[:i + 1] != cur_sys_names[:i + 1]:
break
else:
existing_sys_names = cur_sys_names[:i + 1]
# What parts of the hierarchy for this varname need to be written that
# were not already written above this
remaining_sys_path_parts = sys_names[len(existing_sys_names):]
# Write the Systems in the var name path
indent = len(existing_sys_names) * indent_inc
for i, sys_name in enumerate(remaining_sys_path_parts):
out_stream.write(indent * ' ' + sys_name + '\n')
indent += indent_inc
cur_sys_names = sys_names
row = '{:{align}{width}}'.format(indent * ' ' + abs_name.split('.')[-1],
align=align, width=max_varname_len)
_write_variable(out_stream, row, column_names, var_dict[abs_name], print_arrays)
else:
for name in var_list:
row = '{:{align}{width}}'.format(name[rel_idx:], align=align, width=max_varname_len)
_write_variable(out_stream, row, column_names, var_dict[name], print_arrays)
out_stream.write('\n\n')
[docs]def write_source_table(source_dicts, out_stream):
"""
Write tables of cases and their respective sources.
Parameters
----------
source_dicts : dict or list of dicts
Dict of source and cases.
out_stream : file-like object
Where to send human readable output.
"""
if out_stream is None:
return
# use table_builder if we are in a notebook and are using the default out_stream
use_html = notebook and out_stream is _DEFAULT_OUT_STREAM
if out_stream is _DEFAULT_OUT_STREAM:
out_stream = sys.stdout
elif not isinstance(out_stream, TextIOBase):
raise TypeError("Invalid output stream specified for 'out_stream'.")
if not source_dicts:
out_stream.write('No data found.\n')
return
if not isinstance(source_dicts, list):
source_dicts = [source_dicts]
for source_dict in source_dicts:
if use_html:
display(HTML(str(generate_table(source_dict, headers='keys', tablefmt='html'))))
else:
for key, value in source_dict.items():
if value:
out_stream.write(f'{key}\n')
for val in value:
out_stream.write(f' {val}\n')
def _write_variable(out_stream, row, column_names, var_dict, print_arrays):
"""
For one variable, write name, values, residuals, and metadata to out_stream.
Parameters
----------
out_stream : file-like object
Where to send human readable output.
Set to None to suppress.
row : str
The string containing the contents of the beginning of this row output.
Contains the name of the System or varname, possibley indented to show hierarchy.
column_names : list of str
Indicates which columns will be written in this row.
var_dict : dict
Contains the values to be written in this row. Keys are columns names.
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.
"""
if out_stream is None:
return
elif out_stream is _DEFAULT_OUT_STREAM:
out_stream = sys.stdout
left_column_width = len(row)
have_array_values = [] # keep track of which values are arrays
print_options = np.get_printoptions()
np_precision = print_options['precision']
for column_name in column_names:
row += column_spacing * ' '
if isinstance(var_dict[column_name], np.ndarray) and \
var_dict[column_name].size > 1:
have_array_values.append(column_name)
norm = np.linalg.norm(var_dict[column_name])
out = '|{}|'.format(str(np.round(norm, np_precision)))
else:
out = str(var_dict[column_name])
row += '{:{align}{width}}'.format(out, align=align,
width=column_widths[column_name])
out_stream.write(row + '\n')
if print_arrays:
for column_name in have_array_values:
out_stream.write("{} {}:\n".format(
left_column_width * ' ', column_name))
out_str = pprint.pformat(var_dict[column_name])
indented_lines = [(left_column_width + indent_inc) * ' ' +
s for s in out_str.splitlines()]
out_stream.write('\n'.join(indented_lines) + '\n')
