"""Code for generating N2 diagram."""
import inspect
import os
import pathlib
import sys
from operator import itemgetter
import networkx as nx
import numpy as np
import openmdao.utils.hooks as hooks
from openmdao.core.explicitcomponent import ExplicitComponent
from openmdao.core.indepvarcomp import IndepVarComp
from openmdao.core.parallel_group import ParallelGroup
from openmdao.core.group import Group
from openmdao.core.problem import Problem, _SetupStatus
from openmdao.core.implicitcomponent import ImplicitComponent
from openmdao.core.constants import _UNDEFINED
from openmdao.components.exec_comp import ExecComp
from openmdao.components.meta_model_structured_comp import MetaModelStructuredComp
from openmdao.components.meta_model_unstructured_comp import MetaModelUnStructuredComp
from openmdao.drivers.doe_driver import DOEDriver
from openmdao.recorders.case_reader import CaseReader
from openmdao.solvers.nonlinear.newton import NewtonSolver
from openmdao.utils.array_utils import convert_ndarray_to_support_nans_in_json
from openmdao.utils.class_util import overrides_method
from openmdao.utils.general_utils import default_noraise
from openmdao.utils.mpi import MPI
from openmdao.utils.notebook_utils import notebook, display, HTML, IFrame, colab
from openmdao.utils.om_warnings import issue_warning
from openmdao.utils.reports_system import register_report_hook
from openmdao.utils.file_utils import _load_and_exec, _to_filename
from openmdao.visualization.htmlpp import HtmlPreprocessor
from openmdao import __version__ as openmdao_version
_MAX_ARRAY_SIZE_FOR_REPR_VAL = 1000 # If var has more elements than this do not pass to N2
_MAX_OPTION_SIZE = int(1e4) # If option value is bigger than this do not pass to N2
_default_n2_filename = 'n2.html'
def _get_array_info(system, vec, name, prom, var_dict, from_src=True):
ndarray_to_convert = vec._abs_get_val(name, flat=False) if vec else \
system.get_val(prom, from_src=from_src)
var_dict['val'] = convert_ndarray_to_support_nans_in_json(ndarray_to_convert)
# Find the minimum indices and value
min_indices = np.unravel_index(np.nanargmin(ndarray_to_convert, axis=None),
ndarray_to_convert.shape)
var_dict['val_min_indices'] = min_indices
var_dict['val_min'] = ndarray_to_convert[min_indices]
# Find the maximum indices and value
max_indices = np.unravel_index(np.nanargmax(ndarray_to_convert, axis=None),
ndarray_to_convert.shape)
var_dict['val_max_indices'] = max_indices
var_dict['val_max'] = ndarray_to_convert[max_indices]
def _get_var_dict(system, typ, name, is_parallel, is_implicit, values):
if name in system._var_abs2meta[typ]:
meta = system._var_abs2meta[typ][name]
prom = system._var_abs2prom[typ][name]
val = np.asarray(meta['val'])
is_dist = MPI is not None and meta['distributed']
var_dict = {
'name': prom,
'type': typ,
'dtype': type(val).__name__,
'is_discrete': False,
'distributed': is_dist,
'shape': str(meta['shape']),
'desc': meta['desc']
}
if typ == 'output':
var_dict['implicit'] = is_implicit
vec = system._outputs
else: # input
if MPI:
# for inputs if we're under MPI, we only retrieve the value currently stored
# in the input vector and not from the connected source because that source
# could be remote.
vec = system._inputs
else:
vec = None
# if 'vec' is not None at this point, we can retrieve the value using vec._abs_get_val,
# which is a faster call than system.get_val.
if meta['units'] is None:
var_dict['units'] = 'None'
else:
var_dict['units'] = meta['units']
try:
if values and val.size < _MAX_ARRAY_SIZE_FOR_REPR_VAL:
if not MPI:
# Get the current value
_get_array_info(system, vec, name, prom, var_dict, from_src=True)
elif is_parallel or is_dist:
# we can't access non-local values, so just get the initial value
var_dict['val'] = val
var_dict['initial_value'] = True
else:
# get the current value but don't try to get it from the source,
# which could be remote under MPI
_get_array_info(system, vec, name, prom, var_dict, from_src=False)
except Exception as err:
issue_warning(str(err))
else: # discrete
meta = system._var_discrete[typ][name]
val = meta['val']
var_dict = {
'name': name,
'type': typ,
'dtype': type(val).__name__,
'is_discrete': True,
}
if values:
if MPI is None or isinstance(val, (int, str, list, dict, complex, np.ndarray)):
var_dict['val'] = default_noraise(system.get_val(name))
if 'surrogate_name' in meta:
var_dict['surrogate_name'] = meta['surrogate_name']
return var_dict
def _serialize_single_option(option):
"""
Return a json-safe equivalent of the option.
The default_noraise function performs the datatype serialization, while this function takes
care of attributes specific to options dicts.
Parameters
----------
option : object
Option to be serialized.
Returns
-------
object
JSON-safe serialized object.
"""
if not option['recordable']:
return 'Not Recordable'
val = option['val']
if val is _UNDEFINED:
return str(val)
if sys.getsizeof(val) > _MAX_OPTION_SIZE:
return 'Too Large to Display'
return default_noraise(val)
def _get_tree_dict(system, values=True, is_parallel=False):
"""
Get a dictionary representation of the system hierarchy.
Parameters
----------
system : System
The System at the root of the hierarchy
values : bool
If True, include variable values. If False, all values will be None.
is_parallel : bool
If True, values can be remote and are not available.
"""
tree_dict = {
'name': system.name if system.name else 'root',
'type': 'subsystem' if system.name else 'root',
'class': ':'.join((type(system).__module__, type(system).__qualname__)),
'expressions': None,
'nonlinear_solver': "",
'nonlinear_solver_options': None,
'linear_solver': "",
'linear_solver_options': None,
}
is_implicit = False
if isinstance(system, Group):
if MPI and isinstance(system, ParallelGroup):
is_parallel = True
tree_dict['component_type'] = None
tree_dict['subsystem_type'] = 'group'
tree_dict['is_parallel'] = is_parallel
children = [_get_tree_dict(s, values, is_parallel)
for s in system._subsystems_myproc]
if system.comm.size > 1:
if system._subsystems_myproc:
sub_comm = system._subsystems_myproc[0].comm
if sub_comm.rank != 0:
children = []
children_lists = system.comm.allgather(children)
children = []
for children_list in children_lists:
children.extend(children_list)
if system.linear_solver:
tree_dict['linear_solver'] = system.linear_solver.SOLVER
tree_dict['linear_solver_options'] = {
k: _serialize_single_option(opt)
for k, opt in system.linear_solver.options._dict.items()
}
if system.nonlinear_solver:
tree_dict['nonlinear_solver'] = system.nonlinear_solver.SOLVER
tree_dict['nonlinear_solver_options'] = {
k: _serialize_single_option(opt)
for k, opt in system.nonlinear_solver.options._dict.items()
}
if system.nonlinear_solver.SOLVER == NewtonSolver.SOLVER:
tree_dict['solve_subsystems'] = system._nonlinear_solver.options['solve_subsystems']
else:
tree_dict['subsystem_type'] = 'component'
tree_dict['is_parallel'] = is_parallel
if isinstance(system, ImplicitComponent):
is_implicit = True
tree_dict['component_type'] = 'implicit'
if overrides_method('solve_linear', system, ImplicitComponent):
tree_dict['linear_solver'] = "solve_linear"
elif system.linear_solver:
tree_dict['linear_solver'] = system.linear_solver.SOLVER
tree_dict['linear_solver_options'] = {
k: _serialize_single_option(opt)
for k, opt in system.linear_solver.options._dict.items()
}
if overrides_method('solve_nonlinear', system, ImplicitComponent):
tree_dict['nonlinear_solver'] = "solve_nonlinear"
elif system.nonlinear_solver:
tree_dict['nonlinear_solver'] = system.nonlinear_solver.SOLVER
tree_dict['nonlinear_solver_options'] = {
k: _serialize_single_option(opt)
for k, opt in system.nonlinear_solver.options._dict.items()
}
elif isinstance(system, ExecComp):
tree_dict['component_type'] = 'exec'
tree_dict['expressions'] = system._exprs
elif isinstance(system, (MetaModelStructuredComp, MetaModelUnStructuredComp)):
tree_dict['component_type'] = 'metamodel'
elif isinstance(system, IndepVarComp):
tree_dict['component_type'] = 'indep'
elif isinstance(system, ExplicitComponent):
tree_dict['component_type'] = 'explicit'
else:
tree_dict['component_type'] = None
children = []
for typ in ['input', 'output']:
for abs_name in system._var_abs2meta[typ]:
children.append(_get_var_dict(system, typ, abs_name,
is_parallel, is_implicit, values))
for prom_name in system._var_discrete[typ]:
children.append(_get_var_dict(system, typ, prom_name,
is_parallel, is_implicit, values))
tree_dict['children'] = children
options = {}
slv = {'linear_solver', 'nonlinear_solver'}
for k, opt in system.options._dict.items():
if k in slv:
# need to handle solver option separately because it can be a class, instance or None
try:
val = opt['val']
except KeyError:
val = opt['value']
try:
options[k] = val.SOLVER
except AttributeError:
options[k] = val
else:
options[k] = _serialize_single_option(opt)
tree_dict['options'] = options
return tree_dict
def _get_declare_partials(system):
"""
Get a list of the declared partials.
Parameters
----------
system : <System>
A System in the model.
Returns
-------
list
A list containing all the declared partials (strings in the form "of > wrt" )
beginning from the given system on down.
"""
declare_partials_list = []
for of, wrt in system._declared_partials_iter():
if of != wrt:
declare_partials_list.append(f"{of} > {wrt}")
return declare_partials_list
def _get_viewer_data(data_source, values=_UNDEFINED, case_id=None):
"""
Get the data needed by the N2 viewer as a dictionary.
Parameters
----------
data_source : <Problem> or <Group> or str or pathlib.Path
A Problem or Group or case recorder filename containing the model or model data.
If the case recorder file from a parallel run has separate metadata, the
filenames can be specified with a comma, e.g.: case.sql_0,case.sql_meta
values : bool or _UNDEFINED
If True, include variable values. If False, all values will be None.
If unspecified, this behaves as if set to True unless the data source is a Problem or
model for which setup is not complete, in which case it behaves as if set to False.
case_id : int or str or None
Case name or index of case in SQL file.
Returns
-------
dict
A dictionary containing information about the model for use by the viewer.
"""
if isinstance(data_source, Problem):
root_group = data_source.model
if not isinstance(root_group, Group):
# this function only makes sense when the model is a Group
msg = f"The model is of type {root_group.__class__.__name__}, " \
"viewer data is only available if the model is a Group."
raise TypeError(msg)
driver = data_source.driver
driver_name = driver.__class__.__name__
driver_type = 'doe' if isinstance(driver, DOEDriver) else 'optimization'
driver_options = {key: _serialize_single_option(driver.options._dict[key])
for key in driver.options}
if driver_type == 'optimization' and hasattr(driver, 'opt_settings'):
driver_opt_settings = driver.opt_settings
else:
driver_opt_settings = None
# set default behavior for values flag
if values is _UNDEFINED:
values = (data_source._metadata is not None and
data_source._metadata['setup_status'] >= _SetupStatus.POST_FINAL_SETUP)
elif isinstance(data_source, Group):
if not data_source.pathname: # root group
root_group = data_source
driver_name = None
driver_type = None
driver_options = None
driver_opt_settings = None
else:
# this function only makes sense when it is at the root
msg = f"Viewer data is not available for sub-Group '{data_source.pathname}'."
raise TypeError(msg)
# set default behavio r for values flag
if values is _UNDEFINED:
values = (data_source._problem_meta is not None and
data_source._problem_meta['setup_status'] >= _SetupStatus.POST_FINAL_SETUP)
elif isinstance(data_source, str) or isinstance(data_source, pathlib.Path):
if isinstance(data_source, str) and ',' in data_source:
filenames = data_source.split(',')
cr = CaseReader(filenames[0], metadata_filename=filenames[1])
else:
cr = CaseReader(data_source)
data_dict = cr.problem_metadata
# set default behavior for values flag
if values is _UNDEFINED:
values = True
def set_values(children, stack, case):
"""
Set variable values in model tree from the specified Case.
If case is None, set all values to None.
"""
for child in children:
# if 'val' in child
if child['type'] == 'subsystem':
stack.append(child['name'])
set_values(child['children'], stack, case)
stack.pop()
elif child['type'] == 'input':
if case is None:
child.pop('val')
for key in ['val_min', 'val_max', 'val_min_indices', 'val_max_indices']:
del child[key]
elif case.inputs is None:
child['val'] = 'N/A'
else:
path = child['name'] if not stack else '.'.join(stack + [child['name']])
child['val'] = case.inputs[path]
elif child['type'] == 'output':
if case is None:
child.pop('val')
for key in ['val_min', 'val_max', 'val_min_indices', 'val_max_indices']:
del child[key]
elif case.outputs is None:
child['val'] = 'N/A'
else:
path = child['name'] if not stack else '.'.join(stack + [child['name']])
try:
child['val'] = case.outputs[path]
except KeyError:
child['val'] = 'N/A'
if values is False:
set_values(data_dict['tree']['children'], [], None)
elif case_id is not None:
case = cr.get_case(case_id)
print(f"Using source: {case.source}\nCase: {case.name}")
set_values(data_dict['tree']['children'], [], case)
# Delete the variables key since it's not used in N2
if 'variables' in data_dict:
del data_dict['variables']
# Older recordings might not have this.
if 'md5_hash' not in data_dict:
data_dict['md5_hash'] = None
return data_dict
else:
raise TypeError(f"Viewer data is not available for '{data_source}'."
"The source must be a Problem, model or the filename of a recording.")
data_dict = {}
data_dict['tree'] = _get_tree_dict(root_group, values=values)
data_dict['md5_hash'] = root_group._generate_md5_hash()
connections_list = []
G = root_group.compute_sys_graph(comps_only=True)
scc = nx.strongly_connected_components(G)
strongdict = {}
sys_idx_names = []
for i, strong_comp in enumerate(scc):
for c in strong_comp:
strongdict[c] = i # associate each comp with a strongly connected component
if len(strong_comp) > 1:
# these IDs are only used when back edges are present
for name in strong_comp:
sys_idx_names.append(name)
sys_idx = {} # map of pathnames to index of pathname in list (systems in cycles only)
comp_orders = {name: i for i, name in enumerate(root_group._ordered_comp_name_iter())}
for name in sorted(sys_idx_names):
sys_idx[name] = len(sys_idx)
# 1 is added to the indices of all edges in the matrix so that we can use 0 entries to
# indicate that there is no connection.
matrix = np.zeros((len(comp_orders), len(comp_orders)), dtype=np.int32)
edge_ids = []
for i, edge in enumerate(G.edges()):
src, tgt = edge
if strongdict[src] == strongdict[tgt]:
matrix[comp_orders[src], comp_orders[tgt]] = i + 1 # bump edge index by 1
edge_ids.append((sys_idx[src], sys_idx[tgt]))
else:
edge_ids.append(None)
for edge_i, (src, tgt) in enumerate(G.edges()):
if strongdict[src] == strongdict[tgt]:
start = comp_orders[src]
end = comp_orders[tgt]
# get a view here so we can remove this edge from submat temporarily to eliminate
# an 'if' check inside the nested list comprehension for edges_list
rem = matrix[start:start + 1, end:end + 1]
rem[0, 0] = 0
if end < start:
start, end = end, start
submat = matrix[start:end + 1, start:end + 1]
nz = submat[submat > 0]
rem[0, 0] = edge_i + 1 # put removed edge back
if nz.size > 1:
nz -= 1 # convert back to correct edge index
edges_list = [edge_ids[i] for i in nz]
edges_list = sorted(edges_list, key=itemgetter(0, 1))
for vsrc, vtgtlist in G.get_edge_data(src, tgt)['conns'].items():
for vtgt in vtgtlist:
connections_list.append({'src': vsrc, 'tgt': vtgt,
'cycle_arrows': edges_list})
continue
for vsrc, vtgtlist in G.get_edge_data(src, tgt)['conns'].items():
for vtgt in vtgtlist:
connections_list.append({'src': vsrc, 'tgt': vtgt})
connections_list = sorted(connections_list, key=itemgetter('src', 'tgt'))
data_dict['sys_pathnames_list'] = list(sys_idx)
data_dict['connections_list'] = connections_list
data_dict['abs2prom'] = root_group._var_abs2prom
data_dict['driver'] = {
'name': driver_name,
'type': driver_type,
'options': driver_options,
'opt_settings': driver_opt_settings
}
data_dict['design_vars'] = root_group.get_design_vars(use_prom_ivc=False)
data_dict['responses'] = root_group.get_responses(use_prom_ivc=False)
data_dict['declare_partials_list'] = _get_declare_partials(root_group)
return data_dict
[docs]def n2(data_source, outfile=_default_n2_filename, path=None, values=_UNDEFINED, case_id=None,
show_browser=True, embeddable=False, title=None, display_in_notebook=True):
"""
Generate an HTML file containing a tree viewer.
Optionally opens a web browser to view the file.
Parameters
----------
data_source : <Problem> or str
The Problem or case recorder database containing the model or model data.
outfile : str, optional
The name of the final output file.
path : str, optional
If specified, the n2 viewer will begin in a state that is zoomed in on the selected path.
This path should be the absolute path of a system in the model.
values : bool or _UNDEFINED
If True, include variable values. If False, all values will be None.
If unspecified, this behaves as if set to True unless the data source is a Problem or
model for which setup is not complete, in which case it behaves as if set to False.
case_id : int, str, or None
Case name or index of case in SQL file if data_source is a database.
show_browser : bool, optional
If True, pop up the system default web browser to view the generated html file.
Defaults to True.
embeddable : bool, optional
If True, gives a single HTML file that doesn't have the <html>, <DOCTYPE>, <body>
and <head> tags. If False, gives a single, standalone HTML file for viewing.
title : str, optional
The title for the diagram. Used in the HTML title.
display_in_notebook : bool, optional
If True, display the N2 diagram in the notebook, if this is called from a notebook.
Defaults to True.
"""
# grab the model viewer data
try:
model_data = _get_viewer_data(data_source, values=values, case_id=case_id)
err_msg = ''
except TypeError as err:
model_data = {}
err_msg = str(err)
issue_warning(err_msg)
# If MPI is active only display one copy of the viewer.
# If the data_source is a Problem, only run on the root proc of its comm.
# Otherwise, only run on the global root proc.
if MPI:
try:
comm = data_source.comm
except AttributeError:
comm = MPI.COMM_WORLD
if comm.rank != 0:
return
options = {}
model_data['options'] = options
import openmdao
openmdao_dir = os.path.dirname(inspect.getfile(openmdao))
vis_dir = os.path.join(openmdao_dir, "visualization/n2_viewer")
if title:
title = f"OpenMDAO Model Hierarchy and N2 diagram: {title}"
else:
title = "OpenMDAO Model Hierarchy and N2 diagram"
html_vars = {
'title': title,
'embeddable': "embedded-diagram" if embeddable else "non-embedded-diagram",
'openmdao_version': openmdao_version,
'model_data': model_data,
'initial_path': path
}
if err_msg:
with open(outfile, 'w') as f:
f.write(err_msg)
else:
HtmlPreprocessor(os.path.join(vis_dir, "index.html"),
outfile, allow_overwrite=True, var_dict=html_vars,
json_dumps_default=default_noraise, verbose=False).run()
if notebook:
if display_in_notebook:
# display in Jupyter Notebook
outfile = os.path.relpath(outfile)
if not colab:
display(IFrame(src=outfile, width="100%", height=700))
else:
display(HTML(outfile))
elif show_browser:
# open it up in the browser
from openmdao.utils.webview import webview
webview(outfile)
# N2 report definition
def _run_n2_report(prob, report_filename=_default_n2_filename):
n2_filepath = prob.get_reports_dir() / report_filename
try:
n2(prob, show_browser=False, outfile=n2_filepath, display_in_notebook=False)
except RuntimeError as err:
# We ignore this error
if str(err) != "Can't compute total derivatives unless " \
"both 'of' or 'wrt' variables have been specified.":
raise err
def _run_n2_report_w_errors(prob, report_filename=_default_n2_filename):
if prob._any_rank_has_saved_errors():
n2_filepath = prob.get_reports_dir() / report_filename
# only run the n2 here if we've had setup errors. Normally we'd wait until
# after final_setup in order to have correct values for all of the I/O variables.
try:
n2(prob, show_browser=False, outfile=n2_filepath, display_in_notebook=False)
except Exception as err:
# We ignore this error
if str(err) != "Can't compute total derivatives unless " \
"both 'of' or 'wrt' variables have been specified.":
prob.model._collect_error(str(err))
# errors will result in exit at the end of the _check_collected_errors method
def _n2_report_register():
register_report_hook('n2', 'final_setup', 'Problem', post=_run_n2_report,
description='N2 diagram', report_filename=_default_n2_filename)
register_report_hook('n2', '_check_collected_errors', 'Problem', pre=_run_n2_report_w_errors,
description='N2 diagram')
def _n2_setup_parser(parser):
"""
Set up the openmdao subparser for the 'openmdao n2' command.
Parameters
----------
parser : argparse subparser
The parser we're adding options to.
"""
parser.add_argument('file', nargs=1,
help='Python script or recording containing the model. '
'If metadata from a parallel run was recorded in a separate file, '
'specify both database filenames delimited with a comma.')
parser.add_argument('-o', default=_default_n2_filename, action='store', dest='outfile',
help='html output file.')
parser.add_argument('--no_values', action='store_true', dest='no_values',
help="don't display variable values.")
parser.add_argument('--no_browser', action='store_true', dest='no_browser',
help="don't display in a browser.")
parser.add_argument('--embed', action='store_true', dest='embeddable',
help="create embeddable version.")
parser.add_argument('--title', default=None, action='store', dest='title',
help='diagram title.')
parser.add_argument('--path', default=None, action='store', dest='path',
help='initial system path to zoom into.')
parser.add_argument('--problem', default=None, action='store', dest='problem_name',
help='name of sub-problem, if target is a sub-problem')
def _n2_cmd(options, user_args):
"""
Process command line args and call n2 on the specified file.
Parameters
----------
options : argparse Namespace
Command line options.
user_args : list of str
Command line options after '--' (if any). Passed to user script.
"""
filename = _to_filename(options.file[0])
probname = options.problem_name
if filename.endswith('.py'):
# disable the reports system, we only want the N2 report and then we exit
os.environ['OPENMDAO_REPORTS'] = '0'
def _view_model_w_errors(prob):
# if problem name is not specified, use top-level problem (no delimiter in pathname)
pathname = prob._metadata['pathname']
if (probname is None and '/' not in pathname) or (probname == prob._name):
errs = prob._metadata['saved_errors']
if errs:
# only run the n2 here if we've had setup errors. Normally we'd wait until
# after final_setup in order to have correct values for all of the variables.
n2(prob, outfile=options.outfile, show_browser=not options.no_browser,
values=not options.no_values, title=options.title, path=options.path,
embeddable=options.embeddable)
# errors will result in exit at the end of the _check_collected_errors method
else:
# no errors, generate n2 after final_setup
def _view_model_no_errors(prob):
n2(prob, outfile=options.outfile, show_browser=not options.no_browser,
values=not options.no_values, title=options.title, path=options.path,
embeddable=options.embeddable)
hooks._register_hook('final_setup', 'Problem',
post=_view_model_no_errors, exit=True)
hooks._setup_hooks(prob)
hooks._register_hook('_check_collected_errors', 'Problem', pre=_view_model_w_errors)
_load_and_exec(options.file[0], user_args)
else:
# assume the file is a recording, run standalone
n2(filename, outfile=options.outfile, title=options.title, path=options.path,
values=False if options.no_values else _UNDEFINED,
show_browser=not options.no_browser,
embeddable=options.embeddable)
