def _process_dashboard_args(database_paths, no_browser, port):
"""Check arguments and find free port if none was given.
The no_browser and port arguments override settings from the databases if not None.
Args:
database_paths (str or pathlib.Path or list of them): Path(s) to an sqlite3
file which typically has the file extension ``.db``. See :ref:`logging` for
details.
no_browser (bool or None): Whether or not to open the dashboard in the browser.
port (int or None): Port where to display the dashboard.
Returns:
database_paths (str or pathlib.Path or list of them):
Path(s) to an sqlite3 file which typically has the file extension ``.db``.
See :ref:`logging` for details.
no_browser (bool):
Whether or not to open the dashboard in the browser.
port (int): port where to display the dashboard.
"""
if not isinstance(database_paths, (list, tuple)):
database_paths = [database_paths]
for single_database_path in database_paths:
if not isinstance(single_database_path, (str, pathlib.Path)):
raise TypeError(
f"database_paths must be string or pathlib.Path. ",
"You supplied {type(single_database_path)}.",
)
database_name_to_path = create_short_database_names(path_list=database_paths)
all_options = []
for single_database_path in database_paths:
database = load_database(single_database_path)
dash_options = read_scalar_field(database, "dash_options")
all_options.append(dash_options)
if port is None:
ports = {d.pop("port", None) for d in all_options}
ports = {p for p in ports if p is not None}
if len(ports) == 0:
port = find_free_port()
else:
port = ports.pop()
if len(ports) > 1:
warnings.warn(f"You supplied more than one port. {port} will be used.")
if not isinstance(port, int):
raise TypeError(f"port must be an integer. You supplied {type(port)}.")
if no_browser is None:
no_browser_vals = {d.pop("no_browser", False) for d in all_options}
no_browser = no_browser_vals.pop()
if len(no_browser_vals) > 1:
no_browser = False
warnings.warn(
"You supplied both True and False for no_browser. It is set to False."
)
return database_name_to_path, no_browser, port
def monitoring_app(doc, database_name, session_data):
"""Create plots showing the development of the criterion and parameters.
Options are loaded from the database. Supported options are:
- rollover (int): How many iterations to keep before discarding.
Args:
doc (bokeh.Document): Argument required by bokeh.
database_name (str): Short and unique name of the database.
session_data (dict): Infos to be passed between and within apps.
Keys of this app's entry are:
- last_retrieved (int): last iteration currently in the ColumnDataSource.
- database_path (str or pathlib.Path)
- callbacks (dict): dictionary to be populated with callbacks.
"""
database = load_database(session_data["database_path"])
start_params = read_scalar_field(database, "start_params")
dash_options = read_scalar_field(database, "dash_options")
rollover = dash_options["rollover"]
tables = ["criterion_history", "params_history"]
criterion_history, params_history = _create_bokeh_data_sources(
database=database, tables=tables
)
session_data["last_retrieved"] = 1
# create initial bokeh elements without callbacks
initial_convergence_plots = _create_initial_convergence_plots(
criterion_history=criterion_history,
params_history=params_history,
start_params=start_params,
)
activation_button = Toggle(
active=False,
label="Start Updating from Database",
button_type="danger",
width=50,
height=30,
name="activation_button",
)
# add elements to bokeh Document
bokeh_convergence_elements = [Row(activation_button)] + initial_convergence_plots
convergence_tab = Panel(
child=Column(*bokeh_convergence_elements), title="Convergence Tab"
)
tabs = Tabs(tabs=[convergence_tab])
doc.add_root(tabs)
# add callbacks
activation_callback = partial(
_activation_callback,
button=activation_button,
doc=doc,
database=database,
session_data=session_data,
rollover=rollover,
tables=tables,
)
activation_button.on_change("active", activation_callback)
def test_gradient_status_table(database):
assert read_scalar_field(database, "gradient_status") == 0
def test_optimization_status_table(database):
assert read_scalar_field(database, "optimization_status") == "success"
def test_start_params_table(database):
params = pd.DataFrame()
params["name"] = list("abc")
assert_frame_equal(read_scalar_field(database, "start_params"), params)
def test_update_scalar_field(database):
upd_db.update_scalar_field(database=database,
table="optimization_status",
value="failure")
assert read_scalar_field(database, "optimization_status") == "failure"
def test_db_options_table(database):
assert read_scalar_field(database, "db_options") == {"a": 3}
