def test_extend_with_paramspec(some_paramspecs):
ps1 = some_paramspecs[1]['ps1']
ps2 = some_paramspecs[1]['ps2']
ps3 = some_paramspecs[1]['ps3']
ps4 = some_paramspecs[1]['ps4']
ps5 = some_paramspecs[1]['ps5']
ps6 = some_paramspecs[1]['ps6']
ps1_base = ps1.base_version()
ps2_base = ps2.base_version()
ps3_base = ps3.base_version()
ps4_base = ps4.base_version()
ps5_base = ps5.base_version()
ps6_base = ps6.base_version()
idps_bare = InterDependencies_(standalones=(ps1_base, ))
idps_extended = InterDependencies_(inferences={ps3_base: (ps1_base, )})
assert idps_bare._extend_with_paramspec(ps3) == idps_extended
idps_bare = InterDependencies_(standalones=(ps2_base, ),
inferences={ps3_base: (ps1_base, )})
idps_extended = InterDependencies_(inferences={
ps3_base: (ps1_base, ),
ps4_base: (ps2_base, )
})
assert idps_bare._extend_with_paramspec(ps4) == idps_extended
idps_bare = InterDependencies_(standalones=(ps1_base, ps2_base))
idps_extended = InterDependencies_(inferences={
ps3_base: (ps1_base, ),
ps4_base: (ps2_base, )
},
dependencies={
ps5_base: (ps3_base, ps4_base),
ps6_base: (ps3_base, ps4_base)
})
assert (idps_bare._extend_with_paramspec(ps3)._extend_with_paramspec(
ps4)._extend_with_paramspec(ps5)._extend_with_paramspec(ps6)
) == idps_extended
class DataSet(Sized):
# the "persistent traits" are the attributes/properties of the DataSet
# that are NOT tied to the representation of the DataSet in any particular
# database
persistent_traits = ('name', 'guid', 'number_of_results', 'parameters',
'paramspecs', 'exp_name', 'sample_name', 'completed',
'snapshot', 'run_timestamp_raw', 'description',
'completed_timestamp_raw', 'metadata')
def __init__(self,
path_to_db: str = None,
run_id: Optional[int] = None,
conn: Optional[ConnectionPlus] = None,
exp_id=None,
name: str = None,
specs: Optional[SpecsOrInterDeps] = None,
values=None,
metadata=None) -> None:
"""
Create a new DataSet object. The object can either hold a new run or
an already existing run. If a run_id is provided, then an old run is
looked up, else a new run is created.
Args:
path_to_db: path to the sqlite file on disk. If not provided, the
path will be read from the config.
run_id: provide this when loading an existing run, leave it
as None when creating a new run
conn: connection to the DB; if provided and `path_to_db` is
provided as well, then a ValueError is raised (this is to
prevent the possibility of providing a connection to a DB
file that is different from `path_to_db`)
exp_id: the id of the experiment in which to create a new run.
Ignored if run_id is provided.
name: the name of the dataset. Ignored if run_id is provided.
specs: paramspecs belonging to the dataset. Ignored if run_id is
provided.
values: values to insert into the dataset. Ignored if run_id is
provided.
metadata: metadata to insert into the dataset. Ignored if run_id
is provided.
"""
if path_to_db is not None and conn is not None:
raise ValueError("Both `path_to_db` and `conn` arguments have "
"been passed together with non-None values. "
"This is not allowed.")
self._path_to_db = path_to_db or get_DB_location()
self.conn = make_connection_plus_from(conn) if conn is not None else \
connect(self.path_to_db)
self._run_id = run_id
self._debug = False
self.subscribers: Dict[str, _Subscriber] = {}
self._interdeps: InterDependencies_
if run_id is not None:
if not run_exists(self.conn, run_id):
raise ValueError(f"Run with run_id {run_id} does not exist in "
f"the database")
self._completed = completed(self.conn, self.run_id)
run_desc = self._get_run_description_from_db()
if run_desc._old_style_deps:
# TODO: what if the old run had invalid interdep.s?
old_idps: InterDependencies = cast(InterDependencies,
run_desc.interdeps)
self._interdeps = old_to_new(old_idps)
else:
new_idps: InterDependencies_ = cast(InterDependencies_,
run_desc.interdeps)
self._interdeps = new_idps
self._metadata = get_metadata_from_run_id(self.conn, run_id)
self._started = self.run_timestamp_raw is not None
else:
# Actually perform all the side effects needed for the creation
# of a new dataset. Note that a dataset is created (in the DB)
# with no parameters; they are written to disk when the dataset
# is marked as started
if exp_id is None:
if len(get_experiments(self.conn)) > 0:
exp_id = get_last_experiment(self.conn)
else:
raise ValueError("No experiments found."
"You can start a new one with:"
" new_experiment(name, sample_name)")
name = name or "dataset"
_, run_id, __ = create_run(self.conn,
exp_id,
name,
generate_guid(),
parameters=None,
values=values,
metadata=metadata)
# this is really the UUID (an ever increasing count in the db)
self._run_id = run_id
self._completed = False
self._started = False
if isinstance(specs, InterDependencies_):
self._interdeps = specs
elif specs is not None:
self._interdeps = old_to_new(InterDependencies(*specs))
else:
self._interdeps = InterDependencies_()
self._metadata = get_metadata_from_run_id(self.conn, self.run_id)
@property
def run_id(self):
return self._run_id
@property
def path_to_db(self):
return self._path_to_db
@property
def name(self):
return select_one_where(self.conn, "runs", "name", "run_id",
self.run_id)
@property
def table_name(self):
return select_one_where(self.conn, "runs", "result_table_name",
"run_id", self.run_id)
@property
def guid(self):
return get_guid_from_run_id(self.conn, self.run_id)
@property
def snapshot(self) -> Optional[dict]:
"""Snapshot of the run as dictionary (or None)"""
snapshot_json = self.snapshot_raw
if snapshot_json is not None:
return json.loads(snapshot_json)
else:
return None
@property
def snapshot_raw(self) -> Optional[str]:
"""Snapshot of the run as a JSON-formatted string (or None)"""
if is_column_in_table(self.conn, "runs", "snapshot"):
return select_one_where(self.conn, "runs", "snapshot", "run_id",
self.run_id)
else:
return None
@property
def number_of_results(self):
sql = f'SELECT COUNT(*) FROM "{self.table_name}"'
cursor = atomic_transaction(self.conn, sql)
return one(cursor, 'COUNT(*)')
@property
def counter(self):
return select_one_where(self.conn, "runs", "result_counter", "run_id",
self.run_id)
@property
def parameters(self) -> str:
if self.pristine:
psnames = [ps.name for ps in self.description.interdeps.paramspecs]
return ','.join(psnames)
else:
return select_one_where(self.conn, "runs", "parameters", "run_id",
self.run_id)
@property
def paramspecs(self) -> Dict[str, Union[ParamSpec, ParamSpecBase]]:
params: Sequence
if self.pristine:
params = self.description.interdeps.paramspecs
else:
params = self.get_parameters()
return {ps.name: ps for ps in params}
@property
def exp_id(self) -> int:
return select_one_where(self.conn, "runs", "exp_id", "run_id",
self.run_id)
@property
def exp_name(self) -> str:
return get_experiment_name_from_experiment_id(self.conn, self.exp_id)
@property
def sample_name(self) -> str:
return get_sample_name_from_experiment_id(self.conn, self.exp_id)
@property
def run_timestamp_raw(self) -> Optional[float]:
"""
Returns run timestamp as number of seconds since the Epoch
The run timestamp is the moment when the measurement for this run
started.
"""
return get_run_timestamp_from_run_id(self.conn, self.run_id)
@property
def description(self) -> RunDescriber:
return RunDescriber(interdeps=self._interdeps)
@property
def metadata(self) -> Dict:
return self._metadata
def the_same_dataset_as(self, other: 'DataSet') -> bool:
"""
Check if two datasets correspond to the same run by comparing
all their persistent traits. Note that this method
does not compare the data itself.
This function raises if the GUIDs match but anything else doesn't
Args:
other: the dataset to compare self to
"""
if not isinstance(other, DataSet):
return False
guids_match = self.guid == other.guid
for attr in DataSet.persistent_traits:
if getattr(self, attr) != getattr(other, attr):
if guids_match:
raise RuntimeError('Critical inconsistency detected! '
'The two datasets have the same GUID,'
f' but their "{attr}" differ.')
else:
return False
return True
def run_timestamp(self, fmt: str = "%Y-%m-%d %H:%M:%S") -> Optional[str]:
"""
Returns run timestamp in a human-readable format
The run timestamp is the moment when the measurement for this run
started. If the run has not yet been started, this function returns
None.
Consult with `time.strftime` for information about the format.
"""
if self.run_timestamp_raw is None:
return None
else:
return time.strftime(fmt, time.localtime(self.run_timestamp_raw))
@property
def completed_timestamp_raw(self) -> Union[float, None]:
"""
Returns timestamp when measurement run was completed
as number of seconds since the Epoch
If the run (or the dataset) is not completed, then returns None.
"""
return get_completed_timestamp_from_run_id(self.conn, self.run_id)
def completed_timestamp(self,
fmt: str = "%Y-%m-%d %H:%M:%S") -> Optional[str]:
"""
Returns timestamp when measurement run was completed
in a human-readable format
If the run (or the dataset) is not completed, then returns None.
Consult with `time.strftime` for information about the format.
"""
completed_timestamp_raw = self.completed_timestamp_raw
if completed_timestamp_raw:
completed_timestamp: Optional[str] = time.strftime(
fmt, time.localtime(completed_timestamp_raw))
else:
completed_timestamp = None
return completed_timestamp
def _get_run_description_from_db(self) -> RunDescriber:
"""
Look up the run_description from the database
"""
desc_str = get_run_description(self.conn, self.run_id)
return RunDescriber.from_json(desc_str)
def toggle_debug(self):
"""
Toggle debug mode, if debug mode is on all the queries made are
echoed back.
"""
self._debug = not self._debug
self.conn.close()
self.conn = connect(self.path_to_db, self._debug)
def add_parameter(self, spec: ParamSpec):
"""
Add a parameter to the DataSet. To ensure sanity, parameters must be
added to the DataSet in a sequence matching their internal
dependencies, i.e. first independent parameters, next other
independent parameters inferred from the first ones, and finally
the dependent parameters. Note that adding parameters to the DataSet
does not reflect in the DB file until the DataSet is marked as started
"""
if not self.pristine:
raise RuntimeError('Can not add parameters to a DataSet that has '
'been started.')
if self.parameters:
old_params = self.parameters.split(',')
else:
old_params = []
if spec.name in old_params:
raise ValueError(f'Duplicate parameter name: {spec.name}')
self._interdeps = self._interdeps._extend_with_paramspec(spec)
def set_interdependencies(self, interdeps: InterDependencies_) -> None:
"""
Overwrite the interdependencies object (which holds all added
parameters and their relationships) of this dataset
"""
if not isinstance(interdeps, InterDependencies_):
raise TypeError('Wrong input type. Expected InterDepencies_, '
f'got {type(interdeps)}')
self._interdeps = interdeps
def get_parameters(self) -> SPECS:
return get_parameters(self.conn, self.run_id)
def add_metadata(self, tag: str, metadata: Any):
"""
Adds metadata to the DataSet. The metadata is stored under the
provided tag. Note that None is not allowed as a metadata value.
Args:
tag: represents the key in the metadata dictionary
metadata: actual metadata
"""
self._metadata[tag] = metadata
# `add_meta_data` is not atomic by itself, hence using `atomic`
with atomic(self.conn) as conn:
add_meta_data(conn, self.run_id, {tag: metadata})
def add_snapshot(self, snapshot: str, overwrite: bool = False) -> None:
"""
Adds a snapshot to this run
Args:
snapshot: the raw JSON dump of the snapshot
overwrite: force overwrite an existing snapshot
"""
if self.snapshot is None or overwrite:
add_meta_data(self.conn, self.run_id, {'snapshot': snapshot})
elif self.snapshot is not None and not overwrite:
log.warning('This dataset already has a snapshot. Use overwrite'
'=True to overwrite that')
@property
def pristine(self) -> bool:
"""
Is this DataSet pristine? A pristine DataSet has not yet been started,
meaning that parameters can still be added and removed, but results
can not be added.
"""
return not (self._started or self._completed)
@property
def running(self) -> bool:
"""
Is this DataSet currently running? A running DataSet has been started,
but not yet completed.
"""
return self._started and not (self._completed)
@property
def started(self) -> bool:
"""
Has this DataSet been started? A DataSet not started can not have any
results added to it.
"""
return self._started
@property
def completed(self) -> bool:
"""
Is this DataSet completed? A completed DataSet may not be modified in
any way.
"""
return self._completed
@completed.setter
def completed(self, value):
self._completed = value
if value:
mark_run_complete(self.conn, self.run_id)
def mark_started(self) -> None:
"""
Mark this dataset as started. A dataset that has been started can not
have its parameters modified.
Calling this on an already started DataSet is a NOOP.
"""
if not self._started:
self._perform_start_actions()
self._started = True
def _perform_start_actions(self) -> None:
"""
Perform the actions that must take place once the run has been started
"""
paramspecs = new_to_old(self._interdeps).paramspecs
for spec in paramspecs:
add_parameter(self.conn, self.table_name, spec)
update_run_description(self.conn, self.run_id,
self.description.to_json())
set_run_timestamp(self.conn, self.run_id)
def mark_completed(self) -> None:
"""
Mark dataset as complete and thus read only and notify the subscribers
"""
if self.pristine:
raise RuntimeError('Can not mark DataSet as complete before it '
'has been marked as started.')
self.completed = True
for sub in self.subscribers.values():
sub.done_callback()
@deprecate(alternative='mark_completed')
def mark_complete(self):
self.mark_completed()
def add_result(self, results: Dict[str, VALUE]) -> int:
"""
Add a logically single result to existing parameters
Args:
results: dictionary with name of a parameter as the key and the
value to associate as the value.
Returns:
index in the DataSet that the result was stored at
If a parameter exist in the dataset and it's not in the results
dictionary, "Null" values are inserted.
It is an error to provide a value for a key or keyword that is not
the name of a parameter in this DataSet.
It is an error to add results to a completed DataSet.
"""
if self.pristine:
raise RuntimeError('This DataSet has not been marked as started. '
'Please mark the DataSet as started before '
'adding results to it.')
if self.completed:
raise CompletedError('This DataSet is complete, no further '
'results can be added to it.')
try:
parameters = [
self._interdeps._id_to_paramspec[name] for name in results
]
self._interdeps.validate_subset(parameters)
except DependencyError as de:
raise ValueError(
'Can not add result, missing setpoint values') from de
index = insert_values(self.conn, self.table_name, list(results.keys()),
list(results.values()))
return index
def add_results(self, results: List[Dict[str, VALUE]]) -> int:
"""
Adds a sequence of results to the DataSet.
Args:
results: list of name-value dictionaries where each dictionary
provides the values for the parameters in that result. If some
parameters are missing the corresponding values are assumed
to be None
Returns:
the index in the DataSet that the **first** result was stored at
It is an error to provide a value for a key or keyword that is not
the name of a parameter in this DataSet.
It is an error to add results to a completed DataSet.
"""
if self.pristine:
raise RuntimeError('This DataSet has not been marked as started. '
'Please mark the DataSet as started before '
'adding results to it.')
if self.completed:
raise CompletedError('This DataSet is complete, no further '
'results can be added to it.')
expected_keys = frozenset.union(*[frozenset(d) for d in results])
values = [[d.get(k, None) for k in expected_keys] for d in results]
len_before_add = length(self.conn, self.table_name)
insert_many_values(self.conn, self.table_name, list(expected_keys),
values)
return len_before_add
@staticmethod
def _validate_parameters(
*params: Union[str, ParamSpec, _BaseParameter]) -> List[str]:
"""
Validate that the provided parameters have a name and return those
names as a list.
The Parameters may be a mix of strings, ParamSpecs or ordinary
QCoDeS parameters.
"""
valid_param_names = []
for maybeParam in params:
if isinstance(maybeParam, str):
valid_param_names.append(maybeParam)
continue
else:
try:
maybeParam = maybeParam.name
except Exception as e:
raise ValueError(
"This parameter does not have a name") from e
valid_param_names.append(maybeParam)
return valid_param_names
def get_data(self,
*params: Union[str, ParamSpec, _BaseParameter],
start: Optional[int] = None,
end: Optional[int] = None) -> List[List[Any]]:
"""
Returns the values stored in the DataSet for the specified parameters.
The values are returned as a list of lists, SQL rows by SQL columns,
e.g. datapoints by parameters. The data type of each element is based
on the datatype provided when the DataSet was created. The parameter
list may contain a mix of string parameter names, QCoDeS Parameter
objects, and ParamSpec objects (as long as they have a `name` field).
If provided, the start and end arguments select a range of results
by result count (index). If the range is empty - that is, if the end is
less than or equal to the start, or if start is after the current end
of the DataSet – then a list of empty arrays is returned.
For a more type independent and easier to work with view of the data
you may want to consider using
:py:meth:`.get_parameter_data`
Args:
*params: string parameter names, QCoDeS Parameter objects, and
ParamSpec objects
start: start value of selection range (by result count); ignored
if None
end: end value of selection range (by results count); ignored if
None
Returns:
list of lists SQL rows of data by SQL columns. Each SQL row is a
datapoint and each SQL column is a parameter. Each element will
be of the datatypes stored in the database (numeric, array or
string)
"""
valid_param_names = self._validate_parameters(*params)
return get_data(self.conn, self.table_name, valid_param_names, start,
end)
def get_parameter_data(
self,
*params: Union[str, ParamSpec, _BaseParameter],
start: Optional[int] = None,
end: Optional[int] = None) -> Dict[str, Dict[str, numpy.ndarray]]:
"""
Returns the values stored in the DataSet for the specified parameters
and their dependencies. If no paramerers are supplied the values will
be returned for all parameters that are not them self dependencies.
The values are returned as a dictionary with names of the requested
parameters as keys and values consisting of dictionaries with the
names of the parameters and its dependencies as keys and numpy arrays
of the data as values. If some of the parameters are stored as
arrays the remaining parameters are expanded to the same shape as these.
Apart from this expansion the data returned by this method
is the transpose of the date returned by `get_data`.
If provided, the start and end arguments select a range of results
by result count (index). If the range is empty - that is, if the end is
less than or equal to the start, or if start is after the current end
of the DataSet – then a list of empty arrays is returned.
Args:
*params: string parameter names, QCoDeS Parameter objects, and
ParamSpec objects. If no parameters are supplied data for
all parameters that are not a dependency of another
parameter will be returned.
start: start value of selection range (by result count); ignored
if None
end: end value of selection range (by results count); ignored if
None
Returns:
Dictionary from requested parameters to Dict of parameter names
to numpy arrays containing the data points of type numeric,
array or string.
"""
if len(params) == 0:
valid_param_names = get_non_dependencies(self.conn, self.run_id)
else:
valid_param_names = self._validate_parameters(*params)
return get_parameter_data(self.conn, self.table_name,
valid_param_names, start, end)
def get_data_as_pandas_dataframe(self,
*params: Union[str,
ParamSpec,
_BaseParameter],
start: Optional[int] = None,
end: Optional[int] = None) -> \
Dict[str, pd.DataFrame]:
"""
Returns the values stored in the DataSet for the specified parameters
and their dependencies as a dict of :py:class:`pandas.DataFrame` s
Each element in the dict is indexed by the names of the requested
parameters.
Each DataFrame contains a column for the data and is indexed by a
:py:class:`pandas.MultiIndex` formed from all the setpoints
of the parameter.
If no parameters are supplied data will be be
returned for all parameters in the dataset that are not them self
dependencies of other parameters.
If provided, the start and end arguments select a range of results
by result count (index). If the range is empty - that is, if the end is
less than or equal to the start, or if start is after the current end
of the DataSet – then a dict of empty :py:class:`pandas.DataFrame` s is
returned.
Args:
*params: string parameter names, QCoDeS Parameter objects, and
ParamSpec objects. If no parameters are supplied data for
all parameters that are not a dependency of another
parameter will be returned.
start: start value of selection range (by result count); ignored
if None
end: end value of selection range (by results count); ignored if
None
Returns:
Dictionary from requested parameter names to
:py:class:`pandas.DataFrame` s with the requested parameter as
a column and a indexed by a :py:class:`pandas.MultiIndex` formed
by the dependencies.
"""
dfs = {}
datadict = self.get_parameter_data(*params, start=start, end=end)
for name, subdict in datadict.items():
keys = list(subdict.keys())
if len(keys) == 0:
dfs[name] = pd.DataFrame()
continue
if len(keys) == 1:
index = None
elif len(keys) == 2:
index = pd.Index(subdict[keys[1]].ravel(), name=keys[1])
else:
indexdata = tuple(
numpy.concatenate(subdict[key]) if subdict[key].dtype ==
numpy.dtype('O') else subdict[key].ravel()
for key in keys[1:])
index = pd.MultiIndex.from_arrays(indexdata, names=keys[1:])
if subdict[keys[0]].dtype == numpy.dtype('O'):
# ravel will not fully unpack a numpy array of arrays
# which are of "object" dtype. This can happen if a variable
# length array is stored in the db. We use concatenate to
# flatten these
mydata = numpy.concatenate(subdict[keys[0]])
else:
mydata = subdict[keys[0]].ravel()
df = pd.DataFrame(mydata, index=index, columns=[keys[0]])
dfs[name] = df
return dfs
def get_values(self, param_name: str) -> List[List[Any]]:
"""
Get the values (i.e. not NULLs) of the specified parameter
"""
if param_name not in self.parameters:
raise ValueError('Unknown parameter, not in this DataSet')
values = get_values(self.conn, self.table_name, param_name)
return values
def get_setpoints(self, param_name: str) -> Dict[str, List[List[Any]]]:
"""
Get the setpoints for the specified parameter
Args:
param_name: The name of the parameter for which to get the
setpoints
"""
paramspec: ParamSpecBase = self._interdeps._id_to_paramspec[param_name]
if param_name not in self.parameters:
raise ValueError('Unknown parameter, not in this DataSet')
if paramspec not in self._interdeps.dependencies.keys():
raise ValueError(f'Parameter {param_name} has no setpoints.')
setpoints = get_setpoints(self.conn, self.table_name, param_name)
return setpoints
def subscribe(self,
callback: Callable[[Any, int, Optional[Any]], None],
min_wait: int = 0,
min_count: int = 1,
state: Optional[Any] = None,
callback_kwargs: Optional[Dict[str, Any]] = None) -> str:
subscriber_id = uuid.uuid4().hex
subscriber = _Subscriber(self, subscriber_id, callback, state,
min_wait, min_count, callback_kwargs)
self.subscribers[subscriber_id] = subscriber
subscriber.start()
return subscriber_id
def subscribe_from_config(self, name: str) -> str:
"""
Subscribe a subscriber defined in the `qcodesrc.json` config file to
the data of this `DataSet`. The definition can be found at
`subscription.subscribers`.
Args:
name: identifier of the subscriber. Equal to the key of the entry
in 'qcodesrc.json::subscription.subscribers'.
"""
subscribers = qcodes.config.subscription.subscribers
try:
subscriber_info = getattr(subscribers, name)
# the dot dict behind the config does not convert the error and
# actually raises a `KeyError`
except (AttributeError, KeyError):
keys = ','.join(subscribers.keys())
raise RuntimeError(
f'subscribe_from_config: failed to subscribe "{name}" to DataSet '
f'from list of subscribers in `qcodesrc.json` (subscriptions.'
f'subscribers). Chose one of: {keys}')
# get callback from string
parts = subscriber_info.factory.split('.')
import_path, type_name = '.'.join(parts[:-1]), parts[-1]
module = importlib.import_module(import_path)
factory = getattr(module, type_name)
kwargs = {k: v for k, v in subscriber_info.subscription_kwargs.items()}
kwargs['callback'] = factory(self, **subscriber_info.factory_kwargs)
kwargs['state'] = {}
return self.subscribe(**kwargs)
def unsubscribe(self, uuid: str) -> None:
"""
Remove subscriber with the provided uuid
"""
with atomic(self.conn) as conn:
sub = self.subscribers[uuid]
remove_trigger(conn, sub.trigger_id)
sub.schedule_stop()
sub.join()
del self.subscribers[uuid]
def unsubscribe_all(self):
"""
Remove all subscribers
"""
sql = "select * from sqlite_master where type = 'trigger';"
triggers = atomic_transaction(self.conn, sql).fetchall()
with atomic(self.conn) as conn:
for trigger in triggers:
remove_trigger(conn, trigger['name'])
for sub in self.subscribers.values():
sub.schedule_stop()
sub.join()
self.subscribers.clear()
def get_metadata(self, tag):
return get_metadata(self.conn, tag, self.table_name)
def __len__(self) -> int:
return length(self.conn, self.table_name)
def __repr__(self) -> str:
out = []
heading = f"{self.name} #{self.run_id}@{self.path_to_db}"
out.append(heading)
out.append("-" * len(heading))
ps = self.get_parameters()
if len(ps) > 0:
for p in ps:
out.append(f"{p.name} - {p.type}")
return "\n".join(out)