"""
Class definition for SqliteRecorder, which provides dictionary backed by SQLite.
"""
from io import BytesIO
import os.path
import gc
import sqlite3
from itertools import chain
import json
import numpy as np
import pickle
import zlib
from openmdao import __version__ as openmdao_version
from openmdao.recorders.case_recorder import CaseRecorder, PICKLE_VER
from openmdao.utils.mpi import MPI
from openmdao.utils.record_util import dict_to_structured_array
from openmdao.utils.options_dictionary import OptionsDictionary
from openmdao.utils.general_utils import make_serializable, default_noraise
from openmdao.core.driver import Driver
from openmdao.core.system import System
from openmdao.core.problem import Problem
from openmdao.solvers.solver import Solver
from openmdao.utils.om_warnings import issue_warning, CaseRecorderWarning
"""
SQL case database version history.
----------------------------------
14-- OpenMDAO 3.8.1
Metadata pickle and JSON blobs are compressed.
Save metadata separately for parallel runs.
13-- OpenMDAO 3.8.1
Added OpenMDAO version number to recorder file
12-- OpenMDAO 3.6.1
Change key for system metadata to use non-ambiguous separator
11-- OpenMDAO 3.2
IndepVarComps are created automatically, so this changes some bookkeeping.
10-- OpenMDAO 3.0
Added abs_err and rel_err recording to Problem recording
9 -- OpenMDAO 3.0
Changed the character to split the derivatives from 'of,wrt' to 'of!wrt' to allow for commas
in variable names
8 -- OpenMDAO 3.0
Added inputs, outputs, and residuals fields to problem_cases table. Added
outputs and residuals fields to driver_iterations table
7 -- OpenMDAO 3.0
Added derivatives field to table for recording problems.
6 -- OpenMDAO 3.X
Removed abs2prom from the driver_metadata table.
5 -- OpenMDAO 2.5
Added source column (driver name, system/solver pathname) to global iterations table.
4 -- OpenMDAO 2.4
Added variable settings metadata that contains scaling info.
3 -- OpenMDAO 2.4
Storing most data as JSON rather than binary numpy arrays.
2 -- OpenMDAO 2.4, merged 20 July 2018.
Added support for recording derivatives from driver, resulting in a new table.
1 -- Through OpenMDAO 2.3
Original implementation.
"""
format_version = 14
# separator, cannot be a legal char for names
META_KEY_SEP = '!'
[docs]def array_to_blob(array):
"""
Make numpy array into a BLOB.
Convert a numpy array to something that can be written
to a BLOB field in sqlite.
TODO : move this to a util file?
Parameters
----------
array : array
The array that will be converted to a blob.
Returns
-------
blob
The blob created from the array.
"""
out = BytesIO()
np.save(out, array)
out.seek(0)
return sqlite3.Binary(out.read())
[docs]def blob_to_array(blob):
"""
Convert sqlite BLOB to numpy array.
TODO : move this to a util file?
Parameters
----------
blob : blob
The blob that will be converted to an array.
Returns
-------
array
The array created from the blob.
"""
out = BytesIO(blob)
out.seek(0)
return np.load(out, allow_pickle=True)
[docs]class SqliteRecorder(CaseRecorder):
"""
Recorder that saves cases in a sqlite db.
Parameters
----------
filepath : str or Path
Path to the recorder file.
append : bool, optional
Optional. If True, append to an existing case recorder file.
pickle_version : int, optional
The pickle protocol version to use when pickling metadata.
record_viewer_data : bool, optional
If True, record data needed for visualization.
Attributes
----------
_record_viewer_data : bool
Flag indicating whether to record data needed to generate N2 diagram.
connection : sqlite connection object
Connection to the sqlite3 database.
metadata_connection : sqlite connection object
Connection to the sqlite3 database, if metadata is recorded separately.
_record_metadata : Whether this process is recording metadata. Always True
for serial runs, only True for rank 0 of parallel runs.
_abs2prom : {'input': dict, 'output': dict}
Dictionary mapping absolute names to promoted names.
_prom2abs : {'input': dict, 'output': dict}
Dictionary mapping promoted names to absolute names.
_abs2meta : {'name': {}}
Dictionary mapping absolute variable names to their metadata including units,
bounds, and scaling.
_pickle_version : int
The pickle protocol version to use when pickling metadata.
_filepath : str
Path to the recorder file.
_database_initialized : bool
Flag indicating whether or not the database has been initialized.
_started : set
set of recording requesters for which this recorder has been started.
_use_outputs_dir : bool
Flag indicating if the database is being saved in the problem outputs dir.
"""
[docs] def __init__(self, filepath, append=False, pickle_version=PICKLE_VER, record_viewer_data=True):
"""
Initialize the SqliteRecorder.
"""
if append:
raise NotImplementedError("Append feature not implemented for SqliteRecorder")
self.connection = None
self.metadata_connection = None
self._record_metadata = True
self._record_viewer_data = record_viewer_data
self._abs2prom = {'input': {}, 'output': {}}
self._prom2abs = {'input': {}, 'output': {}}
self._abs2meta = {}
self._pickle_version = pickle_version
self._filepath = str(filepath)
self._use_outputs_dir = not (os.path.sep in str(filepath) or '/' in str(filepath))
self._database_initialized = False
self._started = set()
super().__init__(record_viewer_data)
def _initialize_database(self, comm):
"""
Initialize the database.
Parameters
----------
comm : MPI.Comm or <FakeComm> or None
The communicator for the recorder (should be the comm for the Problem).
"""
filepath = None
if MPI and comm and comm.size > 1:
if self._record_on_proc:
if not self._parallel:
# recording only on this proc
filepath = self._filepath
else:
# recording on multiple procs, so a separate file for each recording proc
# plus a file for the common metadata, written by the lowest recording rank
rank = comm.rank
filepath = f"{self._filepath}_{rank}"
print("Note: SqliteRecorder is running on multiple processors. "
f"Cases from rank {rank} are being written to {filepath}.")
if rank == min(self._recording_ranks):
metadata_filepath = f'{self._filepath}_meta'
print("Note: Metadata is being recorded separately as "
f"{metadata_filepath}.")
try:
os.remove(metadata_filepath)
issue_warning("The existing case recorder metadata file, "
f"{metadata_filepath}, is being overwritten.",
category=UserWarning)
except OSError:
pass
self.metadata_connection = sqlite3.connect(metadata_filepath)
else:
self._record_metadata = False
else:
# no MPI or comm size == 1
filepath = self._filepath
if filepath:
try:
os.remove(filepath)
issue_warning(f'The existing case recorder file, {filepath},'
' is being overwritten.', category=UserWarning)
except OSError:
pass
self.connection = sqlite3.connect(filepath)
if self._record_metadata and self.metadata_connection is None:
self.metadata_connection = self.connection
with self.connection as c:
# used to keep track of the order of the case records across all case tables
c.execute("CREATE TABLE global_iterations(id INTEGER PRIMARY KEY, "
"record_type TEXT, rowid INT, source TEXT)")
c.execute("CREATE TABLE driver_iterations(id INTEGER PRIMARY KEY, "
"counter INT, iteration_coordinate TEXT, timestamp REAL, "
"success INT, msg TEXT, inputs TEXT, outputs TEXT, residuals TEXT)")
c.execute("CREATE TABLE driver_derivatives(id INTEGER PRIMARY KEY, "
"counter INT, iteration_coordinate TEXT, timestamp REAL, "
"success INT, msg TEXT, derivatives BLOB)")
c.execute("CREATE INDEX driv_iter_ind on driver_iterations(iteration_coordinate)")
c.execute("CREATE TABLE problem_cases(id INTEGER PRIMARY KEY, "
"counter INT, case_name TEXT, timestamp REAL, "
"success INT, msg TEXT, inputs TEXT, outputs TEXT, residuals TEXT, "
"jacobian BLOB, abs_err REAL, rel_err REAL)")
c.execute("CREATE INDEX prob_name_ind on problem_cases(case_name)")
c.execute("CREATE TABLE system_iterations(id INTEGER PRIMARY KEY, "
"counter INT, iteration_coordinate TEXT, timestamp REAL, "
"success INT, msg TEXT, inputs TEXT, outputs TEXT, residuals TEXT)")
c.execute("CREATE INDEX sys_iter_ind on system_iterations(iteration_coordinate)")
c.execute("CREATE TABLE solver_iterations(id INTEGER PRIMARY KEY, "
"counter INT, iteration_coordinate TEXT, timestamp REAL, "
"success INT, msg TEXT, abs_err REAL, rel_err REAL, "
"solver_inputs TEXT, solver_output TEXT, solver_residuals TEXT)")
c.execute("CREATE INDEX solv_iter_ind on solver_iterations(iteration_coordinate)")
if self._record_metadata:
with self.metadata_connection as m:
m.execute("CREATE TABLE metadata(format_version INT, openmdao_version "
"TEXT, abs2prom BLOB, prom2abs BLOB, abs2meta BLOB, "
"var_settings BLOB,conns BLOB)")
m.execute("INSERT INTO metadata(format_version, openmdao_version, "
"abs2prom, prom2abs) VALUES(?,?,?,?)",
(format_version, openmdao_version, None, None))
m.execute("CREATE TABLE driver_metadata(id TEXT PRIMARY KEY, "
"model_viewer_data TEXT)")
m.execute("CREATE TABLE system_metadata(id TEXT PRIMARY KEY, "
"scaling_factors BLOB, component_metadata BLOB)")
m.execute("CREATE TABLE solver_metadata(id TEXT PRIMARY KEY, "
"solver_options BLOB, solver_class TEXT)")
self._database_initialized = True
if MPI and comm and comm.size > 1:
comm.barrier()
def _cleanup_abs2meta(self):
"""
Convert all abs2meta variable properties to a form that can be dumped as JSON.
"""
for name in self._abs2meta:
for prop in self._abs2meta[name]:
self._abs2meta[name][prop] = make_serializable(self._abs2meta[name][prop])
def _make_var_setting_serializable(self, var_settings):
"""
Convert all var_settings variable properties to a form that can be dumped as JSON.
Parameters
----------
var_settings : dict
Dictionary mapping absolute variable names to variable settings.
Returns
-------
var_settings : dict
Dictionary mapping absolute variable names to var settings that are JSON compatible.
"""
# var_settings is already a copy at the outer level, so we just have to copy the
# inner dicts to prevent modifying the original designvars, objectives, and constraints.
for name, meta in var_settings.items():
meta = meta.copy()
for prop, val in meta.items():
meta[prop] = make_serializable(val)
var_settings[name] = meta
return var_settings
[docs] def startup(self, recording_requester, comm=None):
"""
Prepare for a new run and create/update the abs2prom and prom2abs variables.
Parameters
----------
recording_requester : object
Object to which this recorder is attached.
comm : MPI.Comm or <FakeComm> or None
The MPI communicator for the recorder (should be the comm for the Problem).
"""
# we only want to set up recording once for each recording_requester
if recording_requester in self._started:
return
super().startup(recording_requester, comm)
# grab the system and driver
if isinstance(recording_requester, Driver):
system = recording_requester._problem().model
driver = recording_requester
elif isinstance(recording_requester, System):
system = recording_requester
driver = None
elif isinstance(recording_requester, Problem):
system = recording_requester.model
driver = recording_requester.driver
elif isinstance(recording_requester, Solver):
system = recording_requester._system()
driver = None
else:
raise ValueError('Driver encountered a recording_requester it cannot handle'
': {0}'.format(recording_requester))
if self._use_outputs_dir:
self._filepath = system.get_outputs_dir() / self._filepath
if not self._database_initialized:
self._initialize_database(comm)
states = system._list_states_allprocs()
if driver is None:
desvars = system.get_design_vars(True, get_sizes=False, use_prom_ivc=False)
responses = system.get_responses(True, get_sizes=False, use_prom_ivc=False)
constraints = {}
objectives = {}
for name, data in responses.items():
if data['type'] == 'con':
constraints[name] = data
else:
objectives[name] = data
if self.connection:
if driver is not None:
desvars = driver._designvars
responses = driver._responses
constraints = driver._cons
objectives = driver._objs
inputs = list(system.abs_name_iter('input', local=False, discrete=True))
outputs = list(system.abs_name_iter('output', local=False, discrete=True))
var_order = system._get_vars_exec_order(inputs=True, outputs=True, local=True)
# merge current abs2prom and prom2abs with this system's version
self._abs2prom['input'].update(system._var_allprocs_abs2prom['input'])
self._abs2prom['output'].update(system._var_allprocs_abs2prom['output'])
for v, abs_names in system._var_allprocs_prom2abs_list['input'].items():
if v not in self._prom2abs['input']:
self._prom2abs['input'][v] = abs_names
else:
self._prom2abs['input'][v] = list(set(chain(self._prom2abs['input'][v],
abs_names)))
# for outputs, there can be only one abs name per promoted name
for v, abs_names in system._var_allprocs_prom2abs_list['output'].items():
self._prom2abs['output'][v] = abs_names
# absolute pathname to metadata mappings for continuous & discrete variables
# discrete mapping is sub-keyed on 'output' & 'input'
real_meta_in = system._var_allprocs_abs2meta['input']
real_meta_out = system._var_allprocs_abs2meta['output']
disc_meta_in = system._var_allprocs_discrete['input']
disc_meta_out = system._var_allprocs_discrete['output']
all_var_info = [(outputs, 'output'),
(desvars, 'desvar'), (responses, 'response'),
(objectives, 'objective'), (constraints, 'constraint')]
for varinfo, var_type in all_var_info:
if var_type != 'output':
varinfo = varinfo.items()
for data in varinfo:
# Design variables, constraints and objectives can be requested by input name.
if var_type != 'output':
name, vmeta = data
srcname = vmeta['source']
else:
srcname = name = data
if srcname not in self._abs2meta:
if srcname in real_meta_out:
self._abs2meta[srcname] = real_meta_out[srcname].copy()
elif srcname in disc_meta_out:
self._abs2meta[srcname] = disc_meta_out[srcname].copy()
elif name in system._responses:
for io in self._prom2abs:
if srcname in self._prom2abs[io]:
abs_in = self._prom2abs[io][srcname][0]
self._abs2meta[srcname] = real_meta_in[abs_in].copy()
break
self._abs2meta[srcname]['type'] = []
self._abs2meta[srcname]['explicit'] = srcname not in states
if var_type not in self._abs2meta[srcname]['type']:
self._abs2meta[srcname]['type'].append(var_type)
for name in inputs:
try:
self._abs2meta[name] = real_meta_in[name].copy()
except KeyError:
self._abs2meta[name] = disc_meta_in[name].copy()
self._abs2meta[name]['type'] = ['input']
self._abs2meta[name]['explicit'] = True
# merge current abs2meta with this system's version
for name, meta in self._abs2meta.items():
for io in ('input', 'output'):
if name in system._var_allprocs_abs2meta[io]:
meta.update(system._var_allprocs_abs2meta[io][name])
break
self._cleanup_abs2meta()
# store the updated abs2prom and prom2abs
abs2prom = zlib.compress(json.dumps(self._abs2prom).encode('ascii'))
prom2abs = zlib.compress(json.dumps(self._prom2abs).encode('ascii'))
abs2meta = zlib.compress(json.dumps(self._abs2meta).encode('ascii'))
conns = zlib.compress(json.dumps(
system._problem_meta['model_ref']()._conn_global_abs_in2out).encode('ascii'))
# TODO: seems like we could clobber the var_settings for a desvar in cases where a
# desvar is also a constraint... Make a test case and fix if needed.
var_settings = {}
var_settings.update(desvars)
var_settings.update(objectives)
var_settings.update(constraints)
var_settings = self._make_var_setting_serializable(var_settings)
var_settings['execution_order'] = var_order
var_settings_json = zlib.compress(
json.dumps(var_settings, default=default_noraise).encode('ascii'))
if self._record_metadata:
with self.metadata_connection as m:
m.execute("UPDATE metadata SET " + # nosec: trusted input
"abs2prom=?, prom2abs=?, abs2meta=?, var_settings=?, conns=?",
(abs2prom, prom2abs, abs2meta, var_settings_json, conns))
self._started.add(recording_requester)
[docs] def record_iteration_driver(self, driver, data, metadata):
"""
Record data and metadata from a Driver.
Parameters
----------
driver : Driver
Driver in need of recording.
data : dict
Dictionary containing desvars, objectives, constraints, responses, and System vars.
metadata : dict
Dictionary containing execution metadata.
"""
if not self._database_initialized:
raise RuntimeError(f"{driver.msginfo} attempted to record iteration to "
f"'{self._filepath}', but database is not initialized;"
" `run_model()`, `run_driver()`, or `final_setup()` "
"must be called after adding a recorder.")
if self.connection:
outputs = data['output']
inputs = data['input']
residuals = data['residual']
# convert to list so this can be dumped as JSON
for in_out_resid in (inputs, outputs, residuals):
if in_out_resid is None:
continue
for var in in_out_resid:
in_out_resid[var] = make_serializable(in_out_resid[var])
outputs_text = json.dumps(outputs)
inputs_text = json.dumps(inputs)
residuals_text = json.dumps(residuals)
with self.connection as c:
c = c.cursor() # need a real cursor for lastrowid
c.execute("INSERT INTO driver_iterations(counter, iteration_coordinate, "
"timestamp, success, msg, inputs, outputs, residuals) "
"VALUES(?,?,?,?,?,?,?,?)",
(self._counter, self._iteration_coordinate,
metadata['timestamp'], metadata['success'], metadata['msg'],
inputs_text, outputs_text, residuals_text))
c.execute("INSERT INTO global_iterations(record_type, rowid, source) VALUES(?,?,?)",
('driver', c.lastrowid, driver._get_name()))
[docs] def record_iteration_problem(self, problem, data, metadata):
"""
Record data and metadata from a Problem.
Parameters
----------
problem : Problem
Problem in need of recording.
data : dict
Dictionary containing desvars, objectives, and constraints.
metadata : dict
Dictionary containing execution metadata.
"""
if not self._database_initialized:
raise RuntimeError(f"{problem.msginfo} attempted to record iteration to "
f"'{self._filepath}', but database is not initialized;"
" `run_model()`, `run_driver()`, or `final_setup()` "
"must be called after adding a recorder.")
if self.connection:
outputs = data['output']
inputs = data['input']
residuals = data['residual']
driver = problem.driver
if problem.recording_options['record_derivatives'] and \
driver._designvars and driver._responses:
totals = data['totals']
else:
totals = {}
totals_array = dict_to_structured_array(totals)
totals_blob = array_to_blob(totals_array)
# convert to list so this can be dumped as JSON
for in_out_resid in (inputs, outputs, residuals):
if in_out_resid is None:
continue
for var in in_out_resid:
in_out_resid[var] = make_serializable(in_out_resid[var])
outputs_text = json.dumps(outputs)
inputs_text = json.dumps(inputs)
residuals_text = json.dumps(residuals)
abs_err = data['abs'] if 'abs' in data else None
rel_err = data['rel'] if 'rel' in data else None
with self.connection as c:
c = c.cursor() # need a real cursor for lastrowid
c.execute("INSERT INTO problem_cases(counter, case_name, "
"timestamp, success, msg, inputs, outputs, residuals, jacobian, "
"abs_err, rel_err ) "
"VALUES(?,?,?,?,?,?,?,?,?,?,?)",
(self._counter, metadata['name'],
metadata['timestamp'], metadata['success'], metadata['msg'],
inputs_text, outputs_text, residuals_text, totals_blob,
abs_err, rel_err))
c.execute("INSERT INTO global_iterations(record_type, rowid, source) VALUES(?,?,?)",
('problem', c.lastrowid, metadata['name']))
[docs] def record_iteration_system(self, system, data, metadata):
"""
Record data and metadata from a System.
Parameters
----------
system : System
System in need of recording.
data : dict
Dictionary containing inputs, outputs, and residuals.
metadata : dict
Dictionary containing execution metadata.
"""
if not self._database_initialized:
raise RuntimeError(f"{system.msginfo} attempted to record iteration to "
f"'{self._filepath}', but database is not initialized;"
" `run_model()`, `run_driver()`, or `final_setup()` "
"must be called after adding a recorder.")
if self.connection:
inputs = data['input']
outputs = data['output']
residuals = data['residual']
# convert to list so this can be dumped as JSON
for i_o_r in (inputs, outputs, residuals):
for var, dat in i_o_r.items():
i_o_r[var] = make_serializable(dat)
outputs_text = json.dumps(outputs)
inputs_text = json.dumps(inputs)
residuals_text = json.dumps(residuals)
with self.connection as c:
c = c.cursor() # need a real cursor for lastrowid
c.execute("INSERT INTO system_iterations(counter, iteration_coordinate, "
"timestamp, success, msg, inputs , outputs , residuals ) "
"VALUES(?,?,?,?,?,?,?,?)",
(self._counter, self._iteration_coordinate,
metadata['timestamp'], metadata['success'], metadata['msg'],
inputs_text, outputs_text, residuals_text))
# get the pathname of the source system
source_system = system.pathname
if source_system == '':
source_system = 'root'
c.execute("INSERT INTO global_iterations(record_type, rowid, source) VALUES(?,?,?)",
('system', c.lastrowid, source_system))
[docs] def record_iteration_solver(self, solver, data, metadata):
"""
Record data and metadata from a Solver.
Parameters
----------
solver : Solver
Solver in need of recording.
data : dict
Dictionary containing outputs, residuals, and errors.
metadata : dict
Dictionary containing execution metadata.
"""
if not self._database_initialized:
raise RuntimeError(f"{solver.msginfo} attempted to record iteration to "
f"'{self._filepath}', but database is not initialized;"
" `run_model()`, `run_driver()`, or `final_setup()` "
"must be called after adding a recorder.")
if self.connection:
abs = data['abs']
rel = data['rel']
inputs = data['input']
outputs = data['output']
residuals = data['residual']
# convert to list so this can be dumped as JSON
for i_o_r in (inputs, outputs, residuals):
if i_o_r is None:
continue
for var in i_o_r:
i_o_r[var] = make_serializable(i_o_r[var])
outputs_text = json.dumps(outputs)
inputs_text = json.dumps(inputs)
residuals_text = json.dumps(residuals)
with self.connection as c:
c = c.cursor() # need a real cursor for lastrowid
c.execute("INSERT INTO solver_iterations(counter, iteration_coordinate, "
"timestamp, success, msg, abs_err, rel_err, "
"solver_inputs, solver_output, solver_residuals) "
"VALUES(?,?,?,?,?,?,?,?,?,?)",
(self._counter, self._iteration_coordinate,
metadata['timestamp'], metadata['success'], metadata['msg'],
abs, rel, inputs_text, outputs_text, residuals_text))
# get the pathname of the source system
source_system = solver._system().pathname
if source_system == '':
source_system = 'root'
# get solver type from SOLVER class attribute to determine the solver pathname
solver_type = solver.SOLVER[0:2]
if solver_type == 'NL':
source_solver = source_system + '.nonlinear_solver'
elif solver_type == 'LS':
source_solver = source_system + '.nonlinear_solver.linesearch'
else:
raise RuntimeError("Solver type '%s' not recognized during recording. "
"Expecting NL or LS" % solver.SOLVER)
c.execute("INSERT INTO global_iterations(record_type, rowid, source) VALUES(?,?,?)",
('solver', c.lastrowid, source_solver))
[docs] def record_viewer_data(self, model_viewer_data, key='Driver'):
"""
Record model viewer data.
Parameters
----------
model_viewer_data : dict
Data required to visualize the model.
key : str, optional
The unique ID to use for this data in the table.
"""
if self._record_metadata and self.metadata_connection:
json_data = json.dumps(model_viewer_data, default=default_noraise)
# Note: recorded to 'driver_metadata' table for legacy/compatibility reasons.
try:
with self.metadata_connection as m:
m.execute("INSERT INTO driver_metadata(id, model_viewer_data) VALUES(?,?)",
(key, json_data))
except sqlite3.IntegrityError:
print("Model viewer data has already been recorded for %s." % key)
[docs] def record_derivatives_driver(self, recording_requester, data, metadata):
"""
Record derivatives data from a Driver.
Parameters
----------
recording_requester : object
Driver in need of recording.
data : dict
Dictionary containing derivatives keyed by 'of,wrt' to be recorded.
metadata : dict
Dictionary containing execution metadata.
"""
if self.connection:
data_array = dict_to_structured_array(data)
data_blob = array_to_blob(data_array)
with self.connection as c:
c = c.cursor() # need a real cursor for lastrowid
c.execute("INSERT INTO driver_derivatives(counter, iteration_coordinate, "
"timestamp, success, msg, derivatives) VALUES(?,?,?,?,?,?)",
(self._counter, self._iteration_coordinate,
metadata['timestamp'], metadata['success'], metadata['msg'],
data_blob))
[docs] def shutdown(self):
"""
Shut down the recorder.
"""
# close database connection
if self._record_metadata and self.metadata_connection and \
self.metadata_connection != self.connection:
self.metadata_connection.close()
if self.connection:
self.connection.close()
# sqlite close() does not always write until garbage collection occurs.
# If collection is not forced like this and a reader is immediately opened on
# the same file, it may find a 0-length or malformed db.
# See https://www.sqlite.org/c3ref/close.html for more info
gc.collect()
[docs] def delete_recordings(self):
"""
Delete all the recordings.
"""
if self.connection:
self.connection.execute("DELETE FROM global_iterations")
self.connection.execute("DELETE FROM driver_iterations")
self.connection.execute("DELETE FROM driver_derivatives")
self.connection.execute("DELETE FROM problem_cases")
self.connection.execute("DELETE FROM system_iterations")
self.connection.execute("DELETE FROM solver_iterations")
self.connection.execute("DELETE FROM driver_metadata")
self.connection.execute("DELETE FROM system_metadata")
self.connection.execute("DELETE FROM solver_metadata")
