def standalone_parameters_dataset(dataset): n_params = 3 n_rows = 10**3 params_indep = [ ParamSpecBase(f'param_{i}', 'numeric', label=f'param_{i}', unit='V') for i in range(n_params) ] param_dep = ParamSpecBase(f'param_{n_params}', 'numeric', label=f'param_{n_params}', unit='Ohm') params_all = params_indep + [param_dep] idps = InterDependencies_( dependencies={param_dep: tuple(params_indep[0:1])}, standalones=tuple(params_indep[1:])) dataset.set_interdependencies(idps) dataset.mark_started() dataset.add_results([{ p.name: np.int(n_rows * 10 * pn + i) for pn, p in enumerate(params_all) } for i in range(n_rows)]) dataset.mark_completed() yield dataset
def test_get_data_by_id_order(dataset): """ Test that the added values of setpoints end up associated with the correct setpoint parameter, irrespective of the ordering of those setpoint parameters """ indepA = ParamSpecBase('indep1', "numeric") indepB = ParamSpecBase('indep2', "numeric") depAB = ParamSpecBase('depAB', "numeric") depBA = ParamSpecBase('depBA', "numeric") idps = InterDependencies_(dependencies={ depAB: (indepA, indepB), depBA: (indepB, indepA) }) dataset.set_interdependencies(idps) dataset.mark_started() dataset.add_results([{'depAB': 12, 'indep2': 2, 'indep1': 1}]) dataset.add_results([{'depBA': 21, 'indep2': 2, 'indep1': 1}]) dataset.mark_completed() data = get_data_by_id(dataset.run_id) data_dict = {el['name']: el['data'] for el in data[0]} assert data_dict['indep1'] == 1 assert data_dict['indep2'] == 2 data_dict = {el['name']: el['data'] for el in data[1]} assert data_dict['indep1'] == 1 assert data_dict['indep2'] == 2
def test_numpy_nan(dataset): parameter_m = ParamSpec("m", "numeric") dataset.add_parameters([parameter_m]) data_dict = [{"m": value} for value in [0.0, np.nan, 1.0]] dataset.add_results(data_dict) retrieved = dataset.get_data("m") assert np.isnan(retrieved[1])
def test_numpy_nan(dataset): parameter_m = ParamSpecBase("m", "numeric") idps = InterDependencies_(standalones=(parameter_m, )) dataset.set_interdependencies(idps) dataset.mark_started() data_dict = [{"m": value} for value in [0.0, np.nan, 1.0]] dataset.add_results(data_dict) retrieved = dataset.get_data("m") assert np.isnan(retrieved[1])
def ds_with_vals(self, dataset): """ This fixture creates a DataSet with values that is to be used by all the tests in this class """ idps = InterDependencies_(standalones=(self.x, )) dataset.set_interdependencies(idps) dataset.mark_started() for xv in self.xvals: dataset.add_results([{self.x.name: xv}]) return dataset
def test_numpy_floats(dataset): """ Test that we can insert numpy floats in the data set """ float_param = ParamSpec('y', 'numeric') dataset.add_parameters([float_param]) numpy_floats = [np.float, np.float16, np.float32, np.float64] results = [{"y": tp(1.2)} for tp in numpy_floats] dataset.add_results(results) expected_result = [[tp(1.2)] for tp in numpy_floats] assert np.allclose(dataset.get_data("y"), expected_result, atol=1E-8)
def test_numpy_inf(dataset): """ Test that we can insert and retrieve numpy inf in the data set """ parameter_m = ParamSpec("m", "numeric") dataset.add_parameter(parameter_m) dataset.mark_started() data_dict = [{"m": value} for value in [-np.inf, np.inf]] dataset.add_results(data_dict) retrieved = dataset.get_data("m") assert np.isinf(retrieved).all()
def test_numpy_inf(dataset): """ Test that we can insert and retrieve numpy inf in the data set """ parameter_m = ParamSpecBase("m", "numeric") idps = InterDependencies_(standalones=(parameter_m, )) dataset.set_interdependencies(idps) dataset.mark_started() data_dict = [{"m": value} for value in [-np.inf, np.inf]] dataset.add_results(data_dict) retrieved = dataset.get_data("m") assert np.isinf(retrieved).all()
def test_numpy_floats(dataset): """ Test that we can insert numpy floats in the data set """ float_param = ParamSpecBase('y', 'numeric') idps = InterDependencies_(standalones=(float_param, )) dataset.set_interdependencies(idps) dataset.mark_started() numpy_floats = [np.float, np.float16, np.float32, np.float64] results = [{"y": tp(1.2)} for tp in numpy_floats] dataset.add_results(results) expected_result = [[tp(1.2)] for tp in numpy_floats] assert np.allclose(dataset.get_data("y"), expected_result, atol=1E-8)
def test_numpy_ints(dataset): """ Test that we can insert numpy integers in the data set """ xparam = ParamSpec('x', 'numeric') dataset.add_parameters([xparam]) numpy_ints = [ np.int, np.int8, np.int16, np.int32, np.int64, np.uint, np.uint8, np.uint16, np.uint32, np.uint64 ] results = [{"x": tp(1)} for tp in numpy_ints] dataset.add_results(results) expected_result = len(numpy_ints) * [[1]] assert dataset.get_data("x") == expected_result
def test_write_data_to_text_file_save(tmp_path_factory): dataset = new_data_set("dataset") xparam = ParamSpecBase("x", 'numeric') yparam = ParamSpecBase("y", 'numeric') idps = InterDependencies_(dependencies={yparam: (xparam, )}) dataset.set_interdependencies(idps) dataset.mark_started() results = [{'x': 0, 'y': 1}] dataset.add_results(results) dataset.mark_completed() path = str(tmp_path_factory.mktemp("write_data_to_text_file_save")) dataset.write_data_to_text_file(path=path) assert os.listdir(path) == ['y.dat'] with open(os.path.join(path, "y.dat")) as f: assert f.readlines() == ['0\t1\n']
def test_write_data_to_text_file_save(): dataset = new_data_set("dataset") xparam = ParamSpecBase("x", 'numeric') yparam = ParamSpecBase("y", 'numeric') idps = InterDependencies_(dependencies={yparam: (xparam, )}) dataset.set_interdependencies(idps) dataset.mark_started() results = [{'x': 0, 'y': 1}] dataset.add_results(results) dataset.mark_completed() with tempfile.TemporaryDirectory() as temp_dir: dataset.write_data_to_text_file(path=temp_dir) assert os.listdir(temp_dir) == ['y.dat'] with open(temp_dir + "//y.dat") as f: assert f.readlines() == ['0\t1\n']
def test_missing_keys(dataset): """ Test that we can now have partial results with keys missing. This is for example handy when having an interleaved 1D and 2D sweep. """ x = ParamSpec("x", paramtype='numeric') y = ParamSpec("y", paramtype='numeric') a = ParamSpec("a", paramtype='numeric', depends_on=[x]) b = ParamSpec("b", paramtype='numeric', depends_on=[x, y]) dataset.add_parameter(x) dataset.add_parameter(y) dataset.add_parameter(a) dataset.add_parameter(b) dataset.mark_started() def fa(xv): return xv + 1 def fb(xv, yv): return xv + 2 - yv * 3 results = [] xvals = [1, 2, 3] yvals = [2, 3, 4] for xv in xvals: results.append({"x": xv, "a": fa(xv)}) for yv in yvals: results.append({"x": xv, "y": yv, "b": fb(xv, yv)}) dataset.add_results(results) assert dataset.get_values("x") == [[r["x"]] for r in results] assert dataset.get_values("y") == [[r["y"]] for r in results if "y" in r] assert dataset.get_values("a") == [[r["a"]] for r in results if "a" in r] assert dataset.get_values("b") == [[r["b"]] for r in results if "b" in r] assert dataset.get_setpoints("a")['x'] == [[xv] for xv in xvals] tmp = [list(t) for t in zip(*(itertools.product(xvals, yvals)))] expected_setpoints = [[[v] for v in vals] for vals in tmp] assert dataset.get_setpoints("b")['x'] == expected_setpoints[0] assert dataset.get_setpoints("b")['y'] == expected_setpoints[1]
def test_basic_subscription(dataset, basic_subscriber): xparam = ParamSpecBase(name='x', paramtype='numeric', label='x parameter', unit='V') yparam = ParamSpecBase(name='y', paramtype='numeric', label='y parameter', unit='Hz') idps = InterDependencies_(dependencies={yparam: (xparam, )}) dataset.set_interdependencies(idps) dataset.mark_started() sub_id = dataset.subscribe(basic_subscriber, min_wait=0, min_count=1, state={}) assert len(dataset.subscribers) == 1 assert list(dataset.subscribers.keys()) == [sub_id] expected_state = {} for x in range(10): y = -x**2 dataset.add_results([{'x': x, 'y': y}]) expected_state[x + 1] = [(x, y)] @retry_until_does_not_throw(exception_class_to_expect=AssertionError, delay=0, tries=10) def assert_expected_state(): assert dataset.subscribers[sub_id].state == expected_state assert_expected_state() dataset.unsubscribe(sub_id) assert len(dataset.subscribers) == 0 assert list(dataset.subscribers.keys()) == [] # Ensure the trigger for the subscriber has been removed from the database get_triggers_sql = "SELECT * FROM sqlite_master WHERE TYPE = 'trigger';" triggers = atomic_transaction(dataset.conn, get_triggers_sql).fetchall() assert len(triggers) == 0
def test_numpy_ints(dataset): """ Test that we can insert numpy integers in the data set """ xparam = ParamSpecBase('x', 'numeric') idps = InterDependencies_(standalones=(xparam, )) dataset.set_interdependencies(idps) dataset.mark_started() numpy_ints = [ np.int, np.int8, np.int16, np.int32, np.int64, np.uint, np.uint8, np.uint16, np.uint32, np.uint64 ] results = [{"x": tp(1)} for tp in numpy_ints] dataset.add_results(results) expected_result = len(numpy_ints) * [[1]] assert dataset.get_data("x") == expected_result
def scalar_dataset_with_nulls(dataset): """ A very simple dataset. A scalar is varied, and two parameters are measured one by one """ sp = ParamSpec('setpoint', 'numeric') val1 = ParamSpec('first_value', 'numeric', depends_on=(sp,)) val2 = ParamSpec('second_value', 'numeric', depends_on=(sp,)) for p in [sp, val1, val2]: dataset.add_parameter(p) dataset.mark_started() dataset.add_results([{sp.name: 0, val1.name: 1}, {sp.name: 0, val2.name: 2}]) dataset.mark_completed() yield dataset
def scalar_dataset(dataset): n_params = 3 n_rows = 10**3 params_indep = [ParamSpec(f'param_{i}', 'numeric', label=f'param_{i}', unit='V') for i in range(n_params)] params = params_indep + [ParamSpec(f'param_{n_params}', 'numeric', label=f'param_{n_params}', unit='Ohm', depends_on=params_indep)] for p in params: dataset.add_parameter(p) dataset.mark_started() dataset.add_results([{p.name: np.int(n_rows*10*pn+i) for pn, p in enumerate(params)} for i in range(n_rows)]) dataset.mark_completed() yield dataset
def test_write_data_to_text_file_name_exception(): dataset = new_data_set("dataset") xparam = ParamSpecBase("x", 'numeric') yparam = ParamSpecBase("y", 'numeric') zparam = ParamSpecBase("z", 'numeric') idps = InterDependencies_(dependencies={ yparam: (xparam, ), zparam: (xparam, ) }) dataset.set_interdependencies(idps) dataset.mark_started() results = [{'x': 0, 'y': 1, 'z': 2}] dataset.add_results(results) dataset.mark_completed() with tempfile.TemporaryDirectory() as temp_dir, pytest.raises( Exception, match='desired file name'): dataset.write_data_to_text_file(path=temp_dir, single_file=True, single_file_name=None)
def test_adding_too_many_results(): """ This test really tests the "chunking" functionality of the insert_many_values function of the sqlite.query_helpers module """ dataset = new_data_set("test_adding_too_many_results") xparam = ParamSpecBase("x", "numeric", label="x parameter", unit='V') yparam = ParamSpecBase("y", 'numeric', label='y parameter', unit='Hz') idps = InterDependencies_(dependencies={yparam: (xparam,)}) dataset.set_interdependencies(idps) dataset.mark_started() n_max = qc.SQLiteSettings.limits['MAX_VARIABLE_NUMBER'] vals = np.linspace(0, 1, int(n_max/2)+2) results = [{'x': val} for val in vals] dataset.add_results(results) vals = np.linspace(0, 1, int(n_max/2)+1) results = [{'x': val, 'y': val} for val in vals] dataset.add_results(results) vals = np.linspace(0, 1, n_max*3) results = [{'x': val} for val in vals] dataset.add_results(results)
def test_write_data_to_text_file_length_exception(tmp_path): dataset = new_data_set("dataset") xparam = ParamSpecBase("x", 'numeric') yparam = ParamSpecBase("y", 'numeric') zparam = ParamSpecBase("z", 'numeric') idps = InterDependencies_(dependencies={ yparam: (xparam, ), zparam: (xparam, ) }) dataset.set_interdependencies(idps) dataset.mark_started() results1 = [{'x': 0, 'y': 1}] results2 = [{'x': 0, 'z': 2}] results3 = [{'x': 1, 'z': 3}] dataset.add_results(results1) dataset.add_results(results2) dataset.add_results(results3) dataset.mark_completed() temp_dir = str(tmp_path) with pytest.raises(Exception, match='different length'): dataset.write_data_to_text_file(path=temp_dir, single_file=True, single_file_name='yz')
def test_adding_too_many_results(): """ This test really tests the "chunking" functionality of the insert_many_values function of the sqlite_base module """ dataset = new_data_set("test_adding_too_many_results") xparam = ParamSpec("x", "numeric", label="x parameter", unit='V') yparam = ParamSpec("y", 'numeric', label='y parameter', unit='Hz', depends_on=[xparam]) dataset.add_parameter(xparam) dataset.add_parameter(yparam) dataset.mark_started() n_max = qc.SQLiteSettings.limits['MAX_VARIABLE_NUMBER'] vals = np.linspace(0, 1, int(n_max/2)+2) results = [{'x': val} for val in vals] dataset.add_results(results) vals = np.linspace(0, 1, int(n_max/2)+1) results = [{'x': val, 'y': val} for val in vals] dataset.add_results(results) vals = np.linspace(0, 1, n_max*3) results = [{'x': val} for val in vals] dataset.add_results(results)
def scalar_dataset_with_nulls(dataset): """ A very simple dataset. A scalar is varied, and two parameters are measured one by one """ sp = ParamSpecBase('setpoint', 'numeric') val1 = ParamSpecBase('first_value', 'numeric') val2 = ParamSpecBase('second_value', 'numeric') idps = InterDependencies_(dependencies={val1: (sp, ), val2: (sp, )}) dataset.set_interdependencies(idps) dataset.mark_started() dataset.add_results([{ sp.name: 0, val1.name: 1 }, { sp.name: 0, val2.name: 2 }]) dataset.mark_completed() yield dataset
def test_add_parameter_values(dataset): n = 2 m = n + 1 xparam = ParamSpec('x', 'numeric') dataset.add_parameter(xparam) x_results = [{'x': x} for x in range(n)] dataset.add_results(x_results) yparam = ParamSpec("y", "numeric") match_str = f'Need to have {n} values but got {m}.' match_str = re.escape(match_str) with pytest.raises(ValueError, match=match_str): pytest.deprecated_call(dataset.add_parameter_values, yparam, [y for y in range(m)]) yvals = [y for y in range(n)] # Unlike what the docstring of the method suggests, # `add_parameter_values` does NOT add a new parameter and values for it # "NEXT TO the columns of values of existing parameters". # # In other words, if the initial state of the table is: # # | x | # -------- # | 1 | # | 2 | # # then the state of the table after calling `add_parameter_values` is # going to be: # # | x | y | # --------------- # | 1 | NULL | # | 2 | NULL | # | NULL | 25 | # | NULL | 42 | # # while the docstring suggests the following state: # # | x | y | # --------------- # | 1 | 25 | # | 2 | 42 | # y_expected = [[None]] * n + [[y] for y in yvals] pytest.deprecated_call(dataset.add_parameter_values, yparam, yvals) shadow_ds = make_shadow_dataset(dataset) try: assert y_expected == dataset.get_data(yparam) assert y_expected == shadow_ds.get_data(yparam) dataset.mark_complete() # and now let's test that dataset's connection does not commit anymore # when `atomic` is used dataset.add_results([{yparam.name: -2}]) y_expected_2 = y_expected + [[-2]] assert y_expected_2 == dataset.get_data(yparam) assert y_expected_2 == shadow_ds.get_data(yparam) finally: shadow_ds.conn.close()
def test_subscription_from_config(dataset, basic_subscriber): """ This test is similar to `test_basic_subscription`, with the only difference that another subscriber from a config file is added. """ # This string represents the config file in the home directory: config = """ { "subscription":{ "subscribers":{ "test_subscriber":{ "factory": "qcodes.tests.dataset.test_subscribing.MockSubscriber", "factory_kwargs":{ "lg": false }, "subscription_kwargs":{ "min_wait": 0, "min_count": 1, "callback_kwargs": {} } } } } } """ # This little dance around the db_location is due to the fact that the # dataset fixture creates a dataset in a db in a temporary directory. # Therefore we need to 'backup' the path to the db when using the # default configuration. db_location = qcodes.config.core.db_location with default_config(user_config=config): qcodes.config.core.db_location = db_location assert 'test_subscriber' in qcodes.config.subscription.subscribers xparam = ParamSpecBase(name='x', paramtype='numeric', label='x parameter', unit='V') yparam = ParamSpecBase(name='y', paramtype='numeric', label='y parameter', unit='Hz') idps = InterDependencies_(dependencies={yparam: (xparam, )}) dataset.set_interdependencies(idps) dataset.mark_started() sub_id = dataset.subscribe(basic_subscriber, min_wait=0, min_count=1, state={}) sub_id_c = dataset.subscribe_from_config('test_subscriber') assert len(dataset.subscribers) == 2 assert list(dataset.subscribers.keys()) == [sub_id, sub_id_c] expected_state = {} # Here we are only testing 2 to reduce the CI time for x in range(2): y = -x**2 dataset.add_results([{'x': x, 'y': y}]) expected_state[x + 1] = [(x, y)] @retry_until_does_not_throw( exception_class_to_expect=AssertionError, tries=10) def assert_expected_state(): assert dataset.subscribers[sub_id].state == expected_state assert dataset.subscribers[sub_id_c].state == expected_state assert_expected_state()