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()