def test_validation_one_sp_dim_missing():
"""
If one or more setpoint validators has no shape the validation will fail.
"""
n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
n_points_1.set(10)
n_points_2.set(20)
setpoints_1 = Parameter(
"setpoints_1",
get_cmd=lambda: rand(n_points_1()),
vals=vals.Arrays(shape=(n_points_1, None)),
)
param_sp_without_shape = ParameterWithSetpoints(
"param_6",
get_cmd=lambda: rand(n_points_1()),
setpoints=(setpoints_1, ),
vals=vals.Arrays(shape=(n_points_1, n_points_2)),
)
expected_err_msg = (
r"One or more dimensions have unknown shape "
r"when comparing output: \(<qcodes.parameters.parameter.Parameter: n_points_1 at [0-9]+>, <qcodes.parameters.parameter.Parameter: n_points_2 at [0-9]+>\) to setpoints: "
r"\(<qcodes.parameters.parameter.Parameter: n_points_1 at [0-9]+>, None\)"
)
with pytest.raises(ValueError, match=expected_err_msg):
param_sp_without_shape.validate_consistent_shape()
with pytest.raises(ValueError, match=expected_err_msg):
param_sp_without_shape.validate(param_sp_without_shape.get())
def test_stepping_from_invalid_starting_point():
the_value = -10
def set_function(value):
nonlocal the_value
the_value = value
def get_function():
return the_value
a = Parameter('test',
set_cmd=set_function,
get_cmd=get_function,
vals=Numbers(0, 100),
step=5)
# We start out by setting the parameter to an
# invalid value. This is not possible using initial_value
# as the validator will catch that but perhaps this may happen
# if the instrument can return out of range values.
assert a.get() == -10
with pytest.raises(ValueError):
# trying to set to 10 should raise even with 10 valid
# as the steps demand that we first step to -5 which is not
a.set(10)
# afterwards the value should still be the same
assert a.get() == -10
def test_setpoints_non_parameter_raises():
"""
Test that putting some random function as a setpoint parameter will
raise as expected.
"""
n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
n_points_1.set(10)
n_points_2.set(20)
err_msg = (r"Setpoints is of type <class 'function'> "
r"expcected a QCoDeS parameter")
with pytest.raises(TypeError, match=err_msg):
param_with_setpoints_1 = ParameterWithSetpoints(
'param_1',
get_cmd=lambda: rand(n_points_1()),
setpoints=(lambda x: x, ),
vals=vals.Arrays(shape=(n_points_1, )))
param_with_setpoints_1 = ParameterWithSetpoints(
'param_1',
get_cmd=lambda: rand(n_points_1()),
vals=vals.Arrays(shape=(n_points_1, )))
with pytest.raises(TypeError, match=err_msg):
param_with_setpoints_1.setpoints = (lambda x: x, )
def test_setting_non_gettable_parameter_with_finite_step(caplog):
initial_value = 0
step_size = 0.1
set_value = 1.0
# when the parameter is initially set from
# the initial_value the starting point is unknown
# so this should cause a warning but the parameter should still be set
with caplog.at_level(logging.WARNING):
x = Parameter('x',
initial_value=initial_value,
step=step_size,
set_cmd=None)
assert len(caplog.records) == 1
assert f"cannot sweep x from None to {initial_value}" in str(
caplog.records[0])
assert x.cache.get() == 0
# afterwards the stepping should work as expected.
with caplog.at_level(logging.WARNING):
caplog.clear()
assert_array_almost_equal(
np.array(x.get_ramp_values(set_value, step_size)), (np.arange(
initial_value + step_size, set_value + step_size, step_size)))
x.set(set_value)
assert x.cache.get() == set_value
assert len(caplog.records) == 0
def test_validation_wrong_validator():
"""
If the validator does not match the actual content the validation should
fail
"""
n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
n_points_1.set(10)
n_points_2.set(20)
setpoints_1 = Parameter('setpoints_1',
get_cmd=lambda: rand(n_points_1()),
vals=vals.Arrays(shape=(n_points_1, )))
# output is not consistent with validator
param_with_wrong_validator = ParameterWithSetpoints(
'param_2',
get_cmd=lambda: rand(n_points_2()),
setpoints=(setpoints_1, ),
vals=vals.Arrays(shape=(n_points_1, )))
# this does not raise because the validator shapes are consistent
param_with_wrong_validator.validate_consistent_shape()
# but the output is not consistent with the validator
with pytest.raises(ValueError,
match=r'does not have expected shape'
r' \(10,\), '
r'it has shape \(20,\); '
r'Parameter: param_2'):
param_with_wrong_validator.validate(param_with_wrong_validator())
def test_validation_inconsistent_shape():
"""
Parameters with shapes inconsistent with their setpoints should not
validate
"""
n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
n_points_1.set(10)
n_points_2.set(20)
setpoints_1 = Parameter('setpoints_1',
get_cmd=lambda: rand(n_points_1()),
vals=vals.Arrays(shape=(n_points_1, )))
param_with_diff_length = ParameterWithSetpoints(
'param_1',
get_cmd=lambda: rand(n_points_2()),
setpoints=(setpoints_1, ),
vals=vals.Arrays(shape=(n_points_2, )))
# inconsistent shapes
expected_err_msg = (
r"Shape of output is not consistent "
r"with setpoints. Output is shape "
r"\(<qcodes.parameters.parameter.Parameter: n_points_2 at [0-9]+>,\) "
r"and setpoints are shape "
r"\(<qcodes.parameters.parameter.Parameter: n_points_1 at [0-9]+>,\)")
with pytest.raises(ValueError, match=expected_err_msg):
param_with_diff_length.validate_consistent_shape()
with pytest.raises(ValueError, match=expected_err_msg):
param_with_diff_length.validate(param_with_diff_length.get())
def test_set_on_parameter_marked_as_non_settable_raises():
a = Parameter("param", set_cmd=None)
a.set(2)
assert a.get() == 2
a._settable = False
with pytest.raises(
TypeError,
match="Trying to set a parameter that is not settable."):
a.set(1)
assert a.get() == 2
def test_number_of_validations():
p = Parameter('p',
set_cmd=None,
initial_value=0,
vals=BookkeepingValidator())
# in the set wrapper the final value is validated
# and then subsequently each step is validated.
# in this case there is one step so the final value
# is validated twice.
assert p.vals.values_validated == [0, 0]
p.step = 1
p.set(10)
assert p.vals.values_validated == [0, 0, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
def array_in_str_dataset(experiment, request):
meas = Measurement()
scalar_param = Parameter('textparam', set_cmd=None)
param = ArraySetPointParam()
meas.register_parameter(scalar_param, paramtype='text')
meas.register_parameter(param,
setpoints=(scalar_param, ),
paramtype=request.param)
with meas.run() as datasaver:
for i in ['A', 'B', 'C']:
scalar_param.set(i)
datasaver.add_result((scalar_param, scalar_param.get()),
(param, param.get()))
try:
yield datasaver.dataset
finally:
datasaver.dataset.conn.close()
def array_in_scalar_dataset(experiment):
meas = Measurement()
scalar_param = Parameter('scalarparam', set_cmd=None)
param = ArraySetPointParam()
meas.register_parameter(scalar_param)
meas.register_parameter(param,
setpoints=(scalar_param, ),
paramtype='array')
with meas.run() as datasaver:
for i in range(1, 10):
scalar_param.set(i)
datasaver.add_result((scalar_param, scalar_param.get()),
(param, param.get()))
try:
yield datasaver.dataset
finally:
datasaver.dataset.conn.close()
def test_validation_without_shape():
"""
If the Arrays validator does not have a shape the validation will fail
"""
n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
n_points_1.set(10)
setpoints_1 = Parameter('setpoints_1',
get_cmd=lambda: rand(n_points_1()),
vals=vals.Arrays(shape=(n_points_1, )))
with pytest.raises(RuntimeError,
match=r"A ParameterWithSetpoints must "
r"have a shape defined "
r"for its validator."):
param_without_shape = ParameterWithSetpoints(
'param_5',
get_cmd=lambda: rand(n_points_1()),
setpoints=(setpoints_1, ),
vals=vals.Arrays())
def test_validation_no_validator():
"""
If a parameter does not use array validators it cannot be validated.
"""
n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
n_points_1.set(10)
setpoints_1 = Parameter('setpoints_1',
get_cmd=lambda: rand(n_points_1()),
vals=vals.Arrays(shape=(n_points_1, )))
# output does not have a validator
with pytest.raises(ValueError,
match=r"A ParameterWithSetpoints must have "
r"an Arrays validator got "
r"<class 'NoneType'>"):
param_without_validator = ParameterWithSetpoints(
'param_3',
get_cmd=lambda: rand(n_points_1()),
setpoints=(setpoints_1, ))
def test_validation_shapes():
"""
Test that various parameters with setpoints and shape combinations
validate correctly.
"""
n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
n_points_1.set(10)
n_points_2.set(20)
setpoints_1 = Parameter('setpoints_1',
get_cmd=lambda: rand(n_points_1()),
vals=vals.Arrays(shape=(n_points_1, )))
setpoints_2 = Parameter('setpoints_2',
get_cmd=lambda: rand(n_points_2()),
vals=vals.Arrays(shape=(n_points_2, )))
param_with_setpoints_1 = ParameterWithSetpoints(
"param_1",
get_cmd=lambda: rand(n_points_1()),
setpoints=(setpoints_1, ),
vals=vals.Arrays(shape=(n_points_1, )),
)
assert ("<Arrays, shape: (<qcodes.parameters.parameter."
"Parameter: n_points_1 at" in param_with_setpoints_1.__doc__)
# the two shapes are the same so validation works
param_with_setpoints_1.validate_consistent_shape()
param_with_setpoints_1.validate(param_with_setpoints_1.get())
param_with_setpoints_2 = ParameterWithSetpoints(
'param_2',
get_cmd=lambda: rand(n_points_1(), n_points_2()),
vals=vals.Arrays(shape=(n_points_1, n_points_2)))
param_with_setpoints_2.setpoints = (setpoints_1, setpoints_2)
# 2d
param_with_setpoints_2.validate_consistent_shape()
param_with_setpoints_2.validate(param_with_setpoints_2.get())
def test_get_cache():
time_resolution = time.get_clock_info('time').resolution
sleep_delta = 2 * time_resolution
# Create a gettable parameter
local_parameter = Parameter('test_param', set_cmd=None, get_cmd=None)
before_set = datetime.now()
time.sleep(sleep_delta)
local_parameter.set(1)
time.sleep(sleep_delta)
after_set = datetime.now()
# Check we return last set value, with the correct timestamp
assert local_parameter.cache.get() == 1
assert before_set < local_parameter.cache.timestamp < after_set
# Check that updating the value updates the timestamp
time.sleep(sleep_delta)
local_parameter.set(2)
assert local_parameter.cache.get() == 2
assert local_parameter.cache.timestamp > after_set
class TestMeasure(TestCase):
def setUp(self):
self.p1 = Parameter('P1', initial_value=1, get_cmd=None, set_cmd=None)
def test_simple_scalar(self):
data = Measure(self.p1).run_temp()
self.assertEqual(data.single_set.tolist(), [0])
self.assertEqual(data.P1.tolist(), [1])
self.assertEqual(len(data.arrays), 2, data.arrays)
self.assertNotIn('loop', data.metadata)
meta = data.metadata['measurement']
self.assertEqual(meta['__class__'], 'qcodes.measure.Measure')
self.assertEqual(len(meta['actions']), 1)
self.assertFalse(meta['use_threads'])
ts_start = datetime.strptime(meta['ts_start'], '%Y-%m-%d %H:%M:%S')
ts_end = datetime.strptime(meta['ts_end'], '%Y-%m-%d %H:%M:%S')
self.assertGreaterEqual(ts_end, ts_start)
def test_simple_array(self):
data = Measure(MultiGetter(arr=(1.2, 3.4))).run_temp()
self.assertEqual(data.index0_set.tolist(), [0, 1])
self.assertEqual(data.arr.tolist(), [1.2, 3.4])
self.assertEqual(len(data.arrays), 2, data.arrays)
def test_array_and_scalar(self):
self.p1.set(42)
data = Measure(MultiGetter(arr=(5, 6)), self.p1).run_temp()
self.assertEqual(data.single_set.tolist(), [0])
self.assertEqual(data.P1.tolist(), [42])
self.assertEqual(data.index0_set.tolist(), [0, 1])
self.assertEqual(data.arr.tolist(), [5, 6])
self.assertEqual(len(data.arrays), 4, data.arrays)
def test_validation_sp_no_validator():
"""
If the setpoints do not have an Arrays validator validation
will fail.
"""
n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
n_points_2.set(20)
# setpoints do not have a validator
setpoints_2 = Parameter('setpoints_2', get_cmd=lambda: rand(n_points_2()))
param_sp_without_validator = ParameterWithSetpoints(
'param_4',
get_cmd=lambda: rand(n_points_2()),
setpoints=(setpoints_2, ),
vals=vals.Arrays(shape=(n_points_2, )))
expected_err_msg = (r"Can only validate shapes for "
r"parameters with Arrays validator. "
r"setpoints_2 is a setpoint")
with pytest.raises(ValueError, match=expected_err_msg):
param_sp_without_validator.validate_consistent_shape()
with pytest.raises(ValueError, match=expected_err_msg):
param_sp_without_validator.validate(param_sp_without_validator.get())
def test_get_cache_no_get():
"""
Test that cache.get on a parameter that does not have get is handled
correctly.
"""
local_parameter = Parameter('test_param', set_cmd=None, get_cmd=False)
# The parameter does not have a get method.
with pytest.raises(AttributeError):
local_parameter.get()
# get_latest will fail as get cannot be called and no cache
# is available
with pytest.raises(RuntimeError):
local_parameter.cache.get()
value = 1
local_parameter.set(value)
assert local_parameter.cache.get() == value
local_parameter2 = Parameter('test_param2',
set_cmd=None,
get_cmd=False,
initial_value=value)
with pytest.raises(AttributeError):
local_parameter2.get()
assert local_parameter2.cache.get() == value
def array_in_scalar_dataset_unrolled(experiment):
"""
This fixture yields a dataset where an array-valued parameter is registered
as a 'numeric' type and has an additional single-valued setpoint. We
expect data to be saved as individual scalars, with the scalar setpoint
repeated.
"""
meas = Measurement()
scalar_param = Parameter('scalarparam', set_cmd=None)
param = ArraySetPointParam()
meas.register_parameter(scalar_param)
meas.register_parameter(param,
setpoints=(scalar_param, ),
paramtype='numeric')
with meas.run() as datasaver:
for i in range(1, 10):
scalar_param.set(i)
datasaver.add_result((scalar_param, scalar_param.get()),
(param, param.get()))
try:
yield datasaver.dataset
finally:
datasaver.dataset.conn.close()
def parameters():
n_points_1 = Parameter('n_points_1', set_cmd=None, vals=vals.Ints())
n_points_2 = Parameter('n_points_2', set_cmd=None, vals=vals.Ints())
n_points_3 = Parameter('n_points_3', set_cmd=None, vals=vals.Ints())
n_points_1.set(10)
n_points_2.set(20)
n_points_3.set(15)
setpoints_1 = Parameter('setpoints_1',
get_cmd=lambda: np.arange(n_points_1()),
vals=vals.Arrays(shape=(n_points_1, )))
setpoints_2 = Parameter('setpoints_2',
get_cmd=lambda: np.arange(n_points_2()),
vals=vals.Arrays(shape=(n_points_2, )))
setpoints_3 = Parameter('setpoints_3',
get_cmd=lambda: np.arange(n_points_3()),
vals=vals.Arrays(shape=(n_points_3, )))
yield (n_points_1, n_points_2, n_points_3, setpoints_1, setpoints_2,
setpoints_3)
