def test_datasaver_2d(experiment, DAC, DMM, caplog, n_points_1, n_points_2):
meas = Measurement()
meas.register_parameter(DAC.ch1)
meas.register_parameter(DAC.ch2)
meas.register_parameter(DMM.v1, setpoints=(DAC.ch1, DAC.ch2))
n_points_expected_1 = 5
n_points_expected_2 = 10
meas.set_shapes(
{DMM.v1.full_name: (
n_points_expected_1,
n_points_expected_2,
)})
with meas.run() as datasaver:
for set_v_1 in np.linspace(0, 1, n_points_1):
for set_v_2 in np.linspace(0, 1, n_points_2):
datasaver.add_result((DAC.ch1, set_v_1), (DAC.ch2, set_v_2),
(DMM.v1, DMM.v1()))
ds = datasaver.dataset
caplog.clear()
data = ds.get_parameter_data()
if n_points_1 * n_points_2 == n_points_expected_1 * n_points_expected_2:
assert len(caplog.record_tuples) == 0
for dataarray in data[DMM.v1.full_name].values():
assert dataarray.shape == (n_points_expected_1,
n_points_expected_2)
elif n_points_1 * n_points_2 > n_points_expected_1 * n_points_expected_2:
assert len(caplog.record_tuples) == 3
exp_module = "qcodes.dataset.sqlite.queries"
exp_level = logging.WARNING
exp_msg = ("Tried to set data shape for {} in "
"dataset {} "
"from metadata when loading "
"but found inconsistent lengths {} and {}")
assert caplog.record_tuples[0] == (exp_module, exp_level,
exp_msg.format(
DMM.v1.full_name,
DMM.v1.full_name,
n_points_1 * n_points_2,
n_points_expected_1 *
n_points_expected_2))
assert caplog.record_tuples[1] == (exp_module, exp_level,
exp_msg.format(
DAC.ch1.full_name,
DMM.v1.full_name,
n_points_1 * n_points_2,
n_points_expected_1 *
n_points_expected_2))
assert caplog.record_tuples[2] == (exp_module, exp_level,
exp_msg.format(
DAC.ch2.full_name,
DMM.v1.full_name,
n_points_1 * n_points_2,
n_points_expected_1 *
n_points_expected_2))
def _register_parameters(meas: Measurement,
param_meas: Sequence[ParamMeasT],
setpoints: Optional[Sequence[_BaseParameter]] = None,
shapes: Shapes = None) -> None:
for parameter in param_meas:
if isinstance(parameter, _BaseParameter):
meas.register_parameter(parameter, setpoints=setpoints)
meas.set_shapes(shapes=shapes)
def test_cache_2d_shape(experiment,
DAC,
DMM,
n_points_outer,
n_points_inner,
pws_n_points,
bg_writing,
channel_array_instrument,
cache_size):
meas = Measurement()
meas.register_parameter(DAC.ch1)
meas.register_parameter(DAC.ch2)
meas_parameters = (DMM.v1,
channel_array_instrument.A.dummy_multi_parameter,
channel_array_instrument.A.dummy_scalar_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter_2,
channel_array_instrument.A.dummy_array_parameter,
channel_array_instrument.A.dummy_complex_array_parameter,
channel_array_instrument.A.dummy_complex,
channel_array_instrument.A.dummy_parameter_with_setpoints,
channel_array_instrument.A.dummy_parameter_with_setpoints_complex,
)
channel_array_instrument.A.dummy_start(0)
channel_array_instrument.A.dummy_stop(10)
channel_array_instrument.A.dummy_n_points(pws_n_points)
for param in meas_parameters:
meas.register_parameter(param, setpoints=(DAC.ch1, DAC.ch2))
if cache_size == "too_small":
meas.set_shapes(detect_shape_of_measurement(
meas_parameters,
(int(ceil(n_points_outer/2)), n_points_inner))
)
elif cache_size == "too_large":
meas.set_shapes(detect_shape_of_measurement(
meas_parameters,
(n_points_outer*2, n_points_inner))
)
else:
meas.set_shapes(detect_shape_of_measurement(
meas_parameters,
(n_points_outer, n_points_inner))
)
expected_shapes = {
'dummy_dmm_v1': (n_points_outer, n_points_inner),
'dummy_channel_inst_ChanA_multi_setpoint_param_this': (n_points_outer, n_points_inner, 5),
'dummy_channel_inst_ChanA_multi_setpoint_param_that': (n_points_outer, n_points_inner, 5),
'dummy_channel_inst_ChanA_thisparam': (n_points_outer, n_points_inner),
'dummy_channel_inst_ChanA_thatparam': (n_points_outer, n_points_inner),
'dummy_channel_inst_ChanA_this': (n_points_outer, n_points_inner, 5, 3),
'dummy_channel_inst_ChanA_that': (n_points_outer, n_points_inner, 5, 3),
'dummy_channel_inst_ChanA_this_5_3': (n_points_outer, n_points_inner, 5, 3),
'dummy_channel_inst_ChanA_this_2_7': (n_points_outer, n_points_inner, 2, 7),
'dummy_channel_inst_ChanA_dummy_array_parameter': (n_points_outer, n_points_inner, 5),
'dummy_channel_inst_ChanA_dummy_complex_array_parameter': (n_points_outer, n_points_inner, 5),
'dummy_channel_inst_ChanA_dummy_complex': (n_points_outer, n_points_inner),
'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints': (n_points_outer, n_points_inner, pws_n_points),
'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints_complex': (n_points_outer, n_points_inner, pws_n_points)
}
if cache_size == "correct":
assert meas._shapes == expected_shapes
with meas.run(write_in_background=bg_writing) as datasaver:
dataset = datasaver.dataset
# Check that parameter data and cache data are indential for empty datasets
_assert_parameter_data_is_identical(dataset.get_parameter_data(), dataset.cache.data())
n_points_measured = 0
for v1 in np.linspace(-1, 1, n_points_outer):
for v2 in np.linspace(-1, 1, n_points_inner):
n_points_measured += 1
DAC.ch1.set(v1)
DAC.ch2.set(v2)
meas_vals = [(param, param.get()) for param in meas_parameters]
datasaver.add_result((DAC.ch1, v1),
(DAC.ch2, v2),
*meas_vals)
datasaver.flush_data_to_database(block=True)
param_data_trees = dataset.get_parameter_data()
cache_data_trees = dataset.cache.data()
_assert_partial_cache_is_as_expected(
cache_data_trees,
expected_shapes,
n_points_measured,
param_data_trees,
cache_size == "correct"
)
cache_data_trees = dataset.cache.data()
param_data_trees = dataset.get_parameter_data()
_assert_completed_cache_is_as_expected(cache_data_trees,
param_data_trees,
flatten=cache_size == "too_small",
clip=cache_size == "too_large")
def test_cache_1d_shape(experiment, DAC, DMM, n_points, bg_writing,
channel_array_instrument, setpoints_type,
in_memory_cache):
setpoints_param, setpoints_values = _prepare_setpoints_1d(
DAC, channel_array_instrument,
n_points, setpoints_type
)
meas = Measurement()
meas.register_parameter(setpoints_param)
meas_parameters = (DMM.v1,
channel_array_instrument.A.dummy_multi_parameter,
channel_array_instrument.A.dummy_scalar_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter_2,
channel_array_instrument.A.dummy_array_parameter,
channel_array_instrument.A.dummy_complex_array_parameter,
channel_array_instrument.A.dummy_complex,
channel_array_instrument.A.dummy_parameter_with_setpoints,
channel_array_instrument.A.dummy_parameter_with_setpoints_complex,
)
pws_n_points = 10
channel_array_instrument.A.dummy_start(0)
channel_array_instrument.A.dummy_stop(10)
channel_array_instrument.A.dummy_n_points(pws_n_points)
expected_shapes = {
'dummy_dmm_v1': (n_points, ),
'dummy_channel_inst_ChanA_multi_setpoint_param_this': (n_points, 5),
'dummy_channel_inst_ChanA_multi_setpoint_param_that': (n_points, 5),
'dummy_channel_inst_ChanA_thisparam': (n_points, ),
'dummy_channel_inst_ChanA_thatparam': (n_points, ),
'dummy_channel_inst_ChanA_this': (n_points, 5, 3),
'dummy_channel_inst_ChanA_that': (n_points, 5, 3),
'dummy_channel_inst_ChanA_this_5_3': (n_points, 5, 3),
'dummy_channel_inst_ChanA_this_2_7': (n_points, 2, 7),
'dummy_channel_inst_ChanA_dummy_array_parameter': (n_points, 5),
'dummy_channel_inst_ChanA_dummy_complex_array_parameter': (n_points, 5),
'dummy_channel_inst_ChanA_dummy_complex': (n_points, ),
'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints': (n_points, pws_n_points),
'dummy_channel_inst_ChanA_dummy_parameter_with_setpoints_complex': (n_points, pws_n_points)
}
for param in meas_parameters:
meas.register_parameter(param, setpoints=(setpoints_param,))
meas.set_shapes(detect_shape_of_measurement(
meas_parameters,
(n_points,))
)
n_points_measured = 0
with meas.run(write_in_background=bg_writing,
in_memory_cache=in_memory_cache) as datasaver:
dataset = datasaver.dataset
_assert_parameter_data_is_identical(dataset.get_parameter_data(), dataset.cache.data())
for i, v in enumerate(setpoints_values):
n_points_measured += 1
setpoints_param.set(v)
meas_vals = [(param, param.get()) for param in meas_parameters[:-2]]
meas_vals += expand_setpoints_helper(meas_parameters[-2])
meas_vals += expand_setpoints_helper(meas_parameters[-1])
datasaver.add_result((setpoints_param, v),
*meas_vals)
datasaver.flush_data_to_database(block=True)
cache_data_trees = dataset.cache.data()
param_data_trees = dataset.get_parameter_data()
_assert_partial_cache_is_as_expected(
cache_data_trees,
expected_shapes,
n_points_measured,
param_data_trees,
cache_correct=True
)
cache_data_trees = dataset.cache.data()
param_data_trees = dataset.get_parameter_data()
_assert_completed_cache_is_as_expected(cache_data_trees,
param_data_trees,
flatten=False)
def test_cache_1d(experiment, DAC, DMM, n_points, bg_writing,
channel_array_instrument, setpoints_type,
set_shape, in_memory_cache):
setpoints_param, setpoints_values = _prepare_setpoints_1d(
DAC, channel_array_instrument,
n_points, setpoints_type
)
meas1 = Measurement()
meas1.register_parameter(setpoints_param)
meas_parameters1 = (
DMM.v1,
channel_array_instrument.A.dummy_multi_parameter,
channel_array_instrument.A.dummy_scalar_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter_2,
channel_array_instrument.A.dummy_array_parameter,
channel_array_instrument.A.dummy_complex_array_parameter,
channel_array_instrument.A.dummy_complex,
channel_array_instrument.A.dummy_parameter_with_setpoints,
channel_array_instrument.A.dummy_parameter_with_setpoints_complex,
)
pws_shape_1 = 10
pws_shape_2 = 3
channel_array_instrument.A.dummy_start(0)
channel_array_instrument.A.dummy_stop(10)
channel_array_instrument.A.dummy_n_points(pws_shape_1)
channel_array_instrument.A.dummy_start_2(2)
channel_array_instrument.A.dummy_stop_2(7)
channel_array_instrument.A.dummy_n_points_2(pws_shape_2)
if set_shape:
meas1.set_shapes(
{
DMM.v1.full_name: (n_points,),
channel_array_instrument.A.dummy_multi_parameter.full_names[0]: (n_points, 5),
channel_array_instrument.A.dummy_multi_parameter.full_names[1]: (n_points, 5),
channel_array_instrument.A.dummy_scalar_multi_parameter.full_names[0]: (n_points,),
channel_array_instrument.A.dummy_scalar_multi_parameter.full_names[1]: (n_points,),
channel_array_instrument.A.dummy_scalar_multi_parameter.full_names[0]: (n_points,),
channel_array_instrument.A.dummy_scalar_multi_parameter.full_names[1]: (n_points,),
channel_array_instrument.A.dummy_2d_multi_parameter.full_names[0]: (n_points, 5, 3),
channel_array_instrument.A.dummy_2d_multi_parameter.full_names[1]: (n_points, 5, 3),
channel_array_instrument.A.dummy_2d_multi_parameter_2.full_names[0]: (n_points, 5, 3),
channel_array_instrument.A.dummy_2d_multi_parameter_2.full_names[1]: (n_points, 2, 7),
channel_array_instrument.A.dummy_array_parameter.full_name: (n_points, 5),
channel_array_instrument.A.dummy_complex_array_parameter.full_name: (n_points, 5),
channel_array_instrument.A.dummy_complex.full_name: (n_points,),
channel_array_instrument.A.dummy_parameter_with_setpoints.full_name: (n_points, pws_shape_1),
channel_array_instrument.A.dummy_parameter_with_setpoints_complex.full_name: (n_points, pws_shape_1)
}
)
for param in meas_parameters1:
meas1.register_parameter(param, setpoints=(setpoints_param,))
meas2 = Measurement()
meas2.register_parameter(setpoints_param)
meas_parameters2 = (channel_array_instrument.A.dummy_parameter_with_setpoints_2d,)
if set_shape:
meas2.set_shapes(
{meas_parameters2[0].full_name: (n_points, pws_shape_1, pws_shape_2)})
for param in meas_parameters2:
meas2.register_parameter(param, setpoints=(setpoints_param,))
with meas1.run(
write_in_background=bg_writing,
in_memory_cache=in_memory_cache
) as datasaver1:
with meas2.run(
write_in_background=bg_writing,
in_memory_cache=in_memory_cache
) as datasaver2:
dataset1 = datasaver1.dataset
dataset2 = datasaver2.dataset
_assert_parameter_data_is_identical(dataset1.get_parameter_data(), dataset1.cache.data())
_assert_parameter_data_is_identical(dataset2.get_parameter_data(), dataset2.cache.data())
for i, v in enumerate(setpoints_values):
setpoints_param.set(v)
meas_vals1 = [(param, param.get()) for param in meas_parameters1]#[:-2]]
meas_vals1 += expand_setpoints_helper(meas_parameters1[-2])
meas_vals1 += expand_setpoints_helper(meas_parameters1[-1])
datasaver1.add_result((setpoints_param, v),
*meas_vals1)
datasaver1.flush_data_to_database(block=True)
meas_vals2 = [(param, param.get()) for param in meas_parameters2]
datasaver2.add_result((setpoints_param, v),
*meas_vals2)
datasaver2.flush_data_to_database(block=True)
_assert_parameter_data_is_identical(dataset1.get_parameter_data(),
dataset1.cache.data(),
shaped_partial=set_shape)
_assert_parameter_data_is_identical(dataset2.get_parameter_data(),
dataset2.cache.data(),
shaped_partial=set_shape)
_assert_parameter_data_is_identical(dataset1.get_parameter_data(),
dataset1.cache.data())
if in_memory_cache is False:
assert dataset1.cache._loaded_from_completed_ds is True
assert dataset1.completed is True
assert dataset1.cache.live is in_memory_cache
_assert_parameter_data_is_identical(dataset2.get_parameter_data(),
dataset2.cache.data())
if in_memory_cache is False:
assert dataset2.cache._loaded_from_completed_ds is True
assert dataset2.completed is True
assert dataset1.cache.live is in_memory_cache
def test_cache_standalone(
experiment,
DMM,
n_points,
bg_writing,
channel_array_instrument,
set_shape,
in_memory_cache,
):
meas1 = Measurement()
meas1.register_parameter(DMM.v1)
meas_parameters1 = (
DMM.v1,
channel_array_instrument.A.dummy_multi_parameter,
channel_array_instrument.A.dummy_scalar_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter,
channel_array_instrument.A.dummy_2d_multi_parameter_2,
channel_array_instrument.A.dummy_array_parameter,
channel_array_instrument.A.dummy_complex_array_parameter,
channel_array_instrument.A.dummy_complex,
channel_array_instrument.A.dummy_parameter_with_setpoints,
channel_array_instrument.A.dummy_parameter_with_setpoints_complex,
)
pws_shape_1 = 10
pws_shape_2 = 3
channel_array_instrument.A.dummy_start(0)
channel_array_instrument.A.dummy_stop(10)
channel_array_instrument.A.dummy_n_points(pws_shape_1)
channel_array_instrument.A.dummy_start_2(2)
channel_array_instrument.A.dummy_stop_2(7)
channel_array_instrument.A.dummy_n_points_2(pws_shape_2)
if set_shape:
meas1.set_shapes(
{
DMM.v1.full_name: (n_points,),
channel_array_instrument.A.dummy_multi_parameter.full_names[0]: (
n_points,
5,
),
channel_array_instrument.A.dummy_multi_parameter.full_names[1]: (
n_points,
5,
),
channel_array_instrument.A.dummy_scalar_multi_parameter.full_names[0]: (
n_points,
),
channel_array_instrument.A.dummy_scalar_multi_parameter.full_names[1]: (
n_points,
),
channel_array_instrument.A.dummy_scalar_multi_parameter.full_names[0]: (
n_points,
),
channel_array_instrument.A.dummy_scalar_multi_parameter.full_names[1]: (
n_points,
),
channel_array_instrument.A.dummy_2d_multi_parameter.full_names[0]: (
n_points,
5,
3,
),
channel_array_instrument.A.dummy_2d_multi_parameter.full_names[1]: (
n_points,
5,
3,
),
channel_array_instrument.A.dummy_2d_multi_parameter_2.full_names[0]: (
n_points,
5,
3,
),
channel_array_instrument.A.dummy_2d_multi_parameter_2.full_names[1]: (
n_points,
2,
7,
),
channel_array_instrument.A.dummy_array_parameter.full_name: (
n_points,
5,
),
channel_array_instrument.A.dummy_complex_array_parameter.full_name: (
n_points,
5,
),
channel_array_instrument.A.dummy_complex.full_name: (n_points,),
channel_array_instrument.A.dummy_parameter_with_setpoints.full_name: (
n_points,
pws_shape_1,
),
channel_array_instrument.A.dummy_parameter_with_setpoints_complex.full_name: (
n_points,
pws_shape_1,
),
}
)
for param in meas_parameters1:
meas1.register_parameter(param)
meas2 = Measurement()
meas_parameters2 = (channel_array_instrument.A.dummy_parameter_with_setpoints_2d,)
if set_shape:
meas2.set_shapes(
{meas_parameters2[0].full_name: (n_points, pws_shape_1, pws_shape_2)}
)
for param in meas_parameters2:
meas2.register_parameter(param)
with meas1.run(
write_in_background=bg_writing, in_memory_cache=in_memory_cache
) as datasaver1:
with meas2.run(
write_in_background=bg_writing, in_memory_cache=in_memory_cache
) as datasaver2:
dataset1 = datasaver1.dataset
dataset2 = datasaver2.dataset
_assert_parameter_data_is_identical(
dataset1.get_parameter_data(), dataset1.cache.data()
)
_assert_parameter_data_is_identical(
dataset2.get_parameter_data(), dataset2.cache.data()
)
for _ in range(n_points):
meas_vals1 = [(param, param.get()) for param in meas_parameters1]
datasaver1.add_result(*meas_vals1)
datasaver1.flush_data_to_database(block=True)
meas_vals2 = [(param, param.get()) for param in meas_parameters2]
datasaver2.add_result(*meas_vals2)
datasaver2.flush_data_to_database(block=True)
_assert_parameter_data_is_identical(
dataset1.get_parameter_data(),
dataset1.cache.data(),
shaped_partial=set_shape,
)
_assert_parameter_data_is_identical(
dataset2.get_parameter_data(),
dataset2.cache.data(),
shaped_partial=set_shape,
)
_assert_parameter_data_is_identical(
dataset1.get_parameter_data(), dataset1.cache.data()
)
if in_memory_cache is False:
assert dataset1.cache._loaded_from_completed_ds is True
assert dataset1.completed is True
assert dataset1.cache.live is in_memory_cache
_assert_parameter_data_is_identical(
dataset2.get_parameter_data(), dataset2.cache.data()
)
if in_memory_cache is False:
assert dataset2.cache._loaded_from_completed_ds is True
assert dataset2.completed is True
assert dataset1.cache.live is in_memory_cache
def test_plot_dataset_2d_shaped(experiment, request, nan_setpoints, shifted):
"""
Test plotting of preshaped data on a grid that may or may not be shifted
with and without nans in the set points.
"""
inst = DummyInstrument("dummy", gates=["s1", "m1", "s2"])
request.addfinalizer(inst.close)
inst.m1.get = np.random.randn
meas = Measurement()
meas.register_parameter(inst.s1)
meas.register_parameter(inst.s2)
meas.register_parameter(inst.m1, setpoints=(inst.s1, inst.s2))
outer_shape = 10
inner_shape = 20
meas.set_shapes(
detect_shape_of_measurement((inst.m1, ), (outer_shape, inner_shape)))
shift = 0
with meas.run() as datasaver:
try:
for outer in np.linspace(0, 9, outer_shape):
for inner in np.linspace(0 + shift, 10 + shift, inner_shape):
datasaver.add_result((inst.s1, outer), (inst.s2, inner),
(inst.m1, inst.m1()))
if inner > 7 and outer > 6 and nan_setpoints:
raise TerminateLoopException
if shifted:
shift += 1
except TerminateLoopException:
pass
axes, cbs = plot_dataset(datasaver.dataset)
xlims = axes[0].get_xlim()
ylims = axes[0].get_ylim()
# check that this generates a QuadMesh which is the expected output of pcolormesh
assert any(
isinstance(mplobj, QuadMesh) for mplobj in axes[0].get_children())
if nan_setpoints and shifted:
assert xlims[0] == -0.5
assert xlims[1] == 7.5
assert ylims[0] < 0
assert ylims[0] > -1.0
assert ylims[1] > 16
assert ylims[1] < 17
elif not nan_setpoints and shifted:
assert xlims[0] == -0.5
assert xlims[1] == 9.5
assert ylims[0] < 0
assert ylims[0] > -1.0
assert ylims[1] > 19
assert ylims[1] < 20
elif nan_setpoints and not shifted:
assert xlims[0] == -0.5
assert xlims[1] == 7.5
assert ylims[0] < 0
assert ylims[0] > -1.0
assert ylims[1] > 10
assert ylims[1] < 11
else:
assert xlims[0] == -0.5
assert xlims[1] == 9.5
assert ylims[0] < 0
assert ylims[0] > -1.0
assert ylims[1] > 10
assert ylims[1] < 11
