def test_nest():
n_sample_points = 100
x = ManualParameter("x")
sweep_values_x = np.linspace(-1, 1, n_sample_points)
y = ManualParameter("y")
sweep_values_y = np.linspace(-1, 1, n_sample_points)
m = ManualParameter("m")
m.get = lambda: np.sin(x())
n = ManualParameter("n")
n.get = lambda: np.cos(x()) + 2 * np.sin(y())
sweep_object = sweep(x, sweep_values_x)(m, sweep(y, sweep_values_y)(n))
experiment = new_experiment("sweep_measure", sample_name="sine")
station = Station()
meas = SweepMeasurement(exp=experiment, station=station)
meas.register_sweep(sweep_object)
with meas.run() as datasaver:
for data in sweep_object:
datasaver.add_result(*data.items())
data_set = datasaver._dataset
assert data_set.paramspecs["x"].depends_on == ""
assert data_set.paramspecs["y"].depends_on == ""
assert data_set.paramspecs["m"].depends_on == "x"
assert data_set.paramspecs["n"].depends_on == "x, y"
data_x = data_set.get_data('x')
data_y = data_set.get_data('y')
assert data_x[::n_sample_points + 1] == [[xi] for xi in sweep_values_x]
assert data_y[::n_sample_points + 1] == [[None] for _ in sweep_values_x]
coordinate_layout = itertools.product(sweep_values_x, sweep_values_y)
expected_x, expected_y = zip(*coordinate_layout)
assert [ix for c, ix in enumerate(data_x)
if c % (n_sample_points + 1)] == [[xi] for xi in expected_x]
assert [iy for c, iy in enumerate(data_y)
if c % (n_sample_points + 1)] == [[yi] for yi in expected_y]
def test_bare_function(self):
# not a use case we want to promote, but it's there...
p = ManualParameter('test')
def doubler(x):
p.set(x * 2)
f = Function('f', call_cmd=doubler, args=[Numbers(-10, 10)])
f(4)
self.assertEqual(p.get(), 8)
with self.assertRaises(ValueError):
f(20)
def main():
x = ManualParameter("x", unit="V")
y = ManualParameter("y", unit="V")
m = ManualParameter("m", unit="A")
m.get = lambda: x()**2
n = ManualParameter("n", unit="A")
n.get = lambda: x() - y()**2 + 16
setup = [(lambda: None, tuple())]
cleanup = [(lambda: None, tuple())]
result = do_experiment("cool_experiment/my_sample",
setup,
sweep(x, [0, 1, 2])(m),
cleanup,
return_format=["data_set_path"])
data_set_path = result[0]
data = get_results_from_db_path(data_set_path, return_as_dict=True)
print(data)
def test_szip_measure_prior_to_set():
"""
We can use szip to perform a measurement before setting sweep set points. Test this scenario
"""
x = ManualParameter("x")
v = range(1, 10)
m = ManualParameter("m")
m.get = lambda: 2 * x()
x(0)
count = 0
previous_x = x()
for count, i in enumerate(szip(m, sweep(x, v))):
assert i[
"m"] == 2 * previous_x # Note that at this point, x should already have been incremented
assert count < len(v)
previous_x = x()
assert count == len(v) - 1
def test_inferred():
x = ManualParameter("x", unit="V")
@setter([("xmv", "mV")], inferred_parameters=[("x", "V")])
def xsetter(milivolt_value):
volt_value = milivolt_value / 1000.0 # From mV to V
x.set(volt_value)
return volt_value
m = ManualParameter("m")
m.get = lambda: np.sin(x())
sweep_values = np.linspace(-1000, 1000, 100) # We sweep in mV
sweep_object = nest(sweep(xsetter, sweep_values), m)
experiment = new_experiment("sweep_measure", sample_name="sine")
station = Station()
meas = SweepMeasurement(exp=experiment, station=station)
meas.register_sweep(sweep_object)
with meas.run() as datasaver:
for data in sweep_object:
datasaver.add_result(*data.items())
data_set = datasaver._dataset
assert data_set.paramspecs["x"].depends_on == ""
assert data_set.paramspecs["x"].inferred_from == "xmv"
assert data_set.paramspecs["xmv"].depends_on == ""
assert data_set.paramspecs["m"].depends_on == "x"
expected_xmv = [[xi] for xi in sweep_values]
expected_x = [[xi / 1000] for xi in sweep_values]
assert data_set.get_data('xmv') == expected_xmv
assert data_set.get_data('x') == expected_x
def test_simple():
x = ManualParameter("x")
sweep_values = np.linspace(-1, 1, 100)
m = ManualParameter("m")
m.get = lambda: np.sin(x())
sweep_object = nest(sweep(x, sweep_values), m)
experiment = new_experiment("sweep_measure", sample_name="sine")
station = Station()
meas = SweepMeasurement(exp=experiment, station=station)
meas.register_sweep(sweep_object)
with meas.run() as datasaver:
for data in sweep_object:
datasaver.add_result(*data.items())
data_set = datasaver._dataset
assert data_set.paramspecs["x"].depends_on == ""
assert data_set.paramspecs["m"].depends_on == "x"
expected_x = [[xi] for xi in sweep_values]
assert data_set.get_data('x') == expected_x