def test_multiscan(self):
"""A 2D scan."""
other = LinearMotor()
values_0 = np.linspace(0, 10, 2) * q.mm
values_1 = np.linspace(5, 10, 3) * q.mm
range_0 = Region(self.motor['position'], values_0)
range_1 = Region(other['position'], values_1)
def feedback():
return self.motor.position, other.position
gen = resolve(scan(feedback, [range_0, range_1]))
p_0, p_1, result = zip(*gen)
result_x, result_y = zip(*result)
first_expected = [0 * q.mm, 10 * q.mm]
second_expected = [5 * q.mm, 7.5 * q.mm, 10 * q.mm]
combined = list(product(first_expected, second_expected))
p_0_exp, p_1_exp = zip(*combined)
# test parameter values
compare_sequences(p_0, p_0_exp, assert_almost_equal)
compare_sequences(p_1, p_1_exp, assert_almost_equal)
# feedback result is a tuple in this case, test both parts
compare_sequences(result_x, p_0_exp, assert_almost_equal)
compare_sequences(result_y, p_1_exp, assert_almost_equal)
def test_scan_param_feedback(self):
p = self.motor['position']
values = np.linspace(1, 10, 10) * q.mm
scan_param = Region(p, values)
x, y = zip(*resolve(scan_param_feedback(scan_param, p)))
compare_sequences(x, y, self.assertEqual)
def test_process(self):
def feedback():
return self.motor.position
values = np.linspace(1, 10, 12) * q.mm
param_range = Region(self.motor['position'], values)
x, y = zip(*list(resolve(scan(feedback, param_range))))
compare_sequences(x, y, self.assertEqual)
def test_callback(self):
called = []
motor = LinearMotor()
values = np.linspace(0, 2, 3) * q.mm
qrange = Region(motor['position'], values)
def callback():
called.append(motor.position.to(q.mm).magnitude)
list(resolve(scan(lambda: None, qrange, callbacks={qrange: callback})))
np.testing.assert_almost_equal(called, range(3))
def plot_exposure_scan(min_exposure=1*q.ms, max_exposure=500*q.ms, num_points=10):
"""
Plot the mean value of the detector image for exposure times between
*min_exposure* and *max_exposure*, use *num_points* data points.
Returns: a tuple with exposure times and corresponding mean values.
"""
accum = Accumulate()
region = Region(camera['exposure_time'], np.linspace(min_exposure, max_exposure, num_points))
with camera.recording():
inject(resolve(get_exposure_result(region)), broadcast(PyplotViewer(style='-o')(), accum()))
return zip(*accum.items)
