def test_log_norm(self):
data = np.random.random((200, 200))
peaks = ipf.add_atoms_with_gui(data, norm='log')
fig = plt.figure(1)
x, y = fig.axes[0].transData.transform((100, 100))
assert len(peaks) == 0
fig.canvas.button_press_event(x, y, 1)
assert len(peaks) == 1
fig.canvas.button_press_event(x, y, 1)
assert len(peaks) == 0
def test_many_atoms_input(self):
data = np.random.random((500, 1000))
x, y = np.mgrid[10:990:100j, 10:490:100j]
x, y = x.flatten(), y.flatten()
positions = np.stack((x, y)).T
peaks = ipf.add_atoms_with_gui(data, positions)
assert len(peaks) == len(positions)
fig = plt.figure(1)
x0, y0 = fig.axes[0].transData.transform((peaks[0][0], peaks[0][1]))
fig.canvas.button_press_event(x0, y0, 1)
assert len(peaks) == len(positions) - 1
def test_distance_threshold(self):
data = np.random.random((50, 200))
peaks0 = ipf.add_atoms_with_gui(data, distance_threshold=10)
fig0 = plt.figure(1)
x00, y00 = fig0.axes[0].transData.transform((100, 10))
fig0.canvas.button_press_event(x00, y00, 1)
assert len(peaks0) == 1
x01, y01 = fig0.axes[0].transData.transform((109, 19))
fig0.canvas.button_press_event(x01, y01, 1)
assert len(peaks0) == 0
plt.close(fig0)
peaks1 = ipf.add_atoms_with_gui(data, distance_threshold=4)
fig1 = plt.figure(1)
x10, y10 = fig1.axes[0].transData.transform((100, 10))
fig1.canvas.button_press_event(x10, y10, 1)
assert len(peaks1) == 1
x11, y11 = fig1.axes[0].transData.transform((109, 19))
fig1.canvas.button_press_event(x11, y11, 1)
assert len(peaks1) == 2
plt.close(fig1)
def test_atoms_input(self):
data = np.random.random((50, 200))
peaks = ipf.add_atoms_with_gui(data, [
[120, 20],
[120, 30],
])
fig = plt.figure(1)
x0, y0 = fig.axes[0].transData.transform((100, 10))
assert len(peaks) == 2
fig.canvas.button_press_event(x0, y0, 1)
assert len(peaks) == 3
x1, y1 = fig.axes[0].transData.transform((120, 30))
fig.canvas.button_press_event(x1, y1, 1)
assert len(peaks) == 2
assert peaks[0] == approx([120, 20])
assert peaks[1] == approx([100, 10])
def set_calibration_area(self, manual_list=None):
'''
Area that will be used to calibrate a simulation. The pixel separation
can be set with set_calibration_separation. The average intensity of
the atoms chosen by this separation within the area will be set to 1.
The idea is to calibrate the experimental and simulated images with a
known intensity (e.g., single Mo atom in MoS2 is relatively
consistant).
reference_image can be changed via the Model_refiner.reference_image
attribute.
manual_list must be a list of two lists, each of length two.
For example: [ [0,0], [50, 50] ]
'''
if isinstance(manual_list, list):
self.calibration_area = manual_list
else:
self.calibration_area = add_atoms_with_gui(self.reference_image)
def test_signal_input(self):
s = Signal2D(np.random.random((200, 200)))
ipf.add_atoms_with_gui(s, norm='log')
ipf.add_atoms_with_gui(s, norm='linear')
def test_log_norm_negative_image_values(self):
data = np.random.random((200, 200)) - 10
ipf.add_atoms_with_gui(data, norm='log')