def test_centroid(self):
y = np.ones(10)
centroid, sd = peak_utils.centroid(y)
assert_equal(centroid, 4.5)
y = np.ones(3)
x = np.array([0, 1.0, 9.])
centroid, sd = peak_utils.centroid(y, x=x)
assert_equal(centroid, 4.5)
y = np.ones(2)
centroid, sd = peak_utils.centroid(y, dx=9.)
assert_equal(centroid, 4.5)
def test_centroid(self):
y = np.ones(10)
centroid, sd = peak_utils.centroid(y)
assert_equal(centroid, 4.5)
y = np.ones(3)
x = np.array([0, 1.0, 9.0])
centroid, sd = peak_utils.centroid(y, x=x)
assert_equal(centroid, 4.5)
y = np.ones(2)
centroid, sd = peak_utils.centroid(y, dx=9.0)
assert_equal(centroid, 4.5)
def __call__(self, detector, detector_err):
"""
Start the manual beam find
Parameters
----------
detector : np.ndarray
detector image. Shape `(N, T, Y)` or `(T, Y)`. If N > 1 then only
the first image is processed
detector_err: np.ndarray
uncertainties (sd) associated with detector image
Returns
-------
beam_centre, beam_sd, lopx, hipx, background_pixels :
np.ndarray, np.ndarray, np.ndarray, np.ndarray, list of np.ndarray
Beam centre, standard deviation, lowest pixel in foreground region,
highest pixel in foreground region, each of the entries in
`background_pixels` is an array specifying pixels that are in the
background region.
"""
# assume that the ndim is 2 or 3.
# only process the first detector image (N = 0).
self.detector = detector
self.detector_err = detector_err
n_images = 1
if detector.ndim > 2:
self.detector = detector[0]
self.detector_err = detector_err[0]
n_images = np.size(detector, 0)
# guess peak centre from centroid.
xs = np.sum(self.detector, axis=0)
self._integrate_position, _ = centroid(xs)
self.integrate_position.setValue(self._integrate_position)
self.integrate_width.setValue(self._integrate_width)
self.recalculate_graphs()
self.dialog.exec_()
y1 = int(round(self._low_px - PIXEL_OFFSET))
y2 = int(round(self._high_px + PIXEL_OFFSET))
background_pixels = np.r_[np.arange(self._low_bkg, y1 + 1),
np.arange(y2, self._high_bkg + 1)]
return (np.ones((n_images, )) * self._true_centre, np.ones(
(n_images, )) * self._true_sd, np.ones(
(n_images, )) * self._low_px, np.ones(
(n_images, )) * self._high_px,
[background_pixels for i in range(n_images)])
def __call__(self, detector, detector_err, name):
"""
Start the manual beam find
Parameters
----------
detector : np.ndarray
detector image. Shape `(N, T, Y)` or `(T, Y)`. If N > 1 then only
the first image is processed
detector_err: np.ndarray
uncertainties (sd) associated with detector image
name: str
Name of the dataset
Returns
-------
beam_centre, beam_sd, lopx, hipx, background_pixels :
np.ndarray, np.ndarray, np.ndarray, np.ndarray, list of np.ndarray
Beam centre, standard deviation, lowest pixel in foreground region,
highest pixel in foreground region, each of the entries in
`background_pixels` is an array specifying pixels that are in the
background region.
"""
# assume that the ndim is 2 or 3.
# only process the first detector image (N = 0).
self.detector = detector
self.detector_err = detector_err
n_images = 1
if detector.ndim > 2:
self.detector = detector[0]
self.detector_err = detector_err[0]
n_images = np.size(detector, 0)
# set min/max values for the detector image GUI. Crashes result
# otherwise
self.integrate_position.setMaximum(np.size(self.detector, -1) - 1)
self.integrate_width.setMaximum(np.size(self.detector, -1) - 1)
self.pixels_to_include.setMaximum(np.size(self.detector, 0))
self.true_centre.setMaximum(np.size(self.detector, -1))
# guess peak centre from centroid.
xs = np.sum(self.detector, axis=0)
self._integrate_position, _ = centroid(xs)
self.integrate_position.setValue(self._integrate_position)
self.integrate_width.setValue(self._integrate_width)
self.recalculate_graphs()
# set the title of the window to give context about what dataset is
# being processed.
self.setWindowTitle("Manual beam finder")
if name is not None:
self.setWindowTitle("Manual beam finder: " + name)
self.dialog.exec_()
y1 = int(round(self._low_px - PIXEL_OFFSET))
y2 = int(round(self._high_px + PIXEL_OFFSET))
background_pixels = np.r_[np.arange(self._low_bkg, y1 + 1),
np.arange(y2, self._high_bkg + 1)]
return (
np.ones((n_images, )) * self._true_centre,
np.ones((n_images, )) * self._true_sd,
np.ones((n_images, )) * self._low_px,
np.ones((n_images, )) * self._high_px,
[background_pixels for i in range(n_images)],
)