def find_peaks_automatic(self, x, y, peak_ind):
massif = Massif(self.img_data.img_data)
cur_peak_points = massif.find_peaks([x, y])
if len(cur_peak_points):
self.points.append(np.array(cur_peak_points))
self.points_index.append(peak_ind)
return np.array(cur_peak_points)
def find_peaks_automatic(self, x, y, peak_ind):
massif = Massif(self.img_data.img_data)
cur_peak_points = massif.find_peaks([x, y])
if len(cur_peak_points):
self.points.append(np.array(cur_peak_points))
self.points_index.append(peak_ind)
return np.array(cur_peak_points)
def search_peaks_on_ring(self, peak_index, delta_tth=0.1, algorithm='Massif', min_mean_factor=1,
upper_limit=55000):
if not self.is_calibrated:
return
#transform delta from degree into radians
delta_tth = delta_tth / 180.0 * np.pi
# get appropiate two theta value for the ring number
tth_calibrant_list = self.calibrant.get_2th()
tth_calibrant = np.float(tth_calibrant_list[peak_index])
print tth_calibrant
# get the calculated two theta values for the whole image
if self.geometry._ttha is None:
tth_array = self.geometry.twoThetaArray(self.img_data.img_data.shape)
else:
tth_array = self.geometry._ttha
# create mask based on two_theta position
mask = abs(tth_array - tth_calibrant) <= delta_tth
# init the peak search algorithm
if algorithm == 'Massif':
peak_search_algorithm = Massif(self.img_data.img_data)
elif algorithm == 'Blob':
peak_search_algorithm = BlobDetection(self.img_data.img_data * mask)
peak_search_algorithm.process()
else:
return
# calculate the mean and standard deviation of this area
sub_data = np.array(self.img_data.img_data.ravel()[np.where(mask.ravel())], dtype=np.float64)
sub_data[np.where(sub_data > upper_limit)] = np.NaN
mean = np.nanmean(sub_data)
std = np.nanstd(sub_data)
# set the threshold into the mask (don't detect very low intensity peaks)
threshold = min_mean_factor * mean + std
mask2 = np.logical_and(self.img_data.img_data > threshold, mask)
mask2[np.where(self.img_data.img_data > upper_limit)] = False
size2 = mask2.sum(dtype=int)
keep = int(np.ceil(np.sqrt(size2)))
try:
res = peak_search_algorithm.peaks_from_area(mask2, Imin=mean - std, keep=keep)
except IndexError:
res = []
# Store the result
if len(res):
self.points.append(np.array(res))
self.points_index.append(peak_index)
def search_peaks_on_ring(self,
peak_index,
delta_tth=0.1,
algorithm='Massif',
min_mean_factor=1,
upper_limit=55000):
if not self.is_calibrated:
return
#transform delta from degree into radians
delta_tth = delta_tth / 180.0 * np.pi
# get appropiate two theta value for the ring number
tth_calibrant_list = self.calibrant.get_2th()
tth_calibrant = np.float(tth_calibrant_list[peak_index])
print tth_calibrant
# get the calculated two theta values for the whole image
if self.geometry._ttha is None:
tth_array = self.geometry.twoThetaArray(
self.img_data.img_data.shape)
else:
tth_array = self.geometry._ttha
# create mask based on two_theta position
mask = abs(tth_array - tth_calibrant) <= delta_tth
# init the peak search algorithm
if algorithm == 'Massif':
peak_search_algorithm = Massif(self.img_data.img_data)
elif algorithm == 'Blob':
peak_search_algorithm = BlobDetection(self.img_data.img_data *
mask)
peak_search_algorithm.process()
else:
return
# calculate the mean and standard deviation of this area
sub_data = np.array(self.img_data.img_data.ravel()[np.where(
mask.ravel())],
dtype=np.float64)
sub_data[np.where(sub_data > upper_limit)] = np.NaN
mean = np.nanmean(sub_data)
std = np.nanstd(sub_data)
# set the threshold into the mask (don't detect very low intensity peaks)
threshold = min_mean_factor * mean + std
mask2 = np.logical_and(self.img_data.img_data > threshold, mask)
mask2[np.where(self.img_data.img_data > upper_limit)] = False
size2 = mask2.sum(dtype=int)
keep = int(np.ceil(np.sqrt(size2)))
try:
res = peak_search_algorithm.peaks_from_area(mask2,
Imin=mean - std,
keep=keep)
except IndexError:
res = []
# Store the result
if len(res):
self.points.append(np.array(res))
self.points_index.append(peak_index)
