def base_define(self): #得到基准F值，用来计算dF/F
self.base_F = np.zeros(shape=len(self.cell_group))
self.spike_train = np.zeros(shape=(len(self.cell_group),
len(self.aligned_frame_name)))
for i in range(0, len(self.cell_group)):
self.base_F[i] = pp.sum_a_frame(self.graph_after_align,
self.cell_group[i])
def dF_calculation(self, frame_i):
# for frame_i in range(0,len(self.aligned_frame_name)):# 减少读取次数，每次将一张图的细胞数目读取完
current_frame = cv2.imread(self.aligned_frame_name[frame_i], -1)
target_F = np.zeros(shape=len(self.cell_group))
for j in range(0, len(self.cell_group)):
target_F[j] = pp.sum_a_frame(current_frame, self.cell_group[j])
dF_per_frame = (target_F - self.base_F) / self.base_F
return dF_per_frame
def F_calculation(
self
): # Calculate F value of every cell, in order to produce F series
###############Calculate F first
self.F_series = np.zeros(shape=(self.frame_Num, len(self.cell_group)),
dtype=np.float64)
for i in range(self.frame_Num): #Cycle all cells
for j in range(len(self.cell_group)): #Cycle all frames
self.F_series[i, j] = pp.sum_a_frame(self.frame_sets[i, :, :],
self.cell_group[j])
###################Then calculate dF/F
self.sub_series = np.zeros(shape=(self.frame_Num,
len(self.cell_group)),
dtype=np.float64)
for i in range(self.frame_Num):
for j in range(len(self.cell_group)):
cell_average = self.F_series[:, j].mean()
self.sub_series[i, j] = (self.F_series[i, j] -
cell_average) / cell_average