def Intersection_Graph(self): #做交集图
intersection_map = np.ones(shape=(512, 512), dtype=np.float64)
for i in range(np.shape(self.all_cell_graphs)[2]):
intersection_map = intersection_map * self.all_cell_graphs[:, :, i]
intersection_map = (intersection_map > 0) * 255 #把数值正常化然后*255
pp.save_graph('Intersection', intersection_map, graph_folder, '.png',
8, 1)
def Cell_Graph(self):
# =============================================================================
# 定义在一开始进行
# self.spike_train = pp.read_variable(save_folder+r'\\spike_train.pkl')
# self.cell_group = pp.read_variable(save_folder+r'\\Cell_Group.pkl')
# =============================================================================
cell_tuning = np.zeros(shape=(np.shape(self.spike_train)[0], 1),
dtype=np.float64)
for i in range(np.shape(self.spike_train)[0]): #全部细胞循环
temp_cell_A = 0
for j in range(len(self.frame_set_A)): #叠加平均刺激setA下的细胞反应
temp_cell_A = temp_cell_A + self.spike_train[
i, self.frame_set_A[j]] / len(self.frame_set_A)
temp_cell_B = 0
for j in range(len(self.frame_set_B)):
temp_cell_B = temp_cell_B + self.spike_train[
i, self.frame_set_B[j]] / len(self.frame_set_B)
cell_tuning[i] = temp_cell_A - temp_cell_B
pp.save_variable(cell_tuning, self.map_folder + r'\\' + self.map_name +
'_Cells.pkl') #把这个刺激的cell data存下来
#至此得到的cell tuning是有正负的，我们按照绝对值最大把它放到-1~1的范围里
norm_cell_tuning = cell_tuning / abs(cell_tuning).max()
clip_min_cell = norm_cell_tuning.mean() - 3 * norm_cell_tuning.std()
clip_max_cell = norm_cell_tuning.mean() + 3 * norm_cell_tuning.std()
cell_clipped = np.clip(norm_cell_tuning, clip_min_cell,
clip_max_cell) #对减图进行最大和最小值的clip
#接下来plot出来细胞减图
sub_graph_cell = np.ones(shape=(512, 512), dtype=np.float64) * 127
for i in range(len(cell_clipped)):
x_list, y_list = pp.cell_location(self.cell_group[i])
sub_graph_cell[y_list, x_list] = (cell_clipped[i] + 1) * 127
pp.save_graph(self.map_name + '_Cell', sub_graph_cell, self.map_folder,
'.png', 8, 1)
def Sub_Map(self):
aligned_tif_name = pp.read_variable(save_folder +
r'\\aligned_tif_name.pkl')
average_frame_A = np.zeros(shape=(512, 512), dtype=np.float64)
average_frame_B = np.zeros(shape=(512, 512), dtype=np.float64)
for i in range(0, len(self.frame_set_A)): #得到A的平均图
temp_frame = np.float64(
cv2.imread(aligned_tif_name[self.frame_set_A[i]], -1))
average_frame_A = average_frame_A + temp_frame / len(
self.frame_set_A)
for i in range(0, len(self.frame_set_B)): #得到B的平均图
temp_frame = np.float64(
cv2.imread(aligned_tif_name[self.frame_set_B[i]], -1))
average_frame_B = average_frame_B + temp_frame / len(
self.frame_set_B)
#接下来做减图和clip
sub_graph = average_frame_A - average_frame_B
clip_min = sub_graph.mean() - 3 * sub_graph.std()
clip_max = sub_graph.mean() + 3 * sub_graph.std()
sub_graph_clipped = np.clip(sub_graph, clip_min,
clip_max) #对减图进行最大和最小值的clip
norm_sub_graph = (sub_graph_clipped - sub_graph_clipped.min()) / (
sub_graph_clipped.max() - sub_graph_clipped.min())
#保存原始图片
pp.save_variable(
norm_sub_graph,
self.map_folder + r'\\' + self.map_name + '_Graph.pkl')
#以上得到了clip且归一化了的map
real_sub_map = np.uint8(np.clip(norm_sub_graph * 255, 0, 255))
pp.save_graph(map_name, real_sub_map, self.map_folder, '.png', 8, 1)
#接下来画滤波后的
sub_map_filtered = scimg.filters.gaussian_filter(real_sub_map, 1)
pp.save_graph(map_name + '_Filtered', sub_map_filtered,
self.map_folder, '.png', 8, 1)
def T_Test_Map(self):
t_graph = np.zeros(shape=(512, 512, 3), dtype=np.uint8)
cell_t = [] #定义每个细胞的AB之差t值。效应量= t/sqrt(N)t值越大A越强，为负则越小B越强。
cell_p = [] #定义每个细胞AB差异的显著性p
cell_effect_size = [] #定义每个细胞的AB效应量。
for i in range(0, np.shape(self.spike_train)[0]): #全部细胞循环
set_size = min(len(self.frame_set_A),
len(self.frame_set_B)) #先定义配对的样本大小
temp_A_set = random.sample(
list(self.spike_train[i, self.frame_set_A[:]]),
set_size) #在两个刺激下，都随机选择N个
temp_B_set = random.sample(
list(self.spike_train[i, self.frame_set_B[:]]), set_size)
[temp_t, temp_p] = stats.ttest_rel(temp_A_set, temp_B_set)
cell_t.append(temp_t)
cell_p.append(temp_p) #按顺序把p和t加进来
if temp_p < 0.05: #显著检验
cell_effect_size.append(temp_t / np.sqrt(set_size))
else:
cell_effect_size.append(0)
norm_cell_effect_size = cell_effect_size / abs(
np.asarray(cell_effect_size)).max()
for i in range(0, len(norm_cell_effect_size)):
if norm_cell_effect_size[i] > 0: #大于零为红，小于零为蓝
x_list, y_list = pp.cell_location(self.cell_group[i])
t_graph[
y_list, x_list,
2] = norm_cell_effect_size[i] * 255 #注意CV2读写颜色顺序是BGR= =
#index_graph[y_list,x_list,2] = norm_preference_index[i]*255
else:
x_list, y_list = pp.cell_location(self.cell_group[i])
t_graph[y_list, x_list,
0] = abs(norm_cell_effect_size[i]) * 255
pp.save_graph(self.map_name + r'_T_Graph', t_graph, self.map_folder,
'.png', 8, 1)
def Tuning_Index(self):
preference_index = np.zeros(shape=(np.shape(self.spike_train)[0], 1),
dtype=np.float64)
for i in range(0, np.shape(self.spike_train)[0]): #全部细胞循环
temp_cell_A = 0
for j in range(0, len(self.frame_set_A)): #叠加平均刺激setA下的细胞反应
temp_cell_A = temp_cell_A + self.spike_train[
i, self.frame_set_A[j]] / len(self.frame_set_A)
temp_cell_B = 0
for j in range(0, len(self.frame_set_B)):
temp_cell_B = temp_cell_B + self.spike_train[
i, self.frame_set_B[j]] / len(self.frame_set_B)
preference_index[i] = temp_cell_A - temp_cell_B
norm_preference_index = preference_index / abs(
preference_index).max()
index_graph = np.zeros(shape=(512, 512, 3), dtype=np.uint8)
for i in range(0, len(preference_index)):
if preference_index[i] > 0:
x_list, y_list = pp.cell_location(self.cell_group[i])
index_graph[
y_list, x_list,
2] = norm_preference_index[i] * 255 #注意CV2读写颜色顺序是BGR= =
#index_graph[y_list,x_list,2] = norm_preference_index[i]*255
else:
x_list, y_list = pp.cell_location(self.cell_group[i])
index_graph[y_list, x_list,
0] = abs(norm_preference_index[i]) * 255
#绘图
pp.save_graph(self.map_name + r'_Tuning_Index', index_graph,
self.map_folder, '.png', 8, 1)
def Union_Graph(self):
union_map = np.zeros(shape=(512, 512), dtype=np.float64)
for i in range(np.shape(self.all_cell_graphs)[2]):
union_map = union_map + self.all_cell_graphs[:, :, i]
union_map = (union_map > 0) * 255
pp.save_graph('Union', union_map, graph_folder, '.png', 8, 1)