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 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 show_cell(self): #由于前面筛掉了一些cell，所以这里图需要重新画。
thres_graph = np.zeros(shape=(512, 512), dtype=np.uint8)
for i in range(len(self.cell_group)):
x_list, y_list = pp.cell_location(self.cell_group[i])
thres_graph[y_list, x_list] = 255
RGB_graph = cv2.cvtColor(thres_graph, cv2.COLOR_GRAY2BGR) #转灰度为RGB
base_graph_path = self.save_folder + '\\' + self.save_name
cv2.imwrite(base_graph_path + '.tif', RGB_graph)
cv2.imwrite(base_graph_path + '_resized.tif',
cv2.resize(RGB_graph, (1024, 1024))) #把细胞图放大一倍并保存起来
# pp.show_cell(base_graph_path+'.tif',self.cell_group)# 在细胞图上标上细胞的编号。
pp.show_cell(base_graph_path + '_resized.tif',
self.cell_group) # 在细胞图上标上细胞的编号。
def cell_plot(self):
thres_graph = np.zeros(shape=(512, 512), dtype=np.uint8)
for i in range(len(self.cell_group)):
x_list, y_list = pp.cell_location(self.cell_group[i])
thres_graph[y_list, x_list] = 255
RGB_graph = cv2.cvtColor(thres_graph, cv2.COLOR_GRAY2BGR) #转灰度为RGB
for i in range(len(self.save_folder)):
cv2.imwrite(self.save_folder[i] + r'\\' + self.save_name + '.tif',
RGB_graph)
cv2.imwrite(
self.save_folder[i] + r'\\' + self.save_name + '_resized.tif',
cv2.resize(RGB_graph, (1024, 1024)))
pp.show_cell(self.save_folder[i] + r'\\' + self.save_name +
'_resized.tif', self.cell_group) # 在细胞图上标上细胞的编号。
pp.save_variable(
self.cell_group,
self.save_folder[i] + r'\\' + self.save_name + '_Groups.pkl')
def cell_graph_plot(self,name,features):#注意输入格式，需要features第一个维度是样本数
graph_folder = save_folder+r'\\'+name
pp.mkdir(graph_folder)
for i in range(np.shape(features)[0]):
temp_graph = np.ones(shape = (512,512),dtype = np.uint8)*127#基底图
temp_feature = features[i]
if temp_feature.max() !=0:
norm_temp_feature = (temp_feature/(abs(temp_feature).max())+1)/2#保持0不变，并拉伸至0-1的图
else:
norm_temp_feature = (temp_feature+1)/2
for j in range(len(self.cell_group)):#循环细胞
x_list,y_list = pp.cell_location(self.cell_group[j])
temp_graph[y_list,x_list] = norm_temp_feature[j]*255
temp_graph_name = graph_folder+r'\\'+name+str(i+1)
current_graph_labled = cv2.cvtColor(np.uint8(temp_graph),cv2.COLOR_GRAY2BGR)
cv2.imwrite(temp_graph_name+'.png',np.uint8(cv2.resize(current_graph_labled,(1024,1024))))
pp.show_cell(temp_graph_name+'.png',self.cell_group)
def sub_video(self): #dF video is based on 0.5 graph, use 0 as gray.
#Clip First, Attention Here, data <0 is set as 0, with data >0 clip at 2std
self.sub_series = np.clip(
self.sub_series, 0,
self.sub_series.mean() + 2 * self.sub_series.std())
#Then normalize all data,keep 0 as 0 unchanged.
self.sub_series = self.sub_series / abs(self.sub_series).max()
#Recover cell data to frame
sub_video_context = np.zeros(shape=(self.frame_Num, 512, 512),
dtype=np.uint8)
for i in range(self.frame_Num):
current_frame = np.ones(shape=(512, 512), dtype=np.uint8) * 127
for j in range(len(self.cell_group)):
x_list, y_list = pp.cell_location(self.cell_group[j])
current_frame[y_list, x_list] = np.uint8(
(self.sub_series[i, j] + 1) * 127)
sub_video_context[i, :, :] = current_frame
self.video_plot('Sub_Video_Cell.avi', sub_video_context)
def F_video(
self
): #F_video is 0-1 video on black back ground, show avtivations.
#clip F_series to +-2std
self.F_series = np.clip(self.F_series,
self.F_series.mean() - 2 * self.F_series.std(),
self.F_series.mean() + 2 * self.F_series.std())
# Then normalize F series to (0,1)
self.F_series = (self.F_series - self.F_series.min()) / (
self.F_series.max() - self.F_series.min())
#recover cell data to graph datas.
F_video_context = np.zeros(shape=(self.frame_Num, 512, 512),
dtype=np.uint8)
for i in range(self.frame_Num):
current_frame = np.zeros(shape=(512, 512), dtype=np.uint8)
for j in range(len(self.cell_group)):
x_list, y_list = pp.cell_location(self.cell_group[j])
current_frame[y_list,
x_list] = np.uint8(self.F_series[i, j] * 255)
F_video_context[i, :, :] = current_frame
self.video_plot('F_Video_Cell.avi', F_video_context)