def folder_generation(self,
upper_folder): #给定特定目录，在其下级建立result和Aligned文件夹，并返回目录
result_folder = upper_folder + r'\results'
pp.mkdir(result_folder)
aligned_frame_folder = result_folder + '\Aligned_Frames' #保存对齐过后图片的文件夹
pp.mkdir(aligned_frame_folder)
return result_folder, aligned_frame_folder
def plot_all(self):
all_keys = list(self.all_stim_graph.keys())
plt.figure(figsize=(200, 7))
plt.title('Correlation Plot of Spon & All plots Graph')
plt.xlabel('Frames')
plt.ylabel('Pearson R')
plt.grid() #网格
plt.axhline(y=0, color='#d62728') #红色
plt.ylim((-0.4, 0.4))
plt.xlim((0, self.N_frame))
x_ticks = np.arange(0, self.N_frame, 50)
plt.xticks(x_ticks)
correlation_folder = save_folder + r'\\Correlation_vs_Stim_Maps'
pp.mkdir(correlation_folder)
all_colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']
for i in range(len(all_keys)): #cycle all stim graphs
if self.shuffle_flag == True: # if true, add shuffle data in it.
down_lim = self.Shuffled_Dictionary[all_keys[i]][0]
shuffle_avr = self.Shuffled_Dictionary[all_keys[i]][1]
up_lim = self.Shuffled_Dictionary[all_keys[i]][2]
plt.fill_between(range(self.N_frame), up_lim, 0, color='#dfdfdf')
plt.fill_between(range(self.N_frame), down_lim, 0, color='#dfdfdf')
plt.plot(shuffle_avr, color='#8d8d8d')
current_plot = self.correlation_calculator(
self.all_stim_graph[all_keys[i]])
plt.plot(current_plot, color=all_colors[i], label=all_keys[i])
plt.legend()
plt.savefig(correlation_folder + r'\\All_plots.png')
plt.close('all')
def __init__(self,save_folder,target_graph,clustered_data,correlation_name):
self.target_graph = target_graph
self.clustered_data = clustered_data
self.correlation_name = correlation_name
self.correlation_folder = save_folder+r'\\Correlation_Folder'
pp.mkdir(self.correlation_folder)
def __init__(self, stim_set_A, stim_set_B, map_name, save_folder,
cell_find_type, spike_train, cell_group):
self.map_folder = save_folder + r'\\Stim_Graphs_' + cell_find_type
pp.mkdir(self.map_folder)
self.stim_set_A = stim_set_A
self.stim_set_B = stim_set_B
self.map_name = map_name
self.spike_train = pp.read_variable(spike_train)
self.cell_group = pp.read_variable(cell_group)
def __init__(self, save_folder, spike_train_name, shuffle_times,
shuffle_type):
self.save_folder = save_folder
self.spike_train = pp.read_variable(save_folder + r'\\' +
spike_train_name)
self.N_cell, self.N_frame = np.shape(self.spike_train)
self.shuffled_folder = save_folder + r'\\Shuffled_trains'
pp.mkdir(self.shuffled_folder)
self.shuffle_times = shuffle_times #How many times you want to shuffle
self.shuffle_type = shuffle_type ####in_frame or cross_frames
def condition_spikes(self):
self.radar_folder = save_folder + r'\Cell_Tuning_' + self.cell_type
pp.mkdir(self.radar_folder)
self.stim_set = list(
self.Frame_Stim_Check.keys())[:-1] #所有刺激的set，去掉了Stim-Off
self.cell_condition_data = np.zeros(shape=(np.shape(
self.spike_train)[0], len(self.stim_set)),
dtype=np.float64)
for i in range(0, np.shape(self.spike_train)[0]): #循环细胞
for j in range(0, len(self.stim_set)): #循环全部condition
temp_frame = self.Frame_Stim_Check[str(
self.stim_set[j])] #当前condition的全部帧id
self.cell_condition_data[i, j] = self.spike_train[
i, temp_frame[:]].mean()
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)
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 25 17:02:24 2019
@author: ZR
利用这一段小代码对Spike_Train数据体进行shuffle，可以与未shuffle的进行比较，从而验证结论的可靠性。
"""
import General_Functions.my_tools as pp
pp.mkdir('ab')
def plot_initialize(self): #进行绘图的初始化准备
self.spike_train_folder = self.save_folder + '\Spike_Trains_' + self.cell_find_type
pp.mkdir(self.spike_train_folder)
self.cell_Num, self.frame_Num = np.shape(self.spike_train)
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 14 10:37:05 2019
@author: ZR
"""
import numpy as np
import matplotlib.pyplot as plt
import General_Functions.my_tools as pp
#%%首先得到每个cell对各个condition的dF/F平均。
#注意，如果刺激没有0id的话，这里需要做一些修改。
have_blank = True #condition里是否有condition0，如果有的话则为真，否则为假。
radar_folder = save_folder + r'\Radar_Map'
pp.mkdir(radar_folder)
cell_condition_data = np.zeros(shape=(np.shape(spike_train)[0], len(stim_set)),
dtype=np.float64) #得到每个细胞的condition tuning 数据
for i in range(0, np.shape(spike_train)[0]): #循环细胞
for j in range(0, len(stim_set)): #循环全部condition
temp_frame = Frame_Stim_Check[str(stim_set[j])] #当前condition的全部帧id
cell_condition_data[i, j] = spike_train[i, temp_frame[:]].mean()
#%% 接下来进行雷达图的绘制。
# 中文和负号的正常显示
plt.rcParams['font.sans-serif'] = 'Microsoft YaHei'
plt.rcParams['axes.unicode_minus'] = False
# 使用ggplot的绘图风格
plt.style.use('ggplot')
# 构造数据
if have_blank == True:
cell_condition_data = cell_condition_data[:, 1:] #去掉conditionid的影响。
feature = list(Frame_Stim_Check.keys())[1:(len(Frame_Stim_Check) - 1)]
