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State_convert_directed_graphs.py
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# -*- coding:utf-8 -*-
# @FileName :State_convert_directed_graphs.py
# @Time :2022/6/20 14:41
# @Author :XuYang
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_chord_diagram import chord_diagram
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib
import seaborn as sns
matplotlib.use('Qt5Agg')
sys.path.append(os.path.abspath(".."))
# color_list = ['#845EC2', '#B39CD0', '#D65DB1', '#4FFBDF', '#FFC75F',
# '#D5CABD', '#B0A8B9', '#FF6F91', '#F9F871', '#D7E8F0',
# '#60DB73', '#E8575A', '#008B74', '#00C0A3', '#FF9671',
# '#93DEB1']
"""
Arousal Behavior Class Combine
1、Right turning:[1] (#845EC2) 2、Left turning:[26] (#B39CD0)
3、Sniffing:[2, 4, 10, 11, 12, 16, 22, 25] (#D65DB1)
4、Walking:[3, 6, 7, 19, 30] (#4FFBDF) 5、Trembling:[5, 15, 32, 40] (#FFC75F)
6、Climbing:[8, 29] (#D5CABD) 7、Falling:[9] (#B0A8B9)
8、Immobility:[13, 20, 33, 34] (#FF6F91) 9、Paralysis:[14, 35] (#F9F871)
10、Standing:[17] (#D7E8F0) 11、Trotting:[18, 31] (#60DB73)
12、Grooming:[21] (#E8575A) 13、Flight:[23, 38] (#008B74)
14、Running:[24, 36] (#00C0A3) 15、LORR:[27, 28, 39] (#FF9671)
16、Stepping:[37] (#93DEB1)
"""
"""
1.Locomotion : 4-Walking 11-Trotting 16-Stepping 14-Running 13-Flight 2-Left turning 1-Right turning
2.Exploration : 10-Standing 6-Climbing 7-Falling 3-Sniffing
3.Maintenance : 12-Grooming
4.Non-locomtion : 8-Immobility
5.Posture : 15-LORR 9-Paralysis 5-Trembling
"""
def search_csv(path=".", name=""): # 抓取csv文件
result = []
for item in os.listdir(path):
item_path = os.path.join(path, item)
if os.path.isdir(item_path):
search_csv(item_path, name)
elif os.path.isfile(item_path):
if name + ".csv" == item:
# global csv_result
# csv_result.append(name)
result.append(item_path)
# print(csv_result)
# print(item_path + ";", end="")
# result = item
return result
def read_csv(path='.', name="", column="", element="", state_name=""):
"""
column[0]: file_name column[1]:第一次looming时间点
sheet1：Fwake状态 sheet2：Frorr状态
"""
item_path = os.path.join(path, name)
with open(item_path, 'rb') as f:
csv_data = pd.read_excel(f, sheet_name=state_name)
# df1 = csv_data.set_index([column]) # 选取某一列数据
# sel_data = df1.loc[element] # 根据元素提取特定数据
return csv_data
def pre_data(file_path, dataframe, num, state=""):
# j = 0
A = np.zeros((5, 5))
fre_list = []
looming_time = int(dataframe.at[num, state])
start = looming_time - 300 * 30 # 起始时间
end = looming_time + 300 * 30 # 终止时间
df2 = pd.read_csv(file_path)
data = df2.iloc[start:end, 2:3]
for i in range(1, len(data)):
if data.iloc[i, 0] != data.iloc[i - 1, 0]:
a = data.iloc[i, 0] - 1
b = data.iloc[i - 1, 0] - 1
A[a, b] = A[a, b] + 1
# print(A)
class_type = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0}
for line in data.iloc[:, 0]:
if line not in class_type:
class_type[line] = 0
else:
class_type[line] += 1
class_type = dict(sorted(class_type.items(), key=lambda item: item[0])) # sort dict
# print(class_type)
behavior_fre = list(class_type.values())
behavior_fre_norm = behavior_fre / np.linalg.norm(behavior_fre)
# behavior_fre_norm = behavior_fre
for j in range(len(behavior_fre_norm)):
# A[j, j] = behavior_fre_norm[j]
A[j, j] = 0
return A
# return A, behavior_fre
def del_pre_data(data_list):
del_index = []
del_data = data_list
t = 0
for i in range(len(del_data)):
if np.any(del_data[:, [i]]) == 0 and np.any(del_data[[i], :]) == 0:
# print(i, t, i - t)
del_index.append(i - t)
t = t + 1
for item in del_index:
del_data = np.delete(del_data, item, 1)
del_data = np.delete(del_data, item, 0)
# names = ['Right turning', 'Left turning', 'Sniffing', 'Walking', 'Trembling', 'Climbing', 'Falling',
# 'Immobility', 'Paralysis', 'Standing', 'Trotting', 'Grooming', 'Flight', 'Running', 'LORR', 'Stepping']
names = ['Locomotion', 'Exploration', 'Maintenance', 'Non-locomtion', 'Posture']
# color_list = ['#845EC2', '#B39CD0', '#D65DB1', '#4FFBDF', '#FFC75F',
# '#D5CABD', '#B0A8B9', '#FF6F91', '#F9F871', '#D7E8F0',
# '#60DB73', '#E8575A', '#008B74', '#00C0A3', '#FF9671',
# '#93DEB1'] # old color list
# color_list = ['#A86A74', '#CB4042', '#FF6E00', '#EF8C92', '#89BDDE',
# '#FFB67F', '#FFC408', '#937DAD', '#478FB1', '#FFE2CC',
# '#EFB4C5', '#1d953f', '#B34C5A', '#D35889', '#A8DBD9',
# '#EACAC9'] # new color list
color_list = ['#d7b0b0', '#f3b77c', '#aacf7c', '#c69cc5', '#8BABD3'] # big cluster color
for item in del_index:
del names[item]
del color_list[item]
return del_data, names, color_list
# explicit function to normalize array
def normalize_2d(matrix):
norm = np.linalg.norm(matrix)
matrix = matrix / norm # normalized matrix
return matrix
if __name__ == '__main__':
"""
SP arousal 10min
"""
a = read_csv(path=r'D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2',
name="video_info.xlsx", column="looming_time1", state_name="Male_RoRR") # Male_Wakefulness
file_list_1 = []
for item in a['Video_name'][0:10]:
item = item.replace("-camera-0", "")
file_list1 = search_csv(
path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2"
r"/BeAMapping_correct",
name="{}_Movement_Labels".format(item))
file_list_1.append(file_list1)
file_list_1 = list(np.ravel(file_list_1))
b = read_csv(path=r'D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2',
name="video_info.xlsx", column="looming_time1", state_name="Female_RoRR") # Female_Wakefulness
file_list_2 = []
for item in b['Video_name'][0:10]:
item = item.replace("-camera-0", "")
file_list1 = search_csv(
path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2"
r"/BeAMapping_correct",
name="{}_Movement_Labels".format(item))
file_list_2.append(file_list1)
file_list_2 = list(np.ravel(file_list_2))
"""
所有老鼠数据
"""
# behavior_label = ['Right turning', 'Left turning', 'Sniffing', 'Walking', 'Trembling', 'Climbing', 'Falling',
# 'Immobility', 'Paralysis', 'Standing', 'Trotting', 'Grooming', 'Flight', 'Running', 'LORR',
# 'Stepping']
file_list = file_list_2
dataframe = b
mouse_state = 'RORR'
looming_time = 14
Male_data = np.zeros((5, 5))
Female_data = np.zeros((5, 5))
for x in range(2, looming_time, 2): # 调整间隔时长：5min/10min
# for x in range(1, 2):
state = "looming_time{}".format(x)
for num in range(len(file_list)): # 访问老鼠个体
# for num in range(2, 3):
# sub_list2 = pre_data(file_list_2[0], b, 0, state="looming_time2")
sub_list1 = pre_data(file_list[num], dataframe, num, state=state)
# print(sub_list1)
# data = Male_data + Female_data
Male_data = Male_data + sub_list1
for num in range(len(file_list_1)): # 访问老鼠个体
# for num in range(2, 3):
# sub_list2 = pre_data(file_list_2[0], b, 0, state="looming_time2")
sub_list2 = pre_data(file_list_1[num], a, num, state=state)
# print(sub_list2)
# data = Male_data + Female_data
Female_data = Female_data + sub_list2
all_data = Male_data + Female_data
del_data, names, colors = del_pre_data(all_data)
# all_data = np.zeros((5, 5))
color = ListedColormap(colors)
fig = plt.figure(figsize=(5, 5), dpi=300)
ax = fig.add_subplot(111)
chord_diagram(del_data, gap=0.03, use_gradient=True, sort='distance', cmap=color, names=names,
chord_colors=colors, fontcolor="grey", ax=ax, fontsize=10)
# chord_diagram(del_data, gap=0.03, use_gradient=True, sort='distance', cmap=color,
# chord_colors=colors, fontcolor="grey", ax=ax, fontsize=10)
# sns.heatmap(data=all_data)
# str_grd = "_gradient" if grads[0] else ""
plt.xlabel('Time (s)', fontsize=15)
plt.ylabel('Fraction', fontsize=15)
plt.tight_layout()
plt.show()
plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/State_convert/SP_Arousal_add'
'/big_cluster/All_{}_{}_{}min.tiff'.format(mouse_state, (int(x) / 2 - 1) * 10, (int(x) / 2) * 10), dpi=300)
plt.close()
"""
单只老鼠数据
"""
# file_list = file_list_2
# dataframe = b
# mouse_state = 'Wakefulness'
# looming_time = 4
# Male_data = np.zeros((5, 5))
# Female_data = np.zeros((5, 5))
#
# for x in range(2, looming_time, 2): # 调整间隔时长：5min/10min
# # for x in range(1, 2):
# state = "looming_time{}".format(x)
# for num in range(len(file_list)): # 访问老鼠个体
# # for num in range(2, 3):
# # sub_list2 = pre_data(file_list_2[0], b, 0, state="looming_time2")
# sub_list1, behavior_fre_single = pre_data(file_list[num], dataframe, num, state=state)
# # print(sub_list1)
# # data = Male_data + Female_data
# Male_data = Male_data + sub_list1
#
# # for num in range(len(file_list_1)): # 访问老鼠个体
# # # for num in range(2, 3):
# # # sub_list2 = pre_data(file_list_2[0], b, 0, state="looming_time2")
# # sub_list2 = pre_data(file_list_1[num], a, num, state=state)
# # # print(sub_list2)
# # # data = Male_data + Female_data
# # Female_data = Female_data + sub_list2
#
# all_data = Male_data + Female_data
#
# del_data, names, colors = del_pre_data(all_data)
# # all_data = np.zeros((5, 5))
# color = ListedColormap(colors)
# fig = plt.figure(figsize=(5, 5), dpi=300)
# ax = fig.add_subplot(111)
# # chord_diagram(flux, names, gap=0.03, use_gradient=True, sort='distance', cmap=color,
# # chord_colors=colors,
# # rotate_names=True, fontcolor="grey", ax=ax, fontsize=10)
#
# chord_diagram(del_data, gap=0.03, use_gradient=True, sort='distance', cmap=color, names=names,
# chord_colors=colors, fontcolor="grey", ax=ax, fontsize=10)
# # sns.heatmap(data=all_data)
#
# # str_grd = "_gradient" if grads[0] else ""
# plt.xlabel('Time (s)', fontsize=15)
# plt.ylabel('Fraction', fontsize=15)
# plt.tight_layout()
# plt.show()
# plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/State_convert'
# '/SP_Arousal_add/big_cluster/All_Wakefulness_Male_{}{}_{}_10min.tiff'.format(num,
# mouse_state,
# int(x)), dpi=300)
# plt.close()
#
# # matrix_out = del_data
# # for a in range(len(del_data)):
#
# # matrix_out = pd.DataFrame(del_data)
# # matrix_out['behavior frequency'] = behavior_fre_single
# # matrix_out = matrix_out.rename(
# # columns={0: 'Locomotion', 1: 'Exploration', 2: 'Maintenance', 3: 'Non_locomtion', 4: 'Posture'},
# # index={0: 'Locomotion', 1: 'Exploration', 2: 'Maintenance', 3: 'Non_locomtion', 4: 'Posture'})