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SP_behavior_correlation.py
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# %%
# 徐阳
# 开发时间：2021/9/11 20:01
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter
import matplotlib
from sklearn import *
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler # to standardize the features
matplotlib.use('Qt5Agg')
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)
"""
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=""):
df1 = pd.read_csv(file_path)
looming_time = int(dataframe.at[num, state])
# data = df1.iloc[looming_time - 17995:looming_time, 1:2]
data = df1.iloc[looming_time - 5*30:looming_time+115*30, 1:2] # looming专用 2分钟计算
data1 = data.iloc[:, 0].tolist()
class_type = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0,
11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0}
for line in data1:
if line not in class_type:
class_type[line] = 0
else:
class_type[line] += 1
list_1 = list(class_type.values())
return list_1
def sort_data(list_1):
male_std = []
for i in range(len(list_1)):
male_1 = np.std(list_1[i])
male_std.append(male_1)
dictionary = dict(zip(male_std, list_1))
# dictionary1 = {l: sorted(m) for l, m in dictionary.items()}
# dictionary = sorted(dictionary.keys())
sort_list = []
# convert the dictionary to list using dict.keys
dictlist = list(dictionary.keys())
# sort the list
dictlist.sort()
# Print the corresponding key and value by traversing this list
for key in dictlist:
# print key and value
# print(key, ":", dictionary[key])
sort_list.append(dictionary[key])
return sort_list
if __name__ == '__main__':
"""
SP Arousal 60min
"""
# """
# Wakefulness状态
# """
# 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_Wakefulness") # 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",
# 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_Wakefulness") # 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",
# name="{}_Movement_Labels".format(item))
# file_list_2.append(file_list1)
# file_list_2 = list(np.ravel(file_list_2))
#
# Male_list = []
# for i in range(len(file_list_1)):
# sub_list1 = pre_data(file_list_1[i], a, i, state="looming_time2")
# # print(sub_list1)
# Male_list.append(sub_list1)
# # Male_list = sort_data(Male_list)
#
# Female_list = []
# for i in range(len(file_list_2)):
# sub_list2 = pre_data(file_list_2[i], b, i, state="looming_time2")
# # print(sub_list2)
# Female_list.append(sub_list2)
# # Female_list = sorted(Female_list)
# # Female_list = sort_data(Female_list)
#
# Wake = Male_list + Female_list
# Wake = pd.DataFrame(Wake)
#
# """
# 主成分分析
# """
# # pca = PCA(n_components=3)
# # Wake_pca = pca.fit(Wake.T)
# # print(pca.explained_variance_ratio_)
# # Wake1 = Wake_pca.components_.T
# # Wake1 = pd.DataFrame(Wake1)
# # Wake1.to_csv('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/correlation/wake.csv')
#
# """
# RORR状态
# """
# c = 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_3 = []
# for item in c['Video_name'][0:10]:
# item = item.replace("-camera-0", "")
# file_list3 = search_csv(
# path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2"
# r"/BeAMapping",
# name="{}_Movement_Labels".format(item))
# file_list_3.append(file_list3)
# file_list_3 = list(np.ravel(file_list_3))
#
# d = 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") # Male_Wakefulness
# file_list_4 = []
# for item in d['Video_name'][0:10]:
# item = item.replace("-camera-0", "")
# file_list4 = search_csv(
# path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2"
# r"/BeAMapping",
# name="{}_Movement_Labels".format(item))
# file_list_4.append(file_list4)
# file_list_4 = list(np.ravel(file_list_4))
#
# for j in range(2, 14, 2):
# state = 'looming_time{}'.format(j)
#
# Male_RORR = []
# for i in range(len(file_list_3)):
# sub_list3 = pre_data(file_list_3[i], c, i, state=state)
# # print(sub_list2)
# Male_RORR.append(sub_list3)
# # Male_RORR = sort_data(Male_RORR)
#
# Female_RORR = []
# for i in range(len(file_list_4)):
# sub_list4 = pre_data(file_list_4[i], d, i, state=state)
# # print(sub_list2)
# Female_RORR.append(sub_list4)
# # Female_RORR = sort_data(Female_RORR)
#
# RORR = Male_RORR + Female_RORR
# RORR = sort_data(RORR)
#
# """
# 主成分分析
# """
# # RORR = pd.DataFrame(RORR)
# # pca = PCA(n_components=3)
# # RORR_pca = pca.fit(RORR.T)
# # print(pca.explained_variance_ratio_)
# # RORR1 = RORR_pca.components_.T
# # RORR1 = pd.DataFrame(RORR1)
# # RORR1.to_csv('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/correlation/RORR_{}min.csv'.format(j * 5))
#
# # all_list = Male_list + Female_list + Female_list2 + Female_list3
# all_list = Wake + RORR
#
# X = np.corrcoef(all_list)
# # ax = sns.heatmap(X, center=0, cmap="YlGnBu")
#
# # sort_list = sort_data(all_list)
# # x_ticks = ['', '', '', 'Wakefulness', '', '', '', '', '', '', 'RoRR', '', '', '', ]
# # y_ticks = ['Wakefulness', 'RoRR']
# """
# 计算相关性，绘制热图
# """
# X = np.corrcoef(all_list)
# fig, ax = plt.subplots(figsize=(7, 6), dpi=300)
# # ax = sns.heatmap(X, center=0, cmap="Spectral", yticklabels=False, xticklabels=False, vmin=-1, vmax=1)
# ax = sns.heatmap(X, center=0, cmap="vlag", yticklabels=False, xticklabels=False, vmin=-1, vmax=1)
# # ax.set_xticklabels(['Wakefulness', 'RoRR'])
#
# cbar = ax.collections[0].colorbar
# # here set the labelsize by 20
# cbar.ax.tick_params(labelsize=25)
# plt.tight_layout()
# plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/correlation'
# '/SP_behavior_Wake_{}min_v2.tiff'.format(j * 5), dpi=300)
# plt.show()
# plt.close()
"""
SP behavior 60min corr SP Arousal 60min
"""
# """
# Wakefulness状态
# """
# a = read_csv(path=r'D:/3D_behavior/Arousal_behavior/Arousal_result_all/SP_behavior_60min',
# name="video_info.xlsx", column="looming_time1", state_name="Male_Wakefulness") # 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/SP_behavior_60min/new_results/BeAMapping-replace",
# 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/SP_behavior_60min',
# name="video_info.xlsx", column="looming_time1", state_name="Female_Wakefulness") # 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/SP_behavior_60min/new_results/BeAMapping-replace",
# name="{}_Movement_Labels".format(item))
# file_list_2.append(file_list1)
# file_list_2 = list(np.ravel(file_list_2))
#
# Male_list = []
# for i in range(len(file_list_1)):
# sub_list1 = pre_data(file_list_1[i], a, i, state="looming_time12")
# # print(sub_list1)
# Male_list.append(sub_list1)
# # Male_list = sort_data(Male_list)
#
# Female_list = []
# for i in range(len(file_list_2)):
# sub_list2 = pre_data(file_list_2[i], b, i, state="looming_time12")
# # print(sub_list2)
# Female_list.append(sub_list2)
# # Female_list = sorted(Female_list)
# # Female_list = sort_data(Female_list)
#
# Wake = Male_list + Female_list
# # Wake = pd.DataFrame(Wake)
#
# """
# RORR状态
# """
# c = 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_3 = []
# for item in c['Video_name'][0:10]:
# item = item.replace("-camera-0", "")
# file_list3 = search_csv(
# path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2"
# r"/BeAMapping",
# name="{}_Movement_Labels".format(item))
# file_list_3.append(file_list3)
# file_list_3 = list(np.ravel(file_list_3))
#
# d = 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") # Male_Wakefulness
# file_list_4 = []
# for item in d['Video_name'][0:10]:
# item = item.replace("-camera-0", "")
# file_list4 = search_csv(
# path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2"
# r"/BeAMapping",
# name="{}_Movement_Labels".format(item))
# file_list_4.append(file_list4)
# file_list_4 = list(np.ravel(file_list_4))
#
# for j in range(12, 14, 2):
# # Wake = []
# RORR = []
# state = 'looming_time{}'.format(j)
#
# # Male_list = []
# # for i in range(len(file_list_1)):
# # sub_list1 = pre_data(file_list_1[i], a, i, state=state)
# # # print(sub_list1)
# # Male_list.append(sub_list1)
# # # Male_list = sort_data(Male_list)
# # Female_list = []
# # for i in range(len(file_list_2)):
# # sub_list2 = pre_data(file_list_2[i], b, i, state=state)
# # # print(sub_list2)
# # Female_list.append(sub_list2)
# # # Female_list = sorted(Female_list)
# # # Female_list = sort_data(Female_list)
# # Wake = Male_list + Female_list
# # # Wake = pd.DataFrame(Wake)
# # Wake = sort_data(Wake)
#
# Male_RORR = []
# for i in range(len(file_list_3)):
# sub_list3 = pre_data(file_list_3[i], c, i, state=state)
# # print(sub_list2)
# Male_RORR.append(sub_list3)
# # Male_RORR = sort_data(Male_RORR)
#
# Female_RORR = []
# for i in range(len(file_list_4)):
# sub_list4 = pre_data(file_list_4[i], d, i, state=state)
# # print(sub_list2)
# Female_RORR.append(sub_list4)
# # Female_RORR = sort_data(Female_RORR)
#
# RORR = Male_RORR + Female_RORR
# RORR = sort_data(RORR)
#
# # all_list = Male_list + Female_list + Female_list2 + Female_list3
# all_list = Wake + RORR
#
# X = np.corrcoef(all_list)
# # ax = sns.heatmap(X, center=0, cmap="YlGnBu")
#
# # sort_list = sort_data(all_list)
# # x_ticks = ['', '', '', 'Wakefulness', '', '', '', '', '', '', 'RoRR', '', '', '', ]
# # y_ticks = ['Wakefulness', 'RoRR']
# """
# 计算相关性，绘制热图
# """
# X = np.corrcoef(all_list)
# fig, ax = plt.subplots(figsize=(7, 6), dpi=300)
# # ax = sns.heatmap(X, center=0, cmap="Spectral", yticklabels=False, xticklabels=False, vmin=-1, vmax=1)
# ax = sns.heatmap(X, center=0, cmap="vlag", yticklabels=False, xticklabels=False, vmin=-1, vmax=1)
# # ax.set_xticklabels(['Wakefulness', 'RoRR'])
#
# cbar = ax.collections[0].colorbar
# # here set the labelsize by 20
# cbar.ax.tick_params(labelsize=25)
# plt.tight_layout()
# plt.show()
# # plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/correlation/Wake_RORR'
# # '/SP_Wake_RORR_{}min_v3.tiff'.format(j * 5), dpi=300, transparent=True)
# # plt.close()
"""
looming wake corr Rorr
"""
"""
Wakefulness状态
"""
a = read_csv(path=r'D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new',
name="video_info.xlsx", column="looming_time1", state_name="Male_Wakefulness") # Male_Wakefulness
file_list_1 = []
for item in a['Video_name'][0:5]:
item = item.replace("-camera-0", "")
file_list1 = search_csv(
path=r"D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new\BeAOutputs\csv_file_output_new",
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_analysis_new\Arousal_result_final\looming_new',
name="video_info.xlsx",
column="looming_time1", state_name="Female_Wakefulness") # Female_Wakefulness
file_list_2 = []
for item in b['Video_name'][0:6]:
item = item.replace("-camera-0", "")
file_list1 = search_csv(
path=r"D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new\BeAOutputs\csv_file_output_new",
name="{}_Movement_Labels".format(item))
file_list_2.append(file_list1)
file_list_2 = list(np.ravel(file_list_2))
Male_list = []
for i in range(len(file_list_1)):
sub_list1 = pre_data(file_list_1[i], a, i, state="looming_time1")
# print(sub_list1)
Male_list.append(sub_list1)
Male_list = sort_data(Male_list)
Female_list = []
for i in range(len(file_list_2)):
sub_list2 = pre_data(file_list_2[i], b, i, state="looming_time1")
# print(sub_list2)
Female_list.append(sub_list2)
Female_list = sorted(Female_list)
# Female_list = sort_data(Female_list)
Wake = Male_list + Female_list
# Wake = pd.DataFrame(Wake)
"""
RORR状态
"""
c = read_csv(path=r'D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new',
name="video_info.xlsx", column="looming_time1", state_name="Male_RoRR") # Male_Wakefulness
file_list_3 = []
for item in c['Video_name'][0:5]:
item = item.replace("-camera-0", "")
file_list3 = search_csv(
path=r"D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new\BeAOutputs\csv_file_output_new",
name="{}_Movement_Labels".format(item))
file_list_3.append(file_list3)
file_list_3 = list(np.ravel(file_list_3))
d = read_csv(path=r'D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new',
name="video_info.xlsx",
column="looming_time1", state_name="Female_RoRR") # Female_Wakefulness
file_list_4 = []
for item in d['Video_name'][0:6]:
item = item.replace("-camera-0", "")
file_list4 = search_csv(
path=r"D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new\BeAOutputs\csv_file_output_new",
name="{}_Movement_Labels".format(item))
file_list_4.append(file_list4)
file_list_4 = list(np.ravel(file_list_4))
for j in range(1, 5, 1):
# Wake = []
RORR = []
state = 'looming_time{}'.format(j)
# Male_list = []
# for i in range(len(file_list_1)):
# sub_list1 = pre_data(file_list_1[i], a, i, state=state)
# # print(sub_list1)
# Male_list.append(sub_list1)
# # Male_list = sort_data(Male_list)
# Female_list = []
# for i in range(len(file_list_2)):
# sub_list2 = pre_data(file_list_2[i], b, i, state=state)
# # print(sub_list2)
# Female_list.append(sub_list2)
# # Female_list = sorted(Female_list)
# # Female_list = sort_data(Female_list)
# Wake = Male_list + Female_list
# # Wake = pd.DataFrame(Wake)
# Wake = sort_data(Wake)
Male_RORR = []
for i in range(len(file_list_3)):
sub_list3 = pre_data(file_list_3[i], c, i, state=state)
# print(sub_list2)
Male_RORR.append(sub_list3)
# Male_RORR = sort_data(Male_RORR)
Female_RORR = []
for i in range(len(file_list_4)):
sub_list4 = pre_data(file_list_4[i], d, i, state=state)
# print(sub_list2)
Female_RORR.append(sub_list4)
# Female_RORR = sort_data(Female_RORR)
RORR = Male_RORR + Female_RORR
# RORR = sort_data(RORR)
# all_list = Male_list + Female_list + Female_list2 + Female_list3
all_list = Wake + RORR
X = np.corrcoef(all_list)
# ax = sns.heatmap(X, center=0, cmap="YlGnBu")
# sort_list = sort_data(all_list)
# x_ticks = ['', '', '', 'Wakefulness', '', '', '', '', '', '', 'RoRR', '', '', '', ]
# y_ticks = ['Wakefulness', 'RoRR']
"""
计算相关性，绘制热图
"""
X = np.corrcoef(all_list)
fig, ax = plt.subplots(figsize=(7, 6), dpi=300)
# ax = sns.heatmap(X, center=0, cmap="Spectral", yticklabels=False, xticklabels=False, vmin=-1, vmax=1)
ax = sns.heatmap(X, center=0, cmap="vlag", yticklabels=False, xticklabels=False, vmin=-1, vmax=1)
# ax.set_xticklabels(['Wakefulness', 'RoRR'])
cbar = ax.collections[0].colorbar
# here set the labelsize by 20
cbar.ax.tick_params(labelsize=25)
plt.tight_layout()
plt.show()
plt.savefig(r'D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Analysis\corr_matrix\looming'
'/Wake_RORR_looming_time{}_v23.tiff'.format(j), dpi=300, transparent=True)
plt.close()