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state_convert_sequence.py
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# -*- coding:utf-8 -*-
# @FileName :state_convert_sequence.py
# @Time :2022/4/24 10:19
# @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
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)
"""
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((16, 16))
fre_list = []
looming_time = int(dataframe.at[num, state])
start = looming_time - 600 * 30 # 起始时间
end = looming_time + 0 * 30 # 终止时间
df2 = pd.read_csv(file_path)
data = df2.iloc[start:end, 1:2]
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, 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 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())
# if behavior_fre.count(0) == 15:
# behavior_fre[8] = 50
# behavior_fre[14] = 9000 - behavior_fre[8]
# # print(behavior_fre)
A = normalize_2d(A)
behavior_fre_norm = behavior_fre / np.linalg.norm(behavior_fre)
for j in range(len(behavior_fre_norm)):
# A[j, j] = behavior_fre_norm[j]
A[j, j] = 0
return A
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']
color_list = ['#845EC2', '#B39CD0', '#D65DB1', '#4FFBDF', '#FFC75F',
'#D5CABD', '#B0A8B9', '#FF6F91', '#F9F871', '#D7E8F0',
'#60DB73', '#E8575A', '#008B74', '#00C0A3', '#FF9671',
'#93DEB1']
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
def getNumofCommonSubstr(str1, str2):
lstr1 = len(str1)
lstr2 = len(str2)
record = [[0 for i in range(lstr2 + 1)] for j in range(lstr1 + 1)]
# 开辟列表空间 为什么要多一位呢?主要是不多一位的话,会存在边界问题
# 多了一位以后就不存在超界问题
maxNum = 0 # 最长匹配长度
p = 0 # 匹配的起始位
for i in range(lstr1):
for j in range(lstr2):
if str1[i] == str2[j]:
# 相同则累加
record[i + 1][j + 1] = record[i][j] + 1
if record[i + 1][j + 1] > maxNum:
# 获取最大匹配长度
maxNum = record[i + 1][j + 1]
# 记录最大匹配长度的终止位置
p = i + 1
start1 = i
start2 = j
return str1[p - maxNum:p], maxNum, start1, start2
if __name__ == '__main__':
"""
SP Arousal wake 60min
"""
a = read_csv(path=r'D:/3D_behavior/Arousal_behavior/Arousal_result_all/SP_behavior_60min',
name="video_info.xlsx", column="looming_time3", state_name="Male_Wakefulness") # Male_Wakefulness
file_list_1 = []
# for item in a['Video_name'][0:len(a['Video_name'])]:
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="{}_Feature_Space".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_time3", state_name="Female_Wakefulness") # Female_Wakefulness
file_list_2 = []
# for item in b['Video_name'][0:len(a['Video_name'])]:
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="{}_Feature_Space".format(item))
file_list_2.append(file_list1)
file_list_2 = list(np.ravel(file_list_2))
movement_label_all = []
for i in range(len(file_list_2)):
data_1 = pd.read_csv(file_list_2[i])
movement_label = data_1['new_label'].to_list()
movement_label = [str(x) for x in movement_label]
movement_label = ' '.join(movement_label)
movement_label_all.append(movement_label)
for j in range(len(movement_label_all) - 1):
res = getNumofCommonSubstr(movement_label_all[j], movement_label_all[j + 1])
print(res)