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state_correlation_PCA.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
from sklearn.decomposition import PCA
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 - 5 * 30:looming_time + 115 * 30, 1:2]
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__':
a = read_csv(path=r'D:/3D_behavior/Arousal_behavior/Arousal_result_all',
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_result_all/BeAMapping/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',
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_result_all/BeAMapping/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_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_list2 = []
# for i in range(len(file_list_2)):
# sub_list3 = pre_data(file_list_2[i], b, i, state="looming_time2")
# # print(sub_list2)
# Female_list2.append(sub_list3)
# Female_list2 = sort_data(Female_list2)
#
# Female_list3 = []
# for i in range(len(file_list_2)):
# sub_list4 = pre_data(file_list_2[i], b, i, state="looming_time3")
# # print(sub_list2)
# Female_list3.append(sub_list4)
# # Female_list3 = sort_data(Female_list3)
#
# Female_list4 = []
# for i in range(len(file_list_2)):
# sub_list4 = pre_data(file_list_2[i], b, i, state="looming_time4")
# # print(sub_list2)
# Female_list4.append(sub_list4)
# # Female_list3 = sort_data(Female_list3)
result_list = Male_list
Behavior_cal = []
for j in range(len(result_list)):
t = 0
for i in range(len(result_list[j])):
if result_list[j][i] != 0:
t = t + 1
# print(t)
Behavior_cal.append(t)
print(Behavior_cal)
all = Male_list + Female_list
concat_list = [j for i in all for j in i]
label_fre = []
label = [i for i in range(1, 17)]
for i in range(len(Male_list[0])):
label_fre1 = [label[i], Male_list[0][i]]
label_fre.append(label_fre1)
print(label_fre)
# X = np.array(Female_list3)
# pca = PCA(n_components=1) # 降到2维
# pca.fit(X) # 训练
# newX = pca.fit_transform(X) # 降维后的数据
# newX = newX.tolist()
# # PCA(copy=True, n_components=2, whiten=False)
# print(pca.explained_variance_ratio_) # 输出贡献率
# print(newX) # 输出降维后的数据
# explicit function to normalize array
def normalize(arr, t_min, t_max):
norm_arr = []
diff = t_max - t_min
diff_arr = max(arr) - min(arr)
for i in arr:
temp = (((i - min(arr)) * diff) / diff_arr) + t_min
norm_arr.append(temp)
return norm_arr
range_to_normalize = (0, 1)
normalized_array_1d = normalize(concat_list,
range_to_normalize[0],
range_to_normalize[1])
X = np.array_split(normalized_array_1d, len(file_list_1)+len(file_list_2)) # reshape:文件个数
all_data = pd.DataFrame(X)
all_data = np.transpose(all_data)
all_data.to_csv("D:\\3D_behavior\\Arousal_behavior\\Arousal_result_all\\Analysis_result\\State_space"
"\\looming_behavior_fre\\Wakefulness.csv")