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state_space_test.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
# color_list = sns.color_palette("Spectral", 40)
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, movement_label_num):
with open(file_path, 'rb') as f:
csv_data = pd.read_csv(f)
label = csv_data.loc[:, 'new_label']
segBoundary = csv_data.loc[:, 'segBoundary']
seg_space_list = []
seg_before_list = []
# movement_label_num = 3
# x = 4290
for i in range(0, len(segBoundary), 1):
# for i in range(641, 671, 1):
if label[i] == movement_label_num:
if i == 0:
seg_space = segBoundary[i]
seg_space_list.append(seg_space)
seg_before = 0
seg_before_list.append(seg_before)
else:
seg_space = segBoundary[i] - segBoundary[i - 1]
seg_space_list.append(seg_space)
seg_before = segBoundary[i - 1]
seg_before_list.append(seg_before)
# seg_before_list.insert(0, seg_before_list[0])
x_range_list = []
for i in range(0, len(seg_before_list), 1):
x_left = seg_before_list[i]
x_broken = seg_space_list[i]
x_range_list.append((x_left, x_broken))
# x_range_list.insert(0, [0, seg_before_list[0]])
return x_range_list
def data_combine(file_path):
data = []
for i in range(1, 17):
behavior = pre_data(file_path, i)
data.append(behavior)
return data
if __name__ == '__main__':
a = read_csv(path=r'D:/3D_behavior/Arousal_behavior/Arousal_result_all',
name="video_info.xlsx", column="looming_time3", state_name="Male_RoRR") # Male_Wakefulness
file_list_1 = []
# for item in a['Video_name'][0:len(a['Video_name'])]:
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="{}_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',
name="video_info.xlsx", column="looming_time3", state_name="Female_RoRR") # Female_Wakefulness
file_list_2 = []
# for item in b['Video_name'][0:len(a['Video_name'])]:
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="{}_Feature_Space".format(item))
file_list_2.append(file_list1)
file_list_2 = list(np.ravel(file_list_2))
data = []
for j in range(1, 17):
with open(file_list_2[4], 'rb') as f:
csv_data = pd.read_csv(f)
label = csv_data.loc[:, 'new_label']
segBoundary = csv_data.loc[:, 'segBoundary']
seg_space_list = []
seg_before_list = []
# movement_label_num = 3
# x = 4290
# movement_label_num = [i for i in range(1, 17)]
# for i in range(0, len(segBoundary), 1):
for i in range(815, 848, 1):
if label[i] == j:
if i == 0:
seg_space = segBoundary[i]
seg_space_list.append(seg_space)
seg_before = 0
seg_before_list.append(seg_before)
else:
seg_space = segBoundary[i] - segBoundary[i - 1]
seg_space_list.append(seg_space)
seg_before = segBoundary[i - 1]
seg_before_list.append(seg_before)
# seg_before_list.insert(0, seg_before_list[0])
x_range_list = []
for i in range(0, len(seg_before_list), 1):
x_left = seg_before_list[i]
x_broken = seg_space_list[i]
x_range_list.append((x_left, x_broken))
# behavior = pre_data(file_path, i)
data.append(x_range_list)
# Male_data = []
# for i in range(4, 5):
# single_data = data_combine(file_list_1[i])
# Male_data.append(single_data)
#
# Female_data = []
# for i in range(4, 5):
# single_data = data_combine(file_list_2[i])
# Female_data.append(single_data)
plt.figure(figsize=(5, 1), dpi=300)
# plt.figure(figsize=(15, 3), dpi=300)
# for j in range(len(Male_data)):
# for i in range(len(Male_data[0])):
# plt.broken_barh(Male_data[j][i], (j, 0.8), facecolors=color_list[i])
# plt.broken_barh(Female_data[j][i], (j + 5, 0.8), facecolors=color_list[i])
for i in range(len(data)):
plt.broken_barh(data[i], (0, 0.8), facecolors=color_list[i])
# plt.axhline(y=4.9, linewidth=1.0, color='black', linestyle='--')
# plt.yticks([2.5, 7.5], ['Male', 'Female'])
# plt.xticks([0, 9000, 18000], ['0', '5', '10'])
plt.xticks([0, 9000, 18000, 27000, 36000, 45000], ['0', '5', '10', '15', '20', '25'])
plt.tight_layout()
plt.axis('off')
# plt.spines['right'].set_visible(False)
# plt.spines['top'].set_visible(False)
plt.show()