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SP_behavior_looming_state_space.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
matplotlib.use('Qt5Agg')
# color_list = sns.color_palette("Spectral", 40)
"""
Arousal Behavior Class Combine-SP behavior 2022.09.23
2、Running:[23, 24, 38] 3、Trotting:[6, 7, 36]
4、Walking:[19, 30, 31] 5、Stepping:[10, 18]
6、Left turning:[26] 7、Right turning:[16]
8、Rising:[17] 9、Standing:[29]
10、Climbing:[8, 9]
11、Sniffing:[2, 3, 4, 11, 21, 22, 25, 33, 37]
12、Grooming:[20, 34, 40] 13、Immobility:[1, 12, 13]
14、LORR:[27, 28, 39] 15、Paralysis:[5]
16、Twitching:[14, 15, 32, 35]
"""
behavior_dict = {
'flight': '#f25832',
'Running': '#cd5c5c',
'Trotting': '#fc7c59',
'Walking': '#ff9e80',
'Stepping': '#ffbfa9',
'Left turning': '#d3afa4',
'Right turning': '#e3c9c2',
'Rising': '#f4a460',
'Standing': '#ffcc00',
'Climbing': '#ffe735',
'Sniffing': '#ff6e00',
'Grooming': '#48a36d',
'Immobility': '#c896c8',
'LORR': '#4798b3',
'Paralysis': '#8bb9cc',
'Twitching': '#c5dce5'
}
color_list = list(behavior_dict.values())
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, special_time_start, special_time_end):
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 = []
# special_time_end = 27000
# special_time_start = 0
# movement_label_num = 15
for j in range(1, len(segBoundary), 1):
if segBoundary[j] >= special_time_end > segBoundary[j - 1]:
stop_num = j
for k in range(1, len(segBoundary), 1):
if special_time_start == 0:
start_num = 0
elif segBoundary[k] >= special_time_start > segBoundary[k - 1]:
start_num = k - 1
for i in range(start_num, stop_num + 1, 1):
# for i in range(641, 671, 1):
if label[i] == movement_label_num:
if i == start_num:
if i == 0:
seg_space = segBoundary[i]
seg_space_list.append(seg_space)
seg_before = 0
seg_before_list.append(seg_before)
else:
# print(i)
seg_space = segBoundary[i] - special_time_start
seg_space_list.append(seg_space)
seg_before = special_time_start
seg_before_list.append(seg_before)
# print(seg_before_list, seg_space_list)
elif i == stop_num:
seg_space = special_time_end - segBoundary[i - 1]
seg_space_list.append(seg_space)
seg_before = segBoundary[i - 1]
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_space_list.remove(seg_space_list[1])
# seg_before_list.remove(seg_before_list[1])
# 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] - special_time_start
x_broken = seg_space_list[i]
if x_left < 0:
x_broken = seg_space_list[i] + x_left
x_left = 0
elif x_broken < 0:
x_broken = 0
else:
x_left = seg_before_list[i] - special_time_start
x_broken = seg_space_list[i]
x_range_list.append((x_left, x_broken))
# seg_before_list.insert(0, seg_before_list[0])
return x_range_list
def data_combine(file_path, special_time_start, special_time_end):
# special_time1 = special_time_start * 60 * 30
# special_time2 = special_time_end * 60 * 30
special_time1 = special_time_start
special_time2 = special_time_end
data = []
for i in range(1, 17):
behavior = pre_data(file_path, i, special_time1, special_time2)
data.append(behavior)
return data
if __name__ == '__main__':
"""
Arousal looming
"""
# mouse_state = 'RoRR'
# a = read_csv(path=r'D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new',
# name="video_info.xlsx", column="looming_time4",
# state_name="Male_{}".format(mouse_state)) # 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_analysis_new\Arousal_result_final\looming_new\BeAOutputs\csv_file_output",
# 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_analysis_new\Arousal_result_final\looming_new',
# name="video_info.xlsx", column="looming_time4",
# state_name="Female_{}".format(mouse_state)) # 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_analysis_new\Arousal_result_final\looming_new\BeAOutputs\csv_file_output",
# name="{}_Feature_Space".format(item))
# file_list_2.append(file_list1)
# file_list_2 = list(np.ravel(file_list_2))
# # time = 4
# for time in range(1, 5):
#
# Male_data = []
# Male_time = []
# for i in range(0, len(file_list_1)):
# # single_data = data_combine(file_list_1[i], 10, 15)
# # print(a['looming_time{}'.format(time)][i] + 0 * 30, a['looming_time{}'.format(time)][i] + 120 * 30)
#
# single_data = data_combine(file_list_1[i], a['looming_time{}'.format(time)][i],
# a['looming_time{}'.format(time)][i] + 120 * 30)
# # single_data = data_combine(file_list_1[i], 0, 108000)
#
# Male_data.append(single_data)
#
# Female_data = []
# for i in range(0, len(file_list_2)):
# # single_data = data_combine(file_list_2[i], 10, 15)
# # single_data = data_combine(file_list_2[i], 0, 108000)
# single_data = data_combine(file_list_2[i], b['looming_time{}'.format(time)][i],
# b['looming_time{}'.format(time)][i] + 120 * 30)
# # single_data = data_combine(file_list_2[i], 6618, 6618 + 2 * 1800)
# Female_data.append(single_data)
#
# # plt.figure(figsize=(10, 3), dpi=300)
# fig = plt.figure(figsize=(4, 3), dpi=300)
# ax = fig.add_subplot(111)
# for j in range(len(Male_data)):
# for i in range(len(Female_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(Female_data[5])):
# plt.broken_barh(Female_data[5][i], (10, 0.8), facecolors=color_list[i])
#
# plt.axhline(y=4.9, linewidth=1.5, color='black', linestyle='--')
# plt.yticks([2.5, 7.5], ['Males', 'Females'], fontsize=12, rotation=90)
# # plt.xticks([0, 18000], ['0', '10'], fontsize=12)
# plt.xticks([0, 3600], ['0', '2'])
# # plt.xticks([0, 18000, 36000, 54000, 72000, 90000, 108000], ['0', '10', '20', '30', '40', '50', '60'])
# plt.tight_layout()
# # plt.axis('off')
# ax.spines['right'].set_visible(False)
# ax.spines['top'].set_visible(False)
# for axis in ['top', 'bottom', 'left', 'right']:
# ax.spines[axis].set_linewidth(1.5)
# plt.show()
# plt.savefig(
# 'D:/3D_behavior/Arousal_behavior/Arousal_analysis_new/Analysis/state_space/looming_{}_stage_{}_v22.tiff'
# .format(mouse_state, time), dpi=300)
#
# plt.close()
"""
test code
"""
# file_path = file_list_1[3]
# special_time_start = a['looming_time{}'.format(4)][0]
# special_time_end = a['looming_time{}'.format(4)][0] + 2 * 1800
#
# all_data = []
# for movement_label_num in range(0, 16):
# # movement_label_num = 10
#
# 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 = []
#
# # special_time_end = 27000
# # special_time_start = 0
# # movement_label_num = 15
# for j in range(1, len(segBoundary), 1):
# if segBoundary[j] >= special_time_end > segBoundary[j - 1]:
# stop_num = j
#
# for k in range(1, len(segBoundary), 1):
# if special_time_start == 0:
# start_num = 0
#
# elif segBoundary[k] >= special_time_start > segBoundary[k - 1]:
# start_num = k - 1
#
# for i in range(start_num +1, stop_num + 1, 1):
# # for i in range(641, 671, 1):
# if label[i] == movement_label_num:
# if i == start_num:
# if i == 0:
# seg_space = segBoundary[i]
# seg_space_list.append(seg_space)
# seg_before = 0
# seg_before_list.append(seg_before)
# else:
# # print(i)
# seg_space = segBoundary[i] - special_time_start
# seg_space_list.append(seg_space)
# seg_before = special_time_start
# seg_before_list.append(seg_before)
# # print(seg_before_list, seg_space_list)
#
# elif i == stop_num:
# seg_space = special_time_end - segBoundary[i - 1]
# seg_space_list.append(seg_space)
#
# seg_before = segBoundary[i - 1]
# 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_space_list.remove(seg_space_list[1])
# # seg_before_list.remove(seg_before_list[1])
#
# # seg_before_list.insert(0, seg_before_list[0])
# # for i in range(len(seg_before_list)):
# # if seg_before_list[i] < special_time_start:
# # seg_space_list[i] =
# # seg_before_list[i] = special_time_start
#
# x_range_list = []
#
# for i in range(0, len(seg_before_list), 1):
# x_left = seg_before_list[i] - special_time_start
# x_broken = seg_space_list[i]
#
# if x_left < 0:
# x_broken = seg_space_list[i] + x_left
# x_left = 0
# else:
# x_left = seg_before_list[i] - special_time_start
# x_broken = seg_space_list[i]
#
# x_range_list.append((x_left, x_broken))
#
# all_data.append(x_range_list)
"""
单只老鼠使用
"""
mouse_state = 'Wakefulness'
a = read_csv(path=r'D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Arousal_result_final\looming_new',
name="video_info.xlsx", column="looming_time4",
state_name="Male_{}".format(mouse_state)) # 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_analysis_new\Arousal_result_final\looming_new\BeAOutputs\csv_file_output_new",
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_analysis_new\Arousal_result_final\looming_new',
name="video_info.xlsx", column="looming_time4",
state_name="Female_{}".format(mouse_state)) # 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_analysis_new\Arousal_result_final\looming_new\BeAOutputs\csv_file_output_new",
name="{}_Feature_Space".format(item))
file_list_2.append(file_list1)
file_list_2 = list(np.ravel(file_list_2))
"""
单只老鼠所有结果
"""
# Male_data = []
# single_data = data_combine(file_list_1[4], 0, 10*1800)
# Male_data.append(single_data)
#
# # fig = plt.figure(figsize=(12.5, 1), dpi=300)
# fig = plt.figure(figsize=(5, 1), dpi=300)
# ax = fig.add_subplot(111)
# 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])
#
# plt.tight_layout()
# plt.axis('off')
#
# plt.savefig(r'D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Analysis\state_space\M_15/'
# 'Wake_all_v23.tiff', transparent=True, dpi=300)
# plt.close()
"""
单只老鼠looming后时刻的数据
"""
mouse_state = "Wakefulness"
# time = 1
for time in range(1, 5):
mouse_num = 4
Male_data = []
single_data = data_combine(file_list_1[mouse_num], a['looming_time{}'.format(time)][mouse_num] - 5 * 30,
a['looming_time{}'.format(time)][mouse_num] + 115 * 30)
Male_data.append(single_data)
fig = plt.figure(figsize=(5, 1), dpi=300)
ax = fig.add_subplot(111)
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])
plt.tight_layout()
plt.axis('off')
plt.savefig(r'D:\3D_behavior\Arousal_behavior\Arousal_analysis_new\Analysis\state_space\M_15/'
'{}_looming_time_{}_v23.tiff'.format(mouse_state, time), transparent=True, dpi=300)
plt.close()