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Arousal_State_convert_plot.py
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"""
figure plot
"""
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(".."))
"""
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())
movement_names = list(behavior_dict.keys())
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 = movement_names
# color_list = ['#845EC2', '#B39CD0', '#D65DB1', '#4FFBDF', '#FFC75F',
# '#D5CABD', '#B0A8B9', '#FF6F91', '#F9F871', '#D7E8F0',
# '#60DB73', '#E8575A', '#008B74', '#00C0A3', '#FF9671',
# '#93DEB1']
color_lists = color_list
for item in del_index:
del names[item]
del color_lists[item]
return del_data, names, color_lists
# explicit function to normalize array
def normalize_2d(matrix):
norm = np.linalg.norm(matrix)
matrix = matrix / norm # normalized matrix
return matrix
if __name__ == '__main__':
"""
SP Arousal 60min
"""
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="Female_RoRR") # 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_correct",
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="Male_RoRR") # 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_correct",
name="{}_Movement_Labels".format(item))
file_list_2.append(file_list1)
file_list_2 = list(np.ravel(file_list_2))
"""
SP behavior 60min
"""
# 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))
"""
test code
"""
# Male_list = []
# Female_list = []
# # pre_data(file_path, dataframe, num, state="")
# for i in range(len(file_list_1)):
# sub_list1 = pre_data(file_list_1[i], a, i, state="looming_time4")
# Male_list.append(sub_list1)
#
# for j in range(len(file_list_2)):
# sub_list2 = pre_data(file_list_2[j], b, j, state="looming_time4")
# Female_list.append(sub_list2)
#
# data1 = np.zeros((16, 16))
# for item in Male_list:
# Male_data = data1 + item
#
# for item in Female_list:
# Female_data = data1 + item
# j = 1
# A = np.zeros((16, 16))
# start = 0 * 60 * 30
# end = 10 * 60 * 30
# fre_list = []
# df2 = pd.read_csv(file_list_1[j])
#
# 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
#
# 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
# behavior_fre = list(class_type.values())
# 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
"""
RORR状态 单只老鼠
"""
# for x in range(1, 13):
# state = "looming_time{}".format(x)
# for num in range(len(file_list_2)):
# # sub_list2 = pre_data(file_list_2[0], b, 0, state="looming_time2")
# sub_list2 = pre_data(file_list_2[num], b, num, state=state)
# # data = Male_data + Female_data
#
# del_data, names, colors = del_pre_data(sub_list2)
#
# color = ListedColormap(colors)
# fig = plt.figure(figsize=(5, 5), dpi=300)
# ax = fig.add_subplot(111)
# # chord_diagram(flux, names, gap=0.03, use_gradient=True, sort='distance', cmap=color,
# # chord_colors=colors,
# # rotate_names=True, fontcolor="grey", ax=ax, fontsize=10)
# chord_diagram(del_data, gap=0.03, use_gradient=True, sort='distance', cmap=color,
# chord_colors=colors, fontcolor="grey", ax=ax, fontsize=10)
#
# # str_grd = "_gradient" if grads[0] else ""
#
# plt.xlabel('Time (s)', fontsize=15)
# plt.ylabel('Fraction', fontsize=15)
# plt.tight_layout()
# plt.show()
# plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/State_convert'
# '/SP_Arousal_add/{}_RORR{}_5min.tiff'.format(file_list_2[num][-43:-26], x), dpi=300)
# plt.close()
"""
Wake状态 单只老鼠数据
"""
# file_list = file_list_2
# dataframe = b
# mouse_state = 'RORR'
# looming_time = 13
# # all_data = np.zeros((16, 16))
# for x in range(2, looming_time, 2): # 调整间隔时长：5min/10min
# # for x in range(1, 2):
# state = "looming_time{}".format(x)
# for num in range(len(file_list)): # 访问老鼠个体
# # for num in range(2, 3):
# # sub_list2 = pre_data(file_list_2[0], b, 0, state="looming_time2")
# sub_list1 = pre_data(file_list[num], dataframe, num, state=state)
# # data = Male_data + Female_data
# # all_data = all_data + sub_list1
#
# del_data, names, colors = del_pre_data(sub_list1)
#
# color = ListedColormap(colors)
# fig = plt.figure(figsize=(5, 5), dpi=300)
# ax = fig.add_subplot(111)
# # chord_diagram(flux, names, gap=0.03, use_gradient=True, sort='distance', cmap=color,
# # chord_colors=colors,
# # rotate_names=True, fontcolor="grey", ax=ax, fontsize=10)
# chord_diagram(del_data, gap=0.03, use_gradient=True, sort='distance', cmap=color,
# chord_colors=colors, fontcolor="grey", ax=ax, fontsize=10)
#
# # str_grd = "_gradient" if grads[0] else ""
#
# plt.xlabel('Time (s)', fontsize=15)
# plt.ylabel('Fraction', fontsize=15)
# plt.tight_layout()
# plt.show()
# plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/State_convert'
# '/SP_Arousal_add/Female_10min/{}_{}{}_10min.tiff'.format(file_list[num][-43:-26], mouse_state,
# int(x / 2)), dpi=300)
# plt.close()
"""
Wake状态 所有老鼠数据
"""
# behavior_label = ['Right turning', 'Left turning', 'Sniffing', 'Walking', 'Trembling', 'Climbing', 'Falling',
# 'Immobility', 'Paralysis', 'Standing', 'Trotting', 'Grooming', 'Flight', 'Running', 'LORR',
# 'Stepping']
file_list = file_list_2
dataframe = b
mouse_state = 'RORR'
looming_time = 14
Male_data = np.zeros((16, 16))
Female_data = np.zeros((16, 16))
for x in range(2, looming_time, 2): # 调整间隔时长：5min/10min
# for x in range(1, 2):
state = "looming_time{}".format(x)
for num in range(len(file_list)): # 访问老鼠个体
# for num in range(2, 3):
# sub_list2 = pre_data(file_list_2[0], b, 0, state="looming_time2")
sub_list1 = pre_data(file_list[num], dataframe, num, state=state)
# data = Male_data + Female_data
Male_data = Male_data + sub_list1
for num in range(len(file_list_1)): # 访问老鼠个体
# for num in range(2, 3):
# sub_list2 = pre_data(file_list_2[0], b, 0, state="looming_time2")
sub_list2 = pre_data(file_list_1[num], a, num, state=state)
# data = Male_data + Female_data
Female_data = Female_data + sub_list2
all_data = Male_data + Female_data
del_data, names, colors = del_pre_data(all_data)
all_data = np.zeros((16, 16))
color = ListedColormap(colors)
fig = plt.figure(figsize=(5, 5), dpi=300)
ax = fig.add_subplot(111)
# chord_diagram(flux, names, gap=0.03, use_gradient=True, sort='distance', cmap=color,
# chord_colors=colors,
# rotate_names=True, fontcolor="grey", ax=ax, fontsize=10)
chord_diagram(del_data, gap=0.03, use_gradient=True, sort='distance', cmap=color,
chord_colors=colors, fontcolor="grey", ax=ax, fontsize=10)
# str_grd = "_gradient" if grads[0] else ""
plt.xlabel('Time (s)', fontsize=15)
plt.ylabel('Fraction', fontsize=15)
plt.tight_layout()
plt.show()
plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/State_convert/SP_Arousal_add'
'/all_v5/All_{}{}_10min_v9.tiff'.format(mouse_state, int(x / 2)), dpi=300)
plt.close()