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zs_ve_cali_2.py
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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:
Wakefulness: '#f2b67c'
RORR: '#808080'
post-RORR1: '#8aaad2'
post-RORR2: '#d6afaf'
post-RORR3: '#d3d3d3'
color_list_shadow = ['#a8a8a8', '#9ba7ca', '#c29799']
color_list_line = ['#000000', '#0c5172', '#851717']
'''
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 velocity(file_path, num): # Cali velocity
"""
nose:0 left_ear:1 Right_ear:2 neck:3 left_front_limb:4 right_front_limb:5
left_hind_limb:6 right_hind_limb:7 left_front_claw:8 right_front_claw:9
left_hind_claw:10 right_hind_claw:11 back:12 root_tail:13
mid_tail:14 tip_tail:15
"""
with open(file_path[num], 'rb') as f:
df = pd.read_csv(f)
df1 = df.iloc[2:, 36:39] # select back vector
df1 = df1.astype(float)
v = df1.diff()
v_x = v.iloc[start_time[num]:end_time[num] + 1, 0].tolist()
v_y = v.iloc[start_time[num]:end_time[num] + 1, 1].tolist()
v_z = v.iloc[start_time[num]:end_time[num] + 1, 2].tolist()
v_list = []
for j in range(0, len(v_x)):
absolute_v = np.sqrt(np.square(v_x[j]) + np.square(v_y[j]) + +np.square(v_z[j])) # Cali absolute velocity
# absolute_v = smooth(absolute_v, 30)
v_list.append(absolute_v)
v_smooth = list(savgol_filter(v_list, 29, 2))
return v_smooth
def sns_data(dataframe, file_list, be_looming_time, after_looming_time, typename="", row_name=''):
global start_time
global end_time
global start_label
global end_label
start_time = []
end_time = []
for i in dataframe[row_name][0:8]:
start_label = int(i) - 30 * be_looming_time
end_label = int(i) + 30 * after_looming_time
start_time.append(start_label)
end_time.append(end_label)
# dataframe['start_time'] = start_time
# dataframe['end_time'] = end_time
v_list = []
for i in range(len(file_list)):
# velocity(csv_result, i)
v_list.append(velocity(file_list, i))
# print(v_list)
back_v_all = {"time": [], "value": [], "type": []}
time_list = [i for i in range(0, end_label - start_label + 1)]
time_list_all = []
for i in range(len(v_list)):
time_list_all.append(time_list)
back_v_all["type"].append([typename] * (end_label - start_label + 1))
time_list_all = sum(time_list_all, [])
back_v_all["time"] = time_list_all
back_v_all["value"] = v_list
back_v_all["value"] = sum(back_v_all["value"], [])
back_v_all["type"] = sum(back_v_all["type"], [])
df = pd.DataFrame(back_v_all)
return df
def single_sns_data(dataframe, file_list, num, be_looming_time, after_looming_time, row_name="", typename=""):
start_time_label = int(dataframe[row_name][num]) - 30 * be_looming_time
end_time_label = int(dataframe[row_name][num]) + 30 * after_looming_time
with open(file_list, 'rb') as f:
df = pd.read_csv(f)
df1 = df.iloc[2:, 36:39] # select back vector
df1 = df1.astype(float)
v = df1.diff()
v_x = v.iloc[start_time_label:end_time_label + 1, 0].tolist()
v_y = v.iloc[start_time_label:end_time_label + 1, 1].tolist()
v_z = v.iloc[start_time_label:end_time_label + 1, 2].tolist()
v_list = []
for j in range(0, len(v_x)):
absolute_v = np.sqrt(np.square(v_x[j]) + np.square(v_y[j]) + +np.square(v_z[j])) # Cali absolute velocity
# absolute_v = smooth(absolute_v, 30)
v_list.append(absolute_v)
v_smooth = list(savgol_filter(v_list, 29, 2))
# back_v_all = {"time": [], "value": [], "type": []}
back_v_all = {"time": [], "value": []}
time_list = [i for i in range(0, end_time_label - start_time_label + 1)]
# back_v_all["type"] = [typename] * (end_time_label - start_time_label + 1)
back_v_all["time"] = time_list
back_v_all["value"] = v_smooth
df = pd.DataFrame(back_v_all)
return df
if __name__ == '__main__':
"""
速度柱状图
"""
# 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_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/Spontaneous_arousal/SP_Arousal_result_add2"
# r"/3Dskeleton/Calibrated_3DSkeleton",
# name="{}_Cali_Data3d".format(item))
# file_list_1.append(file_list1)
# file_list_1 = list(np.ravel(file_list_1))
# #
# # sns_data_1 = sns_data(a, file_list_1, 7, 53, typename="Male_Wakefulness", row_name='looming_time1')
# # # csv_result.clear()
# #
# 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="Female_RoRR")
# file_list_2 = []
# for item in b['Video_name'][0:10]:
# item = item.replace("-camera-0", "")
# file_list2 = search_csv(
# path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/Spontaneous_arousal/SP_Arousal_result_add2"
# r"/3Dskeleton/Calibrated_3DSkeleton",
# name="{}_Cali_Data3d".format(item))
# file_list_2.append(file_list2)
# file_list_2 = list(np.ravel(file_list_2))
# #
# # sns_data_2 = sns_data(b, file_list_2, 7, 53, typename="Female_RoRR_1", row_name='looming_time1')
# # sns_data_3 = sns_data(b, file_list_2, 5, 55, typename="Female_RoRR_2", row_name='looming_time2')
# # sns_data_4 = sns_data(b, file_list_2, 3, 57, typename="Female_RoRR_3", row_name='looming_time3')
# # # sns_data = sns_data_1.append(sns_data_4)
# # # sns_data = pd.concat([sns_data_1, sns_data_2, sns_data_3, sns_data_4])
# # sns_data = pd.concat([sns_data_2, sns_data_1, sns_data_4])
#
# '''
# 单只典型鼠
# '''
# num = 1
# # file_path = file_list_1[0]
# # sns_data_0 = single_sns_data(a, file_list_1[num], num, 4, 56,
# # typename="Wakefulness", row_name='looming_time1')
# # sns_data_1 = single_sns_data(b, file_list_2[num], num, 9, 51,
# # typename="Female_RoRR_3", row_name='looming_time1')
# # sns_data_2 = single_sns_data(b, file_list_2[num], num, 7, 53,
# # typename="Female_RoRR_1", row_name='looming_time2')
# # sns_data_3 = single_sns_data(b, file_list_2[num], num, 7, 53,
# # typename="Female_RoRR_2", row_name='looming_time3')
# # sns_data_4 = single_sns_data(b, file_list_2[num], num, 7, 53,
# # typename="Female_RoRR_4", row_name='looming_time4')
#
# # sns_data_0 = single_sns_data(a, file_list_1[num], num, 299, 0,
# # row_name='looming_time1')
# # sns_data_1 = single_sns_data(b, file_list_2[num], num, 299, 0,
# # row_name='looming_time1')
# # sns_data_2 = single_sns_data(b, file_list_2[num], num, 299, 0,
# # row_name='looming_time2')
# # sns_data_3 = single_sns_data(b, file_list_2[num], num, 299, 0,
# # row_name='looming_time3')
# # sns_data_4 = single_sns_data(b, file_list_2[num], num, 299, 0,
# # row_name='looming_time4')
# # sns_data_5 = single_sns_data(b, file_list_2[num], num, 299, 0,
# # row_name='looming_time5')
# for x in range(11, 12, 1):
# # x = 1
# row_name = 'looming_time{}'.format(x)
# sns_data_1 = single_sns_data(b, file_list_2[num], num, 299, 0,
# row_name=row_name)
#
# # sns_data = pd.concat([sns_data_0, sns_data_1, sns_data_2, sns_data_3, sns_data_4])
# # sns_data = pd.concat([sns_data_1, sns_data_2, sns_data_3])
# color_list_shadow = ['#a8a8a8', '#9ba7ca', '#c29799']
# # color_list_line = ['#000000', '#0c5172', '#851717']
# color_list_line = ['#851717']
# fig = plt.figure(figsize=(5, 2), dpi=300)
#
# ax = sns.barplot(x="time", y="value", data=sns_data_1, color='#d6afaf')
#
# # # sns.lineplot(data=sns_data_0, x="time", y="value", hue='type', palette=color_list_line)
# # ax = sns.lineplot(data=sns_data_4, x="time", y="value", color='#f2b67c', linewidth=4)
# # # sns.lineplot(data=sns_data, x="time", y="value", hue='type')
# # plt.axvspan(150, 300, color='gray', alpha=0.3, lw=0)
# # plt.rcParams.update({'font.family': 'Arial'})
# # x = [i for i in range(0, 1801, 300)]
# # labels = ['0', '10', '20', '30', '40', '50', '60']
# plt.ylim(0, 15, 5)
# # plt.xticks(x, labels, fontsize=24)
# # plt.yticks([0, 5, 10, 15, 20], fontsize=24)
# # plt.legend(fontsize=6)
# # plt.xlabel('Time (s)', fontsize=26)
# # plt.ylabel('Speed (cm/s)', fontsize=26)
# # # plt.title("Male-15 velocity after looming stimulate", fontsize=8)
# # # plt.title("Male-15 velocity after looming stimulate during RORR", fontsize=8)
# # # plt.title("Wakefulness", fontsize=8)
# # # figure.subplots_adjust(bottom=0.2, right=0.8, top=0.9)
# # ax.spines['top'].set_visible(False)
# # ax.spines['right'].set_visible(False)
# # ax.spines['bottom'].set_linewidth(3.5)
# # ax.spines['left'].set_linewidth(3.5)
# plt.axis('off')
# plt.tight_layout()
#
# plt.show()
# # plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/velocity/SP_Arousal_add'
# # '/F_2_{}.tiff'.format(row_name), dpi=300)
# # plt.close()
"""
速度折线图
"""
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'][0:9]:
item = item.replace("-camera-0", "")
file_list1 = search_csv(
path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/3Dskeleton/Calibrated_3DSkeleton",
name="{}_Cali_Data3d".format(item))
file_list_1.append(file_list1)
file_list_1 = list(np.ravel(file_list_1))
#
# sns_data_1 = sns_data(a, file_list_1, 7, 53, typename="Male_Wakefulness", row_name='looming_time1')
# # csv_result.clear()
#
b = read_csv(path=r'D:/3D_behavior/Arousal_behavior/Arousal_result_all',
name="video_info.xlsx", column="looming_time1", state_name="Male_RoRR")
file_list_2 = []
for item in b['Video_name'][0:10]:
item = item.replace("-camera-0", "")
file_list2 = search_csv(
path=r"D:/3D_behavior/Arousal_behavior/Arousal_result_all/3Dskeleton/Calibrated_3DSkeleton",
name="{}_Cali_Data3d".format(item))
file_list_2.append(file_list2)
file_list_2 = list(np.ravel(file_list_2))
#
# sns_data_2 = sns_data(b, file_list_2, 7, 53, typename="Female_RoRR_1", row_name='looming_time1')
# sns_data_3 = sns_data(b, file_list_2, 5, 55, typename="Female_RoRR_2", row_name='looming_time2')
# sns_data_4 = sns_data(b, file_list_2, 3, 57, typename="Female_RoRR_3", row_name='looming_time3')
# # sns_data = sns_data_1.append(sns_data_4)
# # sns_data = pd.concat([sns_data_1, sns_data_2, sns_data_3, sns_data_4])
# sns_data = pd.concat([sns_data_2, sns_data_1, sns_data_4])
'''
单只典型鼠
'''
num = 4
# file_path = file_list_1[0]
# sns_data_0 = single_sns_data(a, file_list_1[num], num, 4, 56,
# typename="Wakefulness", row_name='looming_time1')
# sns_data_1 = single_sns_data(b, file_list_2[num], num, 9, 51,
# typename="Female_RoRR_3", row_name='looming_time1')
# sns_data_2 = single_sns_data(b, file_list_2[num], num, 7, 53,
# typename="Female_RoRR_1", row_name='looming_time2')
# sns_data_3 = single_sns_data(b, file_list_2[num], num, 7, 53,
# typename="Female_RoRR_2", row_name='looming_time3')
# sns_data_4 = single_sns_data(b, file_list_2[num], num, 7, 53,
# typename="Female_RoRR_4", row_name='looming_time4')
# sns_data_0 = single_sns_data(a, file_list_1[num], num, 299, 0,
# row_name='looming_time1')
# sns_data_1 = single_sns_data(b, file_list_2[num], num, 299, 0,
# row_name='looming_time1')
# sns_data_2 = single_sns_data(b, file_list_2[num], num, 299, 0,
# row_name='looming_time2')
# sns_data_3 = single_sns_data(b, file_list_2[num], num, 299, 0,
# row_name='looming_time3')
# sns_data_4 = single_sns_data(b, file_list_2[num], num, 299, 0,
# row_name='looming_time4')
# sns_data_5 = single_sns_data(b, file_list_2[num], num, 299, 0,
# row_name='looming_time5')
for j in range(1, 2, 1):
# x = 1
row_name = 'looming_time{}'.format(j)
# sns_data_1 = single_sns_data(b, file_list_2[num], num, 6, 54,
# row_name=row_name)
sns_data_1 = single_sns_data(b, file_list_2[num], num, 9, 51,
row_name=row_name)
# sns_data = pd.concat([sns_data_0, sns_data_1, sns_data_2, sns_data_3, sns_data_4])
# sns_data = pd.concat([sns_data_1, sns_data_2, sns_data_3])
color_list_shadow = ['#a8a8a8', '#9ba7ca', '#c29799']
# color_list_line = ['#000000', '#0c5172', '#851717']
color_list_line = ['#851717']
fig = plt.figure(figsize=(10, 8), dpi=300)
# ax = sns.barplot(x="time", y="value", data=sns_data_1, color='#d6afaf')
# sns.lineplot(data=sns_data_0, x="time", y="value", hue='type', palette=color_list_line)
ax = sns.lineplot(data=sns_data_1, x="time", y="value", color='black', linewidth=5)
# sns.lineplot(data=sns_data, x="time", y="value", hue='type')
plt.axvspan(150, 300, color='gray', alpha=0.3, lw=0)
plt.rcParams.update({'font.family': 'Arial'})
x = [i for i in range(0, 1801, 300)]
labels = ['0', '10', '20', '30', '40', '50', '60']
# plt.ylim(0, 15, 5)
plt.xticks(x, labels, fontsize=48)
plt.yticks([0, 5, 10, 15, 20], fontsize=48)
plt.legend(fontsize=6)
# plt.xlabel('Time (s)', fontsize=30)
# plt.ylabel('Speed (cm/s)', fontsize=30)
ax.set(xlabel=None)
ax.set(ylabel=None)
# plt.title("Male-15 velocity after looming stimulate", fontsize=8)
# plt.title("Male-15 velocity after looming stimulate during RORR", fontsize=8)
# plt.title("Wakefulness", fontsize=8)
# figure.subplots_adjust(bottom=0.2, right=0.8, top=0.9)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_linewidth(3.5)
ax.spines['left'].set_linewidth(3.5)
ax.get_legend().remove()
# plt.axis('off')
plt.tight_layout()
plt.show()
plt.savefig('D:/3D_behavior/Arousal_behavior/Arousal_result_all/Analysis_result/velocity/Male'
'/M_15_RORR_{}_v3.tiff'.format(j), dpi=300)
plt.close()