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zs_ve_cali_1.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
csv_result = []
def search_csv(path=".", name=""): # 抓取csv文件
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)
csv_result.append(item_path)
# print(item_path + ";", end="")
return csv_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, 3))
return v_smooth
def sns_data(dataframe, file_list, be_looming_time, after_looming_time, typename=""):
global start_time
global end_time
global start_label
global end_label
start_time = []
end_time = []
for i in dataframe['time']:
start_label = i - 30 * be_looming_time
end_label = 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
if __name__ == '__main__':
a = read_csv(path=r'D:/3D_behavior/Arousal_behavior/results-20211217/3Dskeleton',
name="looming_tag_time.xlsx", column="time", state_name="Frorr")
for item in a['name']:
file_list1 = search_csv(
path=r"D:/3D_behavior/Arousal_behavior/results-20211217/3Dskeleton/Calibrated_3DSkeleton",
name="{}_Cali_Data3d".format(item))
sns_data_1 = sns_data(a, file_list1, 3, 30, typename="Frorr")
csv_result.clear()
# b = read_csv(path=r'D:/3D_behavior/Arousal_behavior/results-20211217/3Dskeleton',
# name="looming_tag_time.xlsx", column="time", state_name="Mrorr")
# for item in b['name']:
# file_list2 = search_csv(path=r"D:/3D_behavior/Arousal_behavior/results-20211217/3Dskeleton/Calibrated_3DSkeleton",
# name="{}_Cali_Data3d".format(item))
#
# sns_data_2 = sns_data(b, file_list2, 3, 30, typename="Mrorr")
# sns_data = sns_data_1.append(sns_data_2)
# sns.lineplot(data=sns_data, x="time", y="value", hue="type")
# with open(file_list1[0], '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[0]:end_time[0] + 1, 0].tolist()
# v_y = v.iloc[start_time[0]:end_time[0] + 1, 1].tolist()
# v_z = v.iloc[start_time[0]:end_time[0] + 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, 3))