+from flask import Flask

+from flask_restx import Api

+from auth import Auth

+from action import Action

+

+app = Flask(__name__)

+

+print("Api Start")

+api = Api(    app,

+    version='0.1',

+    title="trafficagent",

+    description="API Server",

+    terms_url="/",

+    contact="[email protected]",

+    license="MIT")

+

+api.add_namespace(Auth, '/auth')

+print("Api Add Auth")

+

+api.add_namespace(Action, '/action')

+

+if __name__ == "__main__":

+    app.run(debug=False, host='0.0.0.0', port=8080)

+    print("Flask Start")(파일 끝에 줄바꿈 문자 없음)

+import pandas as pd

+from statsmodels.tsa.statespace.sarimax import SARIMAX

+import matplotlib.pyplot as plt

+from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

+import plotly.graph_objects as go

+from scipy import signal

+from datetime import datetime

+import plotly.express as px

+from tools.algo.humidity import absolute_humidity

+

+

+if __name__ == "__main__":

+    df = pd.read_csv("/home/juni/PycharmProjects/failure_analysis/data/weather/202007010000_202308310000_f.csv")

+    ah = absolute_humidity(df["상대습도"], df["기온"])

+    df['관측시각'] = df['관측시각'].apply(lambda x: datetime.strptime(f"{x}", '%Y%m%d%H%M'))

+    df["절대습도"] = ah

+    # fig = go.Figure()

+    #

+    # fig.add_trace(

+    #     go.Scatter(x=df["관측시각"], y=df["절대습도"])

+    # )

+    # fig.add_trace(

+    #     go.Scatter(x=df["관측시각"], y=signal.savgol_filter(

+    #         df["절대습도"],72,3)

+    #     ))

+    # fig.show()

+    # log_df = np.log(df["절대습도"])

+    diff_1 = (df["절대습도"].diff(periods=1).iloc[1:])

+    diff_2 = diff_1.diff(periods=1).iloc[1:]

+    # plot_acf(diff_2)

+    # plot_pacf(diff_2)

+    plt.show()

+    model = SARIMAX(df["절대습도"], order=(1,0,2), seasonal_order=(0,1,2,24))

+    model_fit = model.fit()

+    # ARIMA_model = pm.auto_arima(df['절대습도'],

+    #                             start_p=1,

+    #                             start_q=1,

+    #                             test='adf',  # use adftest to find optimal 'd'

+    #                             max_p=3, max_q=3,  # maximum p and q

+    #                             m=24,  # frequency of series (if m==1, seasonal is set to FALSE automatically)

+    #                             d=None,  # let model determine 'd'

+    #                             D=2, #order of the seasonal differencing

+    #                             seasonal=True,  # No Seasonality for standard ARIMA

+    #                             trace=False,  # logs

+    #                             error_action='warn',  # shows errors ('ignore' silences these)

+    #                             suppress_warnings=False,

+    #                             stepwise=True)

+    print(model_fit.summary())

+    df['forecast'] = model_fit.predict(start=-100, end=-1, dynamic=True)

+    # df[['절대습도', 'forecast']].plot(figsize=(12, 8))

+    fig = px.line(df[['절대습도', 'forecast']])

+    fig.show()

+import numpy as np

+import pandas as pd

+from scipy.stats import pearsonr, spearmanr, kendalltau, pointbiserialr

+import plotly.express as px

+import random

+

+from tools.algo.interpolation import interpolate_value

+from tools.algo.humidity import absolute_humidity

+

+if __name__ == "__main__":

+    df_weather = pd.read_csv("/home/juni/PycharmProjects/failure_analysis/data/weather/202007010000_202308310000_f.csv")

+    df_failure = pd.read_excel("/home/juni/PycharmProjects/failure_analysis/data/failure/대옹 작업내용.xlsx")

+    times = df_failure["작업 시작 시간"]

+    temp = []

+    humidity = []

+    i=0

+    for time in times:

+        temp.append(interpolate_value(df_weather, timestamp=time, col_key="기온", k=6))

+        humidity.append(interpolate_value(df_weather, timestamp=time, col_key="상대습도", k=6))

+

+    absolute_humidity = absolute_humidity(humidity, temp)

+

+    df_failure["기온"] = temp

+    df_failure["상대습도"] = humidity

+    df_failure["절대습도"] = absolute_humidity

+    df_failure['불량 여부'] = np.where(df_failure['불량 여부'] == 'F', 0, df_failure['불량 여부'])

+    df_failure['불량 여부'] = np.where(df_failure['불량 여부'] == 'T', 1, df_failure['불량 여부'])

+

+

+    df = df_failure.copy()

+    correlation_manufacturing_abhumidity = pointbiserialr(df["절대습도"], df["불량 여부"])

+    correlation_manufacturing_rehumidity = pointbiserialr(df["상대습도"], df["불량 여부"])

+    correlation_manufacturing_temp = pointbiserialr(df["기온"], df["불량 여부"])

+    print(correlation_manufacturing_abhumidity)

+    print(correlation_manufacturing_rehumidity)

+    print(correlation_manufacturing_temp)

+

+    df_failure.to_csv("merged_data.csv")

+    pass(파일 끝에 줄바꿈 문자 없음)

+    relative_humidity = np.array(relative_humidity)

+    temperature = np.array(temperature)

+if __name__ == "__main__":

+    df = pd.read_csv('data/202007010000_202308010000.csv')

+    if type(timestamp) is str:

+        target_time = datetime.strptime(timestamp, '%Y%m%d%H%M')

+    else:

+        target_time = timestamp

+    y = relevant_df[col_key]

+    df = pd.read_csv("/home/juni/PycharmProjects/failure_analysis/data/weather/202007010000_202308310000_f.csv",