from flask import Flask, jsonify, request import pandas as pd import numpy as np import statsmodels.api as sm from datetime import datetime app = Flask(__name__) @app.route('/option0', methods=['POST']) def option0(): arr0 = ['2019/1', '2019/2', '2019/3', '2019/4', '2019/5', '2019/6', '2019/7', '2019/8', '2019/9', '2019/10', '2019/11', '2019/12', '2020/1', '2020/2', '2020/3', '2020/4', '2020/5', '2020/6', '2020/7', '2020/8', '2020/9', '2020/10', '2020/11', '2020/12'] date_arr = [] for date_str in arr0: date_obj = datetime.strptime(date_str, '%Y/%m') date_arr.append(date_obj.timestamp()) arr1 = np.array(request.form['data'].split(), dtype=float) data_array = np.vstack((date_arr, arr1)).T.astype(float) df = pd.DataFrame(data_array, columns=['x', 'y']) df = df.dropna() acf, q, p = sm.tsa.acf(df['y'], nlags=20, qstat=True) if (p < 0.05).any(): short_term_dependency = '时间序列具有短期依赖性' else: short_term_dependency = '时间序列没有短期依赖性' acf, q, p = sm.tsa.acf(df['y'], nlags=20, fft=True, qstat=True) if (p < 0.05).any(): has_periodicity = '时间序列具有周期性' else: has_periodicity = '时间序列没有周期性' adf_result = sm.tsa.stattools.adfuller(df['y']) if adf_result[1] < 0.05: is_stationary = '时间序列是平稳的' else: is_stationary = '时间序列不是平稳的' res = sm.tsa.seasonal_decompose(df['y'], model='additive', period=12) if np.isnan(res.seasonal).any(): has_seasonality = '时间序列没有明显的季节性变化' else: has_seasonality = '时间序列存在季节性变化' result = { print(short_term_dependency, has_periodicity, is_stationary, has_seasonality) } return jsonify(result),如何修改才能正常运行

有几个问题需要修改：

1. `print` 函数不应该出现在字典中，应该直接将结果保存在字典中。

2. 在 `jsonify` 函数中，字典的键和值之间应该是冒号 `:`，而不是逗号 `,`。

3. `if (p < 0.05).any():` 这一行代码中，括号的位置应该调整为 `if (p < 0.05).any():`，否则会出现语法错误。

修改后的代码如下所示：

from flask import Flask, jsonify, request 
import pandas as pd 
import numpy as np 
import statsmodels.api as sm 
from datetime import datetime  

app = Flask(__name__) 

@app.route('/option0', methods=['POST']) 
def option0(): 
    arr0 = ['2019/1', '2019/2', '2019/3', '2019/4', '2019/5', 
            '2019/6', '2019/7', '2019/8', '2019/9', '2019/10', 
            '2019/11', '2019/12', '2020/1', '2020/2', '2020/3', 
            '2020/4', '2020/5', '2020/6', '2020/7', '2020/8', 
            '2020/9', '2020/10', '2020/11', '2020/12'] 
    date_arr = [] 
    for date_str in arr0: 
        date_obj = datetime.strptime(date_str, '%Y/%m') 
        date_arr.append(date_obj.timestamp()) 
    arr1 = np.array(request.form['data'].split(), dtype=float) 
    data_array = np.vstack((date_arr, arr1)).T.astype(float) 
    df = pd.DataFrame(data_array, columns=['x', 'y']) 
    df = df.dropna() 
    acf, q, p = sm.tsa.acf(df['y'], nlags=20, qstat=True) 
    if (p < 0.05).any(): 
        short_term_dependency = '时间序列具有短期依赖性' 
    else: 
        short_term_dependency = '时间序列没有短期依赖性' 
    acf, q, p = sm.tsa.acf(df['y'], nlags=20, fft=True, qstat=True) 
    if (p < 0.05).any(): 
        has_periodicity = '时间序列具有周期性' 
    else: 
        has_periodicity = '时间序列没有周期性' 
    adf_result = sm.tsa.stattools.adfuller(df['y']) 
    if adf_result[1] < 0.05: 
        is_stationary = '时间序列是平稳的' 
    else: 
        is_stationary = '时间序列不是平稳的' 
    res = sm.tsa.seasonal_decompose(df['y'], model='additive', period=12) 
    if np.isnan(res.seasonal).any(): 
        has_seasonality = '时间序列没有明显的季节性变化' 
    else: 
        has_seasonality = '时间序列存在季节性变化' 
    result = { 
        'short_term_dependency': short_term_dependency, 
        'has_periodicity': has_periodicity, 
        'is_stationary': is_stationary, 
        'has_seasonality': has_seasonality 
    } 
    return jsonify(result)