如何每15秒执行循环并在同一图表中更新数据分析结果？

Hey there! Let's break down the two core issues in your code and fix them step by step:

1. New File Filtering Logic Issues #

• First off, your Save_data function references T_start and T_step variables that are defined in the main loop—this will throw a NameError (which your current ValueError catch won't handle).
• More importantly, the file filtering logic is flawed: using T_start - T_step means you might reprocess the same files in every loop. Instead, you need to track the last time you finished processing files and only handle files modified after that timestamp.

Here's the fixed Save_data function:

def Save_data(path, last_processed_time):
    my_data = {}
    for filename in os.listdir(path):
        file_path = os.path.join(path, filename)
        file_mtime = os.path.getmtime(file_path)
        # Only process files modified after the last check
        if file_mtime > last_processed_time:
            try:
                data = np.genfromtxt(file_path, skip_header=skip_head)
                my_data[filename] = data[:, [0, 1]]
            except IndexError:
                # Skip broken files instead of returning early
                continue
    return my_data

2. Chart Auto-Update Issues #

• plt.show() blocks your program until you close the chart window, which stops the loop entirely.
• You're reusing plt.figure(1) but never clearing old plot content, so new data will just overlap with the old (even if the window wasn't blocked).

Fix the plotting function with matplotlib's interactive mode:

import matplotlib.pyplot as plt

# Enable interactive mode for real-time updates
plt.ion()

def PlotAverage(aver_voltage, std_dev, title, x):
    plt.figure(1)
    # Clear existing plot content before drawing new data
    plt.clf()
    plt.errorbar(x, aver_voltage, yerr=std_dev, fmt='r', ecolor='gray', label=title)
    plt.title(title)
    plt.xlabel('Time [mus]')
    plt.ylabel('Voltage [V]')
    plt.legend()
    # Refresh the chart without blocking
    plt.draw()
    plt.pause(0.001)  # Give a tiny buffer for rendering

Fixed Main Loop #

Update the main loop to track the last processed time and handle edge cases properly:

import time
import numpy as np
import os

# Initialize timestamp to catch all existing files on first run
last_processed_time = time.time()
T_step = 15

while True:
    try:
        current_time = time.time()
        # Fetch only files added/modified since last loop
        my_data = Save_data(path, last_processed_time)
        
        if not my_data:
            print("\nNo new files in the folder since last check.")
            last_processed_time = current_time
            time.sleep(T_step)
            continue
        
        # Run your analysis pipeline
        tot_voltage, aver_voltage = Average_signal(my_data)
        std_dev = np.std(tot_voltage, axis=1)
        x = TimeVector(my_data, tot_voltage)
        
        # Update the chart
        PlotAverage(aver_voltage, std_dev, title, x)
        
        # Update timestamp to avoid reprocessing files
        last_processed_time = current_time
        
        # Adjust sleep time to account for processing duration (optional)
        elapsed = time.time() - current_time
        time.sleep(max(0, T_step - elapsed))
        
    except ValueError as e:
        print(f"\nValue error occurred: {e}")
        time.sleep(T_step)
        continue
    except Exception as e:
        # Catch unexpected errors to prevent crash
        print(f"\nUnexpected error: {e}")
        time.sleep(T_step)
        continue

Quick Notes: #

• Make sure skip_head, path, and title are properly defined in your code.
• Double-check that Average_signal and TimeVector work with the my_data structure returned by the updated Save_data.

内容的提问来源于stack exchange，提问作者summerfield