我用 Python 创建了一个看门狗。它监视一个文件夹 (test_folder)。如果创建了某个文件，我调用一个函数打开文件并创建一个 jpeg，这是再次保存在 test_folder 中。
第一次迭代工作正常，但是当我上传不同的文件（不同的内容，甚至名称略有不同）时出现错误：
Event: C:/Users/WEL4HO/Desktop/test_folder\AD1__Sampling2000KHz_AEKi-0.parquet created!
Exception in thread Thread-1:
Traceback (most recent call last):
File "C:\Users\WEL4HO\.conda\envs\anaconda\lib\threading.py", line 980, in _bootstrap_inner
self.run()
File "C:\Users\WEL4HO\.conda\envs\anaconda\lib\site-packages\watchdog\observers\api.py", line 205, in run
self.dispatch_events(self.event_queue)
File "C:\Users\WEL4HO\.conda\envs\anaconda\lib\site-packages\watchdog\observers\api.py", line 381, in dispatch_events
handler.dispatch(event)
File "C:\Users\WEL4HO\.conda\envs\anaconda\lib\site-packages\watchdog\events.py", line 271, in dispatch
self.on_any_event(event)
File "C:\Users\WEL4HO\Desktop\test_folder\watchdog3.py", line 34, in on_any_event
Final.GAF(event.src_path, 'test_watchdog.jpeg')
File "C:\Users\WEL4HO\Desktop\test_folder\Final.py", line 18, in GAF
df = pq.read_table(source=parquet_file).to_pandas()
File "C:\Users\WEL4HO\.conda\envs\anaconda\lib\site-packages\pyarrow\parquet\__init__.py", line 2737, in read_table
dataset = _ParquetDatasetV2(
File "C:\Users\WEL4HO\.conda\envs\anaconda\lib\site-packages\pyarrow\parquet\__init__.py", line 2340, in __init__
[fragment], schema=schema or fragment.physical_schema,
File "pyarrow\_dataset.pyx", line 870, in pyarrow._dataset.Fragment.physical_schema.__get__
File "pyarrow\error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow\error.pxi", line 111, in pyarrow.lib.check_status
PermissionError: [WinError 32] Failed to open local file 'C:/Users/WEL4HO/Desktop/test_folder/AD1__Sampling2000KHz_AEKi-0.parquet'. Detail: [Windows error 32] The process cannot access the file because it is being used by another process.
这是我的看门狗：
import sys
import time
import logging
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
import Final
import os
class OnMyWatch:
watchDirectory = r'C:/Users/WEL4HO/Desktop/test_folder'
def __init__(self):
self.observer = Observer()
def run(self):
event_handler = Handler()
self.observer.schedule(event_handler, self.watchDirectory, recursive = True)
self.observer.start()
try:
while True:
time.sleep(1)
except:
self.observer.stop()
self.observer.join()
class Handler(FileSystemEventHandler):
@staticmethod
def on_any_event(event):
if event.is_directory:
return None
elif event.event_type == 'created':
print(f'Event: {event.src_path} created!')
#folder event.src_path -> folder raw -> file
if event.src_path.endswith('Sampling2000KHz_AEKi-0.parquet'):
Final.GAF(event.src_path, 'GAF_photo.jpeg')
if __name__ == '__main__':
watch = OnMyWatch()
watch.run()
这是我调用函数的文件：
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
from pyts.image import GramianAngularField
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
from pathlib import Path
from scipy.signal import savgol_filter
import pandas as pd
import datetime
def GAF (path, outputname):
Sampling_rate = 2000000 #' Ae sample rate'
parquet_file = path
#convert data into numpy array
df = pq.read_table(source=parquet_file).to_pandas()
acoustic_emission_1 = df.values # convert the data into numpy array
time = len(acoustic_emission_1)/(Sampling_rate) #process length
#downsampling
downsampling_rate = 90
acoustic_emission_1_clip= acoustic_emission_1[:len(acoustic_emission_1)-(len(acoustic_emission_1)%downsampling_rate)]
acoustic_emission_1_downsampled = acoustic_emission_1_clip.reshape(-1, downsampling_rate).mean(axis=1)
samplingrate_downsampled = (Sampling_rate)/(downsampling_rate)
#positive data in arr
arr = np.array([], dtype=np.uint16)
counter=0
negative_values=0
for x in np.nditer(acoustic_emission_1_downsampled):
if x < 0:
negative_values=negative_values+1
pass
else:
arr = np.append(arr, acoustic_emission_1_downsampled[counter])
counter=counter+1
#new samplingrate
u=(negative_values)/time
samplingrate_new=samplingrate_downsampled-u
#filter
window_length=int(round((len(arr)*0.285),0))
if (window_length % 2) == 0:
window_length=window_length+1
else:
pass
print ('window_length:',window_length)
y_filtered = savgol_filter(arr,window_length, 3)
print ('nach filter', len(y_filtered))
print ('Y filtered:', y_filtered)
#x-axis
length=len(y_filtered) #länge AE werte
x = np.array([], dtype=int) #array x achse (zeit) leer
counter=0
i=0
while i==0:
x = np.append(x, counter)
if counter == length-1:
break
counter=counter+1
print ('x achse:', len(x))
X=np.array([y_filtered, x], dtype=object)
print (X)
print (length)
# Transform the time series into Gramian Angular Fields
gasf = GramianAngularField(image_size=1000, method='summation')
X_gasf = gasf.transform(X)
# Show the images for the first time series
fig = plt.figure(figsize=(20, 10))
grid = ImageGrid(fig, 111,
nrows_ncols=(1, 1),
axes_pad=0.15,
share_all=True,
cbar_location="right",
cbar_mode="single",
cbar_size="7%",
cbar_pad=0.3,
)
images = [X_gasf[0]]
titles = ['Summation']
for image, title, ax in zip(images, titles, grid):
im = ax.imshow(image, cmap='rainbow', origin='lower')
ax.set_title(title, fontdict={'fontsize': 12})
ax.cax.colorbar(im)
ax.cax.toggle_label(True)
plt.suptitle('Gramian Angular Field', y=0.98, fontsize=16)
fig.savefig(outputname, dpi=300)
plt.close(fig)
del fig
del df
nvm，我修好了。我补充说：
init_size = -1
while True:
current_size = os.path.getsize(event.src_path)
if current_size == init_size:
break
else:
init_size = os.path.getsize(event.src_path)
time.sleep(2)
print("file copy has now finished")