load two layer of cnn

some-test/noise_take_ecg.py

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+import numpy as np
+import pywt
+import wfdb
+from matplotlib import pyplot as plt
+
+# file error
+
+# use db5 ,level9 do deviose
+ECGDATAPATH = './MIT-BIT/'
+
+# from ECG_read_weiyuqing_without_wfdb import PATH
+# record = wfdb.rdrecord(ECGDATAPATH + number, channel_names=['MLII'])
+record = wfdb.rdrecord(ECGDATAPATH + '100', channel_names=['MLII'])
+data = record.p_signal.flatten()
+
+coeffs = pywt.wavedec(data=data, wavelet='db5', level=9)
+cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs
+
+threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1))))
+cD1.fill(0)
+cD2.fill(0)
+for i in range(1, len(coeffs) - 2):
+    coeffs[i] = pywt.threshold(coeffs[i], threshold)
+
+rdata = pywt.waverec(coeffs=coeffs, wavelet='db5')
+
+plt.figure(figsize=(20, 4))
+plt.plot(data)
+plt.show()
+plt.plot(rdata)
+plt.show()

some-test/pytorch_gpu_test.py

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+import torch
+torch.cuda.is_available()
+print(torch.cuda.is_available())
+print(torch.__version__)
+
+
+print(torch.cuda.is_available() )# cuda是否可用
+print(torch.cuda.device_count() )# gpu数量
+print(torch.cuda.current_device())# 当前设备索引, 从0开始
+print(torch.cuda.get_device_name(0))# 返回gpu名字

some-test/test_plt_show.py

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+## read_img.py
+
+import cv2
+import os
+import matplotlib.pyplot as plt
+from PIL import Image
+
+source_path='E:/CToE/Pictures'
+
+def show_pic():
+    # img=cv2.imread(os.path.join(source_path,'t1.png'))
+    img=Image.open(os.path.join(source_path,'1.png'))
+    #   plt.imshow(img)
+    info = plt.imshow(img)
+    print(info)
+    plt.show()
+
+if __name__ == '__main__':
+    show_pic()

some-test/tf_run_in_gpu.py

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+import tensorflow as tf
+
+tf.test.is_gpu_available()
+
+# Tensorflow 2.10是最后一个在本地windows上支持GPU的版本。从2.11版本开始，需要在windows WLS2（适用于 Linux 的 Windows 子系统）上安装才能使用GPU。所以要在native-windows上使用GPU，就只能安装2.10.0版本及以下的版本，或者安装老版的tensorflow-gpu.
+# 指定在cpu上运行
+with tf.device('/cpu:0'):
+    cpu_a = tf.random.normal([10000, 1000])
+    cpu_b = tf.random.normal([1000, 2000])
+    cpu_c = tf.matmul(cpu_a, cpu_b)
+print("cpu_a:", cpu_a.device)
+print("cpu_b:", cpu_b.device)
+print("cpu_c:", cpu_c.device)
+# 查看gpu是否可用
+print(tf.config.list_physical_devices('GPU'))
+# 指定在gpu上运行
+with tf.device('/gpu:0'):
+    gpu_a = tf.random.normal([10000, 1000])
+    gpu_b = tf.random.normal([1000, 2000])
+    gpu_c = tf.matmul(gpu_a, gpu_b)
+print("gpu_a:", gpu_a.device)
+print("gpu_b:", gpu_b.device)
+print("gpu_c:", gpu_c.device)
+
+import tensorflow as tf
+import timeit
+
+
+def cpu_run():
+    with tf.device('/cpu:0'):
+        cpu_a = tf.random.normal([10000, 1000])
+        cpu_b = tf.random.normal([1000, 2000])
+        c = tf.matmul(cpu_a, cpu_b)
+    return c
+
+
+def gpu_run():
+    with tf.device('/gpu:0'):
+        gpu_a = tf.random.normal([10000, 1000])
+        gpu_b = tf.random.normal([1000, 2000])
+        c = tf.matmul(gpu_a, gpu_b)
+    return c
+
+
+cpu_time = timeit.timeit(cpu_run, number=10)
+gpu_time = timeit.timeit(gpu_run, number=10)
+print("cpu:", cpu_time, "  gpu:", gpu_time)
+# 检测 tensorflow 能使用的设备情况
+from tensorflow.python.client import device_lib
+
+# os.environ["CUDA_VISIBLE_DEVICES"] = "1"  # 这个可以指定使用哪个设备
+print(device_lib.list_local_devices())

some-test/wfdb_use.py

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+import os
+
+import numpy as np
+import wfdb
+import matplotlib.pyplot
+
+PATH = '../MIT-BIT/'
+
+
+# file error with read_ecg_data(data) parameter data false
+
+def read_ecg_data(data):
+    # get the data from the ecg data file and set begin and end points
+    record = wfdb.rdrecord(PATH + 'data', sampfrom=0, sampto=1500)
+    # return type record
+    print(type(record))
+    print(dir(record))
+    # get the Lead signal （len、filename、number、signalname、Sample rate
+    print(record.p_signal)
+    print(np.shape(record.p_signal))
+
+    print(record.sig_len)
+
+    print(record.record_name)
+
+    print(record.n_sig)
+
+    print(record.sig_name)
+
+    print(record.fs)
+
+    # get type
+    annotations = wfdb.rdann(PATH + data, 'atr')
+    print(type(annotations))
+    print(dir(annotations))
+
+    # get R
+    print(annotations.sample)
+    # get label of Heart beat
+    print(annotations.sample)
+    # num
+    print(annotations.ann_len)
+    # show label of beat
+    print(wfdb.show_ann_labels())
+
+    matplotlib.pyplot.draw_ecg(record.p_signal)
+    return record.p_signal
+
+
+if __name__ == '__main__':
+    read_ecg_data(100)