importnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltfromscipy.signalimportsavgol_filter# 生成示例数据np.random.seed(0)# 设置随机种子以便重现x=np.linspace(0,10,100)y=np.sin(x)+np.random.normal(0,0.5,size=x.size)# 加入噪声的正弦波# 应用 Savitzky-Golay 滤波器y_smooth=savgol_filter(y,win...
One of the most common smoothing techniques used in data analysis is the moving average. A moving average is a way to smooth out data by calculating the average of a set of values over a specific period of time. To implement a moving average in Python, we can use the pandas library. ...
使用rolling窗口进行平滑 df['y_smooth'] = df['y'].rolling(window=5).mean() plt.plot(df['x'], df['y'], label='Noisy Data') plt.plot(df['x'], df['y_smooth'], label='Smoothed Data', color='red') plt.title('Data Smoothing with Pandas') plt.xlabel('x') plt.ylabel('sin(...
(0, 0.1, 100) # 加入噪声 # 应用高斯滤波 y_smooth = gaussian_filter1d(y, sigma=2) # 绘制结果 plt.plot(x, y, 'o', label='Noisy Data') plt.plot(x, y_smooth, '-', label='Smoothed Data', color='red') plt.title('Gaussian Filter for Smooth Curve') plt.xlabel('X-axis') ...
suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point. Examples --- t = np.linspace(-4, 4, 500) y = np...
plot(x, y, label='Noisy Data') plt.plot(x, filtered_y, label='Filtered Data') plt.legend() plt.show() window_length对曲线的平滑作用:window_length的值越小,曲线越贴近真实曲线;window_length值越大,平滑效果越厉害(备注:该值必须为正奇整数)。 k值对曲线的平滑作用:k 值越大,曲线越贴近真实...
(data.y.shape[1]):X =data.tspanY =data.y_noisy[:, spec_id]obj = smooth_bms(x=X, y=Y, scaling=False)obj.fit_bms(nsteps=1e4, maxtime=1800, minr2=0.999, show_update=True, update_every_n_seconds=200)data...
noisy = peak_signal_noise_ratio(original, noisy, data_range=1) psnr_bayes = peak_signal_noise_ratio(original, im_bayes, data_range=1) psnr_visushrink = peak_signal_noise_ratio(original, im_visushrink,data_range=1) psnr_visushrink2 = peak_signal_noise_ratio(original, im_visushrink2, data...
data.y_smooth_sr = np.zeros(data.y.shape) data.y_diff_sr = np.zeros(data.y.shape) # 对每个物种拟合概况并求导(这个过程可能需要一些时间) for spec_id in range(data.y.shape[1]): X = data.tspan Y = data.y_noisy[:, spec_id] ...
(x=x,y=y_noise,mode='markers',marker=dict(size=6,color='#5E88FC',symbol='circle-open'),name='Noisy Sine'))fig.add_trace(go.Scatter(x=x,y=smoothTriangle(y_noise,10),# setting degree to 10mode='markers',marker=dict(size=6,color='#C190F0',symbol='triangle-up'),name='Moving ...