plt.savefig('high_resolution_plot.png', dpi=300) 使用savefig函数保存图形为文件: 在上述代码中,plt.savefig函数将当前图形保存为名为high_resolution_plot.png的文件,并设置DPI为300,以确保高清质量。 (可选)关闭图形,释放资源: 在保存图形后,如果你不再需要显示该图形,可以使用plt.close()来关闭图形窗口...
ax=plt.subplots(figsize=(8,6))ax.plot(x,y,label='Damped sine wave')ax.set_title('Damped Sine Wave from how2matplotlib.com')ax.set_xlabel('X-axis')ax.set_ylabel('Y-axis')ax.legend()# 设置DPI并保存fig.set_dpi(300)plt.savefig('high_res_plot...
plt.savefig('plot.png', dpi=300) # Save the figure with a higher resolution Powered By Color distortion due to clipping Color distortion occurs when data contains extreme values outside the expected color range. This causes parts of the data to be clipped and assigned the minimum or maximum...
Additional savefig Options In addition to the basic functionality of saving the chart to a file, .savefig() also has a number of useful optional arguments. dpi can be used to set the resolution of the file to a numeric value. transparent can be set to True, which causes the background ...
(0) img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8) buf.close() img = cv2.imdecode(img_arr, 1) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) return img# you can get a high-resolution image as ...
lines.Line2D object # Confidence interval of fit (higher resolution due to generated fit-vals with higher density) # NOTE: the comma "," needs to be left out, otherwise: "TypeError: cannot unpack non-iterable PolyCollection object" line2_label = "{}% CI".format(str(round(CI * 100)))...
This dataset provides offline high-resolution imagery of atmospheric sulfur dioxide (SO2) concentrations. 此星光明 2024/02/02 2130 从1维到6维,一文读懂多维数据可视化策略 其他 选自towardsdatascience 作者:Dipanjan Sarkar 机器之心编译参与:Jane W、乾树、黄小天数据聚合、汇总和可视化是支撑数据分析领域的三...
matplotlib.figure.Figure.savefig python #First create some toy data: x = np.linspace(0, 2*np.pi, 400) y = np.sin(x**2) #Creates just a figure and only one subplot fig, ax = plt.subplots() ax.plot(x, y) ax.set_title('Simple plot') #Creates two subplots and unpacks the out...
Map boundaries at low and high resolution Notice that the low-resolution coastlines are not suitable for this level of zoom, while high-resolution works just fine. The low level would work just fine for a global view, however, and would be much faster than loading the high-resolution border...
arange(0, n_generations) # compute the medians med_low_mut = med(data_low_mut) med_high_mut = med(data_high_mut) plot(x, med_low_mut) plot(x, med_high_mut) savefig('medians1.png') We can use xlim and lim to see a bit better: xlim(-5, 400) ylim(-5000, 100) ...