![]() The source code has been run and debugged. The source code has been entered (note the powerful P圜harm's code completion!) The file main.py was created and opened for editing. So, what has been done with the help of P圜harm? When this command is run, the > prompt appears after the output in the Run tool window, and you can execute your own commands. This command corresponds to running a run/debug configuration for the main.py file with the Run with Python console checkbox selected: Right-click the editor background and choose the Run File in Console command: Mind the only row of figures in the Data tab in the SciView - it's explained by the fact that the area array is one-dimensional. See Managing Variables Loading Policy for more information. It is recommended to switch to the On demand mode by selecting the corresponding loading policy. When you process excessive amount of data, you might experience degradation of debugging performance if the debugger loads variable's values synchronously or asynchronously. ![]() If you click the View as Array link nearby the area array, the Data tab in the SciView window opens: Installation Installing an official release Matplotlib releases are available as wheel packages for macOS, Windows and Linux on PyPI. Next, look at the Variables tab of the Debug tool window. If we execute this line (for example, by clicking the button on the stepping toolbar of the Debug tool window), we'll see the graph: It means that the debugger has stopped at the line with the breakpoint, but has not yet executed it. The line with the first breakpoint is blue highlighted. This is the result of the inline debugging, which is enabled. You see the Debug tool window and the grey characters in the editor. Right-click the editor background and from the context menu choose Debug 'main'. This line appears twice in our example code, and so there will be two breakpoints. Now click the icon or press Control+Enter on the line with the y versus x plot cell mark. In the gutter, click the icon Control+Enter on line with the scatter plot cell mark. Modify the main.py file by adding the "#%%" lines. In the scientific mode, you can execute fragments of your code by creating code cells. You can modify the project code to plot only one graph at a time. Clicking the preview thumbnail displays the respective graph: The code is executed and shows two graphs in the SciView. Process warnings shown for the numpy and matplotlib imports and enable the packages in the project. Plt.plot(X, S, color="red", linewidth=2.5, linestyle="-") Plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-") ![]() X = np.linspace(-np.pi, np.pi, 256,endpoint=True) Plt.scatter(x, y, s=area, c=colors, alpha=0.5) Area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii ![]()
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