PyCharm 2018.1 Help

Scientific Mode Tutorial

In this tutorial, you operate in Scientific Mode and use Matplotlib and NumPy packages to run and debug a Python code with data visualization.

Before you start, ensure the following is installed:

Creating a Scientific project

To create a PyCharm project with Scientific mode settings predefined:

  1. Open the New Project wizard (File | New | Project ).
  2. Choose Scientific project type.
  3. In the project settings dialog window, specify a project name, ensure the Conda is selected as the new environment, and alter the default data folder (if needed), then click Create.
  4. Open the main.py file and add the following code sample:
    import numpy as np import matplotlib.pyplot as plt N = 50 x = np.random.rand(N) y = np.random.rand(N) colors = np.random.rand(N) area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii plt.scatter(x, y, s=area, c=colors, alpha=0.5) plt.show() X = np.linspace(-np.pi, np.pi, 256,endpoint=True) C,S = np.cos(X), np.sin(X) plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-") plt.plot(X, S, color="red", linewidth=2.5, linestyle="-") plt.xlim(X.min()*1.1, X.max()*1.1) plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$']) plt.ylim(C.min()*1.1,C.max()*1.1) plt.yticks([-1, 0, +1], [r'$-1$', r'$0$', r'$+1$']) plt.show()
  5. Process warnings shown for the numpy and matplotlib imports and enable the packages in the project.

Running

Run your scientific project (Shift+F10). The code is executed and shows two graphs in the SciView. Clicking the preview thumbnail displays the respective graph:

py matplotlib run

You can modify the project code to plot only one graph at a time. In the scientific mode, you can execute fragments of your code by creating code cells.

To execute code cells:

  1. Modify the main.py file by adding the "#%%" lines.
    adding cells to the Python code
  2. In the left gutter, click the icon run from left gutter icon (Ctrl+Enter) on line with the scatter plot cell mark. Only the scatter graph will be built.
  3. Now click the icon run from left gutter icon or press Ctrl+Enter on the line with the y versus x plot cell mark. The corresponding graph should appear.

Debugging

Let's put a breakpoint at the line:

plt.show()

This line appears twice in our example code, and so there will be two breakpoints.

Right-click the editor background and on the context menu choose Debug 'sample'.

You see the Debug tool window and the grey characters in the editor. This is the result of the inline debugging, which is enabled.

The line with the first breakpoint is blue highlighted. It means that the debugger has stopped at the line with the breakpoint, but has not yet executed it. If we execute this line (for example, by clicking the frames step over button on the stepping toolbar of the Debug tool window), we'll see the graph:

py matplotlib debug

Next, look at the Variables tab of the Debug tool window. If you click the View as Array link nearby the area array, the Data tab in the SciView window will open:

py matplotlib view as array

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.

Running in console

Right-click the editor background and choose the Run File in Console command:

py run file in console

This command corresponds to running a run/debug configuration for the main.py file with the Run with Python console checkbox selected:

py run config for command line

When this command is run, the >>> prompt appears after the output in the Run tool window, and you can execute your own commands.

Summary

So, what has been done with the help of PyCharm?

  • The file main.py was created and opened for editing.
  • The source code has been entered (note the powerful PyCharm's code completion!)
  • The source code has been run and debugged.
  • Finally, we ran the file in console.
Last modified: 19 April 2018

See Also