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Scientific project tutorial

Last modified: 11 February 2024

Available only in PyCharm Professional: download to try or compare editions

In this tutorial, you will create a Scientific project to run and debug Python code with data visualization.

Before you start, make sure that conda is installed.

Creating a Scientific project

Create a PyCharm project with the scientific 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 that 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 ShiftF10. The code is executed and shows two graphs in the Plots tool window. Clicking the preview thumbnail displays the respective graph:

Preview the graph

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

Execute code cells

  1. Modify the main.py file by adding the "#%%" lines.

    Adding cells to the Python code

  2. In the gutter, click the icon Run on the line with the scatter plot cell mark. Only the scatter graph will be built.

  3. Now click the icon Run 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 from the context menu choose Debug <project_name>.

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 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 Step Over on the stepping toolbar of the Debug tool window), we'll see the graph:

Debugging the matplotlib code

Next, look at the Variables tab of the Debug tool window. If you click the View as Array link next to the area array, the Data View tool window opens:

View as array

When you process excessive amount of data, you might experience degradation of debugging performance if the debugger loads variable's values synchronously or asynchronously. It is recommended to switch to the On demand mode by selecting the corresponding loading policy. See Managing Variables Loading Policy for more information.

Mind the only column in the table - 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.

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

Run with Python console

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.

See also

Procedures

Reference

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