Python is compatible Mac, PC and Linux computers. In this tutorial, the free Python integrated development environment (IDE) that we’ll use to run projects is called PyCharm. We’ll show you how. 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: Conda interpreter. Matplotlib package. Creating a Scientific project Create a PyCharm project with the scientific settings predefined.
In this tutorial, you operate in Scientific Mode and use Matplotlib and NumPy packages to run and debug a Python code with data visualization.
Adobe photoshop mac download and crack. Before you start, ensure the following is installed:
Conda interpreter
Matplotlib package
NumPy package
Creating a Scientific project
Create a PyCharm project with the scientific settings predefined
Open the New Project wizard (File | New | Project ).
Choose Scientific project type.
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.
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()Process warnings shown for the
numpyandmatplotlibimports 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:
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:
Modify the main.py file by adding the '#%%' lines.
In the gutter, click the icon Ctrl+Enter on line with the scatter plot cell mark. Only the scatter graph will be built.
Now click the 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:
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 'main'.
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 button on the stepping toolbar of the Debug tool window), we'll see the graph:
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 opens:
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 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:
This command corresponds to running a run/debug configuration for the main.py file with the Run with Python console checkbox selected:
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.
- Pycharm Tutorial
- Pycharm Useful Resources
- Selected Reading
In this chapter, you will learn in detail about the installation process of PyCharm on your local computer.
Steps Involved
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You will have to follow the steps given below to install PyCharm on your system. These steps show the installation procedure starting from downloading the PyCharm package from its official website to creating a new project.
Step 1
Download the required package or executable from the official website of PyCharm https://www.jetbrains.com/pycharm/download/#section=windowsHere you will observetwo versions of package for Windows as shown in the screenshot given below −
Note that the professional package involves all the advanced features and comes with free trial for few days and the user has to buy a licensed key for activation beyond the trial period. Community package is for free and can be downloaded and installed as and when required. It includes all the basic features needed for installation. Note that we will continue with community package throughout this tutorial.
Step 2
Download the community package (executable file) onto your system and mention a destination folder as shown below −
Step 3
Now, begin the installation procedure similar to any other software package.
Step 4
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Once the installation is successful, PyCharm asks you to import settings of the existing package if any.
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This helps in creating a new project of Python where you can work from the scratch. Note that unlike other IDEs, PyCharm only focusses on working with projects of Python scripting language.