If you are an aspiring data scientist today or someone in the field of Computer Science in general, it is impossible for you to not be a little familiar with Python. As this high level, general purpose programming language is rising in popularity, its strengths and impact are becoming more and more prominent. New developers want to delve into data analytics possible with Python’s elite data visualization and analysis tools.

1. Pycharm License Server 2017
2. Pycharm Professional License Server Free
3. Professional License Mass

According to a survey done by JetBrains, “Python is the primary language used by 84% of programmers who use Python. Furthermore, almost 58% of developers use Python for data analysis while 52% use it for web development. The use of Python for DevOps, machine learning, and web crawling or web scraping follow close behind along with a multitude of other uses.”

So I got pycharm professional version. Its well worth it since I'm stuck with Windows atm, and pycharm is amazing, it helps me bypass all of the obstacles I run into wit Windows. The best tool I've come across. So the way I want to do the licensing thing is to create a server which will hold the license. PyCharm crack is a powerful Python IDE built to allow developers and programmer to code in Python, JavaScript, CSS, and also TypeScript.

PyCharm – a Cross-Platform IDE for Python Developers

In order to get the most out of Python, especially in terms of data analysis, it is important to find an integrated development environment that offers the most in terms of editing code and visualizing results. PyCharm is an IDE developed by JetBrains, the brains behind big development tools like PhpStorm.

The primary component of PyCharm, its code editor, offers intelligent context-based auto-completion of code, code suggestions, and code snippets. It allows programmers to create logical code blocks to separate program modules. The editor is efficient in identifying and highlighting errors as code is written. Code navigation has never been easier as PyCharm allows programmers to quickly jump to a particular snippet, object, or class in the source code. PyCharm also has tons of refactoring features which makes it easy for developers to make organized changes. Support for web technologies like HTML, CSS, JavaScript, and more combined with PyCharm’s live edit and view webpage environment makes it a powerful tool for web development in Python.

“Literate Programming” with Jupyter Notebook

Another IDE that comes into play when talking about Python is Jupyter Notebook, formerly known as IPython Notebook. Jupyter Notebook is especially important in giving shape to what Donald Knuth, a computer scientist from Stanford, famously called “literate programming”. Literate programming is a standard form of programming that focuses on the human readability of code. It allows programmers to give shape to the logical units of their code, the meaning of those code units, and their results. Compiled, a notebook presents code as a complete and understandable thought process and its technological manifestation.

To support literate programming, Jupyter Notebook has a multitude of tools available which provide complete liberty to edit code with its relevant supporting prose. Starting at the basic level, notebooks (the files in which code is written) have the ability to separate code into “cells”. Cells make it easy to differentiate between specific functionality. Apart from code cells, there are markup cells available where it is easy to type code descriptions, significance, or results. Editing options for markup cells are endless; you can play around with text formats, images, and even mathematical equations and diagrams.

With the extensive support for integrating Jupyter Notebook in PyCharm, developers have found it a whole lot of easier to create, execute and debug source codes while examining their outputs simultaneously.

What features are included for Jupyter Notebooks in PyCharm?

PyCharm allows you to make changes to your source document in a number of ways:

• Editing and making previews
• Use notebook as source code with definitions in form of texts
• Live previews along with debugging
• Options for Auto-saving your code
• Highlighting of all types of Error and syntax mistakes
• Ability to add line comments
• Ability to execute and preview results simultaneously
• Allows using the dedicated Jupyter Notebook Debugger

Let’s you recognize .ipynb files with the icon

Jupyter Notebook in PyCharm

Jupyter Notebook’s powerful code writing and editing capabilities and PyCharm’s elite dedicated debugging module for Jupyter, code navigation, framework support, plugin support, and error detection combined can form a development environment which lacks little.

Now the question is how to achieve an integrated development environment that combines the functionalities of PyCharm and Jupyter Notebook. The short answer is that this is currently only possible with a licensed version of PyCharm Professional. PyCharm Professional is not free. However, you can get a free license if you are affiliated with an educational institute and have a .edu email address.

The long answer to the aforementioned question of how to integrate Jupyter Notebook with PyCharm is given below:

1. First, you should create a new project.
2. In that project, create a new ipynb file by going to File > New… > Jupyter Notebook.This should open up a new notebook file.
3. If you don’t have the Jupyter Notebook package installed, an error will appear above the newly opened ipynb file. The error reads “Jupyter package is not installed” and next to it you will have the option to “Install jupyter package”. Click on “Install jupyter package”. This will start the installation process which you can view by clicking on the running processes in the bottom right corner of the PyCharm window.
4. To start exploring Jupyter Notebook in PyCharm, create code cells and execute them.
5. To launch the Jupyter server, execute the code cell.The Jupyter server is then launched using 8888 port by default on the localhost. You can view these configurations in the server’s tool window.Once launched, you can view the server above your source code window and next to it you can view the kernel created as “Python 2” or “Python 3”.
6. You can now access the variables tab in PyCharm to view how the values of your variables change as you execute code cells. This helps in debugging.
7. You can even set breakpoints at lines of code and then click on the Run icon, , and select “Debug Cell” (or alternatively use the shortcut Alt + Shift + Enter) to begin debugging.
8. The following tabs at the bottom of the PyCharm window are essential to using Jupyter Notebook: The “TODO” tab is where you can view TODO comments and easily navigate to them by double-clicking on them in the TODO tab. The “Jupyter” tab is the Jupyter Server log. The “Terminal” is the python terminal where you can write python commands. The “Python Console” is the console where you can view code and its output line by line.

Getting along with the User Interface

Out of the many components of the user interface, let us begin exploring the ones you can work with.

Viewing Modes

PyCharm offers three viewing modes to edit your Jupyter notebook files:

1. Editor Only Mode

This allows adding and editing notebook cells.

2. Split View Mode

The split view mode lets you both add cells and preview their output. This is also the default-viewing mode for all Jupyter notebooks in PyCharm.

3. Preview Only Mode

Here you can preview your code execution results, raw cells and code markdown.

Toolbar

The toolbar provides a number of shortcuts that provide quick access to all basic operations you are going to work with.

The Server Log

The Server log appears when you launch any of the Jupyter Servers. It shows the current state of the server and link to the notebook that you are currently working on.

The Variables Tab

This tab provides a detailed report on the variable values present in the executed cell.

Now that you are familiar with the basics of editing and debugging Jupyter Notebooks in PyCharm, you can go ahead and install the Jupyter package in PyCharm for yourself. From here on, you can fully explore its features and use them to your satisfaction!

PyCharm can be installed from here.

This tutorial assumes you are familiar with the process of building Mantid (with separate source and build directories inside a root directory), and that you have built a working version. If you are unclear about this see here.

1. Once PyCharm is open, set up the project. Go to File->Open and select the root directory in which both your source and build directories reside.

   Go to File->Settings, then under Project you will set two sub-menus ProjectInterpreter and ProjectStructure. The interpreter defines the python executable that will be used to run your code, and the structure menu allows you to decide which folders within the project to include and index.

2. In the ProjectInterpreter sub menu, at the top select the options button and click Add..., a new window should appear titled “Add Python Interpreter”. In the menu on the left, select “System Interpreter” (a version of Python with all the correct variables set already exists within Mantid). Click on the ... to open a file browser, and navigate to;

   This is the interpreter, so select “Ok” and apply the changes. This should bring up a list of all the packages associated to the interpreter. There should be many packages, however you should not see PyQt (but instead QtPy).

3. In the ProjectStructure sub menu you should see your root directory with the source/build directories both visible (if not, add them). The folder structure should be present in the centre of the window allowing you to mark folders orange (excluded) or blue (source). Source directories will be searched for python code.

   Within the source directory add the following to your sources:

   If you are writing scripts in any other directories, you can also mark them as sources. This helps PyCharm give better auto-complete and import suggestions during development.

   Additionally, in the Mantid build directory add the following as source folders:

   here we are setting up PyCharm for the Debug build, you would use /bin/Release instead if you are building mantid in release mode.

4. The environment needs to be set up before running the configuration. Follow the instructions below to use either the EnvFile plugin (recommended) or manual path setup.

NOTE : In some cases, imports in the code will still be highlighted red when they come from folders within the script/ folder, or from other folders entirely. To fix this simply add the relevant folder that contains the module you are importing in the same fashion as step 3 above.

Running python code from within PyCharm which depends on the python API, or PyQt for example requires one extra step. Because the source root labelling from the previous section only affects PyCharm searching and not the run configuration, before running the file we must set up the run configuration correctly.

4. Install the EnvFile plugin by Borys Pierov. The plugin can be installed in multiple ways:
   1. Open Settings(CTRL + SHIFT + S), to go Plugins and search for EnvFile. Install and restart PyCharm.
   2. Go to the plugin’s webpage, download and install it.
5. To edit the configurations go to Run->Run… and select Edit Configurations. Notice that there is now a EnvFile tab under the configuration’s name.- Note that you have to do that for each configuration, or you can change the template configuration, and all configuration that use that template will have the EnvFile setup.
6. Open the EnvFile tab, check EnableEnvFile and SubstituteEnvironmentalVariables(...) - this allows setting up the third-party paths dynamically.
7. Click the + (plus) on the right side, select the pycharm.env file in the root of the build directory.

For running the Workbench continue onto Workbench, and follow the instructions to set up the Script Path and Working Directory.

Pycharm License Server 2017

Advantages of this approach:

• You can have multiple instances of PyCharm running with environment configuration for separate repositories. This is otherwise not possible, as all PyCharm instances seem to share a parent process and environment. (as is the case of 11/01/2019, it might change in the future)
• This makes possible switching projects for multiple repositories via the File > Open Recent … menu, as when the new project is opened its environment won’t be poluted with environment variables from the last one.
  • This can cause errors when the external dependencies aren’t quite the same between all the repositories, as some packages might be missing, or be different versions.

Disadvantages:

• Additional setup for each configuration necessary. Thankfully, if the template is edited to have the correct EnvFile setup, all copies of it will have it too. Copying an already existing configuration also copies the EnvFile setup.

This can be done in two ways:

• Open PyCharm using pycharm.bat which can be found in the build directory (this sets some additional environment variables compared with simply opening PyCharm directly).

  • This is preferred if you only have 1 repository with which PyCharm is used. If you need to use PyCharm on multiple repositories, it is recommended that you use the EnvFile extension.

• To edit the configurations go to Run->Run... and select EditConfigurations. This should open up a sub window. Hit the green + in the top left to create a new configuration and name it. In order to tell PyCharm where to look for python modules and libraries we need to add some folders to the PATH environment variable. Click on the ... next to the Environment Variables box, and hit the + icon. In the Name column enter “PATH”, in the value column enter the following;

The semi-colon delimited list of paths should end in ;%PATH% so that we prepend to the existing list of paths rather than overwriting them.

You should now be able to run and debug the scripts using the newly created configuration, by adding the full path of the file in the Scriptpath box at the top of the configuration window.

As an example, create a new file in <MantidSourceDirectory>/scripts/ called test.py. Copy into it the Python code below.

To test that the above instructions have worked, you can simply create a new Python file with the following content (for PyQt5)

This does not require a PyCharm Professional license for debugging, but requires additional setup for running unit tests.

1. Go to your Run/Debug Configurations.
2. Open Templates > Python tests > Unittests configuration.
3. Set the working directory to <MantidBuildDir>/bin/Debug, for a Debug build, or <MantidBuildDir>/bin/Release for a Release build.
4. Add the EnvFile to the Unittests configuration, instructions in Running Files in the Debugger with EnvFile extension.
5. You should now be able to click the Run/Debug icons next to each unit test method or class to run/debug them.

A PyCharm Professional license is required to use the Remote Debugging feature.

This functionality is useful for debugging python code that is spawned in separate threads, such as Python algorithms and system tests.

The remote debugger needs to be added as a configuration to be used easily:

1. Click the Add Configuration button at the top of the main window or click Run->EditConfigurations...
2. Click the + button and add “Python Debug Server” to the list of configurations.
3. Give it a name, and set the port number to 44444.
4. Leave “Suspend after connect” ticked if you would like any connections to the debugger to act as a breakpoint. It may be useful to untick this if you would like to hit a breakpoint in a loop inside an algorithm that runs many times but does not always hit that breakpoint.
5. Click OK.

You will also need to install the python package pydevd_pycharm which can be done by:

1. Navigating to the directory that contains the python interpreter <MantidSourceDirectory>/external/src/ThirdParty/lib/python3.8/
2. Running the following in the terminal .python-mpipinstallpydevd_pycharm

To use the remote debugger:

1. Select the remote debugger from the drop down list of configurations.

2. Click the green bug icon to start the debugger.

3. Copy and paste the two lines shown in the terminal into the code you wish to debug:

4. Start Mantid or the test you wish to debug (do not stop the remote debugger).

5. If “Suspend after connect” has been ticked the point at which the two lines have been pasted will act as a breakpoint. Otherwise, the code will stop at the next breakpoint after the pasted lines.

6. You can now use the PyCharm debugger as normal.

1. Use the native python interpreter (/usr/bin/python3) rather than from <MantidSourceDirectory>/external/src/ThirdParty/lib/python3.8/python.exe

2. In the ProjectStructure sub menu you should see your root directory with the source/build directories both visible (if not, add them). The folder structure should be present in the centre of the window allowing you to mark folders orange (excluded) or blue (source). Source directories will be searched for python code.

   Within the source directory add the following to your sources:

   If you are writing scripts in any other directories, you can also mark them as sources. This helps PyCharm give better auto-complete and import suggestions during development.

   Additionally, in the Mantid build directory add the following as source folders:

   It is recommended that you add the whole build folder to excluded. This will not interfere with the bin directory, inside the build, being used as a source folder. It will just limit the scope that PyCharm searches for files, classes, etc.

3. Go to Run->Run… and select Edit Configurations. Go to Templates > Python. Make <MantidBuildDirectory>/bin; the WorkingDirectory. This will then be used for all Python configurations you make.

Pycharm Professional License Server Free

You can install non-default plugins by pressing Ctrl+Alt+S to open the Settings/Preferences dialog and then going to Plugins.From here you can manage plugins, or add new ones by clicking Browse repositories.

The following non-default plugins are things our team has found useful for Mantid development:

• Markdown support - Side by side rendering of markdown documents such as``.md`` , .rst (requires Graphviz to show graphs in preview)
• dotplugin - Syntax highlighting for DOT
• BashSupport - Syntax highlighting for BASH scripts
• CMD Support - Syntax highlighting for .BAT ~scripts

Please add to this list if you find a useful plugin of your own

Note: Requires PyCharm Professional.

PyCharm supports deployment and syncronisation of written code to a remote server via SSH.

Professional License Mass

Open a local copy of the project and then follow the the guides here for configuring the remote interpreter and creating a deployment configuration.