Contents

Integrated Development Environments (IDEs)

The teaching contents on this website build on so-called Application Programming Inferaces (APIs) and Integrated Development Environments (IDEs). An API represents a computing interface that enables interactions between multiple software intermediaries. Modular programming becomes easy with an API, because it systematically hides complex information that is not necessarily needed to write code according to industry standards. For instance, an API can define the interface between an application (such as Python or Word) and an Operating System (OS) such as Windows, Linux, or macOS. An IDE enables the definition of a project to use for example a specific Python Conda Environment and it enables robust coding by pointing out issues directly in the code, even before it was executed once. Powerful IDEs go even further and provide assistance in documenting code with markdown (.md files) and direct pipes into git (see section on the usage of git).

Note

These pages are written with the web-based IDE JupyterLab, which is suitable to follow the course contents. IDEs such as PyCharm or Spyder are more suitable to setup projects locally (e.g., for course assignments).

Anaconda

Anaconda Navigator

Anaconda is basically a Python distribution that enables the usage of a couple of IDEs. Today, Anaconda is not limited to Python anymore and also comes with interfaces and environments for R.

Note

Anaconda represents the baseline for many applications presented on this website. It enables the built-in installation of programming language interpreters (e.g., Python and R), as well as IDEs such as PyCharm, Spyder, or JupyterLab (Notebook).

The very first step to get started with Anaconda consists in downloading and installing Anaconda. On Windows, Anaconda (download) should be installed in the LOCAL user folder (e.g., C:\users\<your-user-name>\AppData\Local). Linux or macOS users find download and installation instructions directly at the developer’s website, tailored for their specific distribution. Click here for Linux installation instructions and click here for macOS installation instructions.

After the successful installation of Anaconda, IDEs for Python programming or markdown editing can be directly installed by launching the Anaconda navigator. ``conda`` environments can be created later on following the instructions in the Python (fundamentals) section.

Miniconda

Anaconda may create large environments that require several gigabytes of storage. To install lightweight environments, use Miniconda. Miniconda does not include Anaconda Navigator and to enable working with Jupyter notebooks (in Windows):

  1. Click on Start.

  2. Type Anaconda Prompt and hit enter (use Miniconda3). A Terminal window (black background) opens.

  3. In Anaconda prompt type conda install jupyter and confirm with y when the Terminal asks Proceed ([y]/n)?.

To work with Jupyter notebooks (open, create or modify), type jupyter lab (or jupyter notebook) in Anaconda Prompt (Miniconda3) and hit Enter. The JupyterLab application will open in the default webbrowser.

PyCharm (via Anaconda Navigator)

Jetbrains PyCharm (Community Edition) is an open-access IDe for non-commercial use. Alternatives are Spyder IDE (for Python) or RStudio (R and Python). However, before launching any project in an IDE, the installation of an interpreter (e.g., Python or R) is necessary (we already installed an interpreter with Anaconda).

PyCharm is available as an Anaconda Navigator (i.e., Anaconda’s graphical user interface) module. To enable PyCharm in Anaconda, download PyCharm from the developer’s website and install PyCharm. A reboot may be required after the installation.

After the installation of PyCharm for Anaconda, open Anaconda Navigator:

  1. Open Anaconda Navigator and make sure to be in the Home tab.

  2. If the installation of PyCharm was successful, click on the Launch button to open PyCharm.

  3. In PyCharm click on + Create New Project

  4. Option 1 (preferred): Use the developer’s up-to-date documentation for setting up PyCharm with Anaconda.

  5. Option 2 (quick to read, but may fail): A window will open - enter:

    • Location - Select a local directory for the project (e.g., C:/hydro/project)

    • Project Interpreter - Check the Existing interpreter box and select the above-installed conda environment ``hypy` <hypy_install.html#conda-env>`__ (e.g., C:\users\<your-user-name>\AppData\Local\Continuum\anaonda3\envs\)

    • Click on the Create button.

All set - you are ready to work with Python, markdown (documentation), and git.

Note

Python users read more about setting up conda environments on the Python (basics) page.

JupyterLab

Via Anaconda Navigator

JupyterLab is a product of the nonprofit organization Project Jupyter, which develops “open-source software, open-standards, and services for interactive computing across dozens of programming languages. A Jupyter notebook (.ipynb file) enables the combination of markdown text blocks with executable code blocks. Essentially, a Jupyter notebook is a JavaScript Object Notation (JSON) file. The structure of JSON files enables the easy export of .ipynb notebooks to many other open standard output formats such as HTML, LaTeX, markdown, Python, presentation slides, or PDF. The Jupyter kernels support the three core programming languages Julia, Python and R, and many more Jupyter kernels (currently 49) for other programming languages exist.

  1. Open Anaconda Navigator and make sure to be in the Home tab.

  2. Look for JupyterLab and click on the Install button (if already installed, there is only a Launch button visible).

  3. After successful installation, open JupyterLab, by clicking on the Launch button.

  4. JupyterLab opens in the default web browser, where Jupyter notebooks (.ipynb) or Python files can be created and edited.

Tip

Get familiar with JupyterLab, by creating files, adding new Markdown or Python cells and Running cells.

The essentials of markdown are explained on the Markdown and Documentation page (short read). Learning Python is more than a short read and the Python (basics) walks you through the course contents to learn Python (takes time).

Note

Anaconda Navigator alternatively provides the application Jupyter Notebook. However, JupyterLab is the Project Jupyter’s next-generation user interface, which is more flexible and powerful. This is why this website refers to JupyterLab rather than the Jupyter Notebook app.

Via Anaconda Prompt

Open Anaconda Prompt, which represents a Terminal window with black background and a blinking cursor.

If you are working with Miniconda, install the Jupyter Notebook app by typing conda install jupyter and confirm with y when Anaconda Prompt asks Proceed ([y]/n)?.

To start the JupyterLab app and open, create, or modify Jupyter notebooks, type jupyter lab (jupyter notebook for Jupyter Notebook) in Anaconda Prompt (Miniconda3) and hit Enter. The Jupyter Notebook application will open in the default webbrowser.

Extensions and spellchecker

Many additional features for JupyterLab are available through nbextensions, which can be installed through Anaconda Prompt:

conda install -c conda-forge jupyter_contrib_nbextensions

When reading through the Python tutorials on this website, you will probably find one or another spelling mistake (please report mistakes!). In particular, the Python pages are affected, because they were created with Jupyter Lab, where there is no spell checker pre-installed. To avoid at least the most unpleasant errors you can install a spellchecker in jupyter. One solution is to install @ijmbbarrs spellchecker, which requires installing nodejs (through Anaconda Prompt and in addition to nbextensions):

conda install -c conda-forge nodejs
jupyter labextension install @ijmbarr/jupyterlab_spellchecker

The spellchecker uses Typo.js as dictionary and only identifies misspelled words without proposing corrections. More details on spellchecking are available at the developer’s website.

In the case that several warning messages occur when starting JupyterLab (such as [W 18:49:22.283 NotebookApp] Config option template_path not recognized by LenvsHTMLExporter. Did you mean one of: template_file, template_name, template_paths?), downgrade jupyter notebook from version 6.x to 5.6.1 (there is currently an issue with the temp_path variable):

conda install "nbconvert=5.6.1"
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