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Let’s modify the script so it makes separate figures for each profile instead of a single one.

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If you want your script to open multiple figure windows at the same time, you need to number your figures using plt.figure(). This means that you can run commands again. You should now have the $ prompt back in your Bash window. The Python script will continue running until it reaches the end and exits.

#PYTHON JUPYTER NOTEBOOK STANDALONE UPDATE#

If it’s not in your own code, then ignore the warning and update your Python distribution soon.Ĭlose the figure window.

If the deprecated command is i code, you should fix it so it stops warning you and doesn’t break later on. The command still runs, though, but it might stop working in the future. Please use setCollectionBehavior insteadĪ command that’s deprecated is one that you are discuraged from using because it’s been replaced by something else.

13: 31: 40.589 python setCanCycle: is deprecated.

#PYTHON JUPYTER NOTEBOOK STANDALONE CODE#

The Python code is also paused – it will wait until the figure is closed to continue past the plt.show() line! This shows that Bash is “busy” and won’t respond until you close the figure (you can open a new Bash window if you need to run something while the figure is open). Notice that the Bash window doesn’t show a $ before the text prompt while the figure window is open. There are some tools along the bottom of this window that you can use to navigate and save the figure. Python opened up a window that shows the figure we could see inside the notebook. You can also just add a # at the start of the line. In many text editors, the shortcut Command-/ (or Control-/) will comment out a line. Go into the text file and comment out this line. The iPython magic command %matplotlib inline makes plots appear within the Jupyter notebook instead of in a separate window, but the regular Python interpreter doesn’t understand iPython magic commands. To run the script from the command line, type:įile 'topo_profiler.py', line 20 %matplotlib inline Open a new Bash window and navigate (using cd, ls and pwd) to the directory that contains your topo_profiler.py file. These are line numbers from the Jupyter notebook and are also commented out. You should also see some lines that look like this: # In. These are comments and the Python interpreter will not read them. If you exported the file through the Jupyter notebook menu, any Markdown (text) cells should be prefixed with a #. Most code-friendly text editors will color the text in the file according to the language they are written in! Then open the file topo_profiler.py in your chosen text editor. Call your file topo_profiler.py and make sure that the file is in the same directory that contains the folder data where the file topo.asc is. py file using whichever method is easier. Go ahead and convert your Jupyter notebook into a.

Copy the script and paste it into a simple text file (using a text editor like TextWrangler or Notepad++) and save the file with a.

We can convert this code into a command-line Python script in two different ways: You should copy the code from the Jupyter notebook you were working in before to a new notebook to make sure nothing sneaks into the script: Let’s look back at the script we wrote earlier that plots the West-to-East topographic profiles across our data. We will first run this script locally (in your own computer) before moving it to, the CSDMS HPCC, and running it on a remote server with the commands you learned during the Bash lessons. However, you should quickly move your prototype code into stand-alone scripts that are platform independent and better suited for version control.īefore we dive further into Python, we are going to convert the scripts we wrote in the first lesson into stand-alone Python scripts that can run from the command line. The Jupyter notebooks are valuable in the early stages of your code development workflow, where you gradually build your program by tinkering with the code and exploring the data.

py that are run from the command line or within an IDE (Integrated Development Environment) such as Spyder. Stand-alone Python programs are text files with the extension.

The Jupyter notebooks, however, can only run inside their specific graphical environment. We started out using the Jupyter (iPython) notebooks in these lessons because they are a friendly environment to try out individual commands and write short scripts.

Run Python scripts from the command line.

Python jupyter notebook standalone