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414 lines
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414 lines
15 KiB
WRF Workshop 2017 |
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===================== |
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Welcome wrf-python tutorial attendees! |
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The instructions below should be completed prior to arriving at the tutorial. |
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There will not be enough time to do this during the tutorial. |
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Prerequisites |
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--------------- |
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This tutorial assumes that you have basic knowledge of how to type commands |
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in to a command terminal using your preferred operating system. You |
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should know some basic directory commands like *cd*, *mkdir*, *cp*, *mv*. |
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Regarding Python, to understand the examples in this tutorial, you |
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should have some experience with Python basics. Below is a list of some |
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Python concepts that you will see in the examples, but don't worry if you aren't |
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familiar with everything. |
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- Opening a Python interpreter and entering commands. |
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- Importing packages via the import statement. |
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- Familiarity with some of the basic Python types: str, list, tuple, dict, bool, float, int, None. |
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- Creating a list, tuple, or dict with "[ ]", "( )", "{ }" syntax (e.g. my_list = [1,2,3,4,5]). |
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- Accessing dict/list/tuple items with the "x[ ]" syntax (e.g. my_list_item = my_list[0]). |
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- Slicing str/list/tuple with the ":" syntax (e.g. my_slice = my_list[1:3]). |
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- Using object methods and attributes with the "x.y" syntax (e.g. my_list.append(6)). |
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- Calling functions (e.g. result = some_function(x, y)) |
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- Familiarity with numpy would be helpful, as only a very brief introduction |
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is provided. |
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- Familiarity with matplotlib would be helpful, as only a very brief |
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introduction is provided. |
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If you are completely new to Python, that shouldn't be a problem, since |
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most of the examples consist of basic container types and function calls. It |
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would be helpful to look at some introductory material before arriving at the |
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tutorial. If you've programmed before, picking up Python isn't too difficult. |
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Here are some links: |
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https://www.learnpython.org/ |
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https://developers.google.com/edu/python/ |
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Step 1: Open a Command Terminal |
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-------------------------------- |
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To begin, you will first need to know how to open a command line terminal for |
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your operating system. |
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For Windows: |
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.. code-block:: none |
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WINDOWS + r |
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type cmd in the run window |
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For Mac: |
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.. code-block:: none |
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Finder -> Applications -> Utilities -> Terminal |
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For Linux: |
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.. code-block:: none |
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Try one of the following: |
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CTRL + ALT + T |
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CTRL + ALT + F2 |
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Step 2: Download Miniconda |
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---------------------------- |
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For this tutorial, you will need to download and install Miniconda. We are |
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going to use Python 2.7, but it should also work with Python 3.5+. However, |
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due to limitations with open source compilers on conda-forge, only Python 2.7 |
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is available for Windows. |
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Please use the appropriate link below to download Miniconda for your operating |
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system. |
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.. note:: |
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64-bit OS recommended |
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`Win64 <https://repo.continuum.io/miniconda/Miniconda2-latest-Windows-x86_64.exe>`_ |
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`Mac <https://repo.continuum.io/miniconda/Miniconda2-latest-MacOSX-x86_64.sh>`_ |
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`Linux <https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh>`_ |
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For more information, see: https://conda.io/miniconda.html |
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.. note:: |
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**What is Miniconda?** |
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If you have used the Anaconda distribution for Python before, then you will be |
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familiar with Miniconda. The Anaconda Python distribution includes numerous |
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scientific packages out of the box, which can be difficult for users to build and |
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install. More importantly, Anaconda includes the conda package manager. |
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The conda package manager is a utility (similar to yum or apt-get) that installs |
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packages from a repository of pre-compiled Python packages. These repositories |
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are called channels. Conda makes it easy for Python users to install and |
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uninstall packages, and also can be used to create isolated Python environments |
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(more on that later). |
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Miniconda is a bare bones implementation of Anaconda and only includes the |
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conda package manager. Since we are going to use the conda-forge channel to |
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install our scientific packages, Miniconda avoids any complications between |
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packages provided by Anaconda and conda-forge. |
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Step 3: Install Miniconda |
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---------------------------- |
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Windows: |
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1. Browse to the directory where you downloaded Miniconda2-latest-Windows-x86_64.exe. |
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2. Double click on Miniconda2-latest-Windows-x86_64.exe. |
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3. Follow the instructions. |
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4. Restart your command terminal. |
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Mac and Linux: |
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For Mac and Linux, the installer is a bash script. |
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1. Using a terminal, you need to execute the bash shell script that you downloaded by |
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doing:: |
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bash /path/to/Miniconda2-latest-MacOSX-x86_64.sh [Mac] |
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bash /path/to/Miniconda2-latest-Linux-x86_64.sh [Linux] |
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2. Follow the instructions. |
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3. At the end of the installation, it will ask if you want to add the |
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miniconda2 path to your bash environment. If you are unsure what to do, |
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you should say "yes". If you say "no", we're going to assume you know |
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what you are doing. |
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If you said "yes", then once you restart your shell, the miniconda2 Python |
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will be found instead of the system Python when you type the "python" |
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command. If you want to undo this later, then you can edit |
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either ~/.bash_profile or ~/.bashrc (depending on OS used) and |
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comment out the line that looks similar to:: |
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# added by Miniconda2 4.1.11 installer |
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export PATH="/path/to/miniconda2/bin:$PATH" |
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4. Restart your command terminal. |
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5. [Linux and Mac Users Only] Miniconda only works with bash. If bash is |
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not your default shell, then you need to activate the bash shell by typing |
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the following in to your command terminal:: |
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bash |
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6. Verify that your system is using the correct Python interpreter by typing |
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the following in to your command terminal:: |
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which python |
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You should see the path to your miniconda installation. If not, see the |
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note below. |
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.. note:: |
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If you have already installed another Python distribution, like Enthought |
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Canopy, you will need to comment out any PATH entries for that distribution |
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in your .bashrc or .bash_profile. Otherwise, your shell environment may |
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pick to wrong Python installation. |
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If bash is not your default shell type, and the PATH variable has been |
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set in .bash_profile by the miniconda installer, try executing |
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"bash -l" instead of the "bash" command in step 5. |
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Step 4: Set Up the Conda Environment |
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-------------------------------------- |
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If you are new to the conda package manager, one of the nice features of conda |
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is that you can create isolated Python environments that prevent package |
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incompatibilities. This is similar to the *virtualenv* package that some |
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Python users may be familiar with. However, conda is not compatible with |
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virtualenv, so only use conda environments when working with conda. |
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The name of our conda environment for this tutorial is: **tutorial_2017**. |
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Follow the instructions below to create the tutorial_2017 environment. |
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1. Open a command terminal if you haven't done so. |
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2. [Linux and Mac Users Only] The conda package manager only works with bash, |
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so if bash is not your current shell, type:: |
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bash |
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3. Add the conda-forge channel to your conda package manager. |
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Type or copy this command in to your command terminal:: |
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conda config --add channels conda-forge |
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.. note:: |
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Conda-forge is a community driven collection of packages that are |
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continually tested to ensure compatibility. We highly recommend using |
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conda-forge when working with conda. See https://conda-forge.github.io/ |
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for more details on this excellent project. |
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4. Create the conda environment for the tutorial. |
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Type or copy this command in to your command terminal:: |
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conda create -n tutorial_2017 python=2.7 matplotlib=1.5.3 cartopy netcdf4 jupyter git ffmpeg wrf-python |
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Type "y" when prompted. It will take several minutes to install everything. |
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This command creates an isolated Python environment named *tutorial_2017*, and installs |
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the python interpreter, matplotlib, cartopy, netcdf4, jupyter, git, ffmpeg, and wrf-python |
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packages. |
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.. note:: |
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When the installation completes, your command terminal may post a message similar to: |
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.. code-block:: none |
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If this is your first install of dbus, automatically load on login with: |
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mkdir -p ~/Library/LaunchAgents |
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cp /Users/ladwig/miniconda2/envs/tutorial_test/org.freedesktop.dbus-session.plist ~/Library/LaunchAgents/ |
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launchctl load -w ~/Library/LaunchAgents/org.freedesktop.dbus-session.plist |
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This is indicating that the dbus package can be set up to automatically load on login. You |
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can either ignore this message or type in the commands as indicated. The tutorial should work fine |
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in either case. |
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.. note:: |
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In this tutorial, we need to use matplotlib v1.5.3 due to some issues with cartopy, which |
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should be fixed in a later version of cartopy. Be sure to supply the version number as |
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indicated in the command above. |
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5. Activate the conda environment. |
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To activate the tutorial_2017 Python environment, type the following |
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in to the command terminal: |
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For Linux and Mac (using bash):: |
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source activate tutorial_2017 |
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For Windows:: |
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activate tutorial_2017 |
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You should see (tutorial_2017) on your command prompt. |
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To deactivate your conda environment, type the following in to the |
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command terminal: |
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For Linux and Mac:: |
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source deactivate |
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For Windows:: |
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deactivate tutorial_2017 |
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Step 5: Download the Student Workbook |
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--------------------------------------- |
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The student workbook for the tutorial is available on GitHub. The tutorial_2017 |
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conda environment includes the git application needed to download the repository. |
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These instructions download the tutorial in to your home directory. If you want |
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to place the tutorial in to another directory, we're going to assume you know |
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how to do this yourself. |
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To download the student workbook, follow these instructions: |
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1. Activate the tutorial_2017 conda environment following the instructions |
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in the previous step (*source activate tutorial_2017* or |
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*activate tutorial_2017*). |
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2. Change your working directory to the home directory by typing the |
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following command in to the command terminal: |
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For Linux and Mac:: |
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cd ~ |
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For Windows:: |
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cd %HOMEPATH% |
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3. Download the git repository for the tutorial by typing the following |
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in to the command terminal:: |
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git clone https://github.com/NCAR/wrf_python_tutorial.git |
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4. There may be additional changes to the tutorial after you have downloaded |
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it. To pull down the latest changes, type the following in to the |
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command terminal: |
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For Linux and Mac:: |
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source activate tutorial_2017 |
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cd ~/wrf_python_tutorial |
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git pull |
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For Windows:: |
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activate tutorial_2017 |
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cd %HOMEPATH%\wrf_python_tutorial |
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git pull |
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.. note:: |
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If you try the "git pull" command and it returns an error indicating |
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that you have made changes to the workbook, this is probably because |
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you ran the workbook and it contains the cell output. To fix this, |
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first do a checkout of the workbook, then do the pull. |
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.. code-block:: none |
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git checkout -- wrf_workshop_2017.ipynb |
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git pull |
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Step 6: Verify Your Environment |
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---------------------------------- |
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Verifying that your environment is correct involves importing a few |
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packages and checking for errors (you may see some warnings for matplotlib |
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or xarray, but you can safely ignore these). |
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1. Activate the tutorial_2017 conda environment if it isn't already active |
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(see instructions above). |
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2. Open a python terminal by typing the following in to the command |
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terminal:: |
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python |
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3. Now type the following in to the Python interpreter:: |
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>>> import netCDF4 |
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>>> import matplotlib |
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>>> import xarray |
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>>> import wrf |
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4. You can exit the Python interpreter using **CTRL + D** |
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Step 7: Obtain WRF Output Files |
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---------------------------------- |
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For this tutorial, we strongly recommend that you use your own WRF output files. |
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The tutorial includes an easy way to point to your own data files. The WRF |
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output files should all be from the same WRF run and use the same domain. |
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If your files are located on another system (e.g. yellowstone), then copy 2 or |
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3 of these files to your local computer prior to the tutorial. |
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If you do not have any of your own WRF output files, then you can download the |
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instructor data files from a link that should have been provided to you in an |
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email prior to the tutorial. |
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If you are using the link provided in the email for your data, you can follow |
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the instructions below to place your data in the default location for your |
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workbook. |
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1. The link in the email should take you to a location on an Amazon cloud |
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drive. |
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2. If you hover your mouse over the wrf_tutorial_data.zip file, you'll see |
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an empty check box appear next to the file name. Click this check |
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box. |
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3. At the bottom of the screen, you'll see a Download button next to a |
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cloud icon. Click this button to start the download. |
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4. The download was most likely placed in to your ~/Downloads folder |
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[%HOMEPATH%\\Downloads for Windows]. Using your preferred method of choice |
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for unzipping files, unzip this file in to your home directory. Your data |
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should now be in ~/wrf_tutorial_data |
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[%HOMEPATH%\\wrf_tutorial_data for Windows]. |
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5. Verify that you have three WRF output files in that directory. |
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Getting Help |
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---------------- |
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If you experience problems during this installation, please send a question |
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to the :ref:`google-group` support mailing list. |
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We look forward to seeing you at the tutorial!
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