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  1. 3
      doc/source/tutorial.rst
  2. 439
      doc/source/tutorials/boise_2018.rst

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doc/source/tutorial.rst
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@ -12,7 +12,7 @@ Upcoming Tutorials @@ -12,7 +12,7 @@ Upcoming Tutorials
.. toctree::
:maxdepth: 1
tutorials/wrf_workshop_2018.rst
tutorials/boise_2018.rst
Past Tutorials
@ -23,5 +23,6 @@ Past Tutorials @@ -23,5 +23,6 @@ Past Tutorials
tutorials/wrf_workshop_2017.rst
tutorials/tutorial_03_2018.rst
tutorials/wrf_workshop_2018.rst

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doc/source/tutorials/boise_2018.rst
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WRF-Python and VAPOR Workshop 2018 (Boise State University)
=============================================================
The Department of Geosciences at Boise State University is partnering with
staff from the National Center for Atmospheric Research (NCAR) to host a free,
2-day workshop in the Environmental Research Building (ERB) lab 2104 at
Boise State University on September 26-27, 2018. The tutorial will be centered
on the WRF-Python and VAPOR tools for analyzing and visualizing data from the
Weather Research and Forecasting (WRF) regional weather and climate model.
Users must be registered to attend this tutorial (see :ref:`registration`).
Location
---------------------
September 26-27, 2018 9:00 AM - 4:00 PM
Boise State University, Environmental Research Building (ERB) lab #2104.
WRF-Python Overview
---------------------
WRF-Python is a collection of diagnostic and interpolation routines for use
with output from the Weather Research and Forecasting (WRF-ARW) Model. The
package provides over 30 diagnostic calculations,
several interpolation routines, and utilities to help with plotting
via cartopy, basemap, or PyNGL. The functionality is similar to what is
provided by the NCL WRF package.
.. note::
WRF-Python is NOT a tool for running the WRF-ARW model using Python.
This tutorial provides an introduction to wrf-python. The tutorial is beginner
friendly for new users of wrf-python, but this is NOT an introduction to the
Python programming language (see :ref:`prereq_boise`). Due to limited seating,
if you do not have any previous experience with Python, please do not register
for this tutorial.
.. note::
For online training that provides an introduction to the Python
programming language itself, please see the
`Unidata Python Training Page <https://unidata.github.io/online-python-training/>`_.
Computers will be provided, but feel free to use your own laptop if you prefer.
We will be covering how to install wrf-python via conda as part of the
tutorial.
Students are encouraged to bring their own data sets, but data will be provided
if this is not an option. Students will be provided a jupyter notebook workbook
which can be modified to accommodate their data.
Topics include:
- How to install wrf-python via conda
- A brief introduction to jupyter notebook
- Overview of WRF data files
- WRF-Python basics
- Plotting with cartopy
- Overview of OpenMP features and other performance tips
- Open lab for students
.. _registration:
Registration
---------------
Please register prior to September 19, 2018. The registration form is here:
`Registration Form <https://goo.gl/forms/ASb8bP7Bz2Boxye23>`_
Registration consists of a brief survey, which will help give the instructor
a brief overview of your background and will help tailor the tutorial to
your expectations.
.. _prereq_boise:
Prerequisites
---------------
This tutorial assumes that you have basic knowledge of how to type commands
in to a terminal window using your preferred operating system. You
should know some basic directory commands like *cd*, *mkdir*, *cp*, *mv*.
This tutorial assumes that you have prior experience programming in Python.
Below is a list of some Python concepts that you will see in the examples,
but don't worry if you aren't familiar with everything.
- Opening a Python interpreter and entering commands.
- Importing packages via the import statement.
- Familiarity with some of the basic Python types: str, list, tuple, dict, bool, float, int, None.
- Creating a list, tuple, or dict with "[ ]", "( )", "{ }" syntax (e.g. my_list = [1,2,3,4,5]).
- Accessing dict/list/tuple items with the "x[ ]" syntax (e.g. my_list_item = my_list[0]).
- Slicing str/list/tuple with the ":" syntax (e.g. my_slice = my_list[1:3]).
- Using object methods and attributes with the "x.y" syntax (e.g. my_list.append(6)).
- Calling functions (e.g. result = some_function(x, y))
- Familiarity with numpy would be helpful, as only a very brief introduction
is provided.
- Familiarity with matplotlib would be helpful, as only a very brief
introduction is provided.
-------------------------------------------------
Instructions for Computer Lab Installation
-------------------------------------------------
Step 1: Download Miniconda
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For this tutorial, you will need to download and install Miniconda. We are
going to use Python 3.6+.
Please use the appropriate link below to download Miniconda for your operating
system.
.. note::
64-bit OS recommended
`Win64 <https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe>`_
`Mac <https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh>`_
`Linux <https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh>`_
For more information, see: https://conda.io/miniconda.html
.. note::
**What is Miniconda?**
If you have used the Anaconda distribution for Python before, then you will
be familiar with Miniconda. The Anaconda Python distribution includes numerous
scientific packages out of the box, which can be difficult for users to build and
install. More importantly, Anaconda includes the conda package manager.
The conda package manager is a utility (similar to yum or apt-get) that installs
packages from a repository of pre-compiled Python packages. These repositories
are called channels. Conda makes it easy for Python users to install and
uninstall packages, and also can be used to create isolated Python environments
(more on that later).
Miniconda is a bare bones implementation of Anaconda and only includes the
conda package manager. Since we are going to use the conda-forge channel to
install our scientific packages, Miniconda avoids any complications between
packages provided by Anaconda and conda-forge.
Step 2: Install Miniconda
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Windows:
1. Browse to the directory where you downloaded Miniconda3-latest-Windows-x86_64.exe.
2. Double click on Miniconda3-latest-Windows-x86_64.exe.
3. Follow the instructions.
4. For Windows 10, use the Anaconda command prompt found under the Anaconda2
menu (Start Menu -> Anaconda2 -> Anaconda Prompt). Otherwise, open a
regular command prompt.
Mac and Linux:
For Mac and Linux, the installer is a bash script.
1. Using a terminal, you need to execute the bash shell script that you downloaded by
doing::
bash /path/to/Miniconda3-latest-MacOSX-x86_64.sh [Mac]
bash /path/to/Miniconda3-latest-Linux-x86_64.sh [Linux]
2. Follow the instructions.
3. At the end of the installation, it will ask if you want to add the
miniconda3 path to your bash environment. If you are unsure what to do,
you should say "yes". If you say "no", we're going to assume you know
what you are doing.
If you said "yes", then once you restart your shell, the miniconda3 Python
will be found instead of the system Python when you type the "python"
command. If you want to undo this later, then you can edit
either ~/.bash_profile or ~/.bashrc (depending on OS used) and
comment out the line that looks similar to::
# added by Miniconda3 x.x.x installer
export PATH="/path/to/miniconda3/bin:$PATH"
4. Restart your command terminal.
5. [Linux and Mac Users Only] Miniconda only works with bash. If bash is
not your default shell, then you need to activate the bash shell by typing
the following in to your command terminal::
bash
6. Verify that your system is using the correct Python interpreter by typing
the following in to your command terminal::
which python
You should see the path to your miniconda installation. If not, see the
note below.
.. note::
If you have already installed another Python distribution, like Enthought
Canopy, you will need to comment out any PATH entries for that distribution
in your .bashrc or .bash_profile. Otherwise, your shell environment may
pick to wrong Python installation.
If bash is not your default shell type, and the PATH variable has been
set in .bash_profile by the miniconda installer, try executing
"bash -l" instead of the "bash" command in step 5.
Step 3: Set Up the Conda Environment
--------------------------------------
If you are new to the conda package manager, one of the nice features of conda
is that you can create isolated Python environments that prevent package
incompatibilities. This is similar to the *virtualenv* package that some
Python users may be familiar with. However, conda is not compatible with
virtualenv, so only use conda environments when working with conda.
The name of our conda environment for this tutorial is: **tutorial_backup**.
Follow the instructions below to create the tutorial_backup environment.
1. Open a command terminal if you haven't done so.
2. [Linux and Mac Users Only] The conda package manager only works with bash,
so if bash is not your current shell, type::
bash
3. Add the conda-forge channel to your conda package manager.
Type or copy the command below in to your command terminal. You should
run this command even if you have already done it in the past.
This will ensure that conda-forge is set as the highest priority channel.
::
conda config --add channels conda-forge
.. note::
Conda-forge is a community driven collection of packages that are
continually tested to ensure compatibility. We highly recommend using
conda-forge when working with conda. See https://conda-forge.github.io/
for more details on this excellent project.
4. Create the backup conda environment for the tutorial.
Students will create a conda environment during the tutorial, but if
they run in to problems, we're going to create a backup environment.
Type or copy this command in to your command terminal::
conda create -n tutorial_backup python=3.6 matplotlib cartopy netcdf4 jupyter git ffmpeg wrf-python
Type "y" when prompted. It will take several minutes to install everything.
This command creates an isolated Python environment named *tutorial_backup*, and installs
the python interpreter, matplotlib, cartopy, netcdf4, jupyter, git, ffmpeg, and wrf-python
packages.
.. note::
When the installation completes, your command terminal might post a message similar to:
.. code-block:: none
If this is your first install of dbus, automatically load on login with:
mkdir -p ~/Library/LaunchAgents
cp /path/to/miniconda3/envs/tutorial_test/org.freedesktop.dbus-session.plist ~/Library/LaunchAgents/
launchctl load -w ~/Library/LaunchAgents/org.freedesktop.dbus-session.plist
This is indicating that the dbus package can be set up to automatically load on login. You
can either ignore this message or type in the commands as indicated on your command terminal.
The tutorial should work fine in either case.
5. Activate the conda environment.
To activate the tutorial_backup Python environment, type the following
in to the command terminal:
For Linux and Mac (using bash)::
source activate tutorial_backup
For Windows::
activate tutorial_backup
You should see (tutorial_backup) on your command prompt.
To deactivate your conda environment, type the following in to the
command terminal:
For Linux and Mac::
source deactivate
For Windows::
deactivate tutorial_backup
Step 4: Download the Student Workbook
---------------------------------------
The student workbook for the tutorial is available on GitHub. The tutorial_backup
conda environment includes the git application needed to download the repository.
These instructions download the tutorial in to your home directory. If you want
to place the tutorial in to another directory, we're going to assume you know
how to do this yourself.
To download the student workbook, follow these instructions:
1. Activate the tutorial_backup conda environment following the instructions
in the previous step (*source activate tutorial_backup* or
*activate tutorial_backup*).
2. Change your working directory to the home directory by typing the
following command in to the command terminal:
For Linux and Mac::
cd ~
For Windows::
cd %HOMEPATH%
3. Download the git repository for the tutorial by typing the following
in to the command terminal::
git clone https://github.com/NCAR/wrf_python_tutorial.git
4. There may be additional changes to the tutorial after you have downloaded
it. To pull down the latest changes, type the following in to the
command terminal:
For Linux and Mac::
source activate tutorial_backup
cd ~/wrf_python_tutorial/boise_workshop_2018
git pull
For Windows::
activate tutorial_2018
cd %HOMEPATH%\wrf_python_tutorial\boise_workshop_2018
git pull
.. note::
If you try the "git pull" command and it returns an error indicating
that you have made changes to the workbook, this is probably because
you ran the workbook and it contains the cell output. To fix this,
first do a checkout of the workbook, then do the pull.
.. code-block:: none
git checkout -- .
git pull
Step 5: Verify Your Environment
----------------------------------
Verifying that your environment is correct involves importing a few
packages and checking for errors (you may see some warnings for matplotlib
or xarray, but you can safely ignore these).
1. Activate the tutorial_backup conda environment if it isn't already active
(see instructions above).
2. Open a python terminal by typing the following in to the command
terminal::
python
3. Now type the following in to the Python interpreter::
>>> import netCDF4
>>> import matplotlib
>>> import xarray
>>> import wrf
4. You can exit the Python interpreter using **CTRL + D**
Step 6: Obtain WRF Output Files
----------------------------------
A link will be provided in an email prior to the tutorial for the WRF-ARW
data files used for the examples. If you did not receive this email, the link
will also be provided at the tutorial itself.
You also have the option of using your own data files for the tutorial by
modifying the first Jupyter Notebook cell to point to your data set.
However, there is no guarantee that every cell in your workbook will work
without some modifications (e.g. cross section lines will be drawn outside of
your domain).
1. The link in the email should take you to a location on an Amazon cloud
drive.
2. If you hover your mouse over the wrf_tutorial_data.zip file, you'll see
an empty check box appear next to the file name. Click this check
box.
3. At the bottom of the screen, you'll see a Download button next to a
cloud icon. Click this button to start the download.
4. The download was most likely placed in to your ~/Downloads folder
[%HOMEPATH%\\Downloads for Windows]. Using your preferred method of choice
for unzipping files, unzip this file in to your home directory. Your data
should now be in ~/wrf_tutorial_data
[%HOMEPATH%\\wrf_tutorial_data for Windows].
5. Verify that you have three WRF output files in that directory.
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