VIC Model Parameters¶
To convert ASCII parameters to NetCDF parameters for usage in the VIC 5.0 Image Driver, we have instructions for installing Tonic and its dependencies and then reading in and converting ASCII parameters to NetCDF.
Option 1: Installing Tonic Using Anaconda¶
The easiest way to install Tonic and its dependencies is to use the Anaconda Python distribution. The instructions below assume the user is using the
tonic supports the use of Python 2.7 or 3.4+.
To install Anaconda, follow these two simple steps (check to make sure the installer version is the most current)
download and run the Anaconda installer: http://continuum.io/downloads
setup a virtual environment for Tonic
conda create -n tonic python=3.4 netcdf4 pandas numpy scipy matplotlib xray basemap source activate tonic
Note: you'll need to do the
source activate tonic to activate the tonic virtual environment in any new shells.
Now, download the Tonic source code:
git clone firstname.lastname@example.org:UW-Hydro/tonic.git cd tonic
From the Tonic source code repository, Tonic can be installed using Python's
python setup.py install
This installs a top level script,
vic_utils, into your
$CONDA_ROOT/envs/vic_test/bin directory and the
tonic package into your conda environment (e.g.
$CONDA_ROOT/lib/.../site-packages/tonic). Note that for most
$CONDA_ROOT represents the directory where the root
conda installation is (e.g.
If you don't want to use the Anaconda installation I've shown above, you can build the package in your local python installation using:
python setup.py develop
Below are two methods for installing
tonic if you do not use
anaconda. This may or may not require write permissions. Because this is not our preferred method for installing
tonic, the user should read the Python documentation for further instructions on how to install Python packages.
Option 2a: Using a local Python Install (With Write Permissions)¶
If you have write permissions to the location of your Python distribution, you can just run
python setup.py install
from the top level Tonic directory. This will install
tonic into your
Option 2b: Using a local Python Install (Without Write Permissions)¶
If you do not have write permissions, you can install Tonic in your local
$PYTHONPATH by following these steps:
lib/python directory in your
mkdir -p $HOME/lib/python/
Add this library path to your
$PYTHONPATH in your
python setup.py install --home=$HOME
Testing your install¶
From the following command line:
vic_utils -h python -c 'import tonic'
If you don't get any errors, you should be ready to use
Using Tonic to convert VIC 4 parameters to netCDF format for VIC 5¶
Option 1: Using the
vic_utils command-line utility¶
Tonic includes a command-line utility for converting VIC style parameters to gridded netCDF. Usage of the utility can be found by running:
vic_utils grid_params --help
Option 2: Using the
This option allows the user to specify the format of the individual VIC parameter files. This can be quite useful if the format does not match the assumptions used by the command-line utility.
from tonic.models.vic.grid_params import soil, snow, veg, veg_class, Cols, Desc n_veg_classes = 4 root_zones = 3 # Read the soil parameters soil_dict = soil('~/workdir/Stehekin_soil.txt', c=Cols(nlayers=3)) # Read the snow parameters snow_dict = snow('~/workdir/Stehekin_snowbands.txt', soil_dict, c=Cols(snow_bands=5)) # Read the veg parameter file veg_dict = veg('~/workdir/Stehekin_vegparam.txt', soil_dict, lai_index=False, veg_classes=n_veg_classes) # Read the veg library file veg_lib = veg_class('~/workdir/Stehekin_veglib.txt', skiprows=1) # Determine the grid shape target_grid, target_attrs = calc_grid(soil_dict['lats'], soil_dict['lons']) # Grid all the parameters grid_dict = grid_params(soil_dict, target_grid, version=version veg_dict=veg_dict, veglib_dict=veg_lib, snow_dict=snow_dict) # Write a netCDF file with all the parameters write_netcdf('~/workdir/example.params.vic5.nc', target_attrs, target_grid=target_grid, soil_grid=grid_dict['soil_dict'], snow_grid=grid_dict['snow_dict'], veglib_dict=veg_lib, veg_grid=grid_dict['veg_dict'], version='5.0.dev')