$ conda install notebook pymt_sedfluxImport the Sedflux3D component from pymt. All of the components available to pymt are located in pymt.components. Here I've renamed the component to be Model to show that you could run these same commands with other models as well. For instance, you could instead import Child with from pymt.components import Child as Model and repeat this exercise with Child instead.
In [1]:
from __future__ import print_function
# Some magic to make plots appear within the notebook
%matplotlib inline
import numpy as np # In case we need to use numpy
In [2]:
from pymt import plugins
Instantiate the model. If you wanted, you could have multiple instances of the same model - each with their own state. A PyMT component is instantiate without any arguments.
In [3]:
model = plugins.Sedflux3D()
PyMT adds a docstring to the model. It is important to us that credit it given where credit is due. We want to make it clear that when we include a component in our framework the component is not ours but belong to the model's author. To that end, the model documentation includes a list of model authors, DOIs, as well as full citations to use if someone uses the model. In addition, to keep models from being black boxes, and because they are all open source, we provide links to source code.
In [4]:
# help(model)
In [5]:
rm -rf _model # Clean up for the next step
The following code is generic for all pymt components - not just Sedflux3D. First we instantiate the component and then call its setup method to create a model simulation with the necessary input files (note that this is not a BMI method but something added by PyMT). setup takes an optional argument that gives a path to a folder that will contain the necessary input files (if not provided, a temporary folder will be used). setup returns the name of the config file and the path to the folder containing it (we'll use this information later when we run initialize).
In [6]:
config_file, initdir = model.setup('_model', run_duration=3650000.)
If we look inside the _model folder, we can see all of the necessary sedflux input files ready to run.
In [7]:
ls _model
The initialize method reads all of the input files and gets the model ready to update through time.
In [8]:
model.initialize(config_file, dir=initdir)
In [9]:
model.set_value("channel_exit_water_flow__speed", 1.2)
model.set_value("channel_exit_x-section__mean_of_width", 400.)
model.set_value("channel_exit_x-section__mean_of_depth", 4.)
model.set_value("channel_exit_water_sediment~suspended__mass_concentration", .01)
Out[9]:
Here we update the model for 10 time steps, printing the model time after each step.
In [10]:
for t in range(10):
model.update()
print(model.get_current_time())
From the output it appears that sedflux is operating on a 1 year time step and is using units of days. We can double check that assumption.
In [11]:
model.time_units
Out[11]:
Yep, days. And the time step...
In [12]:
model.get_time_step()
Out[12]:
The update_until method gives us more control by allowing us to update to a particular time. Using the units keyword allows us to specify the "until" time. Here we update the model until 20 years.
In [13]:
model.update_until(200., units='year')
model.get_current_time()
Out[13]:
In [14]:
model.time_units = 'year'
print(model.get_current_time())
We've covered how to setup and run a model, now we'll have a look at how to get data out of the model. This is done with the get_value method but first we need to look at what variables the model provides. This is held as a tuple of variable names and accessed through the output_var_names attribute.
In [15]:
model.output_var_names
Out[15]:
In [16]:
model.input_var_names
Out[16]:
So to get the values of, say, water depth,
In [17]:
model.get_value('sea_water__depth')
Out[17]:
As with previous methods, you can use the units keyword to specify units. Here we get water depths in units of parsecs.
In [18]:
model.get_value('sea_water__depth', units='parsec')
Out[18]:
In [19]:
model.quick_plot('sea_water__depth', vmin=-200, vmax=200, cmap='BrBG_r')
In [22]:
model.set_value("channel_exit_water_sediment~suspended__mass_concentration", .5)
for t in range(10):
model.update()
model.quick_plot('sea_water__depth', vmin=-200, vmax=200, cmap='BrBG_r')
In [ ]: