pymt_dbseabed is a package that uses the bmi_dbseabed pacakge to convert dbSEABED datasets into a reusable, plug-and-play data component for PyMT modeling framework developed by Community Surface Dynamics Modeling System (CSDMS). This allows dbSEABED datasets to be easily coupled with other datasets or models that expose a Basic Model Interface.

Installing pymt

Installing pymt from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge

Note: Before installing pymt, you may want to create a separate environment into which to install it. This can be done with,

conda create -n pymt python=3
conda activate pymt

Once the conda-forge channel has been enabled, pymt can be installed with:

conda install pymt

It is possible to list all of the versions of pymt available on your platform with:

conda search pymt --channel conda-forge

Installing pymt_dbseabed

To install pymt_dbseabed, use pip

pip install pymt_dbseabed

or conda

conda install -c conda-forge pymt_dbseabed

Coding Example

You can learn more details about the coding example from the tutorial notebook.

import matplotlib.pyplot as plt
import numpy as np

from pymt.models import DbSeabedData

# initiate a data component
data_comp = DbSeabedData()
data_comp.initialize('config_file.yaml')

# get variable info
var_name = data_comp.output_var_names[0]
var_unit = data_comp.var_units(var_name)
var_location = data_comp.var_location(var_name)
var_type = data_comp.var_type(var_name)
var_grid = data_comp.var_grid(var_name)
var_itemsize = data_comp.var_itemsize(var_name)
var_nbytes = data_comp.var_nbytes(var_name)

print(f'{var_name=} \n{var_unit=} \n{var_location=} \n{var_type=} \n{var_grid=} \n{var_itemsize=} \n{var_nbytes=}')

# get variable grid info
grid_type = data_comp.grid_type(var_grid)
grid_rank = data_comp.grid_ndim(var_grid)
grid_shape = data_comp.grid_shape(var_grid)
grid_spacing = data_comp.grid_spacing(var_grid)
grid_origin = data_comp.grid_origin(var_grid)

print(f'{grid_type=} \n{grid_rank=} \n{grid_shape=} \n{grid_spacing=} \n{grid_origin=}')

# get variable data
data = data_comp.get_value(var_name)
data_2D = data.reshape(grid_shape)

# get X, Y extent for plot
min_y, min_x = grid_origin
max_y = min_y + grid_spacing[0]*(grid_shape[0]-1)
max_x = min_x + grid_spacing[1]*(grid_shape[1]-1)
dy = grid_spacing[0]/2
dx = grid_spacing[1]/2
extent = [min_x - dx, max_x + dx, min_y - dy, max_y + dy]

# plot data
fig, ax = plt.subplots(1,1, figsize=(9,5))
im = ax.imshow(data_2D, extent=extent)
fig.colorbar(im)
plt.xlabel('X')
plt.ylabel('Y')
plt.title("dbSEABED dataset (Carbonate in %)")

# finalize the data component
data_comp.finalize()