Package: bmi-roms
bmi_romsbmi_roms package is an implementation of the Basic Model Interface (BMI) for
theROMS modeldatasets. This package wraps the dataset with BMI for
data control and query. This package is not implemented for people to use but is the key element to convert the ROMS dataset into
a data component (pymt_roms) for
thePyMTmodeling framework developed
by Community Surface Dynamics Modeling System (CSDMS).The current implementation supports 2D, 3D and 4D ROMS output datasets defined with geospatial and/or time dimensions
(e.g., dataset defined with dimensions as [time, s_rho, eta_rho, xi_rho])If you have any suggestion to improve the current function, please create a github issuehere.Get StartedInstall packageStable ReleaseThe bmi_roms package and its dependencies can be installed with pip$ pip install bmi_romsor conda$ conda install -c conda-forge bmi_romsFrom SourceAfter downloading the source code, run the following command from top-level folder
(the one that contains setup.py) to install bmi_roms.$ pip install -e .Code ExampleLearn more details from thetutorial notebookprovided in this package and launch binder to run the notebook.frombmi_romsimportBmiRomsimportnumpyasnpimportmatplotlib.pyplotaspltdata_comp=BmiRoms()data_comp.initialize('config_file.yaml')# get variable infoforvar_nameindata_comp.get_output_var_names():var_unit=data_comp.get_var_units(var_name)var_location=data_comp.get_var_location(var_name)var_type=data_comp.get_var_type(var_name)var_grid=data_comp.get_var_grid(var_name)var_itemsize=data_comp.get_var_itemsize(var_name)var_nbytes=data_comp.get_var_nbytes(var_name)print('variable_name:{}\nvar_unit:{}\nvar_location:{}\nvar_type:{}\nvar_grid:{}\nvar_itemsize:{}''\nvar_nbytes:{}\n'.format(var_name,var_unit,var_location,var_type,var_grid,var_itemsize,var_nbytes))# get time infostart_time=data_comp.get_start_time()end_time=data_comp.get_end_time()time_step=data_comp.get_time_step()time_unit=data_comp.get_time_units()time_steps=int((end_time-start_time)/time_step)+1print('start_time:{}\nend_time:{}\ntime_step:{}\ntime_unit:{}\ntime_steps:{}\n'.format(start_time,end_time,time_step,time_unit,time_steps))# get variable grid infoforvar_nameindata_comp.get_output_var_names():var_grid=data_comp.get_var_grid(var_name)grid_rank=data_comp.get_grid_rank(var_grid)grid_size=data_comp.get_grid_size(var_grid)grid_shape=np.empty(grid_rank,int)data_comp.get_grid_shape(var_grid,grid_shape)grid_spacing=np.empty(grid_rank)data_comp.get_grid_spacing(var_grid,grid_spacing)grid_origin=np.empty(grid_rank)data_comp.get_grid_origin(var_grid,grid_origin)print('var_name:{}\ngrid_id:{}\ngrid_rank:{}\ngrid_size:{}\ngrid_shape:{}\ngrid_spacing:{}\ngrid_origin:{}\n'.format(var_name,var_grid,grid_rank,grid_size,grid_shape,grid_spacing,grid_origin))# get variable datadata=np.empty(1026080,'float64')data_comp.get_value('time-averaged salinity',data)data_3D=data.reshape([40,106,242])# get lon and lat datalat=np.empty(25652,'float64')data_comp.get_value('latitude of RHO-points',lat)lon=np.empty(25652,'float64')data_comp.get_value('longitude of RHO-points',lon)# make a contour plotfig=plt.figure(figsize=(10,7))im=plt.contourf(lon.reshape([106,242]),lat.reshape([106,242]),data_3D[0],levels=36)fig.colorbar(im)plt.axis('equal')plt.xlabel('Longitude [degree_east]')plt.ylabel('Latitude [degree_north]')plt.title('ROMS model data of time-averaged salinity')