bluemath_tk.wrappers.swash package

Submodules

bluemath_tk.wrappers.swash.swash_example module

bluemath_tk.wrappers.swash.swash_wrapper module

class bluemath_tk.wrappers.swash.swash_wrapper.ChySwashModelWrapper(*args, **kwargs)

Bases: SwashModelWrapper

Wrapper for the SWASH model with friction.

build_case(case_context: dict, case_dir: str) → None

Build the input files for a case.

Parameters:

• case_context (dict) – The case context.

• case_dir (str) – The case directory.

default_Cf = 0.0002

class bluemath_tk.wrappers.swash.swash_wrapper.HySwashVeggyModelWrapper(*args, **kwargs)

Bases: SwashModelWrapper

Wrapper for the SWASH model with vegetation.

build_case(case_context: dict, case_dir: str) → None

Build the input files for a case.

Parameters:

• case_context (dict) – The case context.

• case_dir (str) – The case directory.

class bluemath_tk.wrappers.swash.swash_wrapper.SwashModelWrapper(*args, **kwargs)

Bases: BaseModelWrapper

Wrapper for the SWASH model. https://swash.sourceforge.io/online_doc/swashuse/swashuse.html#input-and-output-files

default_parameters

The default parameters type for the wrapper.

Type:

dict

available_launchers

The available launchers for the wrapper.

Type:

dict

postprocess_functions

The postprocess functions for the wrapper.

Type:

dict

build_cases -> None

Create the cases folders and render the input files.

list_available_postprocess_vars -> List[str]

List available postprocess variables.

_read_tabfile -> pd.DataFrame

Read a tab file and return a pandas DataFrame.

_convert_case_output_files_to_nc -> xr.Dataset

Convert tab files to netCDF file.

get_case_percentage_from_file -> float

Get the case percentage from the output log file.

monitor_cases -> pd.DataFrame

Monitor the cases and log relevant information.

postprocess_case -> xr.Dataset

Convert tab ouput files to netCDF file.

join_postprocessed_files -> xr.Dataset

Join postprocessed files in a single Dataset.

find_maximas -> Tuple[np.ndarray, np.ndarray]

Find the individual maxima of an array.

get_waterlevel -> xr.Dataset

Get water level from the output netCDF file.

calculate_runup2 -> xr.Dataset

Calculates runup 2% (Ru2) from the output netCDF file.

calculate_runup -> xr.Dataset

Stores runup from the output netCDF file.

calculate_setup -> xr.Dataset

Calculates mean setup (Msetup) from the output netCDF file.

calculate_statistical_analysis -> xr.Dataset

Calculates zero-upcrossing analysis to obtain individual wave heights (Hi) and wave periods (Ti).

calculate_spectral_analysis -> xr.Dataset

Makes a water level spectral analysis (scipy.signal.welch) then separates incident waves, infragravity waves, very low frequency waves.

available_launchers = {'docker_mpi': 'docker run --rm -v .:/case_dir -w /case_dir geoocean/rocky8 mpirun -np 2 swash_mpi.exe', 'docker_serial': 'docker run --rm -v .:/case_dir -w /case_dir geoocean/rocky8 swash_serial.exe', 'geoocean-cluster': 'launchSwash.sh', 'mpi': 'mpirun -np 2 swash_mpi.exe', 'serial': 'swash_serial.exe'}

build_case(case_context: dict, case_dir: str) → None

Build the input files for a case.

Parameters:

• case_context (dict) – The case context.

• case_dir (str) – The case directory.

calculate_runup(case_num: int, case_dir: str, output_nc: Dataset) → Dataset

Stores runup from the output netCDF file.

Parameters:

• case_num (int) – The case number.

• case_dir (str) – The case directory.

• output_nc (xr.Dataset) – The output netCDF file.

Returns:

The runup.

Return type:

xr.Dataset

calculate_runup2(case_num: int, case_dir: str, output_nc: Dataset) → Dataset

Calculates runup 2% (Ru2) from the output netCDF file.

Parameters:

• case_num (int) – The case number.

• case_dir (str) – The case directory.

• output_nc (xr.Dataset) – The output netCDF file.

Returns:

The runup 2% (Ru2).

Return type:

xr.Dataset

calculate_setup(case_num: int, case_dir: str, output_nc: Dataset) → Dataset

Calculates mean setup (Msetup) from the output netCDF file.

Parameters:

• case_num (int) – The case number.

• case_dir (str) – The case directory.

• output_nc (xr.Dataset) – The output netCDF file.

Returns:

The mean setup (Msetup).

Return type:

xr.Dataset

calculate_spectral_analysis(case_num: int, case_dir: str, output_nc: Dataset) → Dataset

Makes a water level spectral analysis (scipy.signal.welch) then separates incident waves, infragravity waves, very low frequency waves.

Parameters:

• case_num (int) – The case number.

• case_dir (str) – The case directory.

• output_nc (xr.Dataset) – The output netCDF file.

Returns:

The spectral analysis.

Return type:

xr.Dataset

calculate_statistical_analysis(case_num: int, case_dir: str, output_nc: Dataset) → Dataset

Calculates zero-upcrossing analysis to obtain individual wave heights (Hi) and wave periods (Ti).

Parameters:

• case_num (int) – The case number.

• case_dir (str) – The case directory.

• output_nc (xr.Dataset) – The output netCDF file.

Returns:

The statistical analysis.

Return type:

xr.Dataset

default_parameters = {'Hs': {'description': 'Significant wave height.', 'type': <class 'float'>, 'value': None}, 'Hs_L0': {'description': 'Wave height at deep water.', 'type': <class 'float'>, 'value': None}, 'WL': {'description': 'Water level.', 'type': <class 'float'>, 'value': None}, 'comptime': {'description': 'The computational time.', 'type': <class 'int'>, 'value': 180}, 'deltat': {'description': 'The time step.', 'type': <class 'int'>, 'value': 1}, 'dxinp': {'description': 'The input spacing.', 'type': <class 'float'>, 'value': 1.0}, 'gamma': {'description': 'The gamma parameter.', 'type': <class 'int'>, 'value': 2}, 'n_nodes_per_wavelength': {'description': 'The number of nodes per wavelength.', 'type': <class 'int'>, 'value': 60}, 'vegetation_height': {'description': 'The vegetation height.', 'type': <class 'float'>, 'value': None}, 'warmup': {'description': 'The warmup time.', 'type': <class 'int'>, 'value': 0}}

find_maximas(x: ndarray) → Tuple[ndarray, ndarray]

Find the individual maxima of an array.

Parameters:

x (np.ndarray) – The array (should be the water level time series).

Returns:

The peaks and the values of the peaks.

Return type:

Tuple[np.ndarray, np.ndarray]

get_case_percentage_from_file(output_log_file: str) → str

Get the case percentage from the output log file.

Parameters:

output_log_file (str) – The output log file.

Returns:

The case percentage.

Return type:

str

join_postprocessed_files(postprocessed_files: List[Dataset]) → Dataset

Join postprocessed files in a single Dataset.

Parameters:

postprocessed_files (list) – The postprocessed files.

Returns:

The joined Dataset.

Return type:

xr.Dataset

list_available_postprocess_vars() → List[str]

List available postprocess variables.

Returns:

The available postprocess variables.

Return type:

List[str]

monitor_cases(value_counts: str = None) → DataFrame | dict

Monitor the cases based on different model log files.

postprocess_case(case_num: int, case_dir: str, output_vars: List[str] = None, overwrite_output: bool = True, overwrite_output_postprocessed: bool = True, remove_tab: bool = False, remove_nc: bool = False) → Dataset

Convert tab output files to netCDF file.

Parameters:

• case_num (int) – The case number.

• case_dir (str) – The case directory.

• output_vars (list, optional) – The output variables to postprocess. Default is None.

• overwrite_output (bool, optional) – Overwrite the output.nc file. Default is True.

• overwrite_output_postprocessed (bool, optional) – Overwrite the output_postprocessed.nc file. Default is True.

• remove_tab (bool, optional) – Remove the tab files. Default is False.

• remove_nc (bool, optional) – Remove the netCDF file. Default is False.

Returns:

The postprocessed Dataset.

Return type:

xr.Dataset

postprocess_functions = {'Hfreqs': 'calculate_spectral_analysis', 'Hrms': 'calculate_statistical_analysis', 'Msetup': 'calculate_setup', 'Ru2': 'calculate_runup2', 'Runlev': 'calculate_runup'}

bluemath_tk.wrappers.swash.swash_wrapper.convert_seconds_to_hour_minutes_seconds(seconds)

Module contents