model

Retrieves tide model parameters for named tide models and from model definition files

Source code

General Attributes and Methods

pyTMD.io.load_database(extra_databases: list = [])[source]

Load the JSON database of model files

Parameters:

extra_databases: list, default []: A list of additional databases to load, as either JSON file paths or dictionaries

Returns:

parameters: dict: Database of model parameters

class pyTMD.io.model(directory: str | Path | None = None, **kwargs)[source]

Retrieves tide model parameters for named models or from a model definition file

Attributes:

compressed: bool: Model files are gzip compressed
directory: str, pathlib.Path or None, default None: Working data directory for tide models
extra_databases: list, default []: Additional databases for model parameters
verify: bool: Verify that all model files exist

from_database(m: str, group: tuple = ('z', 'u', 'v'))[source]

Create a model object from database of known tidal models

Parameters:

m: str: Model name
group: tuple, default (‘z’, ‘u’, ‘v’): Model variable(s) to extract

from_file(definition_file: str | Path | IOBase, **kwargs)[source]

Create a model object from an input definition file

Parameters:

definition_file: str, pathlib.Path or io.IOBase: Model definition file for creating model object

from_dict(d: dict)[source]

Create a model object from a dictionary of parameters

Parameters:

d: dict: Model object parameters

to_dict(**kwargs)[source]

Create a dictionary from a model object

Parameters:

fields: list, default all: List of model attributes to output
serialize: bool, default False: Serialize dictionary for JSON output

property gzip: str: Returns suffix for gzip compression

property corrections: str: Returns the corrections group for the model

property file_format: str: Returns the file format for the model

property multifile: bool: Returns if the model uses individual files for constituents

property crs: Coordinate reference system of the model

static known_formats(**kwargs) → list[source]: Returns list of known model formats

static ocean_elevation(**kwargs) → list[source]: Returns list of ocean tide elevation models

static load_elevation(**kwargs) → list[source]: Returns list of load tide elevation models

static ocean_current(**kwargs) → list[source]: Returns list of tidal current models

static OTIS(**kwargs) → list[source]: Returns list of OTIS formatted models

static ATLAS_compact(**kwargs) → list[source]: Returns list of ATLAS-compact formatted models

static TMD3(**kwargs) → list[source]: Returns list of TMD3 formatted models

static ATLAS(**kwargs) → list[source]: Returns list of ATLAS-netcdf formatted models

static GOT(**kwargs) → list[source]: Returns list of GOT formatted models

static FES(**kwargs) → list[source]: Returns list of FES formatted models

pathfinder(model_file: str | Path | list)[source]

Completes file paths and appends gzip suffix

Parameters:

model_file: str, pathlib.Path or list: Model file(s) to complete

validate_format()[source]: Asserts that the model format is a known type

serialize(d: dict)[source]

Encodes dictionary to be JSON serializable

Parameters:

d: dict: Parameters to serialize

parse_constituents(group: str = 'z', **kwargs) → list[source]

Parses tide model files for a list of model constituents

Parameters:

group: str, default ‘z’: Model group

static parse_file(model_file: str | Path, raise_error: bool = False)[source]

Parses a model file for a tidal constituent name

Parameters:

model_file: str or pathlib.Path: Tide model file to parse
raise_error: bool, default False: Raise ValueError if constituent is not found in file name

Returns:

constituent: str or list: Constituent name

reduce_constituents(constituents: str | list, group: str = 'z')[source]

Reduce model files to a subset of constituents

Parameters:

constituents: str or list: List of constituents names
group: str, default ‘z’: Model group

open_dataset(**kwargs)[source]

Open model files as an xarray Dataset

Parameters:

kwargs: dict: Additional keyword arguments for opening model files

Returns:

ds: xarray.Dataset: Tide model data

open_datatree(group: tuple = ('z', 'u', 'v'), **kwargs)[source]

Open model files as an xarray DataTree

Parameters:

group: tuple, default (‘z’, ‘u’, ‘v’): List of model types to extract
kwargs: dict: Additional keyword arguments for opening model files

Returns:

dtree: xr.DataTree: Tide model data