floatcsep.model.Model

class floatcsep.model.Model(name, model_path, forecast_unit=1, use_db=False, func=None, func_kwargs=None, zenodo_id=None, giturl=None, repo_hash=None, authors=None, doi=None, **kwargs)[source]

Class defining a forecast generating Model. Initializes a model source either from filesystem or web repositories, contains information about the Model’s typology, and maps accordingly to a forecast generating function.

Parameters:

  • name (str) – Name of the model

  • model_path (str) – Relative path of the model (file or runnable code) to the Experiment’s instance path

  • forecast_unit (float) – Temporal unit of the forecast. Default to years in time-independent models and days in time-dependent

  • use_db (bool) – Flag the use of a database for speed/memory purposes

  • func (str, Callable) – Forecast generating function (for code models)

  • func_kwargs (dict) – Arguments to pass into func

  • zenodo_id (int) – Zenodo ID or record of the Model

  • giturl (str) – Link to a git repository

  • repo_hash (str) – Specific commit/branch/tag hash.

  • authors (list[str]) – Authors’ names metadata

  • doi – Digital Object Identifier metadata:

__init__(name, model_path, forecast_unit=1, use_db=False, func=None, func_kwargs=None, zenodo_id=None, giturl=None, repo_hash=None, authors=None, doi=None, **kwargs)[source]

Methods

__init__(name, model_path[, forecast_unit, ...])

as_dict([excluded])

returns:

Dictionary with relevant attributes. Model can be reinstantiated

build_model()

create_forecast(tstring, **kwargs)

Creates a forecast from the model source and a given time window

forecast_from_file(start_date, end_date, ...)

Generates a forecast from a file, by parsing and scaling it to the desired time window.

forecast_from_func(start_date, end_date, ...)

from_dict(record, **kwargs)

Returns a Model instance from a dictionary containing the required atrributes.

get_forecast([tstring, region])

Wrapper that just returns a forecast, which should hide the access method (db storage, ti_td, etc.) under the hood

get_source([zenodo_id, giturl, force])

Search, download or clone the model source in the filesystem, zenodo and git, respectively.

init_db([dbpath, force])

Initializes the database if use_db is True.

prepare_args(start, end, **kwargs)

rm_db()

Clean up the generated HDF5 File

run_model()

stage([timewindows])

Pre-steps to make the model runnable before integrating

Attributes

dir

Returns: The directory containing the model source.