# -*- coding: utf-8 -*-
"""
Control parameters for the TorchANI step in a SEAMM flowchart
"""
import logging
import seamm
import pprint # noqa: F401
logger = logging.getLogger(__name__)
[docs]
class TorchANIParameters(seamm.Parameters):
"""
The control parameters for TorchANI.
The developer will add a dictionary of Parameters to this class.
The keys are parameters for the current plugin, which themselves
might be dictionaries.
You need to replace the "time" example below with one or more
definitions of the control parameters for your plugin and application.
Parameters
----------
parameters : {"kind", "default", "default_units", "enumeration",
"format_string", description", help_text"}
A dictionary containing the parameters for the current step.
Each key of the dictionary is a dictionary that contains the
the following keys: kind, default, default_units, enumeration,
format_string, description and help text.
parameters["kind"]: custom
Specifies the kind of a variable. While the "kind" of a variable might
be a numeric value, it may still have enumerated custom values
meaningful to the user. For instance, if the parameter is
a convergence criterion for an optimizer, custom values like "normal",
"precise", etc, might be adequate. In addition, any
parameter can be set to a variable of expression, indicated by having
"$" as the first character in the field. For example, $OPTIMIZER_CONV.
parameters["default"] : "integer" or "float" or "string" or "boolean" or
"enum" The default value of the parameter, used to reset it.
parameters["default_units"] : str
The default units, used for resetting the value.
parameters["enumeration"] : tuple
A tuple of enumerated values.
parameters["format_string"] : str
A format string for "pretty" output.
parameters["description"] : str
A short string used as a prompt in the GUI.
parameters["help_text"] : tuple
A longer string to display as help for the user.
Examples
--------
.. code-block:: python
parameters = {
"time": {
"default": 100.0,
"kind": "float",
"default_units": "ps",
"enumeration": tuple(),
"format_string": ".1f",
"description": "Simulation time:",
"help_text": ("The time to simulate in the dynamics run.")
},
}
See Also
--------
TorchANI, TkTorchANI, TorchANI
TorchANIParameters, TorchANIStep
"""
# The parameters for TorchANI
parameters = {
"time": {
"default": 100.0,
"kind": "float",
"default_units": "ps",
"enumeration": tuple(),
"format_string": ".1f",
"description": "Simulation time:",
"help_text": ("The time to simulate in the dynamics run."),
},
}
def __init__(self, defaults={}, data=None):
"""
Initialize the parameters, by default with the parameters defined above
Parameters
----------
defaults: dict
A dictionary of parameters to initialize. The parameters
above are used first and any given will override/add to them.
data: dict
A dictionary of keys and a subdictionary with value and units
for updating the current, default values.
Returns
-------
None
"""
logger.debug("TorchANIParameters.__init__")
super().__init__(
defaults={**TorchANIParameters.parameters, **defaults}, data=data
)