mdsuite.transformations package

Submodules

• mdsuite.transformations.integrated_heat_current module
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• mdsuite.transformations.ionic_current module
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• mdsuite.transformations.kinaci_integrated_heat_current module
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• mdsuite.transformations.map_molecules module
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• mdsuite.transformations.momentum_flux module
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• mdsuite.transformations.scale_coordinates module
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• mdsuite.transformations.test_trafos module
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• mdsuite.transformations.thermal_flux module
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• mdsuite.transformations.transformation_dict module
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• mdsuite.transformations.transformations module
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• mdsuite.transformations.translational_dipole_moment module
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• mdsuite.transformations.unwrap_coordinates module
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• mdsuite.transformations.unwrap_via_indices module
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• mdsuite.transformations.velocity_from_positions module
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• mdsuite.transformations.wrap_coordinates module
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Module contents

MDSuite: A Zincwarecode package.

License

This program and the accompanying materials are made available under the terms of the Eclipse Public License v2.0 which accompanies this distribution, and is available at https://www.eclipse.org/legal/epl-v20.html

SPDX-License-Identifier: EPL-2.0

Copyright Contributors to the Zincwarecode Project.

Contact Information

email: zincwarecode@gmail.com github: https://github.com/zincware web: https://zincwarecode.com/

Citation

If you use this module please cite us with:

Summary

class mdsuite.transformations.CoordinateUnwrapper

Bases: SingleSpeciesTrafo

Unwrap coordinates by checking if particles moved from one side of the box to the other within one time step

transform_batch(batch: Dict[str, Tensor], carryover: Optional[Any] = None) → Tuple[Tensor, dict]

Implement parent class abstract method.

class mdsuite.transformations.CoordinateWrapper(center_box: bool = True)

Bases: SingleSpeciesTrafo

Wrap coordinates into the simulation box

transform_batch(batch: Dict[str, Tensor], carryover: Optional[Any] = None) → Tensor

Implement parent class abstract method.

class mdsuite.transformations.IntegratedHeatCurrent

Bases: MultiSpeciesTrafo

Transformation to calculate the integrated heat current (positions * energies)

transform_batch(batch: Dict[str, Dict[str, Tensor]], carryover: Optional[Any] = None) → Tensor

Do the actual transformation. :param batch: The batch to be transformed. structure is

{‘Species1’: {‘Property1’: tensordata, …}, …}

Parameters:

carryover (any) – if the transformation batching is only possible with carryover, this argument will provide it, see below.

Returns:

• Either the transformed batch (tf.Tensor)

• Or tuple of (<transformed batch>, <carryover>),

• where the carryover can have any type.

• The carryover will be used as the optional argument for the next batch

class mdsuite.transformations.IonicCurrent

Bases: MultiSpeciesTrafo

Transformation to calculate the ionic current (charge * velocities)

transform_batch(batch: Dict[str, Dict[str, Tensor]], carryover: Optional[Any] = None) → Tensor

Do the actual transformation. :param batch: The batch to be transformed. structure is

{‘Species1’: {‘Property1’: tensordata, …}, …}

Parameters:

carryover (any) – if the transformation batching is only possible with carryover, this argument will provide it, see below.

Returns:

• Either the transformed batch (tf.Tensor)

• Or tuple of (<transformed batch>, <carryover>),

• where the carryover can have any type.

• The carryover will be used as the optional argument for the next batch

class mdsuite.transformations.KinaciIntegratedHeatCurrent

Bases: MultiSpeciesTrafo

Transformation to calculate the Kinaci integrated heat current

transform_batch(batch: Dict[str, Dict[str, Tensor]], carryover: Optional[Tensor] = None) → Tuple[Tensor, Tensor]

Do the actual transformation. :param batch: The batch to be transformed. structure is

{‘Species1’: {‘Property1’: tensordata, …}, …}

Parameters:

carryover (any) – if the transformation batching is only possible with carryover, this argument will provide it, see below.

Returns:

• Either the transformed batch (tf.Tensor)

• Or tuple of (<transformed batch>, <carryover>),

• where the carryover can have any type.

• The carryover will be used as the optional argument for the next batch

class mdsuite.transformations.MolecularMap

Bases: Transformations

Class for mapping atoms in a database to molecules.

scale_function

A dictionary referencing the memory/time scaling function of the transformation.

Type:

dict

molecules

Molecule dictionary to use as reference. e.g.

{'emim': {'smiles': 'CCN1C=C[N+](+C1)C', 'amount': 20}, 'PF6':
{'smiles': 'F[P-](F)(F)(F)(F)F', 'amount': 20}}

would be the input for the emim-PF6 ionic liquid.

Type:

dict

run_transformation(molecules: List[Molecule])

Perform the transformation.

Parameters:

molecules (List[Molecule]) – A list of MDSuite Molecule objects. For each, a molecule will be mapped.

Return type:

Update the experiment database.

class mdsuite.transformations.MomentumFlux

Bases: MultiSpeciesTrafo

Transformation to calculate the momentum flux

transform_batch(batch: Dict[str, Dict[str, Tensor]], carryover: Optional[Any] = None) → Tensor

Do the actual transformation. :param batch: The batch to be transformed. structure is

{‘Species1’: {‘Property1’: tensordata, …}, …}

Parameters:

carryover (any) – if the transformation batching is only possible with carryover, this argument will provide it, see below.

Returns:

• Either the transformed batch (tf.Tensor)

• Or tuple of (<transformed batch>, <carryover>),

• where the carryover can have any type.

• The carryover will be used as the optional argument for the next batch

class mdsuite.transformations.ScaleCoordinates

Bases: SingleSpeciesTrafo

Scale coordinates by multiplying them with the box size

transform_batch(batch: Dict[str, Tensor], carryover: Optional[Any] = None) → Tensor

Implement parent class abstract method.

class mdsuite.transformations.ThermalFlux

Bases: MultiSpeciesTrafo

Transformation to calculate the integrated heat current (positions * energies)

transform_batch(batch: Dict[str, Dict[str, Tensor]], carryover: Optional[Any] = None) → Tensor

Do the actual transformation. :param batch: The batch to be transformed. structure is

{‘Species1’: {‘Property1’: tensordata, …}, …}

Parameters:

carryover (any) – if the transformation batching is only possible with carryover, this argument will provide it, see below.

Returns:

• Either the transformed batch (tf.Tensor)

• Or tuple of (<transformed batch>, <carryover>),

• where the carryover can have any type.

• The carryover will be used as the optional argument for the next batch

class mdsuite.transformations.Transformations(input_properties: Optional[Iterable[PropertyInfo]] = None, output_property: Optional[PropertyInfo] = None, scale_function=None, dtype=tf.float64)

Bases: object

Parent class for MDSuite transformations.

database

database class object for data loading and storing

Type:

Database

experiment

Experiment class instance to update

Type:

object

batch_size

batch size for the computation

Type:

int

n_batches

Number of batches to be looped over

Type:

int

remainder

Remainder amount to add after the batches are looped over.

Type:

int

data_manager

data manager for handling the data transfer

Type:

DataManager

memory_manager

memory manager for the computation.

Type:

MemoryManager

property database

Update the database

replace for https://github.com/zincware/MDSuite/issues/404

property experiment: Experiment

//github.com/zincware/MDSuite/issues/404

Type:

TODO replace for https

find_property_per_config(sp_name, prop) → Union[None, str]

find_property_single_val(sp_name, prop)

get_generator_type_spec_and_const_data(species_names)

get_prop_through_transformation(sp_name, prop)

abstract run_transformation(species: Optional[Iterable[str]] = None)

class mdsuite.transformations.TranslationalDipoleMoment

Bases: MultiSpeciesTrafo

Transformation to calculate the translational dipole moment (charge * positions)

transform_batch(batch: Dict[str, Dict[str, Tensor]], carryover: Optional[Any] = None) → Tensor

Do the actual transformation. :param batch: The batch to be transformed. structure is

{‘Species1’: {‘Property1’: tensordata, …}, …}

Parameters:

carryover (any) – if the transformation batching is only possible with carryover, this argument will provide it, see below.

Returns:

• Either the transformed batch (tf.Tensor)

• Or tuple of (<transformed batch>, <carryover>),

• where the carryover can have any type.

• The carryover will be used as the optional argument for the next batch

class mdsuite.transformations.UnwrapViaIndices

Bases: SingleSpeciesTrafo

Unwrap corrdinates via the box images (pos + box_length * box_image_idx)

transform_batch(batch: Dict[str, Tensor], carryover: Optional[Any] = None) → Tensor

Implement parent class abstract method.

class mdsuite.transformations.VelocityFromPositions

Bases: SingleSpeciesTrafo

Calculate the velocity based on the particle positions via simple forward derivative, i.e. v(t) = (x(t+dt)-x(t))/dt. The last velocity of the trajectory cannot be computed and is copied from the second to last.

transform_batch(batch: Dict[str, Tensor], carryover: Optional[Any] = None) → Tensor

Implement parent class abstract method.