Source code for espressomd.detail.walberla

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# Copyright (C) 2020-2023 The ESPResSo project
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# This file is part of ESPResSo.
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# ESPResSo is free software: you can redistribute it and/or modify
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import os
import itertools
import numpy as np

import espressomd.shapes
import espressomd.code_features
from espressomd.script_interface import ScriptInterfaceHelper, script_interface_register


[docs]@script_interface_register class LatticeWalberla(ScriptInterfaceHelper): """ Interface to a waBLerla lattice. """ _so_name = "walberla::LatticeWalberla" _so_creation_policy = "GLOBAL" def __init__(self, *args, **kwargs): if not espressomd.code_features.has_features("WALBERLA"): raise NotImplementedError("Feature WALBERLA not compiled in") if "sip" not in kwargs: params = self.default_params() params.update(kwargs) super().__init__(*args, **params) self._params = {k: getattr(self, k) for k in self.valid_keys()} else: super().__init__(**kwargs)
[docs] def valid_keys(self): return {"agrid", "n_ghost_layers"}
[docs] def required_keys(self): return self.valid_keys()
[docs] def default_params(self): return {}
[docs] def get_node_indices_inside_shape(self, shape): if not isinstance(shape, espressomd.shapes.Shape): raise ValueError( "Parameter 'shape' must be derived from espressomd.shapes.Shape") agrid = self.agrid idxs = itertools.product(*map(range, self.shape)) for idx in idxs: pos = (np.asarray(idx) + 0.5) * agrid if shape.is_inside(position=pos): yield idx
[docs] def get_shape_bitmask(self, shape): """Create a bitmask for the given shape.""" if not isinstance(shape, espressomd.shapes.Shape): raise ValueError( "Parameter 'shape' must be derived from espressomd.shapes.Shape") mask_flat = shape.call_method("rasterize", grid_size=self.shape, grid_spacing=self.agrid, grid_offset=0.5) return np.reshape(mask_flat, self.shape).astype(bool)
[docs]class LatticeModel:
[docs] def save_checkpoint(self, path, binary): tmp_path = path + ".__tmp__" self.call_method("save_checkpoint", path=tmp_path, mode=int(binary)) os.rename(tmp_path, path)
[docs] def load_checkpoint(self, path, binary): return self.call_method("load_checkpoint", path=path, mode=int(binary))
[docs] def get_nodes_inside_shape(self, shape=None): """ Provide a generator for iterating over all nodes inside the given shape. Parameters ---------- shape : :class:`espressomd.shapes.Shape` Shape to use as filter. """ for idx in self.lattice.get_node_indices_inside_shape(shape): yield self[idx]
[docs] def get_shape_bitmask(self, shape=None): """ Create a bitmask for the given shape. Parameters ---------- shape : :class:`espressomd.shapes.Shape` Shape to rasterize. """ return self.lattice.get_shape_bitmask(shape=shape)
[docs]def get_slice_bounding_box(slices, grid_size): shape = [] slice_lower_corner = [] slice_upper_corner = [] for i in range(3): indices = np.arange(grid_size[i]) if isinstance(slices[i], slice): if slices[i].step not in [None, 1]: raise NotImplementedError( "Slices with step != 1 are not supported") indices = indices[slices[i]] else: if isinstance(slices[i], (int, np.integer)): indices = [indices[slices[i]]] else: raise NotImplementedError( "Tuple-based indexing is not supported") if len(indices) == 0: slice_lower_corner.append(0) slice_upper_corner.append(0) shape.append(0) elif isinstance(slices[i], (int, np.integer)): slice_lower_corner.append(indices[0]) slice_upper_corner.append(indices[0] + 1) else: slice_lower_corner.append(indices[0]) slice_upper_corner.append(indices[-1] + 1) shape.append(len(indices)) return {"slice_lower_corner": slice_lower_corner, "slice_upper_corner": slice_upper_corner, "shape": shape}
[docs]class VTKOutputBase(ScriptInterfaceHelper): def __init__(self, *args, **kwargs): if not espressomd.code_features.has_features("WALBERLA"): raise NotImplementedError("Feature WALBERLA not compiled in") if "sip" not in kwargs: params = self.default_params() params.update(kwargs) if isinstance(params["observables"], str): params["observables"] = [params["observables"]] super().__init__(*args, **params) else: super().__init__(**kwargs)
[docs] def valid_observables(self): return set(self.call_method("get_valid_observable_names"))
[docs] def valid_keys(self): return {"delta_N", "execution_count", "observables", "identifier", "base_folder", "prefix", "enabled"}
[docs] def default_params(self): return {"delta_N": 0, "enabled": True, "execution_count": 0, "base_folder": "vtk_out", "prefix": "simulation_step"}