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cem

CEMMixin

Bases: OverrideResolveMixin

Adds Combinatorial Equilibrium Modeling (CEM) form-finding methods to a data class.

Source code in src/torch_structure/mixins/cem.py 
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class CEMMixin(OverrideResolveMixin):
    """Adds Combinatorial Equilibrium Modeling (CEM) form-finding methods to a data class."""

    def mpcem(
        self,
        inplace: bool=False,
        coords=None,
        load=None,
        is_support=None,
        is_origin_node=None,
        cem_edge_index=None,
        is_trail_edge=None,
        length=None,
        force_sign=None,
        force=None,
        constraint_plane=None,
        max_iter=100,
        tolerance=1e-5,
        damping_factor=0.5,
        verbose=False,
        track_history=False,
        callback=None,
        coords_out=None,
        force_out=None,
        load_out=None,
    ):
        """
        Applies the Message Passing Combinatorial Equilibrium Modeling (MP-CEM)
        algorithm to this structure and updates `coords`, `load`, and `force`.

        See [`torch_structure.formfinding.cem`](../formfinding/cem.md).
        """
        # Node inputs
        coords = self._resolve_override("coords", coords)
        load = self._resolve_override("load", load)
        is_support = self._resolve_override("is_support", is_support)
        is_origin_node = self._resolve_override("is_origin_node", is_origin_node)
        constraint_plane = self._resolve_override("constraint_plane", constraint_plane, required=False)

        # Edge inputs
        edge_mask = self.cem_edge_mask
        cem_edge_index = self._resolve_override("cem_edge_index", cem_edge_index)
        is_trail_edge = self._resolve_override("is_trail_edge", is_trail_edge, mask=edge_mask)
        length = self._resolve_override("length", length, mask=edge_mask)
        force_sign = self._resolve_override("force_sign", force_sign, mask=edge_mask)
        force = self._resolve_override("force", force, mask=edge_mask)

        # Output keys
        coords_key = "coords" if coords_out is None else coords_out
        load_key  = "load"  if load_out  is None else load_out
        force_key = "force" if force_out is None else force_out

        # MP-CEM computation
        new_coords, new_semi_directed_force, new_reaction_force, new_load = mpcem_algorithm(
            coords,
            load,
            is_support,
            is_origin_node,
            cem_edge_index,
            is_trail_edge,
            length,
            force_sign,
            force,
            constraint_plane,
            max_iter=max_iter,
            tolerance=tolerance,
            damping_factor=damping_factor,
            verbose=verbose,
            track_history=track_history,
            callback=callback,
        )
        new_force = self.edge_attr_to_undirected(
            new_semi_directed_force,
            edge_mask,
            batched=track_history)

        # Update data object
        new_data = self if inplace else self.clone()

        if track_history:
            self._track_history(coords_key, new_coords)
            new_data[coords_key] = new_coords[-1]
            self._track_history(load_key, new_load)
            new_data[load_key] = new_load[-1]
            self._track_history(force_key, new_force)
            new_data[force_key] = new_force[-1]
        else:
            new_data[coords_key] = new_coords
            new_data[load_key] = new_load
            new_data[force_key] = new_force

        new_data.reaction_force = new_reaction_force

        if not inplace:
            return new_data

    def cem(
        self,
        inplace: bool=False,
        coords=None,
        load=None,
        is_support=None,
        is_origin_node=None,
        cem_edge_index=None,
        is_trail_edge=None,
        length=None,
        force_sign=None,
        force=None,
        sequence=None,
        max_iter=100,
        tolerance=1e-5,
        damping_factor=0.0,
        enhanced_first_iteration=False,
        verbose=False,
        track_history=False,
    ):
        """
        Applies the Combinatorial Equilibrium Modeling (CEM) algorithm to this
        structure and updates `coords` and `force`.

        See [`torch_structure.formfinding.cem`](../formfinding/cem.md).
        """
        # Node inputs
        if coords is None:
            coords = self.coords
        if load is None:
            load = self.load
        if is_support is None:
            is_support = self.is_support
        if is_origin_node is None:
            is_origin_node = self.is_origin_node
        if sequence is None:
            sequence = self.sequence

        # Edge inputs
        edge_mask = self.cem_edge_mask
        if cem_edge_index is None:
            cem_edge_index = self.cem_edge_index
        if is_trail_edge is None:
            is_trail_edge = self.is_trail_edge[edge_mask]
        if length is None:
            length = self.length[edge_mask]
        if force_sign is None:
            force_sign = self.force_sign[edge_mask]
        if force is None:
            force = self.force[edge_mask]

        # CEM computation
        new_coords, new_semi_directed_force, new_reaction_force = cem_algorithm(
            coords,
            load,
            is_support,
            is_origin_node,
            cem_edge_index,
            is_trail_edge,
            length,
            force_sign,
            force,
            sequence,
            max_iter=max_iter,
            tolerance=tolerance,
            damping_factor=damping_factor,
            enhanced_first_iteration=enhanced_first_iteration,
            verbose=verbose,
            track_history=track_history,
        )
        new_force = self.edge_attr_to_undirected(
            new_semi_directed_force,
            edge_mask,
            batched=track_history)

        # Update data object
        new_data = self if inplace else self.clone()

        if track_history:
            self._track_history("coords", new_coords)
            new_data.coords = new_coords[-1]
            self._track_history("force", new_force)
            new_data.force = new_force[-1]
        else:
            new_data.coords = new_coords
            new_data.force = new_force

        new_data.reaction_force = new_reaction_force

        if not inplace:
            return new_data

    def seqcem(
        self,
        inplace: bool=False,
        coords=None,
        load=None,
        is_support=None,
        is_origin_node=None,
        cem_edge_index=None,
        is_trail_edge=None,
        length=None,
        force_sign=None,
        force=None,
        sequence=None,
        max_iter=100,
        tolerance=1e-5,
        damping_factor=0.0,
        enhanced_first_iteration=False,
        verbose=False,
        track_history=False,
    ):
        """
        Applies the sequential variant of the Combinatorial Equilibrium
        Modeling (CEM) algorithm to this structure and updates `coords` and
        `force`.

        See [`torch_structure.formfinding.cem`](../formfinding/cem.md).
        """
        # Node inputs
        if coords is None:
            coords = self.coords
        if load is None:
            load = self.load
        if is_support is None:
            is_support = self.is_support
        if is_origin_node is None:
            is_origin_node = self.is_origin_node
        if sequence is None:
            sequence = self.sequence

        # Edge inputs
        edge_mask = self.cem_edge_mask
        if cem_edge_index is None:
            cem_edge_index = self.cem_edge_index
        if is_trail_edge is None:
            is_trail_edge = self.is_trail_edge[edge_mask]
        if length is None:
            length = self.length[edge_mask]
        if force_sign is None:
            force_sign = self.force_sign[edge_mask]
        if force is None:
            force = self.force[edge_mask]

        # Sequential CEM computation
        new_coords, new_semi_directed_force, new_reaction_force = seq_cem_algorithm(
            coords,
            load,
            is_support,
            is_origin_node,
            cem_edge_index,
            is_trail_edge,
            length,
            force_sign,
            force,
            sequence,
            max_iter=max_iter,
            tolerance=tolerance,
            damping_factor=damping_factor,
            enhanced_first_iteration=enhanced_first_iteration,
            verbose=verbose,
            track_history=track_history,
        )
        new_force = self.edge_attr_to_undirected(
            new_semi_directed_force,
            edge_mask,
            batched=track_history)

        # Update data object
        new_data = self if inplace else self.clone()

        if track_history:
            self._track_history("coords", new_coords)
            new_data.coords = new_coords[-1]
            self._track_history("force", new_force)
            new_data.force = new_force[-1]
        else:
            new_data.coords = new_coords
            new_data.force = new_force

        new_data.reaction_force = new_reaction_force

        if not inplace:
            return new_data

cem(inplace=False, coords=None, load=None, is_support=None, is_origin_node=None, cem_edge_index=None, is_trail_edge=None, length=None, force_sign=None, force=None, sequence=None, max_iter=100, tolerance=1e-05, damping_factor=0.0, enhanced_first_iteration=False, verbose=False, track_history=False)

Applies the Combinatorial Equilibrium Modeling (CEM) algorithm to this structure and updates coords and force.

See torch_structure.formfinding.cem.

Source code in src/torch_structure/mixins/cem.py 
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def cem(
    self,
    inplace: bool=False,
    coords=None,
    load=None,
    is_support=None,
    is_origin_node=None,
    cem_edge_index=None,
    is_trail_edge=None,
    length=None,
    force_sign=None,
    force=None,
    sequence=None,
    max_iter=100,
    tolerance=1e-5,
    damping_factor=0.0,
    enhanced_first_iteration=False,
    verbose=False,
    track_history=False,
):
    """
    Applies the Combinatorial Equilibrium Modeling (CEM) algorithm to this
    structure and updates `coords` and `force`.

    See [`torch_structure.formfinding.cem`](../formfinding/cem.md).
    """
    # Node inputs
    if coords is None:
        coords = self.coords
    if load is None:
        load = self.load
    if is_support is None:
        is_support = self.is_support
    if is_origin_node is None:
        is_origin_node = self.is_origin_node
    if sequence is None:
        sequence = self.sequence

    # Edge inputs
    edge_mask = self.cem_edge_mask
    if cem_edge_index is None:
        cem_edge_index = self.cem_edge_index
    if is_trail_edge is None:
        is_trail_edge = self.is_trail_edge[edge_mask]
    if length is None:
        length = self.length[edge_mask]
    if force_sign is None:
        force_sign = self.force_sign[edge_mask]
    if force is None:
        force = self.force[edge_mask]

    # CEM computation
    new_coords, new_semi_directed_force, new_reaction_force = cem_algorithm(
        coords,
        load,
        is_support,
        is_origin_node,
        cem_edge_index,
        is_trail_edge,
        length,
        force_sign,
        force,
        sequence,
        max_iter=max_iter,
        tolerance=tolerance,
        damping_factor=damping_factor,
        enhanced_first_iteration=enhanced_first_iteration,
        verbose=verbose,
        track_history=track_history,
    )
    new_force = self.edge_attr_to_undirected(
        new_semi_directed_force,
        edge_mask,
        batched=track_history)

    # Update data object
    new_data = self if inplace else self.clone()

    if track_history:
        self._track_history("coords", new_coords)
        new_data.coords = new_coords[-1]
        self._track_history("force", new_force)
        new_data.force = new_force[-1]
    else:
        new_data.coords = new_coords
        new_data.force = new_force

    new_data.reaction_force = new_reaction_force

    if not inplace:
        return new_data

mpcem(inplace=False, coords=None, load=None, is_support=None, is_origin_node=None, cem_edge_index=None, is_trail_edge=None, length=None, force_sign=None, force=None, constraint_plane=None, max_iter=100, tolerance=1e-05, damping_factor=0.5, verbose=False, track_history=False, callback=None, coords_out=None, force_out=None, load_out=None)

Applies the Message Passing Combinatorial Equilibrium Modeling (MP-CEM) algorithm to this structure and updates coords, load, and force.

See torch_structure.formfinding.cem.

Source code in src/torch_structure/mixins/cem.py 
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def mpcem(
    self,
    inplace: bool=False,
    coords=None,
    load=None,
    is_support=None,
    is_origin_node=None,
    cem_edge_index=None,
    is_trail_edge=None,
    length=None,
    force_sign=None,
    force=None,
    constraint_plane=None,
    max_iter=100,
    tolerance=1e-5,
    damping_factor=0.5,
    verbose=False,
    track_history=False,
    callback=None,
    coords_out=None,
    force_out=None,
    load_out=None,
):
    """
    Applies the Message Passing Combinatorial Equilibrium Modeling (MP-CEM)
    algorithm to this structure and updates `coords`, `load`, and `force`.

    See [`torch_structure.formfinding.cem`](../formfinding/cem.md).
    """
    # Node inputs
    coords = self._resolve_override("coords", coords)
    load = self._resolve_override("load", load)
    is_support = self._resolve_override("is_support", is_support)
    is_origin_node = self._resolve_override("is_origin_node", is_origin_node)
    constraint_plane = self._resolve_override("constraint_plane", constraint_plane, required=False)

    # Edge inputs
    edge_mask = self.cem_edge_mask
    cem_edge_index = self._resolve_override("cem_edge_index", cem_edge_index)
    is_trail_edge = self._resolve_override("is_trail_edge", is_trail_edge, mask=edge_mask)
    length = self._resolve_override("length", length, mask=edge_mask)
    force_sign = self._resolve_override("force_sign", force_sign, mask=edge_mask)
    force = self._resolve_override("force", force, mask=edge_mask)

    # Output keys
    coords_key = "coords" if coords_out is None else coords_out
    load_key  = "load"  if load_out  is None else load_out
    force_key = "force" if force_out is None else force_out

    # MP-CEM computation
    new_coords, new_semi_directed_force, new_reaction_force, new_load = mpcem_algorithm(
        coords,
        load,
        is_support,
        is_origin_node,
        cem_edge_index,
        is_trail_edge,
        length,
        force_sign,
        force,
        constraint_plane,
        max_iter=max_iter,
        tolerance=tolerance,
        damping_factor=damping_factor,
        verbose=verbose,
        track_history=track_history,
        callback=callback,
    )
    new_force = self.edge_attr_to_undirected(
        new_semi_directed_force,
        edge_mask,
        batched=track_history)

    # Update data object
    new_data = self if inplace else self.clone()

    if track_history:
        self._track_history(coords_key, new_coords)
        new_data[coords_key] = new_coords[-1]
        self._track_history(load_key, new_load)
        new_data[load_key] = new_load[-1]
        self._track_history(force_key, new_force)
        new_data[force_key] = new_force[-1]
    else:
        new_data[coords_key] = new_coords
        new_data[load_key] = new_load
        new_data[force_key] = new_force

    new_data.reaction_force = new_reaction_force

    if not inplace:
        return new_data

seqcem(inplace=False, coords=None, load=None, is_support=None, is_origin_node=None, cem_edge_index=None, is_trail_edge=None, length=None, force_sign=None, force=None, sequence=None, max_iter=100, tolerance=1e-05, damping_factor=0.0, enhanced_first_iteration=False, verbose=False, track_history=False)

Applies the sequential variant of the Combinatorial Equilibrium Modeling (CEM) algorithm to this structure and updates coords and force.

See torch_structure.formfinding.cem.

Source code in src/torch_structure/mixins/cem.py 
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def seqcem(
    self,
    inplace: bool=False,
    coords=None,
    load=None,
    is_support=None,
    is_origin_node=None,
    cem_edge_index=None,
    is_trail_edge=None,
    length=None,
    force_sign=None,
    force=None,
    sequence=None,
    max_iter=100,
    tolerance=1e-5,
    damping_factor=0.0,
    enhanced_first_iteration=False,
    verbose=False,
    track_history=False,
):
    """
    Applies the sequential variant of the Combinatorial Equilibrium
    Modeling (CEM) algorithm to this structure and updates `coords` and
    `force`.

    See [`torch_structure.formfinding.cem`](../formfinding/cem.md).
    """
    # Node inputs
    if coords is None:
        coords = self.coords
    if load is None:
        load = self.load
    if is_support is None:
        is_support = self.is_support
    if is_origin_node is None:
        is_origin_node = self.is_origin_node
    if sequence is None:
        sequence = self.sequence

    # Edge inputs
    edge_mask = self.cem_edge_mask
    if cem_edge_index is None:
        cem_edge_index = self.cem_edge_index
    if is_trail_edge is None:
        is_trail_edge = self.is_trail_edge[edge_mask]
    if length is None:
        length = self.length[edge_mask]
    if force_sign is None:
        force_sign = self.force_sign[edge_mask]
    if force is None:
        force = self.force[edge_mask]

    # Sequential CEM computation
    new_coords, new_semi_directed_force, new_reaction_force = seq_cem_algorithm(
        coords,
        load,
        is_support,
        is_origin_node,
        cem_edge_index,
        is_trail_edge,
        length,
        force_sign,
        force,
        sequence,
        max_iter=max_iter,
        tolerance=tolerance,
        damping_factor=damping_factor,
        enhanced_first_iteration=enhanced_first_iteration,
        verbose=verbose,
        track_history=track_history,
    )
    new_force = self.edge_attr_to_undirected(
        new_semi_directed_force,
        edge_mask,
        batched=track_history)

    # Update data object
    new_data = self if inplace else self.clone()

    if track_history:
        self._track_history("coords", new_coords)
        new_data.coords = new_coords[-1]
        self._track_history("force", new_force)
        new_data.force = new_force[-1]
    else:
        new_data.coords = new_coords
        new_data.force = new_force

    new_data.reaction_force = new_reaction_force

    if not inplace:
        return new_data