diff --git a/.gitignore b/.gitignore index cb8124b..6203800 100644 --- a/.gitignore +++ b/.gitignore @@ -203,3 +203,8 @@ coverage # Dedicated folder for personal projects **/scratch/ + + +AGENTS.md +repository_audit.md +**/tests/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 00ca4e2..02caafd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,14 +1,33 @@ repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v2.3.0 - hooks: - - id: check-xml - - id: check-merge-conflict - - id: mixed-line-ending - - id: end-of-file-fixer - - id: trailing-whitespace -# black repo for python formatting -- repo: https://github.com/ambv/black - rev: 22.12.0 - hooks: - - id: black + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: check-added-large-files + - id: check-ast + - id: check-case-conflict + - id: check-json + - id: check-merge-conflict + - id: check-symlinks + - id: check-toml + - id: check-xml + - id: check-yaml + - id: debug-statements + - id: detect-private-key + - id: end-of-file-fixer + - id: mixed-line-ending + args: [--fix=lf] + - id: trailing-whitespace + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.16.3 + hooks: + - id: ruff-check + args: + - --fix + - --exit-non-zero-on-fix + - --target-version=py310 + - --select=E4,E9,F,I + types_or: [python, pyi] + - id: ruff-format + args: [--target-version=py310] + types_or: [python, pyi] diff --git a/MSUtils/ComBo/ComBoMicrostructureImage.py b/MSUtils/ComBo/ComBoMicrostructureImage.py new file mode 100644 index 0000000..8b4ef8f --- /dev/null +++ b/MSUtils/ComBo/ComBoMicrostructureImage.py @@ -0,0 +1,193 @@ +import numpy as np + +from MSUtils.ComBo.interface_normal import ( + c2c_normal, + combo_normal, + interface_laplacian, +) + + +def _validate_lengths(lengths): + values = np.asarray(lengths, dtype=float) + if values.shape != (3,) or np.any(~np.isfinite(values)) or np.any(values <= 0): + raise ValueError("lengths must contain three positive finite values.") + return tuple(float(value) for value in values) + + +def _periodic_block(array, starts, widths): + indices = ( + np.arange(start, start + width) % size + for start, width, size in zip(starts, widths, array.shape, strict=True) + ) + return array[np.ix_(*indices)] + + +class VoxelInfo: + def __init__(self, coords, fraction_0, normal=None): + self.coords = coords + self.fraction_0 = fraction_0 + self.normal = normal + + +class ComBoMicrostructureImage: + def __init__( + self, + coarse_image=None, + voxel_info_list=None, + lengths=(1.0, 1.0, 1.0), + ): + self.coarse_image = coarse_image + self.voxel_info_list = voxel_info_list if voxel_info_list is not None else [] + self.lengths = _validate_lengths(lengths) + + self.volume_fractions = ( + None if coarse_image is None else self.compute_volume_fractions() + ) + + def compute_volume_fractions(self): + """Compute the volume fraction of each phase.""" + fraction_0 = sum(voxel.fraction_0 for voxel in self.voxel_info_list) + volumes = ( + np.count_nonzero(self.coarse_image == 0) + fraction_0, + np.count_nonzero(self.coarse_image == 1) + + len(self.voxel_info_list) + - fraction_0, + ) + return { + phase: volume / self.coarse_image.size + for phase, volume in enumerate(volumes) + if volume + } + + def downscale( + self, + data_array, + Nx, + Ny, + Nz, + min_vol_fraction=0.0, + L=(1.0, 1.0, 1.0), + pad_window=(0, 0, 0), + normal_mode="combo", + ): + """Downscale a periodic binary image to an ``Nx x Ny x Nz`` ComBo image.""" + data_array = np.ascontiguousarray(data_array) + if data_array.ndim != 3: + raise ValueError("ComBo images must be three-dimensional.") + if ( + np.count_nonzero(data_array == 0) + np.count_nonzero(data_array == 1) + != data_array.size + ): + raise ValueError("ComBo images must use the two phase labels 0 and 1.") + if not 0 <= min_vol_fraction <= 0.5: + raise ValueError("min_vol_fraction must be between 0 and 0.5.") + if any( + not isinstance(size, (int, np.integer)) or size <= 0 + for size in (Nx, Ny, Nz) + ): + raise ValueError("The coarse resolution must contain positive integers.") + lengths = _validate_lengths(L) + nx, ny, nz = data_array.shape + if nx % Nx != 0 or ny % Ny != 0 or nz % Nz != 0: + raise ValueError( + "The image shape must be divisible by the coarse resolution." + ) + dx, dy, dz = nx // Nx, ny // Ny, nz // Nz + pad_window = np.asarray(pad_window) + if ( + pad_window.shape != (3,) + or np.any(pad_window != np.floor(pad_window)) + or np.any(pad_window < 0) + ): + raise ValueError("pad_window must contain three nonnegative integers.") + pad_window = pad_window.astype(int) + has_padding = np.any(pad_window) + + if normal_mode not in {"c2c", "combo"}: + raise ValueError("normal_mode must be 'c2c' or 'combo'.") + + fraction_1 = data_array.reshape(Nx, dx, Ny, dy, Nz, dz).mean(axis=(1, 3, 5)) + fraction_0 = 1.0 - fraction_1 + composite = ( + (fraction_0 > 0.0) + & (fraction_1 > 0.0) + & (fraction_0 >= min_vol_fraction) + & (fraction_1 >= min_vol_fraction) + ) + coarse_data = (fraction_1 > fraction_0).astype(np.uint8) + coarse_data[composite] = 2 + + voxel_sizes = np.asarray(lengths) / data_array.shape + if normal_mode == "combo" and np.any(composite): + interface, voxel_sizes = interface_laplacian(data_array, lengths) + + composite_voxels = [] + for i, j, k in np.argwhere(composite): + block_slices = ( + slice(i * dx, (i + 1) * dx), + slice(j * dy, (j + 1) * dy), + slice(k * dz, (k + 1) * dz), + ) + normal_block = data_array[block_slices] + if normal_mode == "combo": + interface_block = interface[block_slices] + if has_padding: + starts = ( + i * dx - pad_window[0], + j * dy - pad_window[1], + k * dz - pad_window[2], + ) + widths = ( + dx + 2 * pad_window[0], + dy + 2 * pad_window[1], + dz + 2 * pad_window[2], + ) + normal_block = _periodic_block(data_array, starts, widths) + interface_block = _periodic_block(interface, starts, widths) + + normal = ( + c2c_normal(normal_block, voxel_sizes) + if normal_mode == "c2c" + else combo_normal(normal_block, interface_block, voxel_sizes) + ) + composite_voxels.append(VoxelInfo((i, j, k), fraction_0[i, j, k], normal)) + + self.coarse_image = coarse_data + self.voxel_info_list = composite_voxels + self.lengths = lengths + self.volume_fractions = self.compute_volume_fractions() + + def write(self, filename, group_name): + from MSUtils.ComBo.combo_io import write_combo + + if self.coarse_image is None: + raise ValueError("The ComBo image has not been downscaled.") + write_combo(self, filename, group_name) + + @staticmethod + def read(filename, group_name): + from MSUtils.ComBo.combo_io import read_combo + + return read_combo(filename, group_name) + + +def main(): + from MSUtils.ComBo.combo_mesh import write_combo_mesh + from MSUtils.general.h52xdmf import write_xdmf + from MSUtils.general.MicrostructureImage import MicrostructureImage + + image = MicrostructureImage(h5_filename="data/fibers1.h5", dset_name="/img").image + + result = ComBoMicrostructureImage() + result.downscale(image, 80, 80, 30, normal_mode="combo") + result.write("data/combo_normals.h5", "/combo_group") + write_combo_mesh(result, "data/combo_mesh.xdmf") + write_xdmf( + h5_filepath="data/combo_normals.h5", + xdmf_filepath="data/combo_normals.xdmf", + verbose=True, + ) + + +if __name__ == "__main__": + main() diff --git a/MSUtils/ComBo/__init__.py b/MSUtils/ComBo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/MSUtils/ComBo/combo_io.py b/MSUtils/ComBo/combo_io.py new file mode 100644 index 0000000..422c528 --- /dev/null +++ b/MSUtils/ComBo/combo_io.py @@ -0,0 +1,83 @@ +import h5py +import numpy as np + +from MSUtils.general.grid import image_in_order, validate_order + + +def write_combo(combo, filename, group_name): + """Write a ComBo microstructure to an HDF5 group.""" + with h5py.File(filename, "a") as file: + if group_name in file: + del file[group_name] + print(f"Group {group_name} exists, overwriting it.") + + group = file.create_group(group_name) + group.attrs["lengths"] = combo.lengths + group.attrs["permute_order"] = "zyx" + + group.create_dataset( + "coarse_image", + data=np.ascontiguousarray(image_in_order(combo.coarse_image, "zyx")), + compression="gzip", + ) + + coarse_normal = np.zeros((*combo.coarse_image.shape, 3), dtype=np.float32) + coarse_volume_fraction_0 = (combo.coarse_image == 0).astype(np.float32) + for voxel_info in combo.voxel_info_list: + coarse_normal[voxel_info.coords] = voxel_info.normal + coarse_volume_fraction_0[voxel_info.coords] = voxel_info.fraction_0 + + group.create_dataset( + "coarse_normal", + data=np.ascontiguousarray(coarse_normal.transpose(2, 1, 0, 3)), + compression="gzip", + ) + group.create_dataset( + "coarse_volume_fraction_0", + data=np.ascontiguousarray(image_in_order(coarse_volume_fraction_0, "zyx")), + compression="gzip", + ) + + +def read_combo(filename, group_name): + """Read a ComBo microstructure from an HDF5 group.""" + from MSUtils.ComBo.ComBoMicrostructureImage import ( + ComBoMicrostructureImage, + VoxelInfo, + ) + + with h5py.File(filename, "r") as file: + if group_name not in file: + raise ValueError(f"Group {group_name} not found in file {filename}") + + group = file[group_name] + order = validate_order(group.attrs.get("permute_order", "xyz")) + coarse_image = np.ascontiguousarray( + image_in_order(group["coarse_image"][:], order) + ) + if not np.all(np.isin(coarse_image, (0, 1, 2))): + raise ValueError("coarse_image must contain only labels 0, 1, and 2.") + lengths = tuple(group.attrs.get("lengths", (1.0, 1.0, 1.0))) + + composite_coords = np.argwhere(coarse_image == 2) + voxel_info_list = [] + if len(composite_coords): + if not {"coarse_normal", "coarse_volume_fraction_0"} <= set(group): + raise ValueError("Composite boxel fields are missing from the file.") + coarse_normal = group["coarse_normal"][:] + coarse_volume_fraction_0 = group["coarse_volume_fraction_0"][:] + if order == "zyx": + coarse_normal = coarse_normal.transpose(2, 1, 0, 3) + coarse_volume_fraction_0 = coarse_volume_fraction_0.transpose(2, 1, 0) + for coords_array in composite_coords: + coords = tuple(coords_array) + fraction_0 = coarse_volume_fraction_0[coords] + voxel_info_list.append( + VoxelInfo( + coords=coords, + fraction_0=fraction_0, + normal=tuple(coarse_normal[coords]), + ) + ) + + return ComBoMicrostructureImage(coarse_image, voxel_info_list, lengths) diff --git a/MSUtils/ComBo/combo_mesh.py b/MSUtils/ComBo/combo_mesh.py new file mode 100644 index 0000000..7e8300a --- /dev/null +++ b/MSUtils/ComBo/combo_mesh.py @@ -0,0 +1,292 @@ +from pathlib import Path + +import meshio +import numpy as np +from scipy.spatial import ConvexHull + +_CORNERS = np.array( + [ + (0, 0, 0), + (1, 0, 0), + (1, 1, 0), + (0, 1, 0), + (0, 0, 1), + (1, 0, 1), + (1, 1, 1), + (0, 1, 1), + ], + dtype=float, +) +_EDGES = ( + (0, 1), + (1, 2), + (2, 3), + (3, 0), + (4, 5), + (5, 6), + (6, 7), + (7, 4), + (0, 4), + (1, 5), + (2, 6), + (3, 7), +) +_EPS = np.finfo(float).eps +_NORMAL_ZERO_TOLERANCE = np.sqrt(_EPS) + + +def _lower_weighted_sum_cdf(value, widths): + dimension = len(widths) + if dimension == 1: + return value / widths[0] + if dimension == 2: + a, b = widths + result = value**2 - max(value - a, 0.0) ** 2 - max(value - b, 0.0) ** 2 + return min(max(result / (2.0 * a * b), 0.0), 1.0) + + a, b, c = widths + result = ( + value**3 + - max(value - a, 0.0) ** 3 + - max(value - b, 0.0) ** 3 + - max(value - c, 0.0) ** 3 + + max(value - a - b, 0.0) ** 3 + + max(value - a - c, 0.0) ** 3 + + max(value - b - c, 0.0) ** 3 + ) + return min(max(result / (6.0 * a * b * c), 0.0), 1.0) + + +def _plane_offset(lower, spacing, normal, fraction_0): + normal = np.array(normal, dtype=np.longdouble, copy=True) + lower = np.asarray(lower, dtype=np.longdouble) + spacing = np.asarray(spacing, dtype=np.longdouble) + normal[np.abs(normal) < _NORMAL_ZERO_TOLERANCE * np.max(np.abs(normal))] = 0.0 + magnitude = np.linalg.norm(normal) + if not np.isfinite(magnitude) or magnitude == 0.0: + raise ValueError("Composite boxel normals must be finite and nonzero.") + normal /= magnitude + + coefficients = normal * spacing + widths = np.abs(coefficients) + widths = widths[widths > _EPS * widths.max()] + minimum = normal @ lower + coefficients[coefficients < 0.0].sum() + target = 1.0 - fraction_0 + total = widths.sum() + reflected = target > 0.5 + if reflected: + target = 1.0 - target + + low = 0.0 + high = 0.5 * total + for _ in range(44): + middle = 0.5 * (low + high) + if _lower_weighted_sum_cdf(middle, widths) < target: + low = middle + else: + high = middle + + distance = 0.5 * (low + high) + if reflected: + distance = total - distance + return np.asarray(normal, dtype=float), float(minimum + distance) + + +def _grid_points(shape, lengths): + nx, ny, nz = shape + hx, hy, hz = np.asarray(lengths) / shape + points = np.empty(((nx + 1) * (ny + 1) * (nz + 1), 3)) + points[:, 0] = np.repeat(np.arange(nx + 1) * hx, (ny + 1) * (nz + 1)) + points[:, 1] = np.tile(np.repeat(np.arange(ny + 1) * hy, nz + 1), nx + 1) + points[:, 2] = np.tile(np.arange(nz + 1) * hz, (nx + 1) * (ny + 1)) + return points + + +def _pure_hexahedra(coarse_image, index_dtype): + _, ny, nz = coarse_image.shape + pure = np.flatnonzero(coarse_image.ravel() < 2) + i = pure // (ny * nz) + j = pure // nz % ny + k = pure % nz + base = (i * (ny + 1) + j) * (nz + 1) + k + x = (ny + 1) * (nz + 1) + y = nz + 1 + offsets = np.array((0, x, x + y, y, 1, x + 1, x + y + 1, y + 1)) + return (base[:, None] + offsets).astype(index_dtype), coarse_image.ravel()[pure] + + +def _corner_ids(coords, shape, index_dtype): + i, j, k = coords + _, ny, nz = shape + base = (i * (ny + 1) + j) * (nz + 1) + k + x = (ny + 1) * (nz + 1) + y = nz + 1 + return (base + np.array((0, x, x + y, y, 1, x + 1, x + y + 1, y + 1))).astype( + index_dtype + ) + + +def _tetrahedralize( + points, point_ids, extra_points, first_extra_id, face_cache, topology +): + faces = face_cache.get(topology) if topology is not None else None + if faces is None: + faces = ConvexHull(points).simplices + if topology is not None: + face_cache[topology] = faces + + center = points.mean(axis=0) + center_id = first_extra_id + len(extra_points) + extra_points.append(center) + + tetrahedra = np.column_stack( + (np.full(len(faces), center_id, dtype=point_ids.dtype), point_ids[faces]) + ) + determinants = np.linalg.det(points[faces] - center) + negative = determinants < 0.0 + tetrahedra[negative, 2], tetrahedra[negative, 3] = ( + tetrahedra[negative, 3], + tetrahedra[negative, 2].copy(), + ) + return tetrahedra, np.abs(determinants).sum() / 6.0 + + +def _composite_tetrahedra( + info, + shape, + spacing, + extra_points, + first_extra_id, + face_cache, + index_dtype, +): + fraction_0 = info.fraction_0 + lower = np.asarray(info.coords) * spacing + normal, offset = _plane_offset(lower, spacing, info.normal, fraction_0) + + corners = lower + _CORNERS * spacing + signed = corners @ normal - offset + tolerance = 64 * _EPS * max(1.0, np.max(np.abs(corners))) + signed[np.abs(signed) < tolerance] = 0.0 + + intersections = [] + for first, second in _EDGES: + if signed[first] * signed[second] < 0.0: + position = signed[first] / (signed[first] - signed[second]) + intersections.append( + corners[first] + position * (corners[second] - corners[first]) + ) + intersections = np.asarray(intersections).reshape(-1, 3) + if np.count_nonzero(signed == 0.0) + len(intersections) < 3: + raise RuntimeError(f"Could not intersect composite boxel {info.coords}.") + + intersection_ids = ( + first_extra_id + + len(extra_points) + + np.arange(len(intersections), dtype=index_dtype) + ) + extra_points.extend(intersections) + points = np.vstack((corners, intersections)) + point_ids = np.concatenate( + (_corner_ids(info.coords, shape, index_dtype), intersection_ids) + ) + intersection_indices = np.arange(8, len(points)) + + tetrahedra = [] + boxel_volume = spacing.prod() + cacheable = not np.any(signed == 0.0) + for corner_mask, expected_fraction in ( + (signed >= 0.0, fraction_0), + (signed <= 0.0, 1.0 - fraction_0), + ): + corner_indices = np.flatnonzero(corner_mask) + indices = np.concatenate((corner_indices, intersection_indices)) + topology = sum(1 << index for index in corner_indices) if cacheable else None + body_tetrahedra, volume = _tetrahedralize( + points[indices], + point_ids[indices], + extra_points, + first_extra_id, + face_cache, + topology, + ) + error = abs(volume / boxel_volume - expected_fraction) + if error > 1e-10 * (1.0 + abs(expected_fraction)): + raise RuntimeError(f"Failed to preserve volume in boxel {info.coords}.") + tetrahedra.append(body_tetrahedra) + + return tetrahedra + + +def create_combo_mesh(combo): + """Create a ParaView-compatible volume mesh from a two-phase ComBo image.""" + if combo.coarse_image is None: + raise ValueError("The ComBo image has not been downscaled.") + coarse_image = np.asarray(combo.coarse_image) + if coarse_image.ndim != 3: + raise ValueError("coarse_image must be three-dimensional.") + + if not np.all(np.isin(coarse_image, (0, 1, 2))): + raise ValueError("coarse_image must contain only labels 0, 1, and 2.") + + mixed_coords = {tuple(info.coords) for info in combo.voxel_info_list} + if len(mixed_coords) != np.count_nonzero(coarse_image == 2) or any( + coarse_image[coords] != 2 for coords in mixed_coords + ): + raise ValueError("Composite boxel metadata does not match coarse_image.") + + lengths = np.asarray(combo.lengths, dtype=float) + spacing = lengths / coarse_image.shape + maximum_points = np.prod(np.asarray(coarse_image.shape, dtype=np.int64) + 1) + maximum_points += 8 * len(combo.voxel_info_list) + index_dtype = np.int32 if maximum_points <= np.iinfo(np.int32).max else np.int64 + points = _grid_points(coarse_image.shape, lengths) + hexahedra, hexahedron_materials = _pure_hexahedra(coarse_image, index_dtype) + + extra_points = [] + tetrahedra = [] + tetrahedron_materials = [] + face_cache = {} + for info in combo.voxel_info_list: + if not 0.0 < info.fraction_0 < 1.0: + raise ValueError("Composite phase fractions must be between zero and one.") + bodies = _composite_tetrahedra( + info, + coarse_image.shape, + spacing, + extra_points, + len(points), + face_cache, + index_dtype, + ) + for material, body in enumerate(bodies): + tetrahedra.append(body) + tetrahedron_materials.append(np.full(len(body), material, dtype=np.uint8)) + + if extra_points: + points = np.vstack((points, extra_points)) + + cells = [] + material_data = [] + if len(hexahedra): + cells.append(("hexahedron", hexahedra)) + material_data.append(np.asarray(hexahedron_materials, dtype=np.uint8)) + if tetrahedra: + cells.append(("tetra", np.vstack(tetrahedra))) + material_data.append(np.concatenate(tetrahedron_materials)) + + return meshio.Mesh( + points=points, + cells=cells, + cell_data={"material_id": material_data}, + ) + + +def write_combo_mesh(combo, filename): + """Write a two-phase ComBo volume mesh as XDMF with HDF5 data.""" + path = Path(filename) + if path.suffix.lower() not in {".xdmf", ".xmf"}: + raise ValueError("filename must end in .xdmf or .xmf.") + h5_path = path.with_suffix(".h5") + meshio.write(path, create_combo_mesh(combo), file_format="xdmf", data_format="HDF") + return path, h5_path diff --git a/MSUtils/ComBo/interface_normal.py b/MSUtils/ComBo/interface_normal.py new file mode 100644 index 0000000..cc6c806 --- /dev/null +++ b/MSUtils/ComBo/interface_normal.py @@ -0,0 +1,56 @@ +import numpy as np + + +def interface_laplacian(image, lengths=(1.0, 1.0, 1.0)): + """Compute the absolute periodic Laplacian of an image.""" + image = np.asarray(image, dtype=float) + voxel_sizes = np.asarray(lengths, dtype=float) / image.shape + laplacian = np.zeros(image.shape) + for axis, spacing in enumerate(voxel_sizes): + laplacian += ( + np.roll(image, 1, axis) - 2.0 * image + np.roll(image, -1, axis) + ) / spacing**2 + return np.abs(laplacian), voxel_sizes + + +def _phase_centroid(image, voxel_sizes): + axes = [ + (np.arange(size) + 0.5 - 0.5 * size) * spacing + for size, spacing in zip(image.shape, voxel_sizes, strict=True) + ] + return np.array( + [ + image.sum(axis=(1, 2)) @ axes[0], + image.sum(axis=(0, 2)) @ axes[1], + image.sum(axis=(0, 1)) @ axes[2], + ] + ) + + +def c2c_normal(image, voxel_sizes): + """Estimate a normal from the phase-centroid direction.""" + normal = -_phase_centroid(image, voxel_sizes) + magnitude = np.linalg.norm(normal) + if magnitude == 0.0: + raise ValueError("The C2C normal is undefined for coincident phase centroids.") + return normal / magnitude + + +def combo_normal(image, interface_weights, voxel_sizes): + """Estimate a normal from a weighted interface-plane fit.""" + weights = np.abs(interface_weights) + interface = np.where(weights > 1e-5) + weights = weights[interface].flatten() + weights /= weights.sum() + + coordinates = voxel_sizes[:, None] * np.array(interface) + coordinates -= (coordinates * weights).sum(axis=1)[:, None] + moment = coordinates @ (coordinates * weights).T + eigenvalues, eigenvectors = np.linalg.eigh(moment) + normal = eigenvectors[:, np.argmin(eigenvalues)] + normal /= np.linalg.norm(normal) + + if _phase_centroid(image, voxel_sizes) @ normal > 0.0: + normal *= -1.0 + + return normal diff --git a/MSUtils/TPMS/tpms.py b/MSUtils/TPMS/tpms.py index b2270f2..fcc266e 100644 --- a/MSUtils/TPMS/tpms.py +++ b/MSUtils/TPMS/tpms.py @@ -1,15 +1,16 @@ +from typing import Callable, Iterable, Optional + import numpy as np -from typing import Iterable, Optional, Callable -from MSUtils.general.MicrostructureImage import MicrostructureImage from MSUtils.general.h52xdmf import write_xdmf +from MSUtils.general.MicrostructureImage import MicrostructureImage from MSUtils.TPMS.tpms_functions import ( - gyroid, - schwarz_p, diamond, - neovius, + gyroid, iwp, lidinoid, + neovius, + schwarz_p, ) diff --git a/MSUtils/TPMS/tpms_example.py b/MSUtils/TPMS/tpms_example.py index 2259044..0f1e69b 100644 --- a/MSUtils/TPMS/tpms_example.py +++ b/MSUtils/TPMS/tpms_example.py @@ -6,16 +6,12 @@ regenerates the microstructure with optimized parameters, recomputes volume fractions, and writes the optimized result to HDF5. """ -from MSUtils.TPMS.tpms import TPMS -from MSUtils.general.MicrostructureImage import MicrostructureImage + from MSUtils.general.h52xdmf import write_xdmf +from MSUtils.general.MicrostructureImage import MicrostructureImage +from MSUtils.TPMS.tpms import TPMS from MSUtils.TPMS.tpms_functions import ( - gyroid, - schwarz_p, - diamond, - neovius, iwp, - lidinoid, ) if __name__ == "__main__": diff --git a/MSUtils/fans_dashboard/PlotYoungsModulus.py b/MSUtils/fans_dashboard/PlotYoungsModulus.py index 8ecac3f..eab7367 100644 --- a/MSUtils/fans_dashboard/PlotYoungsModulus.py +++ b/MSUtils/fans_dashboard/PlotYoungsModulus.py @@ -1,6 +1,6 @@ +import meshio import numpy as np import plotly.graph_objs as go -import meshio def compute_YoungsModulus3D(C_batch): diff --git a/MSUtils/fans_dashboard/plotting.py b/MSUtils/fans_dashboard/plotting.py index c6f78a6..3842ac8 100644 --- a/MSUtils/fans_dashboard/plotting.py +++ b/MSUtils/fans_dashboard/plotting.py @@ -1,6 +1,7 @@ -import numpy as np import shutil + import matplotlib.pyplot as plt +import numpy as np def setup_mpl_style(fontsize=12): diff --git a/MSUtils/fans_dashboard/postprocessing.py b/MSUtils/fans_dashboard/postprocessing.py index 5428a95..2ec3e63 100644 --- a/MSUtils/fans_dashboard/postprocessing.py +++ b/MSUtils/fans_dashboard/postprocessing.py @@ -1,4 +1,5 @@ import numpy as np + from MSUtils.fans_dashboard.tensortools import Mandel2Full diff --git a/MSUtils/fans_dashboard/tensortools.py b/MSUtils/fans_dashboard/tensortools.py index a8d515d..e52ca7f 100644 --- a/MSUtils/fans_dashboard/tensortools.py +++ b/MSUtils/fans_dashboard/tensortools.py @@ -2,312 +2,171 @@ from scipy.linalg import eigvalsh """ -Utility routines to work with tensors -in Voigt and Mandel notation. +Voigt strain: (ε_11, ε_22, ε_33, 2ε_23, 2ε_13, 2ε_12). +Voigt stress: (σ_11, σ_22, σ_33, σ_23, σ_13, σ_12). +Mandel: (A_11, A_22, A_33, √2 A_12, √2 A_13, √2 A_23). """ +_COMPONENT_ORDER = [0, 1, 2, 5, 4, 3] +_SQRT2 = np.sqrt(2.0) -def VoigtStrain2Mandel(A_voigt, order="voigt"): - """Convert a strain in Voigt notation to Mandel notation. - Parameters - ---------- - A_voigt : ndarray - symmetric 2-tensor in Voigt-like notation - (i.e. engineering shear for off-diagonal components) +def _convert_vector(values, shear_factor, order): + result = np.array(values, dtype=float) + result[..., 3:] *= shear_factor + return result[..., _COMPONENT_ORDER] if order == "voigt" else result - order : str - if 'voigt' the order (xx, yy, zz, yz, xz, xy) and - otherwise, (xx, yy, zz, xy, xz, yz) is assumed; - - Returns - ------- - ndarray - The converted tensor as a 6 vector - """ - f = np.sqrt(0.5) - A_mandel = np.array([1.0, 1.0, 1.0, f, f, f]) * A_voigt +def _convert_matrix(values, shear_factor, order): + result = np.array(values, dtype=float) + result[..., 3:, :] *= shear_factor + result[..., :, 3:] *= shear_factor if order == "voigt": - # Voigt in --> Reordering needed - A_mandel = A_mandel[np.array((0, 1, 2, 5, 4, 3))] - return A_mandel + result = result[..., _COMPONENT_ORDER, :][..., :, _COMPONENT_ORDER] + return result -def VoigtStress2Mandel(A_voigt, order="voigt"): - """Convert a stress in Voigt notation to Mandel notation. - - Parameters - ---------- - A_voigt : ndarray - symmetric 2-tensor in Voigt-like notation - (i.e. off-diagonal components are reported without prefactor) - - order : str - if 'voigt' the order (xx, yy, zz, yz, xz, xy) and - otherwise, (xx, yy, zz, xy, xz, yz) is assumed; - - Returns - ------- - ndarray - The converted tensor as a 6 vector +def VoigtStrain2Mandel(A_voigt, order="voigt"): + """Convert engineering Voigt strain to Mandel notation.""" + return _convert_vector(A_voigt, 1.0 / _SQRT2, order) - """ - f = np.sqrt(2.0) - A_mandel = np.array([1.0, 1.0, 1.0, f, f, f]) * A_voigt - if order == "voigt": - # Voigt in --> Reordering needed - A_mandel = A_mandel[np.array((0, 1, 2, 5, 4, 3))] - return A_mandel +def VoigtStress2Mandel(A_voigt, order="voigt"): + """Convert Voigt stress to Mandel notation.""" + return _convert_vector(A_voigt, _SQRT2, order) def Mandel2VoigtStrain(A_mandel, order="voigt"): - """Convert a tensor in Mandel notation to Voigt (for strains). - - Parameters - ---------- - A_voigt : ndarray - symmetric 2-tensor in Mandel notation - - order : str - if 'voigt' the order (xx, yy, zz, yz, xz, xy) and - otherwise, (xx, yy, zz, xy, xz, yz) is returned on output; - - Returns - ------- - ndarray - The converted tensor as a 6 vector - - """ - f = np.sqrt(2.0) - A_voigt = np.array([1.0, 1.0, 1.0, f, f, f]) * A_mandel - if order == "voigt": - # Voigt in --> Reordering needed - A_voigt = A_voigt[np.array((0, 1, 2, 5, 4, 3))] - return A_voigt + """Convert Mandel strain to engineering Voigt notation.""" + return _convert_vector(A_mandel, _SQRT2, order) def Mandel2VoigtStress(A_mandel, order="voigt"): - """Convert a tensor in Mandel notation to Voigt (for stresses). - - Parameters - ---------- - A_voigt : ndarray - symmetric 2-tensor in Mandel notation - - order : str - if 'voigt' the order (xx, yy, zz, yz, xz, xy) and - otherwise, (xx, yy, zz, xy, xz, yz) is returned on output; - - Returns - ------- - ndarray - The converted tensor as a 6 vector - - """ - f = np.sqrt(0.5) - A_voigt = np.array([1.0, 1.0, 1.0, f, f, f]) * A_mandel - if order == "voigt": - # Voigt in --> Reordering needed - A_voigt[:] = A_voigt[np.array((0, 1, 2, 5, 4, 3))] - return A_voigt + """Convert Mandel stress to Voigt notation.""" + return _convert_vector(A_mandel, 1.0 / _SQRT2, order) def StiffnessVoigt2Mandel(C_v, order="voigt"): - C_m = np.array(C_v) - f = np.sqrt(2.0) - C_m[:, :3] *= f - C_m[:3, :] *= f - if order == "voigt": - idx = np.array((0, 1, 2, 5, 4, 3)) - C_m = C_m[idx[:, None], idx[None, :]] - return C_m + """Convert a Voigt stiffness matrix to Mandel notation.""" + return _convert_matrix(C_v, _SQRT2, order) def ComplianceVoigt2Mandel(S_v, order="voigt"): - S_m = np.array(S_v) - f = np.sqrt(0.5) - S_m[:, :3] *= f - S_m[:3, :] *= f - if order == "voigt": - idx = np.array((0, 1, 2, 5, 4, 3)) - S_m = S_m[idx[:, None], idx[None, :]] - return S_m + """Convert a Voigt compliance matrix to Mandel notation.""" + return _convert_matrix(S_v, 1.0 / _SQRT2, order) def Full2Mandel(A): - original_shape = A.shape[:-2] - A_flat = A.reshape(-1, 3, 3) - f = np.sqrt(2.0) - A_mandel = np.zeros((A_flat.shape[0], 6)) - A_mandel[:, 0] = A_flat[:, 0, 0] - A_mandel[:, 1] = A_flat[:, 1, 1] - A_mandel[:, 2] = A_flat[:, 2, 2] - A_mandel[:, 3] = f * A_flat[:, 0, 1] - A_mandel[:, 4] = f * A_flat[:, 0, 2] - A_mandel[:, 5] = f * A_flat[:, 1, 2] - return A_mandel.reshape(original_shape + (6,)) + """Convert symmetric 3x3 tensors to Mandel notation.""" + A = np.asarray(A, dtype=float) + A_mandel = np.empty(A.shape[:-2] + (6,)) + A_mandel[..., 0] = A[..., 0, 0] + A_mandel[..., 1] = A[..., 1, 1] + A_mandel[..., 2] = A[..., 2, 2] + A_mandel[..., 3] = _SQRT2 * A[..., 0, 1] + A_mandel[..., 4] = _SQRT2 * A[..., 0, 2] + A_mandel[..., 5] = _SQRT2 * A[..., 1, 2] + return A_mandel def Mandel2Full(A_mandel): - original_shape = A_mandel.shape[:-1] - A_mandel_flat = A_mandel.reshape(-1, 6) - f = np.sqrt(0.5) - idx = np.array(((0, 3, 4), (3, 1, 5), (4, 5, 2))) - A = np.zeros((A_mandel_flat.shape[0], 3, 3)) - A = f * A_mandel_flat[:, idx] - f = np.sqrt(2.0) - A[:, 0, 0] *= f - A[:, 1, 1] *= f - A[:, 2, 2] *= f - return A.reshape(original_shape + (3, 3)) + """Convert Mandel vectors to symmetric 3x3 tensors.""" + A_mandel = np.asarray(A_mandel, dtype=float) + A = np.empty(A_mandel.shape[:-1] + (3, 3)) + A[..., 0, 0] = A_mandel[..., 0] + A[..., 1, 1] = A_mandel[..., 1] + A[..., 2, 2] = A_mandel[..., 2] + A[..., 0, 1] = A_mandel[..., 3] / _SQRT2 + A[..., 1, 0] = A[..., 0, 1] + A[..., 0, 2] = A_mandel[..., 4] / _SQRT2 + A[..., 2, 0] = A[..., 0, 2] + A[..., 1, 2] = A_mandel[..., 5] / _SQRT2 + A[..., 2, 1] = A[..., 1, 2] + return A def IsoProjectionKappa(A_mandel): - """Project 2-tensor in Mandel notation onto Id. - - The computation computes the orthogonal projection of an arbitrary, symmetric 2 tensor - encoded as a 6-vector in Mandel notation onto the Id (the 2nd order identit ytensor). - - If given a 2d array, the first index is assumed to represent different microstructures - and the projection is computed in vectorized form, returning a numpy.ndarray. - - Parameters - ---------- - A_mandel : ndarray - If ndim=1, then a single tensor is supplied in terms of a 6 vector according to the Mandel notation. - If ndim=2, shape = (n, 6,), then n different conductivity tensors are provided in the same notation. - - Returns - ------- - ndarray : - If ndim=1, a scalar isotropic conductivity is returned. - If ndim=2, a numpy.ndarray containing the n projections is returned. - """ - if A_mandel.ndim == 1: - kappa = A_mandel[:3].mean() - else: - # vectorized computation - kappa = A_mandel[:, :3].mean(axis=1) - return kappa + """Project Mandel tensors onto the second-order identity.""" + A_mandel = np.asarray(A_mandel) + return A_mandel[..., :3].mean(axis=-1) def IsoProjectionC(C_mandel): - """Project 4-tensor in Mandel notation onto isotropic projectors. - - The computation computes the orthogonal projection of an arbitrary, symmetric 4-tensor - encoded as a 6x6 matrix in Mandel notation onto the two isotropic projectors to compute - the bulk modulus K and the shear modulus G. - - If given a 3d array, the first index is assumed to represent different microstructures - and the projection is computed in vectorized form, returning a numpy.ndarray. - - Parameters - ---------- - A_mandel : ndarray - If ndim=2, then a single tensor is supplied in terms of a 6x6 matrix according to the Mandel notation. - If ndim=3, shape = (n, 6, 6,), then n different bulk and shear moduli are returned. - - Returns - ------- - float or ndarray : - If ndim=2, the bulk modulus is returned. - If ndim=3, a numpy.ndarray containing the n different bulk moduli is returned. - float or ndarray : - If ndim=2, the shear modulus is returned. - If ndim=3, a numpy.ndarray containing the n different shear moduli is returned. - """ - if C_mandel.ndim == 2: - K = C_mandel[:3, :3].mean() - G = (np.trace(C_mandel) - 3.0 * K) / 10.0 - else: - # vectorized computation - K = C_mandel[:, :3, :3].mean(axis=(1, 2)) - G = (np.trace(C_mandel, axis1=1, axis2=2) - 3.0 * K) / 10.0 + """Return the isotropic bulk and shear projections of Mandel matrices.""" + C_mandel = np.asarray(C_mandel) + K = C_mandel[..., :3, :3].mean(axis=(-2, -1)) + G = (np.trace(C_mandel, axis1=-2, axis2=-1) - 3.0 * K) / 10.0 return K, G def Piso1(): - """Returns the first isotropic projector in Mandel notation.""" + """Return the volumetric isotropic projector in Mandel notation.""" P = np.zeros((6, 6)) P[:3, :3] = 1.0 / 3.0 return P def Piso2(): - """Returns the second isotropic projector in Mandel notation.""" - P = np.eye(6) - P = P - Piso1() - return P + """Return the deviatoric isotropic projector in Mandel notation.""" + return np.eye(6) - Piso1() def Ciso(K, G): - """Returns an isotropic stiffness tensor in Mandel notation.""" - if type(K) is np.ndarray: - return (3.0 * K - 2.0 * G)[:, None, None] * Piso1()[None, :, :] + 2.0 * G[ - :, None, None - ] * np.eye(6)[None, :, :] - return (3.0 * K - 2.0 * G) * Piso1() + 2.0 * G * np.eye(6) + """Return isotropic Mandel stiffness matrices from bulk and shear moduli.""" + K = np.asarray(K, dtype=float)[..., None, None] + G = np.asarray(G, dtype=float)[..., None, None] + return 3.0 * K * Piso1() + 2.0 * G * Piso2() def ConvertElasticConstants(**kwargs): - # todo: check for K, G, E, nu - # if any two are available, compute other parameters (6 cases) - el_const = {"E": None, "nu": None, "G": None, "K": None} - el_const.update(kwargs) - E, K, G, nu = el_const["E"], el_const["K"], el_const["G"], el_const["nu"] - has_K = K is not None - has_E = E is not None - has_G = G is not None - has_nu = nu is not None - if has_K + has_G + has_E + has_nu < 2: - raise ValueError( - "Insufficient inputs: at least two independent elastic constants (E, K, G, nu) required, received: " - + str(kwargs) - ) - if has_E: - assert E > 0, f"Youngs modulus must be positive, but received E={E}" - if has_K: - assert K > 0, f"Bulk modulus must be positive, but received K={K}" - if has_G: - assert G > 0, f"Shear modulus must be positive, but received G={G}" - if has_nu: - assert (nu > -1.0) and ( - nu < 0.5 - ), f"Poisson ratio must satisfy -1 < nu < 0.5, but received nu={nu}" - - if not has_E: - if has_K and has_G: - E = 9.0 * K * G / (3.0 * K + G) - nu = E / (2.0 * G) - 1.0 - elif has_K and has_nu: - E = K * 3.0 * (1.0 - 2.0 * nu) - G = E / (2.0 * (1.0 + nu)) - else: - E = G * 2.0 * (1.0 + nu) - K = E / (3.0 * (1.0 - 2.0 * nu)) - else: - if has_nu: - # E, nu given - G = E / (2.0 * (1.0 + nu)) - K = E / (3.0 * (1.0 - 2.0 * nu)) - else: - if has_K: - # E, K given - nu = (3.0 * K - E) / (6.0 * K) - G = E / (2.0 * (1.0 + nu)) - else: - # E, G given - nu = E / (2.0 * G) - 1.0 - K = E / (3.0 * (1.0 - 2.0 * nu)) - el_const["K"] = K - el_const["E"] = E - el_const["G"] = G - el_const["nu"] = nu - - return el_const + """Return all isotropic elastic constants from exactly two inputs.""" + names = {"E", "nu", "G", "K"} + unknown = set(kwargs) - names + if unknown: + raise ValueError(f"Unknown elastic constants: {', '.join(sorted(unknown))}.") + + values = {name: float(value) for name, value in kwargs.items() if value is not None} + if len(values) != 2: + raise ValueError("Exactly two of E, nu, G, and K must be provided.") + if not all(np.isfinite(value) for value in values.values()): + raise ValueError("Elastic constants must be finite.") + for name in ("E", "G", "K"): + if name in values and values[name] <= 0.0: + raise ValueError("E, G, and K must be positive.") + if "nu" in values and not -1.0 < values["nu"] < 0.5: + raise ValueError("nu must satisfy -1 < nu < 0.5.") + + E = values.get("E") + nu = values.get("nu") + G = values.get("G") + K = values.get("K") + pair = set(values) + + if pair == {"E", "nu"}: + G = E / (2.0 * (1.0 + nu)) + K = E / (3.0 * (1.0 - 2.0 * nu)) + elif pair == {"E", "G"}: + if E >= 3.0 * G: + raise ValueError("E and G are not physically consistent.") + nu = E / (2.0 * G) - 1.0 + K = E / (3.0 * (1.0 - 2.0 * nu)) + elif pair == {"E", "K"}: + if E >= 9.0 * K: + raise ValueError("E and K are not physically consistent.") + nu = (3.0 * K - E) / (6.0 * K) + G = E / (2.0 * (1.0 + nu)) + elif pair == {"G", "K"}: + E = 9.0 * K * G / (3.0 * K + G) + nu = E / (2.0 * G) - 1.0 + elif pair == {"G", "nu"}: + E = 2.0 * G * (1.0 + nu) + K = E / (3.0 * (1.0 - 2.0 * nu)) + elif pair == {"K", "nu"}: + E = 3.0 * K * (1.0 - 2.0 * nu) + G = E / (2.0 * (1.0 + nu)) + + return {"E": E, "nu": nu, "G": G, "K": K} def is_spd(matrix): @@ -322,27 +181,6 @@ def is_spd(matrix): return is_symmetric and is_positive_definite, eigenvalues -def compute_volume_fractions(microstructure): - """ - Compute volume fractions from binary microstructure. - - Parameters - ---------- - microstructure : ndarray - Binary microstructure with values 0 and 1 - - Returns - ------- - vf : list - List of volume fractions [vf_phase0, vf_phase1] - """ - total_volume = microstructure.size - volume_phase1 = np.sum(microstructure) - volume_phase0 = total_volume - volume_phase1 - - return [volume_phase0 / total_volume, volume_phase1 / total_volume] - - def compute_VoigtReuss_bounds(phase_tensors, volume_fractions): """ Compute Voigt and Reuss bounds from phase tensors and volume fractions. diff --git a/MSUtils/fans_dashboard/utils.py b/MSUtils/fans_dashboard/utils.py index d898b20..9716599 100644 --- a/MSUtils/fans_dashboard/utils.py +++ b/MSUtils/fans_dashboard/utils.py @@ -1,6 +1,8 @@ +from collections import defaultdict + import h5py import numpy as np -from collections import defaultdict + from MSUtils.fans_dashboard.postprocessing import compute_rank2tensor_measures diff --git a/MSUtils/general/ComBoMicrostructureImage.py b/MSUtils/general/ComBoMicrostructureImage.py deleted file mode 100644 index 690a219..0000000 --- a/MSUtils/general/ComBoMicrostructureImage.py +++ /dev/null @@ -1,405 +0,0 @@ -import h5py -import numpy as np -from numpy.fft import fftn, ifftn, irfftn, rfftn - - -class VoxelInfo: - def __init__( - self, coords, elem_xyz, materials, fractions, fine_scale_block, normal=None - ): - self.coords = coords - self.elem_xyz = elem_xyz - self.materials = materials - self.fractions = fractions - self.normal = normal - self.fine_scale_block = fine_scale_block - - -class ComBoMicrostructureImage: - def __init__( - self, coarse_image=None, fine_image=None, voxel_info_list=None, iface=None - ): - self.coarse_image = coarse_image - self.fine_image = fine_image - self.voxel_info_list = voxel_info_list if voxel_info_list is not None else [] - self.iface = iface - - if coarse_image is not None and voxel_info_list is not None: - self.volume_fractions = self.compute_volume_fractions() - else: - self.volume_fractions = None - - def compute_volume_fractions(self): - """ - Compute the volume (in number of voxels) of each material in the 3D image. - """ - data_array = self.coarse_image - if self.voxel_info_list is not None: - unique_labels = np.unique(data_array[data_array >= 0]) - volumes = { - label: np.sum(data_array == label) / data_array.size - for label in unique_labels - } - - for voxel_info in self.voxel_info_list: - for material, fraction in zip( - voxel_info.materials, voxel_info.fractions, strict=False - ): - if material in volumes: - volumes[material] += fraction / data_array.size - else: - unique_labels = np.unique(data_array) - volumes = { - label: np.sum(data_array == label) / data_array.size - for label in unique_labels - } - - return volumes - - def get_image_laplacian(self, img, L=[1.0, 1.0, 1.0]): - """ - Compute the periodic Laplacian of the input image. - """ - s_img = np.array(img.shape, dtype=int) - - # Step 1: Apply periodic Laplace stencil to the input image - img_stencil = np.zeros(img.shape) - L = np.array(L, dtype=float) - lz = L[0] / s_img[0] - ly = L[1] / s_img[1] - lx = L[2] / s_img[2] - l_vx = np.array([lz, ly, lx]) - - f = 3.0 / (lx * ly / lz + lz * ly / lx + lx * lz / ly) - img_stencil[0, 0, 0] += 2.0 * f * (lx * ly / lz + lx * lz / ly + ly * lz / lx) - img_stencil[(1, -1), 0, 0] += -f * lx * ly / lz - img_stencil[0, (1, -1), 0] += -f * lx * lz / ly - img_stencil[0, 0, (1, -1)] += -f * ly * lz / lx - - even = s_img[2] % 2 == 1 - if even: - iface = np.abs(ifftn(fftn(img.astype(float)) * fftn(img_stencil))) - else: - iface = np.abs(irfftn(rfftn(img.astype(float)) * rfftn(img_stencil))) - - return iface, l_vx - - def supervoxel_normal(self, img, lap_img, l, vol_frac=None): - """ - Get the normal vector from a supervoxel using the Laplacian on the image. - """ - inline_norm = lambda x: np.sqrt(x[0] * x[0] + x[1] * x[1] + x[2] * x[2]) - - assert ( - img.ndim == 3 - ), "error: expecting 3D ndarray (0--> phase 0; else-->phase 1)" - N = np.array(img.shape) - l_combi = l * np.array(img.shape) - w = np.abs(lap_img) - iface = np.where(w > 1e-5) - w = w[iface[0], iface[1], iface[2]].flatten() - w = w / w.sum() - XX = l[:, None] * np.array(iface) - Xbar = (XX * w).sum(axis=1) - XX = XX - Xbar[:, None] - Mmod = XX @ (XX * w).T - eigval, evec = np.linalg.eigh(Mmod) - EV = evec[:, np.argmin(eigval)] - normal_xyz = EV[::-1] / np.sqrt(EV[0] * EV[0] + EV[1] * EV[1] + EV[2] * EV[2]) - - p_sum = img.sum(axis=(1, 2)) - n_red = N[0] * N[1] * N[2] - if vol_frac is None: - c1 = p_sum.sum() / n_red - else: - c1 = vol_frac - - x = [] - for i in range(3): - x.append(np.linspace(-0.5, 0.5, N[i] + 1)[:-1] + 0.5 / N[i]) - - z1_c = (p_sum * x[0]).sum() / (n_red * c1) * l_combi[0] - - p_sum = img.sum(axis=0) - y1_c = (p_sum.sum(axis=1) * x[1]).sum() / (n_red * c1) * l_combi[1] - x1_c = (p_sum.sum(axis=0) * x[2]).sum() / (n_red * c1) * l_combi[2] - - if x1_c * normal_xyz[0] + y1_c * normal_xyz[1] + z1_c * normal_xyz[2] > 0.0: - normal_xyz *= -1.0 - - # normal_classic = np.array([x1_c, y1_c, z1_c]) - # normal_classic = -normal_classic / inline_norm(normal_classic) - # return normal_xyz, normal_classic - return normal_xyz - - def downscale( - self, - data_array, - Nx, - Ny, - Nz, - min_vol_fraction=0.0, - L=[1.0, 1.0, 1.0], - pad_window=[0, 0, 0], - ): - """ - Downscale the given 3D image data_array to the size Nx x Ny x Nz considering periodic boundaries. - """ - coarse_data = np.zeros((Nx, Ny, Nz), dtype=int) - nx, ny, nz = data_array.shape - # Check divisibility - if nx % Nx != 0 or ny % Ny != 0 or nz % Nz != 0: - raise ValueError( - "Non-integer downscale factors detected due to indivisible dimensions. Please ensure that the dimensions of the fine-scale image are divisible by the desired coarse-scale dimensions." - ) - dx, dy, dz = nx // Nx, ny // Ny, nz // Nz - - iface, l_vx = self.get_image_laplacian(data_array, L) - - multi_material_voxels = [] - element_number = -1 - for i in range(Nx): - for j in range(Ny): - for k in range(Nz): - # Using modulo arithmetic to handle the wraparound - block = data_array[ - (i * dx) % nx : ((i + 1) * dx) % nx, - (j * dy) % ny : ((j + 1) * dy) % ny, - (k * dz) % nz : ((k + 1) * dz) % nz, - ] - unique, counts = np.unique(block, return_counts=True) - - fractions = counts / counts.sum() - significant_materials = unique[fractions >= min_vol_fraction] - significant_fractions = fractions[fractions >= min_vol_fraction] - element_number += 1 - - normal = None - if len(significant_materials) == 2: - xb = (i * dx - pad_window[0]) % nx - xe = ((i + 1) * dx + pad_window[0]) % nx - yb = (j * dy - pad_window[1]) % ny - ye = ((j + 1) * dy + pad_window[1]) % ny - zb = (k * dz - pad_window[2]) % nz - ze = ((k + 1) * dz + pad_window[2]) % nz - block_pad = data_array[xb:xe, yb:ye, zb:ze] - - if np.array_equal(np.unique(block), np.unique(block_pad)): - iface_block = iface[xb:xe, yb:ye, zb:ze] - supervoxel = np.where( - block_pad == significant_materials[0], 0, 1 - ) - else: - iface_block = iface[ - (i * dx) % nx : ((i + 1) * dx) % nx, - (j * dy) % ny : ((j + 1) * dy) % ny, - (k * dz) % nz : ((k + 1) * dz) % nz, - ] - supervoxel = np.where( - block == significant_materials[0], 0, 1 - ) - - normal = self.supervoxel_normal( - supervoxel, - lap_img=iface_block, - l=l_vx, - vol_frac=significant_fractions[1], - ) - - if len(significant_materials) == 1: - coarse_data[i, j, k] = significant_materials[0] - elif len(significant_materials) > 1: - coarse_data[i, j, k] = -len(significant_materials) - voxel_info = VoxelInfo( - coords=(i, j, k), - elem_xyz=element_number, - materials=significant_materials, - fractions=significant_fractions, - fine_scale_block=block, - normal=normal, - ) - multi_material_voxels.append(voxel_info) - - self.coarse_image = coarse_data - self.voxel_info_list = multi_material_voxels - self.fine_image = data_array - self.iface = iface - self.volume_fractions = self.compute_volume_fractions() - - def write(self, filename, group_name): - """ - Write the coarse data, fine data, and multi-material voxel information to an HDF5 file. - """ - with h5py.File(filename, "a") as f: - if group_name in f: - del f[group_name] - print(f"Group {group_name} exists, overwriting it.") - - grp = f.create_group(group_name) - - if self.coarse_image is not None: - grp.create_dataset( - "coarse_image", - data=self.coarse_image, - dtype=int, - compression="gzip", - compression_opts=9, - ) - - if self.fine_image is not None: - grp.create_dataset( - "fine_image", - data=self.fine_image, - dtype=np.uint8, - compression="gzip", - compression_opts=9, - ) - - # if self.iface is not None: - # grp.create_dataset("iface", data=self.iface, dtype=np.float16, compression="gzip", compression_opts=9) - - if self.voxel_info_list: - if self.coarse_image is not None: - coarse_normal = np.zeros( - ( - self.coarse_image.shape[0], - self.coarse_image.shape[1], - self.coarse_image.shape[2], - 3, - ) - ) - for voxel_info in self.voxel_info_list: - i, j, k = voxel_info.coords - normal_val = ( - voxel_info.normal - if voxel_info.normal is not None - else (0.0, 0.0, 0.0) - ) - coarse_normal[i, j, k] = normal_val - grp.create_dataset( - "coarse_normal", - data=coarse_normal, - dtype="f4", - compression="gzip", - compression_opts=9, - ) - - max_materials = max( - len(voxel_info.materials) for voxel_info in self.voxel_info_list - ) - block_shape = self.voxel_info_list[0].fine_scale_block.shape - - dtype = np.dtype( - [ - ("coords", "3i4"), - ("elem_xyz", "i4"), - ("num_materials", "i4"), - ("materials", f"{max_materials}i4"), - ("fractions", f"{max_materials}f4"), - ("normal", "3f4"), - ( - "fine_scale_block", - f"{block_shape[0]},{block_shape[1]},{block_shape[2]}i4", - ), - ] - ) - structured_array = np.zeros(len(self.voxel_info_list), dtype=dtype) - - for idx, voxel_info in enumerate(self.voxel_info_list): - materials = np.pad( - voxel_info.materials.astype(np.int32), - (0, max_materials - len(voxel_info.materials)), - constant_values=-1, - ) - fractions = np.pad( - voxel_info.fractions, - (0, max_materials - len(voxel_info.fractions)), - constant_values=-1.0, - ) - num_materials = len(voxel_info.materials) - normal_val = ( - voxel_info.normal - if voxel_info.normal is not None - else (0.0, 0.0, 0.0) - ) - - structured_array[idx] = ( - voxel_info.coords, - voxel_info.elem_xyz, - num_materials, - materials, - fractions, - normal_val, - voxel_info.fine_scale_block, - ) - - grp.create_dataset( - "multi_material_voxels", - data=structured_array, - dtype=dtype, - compression="gzip", - compression_opts=9, - ) - - @staticmethod - def read(filename, group_name): - """ - Read a ComBoMicrostructureImage object from an HDF5 file. - """ - with h5py.File(filename, "r") as f: - if group_name not in f: - raise ValueError(f"Group {group_name} not found in file {filename}") - - group = f[group_name] - - if "coarse_image" in group: - coarse_image = group["coarse_image"][:] - if "fine_image" in group: - fine_image = group["fine_image"][:] - if "iface" in group: - iface = group["iface"][:] - - voxel_info_list = [] - if "multi_material_voxels" in group: - voxel_data = group["multi_material_voxels"] - for voxel in voxel_data: - coords = tuple(voxel["coords"]) - elem_xyz = voxel["elem_xyz"] - materials = voxel["materials"][voxel["materials"] != -1] - fractions = voxel["fractions"][voxel["fractions"] != -1] - normal = tuple(voxel["normal"]) - fine_scale_block = voxel["fine_scale_block"] - voxel_info = VoxelInfo( - coords, elem_xyz, materials, fractions, fine_scale_block, normal - ) - voxel_info_list.append(voxel_info) - - return ComBoMicrostructureImage( - coarse_image, fine_image, voxel_info_list, iface - ) - - -def main(): - from MSUtils.general.h52xdmf import write_xdmf - from MSUtils.general.MicrostructureImage import MicrostructureImage - from MSUtils.general.resize_image import resize_image - - ms = MicrostructureImage(h5_filename="data/fibers1.h5", dset_name="/img") - ms = MicrostructureImage( - image=resize_image(ms.image, target_resolution=[256, 256, 256]) - ) - - combo_micro_img = ComBoMicrostructureImage() - combo_micro_img.downscale(ms.image, 64, 64, 64, pad_window=[2, 2, 2]) - combo_micro_img.write("data/test_combo.h5", "/combo_group") - - write_xdmf( - h5_filepath="data/test_combo.h5", - xdmf_filepath="data/test_combo.xdmf", - microstructure_length=[1, 1, 1], - verbose=True, - ) - - -if __name__ == "__main__": - main() diff --git a/MSUtils/general/MicrostructureImage.py b/MSUtils/general/MicrostructureImage.py index 2bad6c2..ab0fc9f 100644 --- a/MSUtils/general/MicrostructureImage.py +++ b/MSUtils/general/MicrostructureImage.py @@ -1,229 +1,121 @@ -from typing import Dict, List, Optional, Union - import h5py import numpy as np +from MSUtils.general.grid import ( + GridSpec, + image_in_order, + validate_order, +) -class MicrostructureImage: - """ - A class for handling microstructure images stored in HDF5 format. - This class provides methods for reading and writing 3D microstructure images - from/to HDF5 files, handling associated metadata, and computing volume fractions - of different phases present in the image. - """ +class MicrostructureImage: + """A three-dimensional microstructure image with HDF5 persistence.""" def __init__( self, - h5_filename: Optional[str] = None, - dset_name: Optional[str] = None, - image: Optional[np.ndarray] = None, - resolution: Optional[Union[List[int], tuple]] = None, - L: Optional[Union[List[float], tuple]] = None, - metadata: Optional[Dict[str, Union[str, int, float]]] = None, + h5_filename: str | None = None, + dset_name: str | None = None, + image: np.ndarray | None = None, + L: list[float] | tuple | None = None, + metadata: dict[str, object] | None = None, + grid: GridSpec | None = None, ): - """ - Initializes the MicrostructureImage object. - - Args: - h5_filename (Optional[str]): Path to the HDF5 file. - dset_name (Optional[str]): Name of the dataset in the HDF5 file. - image (Optional[np.ndarray]): Numpy array representing the image. - resolution (Optional[Union[List[int], tuple]]): Number of voxels along each axis. - L (Optional[Union[List[float], tuple]]): Physical dimensions of the image along each axis. - metadata (Optional[Dict[str, Union[str, int, float]]]): Metadata associated with the image. - - Note: - Either 'h5_filename' and 'dset_name' must be provided to read an image from a file, - or 'image' must be provided to initialize the object with an image array. - - If 'image' is provided, 'h5_filename' and 'dset_name' are optional and can be used - when writing the image to a file. - - If 'resolution' is not provided, it is set to the shape of the image array. - - If 'L' is not provided, default values are used. - """ self.h5_filename = h5_filename self.dset_name = dset_name - self.image = image - self.metadata = metadata if metadata is not None else {} + self.image = None + self.grid = None + self.metadata = dict(metadata or {}) self.volume_fractions = None - self.shape = None - self.resolution = resolution - self.L = L - - if self.h5_filename and self.dset_name: + if h5_filename and dset_name: self.read() - - if self.image is not None: - self.shape = self.image.shape - if self.resolution is None: - self.resolution = self.shape - if self.L is None: - self.L = [1.0, 1.0, 1.0] # Default physical dimensions + elif image is not None: + image = np.ascontiguousarray(image) + if grid is None: + grid = GridSpec( + shape=image.shape, + lengths=(1.0, 1.0, 1.0) if L is None else tuple(L), + ) + if not isinstance(grid, GridSpec): + raise TypeError("grid must be a GridSpec instance.") + if image.shape != grid.shape: + raise ValueError("grid.shape must match image.shape.") + if L is not None and not np.array_equal(L, grid.lengths): + raise ValueError("L and grid.lengths must match.") + self.image = image + self.grid = grid self.compute_volume_fractions() - def read( - self, h5_filename: Optional[str] = None, dset_name: Optional[str] = None - ) -> None: - """ - Reads the image data and metadata from the specified HDF5 file and dataset. + @property + def shape(self) -> tuple[int, int, int] | None: + return None if self.image is None else self.image.shape - Args: - h5_filename (Optional[str]): Path to the HDF5 file. - dset_name (Optional[str]): Name of the dataset in the HDF5 file. - - Raises: - ValueError: If both h5_filename and dset_name are not specified. - ValueError: If the specified dataset is not found in the HDF5 file. - """ - if h5_filename: - self.h5_filename = h5_filename - if dset_name: - self.dset_name = dset_name + @property + def L(self) -> tuple[float, float, float] | None: + return None if self.grid is None else self.grid.lengths + def read( + self, h5_filename: str | None = None, dset_name: str | None = None + ) -> None: + """Read an HDF5 dataset into canonical in-memory XYZ order.""" + self.h5_filename = h5_filename or self.h5_filename + self.dset_name = dset_name or self.dset_name if not self.h5_filename or not self.dset_name: raise ValueError("Both h5_filename and dset_name must be specified.") - with h5py.File(self.h5_filename, "r") as f: - if self.dset_name in f: - dset = f[self.dset_name] - self.image = dset[...] - # Read metadata - self.metadata = {key: value for key, value in dset.attrs.items()} - - # check for permute_order attribute - if "permute_order" in dset.attrs: - permute_order = dset.attrs["permute_order"] - else: - permute_order = "zyx" # Default order - - if permute_order == "zyx": - self.image = self.image.transpose(2, 1, 0) - elif permute_order != "xyz": - raise ValueError( - f"Invalid permute order {permute_order} in dataset {self.dset_name}" - ) - self.shape = self.image.shape - self.resolution = self.shape - # Read L from attributes if available - if "L" in dset.attrs and isinstance(dset.attrs["L"], (list, tuple)): - self.L = dset.attrs["L"] - else: - self.L = [1.0, 1.0, 1.0] # Default physical dimensions - self.compute_volume_fractions() - else: + with h5py.File(self.h5_filename, "r") as h5_file: + if self.dset_name not in h5_file: raise ValueError( f"No dataset with name {self.dset_name} found in {self.h5_filename}" ) + dataset = h5_file[self.dset_name] + if dataset.ndim != 3: + raise ValueError("Microstructure images must be three-dimensional.") + + order = validate_order(dataset.attrs.get("permute_order", "zyx")) + self.image = np.ascontiguousarray(image_in_order(dataset[...], order)) + self.grid = GridSpec.from_h5_attributes( + self.image.shape, dataset.attrs, order + ) + self.metadata = dict(dataset.attrs) + + self.compute_volume_fractions() def write( self, - h5_filename: Optional[str] = None, - dset_name: Optional[str] = None, + h5_filename: str | None = None, + dset_name: str | None = None, order: str = "zyx", compression_level: int = 6, ) -> None: - """ - Writes the image data and metadata to the specified HDF5 file and dataset. - - Args: - h5_filename (Optional[str]): Path to the HDF5 file. - dset_name (Optional[str]): Name of the dataset in the HDF5 file. - order (str): Order of the dimensions ('xyz' or 'zyx'). - compression_level (int): GZIP compression level (0-9). - - Raises: - ValueError: If both h5_filename and dset_name are not specified. - ValueError: If there is no image to write to the HDF5 file. - ValueError: If an invalid order is specified. - ValueError: If an invalid compression level is specified. - - Note: - If a dataset with the same name already exists in the HDF5 file, it will be overwritten. - """ - if h5_filename: - self.h5_filename = h5_filename - if dset_name: - self.dset_name = dset_name - + """Write the image and grid, optionally storing axes in ZYX order.""" + self.h5_filename = h5_filename or self.h5_filename + self.dset_name = dset_name or self.dset_name if not self.h5_filename or not self.dset_name: raise ValueError("Both h5_filename and dset_name must be specified.") - - if self.image is None: + if self.image is None or self.grid is None: raise ValueError("No image to write to H5 file.") - - if not (0 <= compression_level <= 9): + if self.image.shape != self.grid.shape: + raise ValueError("grid.shape must match image.shape.") + if not 0 <= compression_level <= 9: raise ValueError("Invalid compression level. Must be between 0 and 9.") + order = validate_order(order) - # Permute the image data based on the specified order - if order == "xyz": - permuted_image = self.image - elif order == "zyx": - permuted_image = self.image.transpose(2, 1, 0) - self.L = self.L[::-1] - else: - raise ValueError("Invalid order specified. Use 'xyz' or 'zyx'.") - - with h5py.File(self.h5_filename, "a") as f: - if self.dset_name in f: - del f[self.dset_name] - print( - f"Dataset {self.dset_name} exists in {self.h5_filename}, overwriting it." - ) - dset = f.create_dataset( + with h5py.File(self.h5_filename, "a") as h5_file: + if self.dset_name in h5_file: + del h5_file[self.dset_name] + dataset = h5_file.create_dataset( self.dset_name, - data=permuted_image, + data=np.ascontiguousarray(image_in_order(self.image, order)), compression="gzip", compression_opts=compression_level, ) - dset.attrs["permute_order"] = order - - # Write metadata - if self.metadata: - for key, value in self.metadata.items(): - dset.attrs[key] = value - # Write L as attribute - if self.L is not None: - dset.attrs["L"] = self.L - - print( - f"Image written to dataset {self.dset_name} in {self.h5_filename} " - f"with GZIP level {compression_level} compression." - ) + dataset.attrs.update(self.metadata) + dataset.attrs.update(self.grid.to_h5_attributes(order)) def compute_volume_fractions(self) -> None: - """ - Computes the volume fractions of unique labels in the image. - - The volume fractions are stored in the 'volume_fractions' attribute - as a dictionary mapping labels to their respective volume fractions. - """ - unique_labels = np.unique(self.image) + """Compute the fraction occupied by each unique image label.""" + labels, counts = np.unique(self.image, return_counts=True) self.volume_fractions = { - label: np.sum(self.image == label) / self.image.size - for label in unique_labels + label: count / self.image.size for label, count in zip(labels, counts) } - - -def main(): - ms = MicrostructureImage( - h5_filename="data/sphere.h5", dset_name="/sphere03628/240x240x240/ms" - ) - - for key in sorted(ms.volume_fractions.keys()): - value = ms.volume_fractions[key] - print(f"Volume fraction of phase {key}: {value * 100:.6f}%") - - ms.write( - h5_filename="data/test_MicrostructureImage.h5", - dset_name="ms", - order="zyx", - compression_level=6, - ) - - -if __name__ == "__main__": - main() diff --git a/MSUtils/general/compute_correlation_length.py b/MSUtils/general/compute_correlation_length.py index 73c27fd..c44df54 100644 --- a/MSUtils/general/compute_correlation_length.py +++ b/MSUtils/general/compute_correlation_length.py @@ -1,5 +1,3 @@ -import matplotlib.cm as cm -import matplotlib.pyplot as plt import numpy as np from scipy.fft import fftn, ifftn from scipy.optimize import curve_fit @@ -10,169 +8,24 @@ def exponential_decay(x, l): def compute_correlation_length(img): - """ - Compute the correlation length of an image using FFT and exponential decay fitting. - - Parameters: - - img: The input image as a NumPy array. - - Returns: - - cl: Correlation lengths for each dimension. - - aspect_ratio: Aspect ratio derived from correlation lengths. - - autocorr: The autocorrelation function of the image. - """ - # Compute mean and variance for normalization - c1 = np.mean(img) - c2 = np.mean(img**2) - - # Perform FFT, get power spectrum, and compute autocorrelation - f_img = fftn(img) - power_spectrum = np.abs(f_img) ** 2 - autocorr = ifftn(power_spectrum).real / np.prod(img.shape) - autocorr = (autocorr - c1**2) / (c2 - c1**2) - - # Initialize correlation lengths array - cl = np.zeros(img.ndim) - - # Fit exponential decay to autocorrelation function slices - for i_dim in range(img.ndim): - slice_midpoint = slice(None, img.shape[i_dim] // 2) - slice_others = [ - 0 if dim != i_dim else slice_midpoint for dim in range(img.ndim) - ] - y = autocorr[tuple(slice_others)].ravel() - x = np.arange(y.size) - params, _ = curve_fit(exponential_decay, x, y, p0=[10], bounds=(0, np.inf)) - cl[i_dim] = params[0] - - # Normalize correlation lengths and calculate aspect ratio - normalized_cl = cl / np.min(cl) - aspect_ratio = np.floor(normalized_cl).astype(int) - - return aspect_ratio, cl, autocorr - - -def plot_mid_planes(autocorr): - """ - Visualize the mid-planes (XY, YZ, XZ) of the autocorrelation function for 3D data. - For 2D data, just visualize the data itself. - - Parameters: - - autocorr: The autocorrelation function as a NumPy array. - """ - autocorr_shifted = np.fft.fftshift(autocorr) - - if autocorr.ndim == 3: - nz, ny, nx = autocorr_shifted.shape - fig, axes = plt.subplots(1, 3, figsize=(18, 6), dpi=300) - planes = [ - autocorr_shifted[nz // 2, :, :], - autocorr_shifted[:, ny // 2, :], - autocorr_shifted[:, :, nx // 2], - ] - titles = ["XY Mid-Plane", "YZ Mid-Plane", "XZ Mid-Plane"] - - for ax, plane, title in zip(axes, planes, titles, strict=False): - im = ax.imshow(plane, cmap="viridis") - ax.set_title(title, fontsize=14) - ax.axis("off") - - # Colorbar configuration - cbar_ax = fig.add_axes([0.15, 0.05, 0.7, 0.03]) - fig.colorbar(im, cax=cbar_ax, orientation="horizontal") - - elif autocorr.ndim == 2: - plt.figure(figsize=(6, 6), dpi=300) - plt.imshow(autocorr_shifted, cmap="viridis") - plt.title("2D Autocorrelation", fontsize=14) - plt.axis("off") - plt.colorbar(orientation="horizontal") - - plt.subplots_adjust( - left=0.05, right=0.95, top=0.9, bottom=0.2, wspace=0.3, hspace=0.3 - ) - plt.savefig("data/mid_planes.png", bbox_inches="tight") - plt.show() - - -def visualize_correlation(cl, autocorr): - """ - Visualize the autocorrelation function and exponential fits for each dimension. - Handles both 2D and 3D autocorrelation data and plots only half of the autocorrelation line in each direction. - - Parameters: - - cl: Correlation lengths for each dimension. - - autocorr: The autocorrelation function as a NumPy array. - """ - plot_mid_planes(autocorr) # Handles both 2D and 3D - - # Ensure the autocorrelation function is centered - autocorr_centered = np.fft.fftshift(autocorr) - - fig, ax = plt.subplots(figsize=(12, 12), dpi=300) - if autocorr.ndim == 3: - directions = ["X", "Y", "Z"] # The three directions for 3D data - # Indices for slicing from the center to halfway in each direction - indices = [ - (slice(None), autocorr.shape[1] // 2, autocorr.shape[2] // 2), - (autocorr.shape[0] // 2, slice(None), autocorr.shape[2] // 2), - (autocorr.shape[0] // 2, autocorr.shape[1] // 2, slice(None)), - ] - elif autocorr.ndim == 2: - directions = ["X", "Y"] # The two directions for 2D data - indices = [ - (slice(None), autocorr.shape[1] // 2), - (autocorr.shape[0] // 2, slice(None)), - ] - - colors = cm.viridis(np.linspace(0, 1, len(directions))) - markers = ["o", "s", "^", "d"][: len(directions)] - - for i_dim, direction, color, marker in zip( - range(len(directions)), directions, colors, markers, strict=False - ): - line_full = autocorr_centered[indices[i_dim]].ravel() - mid_point = len(line_full) // 2 # Find the center - line = line_full[mid_point:] # Take only the second half from the center - x = np.arange(len(line)) - ax.plot( - x, - line, - label=f"{direction} Autocorrelation", - color=color, - linestyle="-", - marker=marker, - markersize=5, - ) - ax.plot( - x, - exponential_decay(x, cl[i_dim]), - label=f"{direction} Exponential Fit", - color=color, - linestyle="--", - linewidth=2, - ) - - ax.set_title("Autocorrelation and Exponential Fits", fontsize=24) - ax.set_xlabel("Voxel Separation Distance", fontsize=24) - ax.set_ylabel("Autocorrelation", fontsize=24) - ax.legend(fontsize=20) - ax.grid(True) - plt.tight_layout() - plt.savefig("data/correlation_lengths.png", bbox_inches="tight") - plt.show() - - -def main(): - from MSUtils.general.MicrostructureImage import MicrostructureImage - - ms = MicrostructureImage(h5_filename="data/fibers1.h5", dset_name="/img") - aspect_ratio, cl, autocorr = compute_correlation_length(ms.image) - print(f"Correlation Lengths: {cl}") - print(f"Aspect Ratio: {aspect_ratio}") - - visualize_correlation(cl, autocorr) - - -if __name__ == "__main__": - main() + """Return the aspect ratio, correlation lengths, and normalized autocorrelation.""" + mean = np.mean(img) + spectrum = fftn(img) + autocorr = ifftn(np.abs(spectrum) ** 2).real / img.size + autocorr = (autocorr - mean**2) / (np.mean(img**2) - mean**2) + + correlation_lengths = np.empty(img.ndim) + for axis, size in enumerate(img.shape): + index = [0] * img.ndim + index[axis] = slice(size // 2) + values = autocorr[tuple(index)] + correlation_lengths[axis] = curve_fit( + exponential_decay, + np.arange(values.size), + values, + p0=[10], + bounds=(0, np.inf), + )[0][0] + + aspect_ratio = np.floor(correlation_lengths / correlation_lengths.min()).astype(int) + return aspect_ratio, correlation_lengths, autocorr diff --git a/MSUtils/general/grid.py b/MSUtils/general/grid.py new file mode 100644 index 0000000..100a240 --- /dev/null +++ b/MSUtils/general/grid.py @@ -0,0 +1,66 @@ +from dataclasses import dataclass + +import numpy as np + + +def validate_order(order: str) -> str: + if isinstance(order, bytes): + order = order.decode("utf-8") + if order not in {"xyz", "zyx"}: + raise ValueError("Invalid order specified. Use 'xyz' or 'zyx'.") + return order + + +def values_in_order(values, order: str) -> tuple: + values = tuple(values) + return values[::-1] if order == "zyx" else values + + +def image_in_order(image: np.ndarray, order: str) -> np.ndarray: + return image.transpose(2, 1, 0) if order == "zyx" else image + + +@dataclass(frozen=True) +class GridSpec: + """Description of a cell-centered three-dimensional grid in XYZ order.""" + + shape: tuple[int, int, int] + lengths: tuple[float, float, float] + + def __post_init__(self) -> None: + shape = tuple(self.shape) + lengths = tuple(self.lengths) + + if len(shape) != 3 or any( + isinstance(value, (bool, np.bool_)) + or not isinstance(value, (int, np.integer)) + or value <= 0 + for value in shape + ): + raise ValueError("shape must contain three positive integers.") + if len(lengths) != 3 or any( + isinstance(value, (bool, np.bool_)) + or not isinstance(value, (int, float, np.integer, np.floating)) + or not np.isfinite(value) + or value <= 0 + for value in lengths + ): + raise ValueError("lengths must contain three positive finite values.") + object.__setattr__(self, "shape", tuple(int(value) for value in shape)) + object.__setattr__(self, "lengths", tuple(float(value) for value in lengths)) + + @classmethod + def from_h5_attributes( + cls, shape: tuple[int, int, int], attributes, order: str + ) -> "GridSpec": + return cls( + shape=shape, + lengths=values_in_order(attributes.get("L", (1.0, 1.0, 1.0)), order), + ) + + def to_h5_attributes(self, order: str) -> dict[str, object]: + order = validate_order(order) + return { + "permute_order": order, + "L": values_in_order(self.lengths, order), + } diff --git a/MSUtils/general/h52xdmf.py b/MSUtils/general/h52xdmf.py index 45fb305..39c654c 100644 --- a/MSUtils/general/h52xdmf.py +++ b/MSUtils/general/h52xdmf.py @@ -193,7 +193,7 @@ def crawl_h5_group(group, grid_dict, time=None): grid = ET.SubElement(domain, "Grid", Name=grid_name, GridType="Uniform") subgrid = grid - topology = ET.SubElement( + ET.SubElement( subgrid, "Topology", TopologyType="3DCoRectMesh", diff --git a/MSUtils/general/merge_h5_files.py b/MSUtils/general/merge_h5_files.py index ba1b72a..daf4a2e 100644 --- a/MSUtils/general/merge_h5_files.py +++ b/MSUtils/general/merge_h5_files.py @@ -1,84 +1,45 @@ -import os -import h5py import argparse +from pathlib import Path +import h5py -def copy_contents(src_group, dst_group, verbose=False, overwrite=True): - """ - Recursively copy all datasets and groups from src_group into dst_group. - If a dataset already exists at the same path in dst_group: - - If overwrite=True, the dataset is overwritten. - - If overwrite=False, the dataset is skipped. +def copy_contents(src_group, dst_group, verbose=False, overwrite=True): + """Recursively merge an HDF5 group into another group.""" + for key, value in src_group.attrs.items(): + if overwrite or key not in dst_group.attrs: + dst_group.attrs[key] = value - Parameters - ---------- - src_group : h5py.Group - The source HDF5 group to copy from. - dst_group : h5py.Group - The destination HDF5 group to copy into. - verbose : bool - If True, print detailed information about operations. - overwrite : bool - If True, overwrite existing datasets. If False, skip them. - """ for key, item in src_group.items(): - if isinstance(item, h5py.Group): - # Create or use existing group - if key not in dst_group: - if verbose: - print(f"Creating group: {dst_group.name}/{key}") - new_group = dst_group.create_group(key) - else: - new_group = dst_group[key] - - # Recursively copy the contents - copy_contents(item, new_group, verbose=verbose, overwrite=overwrite) - - elif isinstance(item, h5py.Dataset): - # If dataset doesn't exist in the destination, copy it - if key not in dst_group: - if verbose: - print(f"Copying new dataset: {dst_group.name}/{key}") - dst_group.copy(item, key) - else: - # Dataset exists - if overwrite: - # Overwrite it - if verbose: - print(f"Overwriting dataset: {dst_group.name}/{key}") - del dst_group[key] - dst_group.copy(item, key) - else: - # Skip overwriting - if verbose: - print( - f"Skipping dataset (already exists): {dst_group.name}/{key}" - ) + path = f"{dst_group.name.rstrip('/')}/{key}" + if key not in dst_group: + if verbose: + print(f"Copying: {path}") + src_group.copy(key, dst_group) + continue + + destination = dst_group[key] + if isinstance(item, h5py.Group) and isinstance(destination, h5py.Group): + copy_contents(item, destination, verbose=verbose, overwrite=overwrite) + elif overwrite: + if verbose: + print(f"Overwriting: {path}") + del dst_group[key] + src_group.copy(key, dst_group) + elif verbose: + print(f"Skipping existing object: {path}") def merge_h5_files(output_file, input_files, verbose=False, overwrite=True): - """ - Merge multiple HDF5 files into a single output HDF5 file. - - The entire hierarchy (groups/datasets) from each input file will be merged - into the output file. If any dataset path already exists in the output file: - - If overwrite=True, it will be overwritten with the dataset from the current input file. - - If overwrite=False, it will be skipped. - - Parameters - ---------- - output_file : str - Path to the output HDF5 file. - input_files : list of str - Paths to the input HDF5 files to be merged. - verbose : bool, optional - If True, print detailed information about the merging process. - overwrite : bool, optional - If True (default), existing datasets are overwritten. If False, they are skipped. - """ - mode = "a" if os.path.exists(output_file) else "w" - with h5py.File(output_file, mode) as h5out: + """Merge HDF5 files in order into one output file.""" + input_files = tuple(input_files) + output_path = Path(output_file).resolve() + if any(Path(input_file).resolve() == output_path for input_file in input_files): + raise ValueError( + "The output file is updated in place; omit it from the input files." + ) + + with h5py.File(output_file, "a") as h5out: for infile in input_files: if verbose: print(f"Merging file: {infile}") @@ -89,7 +50,7 @@ def merge_h5_files(output_file, input_files, verbose=False, overwrite=True): print(f"Merging complete. Output saved at {output_file}") -def main(): +def main(argv=None): parser = argparse.ArgumentParser( description="Merge multiple HDF5 files into a single output HDF5 file." ) @@ -100,6 +61,7 @@ def main(): "-i", "--inputs", nargs="+", required=True, help="Input HDF5 files to merge." ) parser.add_argument( + "-v", "--verbose", action="store_true", help="Print detailed information about the merging process.", @@ -109,10 +71,10 @@ def main(): action="store_false", dest="overwrite", default=True, - help="If set, existing datasets are not overwritten, but skipped.", + help="Keep existing objects and attributes.", ) - args = parser.parse_args() + args = parser.parse_args(argv) merge_h5_files( args.output, args.inputs, verbose=args.verbose, overwrite=args.overwrite ) @@ -120,10 +82,3 @@ def main(): if __name__ == "__main__": main() - - # Example usage from Python - # output_file = 'path/to/aggregated_h5_file.h5' - # input_files = [f'path/to/individual/h5_file_{k}.h5' for k in range(0, 20)] - # verbose = False - # overwrite = True - # merge_h5_files(output_file, input_files, verbose=verbose, overwrite=overwrite) diff --git a/MSUtils/general/vtk2h5.py b/MSUtils/general/vtk2h5.py deleted file mode 100644 index a5e2208..0000000 --- a/MSUtils/general/vtk2h5.py +++ /dev/null @@ -1,164 +0,0 @@ -import os -import sys -import h5py -import numpy as np -import pyvista as pv -import argparse -from typing import Sequence, Optional - - -def _init_target(dtype: np.dtype, shape_3d, n_comp: int): - """ - Create target array with sensible fill values: -1 for integer, 0 for float. - Shape: (nx,ny,nz) or (nx,ny,nz,n_comp) - """ - is_int = np.issubdtype(dtype, np.integer) - fill = -1 if is_int else 0 - if n_comp == 1: - tgt = np.full( - shape_3d, fill, dtype=dtype if is_int else np.asarray(0, dtype).dtype - ) - else: - tgt = np.full( - shape_3d + (n_comp,), - fill, - dtype=dtype if is_int else np.asarray(0.0, dtype).dtype, - ) - return tgt - - -def _grid_from_centers(mesh, dec: int = 9): - centers = mesh.cell_centers().points - cx = np.round(centers[:, 0], dec) - cy = np.round(centers[:, 1], dec) - cz = np.round(centers[:, 2], dec) - - xu = np.unique(cx) - yu = np.unique(cy) - zu = np.unique(cz) - nx, ny, nz = len(xu), len(yu), len(zu) - if nx * ny * nz != mesh.n_cells: - raise ValueError( - f"Inferred grid {nx} x {ny} x {nz}={nx*ny*nz} != n_cells={mesh.n_cells}. " - "Check dec (rounding) or input mesh." - ) - - # mean spacing from centers → robust to tiny roundoff - dx = float(np.diff(xu).mean()) if nx > 1 else 0.0 - dy = float(np.diff(yu).mean()) if ny > 1 else 0.0 - dz = float(np.diff(zu).mean()) if nz > 1 else 0.0 - Lx, Ly, Lz = nx * dx, ny * dy, nz * dz - - ix = np.searchsorted(xu, cx) - iy = np.searchsorted(yu, cy) - iz = np.searchsorted(zu, cz) - - return (nx, ny, nz, ix, iy, iz, (Lx, Ly, Lz)) - - -def vtk2h5( - vtk_files: Sequence[str], - h5_file_path: str, - grp_name: str = "images", - data_fields: Optional[Sequence[str]] = None, - overwrite: bool = False, - dec: int = 9, -): - mode = "a" if os.path.exists(h5_file_path) else "w" - with h5py.File(h5_file_path, mode) as h5: - root = h5[grp_name] if grp_name in h5 else h5.create_group(grp_name) - - for vf in vtk_files: - stem = os.path.splitext(os.path.basename(vf))[0] - grp = root[stem] if stem in root else root.create_group(stem) - - try: - mesh = pv.read(vf) - except Exception as e: - print(f"[skip] {vf}: read error -> {e}") - continue - - cd = mesh.cell_data - if not cd: - print(f"[skip] {vf}: no cell data present.") - continue - - fields = ( - list(cd.keys()) - if data_fields is None - else [f for f in data_fields if f in cd] - ) - if not fields: - print(f"[skip] {vf}: none of requested fields {data_fields} found.") - continue - - try: - nx, ny, nz, ix, iy, iz, domL = _grid_from_centers(mesh, dec=dec) - except Exception as e: - print(f"[warn] {vf}: grid inference failed ({e}); writing flat arrays.") - for field in fields: - arr = np.asarray(cd[field]) - if field in grp and not overwrite: - print(f"[skip] {vf}:{field} exists (overwrite=False).") - continue - if field in grp: - del grp[field] - ds = grp.create_dataset( - field, - data=arr, - compression="gzip", - compression_opts=9, - chunks=True, - shuffle=True, - ) - # still record something minimal on fallback - ds.attrs["grid_shape"] = (mesh.n_cells,) - grp.attrs["grid_type"] = mesh.__class__.__name__ - grp.attrs["n_cells"] = int(mesh.n_cells) - continue - - for field in fields: - arr = np.asarray(cd[field]) - n_comp = 1 if arr.ndim == 1 else arr.shape[1] - tgt = _init_target(arr.dtype, (nx, ny, nz), n_comp) - - if n_comp == 1: - tgt[ix, iy, iz] = arr.astype(tgt.dtype, copy=False) - else: - # handle vectors/tensors generically; for 3-vectors keep your VTK->xyz flip - if n_comp == 3: - tgt[ix, iy, iz, :] = arr.astype(tgt.dtype, copy=False)[ - :, [2, 1, 0] - ] - else: - tgt[ix, iy, iz, :] = arr.astype(tgt.dtype, copy=False) - - if field in grp and not overwrite: - print(f"[skip] {vf}:{field} exists (overwrite=False).") - continue - if field in grp: - del grp[field] - - chunks = tuple(min(64, s) for s in tgt.shape) - dset = grp.create_dataset( - field, - data=tgt, - compression="gzip", - compression_opts=9, - chunks=chunks, - shuffle=True, - ) - - # --- Only the attributes you asked for --- - dset.attrs["domain_lengths"] = domL # (Lx, Ly, Lz) - dset.attrs["grid_shape"] = (nx, ny, nz) - - # mirror the same minimal info at the group level - grp.attrs["grid_shape"] = (nx, ny, nz) - grp.attrs["domain_lengths"] = domL - grp.attrs["grid_type"] = mesh.__class__.__name__ - grp.attrs["n_cells"] = int(mesh.n_cells) - - h5.attrs["num_files"] = len(root) - - print(f"Done → {h5_file_path}") diff --git a/MSUtils/lattices/lattice_definitions.py b/MSUtils/lattices/lattice_definitions.py index fdd6fab..ccbe885 100644 --- a/MSUtils/lattices/lattice_definitions.py +++ b/MSUtils/lattices/lattice_definitions.py @@ -410,15 +410,3 @@ def plot_lattice(vertices, edges): # Set axis labels fig.update_layout(scene=dict(xaxis_title="X", yaxis_title="Y", zaxis_title="Z")) fig.show() - - -def check_rigidity(vertices, edges): - n = len(vertices) # number of nodes - m = len(edges) # number of struts - result = m - 3 * n + 6 - if result == 0: - return "Lattice is statically determinate and rigid" - elif result > 0: - return "Lattice is statically indeterminate" - else: - return "Lattice is flexible and unstable" diff --git a/MSUtils/sampling/UniaxialStressStrainSampling.py b/MSUtils/sampling/UniaxialStressStrainSampling.py index 9e8d823..b05bc3a 100644 --- a/MSUtils/sampling/UniaxialStressStrainSampling.py +++ b/MSUtils/sampling/UniaxialStressStrainSampling.py @@ -1,4 +1,5 @@ import numpy as np + import MSUtils.sampling.generate_loadpaths as generate_loadpaths @@ -52,7 +53,6 @@ def UniaxialStressStrainSampling( if __name__ == "__main__": - num_load_paths = 128 # number of load paths to generate num_time_steps = 10 # number of time steps per load path dim = 6 diff --git a/MSUtils/sampling/generate_loadpaths.py b/MSUtils/sampling/generate_loadpaths.py index 2409e34..b883891 100644 --- a/MSUtils/sampling/generate_loadpaths.py +++ b/MSUtils/sampling/generate_loadpaths.py @@ -1,5 +1,6 @@ -import numpy as np from pathlib import Path + +import numpy as np from pyrecest.sampling.hyperspherical_sampler import LeopardiSampler diff --git a/MSUtils/spinodoids/generate_spinodal_microstructure.py b/MSUtils/spinodoids/generate_spinodal_microstructure.py index 1030c15..169b65f 100644 --- a/MSUtils/spinodoids/generate_spinodal_microstructure.py +++ b/MSUtils/spinodoids/generate_spinodal_microstructure.py @@ -1,10 +1,12 @@ from time import time +from typing import List, Optional, Tuple, Union + import numpy as np -from typing import Tuple, List, Union, Optional -from scipy.fft import fftn, ifftn, fftfreq +from scipy.fft import fftfreq, fftn, ifftn from scipy.spatial.transform import Rotation -from MSUtils.general.MicrostructureImage import MicrostructureImage + from MSUtils.general.h52xdmf import write_xdmf +from MSUtils.general.MicrostructureImage import MicrostructureImage def generate_spinodal_microstructure( @@ -95,7 +97,6 @@ def generate_spinodal_microstructure( if __name__ == "__main__": - N = [256, 256, 256] L = [1.0, 1.0, 1.0] wavenumber = [20.0, 20.0, 20.0] diff --git a/MSUtils/voronoi/VoronoiGBErosion.py b/MSUtils/voronoi/VoronoiGBErosion.py index 4ca4d88..8a9bed4 100644 --- a/MSUtils/voronoi/VoronoiGBErosion.py +++ b/MSUtils/voronoi/VoronoiGBErosion.py @@ -1,3 +1,4 @@ +import json from collections import defaultdict from pathlib import Path from typing import Self @@ -8,7 +9,6 @@ from scipy.spatial import Delaunay from MSUtils.voronoi import VoronoiImage, VoronoiTessellation -import json class PeriodicVoronoiImageErosion: @@ -36,7 +36,7 @@ def __init__( ) # Extrusion factor in both directions of the plane self.L = np.array(voroImg.L) self.eroded_image = None - self.N = np.array(voroImg.resolution) + self.N = np.array(voroImg.shape) self.voroTess = voroTess self.num_crystals = len(np.unique(self.image)) diff --git a/MSUtils/voronoi/VoronoiImage.py b/MSUtils/voronoi/VoronoiImage.py index eb4d337..99e97c1 100644 --- a/MSUtils/voronoi/VoronoiImage.py +++ b/MSUtils/voronoi/VoronoiImage.py @@ -43,7 +43,6 @@ def __init__( h5_filename=h5_filename, dset_name=dset_name, image=image, - resolution=N, L=L, ) self.compute_volume_fractions() diff --git a/README.md b/README.md index 6306686..f6f985a 100644 --- a/README.md +++ b/README.md @@ -57,12 +57,13 @@ Utilities for file I/O, conversions, and practical helpers. - [MSUtils/general/](MSUtils/general/) - [MicrostructureImage.py](MSUtils/general/MicrostructureImage.py) - Core class for microstructure data: read/write HDF5 datasets with permute-order handling, metadata, and volume fraction calculation. - - [ComBoMicrostructureImage.py](MSUtils/general/ComBoMicrostructureImage.py) - Morphologically sound coarse-graining via composite boxels (ComBo) as described in our [paper](https://doi.org/10.1007/s00466-022-02232-4). - [resize_image.py](MSUtils/general/resize_image.py) - Resize and smooth 3D labelled voxelized microstructure images to any target image resolution. - - [vtk2h5.py](MSUtils/general/vtk2h5.py) - Convert VTI/VTU cell-centered meshes into HDF5 datasets inferred on a regular cell-center grid. - [h52xdmf.py](MSUtils/general/h52xdmf.py) - Convert HDF5 datasets into XDMF XML for ParaView. Supports scalar/vector/tensor attributes and time-series handling. - [merge_h5_files.py](MSUtils/general/merge_h5_files.py) - Merge multiple HDF5 files into one by recursively copying groups/datasets. +- [MSUtils/ComBo/](MSUtils/ComBo/) + - [ComBoMicrostructureImage.py](MSUtils/ComBo/ComBoMicrostructureImage.py) - Morphologically sound coarse-graining via composite boxels (ComBo) as described in our [paper](https://doi.org/10.1007/s00466-022-02232-4). + - [MSUtils/sampling/](MSUtils/sampling/) - [generate_loadpaths.py](MSUtils/sampling/generate_loadpaths.py) - Samples quasi-uniform equal-area directions on the unit hypersphere using the [`LeopardiSampler`](https://github.com/FlorianPfaff/pyRecEst/blob/main/pyrecest/sampling/leopardi_sampler.py) (See [paper](https://ftp.gwdg.de/pub/EMIS/journals/ETNA/vol.25.2006/pp309-327.dir/pp309-327.pdf) for further details). Using the sampled directions, produce linear strain ramps to user limits on deviatoric and volumetric strain magnitude and exporting the load paths to JSON that can be used in the input file for [FANS](https://github.com/DataAnalyticsEngineering/FANS). diff --git a/data/sphere.h5 b/data/sphere.h5 index 3d505b1..53174a5 100644 Binary files a/data/sphere.h5 and b/data/sphere.h5 differ diff --git a/pixi.lock b/pixi.lock index 9fe362c..b439f4b 100644 --- a/pixi.lock +++ b/pixi.lock @@ -26,21 +26,29 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.16.1-hb03c661_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.14.1-pl5321h039972f_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.14.1-pl5321h57e6904_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/astropy-base-8.0.1-py314hc02f841_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.4-h4610da3_2.conda + - 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