From 2cb302d4893696bef2a2789e1a1d58573aee3b9a Mon Sep 17 00:00:00 2001 From: cophus Date: Tue, 2 Dec 2025 13:11:35 -0800 Subject: [PATCH 01/43] adding support for 3d dm4 files --> 4dstem --- src/quantem/core/io/file_readers.py | 222 +++++++++++++++++++++++++--- 1 file changed, 200 insertions(+), 22 deletions(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index cb36f1de..c07d9fbb 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -3,6 +3,7 @@ from pathlib import Path import h5py +import numpy as np from quantem.core.datastructures import Dataset as Dataset from quantem.core.datastructures import Dataset2d as Dataset2d @@ -14,57 +15,234 @@ def read_4dstem( file_path: str | PathLike, file_type: str | None = None, dataset_index: int | None = None, + scan_length: int | None = None, + scan_axis: int = 0, + transpose_scan_axes: bool = False, **kwargs, ) -> Dataset4dstem: """ - File reader for 4D-STEM data + File reader for 4D-STEM data. Parameters ---------- - file_path: str | PathLike - Path to data - file_type: str - The type of file reader needed. See rosettasciio for supported formats + file_path : str | PathLike + Path to data. + file_type : str, optional + The type of file reader needed. See RosettaSciIO for supported formats: https://hyperspy.org/rosettasciio/supported_formats/index.html - dataset_index: int, optional + dataset_index : int, optional Index of the dataset to load if file contains multiple datasets. If None, automatically selects the first 4D dataset found. - **kwargs: dict + If no 4D dataset is found but a 3D stack exists, a 3D dataset can be + interpreted as 4D if `scan_length` is provided. + scan_length : int, optional + For 3D datasets shaped (n_frames, ny, nx) (after possibly moving the + scan axis to the front), interpret the data as a raster scan with shape + (scan_y, scan_x, ny, nx), where scan_y = n_frames // scan_length and + scan_x = scan_length. Required if you want to treat a 3D stack as 4D. + scan_axis : int, default 0 + Which axis of a 3D dataset is the scan/time axis before reshaping. + Must be 0 or 1. The specified axis is moved to axis 0 before the + (scan_y, scan_x) reshape. + transpose_scan_axes : bool, default False + Only used when interpreting a 3D dataset as 4D via `scan_length`. + If True, transpose the scan axes after reshaping so that + (scan_y, scan_x) -> (scan_x, scan_y). This effectively swaps the + interpretation of scan rows and columns in the final 4D array. + + **kwargs : dict Additional keyword arguments to pass to the Dataset4dstem constructor. Returns - -------- + ------- Dataset4dstem """ + + def _reshape_3d_to_4d( + imported_data: dict, + *, + dataset_index_local: int | None, + scan_length_local: int, + scan_axis_local: int, + transpose_scan_axes_local: bool, + ) -> dict: + data = imported_data["data"] + if data.ndim != 3: + raise ValueError( + f"Expected 3D data to reshape, got ndim={data.ndim} " + f"with shape {data.shape}" + ) + + if scan_axis_local not in (0, 1): + raise ValueError(f"scan_axis must be 0 or 1, got {scan_axis_local}") + + # Move scan axis to front so it becomes the frame axis + if scan_axis_local != 0: + data = np.moveaxis(data, scan_axis_local, 0) + + n_frames, ny, nx = data.shape + + if scan_length_local <= 0: + raise ValueError(f"scan_length must be positive, got {scan_length_local}") + if n_frames % scan_length_local != 0: + raise ValueError( + f"scan_length={scan_length_local} is not compatible with n_frames={n_frames}; " + f"n_frames % scan_length = {n_frames % scan_length_local}" + ) + + scan_y = n_frames // scan_length_local + scan_x = scan_length_local + + data_4d = data.reshape(scan_y, scan_x, ny, nx) + + if transpose_scan_axes_local: + data_4d = np.transpose(data_4d, (1, 0, 2, 3)) + scan_y, scan_x = scan_x, scan_y + + old_axes = imported_data.get("axes", None) + if old_axes is None or len(old_axes) != 3: + raise ValueError( + "Expected 3 axes for 3D data when reshaping to 4D; " + f"got axes={old_axes}" + ) + + ax_scan_y = { + "scale": 1.0, + "offset": 0.0, + "units": "pixels", + "name": "scan_y", + } + ax_scan_x = { + "scale": 1.0, + "offset": 0.0, + "units": "pixels", + "name": "scan_x", + } + + ax_qy = dict(old_axes[1]) + ax_qx = dict(old_axes[2]) + + imported_data_4d = imported_data.copy() + imported_data_4d["data"] = data_4d + imported_data_4d["axes"] = [ax_scan_y, ax_scan_x, ax_qy, ax_qx] + + original_shape = imported_data["data"].shape + new_shape = data_4d.shape + if dataset_index_local is not None: + print( + f"Using 3D dataset {dataset_index_local} with shape {original_shape} " + f"interpreted as 4D with shape={new_shape} " + f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " + f"transpose_scan_axes={transpose_scan_axes_local})." + ) + else: + print( + f"Using 3D dataset with shape {original_shape} " + f"interpreted as 4D with shape={new_shape} " + f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " + f"transpose_scan_axes={transpose_scan_axes_local})." + ) + + return imported_data_4d + if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") file_reader = importlib.import_module(f"rsciio.{file_type}").file_reader data_list = file_reader(file_path) - # If specific index provided, use it + if not data_list: + raise ValueError(f"No datasets returned by rsciio.{file_type} for '{file_path}'") + + # Case 1: dataset_index specified explicitly if dataset_index is not None: imported_data = data_list[dataset_index] - if imported_data["data"].ndim != 4: + ndim = imported_data["data"].ndim + + if ndim == 4: + # Use 4D as-is + pass + elif ndim == 3: + if scan_length is None: + raise ValueError( + f"Dataset at index {dataset_index} is 3D (shape={imported_data['data'].shape}). " + "To interpret it as 4D-STEM, please provide scan_length." + ) + imported_data = _reshape_3d_to_4d( + imported_data, + dataset_index_local=dataset_index, + scan_length_local=scan_length, + scan_axis_local=scan_axis, + transpose_scan_axes_local=transpose_scan_axes, + ) + else: raise ValueError( - f"Dataset at index {dataset_index} has {imported_data['data'].ndim} dimensions, " - f"expected 4D. Shape: {imported_data['data'].shape}" + f"Dataset at index {dataset_index} has ndim={ndim}, " + f"expected 4D or 3D. Shape: {imported_data['data'].shape}" ) + else: - # Automatically find first 4D dataset + # Case 2: auto-select dataset four_d_datasets = [(i, d) for i, d in enumerate(data_list) if d["data"].ndim == 4] - if len(four_d_datasets) == 0: - print(f"No 4D datasets found in {file_path}. Available datasets:") - for i, d in enumerate(data_list): - print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") - raise ValueError("No 4D dataset found in file") + if four_d_datasets: + dataset_index, imported_data = four_d_datasets[0] + if len(data_list) > 1: + print( + f"File contains {len(data_list)} dataset(s). Using 4D dataset " + f"{dataset_index} with shape {imported_data['data'].shape}" + ) + else: + three_d_datasets = [(i, d) for i, d in enumerate(data_list) if d["data"].ndim == 3] - dataset_index, imported_data = four_d_datasets[0] + if not three_d_datasets: + print(f"No 4D datasets found in {file_path}. Available datasets:") + for i, d in enumerate(data_list): + print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") + raise ValueError("No 4D or 3D dataset found in file") - if len(data_list) > 1: - print( - f"File contains {len(data_list)} dataset(s). Using dataset {dataset_index} with shape {imported_data['data'].shape}" + if scan_length is None: + print(f"No 4D datasets found in {file_path}. Available datasets:") + for i, d in enumerate(data_list): + print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") + raise ValueError( + "File contains only 3D datasets. To interpret one as 4D-STEM, " + "please specify scan_length so that n_frames % scan_length == 0." + ) + + # Choose first 3D dataset compatible with scan_length along scan_axis + candidates: list[tuple[int, dict]] = [] + for i, d in three_d_datasets: + shape = d["data"].shape + if scan_axis < 0 or scan_axis > 2: + raise ValueError(f"scan_axis must be in [0, 2] for 3D data, got {scan_axis}") + n_frames_axis = shape[scan_axis] + if n_frames_axis % scan_length == 0: + candidates.append((i, d)) + + if not candidates: + print(f"3D datasets in {file_path}:") + for i, d in three_d_datasets: + print(f" Dataset {i}: shape {d['data'].shape}") + raise ValueError( + f"No 3D dataset has length along scan_axis={scan_axis} " + f"divisible by scan_length={scan_length}." + ) + + dataset_index, imported_data = candidates[0] + if len(candidates) > 1: + print( + f"Multiple 3D datasets compatible with scan_length={scan_length} " + f"along scan_axis={scan_axis}. Using dataset {dataset_index} " + f"with shape {imported_data['data'].shape}" + ) + + imported_data = _reshape_3d_to_4d( + imported_data, + dataset_index_local=dataset_index, + scan_length_local=scan_length, + scan_axis_local=scan_axis, + transpose_scan_axes_local=transpose_scan_axes, ) imported_axes = imported_data["axes"] From d88ffb5be0cae76f01b9c85f6f2a8c2751519ec2 Mon Sep 17 00:00:00 2001 From: cophus Date: Wed, 3 Dec 2025 09:11:15 -0800 Subject: [PATCH 02/43] initial construction of polar4dstem class --- src/quantem/core/datastructures/__init__.py | 1 + .../core/datastructures/dataset4dstem.py | 5 + .../core/datastructures/polar4dstem.py | 330 ++++++++++++++++++ 3 files changed, 336 insertions(+) create mode 100644 src/quantem/core/datastructures/polar4dstem.py diff --git a/src/quantem/core/datastructures/__init__.py b/src/quantem/core/datastructures/__init__.py index dfb5b47a..ac8f3d64 100644 --- a/src/quantem/core/datastructures/__init__.py +++ b/src/quantem/core/datastructures/__init__.py @@ -2,6 +2,7 @@ from quantem.core.datastructures.vector import Vector as Vector from quantem.core.datastructures.dataset4dstem import Dataset4dstem as Dataset4dstem +from quantem.core.datastructures.polar4dstem import Polar4dstem as Polar4dstem from quantem.core.datastructures.dataset4d import Dataset4d as Dataset4d from quantem.core.datastructures.dataset3d import Dataset3d as Dataset3d from quantem.core.datastructures.dataset2d import Dataset2d as Dataset2d diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 28328636..2d4c860e 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -7,6 +7,8 @@ from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset4d import Dataset4d +from quantem.core.datastructures.polar4dstem import dataset4dstem_polar_transform + from quantem.core.utils.validators import ensure_valid_array from quantem.core.visualization import show_2d from quantem.core.visualization.visualization_utils import ScalebarConfig @@ -751,3 +753,6 @@ def median_filter_masked_pixels(self, mask: np.ndarray, kernel_width: int = 3): self.array[:, :, index_x, index_y] = np.median( self.array[:, :, x_min:x_max, y_min:y_max], axis=(2, 3) ) + + + polar_transform = dataset4dstem_polar_transform \ No newline at end of file diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py new file mode 100644 index 00000000..848469ec --- /dev/null +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -0,0 +1,330 @@ +import numpy as np +from numpy.typing import NDArray +from typing import Any, TYPE_CHECKING + +from quantem.core.datastructures.dataset4d import Dataset4d +# from quantem.core.datastructures.dataset4dstem import Dataset4dstem + +if TYPE_CHECKING: + from .dataset4dstem import Dataset4dstem + +class Polar4dstem(Dataset4d): + """4D-STEM dataset in polar coordinates (scan_y, scan_x, phi, r).""" + + def __init__( + self, + array: NDArray | Any, + name: str, + origin: NDArray | tuple | list | float | int, + sampling: NDArray | tuple | list | float | int, + units: list[str] | tuple | list, + signal_units: str = "arb. units", + metadata: dict | None = None, + _token: object | None = None, + ): + if metadata is None: + metadata = {} + mdata_keys_polar = [ + "polar_radial_min", + "polar_radial_max", + "polar_radial_step", + "polar_num_annular_bins", + "polar_two_fold_rotation_symmetry", + "polar_origin_row", + "polar_origin_col", + "polar_ellipse_params", + ] + for k in mdata_keys_polar: + if k not in metadata: + metadata[k] = None + + super().__init__( + array=array, + name=name, + origin=origin, + sampling=sampling, + units=units, + signal_units=signal_units, + metadata=metadata, + _token=_token, + ) + + @classmethod + def from_array( + cls, + array: NDArray | Any, + name: str | None = None, + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, + signal_units: str = "arb. units", + metadata: dict | None = None, + ) -> "Polar4dstem": + array = ensure_valid_array(array, ndim=4) + if origin is None: + origin = np.zeros(4) + if sampling is None: + sampling = np.ones(4) + if units is None: + units = ["pixels", "pixels", "deg", "pixels"] + if metadata is None: + metadata = {} + return cls( + array=array, + name=name if name is not None else "Polar 4D-STEM dataset", + origin=origin, + sampling=sampling, + units=units, + signal_units=signal_units, + metadata=metadata, + _token=cls._token, + ) + + @property + def n_phi(self) -> int: + return int(self.array.shape[2]) + + @property + def n_r(self) -> int: + return int(self.array.shape[3]) + + +def dataset4dstem_polar_transform( + self: "Dataset4dstem", + origin_row: float | NDArray, + origin_col: float | NDArray, + ellipse_params: tuple[float, float, float] | None = None, + num_annular_bins: int = 180, + radial_min: float = 0.0, + radial_max: float | None = None, + radial_step: float = 1.0, + two_fold_rotation_symmetry: bool = False, + name: str | None = None, + signal_units: str | None = None, +) -> Polar4dstem: + """Return a Polar4dstem with shape (scan_y, scan_x, phi, r).""" + if self.array.ndim != 4: + raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") + + scan_y, scan_x, ny, nx = self.array.shape + + mapping = _precompute_polar_mapping( + ny=ny, + nx=nx, + origin_row=float(origin_row), + origin_col=float(origin_col), + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + + result_dtype = np.result_type(self.array.dtype, np.float32) + out = np.empty( + (scan_y, scan_x, mapping["n_phi"], mapping["n_r"]), + dtype=result_dtype, + ) + + for iy in range(scan_y): + for ix in range(scan_x): + out[iy, ix] = _apply_polar_mapping_single( + self.array[iy, ix], + mapping, + dtype=result_dtype, + ) + + phi_step_deg = mapping["phi_step"] * 180.0 / np.pi + phi_units = "deg" + radial_units = self.units[-1] + + sampling = np.array( + [ + self.sampling[0], + self.sampling[1], + phi_step_deg, + self.sampling[-1] * mapping["radial_step"], + ], + dtype=float, + ) + origin = np.array( + [ + self.origin[0], + self.origin[1], + 0.0, + self.sampling[-1] * mapping["radial_min"], + ], + dtype=float, + ) + units = [ + self.units[0], + self.units[1], + phi_units, + radial_units, + ] + + metadata = dict(self.metadata) + metadata.update( + { + "polar_radial_min": mapping["radial_min"], + "polar_radial_max": mapping["radial_max"], + "polar_radial_step": mapping["radial_step"], + "polar_num_annular_bins": mapping["n_phi"], + "polar_two_fold_rotation_symmetry": two_fold_rotation_symmetry, + "polar_origin_row": float(origin_row), + "polar_origin_col": float(origin_col), + "polar_ellipse_params": tuple(ellipse_params) + if ellipse_params is not None + else None, + } + ) + + return Polar4dstem( + array=out, + name=name if name is not None else f"{self.name}_polar", + origin=origin, + sampling=sampling, + units=units, + signal_units=signal_units if signal_units is not None else self.signal_units, + metadata=metadata, + _token=Polar4dstem._token, + ) + + +def _precompute_polar_mapping( + ny: int, + nx: int, + origin_row: float, + origin_col: float, + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max: float | None, + radial_step: float, + two_fold_rotation_symmetry: bool, +) -> dict[str, Any]: + origin_row = float(origin_row) + origin_col = float(origin_col) + annular_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi + + rows = np.arange(ny, dtype=np.float64) + cols = np.arange(nx, dtype=np.float64) + cc, rr = np.meshgrid(cols, rows, indexing="xy") + x = cc - origin_col + y = rr - origin_row + + if ellipse_params is None: + rr_pix = np.sqrt(x * x + y * y) + tt = np.mod(np.arctan2(y, x), annular_range) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be a length-3 tuple (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = np.deg2rad(theta_deg) + cos_t = np.cos(theta) + sin_t = np.sin(theta) + xc = x * cos_t + y * sin_t + yc = (y * cos_t - x * sin_t) * (a / b) + rr_pix = (b / a) * np.hypot(xc, yc) + tt = np.mod(np.arctan2(yc, xc) + theta, annular_range) + + if radial_step <= 0: + raise ValueError("radial_step must be > 0.") + radial_min = float(radial_min) + + if radial_max is None: + radial_max_eff = float(rr_pix.max()) + else: + radial_max_eff = float(radial_max) + if radial_max_eff <= radial_min + radial_step: + radial_max_eff = radial_min + radial_step + + radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) + n_r = radial_bins.size + if n_r < 1: + raise ValueError("No radial bins defined. Check radial_min, radial_max, and radial_step.") + + n_phi = int(num_annular_bins) + if n_phi < 1: + raise ValueError("num_annular_bins must be >= 1.") + phi_step = annular_range / n_phi + + r_bin = (rr_pix - radial_min) / radial_step + t_bin = tt / phi_step + + r0 = np.floor(r_bin).astype(np.int64) + t0 = np.floor(t_bin).astype(np.int64) + dr = (r_bin - r0).astype(np.float64) + dt = (t_bin - t0).astype(np.float64) + + valid = (r0 >= 0) & (r0 < n_r - 1) + t0 = np.clip(t0, 0, n_phi - 1) + + flat_valid = valid.ravel() + r0v = r0.ravel()[flat_valid] + t0v = t0.ravel()[flat_valid] + drv = dr.ravel()[flat_valid] + dtv = dt.ravel()[flat_valid] + + n_bins = n_phi * n_r + idx00 = r0v + n_r * t0v + idx01 = r0v + n_r * ((t0v + 1) % n_phi) + idx10 = (r0v + 1) + n_r * t0v + idx11 = (r0v + 1) + n_r * ((t0v + 1) % n_phi) + + w00 = (1.0 - drv) * (1.0 - dtv) + w01 = (1.0 - drv) * dtv + w10 = drv * (1.0 - dtv) + w11 = drv * dtv + + weights_sum = np.bincount(idx00, weights=w00, minlength=n_bins) + weights_sum += np.bincount(idx01, weights=w01, minlength=n_bins) + weights_sum += np.bincount(idx10, weights=w10, minlength=n_bins) + weights_sum += np.bincount(idx11, weights=w11, minlength=n_bins) + weights_sum = weights_sum.reshape(n_phi, n_r) + + weights_inv = np.zeros_like(weights_sum, dtype=np.float64) + mask_bins = weights_sum > 0 + weights_inv[mask_bins] = 1.0 / weights_sum[mask_bins] + + return { + "flat_valid": flat_valid, + "idx00": idx00, + "idx01": idx01, + "idx10": idx10, + "idx11": idx11, + "w00": w00, + "w01": w01, + "w10": w10, + "w11": w11, + "weights_inv": weights_inv, + "n_phi": n_phi, + "n_r": n_r, + "radial_bins": radial_bins, + "phi_step": phi_step, + "annular_range": annular_range, + "radial_min": radial_min, + "radial_max": radial_min + radial_step * n_r, + "radial_step": radial_step, + } + + +def _apply_polar_mapping_single( + image: NDArray, + mapping: dict[str, Any], + dtype: Any, +) -> NDArray: + data = np.asarray(image, dtype=np.float64) + flat = data.ravel()[mapping["flat_valid"]] + n_bins = mapping["n_phi"] * mapping["n_r"] + + acc = np.bincount(mapping["idx00"], weights=flat * mapping["w00"], minlength=n_bins) + acc += np.bincount(mapping["idx01"], weights=flat * mapping["w01"], minlength=n_bins) + acc += np.bincount(mapping["idx10"], weights=flat * mapping["w10"], minlength=n_bins) + acc += np.bincount(mapping["idx11"], weights=flat * mapping["w11"], minlength=n_bins) + + acc = acc.reshape(mapping["n_phi"], mapping["n_r"]) + acc *= mapping["weights_inv"] + return acc.astype(dtype, copy=False) + From 13b8d3d1232840adcfc3f93a059bd3df48a4a663 Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 4 Dec 2025 10:53:10 -0800 Subject: [PATCH 03/43] Changing sampling direction for polar4dstem --- .../core/datastructures/polar4dstem.py | 307 ++++++------------ src/quantem/diffraction/polar.py | 0 2 files changed, 107 insertions(+), 200 deletions(-) create mode 100644 src/quantem/diffraction/polar.py diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index 848469ec..6619af5c 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -1,13 +1,14 @@ import numpy as np from numpy.typing import NDArray from typing import Any, TYPE_CHECKING - -from quantem.core.datastructures.dataset4d import Dataset4d -# from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from scipy.ndimage import map_coordinates if TYPE_CHECKING: from .dataset4dstem import Dataset4dstem +from quantem.core.datastructures.dataset4d import Dataset4d + + class Polar4dstem(Dataset4d): """4D-STEM dataset in polar coordinates (scan_y, scan_x, phi, r).""" @@ -37,7 +38,6 @@ def __init__( for k in mdata_keys_polar: if k not in metadata: metadata[k] = None - super().__init__( array=array, name=name, @@ -60,11 +60,13 @@ def from_array( signal_units: str = "arb. units", metadata: dict | None = None, ) -> "Polar4dstem": - array = ensure_valid_array(array, ndim=4) + array = np.asarray(array) + if array.ndim != 4: + raise ValueError("Polar4dstem.from_array expects a 4D array.") if origin is None: - origin = np.zeros(4) + origin = np.zeros(4, dtype=float) if sampling is None: - sampling = np.ones(4) + sampling = np.ones(4, dtype=float) if units is None: units = ["pixels", "pixels", "deg", "pixels"] if metadata is None: @@ -89,10 +91,68 @@ def n_r(self) -> int: return int(self.array.shape[3]) +def _precompute_polar_coords( + ny: int, + nx: int, + origin_row: float, + origin_col: float, + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max: float | None, + radial_step: float, + two_fold_rotation_symmetry: bool, +) -> tuple[NDArray, NDArray, NDArray, float]: + origin_row = float(origin_row) + origin_col = float(origin_col) + if radial_step <= 0: + raise ValueError("radial_step must be > 0.") + if num_annular_bins < 1: + raise ValueError("num_annular_bins must be >= 1.") + if radial_max is None: + r_row_pos = origin_row + r_row_neg = (ny - 1) - origin_row + r_col_pos = origin_col + r_col_neg = (nx - 1) - origin_col + radial_max_eff = float(min(r_row_pos, r_row_neg, r_col_pos, r_col_neg)) + else: + radial_max_eff = float(radial_max) + if radial_max_eff <= radial_min: + radial_max_eff = radial_min + radial_step + radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) + if radial_bins.size == 0: + radial_bins = np.array([radial_min], dtype=np.float64) + if two_fold_rotation_symmetry: + phi_range = np.pi + else: + phi_range = 2.0 * np.pi + phi_bins = np.linspace(0.0, phi_range, num_annular_bins, endpoint=False, dtype=np.float64) + phi_grid, r_grid = np.meshgrid(phi_bins, radial_bins, indexing="ij") + if ellipse_params is None: + x = r_grid * np.cos(phi_grid) + y = r_grid * np.sin(phi_grid) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = np.deg2rad(theta_deg) + alpha = phi_grid - theta + u = (a / b) * r_grid * np.cos(alpha) + v_prime = r_grid * np.sin(alpha) + cos_t = np.cos(theta) + sin_t = np.sin(theta) + x = u * cos_t - v_prime * sin_t + y = u * sin_t + v_prime * cos_t + coords_y = y + origin_row + coords_x = x + origin_col + coords = np.stack((coords_y, coords_x), axis=0) + return coords, phi_bins, radial_bins, radial_max_eff + + def dataset4dstem_polar_transform( self: "Dataset4dstem", - origin_row: float | NDArray, - origin_col: float | NDArray, + origin_row: float | int | NDArray, + origin_col: float | int | NDArray, ellipse_params: tuple[float, float, float] | None = None, num_annular_bins: int = 180, radial_min: float = 0.0, @@ -102,17 +162,16 @@ def dataset4dstem_polar_transform( name: str | None = None, signal_units: str | None = None, ) -> Polar4dstem: - """Return a Polar4dstem with shape (scan_y, scan_x, phi, r).""" if self.array.ndim != 4: raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") - scan_y, scan_x, ny, nx = self.array.shape - - mapping = _precompute_polar_mapping( + origin_row_f = float(origin_row) + origin_col_f = float(origin_col) + coords, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( ny=ny, nx=nx, - origin_row=float(origin_row), - origin_col=float(origin_col), + origin_row=origin_row_f, + origin_col=origin_col_f, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -120,66 +179,52 @@ def dataset4dstem_polar_transform( radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, ) - + n_phi = phi_bins.size + n_r = radial_bins.size result_dtype = np.result_type(self.array.dtype, np.float32) - out = np.empty( - (scan_y, scan_x, mapping["n_phi"], mapping["n_r"]), - dtype=result_dtype, - ) - + out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=result_dtype) for iy in range(scan_y): for ix in range(scan_x): - out[iy, ix] = _apply_polar_mapping_single( - self.array[iy, ix], - mapping, - dtype=result_dtype, + dp = self.array[iy, ix] + out[iy, ix] = map_coordinates( + dp, + coords, + order=1, + mode="constant", + cval=0.0, ) - - phi_step_deg = mapping["phi_step"] * 180.0 / np.pi - phi_units = "deg" - radial_units = self.units[-1] - - sampling = np.array( - [ - self.sampling[0], - self.sampling[1], - phi_step_deg, - self.sampling[-1] * mapping["radial_step"], - ], - dtype=float, - ) - origin = np.array( - [ - self.origin[0], - self.origin[1], - 0.0, - self.sampling[-1] * mapping["radial_min"], - ], - dtype=float, - ) + if two_fold_rotation_symmetry: + phi_range = np.pi + else: + phi_range = 2.0 * np.pi + phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) + sampling = np.zeros(4, dtype=float) + origin = np.zeros(4, dtype=float) + sampling[0:2] = np.asarray(self.sampling)[0:2] + sampling[2] = phi_step_deg + sampling[3] = float(np.asarray(self.sampling)[-1]) * radial_step + origin[0:2] = np.asarray(self.origin)[0:2] + origin[2] = 0.0 + origin[3] = radial_min * float(np.asarray(self.sampling)[-1]) units = [ self.units[0], self.units[1], - phi_units, - radial_units, + "deg", + self.units[-1], ] - metadata = dict(self.metadata) metadata.update( { - "polar_radial_min": mapping["radial_min"], - "polar_radial_max": mapping["radial_max"], - "polar_radial_step": mapping["radial_step"], - "polar_num_annular_bins": mapping["n_phi"], - "polar_two_fold_rotation_symmetry": two_fold_rotation_symmetry, - "polar_origin_row": float(origin_row), - "polar_origin_col": float(origin_col), - "polar_ellipse_params": tuple(ellipse_params) - if ellipse_params is not None - else None, + "polar_radial_min": float(radial_min), + "polar_radial_max": float(radial_max_eff), + "polar_radial_step": float(radial_step), + "polar_num_annular_bins": int(n_phi), + "polar_two_fold_rotation_symmetry": bool(two_fold_rotation_symmetry), + "polar_origin_row": origin_row_f, + "polar_origin_col": origin_col_f, + "polar_ellipse_params": tuple(ellipse_params) if ellipse_params is not None else None, } ) - return Polar4dstem( array=out, name=name if name is not None else f"{self.name}_polar", @@ -190,141 +235,3 @@ def dataset4dstem_polar_transform( metadata=metadata, _token=Polar4dstem._token, ) - - -def _precompute_polar_mapping( - ny: int, - nx: int, - origin_row: float, - origin_col: float, - ellipse_params: tuple[float, float, float] | None, - num_annular_bins: int, - radial_min: float, - radial_max: float | None, - radial_step: float, - two_fold_rotation_symmetry: bool, -) -> dict[str, Any]: - origin_row = float(origin_row) - origin_col = float(origin_col) - annular_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi - - rows = np.arange(ny, dtype=np.float64) - cols = np.arange(nx, dtype=np.float64) - cc, rr = np.meshgrid(cols, rows, indexing="xy") - x = cc - origin_col - y = rr - origin_row - - if ellipse_params is None: - rr_pix = np.sqrt(x * x + y * y) - tt = np.mod(np.arctan2(y, x), annular_range) - else: - if len(ellipse_params) != 3: - raise ValueError("ellipse_params must be a length-3 tuple (a, b, theta_deg).") - a, b, theta_deg = ellipse_params - theta = np.deg2rad(theta_deg) - cos_t = np.cos(theta) - sin_t = np.sin(theta) - xc = x * cos_t + y * sin_t - yc = (y * cos_t - x * sin_t) * (a / b) - rr_pix = (b / a) * np.hypot(xc, yc) - tt = np.mod(np.arctan2(yc, xc) + theta, annular_range) - - if radial_step <= 0: - raise ValueError("radial_step must be > 0.") - radial_min = float(radial_min) - - if radial_max is None: - radial_max_eff = float(rr_pix.max()) - else: - radial_max_eff = float(radial_max) - if radial_max_eff <= radial_min + radial_step: - radial_max_eff = radial_min + radial_step - - radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) - n_r = radial_bins.size - if n_r < 1: - raise ValueError("No radial bins defined. Check radial_min, radial_max, and radial_step.") - - n_phi = int(num_annular_bins) - if n_phi < 1: - raise ValueError("num_annular_bins must be >= 1.") - phi_step = annular_range / n_phi - - r_bin = (rr_pix - radial_min) / radial_step - t_bin = tt / phi_step - - r0 = np.floor(r_bin).astype(np.int64) - t0 = np.floor(t_bin).astype(np.int64) - dr = (r_bin - r0).astype(np.float64) - dt = (t_bin - t0).astype(np.float64) - - valid = (r0 >= 0) & (r0 < n_r - 1) - t0 = np.clip(t0, 0, n_phi - 1) - - flat_valid = valid.ravel() - r0v = r0.ravel()[flat_valid] - t0v = t0.ravel()[flat_valid] - drv = dr.ravel()[flat_valid] - dtv = dt.ravel()[flat_valid] - - n_bins = n_phi * n_r - idx00 = r0v + n_r * t0v - idx01 = r0v + n_r * ((t0v + 1) % n_phi) - idx10 = (r0v + 1) + n_r * t0v - idx11 = (r0v + 1) + n_r * ((t0v + 1) % n_phi) - - w00 = (1.0 - drv) * (1.0 - dtv) - w01 = (1.0 - drv) * dtv - w10 = drv * (1.0 - dtv) - w11 = drv * dtv - - weights_sum = np.bincount(idx00, weights=w00, minlength=n_bins) - weights_sum += np.bincount(idx01, weights=w01, minlength=n_bins) - weights_sum += np.bincount(idx10, weights=w10, minlength=n_bins) - weights_sum += np.bincount(idx11, weights=w11, minlength=n_bins) - weights_sum = weights_sum.reshape(n_phi, n_r) - - weights_inv = np.zeros_like(weights_sum, dtype=np.float64) - mask_bins = weights_sum > 0 - weights_inv[mask_bins] = 1.0 / weights_sum[mask_bins] - - return { - "flat_valid": flat_valid, - "idx00": idx00, - "idx01": idx01, - "idx10": idx10, - "idx11": idx11, - "w00": w00, - "w01": w01, - "w10": w10, - "w11": w11, - "weights_inv": weights_inv, - "n_phi": n_phi, - "n_r": n_r, - "radial_bins": radial_bins, - "phi_step": phi_step, - "annular_range": annular_range, - "radial_min": radial_min, - "radial_max": radial_min + radial_step * n_r, - "radial_step": radial_step, - } - - -def _apply_polar_mapping_single( - image: NDArray, - mapping: dict[str, Any], - dtype: Any, -) -> NDArray: - data = np.asarray(image, dtype=np.float64) - flat = data.ravel()[mapping["flat_valid"]] - n_bins = mapping["n_phi"] * mapping["n_r"] - - acc = np.bincount(mapping["idx00"], weights=flat * mapping["w00"], minlength=n_bins) - acc += np.bincount(mapping["idx01"], weights=flat * mapping["w01"], minlength=n_bins) - acc += np.bincount(mapping["idx10"], weights=flat * mapping["w10"], minlength=n_bins) - acc += np.bincount(mapping["idx11"], weights=flat * mapping["w11"], minlength=n_bins) - - acc = acc.reshape(mapping["n_phi"], mapping["n_r"]) - acc *= mapping["weights_inv"] - return acc.astype(dtype, copy=False) - diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py new file mode 100644 index 00000000..e69de29b From 9b1ecbf9fb43ac3417e855a6e977577d267f66b8 Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 4 Dec 2025 13:22:56 -0800 Subject: [PATCH 04/43] initial commit for RDF class --- src/quantem/__init__.py | 1 + src/quantem/core/datastructures/dataset.py | 34 +++ src/quantem/diffraction/__init__.py | 1 + src/quantem/diffraction/polar.py | 326 +++++++++++++++++++++ 4 files changed, 362 insertions(+) diff --git a/src/quantem/__init__.py b/src/quantem/__init__.py index db96bb4f..36f3c68b 100644 --- a/src/quantem/__init__.py +++ b/src/quantem/__init__.py @@ -3,4 +3,5 @@ from quantem.core import visualization as visualization from quantem import imaging as imaging +from quantem import diffraction as diffraction from quantem import diffractive_imaging as diffractive_imaging diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index d04c08c5..317b669b 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -147,6 +147,11 @@ def sampling(self) -> NDArray: def sampling(self, value: NDArray | tuple | list | float | int) -> None: self._sampling = validate_ndinfo(value, self.ndim, "sampling") + @property + def origin_units(self) -> NDArray: + # Origin expressed in physical units: origin * sampling + return np.asarray(self.origin) * np.asarray(self.sampling) + @property def units(self) -> list[str]: return self._units @@ -289,6 +294,35 @@ def _copy_custom_attributes(self, new_dataset: Self) -> None: # Skip attributes that can't be copied pass + def coords(self, axis: int) -> Any: + """ + Coordinate array for a given axis in pixel units. + + coords(d) = arange(shape[d]) - origin[d] + """ + axis = int(axis) + if axis < 0 or axis >= self.ndim: + raise ValueError(f"axis {axis} out of bounds for ndim={self.ndim}") + + xp = self._xp + n = int(self.shape[axis]) + origin_d = float(np.asarray(self.origin)[axis]) + + return xp.arange(n, dtype=float) - origin_d + + def coords_units(self, axis: int) -> Any: + """ + Coordinate array for a given axis in physical units. + + coords_units(d) = (arange(shape[d]) - origin[d]) * sampling[d] + """ + axis = int(axis) + if axis < 0 or axis >= self.ndim: + raise ValueError(f"axis {axis} out of bounds for ndim={self.ndim}") + + sampling_d = float(np.asarray(self.sampling)[axis]) + return self.coords(axis) * sampling_d + def mean(self, axes: int | tuple[int, ...] | None = None) -> Any: """ Computes and returns mean of the data array. diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index e69de29b..fdbb2b3d 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -0,0 +1 @@ +from quantem.diffraction.polar import RDF as RDF diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index e69de29b..7e87eff3 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -0,0 +1,326 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any, List, Union + +import matplotlib.pyplot as plt +import numpy as np +from numpy.typing import NDArray + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.datastructures.polar4dstem import Polar4dstem +from quantem.core.io.serialize import AutoSerialize +from quantem.core.utils.validators import ensure_valid_array + + +class RDF(AutoSerialize): + """ + Radial distribution / fluctuation electron microscopy analysis helper. + + This class wraps a 4D-STEM (or 2D diffraction) dataset and stores a + polar-transformed representation as a Polar4dstem instance in `self.polar`. + Analysis methods (radial statistics, PDF, FEM, clustering, etc.) are + provided as stubs for now and will be implemented in future revisions. + """ + + _token = object() + + def __init__( + self, + polar: Polar4dstem, + input_data: Any | None = None, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError( + "Use RadialDistributionFunction.from_data() to instantiate this class." + ) + + super().__init__() + self.polar = polar + self.input_data = input_data + + # Placeholders for analysis results (to be populated by future methods) + self.radial_mean: NDArray | None = None + self.radial_var: NDArray | None = None + self.radial_var_norm: NDArray | None = None + + self.pdf_r: NDArray | None = None + self.pdf_reduced: NDArray | None = None + self.pdf: NDArray | None = None + + self.Sk: NDArray | None = None + self.fk: NDArray | None = None + self.bg: NDArray | None = None + self.offset: float | None = None + self.Sk_mask: NDArray | None = None + + # ------------------------------------------------------------------ + # Constructors + # ------------------------------------------------------------------ + @classmethod + def from_data( + cls, + data: Union[NDArray, Dataset2d, Dataset3d, Dataset4dstem, Polar4dstem], + *, + origin_row: float | None = None, + origin_col: float | None = None, + ellipse_params: tuple[float, float, float] | None = None, + num_annular_bins: int = 180, + radial_min: float = 0.0, + radial_max: float | None = None, + radial_step: float = 1.0, + two_fold_rotation_symmetry: bool = False, + ) -> "RadialDistributionFunction": + """ + Create a RadialDistributionFunction object from various input types. + + Parameters + ---------- + data + Supported inputs: + - 2D numpy array (single diffraction pattern) + - 4D numpy array (scan_y, scan_x, ky, kx) + - Dataset2d + - Dataset4dstem + - Polar4dstem + origin_row, origin_col + Diffraction-space origin (in pixels). If None, defaults to the + central pixel of the diffraction pattern. + Other parameters + Passed through to Dataset4dstem.polar_transform when needed. + """ + # Polar input: use directly + if isinstance(data, Polar4dstem): + polar = data + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset4dstem input: polar-transform it + if isinstance(data, Dataset4dstem): + scan_y, scan_x, ny, nx = data.array.shape + if origin_row is None: + origin_row = (ny - 1) / 2.0 + if origin_col is None: + origin_col = (nx - 1) / 2.0 + + polar = data.polar_transform( + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset2d input: wrap as a trivial 4D-STEM (1x1 scan) then polar-transform + if isinstance(data, Dataset2d): + arr2d = data.array + if arr2d.ndim != 2: + raise ValueError("Dataset2d for RDF must be 2D.") + arr4 = arr2d[None, None, ...] # (1, 1, ky, kx) + + ds4 = Dataset4dstem.from_array( + array=arr4, + name=f"{data.name}_as4dstem" if getattr(data, "name", None) else "rdf_4dstem_from_2d", + origin=np.concatenate( + [np.zeros(2, dtype=float), np.asarray(data.origin, dtype=float)] + ), + sampling=np.concatenate( + [np.ones(2, dtype=float), np.asarray(data.sampling, dtype=float)] + ), + units=["pixels", "pixels"] + list(data.units), + signal_units=data.signal_units, + ) + ny, nx = ds4.array.shape[-2:] + if origin_row is None: + origin_row = (ny - 1) / 2.0 + if origin_col is None: + origin_col = (nx - 1) / 2.0 + + polar = ds4.polar_transform( + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset3d input: not yet specified how to interpret + if isinstance(data, Dataset3d): + raise NotImplementedError( + "RadialDistributionFunction.from_data does not yet support Dataset3d inputs." + ) + + # Numpy array input + arr = ensure_valid_array(data) + if arr.ndim == 2: + ds2 = Dataset2d.from_array(arr, name="rdf_input_2d") + return cls.from_data( + ds2, + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + elif arr.ndim == 4: + ds4 = Dataset4dstem.from_array(arr, name="rdf_input_4dstem") + return cls.from_data( + ds4, + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + else: + raise ValueError( + "RadialDistributionFunction.from_data only supports 2D or 4D arrays." + ) + + # ------------------------------------------------------------------ + # Convenience accessors + # ------------------------------------------------------------------ + @property + def qq(self) -> Any: + """ + Scattering vector coordinate array along the radial dimension of `self.polar`, + in physical units (using Polar4dstem.sampling and origin). + """ + # Polar4dstem dims: (scan_y, scan_x, phi, r) + # radial axis is 3 + return self.polar.coords_units(3) + + @property + def radial_bins(self) -> Any: + """ + Radial bin centers in pixel units (convenience alias). + """ + return self.polar.coords(3) + + # ------------------------------------------------------------------ + # Analysis method stubs (py4DSTEM-style API) + # ------------------------------------------------------------------ + def calculate_radial_statistics( + self, + mask_realspace: NDArray | None = None, + plot_results_mean: bool = False, + plot_results_var: bool = False, + figsize: tuple[float, float] = (8, 4), + returnval: bool = False, + returnfig: bool = False, + progress_bar: bool = True, + ): + """ + Stub for radial statistics (FEM-style) calculation on the polar data. + + Intended to compute radial mean, variance, and normalized variance + from self.polar. Not implemented yet. + """ + raise NotImplementedError("calculate_radial_statistics is not implemented yet.") + + def plot_radial_mean( + self, + log_x: bool = False, + log_y: bool = False, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting radial mean intensity vs scattering vector. + """ + raise NotImplementedError("plot_radial_mean is not implemented yet.") + + def plot_radial_var_norm( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting normalized radial variance vs scattering vector. + """ + raise NotImplementedError("plot_radial_var_norm is not implemented yet.") + + def calculate_pair_dist_function( + self, + k_min: float = 0.05, + k_max: float | None = None, + k_width: float = 0.25, + k_lowpass: float | None = None, + k_highpass: float | None = None, + r_min: float = 0.0, + r_max: float = 20.0, + r_step: float = 0.02, + damp_origin_fluctuations: bool = True, + enforce_positivity: bool = True, + density: float | None = None, + plot_background_fits: bool = False, + plot_sf_estimate: bool = False, + plot_reduced_pdf: bool = True, + plot_pdf: bool = False, + figsize: tuple[float, float] = (8, 4), + maxfev: int | None = None, + returnval: bool = False, + returnfig: bool = False, + ): + """ + Stub for pair distribution function (PDF) calculation from radial statistics. + + Intended to estimate S(k), background, and transform to real-space g(r)/G(r). + """ + raise NotImplementedError("calculate_pair_dist_function is not implemented yet.") + + def plot_background_fits( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting background fit vs radial mean intensity. + """ + raise NotImplementedError("plot_background_fits is not implemented yet.") + + def plot_sf_estimate( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting reduced structure factor S(k). + """ + raise NotImplementedError("plot_sf_estimate is not implemented yet.") + + def plot_reduced_pdf( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting reduced PDF g(r). + """ + raise NotImplementedError("plot_reduced_pdf is not implemented yet.") + + def plot_pdf( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting full PDF G(r). + """ + raise NotImplementedError("plot_pdf is not implemented yet.") From 1d0ec612daf018a45bbfee04ad640c8f0eb8c902 Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 1 Jan 2026 12:42:17 -0800 Subject: [PATCH 05/43] initial commit for StrainMap --- .../core/datastructures/dataset4dstem.py | 2 +- src/quantem/core/utils/imaging_utils.py | 88 +++++++++ src/quantem/diffraction/__init__.py | 1 + src/quantem/diffraction/strain.py | 179 ++++++++++++++++++ 4 files changed, 269 insertions(+), 1 deletion(-) create mode 100644 src/quantem/diffraction/strain.py diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 2d4c860e..eac15ac5 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -74,7 +74,7 @@ def __init__( _token : object | None, optional Token to prevent direct instantiation, by default None """ - mdata_keys_4dstem = ["r_to_q_rotation_cw_deg", "ellipticity"] + mdata_keys_4dstem = ["q_to_r_rotation_cw_deg", 'q_transpose', "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): metadata[k] = None diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index c72fb9c3..3d86000d 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -7,6 +7,7 @@ import torch from numpy.typing import NDArray from scipy.ndimage import gaussian_filter +from scipy.ndimage import map_coordinates from quantem.core.utils.utils import generate_batches @@ -546,3 +547,90 @@ def fourier_cropping( result[-h2:, -w2:] = corner_centered_array[-h2:, -w2:] return result + + +def rotate_image( + im, + rotation_deg: float, + origin: tuple[float, float] | None = None, + clockwise: bool = True, + interpolation: str = "bilinear", + mode: str = "constant", + cval: float = 0.0, +): + """Rotate an array about a pixel origin using bilinear/bicubic interpolation. + + Parameters + ---------- + im + Input array; last two dimensions are treated as (H, W). Any leading + dimensions are treated as batch and rotated independently. + rotation_deg + Rotation angle in degrees. Interpreted as clockwise if clockwise=True, + otherwise counterclockwise. + origin + Rotation origin (row, col) in pixel coordinates. If None, uses (H//2, W//2). + clockwise + If True, interpret rotation_deg as clockwise; if False, as counterclockwise. + interpolation + "bilinear" (order=1) or "bicubic" (order=3). + mode, cval + Boundary handling passed to scipy.ndimage.map_coordinates. + + Returns + ------- + out + Rotated array with the same shape as `im`. + """ + im = np.asarray(im) + if im.ndim < 2: + raise ValueError("im must have at least 2 dimensions") + + H, W = im.shape[-2], im.shape[-1] + if origin is None: + r0 = float(H // 2) + c0 = float(W // 2) + else: + r0 = float(origin[0]) + c0 = float(origin[1]) + + interp = str(interpolation).lower() + if interp in {"bilinear", "linear"}: + order = 1 + elif interp in {"bicubic", "cubic"}: + order = 3 + else: + raise ValueError("interpolation must be 'bilinear' or 'bicubic'") + + theta = float(np.deg2rad(rotation_deg)) + if clockwise: + theta = -theta + + ct = float(np.cos(theta)) + st = float(np.sin(theta)) + + r_out, c_out = np.meshgrid( + np.arange(H, dtype=np.float64), + np.arange(W, dtype=np.float64), + indexing="ij", + ) + + c_rel = c_out - c0 + r_rel = r_out - r0 + + c_in = ct * c_rel + st * r_rel + c0 + r_in = -st * c_rel + ct * r_rel + r0 + + coords = np.vstack((r_in.ravel(), c_in.ravel())) + + if im.ndim == 2: + out = map_coordinates(im, coords, order=order, mode=mode, cval=cval) + return out.reshape(H, W) + + prefix = im.shape[:-2] + n = int(np.prod(prefix)) if prefix else 1 + im_flat = im.reshape(n, H, W) + out_flat = np.empty((n, H * W), dtype=np.result_type(im_flat.dtype, np.float64)) + for i in range(n): + out_flat[i] = map_coordinates(im_flat[i], coords, order=order, mode=mode, cval=cval) + return out_flat.reshape(*prefix, H, W) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index fdbb2b3d..6d0e4202 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1 +1,2 @@ from quantem.diffraction.polar import RDF as RDF +from quantem.diffraction.strain import StrainMap as StrainMap diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py new file mode 100644 index 00000000..55c57d9d --- /dev/null +++ b/src/quantem/diffraction/strain.py @@ -0,0 +1,179 @@ +from __future__ import annotations + +from typing import Any + +import numpy as np +from numpy.typing import NDArray + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.io.serialize import AutoSerialize +from quantem.core.utils.imaging_utils import rotate_image +from quantem.core.utils.utils import electron_wavelength_angstrom +from quantem.core.utils.validators import ensure_valid_array +from quantem.core.visualization import ScalebarConfig, show_2d + + +class StrainMap(AutoSerialize): + """ + Nanobeam strain mapping + """ + + _token = object() + + def __init__( + self, + dataset: Dataset4dstem, + input_data: Any | None = None, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError("Use StrainMap.from_data() to instantiate this class.") + super().__init__() + self.dataset = dataset + self.input_data = input_data + self.strain = None + self.metadata: dict[str, Any] = {} + self.transform: Dataset2d | None = None + self.transform_rotated: Dataset2d | None = None + + @classmethod + def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") -> StrainMap: + if isinstance(data, Dataset4dstem): + return cls(dataset=data, input_data=data, _token=cls._token) + + arr = ensure_valid_array(data) + if arr.ndim != 4: + raise ValueError( + "StrainMap.from_data expects a 4D array with shape (scan_r, scan_c, dp_r, dp_c)." + ) + + ds4 = Dataset4dstem.from_array(arr, name=name) + return cls(dataset=ds4, input_data=data, _token=cls._token) + + def preprocess( + self, + mode: str = "linear", + plot_transform: bool = True, + cropping_factor: float = 0.5, + **plot_kwargs: Any, + ) -> StrainMap: + if self.dataset.units[2] == "A": + qrow_sampling_ang = float(self.dataset.sampling[2]) + elif self.dataset.units[2] == "mrad": + wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) + qrow_sampling_ang = float(self.dataset.sampling[2]) / 1000.0 / wavelength + else: + raise ValueError(f"unrecognized diffraction-space unit for axis 2: {self.dataset.units[2]}") + + if self.dataset.units[3] == "A": + qcol_sampling_ang = float(self.dataset.sampling[3]) + elif self.dataset.units[3] == "mrad": + wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) + qcol_sampling_ang = float(self.dataset.sampling[3]) / 1000.0 / wavelength + else: + raise ValueError(f"unrecognized diffraction-space unit for axis 3: {self.dataset.units[3]}") + + self.metadata["sampling_real"] = np.array( + ( + 1.0 / (qrow_sampling_ang * float(self.dataset.shape[2])), + 1.0 / (qcol_sampling_ang * float(self.dataset.shape[3])), + ), + dtype=float, + ) + + if mode == "linear": + im = np.mean(np.abs(np.fft.fft2(self.dataset.array)), axis=(0, 1)) + elif mode == "log": + im = np.mean(np.abs(np.fft.fft2(np.log(self.dataset.array + 1.0))), axis=(0, 1)) + else: + raise ValueError("mode must be 'linear' or 'log'") + + self.transform = Dataset2d.from_array( + np.fft.fftshift(im), + origin=(im.shape[0] // 2, im.shape[1] // 2), + sampling=(1.0, 1.0), + units=("A", "A"), + signal_units="intensity", + ) + + self.transform_rotated = Dataset2d.from_array( + rotate_image( + self.transform.array, + float(self.dataset.metadata["q_to_r_rotation_cw_deg"]), + clockwise=True, + ), + origin=(im.shape[0] // 2, im.shape[1] // 2), + sampling=(1.0, 1.0), + units=("A", "A"), + signal_units="intensity", + ) + + if plot_transform: + self.plot_transform(cropping_factor=cropping_factor, **plot_kwargs) + + return self + + + + + + + + + + def plot_transform( + self, + cropping_factor: float = 0.25, + **plot_kwargs: Any + ): + if self.transform is None or self.transform_rotated is None: + raise ValueError("Run preprocess() first to compute transform images.") + + defaults = dict( + vmax=1.0, + title=("Original Transform", "Rotated Transform"), + scalebar=ScalebarConfig( + sampling=self.metadata["sampling_real"], + units=r"$\mathrm{\AA}$", + length=2, + ), + ) + defaults.update(plot_kwargs) + + fig, ax = show_2d([self.transform, self.transform_rotated], **defaults) + + axes = np.atleast_1d(ax) + for a in axes: + _apply_center_crop_limits(a, self.transform.shape, cropping_factor) + + return fig, ax + + + + + + + +def _apply_center_crop_limits(ax, shape: tuple[int, int], cropping_factor: float) -> None: + cf = float(cropping_factor) + if cf >= 1.0: + return + if not (0.0 < cf <= 1.0): + raise ValueError("cropping_factor must be in (0, 1].") + + H, W = int(shape[0]), int(shape[1]) + r0 = (H - 1) / 2.0 + c0 = (W - 1) / 2.0 + half_h = 0.5 * cf * H + half_w = 0.5 * cf * W + + ax.set_xlim(c0 - half_w, c0 + half_w) + + y0, y1 = ax.get_ylim() + if y0 > y1: + ax.set_ylim(r0 + half_h, r0 - half_h) + else: + ax.set_ylim(r0 - half_h, r0 + half_h) + + From 5ace6c73d0ac44268063d3bb36009773cddb6a9c Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 1 Jan 2026 19:32:20 -0800 Subject: [PATCH 06/43] all basic functions implemented --- .../core/datastructures/dataset4dstem.py | 2 +- src/quantem/core/utils/imaging_utils.py | 27 +- src/quantem/diffraction/strain.py | 794 +++++++++++++++++- 3 files changed, 754 insertions(+), 69 deletions(-) diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index eac15ac5..dd92b914 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -74,7 +74,7 @@ def __init__( _token : object | None, optional Token to prevent direct instantiation, by default None """ - mdata_keys_4dstem = ["q_to_r_rotation_cw_deg", 'q_transpose', "ellipticity"] + mdata_keys_4dstem = ["q_to_r_rotation_ccw_deg", 'q_transpose', "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): metadata[k] = None diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 3d86000d..8a07b3c1 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -558,30 +558,7 @@ def rotate_image( mode: str = "constant", cval: float = 0.0, ): - """Rotate an array about a pixel origin using bilinear/bicubic interpolation. - - Parameters - ---------- - im - Input array; last two dimensions are treated as (H, W). Any leading - dimensions are treated as batch and rotated independently. - rotation_deg - Rotation angle in degrees. Interpreted as clockwise if clockwise=True, - otherwise counterclockwise. - origin - Rotation origin (row, col) in pixel coordinates. If None, uses (H//2, W//2). - clockwise - If True, interpret rotation_deg as clockwise; if False, as counterclockwise. - interpolation - "bilinear" (order=1) or "bicubic" (order=3). - mode, cval - Boundary handling passed to scipy.ndimage.map_coordinates. - - Returns - ------- - out - Rotated array with the same shape as `im`. - """ + """Rotate an array about a pixel origin using bilinear/bicubic interpolation.""" im = np.asarray(im) if im.ndim < 2: raise ValueError("im must have at least 2 dimensions") @@ -603,7 +580,7 @@ def rotate_image( raise ValueError("interpolation must be 'bilinear' or 'bicubic'") theta = float(np.deg2rad(rotation_deg)) - if clockwise: + if not clockwise: theta = -theta ct = float(np.cos(theta)) diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py index 55c57d9d..b5549ff7 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain.py @@ -2,23 +2,23 @@ from typing import Any +import matplotlib.pyplot as plt import numpy as np from numpy.typing import NDArray +from scipy.ndimage import distance_transform_edt from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.datastructures.dataset4d import Dataset4d from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.imaging_utils import rotate_image +from quantem.core.utils.imaging_utils import dft_upsample, rotate_image from quantem.core.utils.utils import electron_wavelength_angstrom from quantem.core.utils.validators import ensure_valid_array from quantem.core.visualization import ScalebarConfig, show_2d class StrainMap(AutoSerialize): - """ - Nanobeam strain mapping - """ - _token = object() def __init__( @@ -36,9 +36,13 @@ def __init__( self.metadata: dict[str, Any] = {} self.transform: Dataset2d | None = None self.transform_rotated: Dataset2d | None = None + self.u: NDArray | None = None + self.v: NDArray | None = None + self.mask_diffraction = np.ones(self.dataset.array.shape[2:]) + self.mask_diffraction_inv = np.zeros(self.dataset.array.shape[2:]) @classmethod - def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") -> StrainMap: + def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") -> "StrainMap": if isinstance(data, Dataset4dstem): return cls(dataset=data, input_data=data, _token=cls._token) @@ -51,28 +55,81 @@ def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") - ds4 = Dataset4dstem.from_array(arr, name=name) return cls(dataset=ds4, input_data=data, _token=cls._token) + + def diffraction_mask( + self, + threshold = None, + edge_blend = 32.0, + plot_mask = True, + figsize = (8,4), + ): + dp_mean = np.mean(self.dataset.array,axis=(0,1)) + mask_init = dp_mean < threshold + mask_init[:,0] = True + mask_init[0,:] = True + mask_init[:,-1] = True + mask_init[-1,:] = True + + self.mask_diffraction = np.sin( + np.clip( + distance_transform_edt(np.logical_not(mask_init)) / edge_blend, + 0.0, + 1.0, + )*np.pi/2, + )**2 + # int_edge = np.sum(dp_mean*self.mask_diffraction) / np.sum(self.mask_diffraction) + int_edge = np.min(dp_mean[self.mask_diffraction>0.99]) + self.mask_diffraction_inv = (1 - self.mask_diffraction) * int_edge + + if plot_mask: + fig,ax = plt.subplots(1,2,figsize=figsize) + ax[0].imshow( + np.log(np.maximum(dp_mean,np.min(dp_mean[dp_mean>0]))), + cmap = 'gray', + ) + ax[1].imshow( + np.log( + dp_mean*self.mask_diffraction + \ + self.mask_diffraction_inv, + ), + cmap = 'gray', + ) + + return self + + def preprocess( self, mode: str = "linear", + q_to_r_rotation_ccw_deg: float | None = None, + q_transpose: bool | None = None, plot_transform: bool = True, - cropping_factor: float = 0.5, + cropping_factor: float = 0.25, **plot_kwargs: Any, - ) -> StrainMap: - if self.dataset.units[2] == "A": + ) -> "StrainMap": + + self.metadata["mode"] = mode + + qrow_unit = str(self.dataset.units[2]) + qcol_unit = str(self.dataset.units[3]) + + if qrow_unit in {"A", "Å"}: qrow_sampling_ang = float(self.dataset.sampling[2]) - elif self.dataset.units[2] == "mrad": + elif qrow_unit == "mrad": wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) qrow_sampling_ang = float(self.dataset.sampling[2]) / 1000.0 / wavelength else: - raise ValueError(f"unrecognized diffraction-space unit for axis 2: {self.dataset.units[2]}") + qrow_sampling_ang = 1.0 + qrow_unit = "pixels" - if self.dataset.units[3] == "A": + if qcol_unit in {"A", "Å"}: qcol_sampling_ang = float(self.dataset.sampling[3]) - elif self.dataset.units[3] == "mrad": + elif qcol_unit == "mrad": wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) qcol_sampling_ang = float(self.dataset.sampling[3]) / 1000.0 / wavelength else: - raise ValueError(f"unrecognized diffraction-space unit for axis 3: {self.dataset.units[3]}") + qcol_sampling_ang = 1.0 + qcol_unit = "pixels" self.metadata["sampling_real"] = np.array( ( @@ -82,30 +139,81 @@ def preprocess( dtype=float, ) - if mode == "linear": - im = np.mean(np.abs(np.fft.fft2(self.dataset.array)), axis=(0, 1)) - elif mode == "log": - im = np.mean(np.abs(np.fft.fft2(np.log(self.dataset.array + 1.0))), axis=(0, 1)) + if qrow_unit == "pixels" and qcol_unit == "pixels": + self.metadata["real_units"] = "1/pixels" + else: + self.metadata["real_units"] = r"$\mathrm{\AA}$" + + if q_to_r_rotation_ccw_deg is None or q_transpose is None: + parent_rot = self.dataset.metadata.get("q_to_r_rotation_ccw_deg", None) + parent_tr = self.dataset.metadata.get("q_transpose", None) + if q_to_r_rotation_ccw_deg is None and parent_rot is not None: + q_to_r_rotation_ccw_deg = float(parent_rot) + if q_transpose is None and parent_tr is not None: + q_transpose = bool(parent_tr) + if (parent_rot is not None or parent_tr is not None) and ( + q_to_r_rotation_ccw_deg is not None or q_transpose is not None + ): + import warnings + + warnings.warn( + f"StrainMap.preprocess: using parent Dataset4dstem metadata " + f"(q_to_r_rotation_ccw_deg={float(q_to_r_rotation_ccw_deg or 0.0)}, " + f"q_transpose={bool(q_transpose or False)}).", + UserWarning, + ) + + if q_to_r_rotation_ccw_deg is None or q_transpose is None: + import warnings + + q_to_r_rotation_ccw_deg = ( + 0.0 if q_to_r_rotation_ccw_deg is None else float(q_to_r_rotation_ccw_deg) + ) + q_transpose = False if q_transpose is None else bool(q_transpose) + warnings.warn( + "StrainMap.preprocess: setting q_to_r_rotation_ccw_deg=0.0 and q_transpose=False.", + UserWarning, + ) + + self.metadata["q_to_r_rotation_ccw_deg"] = float(q_to_r_rotation_ccw_deg) + self.metadata["q_transpose"] = bool(q_transpose) + + if self.metadata["mode"] == "linear": + im = np.mean(np.abs(np.fft.fft2( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + )), axis=(0, 1)) + elif self.metadata["mode"] == "log": + im = np.mean(np.abs(np.fft.fft2(np.log( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + ))), axis=(0, 1)) else: raise ValueError("mode must be 'linear' or 'log'") + im = np.fft.fftshift(im) + self.transform = Dataset2d.from_array( - np.fft.fftshift(im), + im, origin=(im.shape[0] // 2, im.shape[1] // 2), sampling=(1.0, 1.0), - units=("A", "A"), + units=(qrow_unit, qcol_unit), signal_units="intensity", ) + im_plot = self.transform.array + if bool(self.metadata["q_transpose"]): + im_plot = im_plot.T + self.transform_rotated = Dataset2d.from_array( rotate_image( - self.transform.array, - float(self.dataset.metadata["q_to_r_rotation_cw_deg"]), - clockwise=True, + im_plot, + float(self.metadata["q_to_r_rotation_ccw_deg"]), + clockwise=False, ), origin=(im.shape[0] // 2, im.shape[1] // 2), sampling=(1.0, 1.0), - units=("A", "A"), + units=(self.metadata["real_units"], self.metadata["real_units"]), signal_units="intensity", ) @@ -114,48 +222,419 @@ def preprocess( return self - - - - - - - - def plot_transform( - self, - cropping_factor: float = 0.25, - **plot_kwargs: Any + self, + cropping_factor: float = 0.25, + scalebar_fraction: float = 0.25, + **plot_kwargs: Any, ): if self.transform is None or self.transform_rotated is None: raise ValueError("Run preprocess() first to compute transform images.") + sampling = float(np.mean(self.metadata["sampling_real"])) + units = str(self.metadata.get("real_units", r"$\mathrm{\AA}$")) + + W = int(self.transform.shape[1]) + view_w_px = float(W) * float(cropping_factor) + target_units = float(scalebar_fraction) * view_w_px * sampling + sb_len = _nice_length_units(target_units) + defaults = dict( vmax=1.0, title=("Original Transform", "Rotated Transform"), scalebar=ScalebarConfig( - sampling=self.metadata["sampling_real"], - units=r"$\mathrm{\AA}$", - length=2, + sampling=sampling, + units=units, + length=sb_len if sb_len > 0 else None, ), ) defaults.update(plot_kwargs) fig, ax = show_2d([self.transform, self.transform_rotated], **defaults) - axes = np.atleast_1d(ax) - for a in axes: + for a in _flatten_axes(ax): _apply_center_crop_limits(a, self.transform.shape, cropping_factor) return fig, ax + def choose_lattice_vector( + self, + *, + u: tuple[float, float] | NDArray, + v: tuple[float, float] | NDArray, + define_in_rotated: bool = False, + refine_subpixel: bool = True, + refine_subpixel_dft: bool = False, + refine_radius_px: float = 2.0, + refine_log: bool = False, + upsample: int = 16, + plot: bool = True, + cropping_factor: float = 0.25, + **plot_kwargs: Any, + ) -> "StrainMap": + if self.transform is None or self.transform_rotated is None: + raise ValueError("Run preprocess() first to compute transform images.") + + u_rc = np.asarray(u, dtype=float).reshape(2) + v_rc = np.asarray(v, dtype=float).reshape(2) + + rot_ccw = float(self.metadata.get("q_to_r_rotation_ccw_deg", 0.0)) + q_transpose = bool(self.metadata.get("q_transpose", False)) + + if define_in_rotated: + u_rc = _display_vec_to_raw(u_rc, rotation_ccw_deg=rot_ccw, transpose=q_transpose) + v_rc = _display_vec_to_raw(v_rc, rotation_ccw_deg=rot_ccw, transpose=q_transpose) + + if refine_subpixel_dft: + refine_subpixel = True + + if refine_subpixel: + u_rc, v_rc = _refine_lattice_vectors( + self.transform.array, + u_rc=u_rc, + v_rc=v_rc, + radius_px=float(refine_radius_px), + log_fit=bool(refine_log), + refine_dft=bool(refine_subpixel_dft), + upsample=int(upsample), + ) + + self.u = u_rc + self.v = v_rc + self.metadata["lattice_u_rc"] = self.u.copy() + self.metadata["lattice_v_rc"] = self.v.copy() + + if plot: + fig, ax = self.plot_transform(cropping_factor=cropping_factor, **plot_kwargs) + _overlay_lattice_vectors( + ax=ax, + shape=self.transform.shape, + u_rc=self.u, + v_rc=self.v, + rot_ccw_deg=rot_ccw, + q_transpose=q_transpose, + ) + return self + + return self + + + def fit_lattice_vectors( + self, + refine_subpixel: bool = True, + refine_subpixel_dft: bool = False, + refine_radius_px: float = 2.0, + upsample: int = 16, + refine_log: bool = False, + progressbar: bool = True, + ) -> "StrainMap": + from quantem.core.datastructures.dataset3d import Dataset3d + + if self.u is None or self.v is None: + raise ValueError("Run choose_lattice_vector() first to set initial lattice vectors (self.u, self.v).") + + if refine_subpixel_dft: + refine_subpixel = True + + scan_r = self.dataset.shape[0] + scan_c = self.dataset.shape[1] + self.u_fit = Dataset3d.from_shape( + (scan_r, scan_c, 2), + name="u_fits", + signal_units="pixels", + ) + self.v_fit = Dataset3d.from_shape( + (scan_r, scan_c, 2), + name="v_fits", + signal_units="pixels", + ) + + mode = str(self.metadata.get("mode", "linear")).lower() + + it = np.ndindex(scan_r, scan_c) + if progressbar: + try: + from tqdm.auto import tqdm # type: ignore + + it = tqdm(it, total=scan_r * scan_c, desc="fit_lattice_vectors", leave=True) + except Exception: + pass + + u0 = np.asarray(self.u, dtype=float).reshape(2) + v0 = np.asarray(self.v, dtype=float).reshape(2) + + for r, c in it: + dp = self.dataset.array[r, c]*self.mask_diffraction + \ + self.mask_diffraction_inv + + if mode == "linear": + im = np.fft.fftshift(np.abs(np.fft.fft2(dp))) + elif mode == "log": + int_range = np.max(dp) - np.min(dp) + im = np.fft.fftshift(np.abs(np.fft.fft2(np.log(dp + int_range*0.01)))) + else: + raise ValueError("metadata['mode'] must be 'linear' or 'log'") + + if refine_subpixel: + u_rc, v_rc = _refine_lattice_vectors( + im, + u_rc=u0, + v_rc=v0, + radius_px=float(refine_radius_px), + log_fit=bool(refine_log), + refine_dft=bool(refine_subpixel_dft), + upsample=int(upsample), + ) + else: + u_rc = u0 + v_rc = v0 + + self.u_fit.array[r, c, :] = u_rc + self.v_fit.array[r, c, :] = v_rc + + self.metadata["fit_refine_subpixel"] = bool(refine_subpixel) + self.metadata["fit_refine_subpixel_dft"] = bool(refine_subpixel_dft) + self.metadata["fit_refine_radius_px"] = float(refine_radius_px) + self.metadata["fit_refine_log"] = bool(refine_log) + self.metadata["fit_upsample"] = int(upsample) + + return self + + + def plot_lattice_vectors( + self, + subtract_mean: bool = True, + scalebar: bool = False, + **plot_kwargs: Any, + ): + if getattr(self, "u_fit", None) is None or getattr(self, "v_fit", None) is None: + raise ValueError("Run fit_lattice_vectors() first to compute u_fit and v_fit.") + + im0 = self.u_fit.array[:,:,0] + im1 = self.u_fit.array[:,:,1] + im2 = self.v_fit.array[:,:,0] + im3 = self.v_fit.array[:,:,1] + + if subtract_mean: + im0 = im0 - float(np.nanmean(im0)) + im1 = im1 - float(np.nanmean(im1)) + im2 = im2 - float(np.nanmean(im2)) + im3 = im3 - float(np.nanmean(im3)) + + vlim = float(np.nanmax(np.abs(np.stack([im0, im1, im2, im3], axis=0)))) + vmin = -vlim + vmax = vlim + + defaults: dict[str, Any] = dict( + title=("u_r", "u_c", "v_r", "v_c"), + vmin=vmin, + vmax=vmax, + ) + + if scalebar: + s0 = float(self.dataset.sampling[0]) if len(self.dataset.sampling) > 0 else 1.0 + s1 = float(self.dataset.sampling[1]) if len(self.dataset.sampling) > 1 else s0 + sampling_scan = float(np.mean([s0, s1])) + units_scan = str(self.dataset.units[0]) if len(self.dataset.units) > 0 else "pixels" + defaults["scalebar"] = ScalebarConfig(sampling=sampling_scan, units=units_scan) + + defaults.update(plot_kwargs) + + fig, ax = show_2d([im0, im1, im2, im3], **defaults) + return fig, ax + + + def fit_strain( + self, + mask_reference = None, + plot_strain = True, + ): + if self.u_fit is None or self.v_fit is None: + raise ValueError("Run fit_lattice_vectors() first to compute u_fit and v_fit.") + u_fit = self.u_fit.array + v_fit = self.v_fit.array + scan_r, scan_c = int(u_fit.shape[0]), int(u_fit.shape[1]) + if mask_reference is None: + self.u_ref = np.median(u_fit.reshape(-1, 2), axis=0) + self.v_ref = np.median(v_fit.reshape(-1, 2), axis=0) + else: + m = np.asarray(mask_reference, dtype=bool) + self.u_ref = np.array( + ( + np.median(u_fit[m, 0]), + np.median(u_fit[m, 1]), + ), + dtype=float, + ) + self.v_ref = np.array( + ( + np.median(v_fit[m, 0]), + np.median(v_fit[m, 1]), + ), + dtype=float, + ) + Uref = np.stack((self.u_ref, self.v_ref), axis=1).astype(float) + det = float(np.linalg.det(Uref)) + if not np.isfinite(det) or abs(det) < 1e-12: + Uref_inv = np.linalg.pinv(Uref) + else: + Uref_inv = np.linalg.inv(Uref) + # init + self.strain_trans = Dataset4d.from_shape( + (scan_r, scan_c, 2, 2), + name="transformation matrix", + signal_units="fractional", + ) -def _apply_center_crop_limits(ax, shape: tuple[int, int], cropping_factor: float) -> None: + # Loop over probe positions + for r in range(scan_r): + for c in range(scan_c): + U = np.stack((u_fit[r, c, :], v_fit[r, c, :]), axis=1) + self.strain_trans.array[r, c, :, :] = U @ Uref_inv + + # get strains in orthogonal directions + self.strain_raw_err = Dataset2d.from_array( + self.strain_trans.array[:, :, 0, 0] - 1, + name="strain err", + signal_units="fractional", + ) + self.strain_raw_ecc = Dataset2d.from_array( + self.strain_trans.array[:, :, 1, 1] - 1, + name="strain ecc", + signal_units="fractional", + ) + self.strain_raw_erc = Dataset2d.from_array( + self.strain_trans.array[:, :, 1, 0]*0.5 + self.strain_trans.array[:, :, 0, 1]*0.5, + name="strain erc", + signal_units="fractional", + ) + self.strain_rotation = Dataset2d.from_array( + self.strain_trans.array[:, :, 1, 0]*-0.5 + self.strain_trans.array[:, :, 0, 1]*0.5, + name="strain rotation", + signal_units="fractional", + ) + + return self + + + def plot_strain( + self, + ref_u_v=(1.0, 0.0), + ref_angle_degrees=None, + strain_range_percent=(-3.0, 3.0), + rotation_range_degrees=(-2.0, 2.0), + plot_rotation=True, + cmap_strain="PiYG_r", + cmap_rotation="PiYG_r", + layout="horizontal", + figsize=(6, 6), + ): + import matplotlib.pyplot as plt + + if ref_angle_degrees is None: + ref_vec = self.u_ref * float(ref_u_v[0]) + self.v_ref * float(ref_u_v[1]) + ref_angle = float(np.arctan2(ref_vec[1], ref_vec[0])) + else: + ref_angle = float(np.deg2rad(ref_angle_degrees)) + + angle = ref_angle + np.deg2rad(self.metadata["q_to_r_rotation_ccw_deg"]) + print(np.round(np.rad2deg(angle),2)) + + c = float(np.cos(angle)) + s = float(np.sin(angle)) + + err = self.strain_raw_err.array + ecc = self.strain_raw_ecc.array + erc = self.strain_raw_erc.array + + euu = err * (c * c) + 2.0 * erc * (c * s) + ecc * (s * s) + evv = err * (s * s) - 2.0 * erc * (c * s) + ecc * (c * c) + euv = (ecc - err) * (c * s) + erc * (c * c - s * s) + + self.strain_euu = self.strain_raw_err.copy() + self.strain_evv = self.strain_raw_ecc.copy() + self.strain_euv = self.strain_raw_erc.copy() + self.strain_euu.array[...] = euu + self.strain_evv.array[...] = evv + self.strain_euv.array[...] = euv + + if layout == "horizontal": + if plot_rotation: + fig, ax = plt.subplots(1, 4, figsize=figsize) + + ax[0].imshow( + self.strain_euu.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[1].imshow( + self.strain_evv.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[2].imshow( + self.strain_euv.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[3].imshow( + np.rad2deg(self.strain_rotation.array), + vmin=rotation_range_degrees[0], + vmax=rotation_range_degrees[1], + cmap=cmap_rotation, + ) + return fig, ax + + fig, ax = plt.subplots(1, 3, figsize=figsize) + ax[0].imshow( + self.strain_euu.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[1].imshow( + self.strain_evv.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[2].imshow( + self.strain_euv.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + return fig, ax + + raise ValueError("layout must be 'horizontal'") + + +def _nice_length_units(target: float) -> float: + target = float(target) + if not np.isfinite(target) or target <= 0: + return 0.0 + exp = np.floor(np.log10(target)) + base = target / (10.0**exp) + if base < 1.5: + nice = 1.0 + elif base < 3.5: + nice = 2.0 + elif base < 7.5: + nice = 5.0 + else: + nice = 10.0 + return float(nice * (10.0**exp)) + + +def _apply_center_crop_limits(ax: Any, shape: tuple[int, int], cropping_factor: float) -> None: cf = float(cropping_factor) if cf >= 1.0: return @@ -163,8 +642,8 @@ def _apply_center_crop_limits(ax, shape: tuple[int, int], cropping_factor: float raise ValueError("cropping_factor must be in (0, 1].") H, W = int(shape[0]), int(shape[1]) - r0 = (H - 1) / 2.0 - c0 = (W - 1) / 2.0 + r0 = float(H // 2) + c0 = float(W // 2) half_h = 0.5 * cf * H half_w = 0.5 * cf * W @@ -177,3 +656,232 @@ def _apply_center_crop_limits(ax, shape: tuple[int, int], cropping_factor: float ax.set_ylim(r0 - half_h, r0 + half_h) +def _flatten_axes(ax: Any) -> list[Any]: + if isinstance(ax, np.ndarray): + return list(ax.ravel()) + if isinstance(ax, (list, tuple)): + out: list[Any] = [] + for a in ax: + out.extend(_flatten_axes(a)) + return out + return [ax] + + +def _raw_vec_to_display(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: bool) -> NDArray: + v = np.asarray(vec_rc, dtype=float).reshape(2) + dr, dc = float(v[0]), float(v[1]) + + if transpose: + dr, dc = dc, dr + + theta = float(np.deg2rad(rotation_ccw_deg)) + ct = float(np.cos(theta)) + st = float(np.sin(theta)) + + dr2 = ct * dr - st * dc + dc2 = st * dr + ct * dc + return np.array((dr2, dc2), dtype=float) + + +def _display_vec_to_raw(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: bool) -> NDArray: + v = np.asarray(vec_rc, dtype=float).reshape(2) + dr, dc = float(v[0]), float(v[1]) + + theta = float(np.deg2rad(rotation_ccw_deg)) + ct = float(np.cos(theta)) + st = float(np.sin(theta)) + + dr2 = ct * dr + st * dc + dc2 = -st * dr + ct * dc + + if transpose: + dr2, dc2 = dc2, dr2 + + return np.array((dr2, dc2), dtype=float) + + +def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: + r0, c0 = float(center_rc[0]), float(center_rc[1]) + + def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: + dr, dc = float(vec[0]), float(vec[1]) + ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) + ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) + ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") + + _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) + _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) + + +def _overlay_lattice_vectors( + *, + ax: Any, + shape: tuple[int, int], + u_rc: NDArray, + v_rc: NDArray, + rot_ccw_deg: float, + q_transpose: bool, +) -> None: + axs = _flatten_axes(ax) + if not axs: + return + + H, W = int(shape[0]), int(shape[1]) + center_rc = (float(H // 2), float(W // 2)) + + _plot_lattice_vectors(axs[0], center_rc, u_rc, v_rc) + + if len(axs) >= 2: + u_disp = _raw_vec_to_display(u_rc, rotation_ccw_deg=float(rot_ccw_deg), transpose=bool(q_transpose)) + v_disp = _raw_vec_to_display(v_rc, rotation_ccw_deg=float(rot_ccw_deg), transpose=bool(q_transpose)) + _plot_lattice_vectors(axs[1], center_rc, u_disp, v_disp) + + +def _parabolic_vertex_delta(v_m1: float, v_0: float, v_p1: float) -> float: + denom = (v_m1 - 2.0 * v_0 + v_p1) + if denom == 0 or not np.isfinite(denom): + return 0.0 + delta = 0.5 * (v_m1 - v_p1) / denom + if not np.isfinite(delta): + return 0.0 + return float(np.clip(delta, -1.0, 1.0)) + + +def _refine_peak_subpixel( + im: NDArray, + *, + r_guess: float, + c_guess: float, + radius_px: float = 2.0, + log_fit: bool = False, +) -> tuple[float, float]: + im = np.asarray(im, dtype=float) + H, W = im.shape + + r0 = int(np.clip(int(np.round(r_guess)), 0, H - 1)) + c0 = int(np.clip(int(np.round(c_guess)), 0, W - 1)) + rad = int(max(0, int(np.ceil(float(radius_px))))) + + r1 = max(0, r0 - rad) + r2 = min(H, r0 + rad + 1) + c1 = max(0, c0 - rad) + c2 = min(W, c0 + rad + 1) + + win = im[r1:r2, c1:c2] + if win.size == 0: + return float(r_guess), float(c_guess) + + ir, ic = np.unravel_index(int(np.argmax(win)), win.shape) + r_peak = r1 + int(ir) + c_peak = c1 + int(ic) + + if 0 < r_peak < H - 1: + col = im[r_peak - 1 : r_peak + 2, c_peak] + if log_fit: + col = np.log(np.clip(col, 1e-12, None)) + dr = _parabolic_vertex_delta(float(col[0]), float(col[1]), float(col[2])) + else: + dr = 0.0 + + if 0 < c_peak < W - 1: + row = im[r_peak, c_peak - 1 : c_peak + 2] + if log_fit: + row = np.log(np.clip(row, 1e-12, None)) + dc = _parabolic_vertex_delta(float(row[0]), float(row[1]), float(row[2])) + else: + dc = 0.0 + + return float(r_peak) + dr, float(c_peak) + dc + + +def _refine_peak_subpixel_dft( + im: NDArray, + *, + r0: float, + c0: float, + upsample: int, + log_fit: bool = False, +) -> tuple[float, float]: + if int(upsample) <= 1: + return float(r0), float(c0) + + im = np.asarray(im, dtype=float) + F = np.fft.fft2(im) + + up = int(upsample) + du = int(np.ceil(1.5 * up)) + + patch = dft_upsample(F, up=up, shift=(float(r0), float(c0)), device="cpu") + patch = np.asarray(patch, dtype=float) + + i0, j0 = np.unravel_index(int(np.argmax(patch)), patch.shape) + i0 = int(i0) + j0 = int(j0) + + if 0 < i0 < patch.shape[0] - 1: + col = patch[i0 - 1 : i0 + 2, j0] + if log_fit: + col = np.log(np.clip(col, 1e-12, None)) + di = _parabolic_vertex_delta(float(col[0]), float(col[1]), float(col[2])) + else: + di = 0.0 + + if 0 < j0 < patch.shape[1] - 1: + row = patch[i0, j0 - 1 : j0 + 2] + if log_fit: + row = np.log(np.clip(row, 1e-12, None)) + dj = _parabolic_vertex_delta(float(row[0]), float(row[1]), float(row[2])) + else: + dj = 0.0 + + dr = (float(i0) - float(du) + float(di)) / float(up) + dc = (float(j0) - float(du) + float(dj)) / float(up) + + return float(r0) + dr, float(c0) + dc + + +def _refine_lattice_vectors( + im: NDArray, + *, + u_rc: NDArray, + v_rc: NDArray, + radius_px: float = 2.0, + log_fit: bool = False, + refine_dft: bool = True, + upsample: int = 16, +) -> tuple[NDArray, NDArray]: + im = np.asarray(im, dtype=float) + if im.ndim != 2: + raise ValueError("im must be 2D.") + + H, W = im.shape + r_center = float(H // 2) + c_center = float(W // 2) + + def _refine(vec: NDArray) -> NDArray: + vec = np.asarray(vec, dtype=float).reshape(2) + r_guess = r_center + float(vec[0]) + c_guess = c_center + float(vec[1]) + + r1, c1 = _refine_peak_subpixel( + im, + r_guess=float(r_guess), + c_guess=float(c_guess), + radius_px=float(radius_px), + log_fit=bool(log_fit), + ) + + if refine_dft and int(upsample) > 1: + r2, c2 = _refine_peak_subpixel_dft( + im, + r0=float(r1), + c0=float(c1), + upsample=int(upsample), + log_fit=bool(log_fit), + ) + else: + r2, c2 = float(r1), float(c1) + + return np.array((r2 - r_center, c2 - c_center), dtype=float) + + return _refine(u_rc), _refine(v_rc) From 9b943923a2140f92dc1b51c6b7ce351511c7ceb1 Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 3 Jan 2026 12:39:39 -0800 Subject: [PATCH 07/43] faster strain calc, adding h5plugin to read arina data --- pyproject.toml | 1 + src/quantem/diffraction/strain.py | 55 ++++++++++++++++++++++--------- 2 files changed, 41 insertions(+), 15 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 9223ae7c..1c61b262 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,4 +69,5 @@ dev = [ "pre-commit>=4.2.0", "ruff>=0.11.5", "tomli>=2.2.1", + "hdf5plugin>=6.0.0", ] diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py index b5549ff7..bcfa981d 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain.py @@ -59,7 +59,7 @@ def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") - def diffraction_mask( self, threshold = None, - edge_blend = 32.0, + edge_blend = 64.0, plot_mask = True, figsize = (8,4), ): @@ -103,6 +103,7 @@ def preprocess( mode: str = "linear", q_to_r_rotation_ccw_deg: float | None = None, q_transpose: bool | None = None, + skip = None, plot_transform: bool = True, cropping_factor: float = 0.25, **plot_kwargs: Any, @@ -178,18 +179,32 @@ def preprocess( self.metadata["q_to_r_rotation_ccw_deg"] = float(q_to_r_rotation_ccw_deg) self.metadata["q_transpose"] = bool(q_transpose) - if self.metadata["mode"] == "linear": - im = np.mean(np.abs(np.fft.fft2( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - )), axis=(0, 1)) - elif self.metadata["mode"] == "log": - im = np.mean(np.abs(np.fft.fft2(np.log( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - ))), axis=(0, 1)) + if skip is None: + if self.metadata["mode"] == "linear": + im = np.mean(np.abs(np.fft.fft2( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + )), axis=(0, 1)) + elif self.metadata["mode"] == "log": + im = np.mean(np.abs(np.fft.fft2(np.log1p( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + ))), axis=(0, 1)) + else: + raise ValueError("mode must be 'linear' or 'log'") else: - raise ValueError("mode must be 'linear' or 'log'") + if self.metadata["mode"] == "linear": + im = np.mean(np.abs(np.fft.fft2( + self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + )), axis=(0, 1)) + elif self.metadata["mode"] == "log": + im = np.mean(np.abs(np.fft.fft2(np.log1p( + self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + ))), axis=(0, 1)) + else: + raise ValueError("mode must be 'linear' or 'log'") im = np.fft.fftshift(im) @@ -239,8 +254,19 @@ def plot_transform( target_units = float(scalebar_fraction) * view_w_px * sampling sb_len = _nice_length_units(target_units) + # intensity scaling: compute from transform, apply same scaling to both panels + kr = (np.arange(self.transform.shape[0], dtype=float) - self.transform.shape[0] // 2)[:, None] + kc = (np.arange(self.transform.shape[1], dtype=float) - self.transform.shape[1] // 2)[None, :] + qmag = np.sqrt(kr * kr + kc * kc) + im0 = self.transform.array + tmp = im0 * qmag + i0 = np.unravel_index(int(np.nanargmax(tmp)), tmp.shape) + vmin = 0.0 + vmax = im0[i0] + defaults = dict( - vmax=1.0, + vmin=vmin, + vmax=vmax, title=("Original Transform", "Rotated Transform"), scalebar=ScalebarConfig( sampling=sampling, @@ -371,8 +397,7 @@ def fit_lattice_vectors( if mode == "linear": im = np.fft.fftshift(np.abs(np.fft.fft2(dp))) elif mode == "log": - int_range = np.max(dp) - np.min(dp) - im = np.fft.fftshift(np.abs(np.fft.fft2(np.log(dp + int_range*0.01)))) + im = np.fft.fftshift(np.abs(np.fft.fft2(np.log1p(dp)))) else: raise ValueError("metadata['mode'] must be 'linear' or 'log'") From dc4ee09d7941ca56b211d9e5f39099702c04df3d Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 3 Jan 2026 12:41:18 -0800 Subject: [PATCH 08/43] faster strain, adding h5plugin to read arina data --- pyproject.toml | 1 + src/quantem/diffraction/strain.py | 55 +- uv.lock | 3256 +++++++++++++++-------------- 3 files changed, 1670 insertions(+), 1642 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 9223ae7c..1c61b262 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,4 +69,5 @@ dev = [ "pre-commit>=4.2.0", "ruff>=0.11.5", "tomli>=2.2.1", + "hdf5plugin>=6.0.0", ] diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py index b5549ff7..bcfa981d 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain.py @@ -59,7 +59,7 @@ def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") - def diffraction_mask( self, threshold = None, - edge_blend = 32.0, + edge_blend = 64.0, plot_mask = True, figsize = (8,4), ): @@ -103,6 +103,7 @@ def preprocess( mode: str = "linear", q_to_r_rotation_ccw_deg: float | None = None, q_transpose: bool | None = None, + skip = None, plot_transform: bool = True, cropping_factor: float = 0.25, **plot_kwargs: Any, @@ -178,18 +179,32 @@ def preprocess( self.metadata["q_to_r_rotation_ccw_deg"] = float(q_to_r_rotation_ccw_deg) self.metadata["q_transpose"] = bool(q_transpose) - if self.metadata["mode"] == "linear": - im = np.mean(np.abs(np.fft.fft2( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - )), axis=(0, 1)) - elif self.metadata["mode"] == "log": - im = np.mean(np.abs(np.fft.fft2(np.log( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - ))), axis=(0, 1)) + if skip is None: + if self.metadata["mode"] == "linear": + im = np.mean(np.abs(np.fft.fft2( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + )), axis=(0, 1)) + elif self.metadata["mode"] == "log": + im = np.mean(np.abs(np.fft.fft2(np.log1p( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + ))), axis=(0, 1)) + else: + raise ValueError("mode must be 'linear' or 'log'") else: - raise ValueError("mode must be 'linear' or 'log'") + if self.metadata["mode"] == "linear": + im = np.mean(np.abs(np.fft.fft2( + self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + )), axis=(0, 1)) + elif self.metadata["mode"] == "log": + im = np.mean(np.abs(np.fft.fft2(np.log1p( + self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + ))), axis=(0, 1)) + else: + raise ValueError("mode must be 'linear' or 'log'") im = np.fft.fftshift(im) @@ -239,8 +254,19 @@ def plot_transform( target_units = float(scalebar_fraction) * view_w_px * sampling sb_len = _nice_length_units(target_units) + # intensity scaling: compute from transform, apply same scaling to both panels + kr = (np.arange(self.transform.shape[0], dtype=float) - self.transform.shape[0] // 2)[:, None] + kc = (np.arange(self.transform.shape[1], dtype=float) - self.transform.shape[1] // 2)[None, :] + qmag = np.sqrt(kr * kr + kc * kc) + im0 = self.transform.array + tmp = im0 * qmag + i0 = np.unravel_index(int(np.nanargmax(tmp)), tmp.shape) + vmin = 0.0 + vmax = im0[i0] + defaults = dict( - vmax=1.0, + vmin=vmin, + vmax=vmax, title=("Original Transform", "Rotated Transform"), scalebar=ScalebarConfig( sampling=sampling, @@ -371,8 +397,7 @@ def fit_lattice_vectors( if mode == "linear": im = np.fft.fftshift(np.abs(np.fft.fft2(dp))) elif mode == "log": - int_range = np.max(dp) - np.min(dp) - im = np.fft.fftshift(np.abs(np.fft.fft2(np.log(dp + int_range*0.01)))) + im = np.fft.fftshift(np.abs(np.fft.fft2(np.log1p(dp)))) else: raise ValueError("metadata['mode'] must be 'linear' or 'log'") diff --git a/uv.lock b/uv.lock index 2960e9b3..ae9f2360 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 1 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.14'", @@ -11,9 +11,9 @@ resolution-markers = [ name = "absl-py" version = "2.3.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = 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"2025-06-08T17:06:38.034Z" }, + { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276 }, ] From 0852c49218e183244af0d5bd5779c805b8fbad25 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 18 Jan 2026 16:52:56 -0800 Subject: [PATCH 09/43] temp removing function for merge --- src/quantem/core/utils/imaging_utils.py | 63 ------------------------- 1 file changed, 63 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 8a07b3c1..5dced5bc 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -548,66 +548,3 @@ def fourier_cropping( return result - -def rotate_image( - im, - rotation_deg: float, - origin: tuple[float, float] | None = None, - clockwise: bool = True, - interpolation: str = "bilinear", - mode: str = "constant", - cval: float = 0.0, -): - """Rotate an array about a pixel origin using bilinear/bicubic interpolation.""" - im = np.asarray(im) - if im.ndim < 2: - raise ValueError("im must have at least 2 dimensions") - - H, W = im.shape[-2], im.shape[-1] - if origin is None: - r0 = float(H // 2) - c0 = float(W // 2) - else: - r0 = float(origin[0]) - c0 = float(origin[1]) - - interp = str(interpolation).lower() - if interp in {"bilinear", "linear"}: - order = 1 - elif interp in {"bicubic", "cubic"}: - order = 3 - else: - raise ValueError("interpolation must be 'bilinear' or 'bicubic'") - - theta = float(np.deg2rad(rotation_deg)) - if not clockwise: - theta = -theta - - ct = float(np.cos(theta)) - st = float(np.sin(theta)) - - r_out, c_out = np.meshgrid( - np.arange(H, dtype=np.float64), - np.arange(W, dtype=np.float64), - indexing="ij", - ) - - c_rel = c_out - c0 - r_rel = r_out - r0 - - c_in = ct * c_rel + st * r_rel + c0 - r_in = -st * c_rel + ct * r_rel + r0 - - coords = np.vstack((r_in.ravel(), c_in.ravel())) - - if im.ndim == 2: - out = map_coordinates(im, coords, order=order, mode=mode, cval=cval) - return out.reshape(H, W) - - prefix = im.shape[:-2] - n = int(np.prod(prefix)) if prefix else 1 - im_flat = im.reshape(n, H, W) - out_flat = np.empty((n, H * W), dtype=np.result_type(im_flat.dtype, np.float64)) - for i in range(n): - out_flat[i] = map_coordinates(im_flat[i], coords, order=order, mode=mode, cval=cval) - return out_flat.reshape(*prefix, H, W) From 53ac2d5c473e8361393f15be30d0ac0d94a12203 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 18 Jan 2026 16:53:47 -0800 Subject: [PATCH 10/43] adding rotate_image back --- src/quantem/core/utils/imaging_utils.py | 65 +++++++++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 9be6c9c4..ba5fc3e9 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -1049,3 +1049,68 @@ def unwrap_phase_2d_torch( raise ValueError( f'`method` must be one of {{"reliability-sorting", "poisson"}}, got {method!r}' ) + + + +def rotate_image( + im, + rotation_deg: float, + origin: tuple[float, float] | None = None, + clockwise: bool = True, + interpolation: str = "bilinear", + mode: str = "constant", + cval: float = 0.0, +): + """Rotate an array about a pixel origin using bilinear/bicubic interpolation.""" + im = np.asarray(im) + if im.ndim < 2: + raise ValueError("im must have at least 2 dimensions") + + H, W = im.shape[-2], im.shape[-1] + if origin is None: + r0 = float(H // 2) + c0 = float(W // 2) + else: + r0 = float(origin[0]) + c0 = float(origin[1]) + + interp = str(interpolation).lower() + if interp in {"bilinear", "linear"}: + order = 1 + elif interp in {"bicubic", "cubic"}: + order = 3 + else: + raise ValueError("interpolation must be 'bilinear' or 'bicubic'") + + theta = float(np.deg2rad(rotation_deg)) + if not clockwise: + theta = -theta + + ct = float(np.cos(theta)) + st = float(np.sin(theta)) + + r_out, c_out = np.meshgrid( + np.arange(H, dtype=np.float64), + np.arange(W, dtype=np.float64), + indexing="ij", + ) + + c_rel = c_out - c0 + r_rel = r_out - r0 + + c_in = ct * c_rel + st * r_rel + c0 + r_in = -st * c_rel + ct * r_rel + r0 + + coords = np.vstack((r_in.ravel(), c_in.ravel())) + + if im.ndim == 2: + out = map_coordinates(im, coords, order=order, mode=mode, cval=cval) + return out.reshape(H, W) + + prefix = im.shape[:-2] + n = int(np.prod(prefix)) if prefix else 1 + im_flat = im.reshape(n, H, W) + out_flat = np.empty((n, H * W), dtype=np.result_type(im_flat.dtype, np.float64)) + for i in range(n): + out_flat[i] = map_coordinates(im_flat[i], coords, order=order, mode=mode, cval=cval) + return out_flat.reshape(*prefix, H, W) From 22804b2289b9195900a4ef89e87c064b4489c9e4 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 18 Jan 2026 20:23:34 -0800 Subject: [PATCH 11/43] strain mapping updates --- src/quantem/diffraction/__init__.py | 2 +- src/quantem/diffraction/strain.py | 953 ++++++++++++++++++---------- 2 files changed, 608 insertions(+), 347 deletions(-) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 6d0e4202..77bc5f82 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,2 +1,2 @@ from quantem.diffraction.polar import RDF as RDF -from quantem.diffraction.strain import StrainMap as StrainMap +from quantem.diffraction.strain import StrainMapAutocorrelation as StrainMapAutocorrelation diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py index bcfa981d..fa92c847 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain.py @@ -18,7 +18,7 @@ from quantem.core.visualization import ScalebarConfig, show_2d -class StrainMap(AutoSerialize): +class StrainMapAutocorrelation(AutoSerialize): _token = object() def __init__( @@ -28,7 +28,9 @@ def __init__( _token: object | None = None, ): if _token is not self._token: - raise RuntimeError("Use StrainMap.from_data() to instantiate this class.") + raise RuntimeError( + "Use StrainMapAutocorrelation.from_dataset() or StrainMapAutocorrelation.from_array() to instantiate this class." + ) super().__init__() self.dataset = dataset self.input_data = input_data @@ -36,106 +38,129 @@ def __init__( self.metadata: dict[str, Any] = {} self.transform: Dataset2d | None = None self.transform_rotated: Dataset2d | None = None + self.u: NDArray | None = None self.v: NDArray | None = None + + self.u_fit: Dataset3d | None = None + self.v_fit: Dataset3d | None = None + self.u_peak_fit: Dataset3d | None = None + self.v_peak_fit: Dataset3d | None = None + self.mask_diffraction = np.ones(self.dataset.array.shape[2:]) self.mask_diffraction_inv = np.zeros(self.dataset.array.shape[2:]) @classmethod - def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") -> "StrainMap": - if isinstance(data, Dataset4dstem): - return cls(dataset=data, input_data=data, _token=cls._token) + def from_dataset(cls, dataset: Dataset4dstem, *, name: str | None = None) -> "StrainMapAutocorrelation": + if not isinstance(dataset, Dataset4dstem): + raise TypeError("StrainMapAutocorrelation.from_dataset expects a Dataset4dstem instance.") + if name is not None: + dataset.name = name + return cls(dataset=dataset, input_data=dataset, _token=cls._token) - arr = ensure_valid_array(data) + @classmethod + def from_array(cls, array: NDArray, *, name: str = "strain_map_autocorrelation") -> "StrainMapAutocorrelation": + arr = ensure_valid_array(array) if arr.ndim != 4: raise ValueError( - "StrainMap.from_data expects a 4D array with shape (scan_r, scan_c, dp_r, dp_c)." + "StrainMapAutocorrelation.from_array expects a 4D array with shape (scan_r, scan_c, dp_r, dp_c)." ) - ds4 = Dataset4dstem.from_array(arr, name=name) - return cls(dataset=ds4, input_data=data, _token=cls._token) - + return cls(dataset=ds4, input_data=array, _token=cls._token) def diffraction_mask( self, - threshold = None, - edge_blend = 64.0, - plot_mask = True, - figsize = (8,4), + threshold=None, + edge_blend=64.0, + plot_mask=True, + figsize=(8, 4), ): - dp_mean = np.mean(self.dataset.array,axis=(0,1)) + dp_mean = np.mean(self.dataset.array, axis=(0, 1)) mask_init = dp_mean < threshold - mask_init[:,0] = True - mask_init[0,:] = True - mask_init[:,-1] = True - mask_init[-1,:] = True + mask_init[:, 0] = True + mask_init[0, :] = True + mask_init[:, -1] = True + mask_init[-1, :] = True self.mask_diffraction = np.sin( np.clip( distance_transform_edt(np.logical_not(mask_init)) / edge_blend, 0.0, 1.0, - )*np.pi/2, - )**2 - # int_edge = np.sum(dp_mean*self.mask_diffraction) / np.sum(self.mask_diffraction) - int_edge = np.min(dp_mean[self.mask_diffraction>0.99]) + ) + * np.pi + / 2, + ) ** 2 + int_edge = np.min(dp_mean[self.mask_diffraction > 0.99]) self.mask_diffraction_inv = (1 - self.mask_diffraction) * int_edge if plot_mask: - fig,ax = plt.subplots(1,2,figsize=figsize) + fig, ax = plt.subplots(1, 2, figsize=figsize) ax[0].imshow( - np.log(np.maximum(dp_mean,np.min(dp_mean[dp_mean>0]))), - cmap = 'gray', + np.log(np.maximum(dp_mean, np.min(dp_mean[dp_mean > 0]))), + cmap="gray", ) ax[1].imshow( np.log( - dp_mean*self.mask_diffraction + \ - self.mask_diffraction_inv, + dp_mean * self.mask_diffraction + self.mask_diffraction_inv, ), - cmap = 'gray', + cmap="gray", ) - - return self + return self def preprocess( self, mode: str = "linear", q_to_r_rotation_ccw_deg: float | None = None, q_transpose: bool | None = None, - skip = None, + skip=None, plot_transform: bool = True, cropping_factor: float = 0.25, + gamma: float = 0.5, **plot_kwargs: Any, - ) -> "StrainMap": + ) -> "StrainMapAutocorrelation": + mode_in = mode.strip().lower() + if mode_in in {"linear", "patterson", "paterson", "acf", "autocorrelation"}: + mode_norm = "linear" + elif mode_in in {"log", "cepstrum", "cepstral"}: + mode_norm = "log" + elif mode_in in {"gamma", "power", "sqrt"}: + mode_norm = "gamma" + else: + raise ValueError( + "mode must be 'linear', 'log', or 'gamma' (aliases: 'patterson'->'linear', 'cepstrum'/'cepstral'->'log')." + ) - self.metadata["mode"] = mode + self.metadata["mode"] = mode_norm + if mode_norm == "gamma": + self.metadata["gamma"] = gamma - qrow_unit = str(self.dataset.units[2]) - qcol_unit = str(self.dataset.units[3]) + qrow_unit = self.dataset.units[2] + qcol_unit = self.dataset.units[3] if qrow_unit in {"A", "Å"}: - qrow_sampling_ang = float(self.dataset.sampling[2]) + qrow_sampling_ang = self.dataset.sampling[2] elif qrow_unit == "mrad": - wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) - qrow_sampling_ang = float(self.dataset.sampling[2]) / 1000.0 / wavelength + wavelength = electron_wavelength_angstrom(self.dataset.metadata["energy"]) + qrow_sampling_ang = self.dataset.sampling[2] / 1000.0 / wavelength else: qrow_sampling_ang = 1.0 qrow_unit = "pixels" if qcol_unit in {"A", "Å"}: - qcol_sampling_ang = float(self.dataset.sampling[3]) + qcol_sampling_ang = self.dataset.sampling[3] elif qcol_unit == "mrad": - wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) - qcol_sampling_ang = float(self.dataset.sampling[3]) / 1000.0 / wavelength + wavelength = electron_wavelength_angstrom(self.dataset.metadata["energy"]) + qcol_sampling_ang = self.dataset.sampling[3] / 1000.0 / wavelength else: qcol_sampling_ang = 1.0 qcol_unit = "pixels" self.metadata["sampling_real"] = np.array( ( - 1.0 / (qrow_sampling_ang * float(self.dataset.shape[2])), - 1.0 / (qcol_sampling_ang * float(self.dataset.shape[3])), + 1.0 / (qrow_sampling_ang * self.dataset.shape[2]), + 1.0 / (qcol_sampling_ang * self.dataset.shape[3]), ), dtype=float, ) @@ -145,67 +170,53 @@ def preprocess( else: self.metadata["real_units"] = r"$\mathrm{\AA}$" - if q_to_r_rotation_ccw_deg is None or q_transpose is None: - parent_rot = self.dataset.metadata.get("q_to_r_rotation_ccw_deg", None) - parent_tr = self.dataset.metadata.get("q_transpose", None) - if q_to_r_rotation_ccw_deg is None and parent_rot is not None: - q_to_r_rotation_ccw_deg = float(parent_rot) - if q_transpose is None and parent_tr is not None: - q_transpose = bool(parent_tr) - if (parent_rot is not None or parent_tr is not None) and ( - q_to_r_rotation_ccw_deg is not None or q_transpose is not None - ): - import warnings - - warnings.warn( - f"StrainMap.preprocess: using parent Dataset4dstem metadata " - f"(q_to_r_rotation_ccw_deg={float(q_to_r_rotation_ccw_deg or 0.0)}, " - f"q_transpose={bool(q_transpose or False)}).", - UserWarning, - ) + parent_rot = self.dataset.metadata.get("q_to_r_rotation_ccw_deg", None) + parent_tr = self.dataset.metadata.get("q_transpose", None) - if q_to_r_rotation_ccw_deg is None or q_transpose is None: + used_parent = False + if q_to_r_rotation_ccw_deg is None and parent_rot is not None: + q_to_r_rotation_ccw_deg = parent_rot + used_parent = True + if q_transpose is None and parent_tr is not None: + q_transpose = parent_tr + used_parent = True + + if used_parent: import warnings - q_to_r_rotation_ccw_deg = ( - 0.0 if q_to_r_rotation_ccw_deg is None else float(q_to_r_rotation_ccw_deg) + warnings.warn( + "StrainMapAutocorrelation.preprocess: using parent Dataset4dstem metadata " + f"(q_to_r_rotation_ccw_deg={q_to_r_rotation_ccw_deg or 0.0}, " + f"q_transpose={q_transpose or False}).", + UserWarning, ) - q_transpose = False if q_transpose is None else bool(q_transpose) + + if q_to_r_rotation_ccw_deg is None or q_transpose is None: + import warnings + + q_to_r_rotation_ccw_deg = 0.0 if q_to_r_rotation_ccw_deg is None else q_to_r_rotation_ccw_deg + q_transpose = False if q_transpose is None else q_transpose warnings.warn( - "StrainMap.preprocess: setting q_to_r_rotation_ccw_deg=0.0 and q_transpose=False.", + "StrainMapPatterson.preprocess: setting q_to_r_rotation_ccw_deg=0.0 and q_transpose=False.", UserWarning, ) - self.metadata["q_to_r_rotation_ccw_deg"] = float(q_to_r_rotation_ccw_deg) - self.metadata["q_transpose"] = bool(q_transpose) - - if skip is None: - if self.metadata["mode"] == "linear": - im = np.mean(np.abs(np.fft.fft2( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - )), axis=(0, 1)) - elif self.metadata["mode"] == "log": - im = np.mean(np.abs(np.fft.fft2(np.log1p( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - ))), axis=(0, 1)) - else: - raise ValueError("mode must be 'linear' or 'log'") + self.metadata["q_to_r_rotation_ccw_deg"] = q_to_r_rotation_ccw_deg + self.metadata["q_transpose"] = q_transpose + + arr = self.dataset.array if skip is None else self.dataset.array[::skip, ::skip] + dp = arr * self.mask_diffraction[None, None, :, :] + self.mask_diffraction_inv[None, None, :, :] + + if mode_norm == "linear": + dp_proc = dp + elif mode_norm == "log": + dp_proc = np.log1p(dp) + elif mode_norm == "gamma": + dp_proc = np.power(np.clip(dp, 0.0, None), self.metadata["gamma"]) else: - if self.metadata["mode"] == "linear": - im = np.mean(np.abs(np.fft.fft2( - self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - )), axis=(0, 1)) - elif self.metadata["mode"] == "log": - im = np.mean(np.abs(np.fft.fft2(np.log1p( - self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - ))), axis=(0, 1)) - else: - raise ValueError("mode must be 'linear' or 'log'") + raise RuntimeError("Unreachable: normalized mode mapping failed.") + im = np.mean(np.abs(np.fft.fft2(dp_proc)), axis=(0, 1)) im = np.fft.fftshift(im) self.transform = Dataset2d.from_array( @@ -217,13 +228,13 @@ def preprocess( ) im_plot = self.transform.array - if bool(self.metadata["q_transpose"]): + if self.metadata["q_transpose"]: im_plot = im_plot.T self.transform_rotated = Dataset2d.from_array( rotate_image( im_plot, - float(self.metadata["q_to_r_rotation_ccw_deg"]), + self.metadata["q_to_r_rotation_ccw_deg"], clockwise=False, ), origin=(im.shape[0] // 2, im.shape[1] // 2), @@ -246,21 +257,20 @@ def plot_transform( if self.transform is None or self.transform_rotated is None: raise ValueError("Run preprocess() first to compute transform images.") - sampling = float(np.mean(self.metadata["sampling_real"])) - units = str(self.metadata.get("real_units", r"$\mathrm{\AA}$")) + sampling = np.mean(self.metadata["sampling_real"]) + units = self.metadata.get("real_units", r"$\mathrm{\AA}$") - W = int(self.transform.shape[1]) - view_w_px = float(W) * float(cropping_factor) - target_units = float(scalebar_fraction) * view_w_px * sampling + W = self.transform.shape[1] + view_w_px = W * cropping_factor + target_units = scalebar_fraction * view_w_px * sampling sb_len = _nice_length_units(target_units) - # intensity scaling: compute from transform, apply same scaling to both panels kr = (np.arange(self.transform.shape[0], dtype=float) - self.transform.shape[0] // 2)[:, None] kc = (np.arange(self.transform.shape[1], dtype=float) - self.transform.shape[1] // 2)[None, :] qmag = np.sqrt(kr * kr + kc * kc) im0 = self.transform.array tmp = im0 * qmag - i0 = np.unravel_index(int(np.nanargmax(tmp)), tmp.shape) + i0 = np.unravel_index(np.nanargmax(tmp), tmp.shape) vmin = 0.0 vmax = im0[i0] @@ -283,53 +293,57 @@ def plot_transform( return fig, ax - def choose_lattice_vector( self, *, u: tuple[float, float] | NDArray, v: tuple[float, float] | NDArray, define_in_rotated: bool = False, - refine_subpixel: bool = True, - refine_subpixel_dft: bool = False, + refine_gaussian: bool = True, + refine_dft: bool = False, refine_radius_px: float = 2.0, - refine_log: bool = False, upsample: int = 16, + gaussian_maxfev: int = 100, plot: bool = True, cropping_factor: float = 0.25, **plot_kwargs: Any, - ) -> "StrainMap": + ) -> "StrainMapAutocorrelation": if self.transform is None or self.transform_rotated is None: raise ValueError("Run preprocess() first to compute transform images.") u_rc = np.asarray(u, dtype=float).reshape(2) v_rc = np.asarray(v, dtype=float).reshape(2) - rot_ccw = float(self.metadata.get("q_to_r_rotation_ccw_deg", 0.0)) - q_transpose = bool(self.metadata.get("q_transpose", False)) + rot_ccw = self.metadata["q_to_r_rotation_ccw_deg"] + q_transpose = self.metadata["q_transpose"] if define_in_rotated: u_rc = _display_vec_to_raw(u_rc, rotation_ccw_deg=rot_ccw, transpose=q_transpose) v_rc = _display_vec_to_raw(v_rc, rotation_ccw_deg=rot_ccw, transpose=q_transpose) - if refine_subpixel_dft: - refine_subpixel = True - - if refine_subpixel: - u_rc, v_rc = _refine_lattice_vectors( - self.transform.array, - u_rc=u_rc, - v_rc=v_rc, - radius_px=float(refine_radius_px), - log_fit=bool(refine_log), - refine_dft=bool(refine_subpixel_dft), - upsample=int(upsample), - ) + u_fit_abs, v_fit_abs = _refine_lattice_vectors( + self.transform.array, + u_rc=u_rc, + v_rc=v_rc, + radius_px=refine_radius_px, + refine_gaussian=refine_gaussian, + refine_dft=refine_dft, + upsample=upsample, + maxfev=gaussian_maxfev, + ) + + H, W = self.transform.array.shape + center = np.array((H // 2, W // 2), dtype=float) - self.u = u_rc - self.v = v_rc - self.metadata["lattice_u_rc"] = self.u.copy() - self.metadata["lattice_v_rc"] = self.v.copy() + self.u = u_fit_abs[:2] - center + self.v = v_fit_abs[:2] - center + + self.metadata["choose_define_in_rotated"] = define_in_rotated + self.metadata["choose_refine_gaussian"] = refine_gaussian + self.metadata["choose_refine_dft"] = refine_dft + self.metadata["choose_refine_radius_px"] = refine_radius_px + self.metadata["choose_upsample"] = upsample + self.metadata["choose_gaussian_maxfev"] = gaussian_maxfev if plot: fig, ax = self.plot_transform(cropping_factor=cropping_factor, **plot_kwargs) @@ -345,38 +359,46 @@ def choose_lattice_vector( return self - def fit_lattice_vectors( self, - refine_subpixel: bool = True, - refine_subpixel_dft: bool = False, + refine_gaussian: bool = True, + refine_dft: bool = False, refine_radius_px: float = 2.0, upsample: int = 16, - refine_log: bool = False, + gaussian_maxfev: int = 100, progressbar: bool = True, - ) -> "StrainMap": - from quantem.core.datastructures.dataset3d import Dataset3d - + ) -> "StrainMapAutocorrelation": if self.u is None or self.v is None: raise ValueError("Run choose_lattice_vector() first to set initial lattice vectors (self.u, self.v).") - if refine_subpixel_dft: - refine_subpixel = True - scan_r = self.dataset.shape[0] scan_c = self.dataset.shape[1] + + self.u_peak_fit = Dataset3d.from_shape( + (scan_r, scan_c, 5), + name="u_peak_fit", + signal_units="mixed", + ) + self.v_peak_fit = Dataset3d.from_shape( + (scan_r, scan_c, 5), + name="v_peak_fit", + signal_units="mixed", + ) + self.u_fit = Dataset3d.from_shape( (scan_r, scan_c, 2), - name="u_fits", + name="u_fit", signal_units="pixels", ) self.v_fit = Dataset3d.from_shape( (scan_r, scan_c, 2), - name="v_fits", + name="v_fit", signal_units="pixels", ) - mode = str(self.metadata.get("mode", "linear")).lower() + mode = self.metadata.get("mode", "linear").lower() + if mode == "gamma": + g = self.metadata["gamma"] it = np.ndindex(scan_r, scan_c) if progressbar: @@ -390,97 +412,150 @@ def fit_lattice_vectors( u0 = np.asarray(self.u, dtype=float).reshape(2) v0 = np.asarray(self.v, dtype=float).reshape(2) + dp_shape = self.dataset.array.shape[2:] + r_center = dp_shape[0] // 2 + c_center = dp_shape[1] // 2 + for r, c in it: - dp = self.dataset.array[r, c]*self.mask_diffraction + \ - self.mask_diffraction_inv + dp = self.dataset.array[r, c] * self.mask_diffraction + self.mask_diffraction_inv if mode == "linear": im = np.fft.fftshift(np.abs(np.fft.fft2(dp))) elif mode == "log": im = np.fft.fftshift(np.abs(np.fft.fft2(np.log1p(dp)))) + elif mode == "gamma": + im = np.fft.fftshift(np.abs(np.fft.fft2(np.power(np.clip(dp, 0.0, None), g)))) else: - raise ValueError("metadata['mode'] must be 'linear' or 'log'") - - if refine_subpixel: - u_rc, v_rc = _refine_lattice_vectors( - im, - u_rc=u0, - v_rc=v0, - radius_px=float(refine_radius_px), - log_fit=bool(refine_log), - refine_dft=bool(refine_subpixel_dft), - upsample=int(upsample), - ) - else: - u_rc = u0 - v_rc = v0 + raise ValueError("metadata['mode'] must be 'linear', 'log', or 'gamma'") + + u_fit_abs, v_fit_abs = _refine_lattice_vectors( + im, + u_rc=u0, + v_rc=v0, + radius_px=refine_radius_px, + refine_gaussian=refine_gaussian, + refine_dft=refine_dft, + upsample=upsample, + maxfev=gaussian_maxfev, + ) - self.u_fit.array[r, c, :] = u_rc - self.v_fit.array[r, c, :] = v_rc + self.u_peak_fit.array[r, c, :] = u_fit_abs + self.v_peak_fit.array[r, c, :] = v_fit_abs - self.metadata["fit_refine_subpixel"] = bool(refine_subpixel) - self.metadata["fit_refine_subpixel_dft"] = bool(refine_subpixel_dft) - self.metadata["fit_refine_radius_px"] = float(refine_radius_px) - self.metadata["fit_refine_log"] = bool(refine_log) - self.metadata["fit_upsample"] = int(upsample) + self.u_fit.array[r, c, 0] = u_fit_abs[0] - r_center + self.u_fit.array[r, c, 1] = u_fit_abs[1] - c_center + self.v_fit.array[r, c, 0] = v_fit_abs[0] - r_center + self.v_fit.array[r, c, 1] = v_fit_abs[1] - c_center - return self + self.metadata["fit_refine_gaussian"] = refine_gaussian + self.metadata["fit_refine_dft"] = refine_dft + self.metadata["fit_refine_radius_px"] = refine_radius_px + self.metadata["fit_upsample"] = upsample + self.metadata["fit_gaussian_maxfev"] = gaussian_maxfev + return self def plot_lattice_vectors( self, subtract_mean: bool = True, - scalebar: bool = False, - **plot_kwargs: Any, + max_shift: float = 1.0, + cmap: str = "PiYG_r", + axsize: tuple[float, float] | None = None, + figsize: tuple[float, float] | None = None, + **imshow_kwargs: Any, ): - if getattr(self, "u_fit", None) is None or getattr(self, "v_fit", None) is None: + if self.u_fit is None or self.v_fit is None: raise ValueError("Run fit_lattice_vectors() first to compute u_fit and v_fit.") + if self.u is None or self.v is None: + raise ValueError("Run choose_lattice_vector() first to set self.u and self.v.") - im0 = self.u_fit.array[:,:,0] - im1 = self.u_fit.array[:,:,1] - im2 = self.v_fit.array[:,:,0] - im3 = self.v_fit.array[:,:,1] + im0 = self.u_fit.array[:, :, 0] + im1 = self.u_fit.array[:, :, 1] + im2 = self.v_fit.array[:, :, 0] + im3 = self.v_fit.array[:, :, 1] + + du0 = im0 - self.u[0] + du1 = im1 - self.u[1] + dv0 = im2 - self.v[0] + dv1 = im3 - self.v[1] + + max_shift2 = max_shift * max_shift + mu = (du0 * du0 + du1 * du1) <= max_shift2 + mv = (dv0 * dv0 + dv1 * dv1) <= max_shift2 if subtract_mean: - im0 = im0 - float(np.nanmean(im0)) - im1 = im1 - float(np.nanmean(im1)) - im2 = im2 - float(np.nanmean(im2)) - im3 = im3 - float(np.nanmean(im3)) + if np.any(mu): + im0 = im0 - np.mean(im0[mu]) + im1 = im1 - np.mean(im1[mu]) + else: + im0 = im0 - np.mean(im0) + im1 = im1 - np.mean(im1) + + if np.any(mv): + im2 = im2 - np.mean(im2[mv]) + im3 = im3 - np.mean(im3[mv]) + else: + im2 = im2 - np.mean(im2) + im3 = im3 - np.mean(im3) + + vals = [] + if np.any(mu): + vals.append(np.abs(im0[mu])) + vals.append(np.abs(im1[mu])) + if np.any(mv): + vals.append(np.abs(im2[mv])) + vals.append(np.abs(im3[mv])) + + if vals: + vlim = np.max(np.concatenate(vals)) + else: + vlim = np.max(np.abs(np.stack([im0, im1, im2, im3], axis=0))) - vlim = float(np.nanmax(np.abs(np.stack([im0, im1, im2, im3], axis=0)))) vmin = -vlim vmax = vlim - defaults: dict[str, Any] = dict( - title=("u_r", "u_c", "v_r", "v_c"), - vmin=vmin, - vmax=vmax, - ) + cm = plt.get_cmap(cmap).copy() + cm.set_bad(color="black") - if scalebar: - s0 = float(self.dataset.sampling[0]) if len(self.dataset.sampling) > 0 else 1.0 - s1 = float(self.dataset.sampling[1]) if len(self.dataset.sampling) > 1 else s0 - sampling_scan = float(np.mean([s0, s1])) - units_scan = str(self.dataset.units[0]) if len(self.dataset.units) > 0 else "pixels" - defaults["scalebar"] = ScalebarConfig(sampling=sampling_scan, units=units_scan) + m0 = np.ma.array(im0, mask=~mu) + m1 = np.ma.array(im1, mask=~mu) + m2 = np.ma.array(im2, mask=~mv) + m3 = np.ma.array(im3, mask=~mv) - defaults.update(plot_kwargs) + if axsize is None and figsize is None: + axsize = (4.0, 4.0) + if figsize is None: + figsize = (axsize[0] * 4.0, axsize[1]) - fig, ax = show_2d([im0, im1, im2, im3], **defaults) - return fig, ax + fig, ax = plt.subplots(1, 4, figsize=figsize) + + ax[0].imshow(m0, cmap=cm, vmin=vmin, vmax=vmax, **imshow_kwargs) + ax[1].imshow(m1, cmap=cm, vmin=vmin, vmax=vmax, **imshow_kwargs) + ax[2].imshow(m2, cmap=cm, vmin=vmin, vmax=vmax, **imshow_kwargs) + ax[3].imshow(m3, cmap=cm, vmin=vmin, vmax=vmax, **imshow_kwargs) + + ax[0].set_title("u_r") + ax[1].set_title("u_c") + ax[2].set_title("v_r") + ax[3].set_title("v_c") + for a in ax: + a.set_xticks([]) + a.set_yticks([]) + + return fig, ax def fit_strain( self, - mask_reference = None, - plot_strain = True, + mask_reference=None, + plot_strain=True, ): if self.u_fit is None or self.v_fit is None: raise ValueError("Run fit_lattice_vectors() first to compute u_fit and v_fit.") u_fit = self.u_fit.array v_fit = self.v_fit.array - scan_r, scan_c = int(u_fit.shape[0]), int(u_fit.shape[1]) + scan_r, scan_c = u_fit.shape[0], u_fit.shape[1] if mask_reference is None: self.u_ref = np.median(u_fit.reshape(-1, 2), axis=0) @@ -503,26 +578,23 @@ def fit_strain( ) Uref = np.stack((self.u_ref, self.v_ref), axis=1).astype(float) - det = float(np.linalg.det(Uref)) + det = np.linalg.det(Uref) if not np.isfinite(det) or abs(det) < 1e-12: Uref_inv = np.linalg.pinv(Uref) else: Uref_inv = np.linalg.inv(Uref) - # init self.strain_trans = Dataset4d.from_shape( (scan_r, scan_c, 2, 2), name="transformation matrix", signal_units="fractional", ) - # Loop over probe positions for r in range(scan_r): for c in range(scan_c): U = np.stack((u_fit[r, c, :], v_fit[r, c, :]), axis=1) self.strain_trans.array[r, c, :, :] = U @ Uref_inv - # get strains in orthogonal directions self.strain_raw_err = Dataset2d.from_array( self.strain_trans.array[:, :, 0, 0] - 1, name="strain err", @@ -534,12 +606,12 @@ def fit_strain( signal_units="fractional", ) self.strain_raw_erc = Dataset2d.from_array( - self.strain_trans.array[:, :, 1, 0]*0.5 + self.strain_trans.array[:, :, 0, 1]*0.5, + self.strain_trans.array[:, :, 1, 0] * 0.5 + self.strain_trans.array[:, :, 0, 1] * 0.5, name="strain erc", signal_units="fractional", ) self.strain_rotation = Dataset2d.from_array( - self.strain_trans.array[:, :, 1, 0]*-0.5 + self.strain_trans.array[:, :, 0, 1]*0.5, + self.strain_trans.array[:, :, 1, 0] * -0.5 + self.strain_trans.array[:, :, 0, 1] * 0.5, name="strain rotation", signal_units="fractional", ) @@ -555,23 +627,26 @@ def plot_strain( rotation_range_degrees=(-2.0, 2.0), plot_rotation=True, cmap_strain="PiYG_r", - cmap_rotation="PiYG_r", + cmap_rotation=None, layout="horizontal", figsize=(6, 6), + max_shift: tuple[float, float] | None = None, + amp_range: tuple[float, float] | None = None, ): import matplotlib.pyplot as plt + if cmap_rotation is None: + cmap_rotation = cmap_strain + if ref_angle_degrees is None: - ref_vec = self.u_ref * float(ref_u_v[0]) + self.v_ref * float(ref_u_v[1]) - ref_angle = float(np.arctan2(ref_vec[1], ref_vec[0])) + ref_vec = self.u_ref * ref_u_v[0] + self.v_ref * ref_u_v[1] + ref_angle = np.arctan2(ref_vec[1], ref_vec[0]) else: - ref_angle = float(np.deg2rad(ref_angle_degrees)) + ref_angle = np.deg2rad(ref_angle_degrees) angle = ref_angle + np.deg2rad(self.metadata["q_to_r_rotation_ccw_deg"]) - print(np.round(np.rad2deg(angle),2)) - - c = float(np.cos(angle)) - s = float(np.sin(angle)) + c = np.cos(angle) + s = np.sin(angle) err = self.strain_raw_err.array ecc = self.strain_raw_ecc.array @@ -588,62 +663,148 @@ def plot_strain( self.strain_evv.array[...] = evv self.strain_euv.array[...] = euv - if layout == "horizontal": - if plot_rotation: - fig, ax = plt.subplots(1, 4, figsize=figsize) - - ax[0].imshow( - self.strain_euu.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[1].imshow( - self.strain_evv.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[2].imshow( - self.strain_euv.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[3].imshow( - np.rad2deg(self.strain_rotation.array), - vmin=rotation_range_degrees[0], - vmax=rotation_range_degrees[1], - cmap=cmap_rotation, - ) - return fig, ax - - fig, ax = plt.subplots(1, 3, figsize=figsize) - ax[0].imshow( - self.strain_euu.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[1].imshow( - self.strain_evv.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[2].imshow( - self.strain_euv.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, + alpha = None + if max_shift is not None: + if self.u_fit is None or self.v_fit is None or self.u is None or self.v is None: + raise ValueError("max_shift masking requires u_fit, v_fit, u, v to be available.") + + ur = self.u_fit.array[:, :, 0] + uc = self.u_fit.array[:, :, 1] + vr = self.v_fit.array[:, :, 0] + vc = self.v_fit.array[:, :, 1] + + du0 = ur - self.u[0] + du1 = uc - self.u[1] + dv0 = vr - self.v[0] + dv1 = vc - self.v[1] + + su = du0 * du0 + du1 * du1 + sv = dv0 * dv0 + dv1 * dv1 + sdist2 = 0.5 * (su + sv) + + smin, smax = max_shift + mask = np.clip((sdist2 - smin) / (smax - smin), 0.0, 1.0) + alpha = 1.0 - mask + + if amp_range is not None: + if self.u_peak_fit is None or self.v_peak_fit is None: + raise ValueError("amp_range masking requires u_peak_fit and v_peak_fit to be available.") + a = 0.5 * (self.u_peak_fit.array[:, :, 2] + self.v_peak_fit.array[:, :, 2]) + amin, amax = amp_range + a_mask = np.clip((a - amin) / (amax - amin), 0.0, 1.0) + alpha = a_mask if alpha is None else alpha * a_mask + + if alpha is not None: + alpha = np.asarray(alpha, dtype=float) + good = alpha > 0 + alpha_im = np.where(good, alpha, 1.0) + else: + good = None + alpha_im = None + + if layout != "horizontal": + raise ValueError("layout must be 'horizontal'") + + ncols = 4 if plot_rotation else 3 + fig, ax = plt.subplots(1, ncols, figsize=figsize) + + cm_strain = plt.get_cmap(cmap_strain).copy() + cm_strain.set_bad(color="black") + cm_rot = plt.get_cmap(cmap_rotation).copy() + cm_rot.set_bad(color="black") + + euu_pct = self.strain_euu.array * 100 + evv_pct = self.strain_evv.array * 100 + euv_pct = self.strain_euv.array * 100 + rot_deg = np.rad2deg(self.strain_rotation.array) + + if good is not None and np.any(good): + euu_m = np.ma.array(euu_pct, mask=~good) + evv_m = np.ma.array(evv_pct, mask=~good) + euv_m = np.ma.array(euv_pct, mask=~good) + rot_m = np.ma.array(rot_deg, mask=~good) + else: + euu_m = euu_pct + evv_m = evv_pct + euv_m = euv_pct + rot_m = rot_deg + + title_fs = 16 + im0 = ax[0].imshow( + euu_m, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cm_strain, + alpha=alpha_im, + ) + ax[1].imshow( + evv_m, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cm_strain, + alpha=alpha_im, + ) + ax[2].imshow( + euv_m, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cm_strain, + alpha=alpha_im, + ) + + ax[0].set_title(r"$\epsilon_{uu}$", fontsize=title_fs) + ax[1].set_title(r"$\epsilon_{vv}$", fontsize=title_fs) + ax[2].set_title(r"$\epsilon_{uv}$", fontsize=title_fs) + + if plot_rotation: + im3 = ax[3].imshow( + rot_m, + vmin=rotation_range_degrees[0], + vmax=rotation_range_degrees[1], + cmap=cm_rot, + alpha=alpha_im, ) - return fig, ax + ax[3].set_title("Rotation", fontsize=title_fs) - raise ValueError("layout must be 'horizontal'") + for a in ax: + a.set_xticks([]) + a.set_yticks([]) + a.set_facecolor("black") + + fig.subplots_adjust(left=0.02, right=0.98, top=0.90, bottom=0.16, wspace=0.03) + + b0 = ax[0].get_position() + b2 = ax[2].get_position() + left = b0.x0 + right = b2.x1 + width = right - left + + b3 = ax[3].get_position() if plot_rotation else None + + cb_height = 0.04 + cb_pad = 0.03 + y = b0.y0 - cb_pad - cb_height + + cax1 = fig.add_axes([left, y, width, cb_height]) + cbar1 = fig.colorbar(im0, cax=cax1, orientation="horizontal") + cbar1.set_label("Strain (%)", fontsize=title_fs) + cbar1.ax.tick_params(labelsize=12) + + if plot_rotation: + left_r = b3.x0 + width_r = b3.x1 - b3.x0 + cax2 = fig.add_axes([left_r, y, width_r, cb_height]) + cbar2 = fig.colorbar(im3, cax=cax2, orientation="horizontal") + cbar2.set_label("Rotation (deg)", fontsize=title_fs) + cbar2.ax.tick_params(labelsize=12) + + for a in ax: + a.set_aspect("equal") + + return fig, ax def _nice_length_units(target: float) -> float: - target = float(target) if not np.isfinite(target) or target <= 0: return 0.0 exp = np.floor(np.log10(target)) @@ -656,21 +817,20 @@ def _nice_length_units(target: float) -> float: nice = 5.0 else: nice = 10.0 - return float(nice * (10.0**exp)) + return nice * (10.0**exp) def _apply_center_crop_limits(ax: Any, shape: tuple[int, int], cropping_factor: float) -> None: - cf = float(cropping_factor) - if cf >= 1.0: + if cropping_factor >= 1.0: return - if not (0.0 < cf <= 1.0): + if not (0.0 < cropping_factor <= 1.0): raise ValueError("cropping_factor must be in (0, 1].") - H, W = int(shape[0]), int(shape[1]) - r0 = float(H // 2) - c0 = float(W // 2) - half_h = 0.5 * cf * H - half_w = 0.5 * cf * W + H, W = shape + r0 = H // 2 + c0 = W // 2 + half_h = 0.5 * cropping_factor * H + half_w = 0.5 * cropping_factor * W ax.set_xlim(c0 - half_w, c0 + half_w) @@ -694,14 +854,14 @@ def _flatten_axes(ax: Any) -> list[Any]: def _raw_vec_to_display(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: bool) -> NDArray: v = np.asarray(vec_rc, dtype=float).reshape(2) - dr, dc = float(v[0]), float(v[1]) + dr, dc = v[0], v[1] if transpose: dr, dc = dc, dr - theta = float(np.deg2rad(rotation_ccw_deg)) - ct = float(np.cos(theta)) - st = float(np.sin(theta)) + theta = np.deg2rad(rotation_ccw_deg) + ct = np.cos(theta) + st = np.sin(theta) dr2 = ct * dr - st * dc dc2 = st * dr + ct * dc @@ -710,11 +870,11 @@ def _raw_vec_to_display(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: def _display_vec_to_raw(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: bool) -> NDArray: v = np.asarray(vec_rc, dtype=float).reshape(2) - dr, dc = float(v[0]), float(v[1]) + dr, dc = v[0], v[1] - theta = float(np.deg2rad(rotation_ccw_deg)) - ct = float(np.cos(theta)) - st = float(np.sin(theta)) + theta = np.deg2rad(rotation_ccw_deg) + ct = np.cos(theta) + st = np.sin(theta) dr2 = ct * dr + st * dc dc2 = -st * dr + ct * dc @@ -725,17 +885,17 @@ def _display_vec_to_raw(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: return np.array((dr2, dc2), dtype=float) -def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: - r0, c0 = float(center_rc[0]), float(center_rc[1]) +# def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: +# r0, c0 = center_rc - def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: - dr, dc = float(vec[0]), float(vec[1]) - ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) - ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) - ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") +# def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: +# dr, dc = vec[0], vec[1] +# ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) +# ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) +# ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") - _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) - _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) +# _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) +# _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) def _overlay_lattice_vectors( @@ -751,25 +911,25 @@ def _overlay_lattice_vectors( if not axs: return - H, W = int(shape[0]), int(shape[1]) - center_rc = (float(H // 2), float(W // 2)) + H, W = shape + center_rc = (H // 2, W // 2) _plot_lattice_vectors(axs[0], center_rc, u_rc, v_rc) if len(axs) >= 2: - u_disp = _raw_vec_to_display(u_rc, rotation_ccw_deg=float(rot_ccw_deg), transpose=bool(q_transpose)) - v_disp = _raw_vec_to_display(v_rc, rotation_ccw_deg=float(rot_ccw_deg), transpose=bool(q_transpose)) + u_disp = _raw_vec_to_display(u_rc, rotation_ccw_deg=rot_ccw_deg, transpose=q_transpose) + v_disp = _raw_vec_to_display(v_rc, rotation_ccw_deg=rot_ccw_deg, transpose=q_transpose) _plot_lattice_vectors(axs[1], center_rc, u_disp, v_disp) def _parabolic_vertex_delta(v_m1: float, v_0: float, v_p1: float) -> float: - denom = (v_m1 - 2.0 * v_0 + v_p1) + denom = v_m1 - 2.0 * v_0 + v_p1 if denom == 0 or not np.isfinite(denom): return 0.0 delta = 0.5 * (v_m1 - v_p1) / denom if not np.isfinite(delta): return 0.0 - return float(np.clip(delta, -1.0, 1.0)) + return np.clip(delta, -1.0, 1.0) def _refine_peak_subpixel( @@ -778,14 +938,13 @@ def _refine_peak_subpixel( r_guess: float, c_guess: float, radius_px: float = 2.0, - log_fit: bool = False, ) -> tuple[float, float]: im = np.asarray(im, dtype=float) H, W = im.shape r0 = int(np.clip(int(np.round(r_guess)), 0, H - 1)) c0 = int(np.clip(int(np.round(c_guess)), 0, W - 1)) - rad = int(max(0, int(np.ceil(float(radius_px))))) + rad = int(max(0, int(np.ceil(radius_px)))) r1 = max(0, r0 - rad) r2 = min(H, r0 + rad + 1) @@ -794,29 +953,25 @@ def _refine_peak_subpixel( win = im[r1:r2, c1:c2] if win.size == 0: - return float(r_guess), float(c_guess) + return r_guess, c_guess - ir, ic = np.unravel_index(int(np.argmax(win)), win.shape) - r_peak = r1 + int(ir) - c_peak = c1 + int(ic) + ir, ic = np.unravel_index(np.argmax(win), win.shape) + r_peak = r1 + ir + c_peak = c1 + ic if 0 < r_peak < H - 1: col = im[r_peak - 1 : r_peak + 2, c_peak] - if log_fit: - col = np.log(np.clip(col, 1e-12, None)) - dr = _parabolic_vertex_delta(float(col[0]), float(col[1]), float(col[2])) + dr = _parabolic_vertex_delta(col[0], col[1], col[2]) else: dr = 0.0 if 0 < c_peak < W - 1: row = im[r_peak, c_peak - 1 : c_peak + 2] - if log_fit: - row = np.log(np.clip(row, 1e-12, None)) - dc = _parabolic_vertex_delta(float(row[0]), float(row[1]), float(row[2])) + dc = _parabolic_vertex_delta(row[0], row[1], row[2]) else: dc = 0.0 - return float(r_peak) + dr, float(c_peak) + dc + return r_peak + dr, c_peak + dc def _refine_peak_subpixel_dft( @@ -825,44 +980,37 @@ def _refine_peak_subpixel_dft( r0: float, c0: float, upsample: int, - log_fit: bool = False, ) -> tuple[float, float]: - if int(upsample) <= 1: - return float(r0), float(c0) + if upsample <= 1: + return r0, c0 im = np.asarray(im, dtype=float) F = np.fft.fft2(im) - up = int(upsample) + up = upsample du = int(np.ceil(1.5 * up)) - patch = dft_upsample(F, up=up, shift=(float(r0), float(c0)), device="cpu") + patch = dft_upsample(F, up=up, shift=(r0, c0), device="cpu") patch = np.asarray(patch, dtype=float) - i0, j0 = np.unravel_index(int(np.argmax(patch)), patch.shape) - i0 = int(i0) - j0 = int(j0) + i0, j0 = np.unravel_index(np.argmax(patch), patch.shape) if 0 < i0 < patch.shape[0] - 1: col = patch[i0 - 1 : i0 + 2, j0] - if log_fit: - col = np.log(np.clip(col, 1e-12, None)) - di = _parabolic_vertex_delta(float(col[0]), float(col[1]), float(col[2])) + di = _parabolic_vertex_delta(col[0], col[1], col[2]) else: di = 0.0 if 0 < j0 < patch.shape[1] - 1: row = patch[i0, j0 - 1 : j0 + 2] - if log_fit: - row = np.log(np.clip(row, 1e-12, None)) - dj = _parabolic_vertex_delta(float(row[0]), float(row[1]), float(row[2])) + dj = _parabolic_vertex_delta(row[0], row[1], row[2]) else: dj = 0.0 - dr = (float(i0) - float(du) + float(di)) / float(up) - dc = (float(j0) - float(du) + float(dj)) / float(up) + dr = (i0 - du + di) / up + dc = (j0 - du + dj) / up - return float(r0) + dr, float(c0) + dc + return r0 + dr, c0 + dc def _refine_lattice_vectors( @@ -871,42 +1019,155 @@ def _refine_lattice_vectors( u_rc: NDArray, v_rc: NDArray, radius_px: float = 2.0, - log_fit: bool = False, - refine_dft: bool = True, + refine_gaussian: bool = True, + refine_dft: bool = False, upsample: int = 16, + maxfev: int = 100, ) -> tuple[NDArray, NDArray]: + from scipy.optimize import curve_fit + im = np.asarray(im, dtype=float) if im.ndim != 2: raise ValueError("im must be 2D.") H, W = im.shape - r_center = float(H // 2) - c_center = float(W // 2) + r_center = H // 2 + c_center = W // 2 + + def _parabolic_peak_rc_amp(*, r_guess: float, c_guess: float) -> tuple[float, float, float]: + r0 = int(np.clip(int(np.round(r_guess)), 0, H - 1)) + c0 = int(np.clip(int(np.round(c_guess)), 0, W - 1)) + win = im[ + max(0, r0 - 1) : min(H, r0 + 2), + max(0, c0 - 1) : min(W, c0 + 2), + ] + if win.size == 0: + return r_guess, c_guess, 0.0 + + ir, ic = np.unravel_index(np.argmax(win), win.shape) + r_peak = max(0, r0 - 1) + ir + c_peak = max(0, c0 - 1) + ic + + r_ref = r_peak + c_ref = c_peak + + if 0 < r_peak < H - 1: + col = im[r_peak - 1 : r_peak + 2, c_peak] + dr = _parabolic_vertex_delta(col[0], col[1], col[2]) + else: + dr = 0.0 - def _refine(vec: NDArray) -> NDArray: + if 0 < c_peak < W - 1: + row = im[r_peak, c_peak - 1 : c_peak + 2] + dc = _parabolic_vertex_delta(row[0], row[1], row[2]) + else: + dc = 0.0 + + r_sub = r_ref + dr + c_sub = c_ref + dc + r_int = int(np.clip(int(np.round(r_sub)), 0, H - 1)) + c_int = int(np.clip(int(np.round(c_sub)), 0, W - 1)) + amp = im[r_int, c_int] + + return r_sub, c_sub, amp + + def _fit_gaussian_isotropic( + *, + r0: float, + c0: float, + radius_px: float, + maxfev: int, + ) -> tuple[float, float, float, float, float]: + rad = int(max(1, int(np.ceil(radius_px)))) + r0i = int(np.clip(int(np.round(r0)), 0, H - 1)) + c0i = int(np.clip(int(np.round(c0)), 0, W - 1)) + + r1 = max(0, r0i - rad) + r2 = min(H, r0i + rad + 1) + c1 = max(0, c0i - rad) + c2 = min(W, c0i + rad + 1) + + win = im[r1:r2, c1:c2] + if win.size == 0: + return r0, c0, 0.0, 0.0, 0.0 + + ir, ic = np.unravel_index(np.argmax(win), win.shape) + r_peak = r1 + ir + c_peak = c1 + ic + + bg0 = np.median(win) + amp0 = win[ir, ic] - bg0 + sig0 = max(0.75, radius_px / 2.0) + + rr = np.arange(r1, r2, dtype=float)[:, None] + cc = np.arange(c1, c2, dtype=float)[None, :] + RR = np.broadcast_to(rr, win.shape) + CC = np.broadcast_to(cc, win.shape) + + def _g2( + coords: tuple[NDArray, NDArray], + row: float, + col: float, + amp: float, + sigma: float, + background: float, + ) -> NDArray: + r, c = coords + sig = np.maximum(sigma, 1e-12) + return background + amp * np.exp(-((r - row) ** 2 + (c - col) ** 2) / (2.0 * sig * sig)) + + p0 = (r_peak, c_peak, max(0.0, amp0), sig0, bg0) + + rlo = r1 - 0.5 + rhi = (r2 - 1) + 0.5 + clo = c1 - 0.5 + chi = (c2 - 1) + 0.5 + + bounds_lo = (rlo, clo, 0.0, 0.25, -np.inf) + bounds_hi = (rhi, chi, np.inf, radius_px * 4.0, np.inf) + + try: + popt, _ = curve_fit( + _g2, + (RR.ravel(), CC.ravel()), + win.ravel(), + p0=p0, + bounds=(bounds_lo, bounds_hi), + maxfev=maxfev, + ) + row, col, amp, sig, bg = popt + if not (np.isfinite(row) and np.isfinite(col) and np.isfinite(amp) and np.isfinite(sig) and np.isfinite(bg)): + return r0, c0, p0[2], 0.0, 0.0 + return row, col, amp, sig, bg + except Exception: + return r0, c0, p0[2], 0.0, 0.0 + + def _refine_one(vec: NDArray) -> NDArray: vec = np.asarray(vec, dtype=float).reshape(2) - r_guess = r_center + float(vec[0]) - c_guess = c_center + float(vec[1]) + r_guess = r_center + vec[0] + c_guess = c_center + vec[1] - r1, c1 = _refine_peak_subpixel( - im, - r_guess=float(r_guess), - c_guess=float(c_guess), - radius_px=float(radius_px), - log_fit=bool(log_fit), - ) + r_par, c_par, amp_par = _parabolic_peak_rc_amp(r_guess=r_guess, c_guess=c_guess) - if refine_dft and int(upsample) > 1: - r2, c2 = _refine_peak_subpixel_dft( - im, - r0=float(r1), - c0=float(c1), - upsample=int(upsample), - log_fit=bool(log_fit), + if refine_gaussian: + r_fit, c_fit, amp, sig, bg = _fit_gaussian_isotropic( + r0=r_par, + c0=c_par, + radius_px=radius_px, + maxfev=maxfev, ) else: - r2, c2 = float(r1), float(c1) + r_fit, c_fit, amp, sig, bg = r_par, c_par, amp_par, 0.0, 0.0 + + if refine_dft and upsample > 1: + r_dft, c_dft = _refine_peak_subpixel_dft( + im, + r0=r_fit, + c0=c_fit, + upsample=upsample, + ) + r_fit, c_fit = r_dft, c_dft - return np.array((r2 - r_center, c2 - c_center), dtype=float) + return np.array((r_fit, c_fit, amp, sig, bg), dtype=float) - return _refine(u_rc), _refine(v_rc) + return _refine_one(u_rc), _refine_one(v_rc) From a154aa6229e1dae67fc809bef4d7d562a4cea4f9 Mon Sep 17 00:00:00 2001 From: cophus Date: Mon, 19 Jan 2026 11:45:06 -0800 Subject: [PATCH 12/43] renaming parent .py file --- src/quantem/diffraction/__init__.py | 2 +- .../{strain.py => strain_autocorrelation.py} | 20 +++++++++---------- 2 files changed, 11 insertions(+), 11 deletions(-) rename src/quantem/diffraction/{strain.py => strain_autocorrelation.py} (98%) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 77bc5f82..450b9b2f 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,2 +1,2 @@ from quantem.diffraction.polar import RDF as RDF -from quantem.diffraction.strain import StrainMapAutocorrelation as StrainMapAutocorrelation +from quantem.diffraction.strain_autocorrelation import StrainMapAutocorrelation as StrainMapAutocorrelation diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain_autocorrelation.py similarity index 98% rename from src/quantem/diffraction/strain.py rename to src/quantem/diffraction/strain_autocorrelation.py index fa92c847..a084b32d 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain_autocorrelation.py @@ -626,7 +626,7 @@ def plot_strain( strain_range_percent=(-3.0, 3.0), rotation_range_degrees=(-2.0, 2.0), plot_rotation=True, - cmap_strain="PiYG_r", + cmap_strain="RdBu_r", cmap_rotation=None, layout="horizontal", figsize=(6, 6), @@ -885,17 +885,17 @@ def _display_vec_to_raw(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: return np.array((dr2, dc2), dtype=float) -# def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: -# r0, c0 = center_rc +def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: + r0, c0 = center_rc -# def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: -# dr, dc = vec[0], vec[1] -# ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) -# ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) -# ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") + def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: + dr, dc = vec[0], vec[1] + ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) + ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) + ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") -# _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) -# _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) + _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) + _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) def _overlay_lattice_vectors( From d3d3aa5952888ad4441c40b35128426fec30ab95 Mon Sep 17 00:00:00 2001 From: cophus Date: Wed, 28 Jan 2026 20:18:07 -0800 Subject: [PATCH 13/43] initial class --- src/quantem/diffraction/__init__.py | 1 + src/quantem/diffraction/maped.py | 87 +++++++++++++++++++++++++++++ 2 files changed, 88 insertions(+) create mode 100644 src/quantem/diffraction/maped.py diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 450b9b2f..2a79312b 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,2 +1,3 @@ from quantem.diffraction.polar import RDF as RDF from quantem.diffraction.strain_autocorrelation import StrainMapAutocorrelation as StrainMapAutocorrelation +from quantem.diffraction.maped import MAPED as MAPED diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py new file mode 100644 index 00000000..f301454e --- /dev/null +++ b/src/quantem/diffraction/maped.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +from typing import Any, Sequence + +import numpy as np + +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.io.serialize import AutoSerialize +from quantem.core.visualization import show_2d + + +class MAPED(AutoSerialize): + _token = object() + + def __init__(self, datasets: list[Dataset4dstem], _token: object | None = None): + if _token is not self._token: + raise RuntimeError("Use MAPED.from_datasets() to instantiate this class.") + AutoSerialize.__init__(self) + self.datasets = datasets + self.metadata: dict[str, Any] = {} + + @classmethod + def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> "MAPED": + if not isinstance(datasets, Sequence) or isinstance(datasets, (str, bytes)): + raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") + ds_list: list[Dataset4dstem] = [] + for d in datasets: + if not isinstance(d, Dataset4dstem): + raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") + ds_list.append(d) + if len(ds_list) == 0: + raise ValueError("MAPED.from_datasets expects a non-empty sequence of Dataset4dstem instances.") + return cls(datasets=ds_list, _token=cls._token) + + def preprocess( + self, + *, + plot_summary: bool = True, + scale: float | Sequence[float] | None = None, + **plot_kwargs: Any, + ) -> "MAPED": + n = len(self.datasets) + if scale is None: + self.scales = np.ones(n, dtype=float) + elif isinstance(scale, (int, float, np.floating)): + self.scales = np.full(n, float(scale), dtype=float) + else: + self.scales = np.asarray(list(scale), dtype=float) + if self.scales.shape != (n,): + raise ValueError("scale must be a scalar or a sequence with the same length as datasets.") + if np.any(self.scales == 0): + raise ValueError("scale entries must be nonzero.") + + self.dp_mean = [] + self.im_bf = [] + + for d in self.datasets: + if hasattr(d, "get_dp_mean"): + try: + d.get_dp_mean() + except TypeError: + try: + d.get_dp_mean(returnval=False) + except Exception: + pass + + dp = getattr(d, "dp_mean", None) + if dp is None: + dp_arr = np.mean(np.asarray(d.array), axis=(0, 1)) + else: + dp_arr = np.asarray(dp.array if hasattr(dp, "array") else dp) + + im_bf_arr = np.mean(np.asarray(d.array), axis=(2, 3)) + + self.dp_mean.append(np.asarray(dp_arr)) + self.im_bf.append(np.asarray(im_bf_arr)) + + if plot_summary: + show_2d( + [ + [self.im_bf[i] / self.scales[i] for i in range(n)], + [self.dp_mean[i] for i in range(n)], + ], + **plot_kwargs, + ) + + return self From ed4478945cc5a73ed5d502a7c90bdb47bc1e89f5 Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 31 Jan 2026 16:19:06 -0800 Subject: [PATCH 14/43] fixing DFT upsampling / image correlation, adding tests --- src/quantem/core/utils/imaging_utils.py | 365 +++++++----------------- tests/core/utils/test_imaging_utils.py | 75 +++++ 2 files changed, 178 insertions(+), 262 deletions(-) create mode 100644 tests/core/utils/test_imaging_utils.py diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index ba5fc3e9..805ca5b0 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -12,11 +12,17 @@ from quantem.core.utils.utils import generate_batches +def _parabolic_peak(v) -> float: + denom = 4.0 * v[1] - 2.0 * v[2] - 2.0 * v[0] + if denom == 0: + return 0.0 + return float((v[2] - v[0]) / denom) + + def dft_upsample( F: NDArray, up: int, shift: Tuple[float, float], - device: str = "cpu", ): """ Matrix multiplication DFT, from: @@ -25,27 +31,53 @@ def dft_upsample( image registration algorithms," Opt. Lett. 33, 156-158 (2008). http://www.sciencedirect.com/science/article/pii/S0045790612000778 """ - if device == "gpu": - import cupy as cp # type: ignore + M, N = F.shape + pixel_radius = 1.5 + num_row = int(math.ceil(pixel_radius * up)) + num_col = num_row - xp = cp - else: - xp = np + col_freq = np.fft.ifftshift(np.arange(N)) - math.floor(N / 2) + row_freq = np.fft.ifftshift(np.arange(M)) - math.floor(M / 2) - M, N = F.shape - du = np.ceil(1.5 * up).astype(int) - row = np.arange(-du, du + 1) - col = np.arange(-du, du + 1) - r_shift = shift[0] - M // 2 - c_shift = shift[1] - N // 2 - - kern_row = np.exp( - -2j * np.pi / (M * up) * np.outer(row, xp.fft.ifftshift(xp.arange(M)) - M // 2 + r_shift) - ) - kern_col = np.exp( - -2j * np.pi / (N * up) * np.outer(xp.fft.ifftshift(xp.arange(N)) - N // 2 + c_shift, col) - ) - return xp.real(kern_row @ F @ kern_col) + row_coords = np.arange(num_row, dtype=float) - float(shift[0]) + col_coords = np.arange(num_col, dtype=float) - float(shift[1]) + + factor_row = -2j * math.pi / (M * float(up)) + factor_col = -2j * math.pi / (N * float(up)) + + row_kern = np.exp(factor_row * (row_coords[:, None] * row_freq[None, :])).astype(F.dtype) + col_kern = np.exp(factor_col * (col_freq[:, None] * col_coords[None, :])).astype(F.dtype) + + return (row_kern @ F @ col_kern).real + + +def _upsampled_correlation_numpy( + imageCorr: NDArray, + upsampleFactor: int, + xyShift: NDArray, +) -> NDArray: + xyShift = np.round(xyShift * float(upsampleFactor)) / float(upsampleFactor) + globalShift = math.floor(math.ceil(upsampleFactor * 1.5) / 2.0) + upsampleCenter = float(globalShift) - (float(upsampleFactor) * xyShift) + + im_up = dft_upsample(np.conj(imageCorr), upsampleFactor, (float(upsampleCenter[0]), float(upsampleCenter[1]))) + imageCorrUpsample = np.conj(im_up) + + flat_idx = int(np.argmax(imageCorrUpsample.real)) + r = flat_idx // imageCorrUpsample.shape[1] + c = flat_idx % imageCorrUpsample.shape[1] + + dx = 0.0 + dy = 0.0 + patch = imageCorrUpsample.real[r - 1 : r + 2, c - 1 : c + 2] + if patch.shape == (3, 3): + dx = _parabolic_peak(patch[:, 1]) + dy = _parabolic_peak(patch[1, :]) + + xySubShift = np.array([float(r), float(c)], dtype=float) - float(globalShift) + xyShift = xyShift + (xySubShift + np.array([dx, dy], dtype=float)) / float(upsampleFactor) + + return xyShift def cross_correlation_shift( @@ -56,7 +88,6 @@ def cross_correlation_shift( return_shifted_image: bool = False, fft_input: bool = False, fft_output: bool = False, - device: str = "cpu", ): """ Estimate subpixel shift between two 2D images using Fourier cross-correlation. @@ -68,98 +99,78 @@ def cross_correlation_shift( im : ndarray Image to align or its FFT if fft_input=True upsample_factor : int - Subpixel upsampling factor (must be > 1 for subpixel accuracy) - fft_input : bool - If True, assumes im_ref and im are already in Fourier space + Subpixel upsampling factor (torch-equivalent behavior): + - <= 2 : half-pixel refinement (parabolic, then rounded to nearest 0.5 px) + - > 2 : additional DFT upsample refinement + max_shift : float or None + Optional radial cutoff in pixel-shift units (keeps only shifts with |shift| <= max_shift) return_shifted_image : bool If True, return the shifted version of `im` aligned to `im_ref` - device : str - 'cpu' or 'gpu' (requires CuPy) + fft_input : bool + If True, assumes im_ref and im are already in Fourier space + fft_output : bool + If True and return_shifted_image=True, return the shifted image in Fourier space Returns ------- shifts : tuple of float (row_shift, col_shift) to align `im` to `im_ref` image_shifted : ndarray (optional) - Shifted image in real space, only returned if return_shifted_image=True + Shifted image in real space (or Fourier space if fft_output=True) """ - if device == "gpu": - import cupy as cp # type: ignore - - xp = cp - else: - xp = np + F_ref = np.asarray(im_ref) if fft_input else np.fft.fft2(np.asarray(im_ref)) + F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) - # Fourier transforms - F_ref = im_ref if fft_input else xp.fft.fft2(im_ref) - F_im = im if fft_input else xp.fft.fft2(im) + cc = F_ref * np.conj(F_im) + cc_real = np.fft.ifft2(cc).real - # Correlation - cc = F_ref * xp.conj(F_im) - cc_real = xp.real(xp.fft.ifft2(cc)) + M, N = cc_real.shape if max_shift is not None: - x = np.fft.fftfreq(cc.shape[0], 1 / cc.shape[0]) - y = np.fft.fftfreq(cc.shape[1], 1 / cc.shape[1]) - mask = x[:, None] ** 2 + y[None, :] ** 2 >= max_shift**2 - cc_real[mask] = 0.0 + x = np.fft.fftfreq(M) * M + y = np.fft.fftfreq(N) * N + mask = x[:, None] ** 2 + y[None, :] ** 2 > float(max_shift) ** 2 + cc_real = cc_real.copy() + cc_real[mask] = -np.inf - # Coarse peak - peak = xp.unravel_index(xp.argmax(cc_real), cc_real.shape) - x0, y0 = peak + flat_idx = int(np.argmax(cc_real)) + x0 = flat_idx // N + y0 = flat_idx % N - # Parabolic refinement - x_inds = xp.mod(x0 + xp.arange(-1, 2), cc.shape[0]).astype(int) - y_inds = xp.mod(y0 + xp.arange(-1, 2), cc.shape[1]).astype(int) + x_inds = [((x0 + dx) % M) for dx in (-1, 0, 1)] + y_inds = [((y0 + dy) % N) for dy in (-1, 0, 1)] vx = cc_real[x_inds, y0] vy = cc_real[x0, y_inds] - def parabolic_peak(v): - return (v[2] - v[0]) / (4 * v[1] - 2 * v[2] - 2 * v[0]) + dx = _parabolic_peak(vx) + dy = _parabolic_peak(vy) - dx = parabolic_peak(vx) - dy = parabolic_peak(vy) + x0 = np.round((float(x0) + float(dx)) * 2.0) / 2.0 + y0 = np.round((float(y0) + float(dy)) * 2.0) / 2.0 - x0 = (x0 + dx) % cc.shape[0] - y0 = (y0 + dy) % cc.shape[1] - - if upsample_factor <= 1: - shifts = (x0, y0) - else: - # Local DFT upsampling + xy_shift = np.array([x0, y0], dtype=float) - local = dft_upsample(cc, upsample_factor, (x0, y0), device=device) - peak = np.unravel_index(xp.argmax(local), local.shape) - - try: - lx, ly = peak - icc = local[lx - 1 : lx + 2, ly - 1 : ly + 2] - if icc.shape == (3, 3): - dxf = parabolic_peak(icc[:, 1]) - dyf = parabolic_peak(icc[1, :]) - else: - raise ValueError("Subarray too close to edge") - except (IndexError, ValueError): - dxf = dyf = 0.0 - - shifts = np.array([x0, y0]) + (np.array(peak) - upsample_factor) / upsample_factor - shifts += np.array([dxf, dyf]) / upsample_factor + if upsample_factor > 2: + xy_shift = _upsampled_correlation_numpy(cc, int(upsample_factor), xy_shift) - shifts = (shifts + 0.5 * np.array(cc.shape)) % cc.shape - 0.5 * np.array(cc.shape) + shifts = np.empty(2, dtype=float) + shifts[0] = ((xy_shift[0] + M / 2) % M) - M / 2 + shifts[1] = ((xy_shift[1] + N / 2) % N) - N / 2 + shifts = (float(shifts[0]), float(shifts[1])) if not return_shifted_image: return shifts - # Fourier shift image (F_im assumed to be FFT) - kx = xp.fft.fftfreq(F_im.shape[0])[:, None] - ky = xp.fft.fftfreq(F_im.shape[1])[None, :] - phase_ramp = xp.exp(-2j * np.pi * (kx * shifts[0] + ky * shifts[1])) + kx = np.fft.fftfreq(F_im.shape[0])[:, None] + ky = np.fft.fftfreq(F_im.shape[1])[None, :] + phase_ramp = np.exp(-2j * np.pi * (kx * shifts[0] + ky * shifts[1])) F_im_shifted = F_im * phase_ramp + if fft_output: image_shifted = F_im_shifted else: - image_shifted = xp.real(xp.fft.ifft2(F_im_shifted)) + image_shifted = np.fft.ifft2(F_im_shifted).real return shifts, image_shifted @@ -176,7 +187,6 @@ def cross_correlation_shift_torch( xy_shift = align_images_fourier_torch(G1, G2, upsample_factor) - # convert to centered signed shifts as original code M, N = im_ref.shape dx = ((xy_shift[0] + M / 2) % M) - M / 2 dy = ((xy_shift[1] + N / 2) % N) - N / 2 @@ -198,12 +208,10 @@ def align_images_fourier_torch( cc = G1 * G2.conj() cc_real = torch.fft.ifft2(cc).real - # local max (integer) flat_idx = torch.argmax(cc_real) x0 = (flat_idx // cc_real.shape[1]).to(torch.long).item() y0 = (flat_idx % cc_real.shape[1]).to(torch.long).item() - # half pixel shifts: pick ±1 indices with wrap (mod) M, N = cc_real.shape x_inds = [((x0 + dx) % M) for dx in (-1, 0, 1)] y_inds = [((y0 + dy) % N) for dy in (-1, 0, 1)] @@ -211,14 +219,11 @@ def align_images_fourier_torch( vx = cc_real[x_inds, y0] vy = cc_real[x0, y_inds] - # parabolic half-pixel refine - # dx = (vx[2] - vx[0]) / (4*vx[1] - 2*vx[2] - 2*vx[0]) denom_x = 4.0 * vx[1] - 2.0 * vx[2] - 2.0 * vx[0] denom_y = 4.0 * vy[1] - 2.0 * vy[2] - 2.0 * vy[0] dx = (vx[2] - vx[0]) / denom_x if denom_x != 0 else torch.tensor(0.0, device=device) dy = (vy[2] - vy[0]) / denom_y if denom_y != 0 else torch.tensor(0.0, device=device) - # round to nearest half-pixel x0 = torch.round((x0 + dx) * 2.0) / 2.0 y0 = torch.round((y0 + dy) * 2.0) / 2.0 @@ -243,7 +248,6 @@ def upsampled_correlation_torch( xyShift: 2-element tensor (x,y) in image coords; must be half-pixel precision as described. Returns refined xyShift (tensor length 2). """ - assert upsampleFactor > 2 xyShift = torch.round(xyShift * float(upsampleFactor)) / float(upsampleFactor) @@ -254,28 +258,25 @@ def upsampled_correlation_torch( im_up = dftUpsample_torch(conj_input, upsampleFactor, upsampleCenter) imageCorrUpsample = im_up.conj() - # find maximum - # flatten argmax -> unravel to 2D flat_idx = torch.argmax(imageCorrUpsample.real) - # unravel_index xySubShift0 = (flat_idx // imageCorrUpsample.shape[1]).to(torch.long) xySubShift1 = (flat_idx % imageCorrUpsample.shape[1]).to(torch.long) xySubShift = torch.tensor([xySubShift0.item(), xySubShift1.item()]) - # parabolic subpixel refinement dx = 0.0 dy = 0.0 try: - # extract 3x3 patch around found peak r = xySubShift[0].item() c = xySubShift[1].item() patch = imageCorrUpsample.real[r - 1 : r + 2, c - 1 : c + 2] - # if patch is incomplete (near edge) this will raise / have wrong shape -> except if patch.shape == (3, 3): icc = patch - # dx corresponds to row direction (vertical axis) as in original code: - dx = (icc[2, 1] - icc[0, 1]) / (4.0 * icc[1, 1] - 2.0 * icc[2, 1] - 2.0 * icc[0, 1]) - dy = (icc[1, 2] - icc[1, 0]) / (4.0 * icc[1, 1] - 2.0 * icc[1, 2] - 2.0 * icc[1, 0]) + dx = (icc[2, 1] - icc[0, 1]) / ( + 4.0 * icc[1, 1] - 2.0 * icc[2, 1] - 2.0 * icc[0, 1] + ) + dy = (icc[1, 2] - icc[1, 0]) / ( + 4.0 * icc[1, 1] - 2.0 * icc[1, 2] - 2.0 * icc[1, 0] + ) dx = dx.item() dy = dy.item() else: @@ -283,7 +284,6 @@ def upsampled_correlation_torch( except Exception: dx, dy = 0.0, 0.0 - # convert xySubShift to zero-centered by subtracting globalShift xySubShift = xySubShift.to(dtype=torch.get_default_dtype()) xySubShift = xySubShift - globalShift.to(xySubShift.dtype) @@ -312,13 +312,9 @@ def dftUpsample_torch( numRow = int(math.ceil(pixelRadius * upsampleFactor)) numCol = numRow - # prepare the vectors exactly like the numpy version - # col: frequency indices (centered) for N col_freq = torch.fft.ifftshift(torch.arange(N, device=device)) - math.floor(N / 2) - # row: frequency indices (centered) for M row_freq = torch.fft.ifftshift(torch.arange(M, device=device)) - math.floor(M / 2) - # small upsample grid coordinates (integer positions in the UPSAMPLED GRID) col_coords = torch.arange(numCol, device=device, dtype=torch.get_default_dtype()) - float( xyShift[1] ) @@ -326,25 +322,18 @@ def dftUpsample_torch( xyShift[0] ) - # build kernels: note factor signs and denominators match original numpy code - # colKern: shape (N, numCol) factor_col = -2j * math.pi / (N * float(upsampleFactor)) - # outer(col_freq, col_coords) -> shape (N, numCol) colKern = torch.exp(factor_col * (col_freq.unsqueeze(1) * col_coords.unsqueeze(0))).to( imageCorr.dtype ) - # rowKern: shape (numRow, M) factor_row = -2j * math.pi / (M * float(upsampleFactor)) - # outer(row_coords, row_freq) -> shape (numRow, M) rowKern = torch.exp(factor_row * (row_coords.unsqueeze(1) * row_freq.unsqueeze(0))).to( imageCorr.dtype ) - # perform the small-matrix DFT: (numRow, M) @ (M, N) @ (N, numCol) -> (numRow, numCol) imageUpsample = rowKern @ imageCorr @ colKern - # original code took xp.real(...) before returning return imageUpsample.real @@ -362,32 +351,6 @@ def bilinear_kde( ) -> NDArray | tuple[NDArray, NDArray]: """ Compute a bilinear kernel density estimate (KDE) with smooth threshold masking. - - Parameters - ---------- - xa : NDArray - Vertical (row) coordinates of input points. - ya : NDArray - Horizontal (col) coordinates of input points. - values : NDArray - Weights for each (xa, ya) point. - output_shape : tuple of int - Output image shape (rows, cols). - kde_sigma : float - Standard deviation of Gaussian KDE smoothing. - pad_value : float, default = 1.0 - Value to return when KDE support is too low. - threshold : float, default = 1e-3 - Minimum counts_KDE value for trusting the output signal. - lowpass_filter : bool, optional - If True, apply sinc-based inverse filtering to deconvolve the kernel. - max_batch_size : int or None, optional - Max number of points to process in one batch. - - Returns - ------- - NDArray - The estimated KDE image with threshold-masked output. """ rows, cols = output_shape xF = np.floor(xa.ravel()).astype(int) @@ -417,14 +380,12 @@ def bilinear_kde( inds_1D, weights=weights * w[start:end], minlength=rows * cols ) - # Reshape to 2D and apply Gaussian KDE pix_count = pix_count.reshape(output_shape) pix_output = pix_output.reshape(output_shape) pix_count = gaussian_filter(pix_count, kde_sigma) pix_output = gaussian_filter(pix_output, kde_sigma) - # Final image weight = np.minimum(pix_count / threshold, 1.0) image = pad_value * (1.0 - weight) + weight * (pix_output / np.maximum(pix_count, 1e-8)) @@ -456,23 +417,7 @@ def bilinear_array_interpolation( ) -> NDArray: """ Bilinear sampling of values from an array and pixel positions. - - Parameters - ---------- - image: np.ndarray - Image array to sample from - xa: np.ndarray - Vertical interpolation sampling positions of image array in pixels - ya: np.ndarray - Horizontal interpolation sampling positions of image array in pixels - - Returns - ------- - values: np.ndarray - Bilinear interpolation values of array at (xa,ya) positions - """ - xF = np.floor(xa.ravel()).astype("int") yF = np.floor(ya.ravel()).astype("int") dx = xa.ravel() - xF @@ -498,10 +443,7 @@ def bilinear_array_interpolation( values[start:end] += raveled_image[inds_1D] * weights - values = np.reshape( - values, - xa.shape, - ) + values = np.reshape(values, xa.shape) return values @@ -513,20 +455,7 @@ def fourier_cropping( """ Crops a corner-centered FFT array to retain only the lowest frequencies, equivalent to a center crop on the fftshifted version. - - Parameters: - ----------- - corner_centered_array : ndarray - 2D array (typically result of np.fft.fft2) with corner-centered DC - crop_shape : tuple of int - (height, width) of the desired cropped array (could be odd or even depending on arr.shape) - - Returns: - -------- - cropped : ndarray - Cropped array containing only the lowest frequencies, still corner-centered. """ - H, W = corner_centered_array.shape crop_h, crop_w = crop_shape @@ -537,13 +466,9 @@ def fourier_cropping( result = np.zeros(crop_shape, dtype=corner_centered_array.dtype) - # Top-left result[:h1, :w1] = corner_centered_array[:h1, :w1] - # Top-right result[:h1, -w2:] = corner_centered_array[:h1, -w2:] - # Bottom-left result[-h2:, :w1] = corner_centered_array[-h2:, :w1] - # Bottom-right result[-h2:, -w2:] = corner_centered_array[-h2:, -w2:] return result @@ -557,22 +482,6 @@ def compute_fsc_from_halfsets( """ Compute radially averaged Fourier Shell Correlation (FSC) from two half-set reconstructions. - - Parameters - ---------- - halfset_recons : list[torch.Tensor] - Two statistically-independent reconstructions, using half the dataset. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - epsilon: float, optional - Small number to avoid dividing by zero - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - fsc : NDarray - Fourier shell correlation as function of spatial frequency """ r1, r2 = halfset_recons @@ -602,12 +511,10 @@ def compute_fsc_from_halfsets( w0 = 1.0 - d_ind w1 = d_ind - # Flatten arrays cross = cross.reshape(-1) p1 = p1.reshape(-1) p2 = p2.reshape(-1) - # Accumulate cross_b = torch.bincount(inds_f, weights=cross * w0, minlength=num_bins) + torch.bincount( inds_f + 1, weights=cross * w1, minlength=num_bins ) @@ -637,45 +544,14 @@ def compute_spectral_snr_from_halfsets( ): """ Compute spectral SNR from two half-set reconstructions using symmetric/antisymmetric decomposition. - - The method decomposes the Fourier transforms into: - - Symmetric: (F₁ + F₂)/2 → signal + correlated noise - - Antisymmetric: (F₁ - F₂)/2 → uncorrelated noise only - - SSNR(q) = sqrt(signal_power / noise_power) - - where: - - signal_power = (|symmetric|² - |antisymmetric|²)₊ - - noise_power = |antisymmetric|² - - Parameters - ---------- - halfset_recons : list[torch.Tensor] - Two statistically-independent reconstructions, using half the dataset. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - total_dose: float - Total _normalized_ electron dose, e.g. in DirectPtychography this is ~self.num_bf - epsilon: float, optional - Small number to avoid dividing by zero - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - ssnr : NDarray - Radially averaged spectral SNR as function of spatial frequency """ - # Compute Fourier transforms halfset_1, halfset_2 = halfset_recons F1 = torch.fft.fft2(halfset_1) F2 = torch.fft.fft2(halfset_2) - # Symmetric and antisymmetric decomposition symmetric = (F1 + F2) / 2 antisymmetric = (F1 - F2) / 2 - # Power spectra noise_power = antisymmetric.abs() total_power = symmetric.abs() signal_power = (total_power - noise_power).clamp_min(0) @@ -699,11 +575,9 @@ def compute_spectral_snr_from_halfsets( w0 = 1.0 - d_ind w1 = d_ind - # Flatten arrays signal = signal_power.reshape(-1) noise = noise_power.reshape(-1) - # Accumulate signal_b = torch.bincount(inds_f, weights=signal * w0, minlength=num_bins) + torch.bincount( inds_f + 1, weights=signal * w1, minlength=num_bins ) @@ -726,20 +600,6 @@ def radially_average_fourier_array( ): """ Radially average a corner-centered Fourier array. - - Parameters - ---------- - corner_centered_array : list[torch.Tensor] - Fourier array to average radially. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - array_1d : NDarray - Radially averaged Fourier array as function of spatial frequency """ device = corner_centered_array.device nx, ny = corner_centered_array.shape @@ -760,10 +620,8 @@ def radially_average_fourier_array( w0 = 1.0 - d_ind w1 = d_ind - # Flatten arrays array = corner_centered_array.reshape(-1) - # Accumulate array_b = torch.bincount(inds_f, weights=array * w0, minlength=num_bins) + torch.bincount( inds_f + 1, weights=array * w1, minlength=num_bins ) @@ -842,9 +700,7 @@ def add_edges(i1, i2): inc = _find_wrap(phi_f[i1], phi_f[i2]) rel = rel_f[i1] + rel_f[i2] - edges.append( # ty:ignore[possibly-missing-attribute] - torch.stack([i1, i2, rel, inc], dim=1) - ) + edges.append(torch.stack([i1, i2, rel, inc], dim=1)) if wrap_around: add_edges(idx.flatten(), torch.roll(idx, -1, 1).flatten()) @@ -856,7 +712,6 @@ def add_edges(i1, i2): edges = torch.cat(edges, dim=0) edges = edges[edges[:, 2].argsort()] - # return integer tensors only (CPU) return ( edges[:, 0].long(), edges[:, 1].long(), @@ -885,7 +740,6 @@ def union(self, x, y, inc_xy): if rx == ry: return - # phase(y) + oy + inc = phase(x) + ox delta = ox - oy - inc_xy if self.rank[rx] < self.rank[ry]: @@ -963,18 +817,6 @@ def _unwrap_phase_2d_torch_poisson( ): """ Least-squares / Poisson phase unwrapping with optional mask. - - Parameters - ---------- - phi_wrapped : (H, W) tensor - Wrapped phase in (-pi, pi], any device - mask : (H, W) bool tensor, optional - True = valid pixel - - Returns - ------- - phi_unwrapped : (H, W) tensor - Unwrapped phase (same device as input) """ device = phi_wrapped.device dtype = phi_wrapped.dtype @@ -1014,10 +856,10 @@ def _unwrap_phase_2d_torch_poisson( denom = kx**2 + ky**2 + regularization_lambda else: denom = kx**2 + ky**2 - denom[0, 0] = 1.0 # avoid divide by zero + denom[0, 0] = 1.0 phi_hat = -div_hat / denom - phi_hat[0, 0] = 0.0 # fix piston + phi_hat[0, 0] = 0.0 phi = torch.fft.ifftn(phi_hat).real @@ -1051,7 +893,6 @@ def unwrap_phase_2d_torch( ) - def rotate_image( im, rotation_deg: float, diff --git a/tests/core/utils/test_imaging_utils.py b/tests/core/utils/test_imaging_utils.py new file mode 100644 index 00000000..691756dd --- /dev/null +++ b/tests/core/utils/test_imaging_utils.py @@ -0,0 +1,75 @@ +""" +Tests for imaging utilities in quantem.core.utils.imaging_utils +""" + +import numpy as np +import pytest + +torch = pytest.importorskip("torch") + +from quantem.core.utils.imaging_utils import cross_correlation_shift, cross_correlation_shift_torch + + +@pytest.fixture +def spot_image(): + from scipy.ndimage import gaussian_filter + + im = np.zeros((64, 64), dtype=np.float64) + im[32, 32] = 1.0 + im = gaussian_filter(im, 2.0) + im /= np.max(im) + return im + + +def _fourier_shift_numpy(im: np.ndarray, shift_rc: tuple[float, float]) -> np.ndarray: + dr, dc = shift_rc + kr = np.fft.fftfreq(im.shape[0])[:, None] + kc = np.fft.fftfreq(im.shape[1])[None, :] + F = np.fft.fft2(im) + phase = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + return np.fft.ifft2(F * phase).real + + +def _wrap_shift_rc(shift_rc: tuple[float, float], shape: tuple[int, int]) -> tuple[float, float]: + dr, dc = shift_rc + M, N = shape + dr = ((dr + M / 2) % M) - M / 2 + dc = ((dc + N / 2) % N) - N / 2 + return float(dr), float(dc) + + +@pytest.mark.parametrize( + "shift_true, upsample_factor, atol", + [ + ((5.0, -3.0), 1000, 1e-3), + ((-7.123, 1.789), 1000, 1e-3), + ], +) +def test_cross_correlation_shift_numpy_matches_expected(spot_image, shift_true, upsample_factor, atol): + im_ref = spot_image + im = _fourier_shift_numpy(im_ref, shift_true) + expected = _wrap_shift_rc((-shift_true[0], -shift_true[1]), im_ref.shape) + + meas = cross_correlation_shift(im_ref, im, upsample_factor=upsample_factor) + assert meas[0] == pytest.approx(expected[0], abs=atol) + assert meas[1] == pytest.approx(expected[1], abs=atol) + + +@pytest.mark.parametrize( + "shift_true, upsample_factor, atol", + [ + ((5.0, -3.0), 1000, 1e-3), + ((-7.123, 1.789), 1000, 1e-3), + ], +) +def test_cross_correlation_shift_torch_matches_expected(spot_image, shift_true, upsample_factor, atol): + im_ref = spot_image + im = _fourier_shift_numpy(im_ref, shift_true) + expected = _wrap_shift_rc((-shift_true[0], -shift_true[1]), im_ref.shape) + + t_ref = torch.from_numpy(im_ref) + t_im = torch.from_numpy(im) + meas = cross_correlation_shift_torch(t_ref, t_im, upsample_factor=upsample_factor).cpu().numpy() + + assert float(meas[0]) == pytest.approx(expected[0], abs=atol) + assert float(meas[1]) == pytest.approx(expected[1], abs=atol) From f249c6c2b99b0f65db6bcc7dc7c3b6be3f34d78f Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 31 Jan 2026 16:19:17 -0800 Subject: [PATCH 15/43] initial maped class commit --- src/quantem/diffraction/maped.py | 169 ++++++++++++++++++++++++++++++- 1 file changed, 164 insertions(+), 5 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index f301454e..ca14c92f 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -3,6 +3,7 @@ from typing import Any, Sequence import numpy as np +from scipy.signal.windows import tukey from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize @@ -34,7 +35,6 @@ def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> "MAPED": def preprocess( self, - *, plot_summary: bool = True, scale: float | Sequence[float] | None = None, **plot_kwargs: Any, @@ -76,12 +76,171 @@ def preprocess( self.im_bf.append(np.asarray(im_bf_arr)) if plot_summary: + tiles = [[(self.im_bf[i] / self.scales[i]), self.dp_mean[i]] for i in range(n)] + titles = [[f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n)] show_2d( - [ - [self.im_bf[i] / self.scales[i] for i in range(n)], - [self.dp_mean[i] for i in range(n)], - ], + tiles, + titles=titles, **plot_kwargs, ) return self + + + def diffraction_find_origin( + self, + origins=None, + sigma=None, + plot_origins: bool = True, + plot_indices=None, + ): + """ + Choose or automatically find the origin in diffraction space. + + Parameters + ---------- + origins + Optional manual origins. Can be: + - a single (row, col) tuple, applied to all datasets + - a list of (row, col) tuples of length n (one per dataset) + - a list of (row, col) tuples shorter than n, used for plot/inspection only (will error if not broadcastable) + sigma + Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. + plot_origins + If True, plot mean diffraction patterns with overlaid origin markers. + plot_indices + Optional indices to plot. If None, plots all datasets. + + Stores + ------ + self.diffraction_origins : np.ndarray + Array of shape (n, 2) with integer (row, col) origins. + """ + import numpy as _np + + try: + from scipy.ndimage import gaussian_filter as _gaussian_filter + except Exception: # pragma: no cover + _gaussian_filter = None + + n = len(self.datasets) + if not hasattr(self, "dp_mean"): + raise RuntimeError("Run preprocess() first so self.dp_mean exists.") + + if plot_indices is None: + plot_indices_list = list(range(n)) + else: + plot_indices_list = list(plot_indices) + for i in plot_indices_list: + if i < 0 or i >= n: + raise IndexError("plot_indices contains an out-of-range index.") + + if origins is None: + origins_arr = _np.zeros((n, 2), dtype=int) + for i in range(n): + dp = _np.asarray(self.dp_mean[i]) + if sigma is not None and float(sigma) > 0: + if _gaussian_filter is None: + raise ImportError("scipy is required for sigma smoothing (gaussian_filter).") + dp_use = _gaussian_filter(dp.astype(float, copy=False), float(sigma)) + else: + dp_use = dp + ind = int(_np.argmax(dp_use)) + r, c = _np.unravel_index(ind, dp_use.shape) + origins_arr[i, 0] = int(r) + origins_arr[i, 1] = int(c) + else: + if isinstance(origins, tuple) and len(origins) == 2: + origins_arr = _np.tile(_np.asarray(origins, dtype=int)[None, :], (n, 1)) + else: + origins_list = list(origins) + if len(origins_list) != n: + raise ValueError("origins must be a single (row,col) tuple or a list of length n.") + origins_arr = _np.asarray(origins_list, dtype=int) + if origins_arr.shape != (n, 2): + raise ValueError("origins must have shape (n, 2) after conversion.") + + self.diffraction_origins = origins_arr + + if plot_origins: + dp_tiles = [[_np.asarray(self.dp_mean[i]) for i in plot_indices_list]] + titles = [[f"{i} - Mean Diffraction Pattern" for i in plot_indices_list]] + fig, axs = show_2d(dp_tiles, titles=titles, returnfig=True, **{}) + if not isinstance(axs, (list, _np.ndarray)): + axs = [axs] + axs_flat = _np.ravel(axs) + for j, i in enumerate(plot_indices_list): + ax = axs_flat[j] + r, c = self.diffraction_origins[i] + ax.plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) + return fig, axs + + return self + + + def diffraction_align( + self, + edge_blend = 8.0, + padding = None, + weight_scale = 1/8, + plot_aligned = True, + linewidth = 2, + **kwargs, + ): + """ + Refine the diffraction space origins, set padding, align images + + """ + + # window function + from scipy.signal.windows import tukey + w = tukey(self.dp_mean[0].shape[0], alpha=2.0*edge_blend/self.dp_mean[0].shape[0])[:,None] * \ + tukey(self.dp_mean[0].shape[1], alpha=2.0*edge_blend/self.dp_mean[0].shape[1])[None,:] + + # coordinates + r = np.fft.fftfreq(self.dp_mean[0].shape[0],1/self.dp_mean[0].shape[0])[:,None] + c = np.fft.fftfreq(self.dp_mean[0].shape[1],1/self.dp_mean[0].shape[1])[None,:] + + # init + shifts = np.zeros((len(self.dp_mean),2)) + + # correlation alignment + G_ref = np.fft.fft2(w * self.dp_mean[0]) + xy0 = self.diffraction_origins[0] + for ind in range(1,2): + G = np.conj(np.fft.fft2(w * self.dp_mean[ind])) + xy = self.diffraction_origins[ind] + + dr2 = (r - xy0[0] + xy[0])**2 \ + + (c - xy0[1] + xy[1])**2 + im_weight = np.clip(1 - np.sqrt(dr2)/np.mean(self.dp_mean[0].shape)/weight_scale, 0.0, 1.0) + im_weight = np.sin(im_weight*np.pi/2)**2 + + im_corr = np.real(np.fft.ifft2(G_ref * G)) * im_weight + + + + if plot_aligned: + show_2d( + np.fft.fftshift(im_corr), + norm = { + 'upper_quantile':1.0, + }, + **kwargs, + ) + + + def real_space_align( + self + ): + pass + + + + def merge_datasets( + self + ): + pass + + + \ No newline at end of file From 494dc248cb3ecb28ce9d112da6fdb24bf49db234 Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 31 Jan 2026 17:20:00 -0800 Subject: [PATCH 16/43] Updating with weighted correlation --- src/quantem/core/utils/imaging_utils.py | 130 ++++++++++++++++++++++++ src/quantem/diffraction/maped.py | 21 +++- tests/core/utils/test_imaging_utils.py | 56 +++++++++- 3 files changed, 204 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 805ca5b0..e9c22b09 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -337,6 +337,136 @@ def dftUpsample_torch( return imageUpsample.real +def weighted_cross_correlation_shift( + im_ref=None, + im=None, + *, + cc=None, + weight_real=None, + upsample_factor: int = 1, + max_shift=None, + fft_input: bool = False, + return_shifted: bool = False, + shifted_output: str = "real", +): + """ + Weighted peak selection + DFT subpixel refinement for Fourier cross-correlation. + + You can provide either: + - im_ref and im (real-space images, or Fourier-domain if fft_input=True), OR + - cc (the Fourier-domain cross-spectrum), where cc = F_ref * conj(F_im) + + The weight is applied ONLY in real-space correlation to choose the peak location, + but the subpixel refinement uses the true (unweighted) cross-spectrum `cc`. + + Parameters + ---------- + im_ref, im : ndarray or None + Input images (real space) or their FFTs (if fft_input=True). + cc : ndarray or None + Fourier-domain cross-spectrum cc = F_ref * conj(F_im). If provided, im_ref/im are ignored. + weight_real : ndarray or None + Real-space weight image (same shape as correlation). Used only for peak selection. + If None, peak selection is unweighted. + upsample_factor : int + <= 2: half-pixel refinement (parabolic then rounded to nearest 0.5 px) + > 2 : additional DFT upsample refinement via _upsampled_correlation_numpy + max_shift : float or None + Optional radial cutoff (in pixels) applied to the (weighted) real correlation during peak pick. + fft_input : bool + If True, im_ref and im are already Fourier-domain arrays. + return_shifted : bool + If True, also return shifted version of `im` (or its FFT) aligned to `im_ref`. + Requires im to be provided (or fft_input=True with im as FFT). If only cc is provided, + shifted output is unavailable. + shifted_output : {"real","fft"} + Output type for the shifted image. + + Returns + ------- + shift_rc : tuple[float, float] + (d_row, d_col) shift to apply to `im` to align it to `im_ref`. + shifted : ndarray (optional) + Shifted image (real) or FFT (corner-centered) depending on shifted_output. + """ + import numpy as np + + from quantem.core.utils.imaging_utils import _parabolic_peak, _upsampled_correlation_numpy + + if cc is None: + if im_ref is None or im is None: + raise ValueError("Provide either `cc` or both `im_ref` and `im`.") + F_ref = np.asarray(im_ref) if fft_input else np.fft.fft2(np.asarray(im_ref)) + F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) + cc = F_ref * np.conj(F_im) + else: + cc = np.asarray(cc) + F_im = None + + cc_real = np.fft.ifft2(cc).real + M, N = cc_real.shape + + if weight_real is not None: + w = np.asarray(weight_real) + if w.shape != cc_real.shape: + raise ValueError(f"weight_real.shape={w.shape} must match correlation shape {cc_real.shape}.") + cc_pick = cc_real * w + else: + cc_pick = cc_real + + if max_shift is not None: + x = np.fft.fftfreq(M) * M + y = np.fft.fftfreq(N) * N + mask = x[:, None] ** 2 + y[None, :] ** 2 > float(max_shift) ** 2 + cc_pick = cc_pick.copy() + cc_pick[mask] = -np.inf + + flat_idx = int(np.argmax(cc_pick)) + x0 = flat_idx // N + y0 = flat_idx % N + + x_inds = [((x0 + dx) % M) for dx in (-1, 0, 1)] + y_inds = [((y0 + dy) % N) for dy in (-1, 0, 1)] + vx = cc_pick[x_inds, y0] + vy = cc_pick[x0, y_inds] + + dx = _parabolic_peak(vx) + dy = _parabolic_peak(vy) + + x0 = np.round((float(x0) + float(dx)) * 2.0) / 2.0 + y0 = np.round((float(y0) + float(dy)) * 2.0) / 2.0 + xy_shift = np.array([x0, y0], dtype=float) + + if upsample_factor > 2: + xy_shift = _upsampled_correlation_numpy(cc, int(upsample_factor), xy_shift) + + dr = ((xy_shift[0] + M / 2) % M) - M / 2 + dc = ((xy_shift[1] + N / 2) % N) - N / 2 + shift_rc = (float(dr), float(dc)) + + if not return_shifted: + return shift_rc + + if im is None: + raise ValueError("return_shifted=True requires `im` (or its FFT via fft_input=True).") + + if F_im is None: + F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) + + kr = np.fft.fftfreq(M)[:, None] + kc = np.fft.fftfreq(N)[None, :] + phase_ramp = np.exp(-2j * np.pi * (kr * shift_rc[0] + kc * shift_rc[1])) + F_im_shifted = F_im * phase_ramp + + out_mode = str(shifted_output).lower() + if out_mode in {"fft", "fourier"}: + return shift_rc, F_im_shifted + if out_mode in {"real", "image"}: + return shift_rc, np.fft.ifft2(F_im_shifted).real + + raise ValueError("shifted_output must be 'real' or 'fft'.") + + def bilinear_kde( xa: NDArray, ya: NDArray, diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index ca14c92f..a6b21d5b 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -8,6 +8,10 @@ from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.visualization import show_2d +# from quantem.core.utils.imaging_utils import cross_correlation_shift +# from quantem.core.utils.imaging_utils import dft_upsample +# from quantem.core.utils.imaging_utils import correlation_peak_shift_from_real +from quantem.core.utils.imaging_utils import cross_correlation_shift class MAPED(AutoSerialize): @@ -216,8 +220,23 @@ def diffraction_align( im_weight = np.clip(1 - np.sqrt(dr2)/np.mean(self.dp_mean[0].shape)/weight_scale, 0.0, 1.0) im_weight = np.sin(im_weight*np.pi/2)**2 - im_corr = np.real(np.fft.ifft2(G_ref * G)) * im_weight + # im_corr = np.real(np.fft.ifft2(G_ref * G)) * im_weight + # cc_weighted = + cc = G_ref * G + cc_weighted = np.fft.fft2(np.fft.ifft2(cc)) * im_weight + + + shift_rc = cross_correlation_shift( + cc_weighted, + np.ones_like(cc_weighted), # identity: cc = cc_weighted * conj(1) = cc_weighted + fft_input=True, # treat inputs as Fourier-domain + upsample_factor=100, # or whatever you want + ) + + shift = correlation_peak_shift_from_real(im_corr, upsample_factor=100) + + print(shift) if plot_aligned: diff --git a/tests/core/utils/test_imaging_utils.py b/tests/core/utils/test_imaging_utils.py index 691756dd..bc3989d6 100644 --- a/tests/core/utils/test_imaging_utils.py +++ b/tests/core/utils/test_imaging_utils.py @@ -3,16 +3,16 @@ """ import numpy as np +from scipy.ndimage import gaussian_filter import pytest torch = pytest.importorskip("torch") -from quantem.core.utils.imaging_utils import cross_correlation_shift, cross_correlation_shift_torch +from quantem.core.utils.imaging_utils import cross_correlation_shift, cross_correlation_shift_torch, weighted_cross_correlation_shift @pytest.fixture def spot_image(): - from scipy.ndimage import gaussian_filter im = np.zeros((64, 64), dtype=np.float64) im[32, 32] = 1.0 @@ -73,3 +73,55 @@ def test_cross_correlation_shift_torch_matches_expected(spot_image, shift_true, assert float(meas[0]) == pytest.approx(expected[0], abs=atol) assert float(meas[1]) == pytest.approx(expected[1], abs=atol) + +import numpy as np +import pytest + +from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift + + +@pytest.fixture +def peak_grid_images(): + im_ref = np.zeros((80, 80), dtype=float) + im = np.zeros_like(im_ref) + + r_ref = np.array([17, 27, 37, 47], dtype=int) + r_im = np.array([27, 37, 47, 57], dtype=int) # shifted +10 rows + c = np.array([17, 27, 37, 47], dtype=int) + + for rr in r_ref: + for cc in c: + im_ref[rr, cc] = 1.0 + + for rr in r_im: + for cc in c: + im[rr, cc] = 1.0 + + im_ref[37,27] = 3.0 + im[27,27] = 3.0 + + im_ref = gaussian_filter(im_ref,1.0) + im = gaussian_filter(im,1.0) + + # Smooth wrapped radial weight centered at 0 shift + M, N = im_ref.shape + fr = np.fft.fftfreq(M) * M + fc = np.fft.fftfreq(N) * N + dr2 = fr[:, None] ** 2 + fc[None, :] ** 2 + + sigma = 3.0 + weight = np.exp(dr2 / (-2.0*sigma**2)) + + return im_ref, im, weight + + +def test_weighted_cross_correlation_shift_unweighted_prefers_full_overlap(peak_grid_images): + im_ref, im, weight = peak_grid_images + shift = weighted_cross_correlation_shift(im_ref, im, upsample_factor=1000) + assert np.allclose(shift, (-10.0, 0.0), atol=1e-3) + + +def test_weighted_cross_correlation_shift_weighted_prefers_near_zero(peak_grid_images): + im_ref, im, weight = peak_grid_images + shift = weighted_cross_correlation_shift(im_ref, im, weight_real=weight, upsample_factor=1000) + assert np.allclose(shift, (0.0, 0.0), atol=1e-3) From 38a3fc21c6f34d697102947008b3c82a84e923e6 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Feb 2026 16:53:38 -0800 Subject: [PATCH 17/43] maped output --- src/quantem/core/utils/imaging_utils.py | 54 +-- src/quantem/diffraction/maped.py | 541 ++++++++++++++++++++++-- 2 files changed, 521 insertions(+), 74 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index e9c22b09..f6e5b660 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -346,53 +346,27 @@ def weighted_cross_correlation_shift( upsample_factor: int = 1, max_shift=None, fft_input: bool = False, - return_shifted: bool = False, - shifted_output: str = "real", + fft_output: bool = False, + return_shifted_image: bool = False, ): """ Weighted peak selection + DFT subpixel refinement for Fourier cross-correlation. - You can provide either: + Provide either: - im_ref and im (real-space images, or Fourier-domain if fft_input=True), OR - cc (the Fourier-domain cross-spectrum), where cc = F_ref * conj(F_im) The weight is applied ONLY in real-space correlation to choose the peak location, but the subpixel refinement uses the true (unweighted) cross-spectrum `cc`. - Parameters - ---------- - im_ref, im : ndarray or None - Input images (real space) or their FFTs (if fft_input=True). - cc : ndarray or None - Fourier-domain cross-spectrum cc = F_ref * conj(F_im). If provided, im_ref/im are ignored. - weight_real : ndarray or None - Real-space weight image (same shape as correlation). Used only for peak selection. - If None, peak selection is unweighted. - upsample_factor : int - <= 2: half-pixel refinement (parabolic then rounded to nearest 0.5 px) - > 2 : additional DFT upsample refinement via _upsampled_correlation_numpy - max_shift : float or None - Optional radial cutoff (in pixels) applied to the (weighted) real correlation during peak pick. - fft_input : bool - If True, im_ref and im are already Fourier-domain arrays. - return_shifted : bool - If True, also return shifted version of `im` (or its FFT) aligned to `im_ref`. - Requires im to be provided (or fft_input=True with im as FFT). If only cc is provided, - shifted output is unavailable. - shifted_output : {"real","fft"} - Output type for the shifted image. - Returns ------- shift_rc : tuple[float, float] (d_row, d_col) shift to apply to `im` to align it to `im_ref`. shifted : ndarray (optional) - Shifted image (real) or FFT (corner-centered) depending on shifted_output. + If return_shifted=True: shifted image. If fft_output=True returns FFT (corner-centered), + else returns real-space image. """ - import numpy as np - - from quantem.core.utils.imaging_utils import _parabolic_peak, _upsampled_correlation_numpy - if cc is None: if im_ref is None or im is None: raise ValueError("Provide either `cc` or both `im_ref` and `im`.") @@ -415,9 +389,9 @@ def weighted_cross_correlation_shift( cc_pick = cc_real if max_shift is not None: - x = np.fft.fftfreq(M) * M - y = np.fft.fftfreq(N) * N - mask = x[:, None] ** 2 + y[None, :] ** 2 > float(max_shift) ** 2 + fr = np.fft.fftfreq(M) * M + fc = np.fft.fftfreq(N) * N + mask = fr[:, None] ** 2 + fc[None, :] ** 2 > float(max_shift) ** 2 cc_pick = cc_pick.copy() cc_pick[mask] = -np.inf @@ -444,11 +418,11 @@ def weighted_cross_correlation_shift( dc = ((xy_shift[1] + N / 2) % N) - N / 2 shift_rc = (float(dr), float(dc)) - if not return_shifted: + if not return_shifted_image: return shift_rc if im is None: - raise ValueError("return_shifted=True requires `im` (or its FFT via fft_input=True).") + raise ValueError("return_shifted_image=True requires `im` (or its FFT via fft_input=True).") if F_im is None: F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) @@ -458,13 +432,9 @@ def weighted_cross_correlation_shift( phase_ramp = np.exp(-2j * np.pi * (kr * shift_rc[0] + kc * shift_rc[1])) F_im_shifted = F_im * phase_ramp - out_mode = str(shifted_output).lower() - if out_mode in {"fft", "fourier"}: + if fft_output: return shift_rc, F_im_shifted - if out_mode in {"real", "image"}: - return shift_rc, np.fft.ifft2(F_im_shifted).real - - raise ValueError("shifted_output must be 'real' or 'fft'.") + return shift_rc, np.fft.ifft2(F_im_shifted).real def bilinear_kde( diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index a6b21d5b..afb1fc0c 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -8,10 +8,7 @@ from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.visualization import show_2d -# from quantem.core.utils.imaging_utils import cross_correlation_shift -# from quantem.core.utils.imaging_utils import dft_upsample -# from quantem.core.utils.imaging_utils import correlation_peak_shift_from_real -from quantem.core.utils.imaging_utils import cross_correlation_shift +from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift class MAPED(AutoSerialize): @@ -184,8 +181,10 @@ def diffraction_find_origin( def diffraction_align( self, - edge_blend = 8.0, + edge_blend = 16.0, padding = None, + pad_val = 'min', + upsample_factor = 100, weight_scale = 1/8, plot_aligned = True, linewidth = 2, @@ -206,13 +205,13 @@ def diffraction_align( c = np.fft.fftfreq(self.dp_mean[0].shape[1],1/self.dp_mean[0].shape[1])[None,:] # init - shifts = np.zeros((len(self.dp_mean),2)) + self.diffraction_shifts = np.zeros((len(self.dp_mean),2)) # correlation alignment G_ref = np.fft.fft2(w * self.dp_mean[0]) xy0 = self.diffraction_origins[0] - for ind in range(1,2): - G = np.conj(np.fft.fft2(w * self.dp_mean[ind])) + for ind in range(1,len(self.dp_mean)): + G = np.fft.fft2(w * self.dp_mean[ind]) xy = self.diffraction_origins[ind] dr2 = (r - xy0[0] + xy[0])**2 \ @@ -220,46 +219,524 @@ def diffraction_align( im_weight = np.clip(1 - np.sqrt(dr2)/np.mean(self.dp_mean[0].shape)/weight_scale, 0.0, 1.0) im_weight = np.sin(im_weight*np.pi/2)**2 - # im_corr = np.real(np.fft.ifft2(G_ref * G)) * im_weight - # cc_weighted = - - cc = G_ref * G - cc_weighted = np.fft.fft2(np.fft.ifft2(cc)) * im_weight - - - shift_rc = cross_correlation_shift( - cc_weighted, - np.ones_like(cc_weighted), # identity: cc = cc_weighted * conj(1) = cc_weighted - fft_input=True, # treat inputs as Fourier-domain - upsample_factor=100, # or whatever you want + shift, G_shift = weighted_cross_correlation_shift( + im_ref=G_ref, + im=G, + weight_real=im_weight*0+1.0, + upsample_factor = upsample_factor, + fft_input = True, + fft_output = True, + return_shifted_image = True, ) + self.diffraction_shifts[ind,:] = shift - shift = correlation_peak_shift_from_real(im_corr, upsample_factor=100) + # update reference + G_ref = G_ref*(ind/(ind+1)) + G_shift/(ind+1) - print(shift) + # Center shifts + self.diffraction_shifts -= np.mean(self.diffraction_shifts,axis=0)[None,:] + # Generate output image if plot_aligned: + im_aligned = shift_images( + images = self.dp_mean, + shifts_rc = self.diffraction_shifts, + edge_blend = edge_blend, + padding = padding, + pad_val = pad_val, + ) show_2d( - np.fft.fftshift(im_corr), - norm = { - 'upper_quantile':1.0, - }, + im_aligned, **kwargs, ) def real_space_align( - self + self, + num_images=None, + num_iter: int = 3, + edge_blend: float = 1.0, + padding=None, + pad_val: str | float = "median", + upsample_factor: int = 100, + max_shift=None, + shift_method: str = "bilinear", + edge_filter: bool = True, + edge_sigma: float = 2.0, + hanning_filter: bool = False, + plot_aligned: bool = True, + **kwargs, ): - pass + import numpy as np + from scipy.ndimage import gaussian_filter, shift as ndi_shift + from scipy.signal import convolve2d + from scipy.signal.windows import tukey + + from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift + from quantem.core.visualization import show_2d + + if not hasattr(self, "im_bf"): + raise RuntimeError("Run preprocess() first so self.im_bf exists.") + if len(self.im_bf) == 0: + raise RuntimeError("No images found in self.im_bf.") + + H, W = self.im_bf[0].shape + for im in self.im_bf: + if im.shape != (H, W): + raise ValueError("all self.im_bf images must have the same shape") + + n_total = len(self.im_bf) + if num_images is None: + n = n_total + else: + n = int(num_images) + if n <= 0: + raise ValueError("num_images must be positive") + n = min(n, n_total) + + if int(num_iter) < 1: + raise ValueError("num_iter must be >= 1") + + if max_shift is not None: + pad_cc = int(np.ceil(float(max_shift))) + 4 + else: + pad_cc = int(np.ceil(float(edge_blend))) + 4 + + Hp = H + 2 * pad_cc + Wp = W + 2 * pad_cc + r0 = pad_cc + c0 = pad_cc + + w_h = np.ones((H, W), dtype=float) + if hanning_filter: + w_h = np.hanning(H)[:, None] * np.hanning(W)[None, :] + w_h_pad = np.zeros((Hp, Wp), dtype=float) + w_h_pad[r0 : r0 + H, c0 : c0 + W] = w_h + w_h_sum = float(np.sum(w_h_pad)) + if w_h_sum <= 0: + raise RuntimeError("hanning window sum is zero") + + wx = None + if edge_filter: + wx = np.array( + [ + [-1.0, -2.0, -1.0], + [ 0.0, 0.0, 0.0], + [ 1.0, 2.0, 1.0], + ], + dtype=float, + ) + + base_pad = np.zeros((n, Hp, Wp), dtype=float) + for i in range(n): + im0 = np.asarray(self.im_bf[i], dtype=float) + + if edge_filter: + gx = convolve2d(im0, wx, mode="same", boundary="symm") + gy = convolve2d(im0, wx.T, mode="same", boundary="symm") + gx = gaussian_filter(gx, float(edge_sigma), mode="nearest") + gy = gaussian_filter(gy, float(edge_sigma), mode="nearest") + im_use = np.sqrt(gx * gx + gy * gy) + else: + im_use = im0 + + base_pad[i, r0 : r0 + H, c0 : c0 + W] = im_use + + shifts = np.zeros((n, 2), dtype=float) + + for _ in range(int(num_iter)): + G_list = np.empty((n, Hp, Wp), dtype=np.complex128) + + for i in range(n): + im_a = ndi_shift( + base_pad[i], + shift=(float(shifts[i, 0]), float(shifts[i, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + im_mean = float(np.sum(im_a * w_h_pad) / w_h_sum) + im_win = (im_a - im_mean) * w_h_pad + G_list[i] = np.fft.fft2(im_win) + + G_ref = np.mean(G_list, axis=0) + + for i in range(1, n): + drc = weighted_cross_correlation_shift( + im_ref=G_ref, + im=G_list[i], + weight_real=None, + upsample_factor=int(upsample_factor), + max_shift=max_shift, + fft_input=True, + fft_output=False, + return_shifted_image=False, + ) + shifts[i, 0] += float(drc[0]) + shifts[i, 1] += float(drc[1]) + + shifts -= shifts[0][None, :] + + shifts -= np.mean(shifts, axis=0)[None, :] + + self.real_space_shifts = np.zeros((n_total, 2), dtype=float) + self.real_space_shifts[:n, :] = shifts + + if plot_aligned: + im_aligned = shift_images( + images=self.im_bf[:n], + shifts_rc=self.real_space_shifts[:n, :], + edge_blend=float(edge_blend), + padding=padding, + pad_val=pad_val, + shift_method=str(shift_method), + ) + show_2d(im_aligned, **kwargs) + + return self - def merge_datasets( - self + self, + real_space_padding=0, + real_space_edge_blend=1.0, + diffraction_padding=0, + diffraction_edge_blend=0.0, + diffraction_pad_val="min", + shift_method: str = "bilinear", + plot_result: bool = True, + **kwargs, ): - pass + import numpy as np + from scipy.ndimage import shift as ndi_shift + from scipy.signal.windows import tukey + from tqdm import tqdm + + if not hasattr(self, "real_space_shifts"): + raise RuntimeError("Run real_space_align() first so self.real_space_shifts exists.") + if not hasattr(self, "diffraction_shifts"): + raise RuntimeError("Run diffraction_align() first so self.diffraction_shifts exists.") + + n = len(self.datasets) + if n == 0: + raise RuntimeError("No datasets found in self.datasets.") + + arrays = [np.asarray(d.array, dtype=float) for d in self.datasets] + shape0 = arrays[0].shape + if len(shape0) != 4: + raise ValueError("Expected Dataset4dstem arrays with shape (R, C, H, W).") + Rs, Cs, H, W = shape0 + for a in arrays: + if a.shape != (Rs, Cs, H, W): + raise ValueError("All datasets must have the same shape (R, C, H, W).") + + rs_shifts = np.asarray(self.real_space_shifts, dtype=float) + dp_shifts = np.asarray(self.diffraction_shifts, dtype=float) + if rs_shifts.shape != (n, 2): + raise ValueError("self.real_space_shifts must have shape (n, 2).") + if dp_shifts.shape != (n, 2): + raise ValueError("self.diffraction_shifts must have shape (n, 2).") + + real_space_padding = int(real_space_padding) + if real_space_padding < 0: + raise ValueError("real_space_padding must be >= 0.") + + method = str(shift_method).strip().lower() + if method not in {"bilinear", "fourier"}: + raise ValueError("shift_method must be 'bilinear' or 'fourier'.") + + # Real-space taper window (used to weight contributions) + alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) if real_space_edge_blend > 0 else 0.0 + alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) if real_space_edge_blend > 0 else 0.0 + w_rs = tukey(Rs, alpha=alpha_r)[:, None] * tukey(Cs, alpha=alpha_c)[None, :] + w_rs = w_rs.astype(float, copy=False) + + # Diffraction padding (must be large enough to prevent wrap for Fourier shifts) + diffraction_padding = int(diffraction_padding) + if diffraction_padding < 0: + raise ValueError("diffraction_padding must be >= 0.") + max_abs_dp = float(np.max(np.abs(dp_shifts))) if dp_shifts.size else 0.0 + pad_dp_min = int(np.ceil(max_abs_dp)) + 2 + pad_dp = max(diffraction_padding, pad_dp_min) + + Hp = H + 2 * pad_dp + Wp = W + 2 * pad_dp + rp0 = pad_dp + cp0 = pad_dp + + # Diffraction taper window + alpha_hr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) if diffraction_edge_blend > 0 else 0.0 + alpha_hc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) if diffraction_edge_blend > 0 else 0.0 + w_dp = tukey(H, alpha=alpha_hr)[:, None] * tukey(W, alpha=alpha_hc)[None, :] + w_dp = w_dp.astype(float, copy=False) + + # Padded diffraction window (unshifted) + w_dp_pad = np.zeros((Hp, Wp)) + w_dp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = w_dp + + # Pad value in diffraction space (computed from dp_means for speed) + if isinstance(diffraction_pad_val, str): + s = diffraction_pad_val.strip().lower() + dp_means = [np.mean(a, axis=(0, 1)) for a in arrays] + v = np.stack(dp_means, axis=0).reshape(-1) + if s == "min": + pad_val_dp = float(np.min(v)) + elif s == "max": + pad_val_dp = float(np.max(v)) + elif s == "mean": + pad_val_dp = float(np.mean(v)) + elif s == "median": + pad_val_dp = float(np.median(v)) + else: + raise ValueError("diffraction_pad_val must be a float or one of {'min','max','mean','median'}.") + else: + pad_val_dp = float(diffraction_pad_val) + + # Precompute diffraction window shifts per dataset + if method == "fourier": + kr = np.fft.fftfreq(Hp)[:, None] + kc = np.fft.fftfreq(Wp)[None, :] + ramps = [ + np.exp(-2j * np.pi * (kr * float(dp_shifts[i, 0]) + kc * float(dp_shifts[i, 1]))) + for i in range(n) + ] + wdp_shifted = np.empty((n, Hp, Wp)) + Fw0 = np.fft.fft2(w_dp_pad) + for i in range(n): + wtmp = np.fft.ifft2(Fw0 * ramps[i]).real + wtmp = np.clip(wtmp, 0.0, 1.0) + wdp_shifted[i] = wtmp + else: + wdp_shifted = np.empty((n, Hp, Wp)) + for i in range(n): + wtmp = ndi_shift( + w_dp_pad, + shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + wtmp = np.clip(wtmp, 0.0, 1.0) + wdp_shifted[i] = wtmp + + # Edge blend weight for pad value (diffraction space) + edge_w_dp = 1.0 - np.clip(np.max(wdp_shifted, axis=0), 0.0, 1.0) + + Rout = Rs + 2 * real_space_padding + Cout = Cs + 2 * real_space_padding + merged = np.zeros((Rout, Cout, Hp, Wp)) + + dp_local = np.empty((H, W)) + dp_pad = np.zeros((Hp, Wp)) + dp_shifted_tmp = np.empty((Hp, Wp)) + num_tmp = np.zeros((Hp, Wp)) + den_tmp = np.zeros((Hp, Wp)) + out_tmp = np.empty((Hp, Wp)) + + for ro in tqdm(range(Rout), desc="Merging (rows)"): + r_base = float(ro - real_space_padding) + for co in range(Cout): + c_base = float(co - real_space_padding) + + num_tmp.fill(0.0) + den_tmp.fill(0.0) + max_wi = 0.0 + + for i in range(n): + r_in = r_base - float(rs_shifts[i, 0]) + c_in = c_base - float(rs_shifts[i, 1]) + + r0 = int(np.floor(r_in)) + c0 = int(np.floor(c_in)) + if r0 < 0 or r0 >= Rs - 1 or c0 < 0 or c0 >= Cs - 1: + continue + + dr = float(r_in - r0) + dc = float(c_in - c0) + + w00 = (1.0 - dr) * (1.0 - dc) + w10 = dr * (1.0 - dc) + w01 = (1.0 - dr) * dc + w11 = dr * dc + + wi = ( + w00 * w_rs[r0, c0] + + w10 * w_rs[r0 + 1, c0] + + w01 * w_rs[r0, c0 + 1] + + w11 * w_rs[r0 + 1, c0 + 1] + ) + if wi <= 0.0: + continue + if wi > max_wi: + max_wi = wi + + a = arrays[i] + dp_local[:] = ( + w00 * a[r0, c0] + + w10 * a[r0 + 1, c0] + + w01 * a[r0, c0 + 1] + + w11 * a[r0 + 1, c0 + 1] + ) + + dp_pad.fill(0.0) + dp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = dp_local * w_dp + + if method == "fourier": + dp_shifted_tmp[:] = np.fft.ifft2(np.fft.fft2(dp_pad) * ramps[i]).real + else: + dp_shifted_tmp[:] = ndi_shift( + dp_pad, + shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + + num_tmp += wi * dp_shifted_tmp + den_tmp += wi * wdp_shifted[i] + + if max_wi <= 0.0: + merged[ro, co] = 0.0 + continue + + num = num_tmp + edge_w_dp * pad_val_dp + den = den_tmp + edge_w_dp + + np.divide(num, den, out=out_tmp, where=(den > 0.0)) + out_tmp[den <= 0.0] = pad_val_dp + merged[ro, co] = out_tmp + + dataset_merged = Dataset4dstem.from_array(merged) + + self.im_bf_merged = np.mean(merged, axis=(2, 3)) + self.dp_mean_merged = np.mean(merged, axis=(0, 1)) + + dataset_merged.im_bf_merged = self.im_bf_merged + dataset_merged.dp_mean_merged = self.dp_mean_merged + dataset_merged.metadata["im_bf_merged"] = self.im_bf_merged + dataset_merged.metadata["dp_mean_merged"] = self.dp_mean_merged + dataset_merged.metadata["real_space_shifts_rc"] = rs_shifts.copy() + dataset_merged.metadata["diffraction_shifts_rc"] = dp_shifts.copy() + + if plot_result: + show_2d( + [[self.im_bf_merged, self.dp_mean_merged]], + titles=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], + **kwargs, + ) + + return dataset_merged + + +def shift_images( + images, + shifts_rc, + edge_blend: float = 8.0, + padding=None, + pad_val=0.0, + shift_method: str = "bilinear", +): + import numpy as np + from scipy.ndimage import shift as ndi_shift + from scipy.signal.windows import tukey + + images = [np.asarray(im, dtype=float) for im in images] + if len(images) == 0: + raise ValueError("images must be non-empty") + + H, W = images[0].shape + for im in images: + if im.shape != (H, W): + raise ValueError("all images must have the same shape") + + shifts_rc = np.asarray(shifts_rc, dtype=float) + if shifts_rc.shape != (len(images), 2): + raise ValueError("shifts_rc must have shape (len(images), 2)") + + if isinstance(pad_val, str): + s = pad_val.strip().lower() + v = np.stack(images, axis=0).reshape(-1) + if s == "min": + pad_val = float(np.min(v)) + elif s == "max": + pad_val = float(np.max(v)) + elif s == "mean": + pad_val = float(np.mean(v)) + elif s == "median": + pad_val = float(np.median(v)) + else: + raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") + else: + pad_val = float(pad_val) + + if padding is None: + max_shift = float(np.max(np.abs(shifts_rc))) if shifts_rc.size else 0.0 + padding = int(np.ceil(max_shift + float(edge_blend))) + 2 + padding = int(padding) + + alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 + alpha_c = min(1.0, 2.0 * float(edge_blend) / float(W)) if edge_blend > 0 else 0.0 + w = tukey(H, alpha=alpha_r)[:, None] * tukey(W, alpha=alpha_c)[None, :] + w = w.astype(float, copy=False) + + Hp = H + 2 * padding + Wp = W + 2 * padding + + stack_w = np.zeros((len(images), Hp, Wp), dtype=float) + stack = np.zeros_like(stack_w) + + r0 = padding + c0 = padding + stack_w[:, r0 : r0 + H, c0 : c0 + W] = w[None, :, :] + for ind, im in enumerate(images): + stack[ind, r0 : r0 + H, c0 : c0 + W] = im * w + + method = str(shift_method).strip().lower() + if method not in {"bilinear", "fourier"}: + raise ValueError("shift_method must be 'bilinear' or 'fourier'") + + if method == "fourier": + kr = np.fft.fftfreq(Hp)[:, None] + kc = np.fft.fftfreq(Wp)[None, :] + for ind in range(len(images)): + dr, dc = float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1]) + ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + + F = np.fft.fft2(stack[ind]) + stack[ind] = np.fft.ifft2(F * ramp).real + + Fw = np.fft.fft2(stack_w[ind]) + stack_w[ind] = np.fft.ifft2(Fw * ramp).real + stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) + else: + for ind in range(len(images)): + stack[ind] = ndi_shift( + stack[ind], + shift=(float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = ndi_shift( + stack_w[ind], + shift=(float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) + + # edge_w = 1.0 - np.clip(np.max(stack_w, axis=0), 0.0, 1.0) + edge_w = len(images) - np.sum(stack_w, axis=0) + num = np.sum(stack, axis=0) + edge_w * pad_val + den = np.sum(stack_w, axis=0) + edge_w + out = num / den - \ No newline at end of file + return out From 1e14d70ea12b9e4fb61b71d3ecdd90e66ebe9b6f Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Feb 2026 17:13:41 -0800 Subject: [PATCH 18/43] datatype control for merged data --- src/quantem/diffraction/maped.py | 190 +++++++++++++++++++------------ 1 file changed, 118 insertions(+), 72 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index afb1fc0c..9df542f3 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -406,9 +406,13 @@ def merge_datasets( diffraction_edge_blend=0.0, diffraction_pad_val="min", shift_method: str = "bilinear", + dtype=None, + scale_output: bool = False, plot_result: bool = True, **kwargs, ): + import warnings + import numpy as np from scipy.ndimage import shift as ndi_shift from scipy.signal.windows import tukey @@ -419,18 +423,15 @@ def merge_datasets( if not hasattr(self, "diffraction_shifts"): raise RuntimeError("Run diffraction_align() first so self.diffraction_shifts exists.") - n = len(self.datasets) + arrays = [np.asarray(d.array) for d in self.datasets] + n = len(arrays) if n == 0: raise RuntimeError("No datasets found in self.datasets.") - arrays = [np.asarray(d.array, dtype=float) for d in self.datasets] - shape0 = arrays[0].shape - if len(shape0) != 4: - raise ValueError("Expected Dataset4dstem arrays with shape (R, C, H, W).") - Rs, Cs, H, W = shape0 + Rs, Cs, H, W = arrays[0].shape for a in arrays: if a.shape != (Rs, Cs, H, W): - raise ValueError("All datasets must have the same shape (R, C, H, W).") + raise ValueError("All dataset arrays must have the same shape (Rs, Cs, H, W).") rs_shifts = np.asarray(self.real_space_shifts, dtype=float) dp_shifts = np.asarray(self.diffraction_shifts, dtype=float) @@ -439,48 +440,48 @@ def merge_datasets( if dp_shifts.shape != (n, 2): raise ValueError("self.diffraction_shifts must have shape (n, 2).") + if dtype is None: + dtype_out = np.asarray(arrays[0]).dtype + warnings.warn(f"dtype=None; using parent dtype {dtype_out}.", RuntimeWarning) + else: + dtype_out = np.dtype(dtype) + real_space_padding = int(real_space_padding) - if real_space_padding < 0: - raise ValueError("real_space_padding must be >= 0.") + diffraction_padding = int(diffraction_padding) + + Rout = Rs + 2 * real_space_padding + Cout = Cs + 2 * real_space_padding + + Hp = H + 2 * diffraction_padding + Wp = W + 2 * diffraction_padding + rp0 = diffraction_padding + cp0 = diffraction_padding method = str(shift_method).strip().lower() if method not in {"bilinear", "fourier"}: raise ValueError("shift_method must be 'bilinear' or 'fourier'.") - # Real-space taper window (used to weight contributions) - alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) if real_space_edge_blend > 0 else 0.0 - alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) if real_space_edge_blend > 0 else 0.0 - w_rs = tukey(Rs, alpha=alpha_r)[:, None] * tukey(Cs, alpha=alpha_c)[None, :] + if real_space_edge_blend and float(real_space_edge_blend) > 0: + alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) + alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) + w_rs = tukey(Rs, alpha=alpha_r)[:, None] * tukey(Cs, alpha=alpha_c)[None, :] + else: + w_rs = np.ones((Rs, Cs), dtype=float) w_rs = w_rs.astype(float, copy=False) - # Diffraction padding (must be large enough to prevent wrap for Fourier shifts) - diffraction_padding = int(diffraction_padding) - if diffraction_padding < 0: - raise ValueError("diffraction_padding must be >= 0.") - max_abs_dp = float(np.max(np.abs(dp_shifts))) if dp_shifts.size else 0.0 - pad_dp_min = int(np.ceil(max_abs_dp)) + 2 - pad_dp = max(diffraction_padding, pad_dp_min) - - Hp = H + 2 * pad_dp - Wp = W + 2 * pad_dp - rp0 = pad_dp - cp0 = pad_dp - - # Diffraction taper window - alpha_hr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) if diffraction_edge_blend > 0 else 0.0 - alpha_hc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) if diffraction_edge_blend > 0 else 0.0 - w_dp = tukey(H, alpha=alpha_hr)[:, None] * tukey(W, alpha=alpha_hc)[None, :] + if diffraction_edge_blend and float(diffraction_edge_blend) > 0: + alpha_dr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) + alpha_dc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) + w_dp = tukey(H, alpha=alpha_dr)[:, None] * tukey(W, alpha=alpha_dc)[None, :] + else: + w_dp = np.ones((H, W), dtype=float) w_dp = w_dp.astype(float, copy=False) - # Padded diffraction window (unshifted) - w_dp_pad = np.zeros((Hp, Wp)) - w_dp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = w_dp + dp_means = [np.mean(a, axis=(0, 1), dtype=np.float64) for a in arrays] + v = np.stack(dp_means, axis=0).reshape(-1) - # Pad value in diffraction space (computed from dp_means for speed) if isinstance(diffraction_pad_val, str): s = diffraction_pad_val.strip().lower() - dp_means = [np.mean(a, axis=(0, 1)) for a in arrays] - v = np.stack(dp_means, axis=0).reshape(-1) if s == "min": pad_val_dp = float(np.min(v)) elif s == "max": @@ -494,47 +495,44 @@ def merge_datasets( else: pad_val_dp = float(diffraction_pad_val) - # Precompute diffraction window shifts per dataset + wdp_pad = np.zeros((Hp, Wp), dtype=float) + wdp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = w_dp + + wdp_shifted = np.zeros((n, Hp, Wp), dtype=float) if method == "fourier": kr = np.fft.fftfreq(Hp)[:, None] kc = np.fft.fftfreq(Wp)[None, :] - ramps = [ - np.exp(-2j * np.pi * (kr * float(dp_shifts[i, 0]) + kc * float(dp_shifts[i, 1]))) - for i in range(n) - ] - wdp_shifted = np.empty((n, Hp, Wp)) - Fw0 = np.fft.fft2(w_dp_pad) + ramps = [] + Fw = np.fft.fft2(wdp_pad) for i in range(n): - wtmp = np.fft.ifft2(Fw0 * ramps[i]).real - wtmp = np.clip(wtmp, 0.0, 1.0) - wdp_shifted[i] = wtmp + dr, dc = float(dp_shifts[i, 0]), float(dp_shifts[i, 1]) + ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + ramps.append(ramp) + w_i = np.fft.ifft2(Fw * ramp).real + wdp_shifted[i] = np.clip(w_i, 0.0, 1.0) else: - wdp_shifted = np.empty((n, Hp, Wp)) for i in range(n): - wtmp = ndi_shift( - w_dp_pad, + w_i = ndi_shift( + wdp_pad, shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), order=1, mode="constant", cval=0.0, prefilter=False, ) - wtmp = np.clip(wtmp, 0.0, 1.0) - wdp_shifted[i] = wtmp + wdp_shifted[i] = np.clip(w_i, 0.0, 1.0) + ramps = None - # Edge blend weight for pad value (diffraction space) - edge_w_dp = 1.0 - np.clip(np.max(wdp_shifted, axis=0), 0.0, 1.0) + edge_w_dp = 1.0 - np.max(wdp_shifted, axis=0) + edge_w_dp = np.clip(edge_w_dp, 0.0, 1.0) - Rout = Rs + 2 * real_space_padding - Cout = Cs + 2 * real_space_padding - merged = np.zeros((Rout, Cout, Hp, Wp)) + merged = np.zeros((Rout, Cout, Hp, Wp), dtype=np.float64) - dp_local = np.empty((H, W)) - dp_pad = np.zeros((Hp, Wp)) - dp_shifted_tmp = np.empty((Hp, Wp)) - num_tmp = np.zeros((Hp, Wp)) - den_tmp = np.zeros((Hp, Wp)) - out_tmp = np.empty((Hp, Wp)) + dp_local = np.zeros((H, W), dtype=np.float64) + dp_pad = np.zeros((Hp, Wp), dtype=np.float64) + dp_shifted_tmp = np.zeros((Hp, Wp), dtype=np.float64) + num_tmp = np.zeros((Hp, Wp), dtype=np.float64) + den_tmp = np.zeros((Hp, Wp), dtype=np.float64) for ro in tqdm(range(Rout), desc="Merging (rows)"): r_base = float(ro - real_space_padding) @@ -606,21 +604,69 @@ def merge_datasets( num = num_tmp + edge_w_dp * pad_val_dp den = den_tmp + edge_w_dp - np.divide(num, den, out=out_tmp, where=(den > 0.0)) - out_tmp[den <= 0.0] = pad_val_dp - merged[ro, co] = out_tmp + out = np.empty_like(num) + np.divide(num, den, out=out, where=den != 0.0) + out[den == 0.0] = 0.0 + merged[ro, co] = out + + self.im_bf_merged = np.mean(merged, axis=(2, 3), dtype=np.float64) + self.dp_mean_merged = np.mean(merged, axis=(0, 1), dtype=np.float64) + + if np.issubdtype(dtype_out, np.integer): + info = np.iinfo(dtype_out) + dmin = float(info.min) + dmax = float(info.max) + + merged_f = merged # float64 + + if scale_output: + peak = float(np.max(merged_f)) + if peak <= 0.0: + scale = 1.0 + merged_scaled = merged_f + else: + scale = dmax / peak + merged_scaled = merged_f * scale + + if np.issubdtype(dtype_out, np.unsignedinteger): + if float(np.min(merged_scaled)) < 0.0: + warnings.warn( + f"scale_output=True with unsigned dtype {dtype_out}: " + "negative values present; they will be clipped to 0.", + RuntimeWarning, + ) + lo, hi = 0.0, dmax + else: + lo, hi = dmin, dmax + + if float(np.min(merged_scaled)) < lo or float(np.max(merged_scaled)) > hi: + warnings.warn( + f"Output overflow for dtype {dtype_out} after scaling: " + f"data range [{float(np.min(merged_scaled))}, {float(np.max(merged_scaled))}] exceeds " + f"[{lo}, {hi}]. Values will be clipped.", + RuntimeWarning, + ) + + merged_out = np.rint(np.clip(merged_scaled, lo, hi)).astype(dtype_out) + + else: + below = float(np.min(merged_f)) + above = float(np.max(merged_f)) + if below < dmin or above > dmax: + warnings.warn( + f"Output overflow for dtype {dtype_out}: data range [{below}, {above}] exceeds " + f"[{dmin}, {dmax}]. Values will be clipped.", + RuntimeWarning, + ) + merged_out = np.rint(np.clip(merged_f, dmin, dmax)).astype(dtype_out) + else: + merged_out = merged.astype(dtype_out, copy=False) - dataset_merged = Dataset4dstem.from_array(merged) - self.im_bf_merged = np.mean(merged, axis=(2, 3)) - self.dp_mean_merged = np.mean(merged, axis=(0, 1)) + dataset_merged = Dataset4dstem.from_array(array=merged_out) dataset_merged.im_bf_merged = self.im_bf_merged dataset_merged.dp_mean_merged = self.dp_mean_merged - dataset_merged.metadata["im_bf_merged"] = self.im_bf_merged - dataset_merged.metadata["dp_mean_merged"] = self.dp_mean_merged - dataset_merged.metadata["real_space_shifts_rc"] = rs_shifts.copy() - dataset_merged.metadata["diffraction_shifts_rc"] = dp_shifts.copy() if plot_result: show_2d( From 3196aca903ca46bc427706394cd9afc8a66d3a5e Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Feb 2026 17:40:36 -0800 Subject: [PATCH 19/43] adding docstrings --- src/quantem/diffraction/maped.py | 452 +++++++++++++++++++------------ 1 file changed, 278 insertions(+), 174 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 9df542f3..3b5154c0 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1,28 +1,55 @@ from __future__ import annotations +import warnings from typing import Any, Sequence import numpy as np +from scipy.ndimage import gaussian_filter, shift as ndi_shift +from scipy.signal import convolve2d from scipy.signal.windows import tukey +from tqdm import tqdm from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.visualization import show_2d from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift +from quantem.core.visualization import show_2d class MAPED(AutoSerialize): + """ + Merge-Averaged Precession Electron Diffraction (MAPED) helper. + + This class manages a set of 4D-STEM datasets and provides utilities to: + - compute mean BF and mean DP summaries, + - choose/find diffraction origins, + - align diffraction space and real space, + - merge datasets into a single composite Dataset4dstem. + """ + _token = object() def __init__(self, datasets: list[Dataset4dstem], _token: object | None = None): if _token is not self._token: raise RuntimeError("Use MAPED.from_datasets() to instantiate this class.") - AutoSerialize.__init__(self) + super().__init__() self.datasets = datasets self.metadata: dict[str, Any] = {} @classmethod - def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> "MAPED": + def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> MAPED: + """ + Construct a MAPED instance from a non-empty sequence of Dataset4dstem. + + Parameters + ---------- + datasets + Sequence of Dataset4dstem instances. + + Returns + ------- + MAPED + New MAPED instance. + """ if not isinstance(datasets, Sequence) or isinstance(datasets, (str, bytes)): raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") ds_list: list[Dataset4dstem] = [] @@ -30,7 +57,7 @@ def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> "MAPED": if not isinstance(d, Dataset4dstem): raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") ds_list.append(d) - if len(ds_list) == 0: + if not ds_list: raise ValueError("MAPED.from_datasets expects a non-empty sequence of Dataset4dstem instances.") return cls(datasets=ds_list, _token=cls._token) @@ -39,7 +66,19 @@ def preprocess( plot_summary: bool = True, scale: float | Sequence[float] | None = None, **plot_kwargs: Any, - ) -> "MAPED": + ) -> MAPED: + """ + Compute dataset summary images. + + Stores + ------ + self.scales : np.ndarray + Per-dataset scaling factors (n,). + self.dp_mean : list[np.ndarray] + Mean diffraction patterns (H, W), one per dataset. + self.im_bf : list[np.ndarray] + Mean bright-field images (R, C), one per dataset. + """ n = len(self.datasets) if scale is None: self.scales = np.ones(n, dtype=float) @@ -52,8 +91,8 @@ def preprocess( if np.any(self.scales == 0): raise ValueError("scale entries must be nonzero.") - self.dp_mean = [] - self.im_bf = [] + self.dp_mean: list[np.ndarray] = [] + self.im_bf: list[np.ndarray] = [] for d in self.datasets: if hasattr(d, "get_dp_mean"): @@ -67,11 +106,13 @@ def preprocess( dp = getattr(d, "dp_mean", None) if dp is None: - dp_arr = np.mean(np.asarray(d.array), axis=(0, 1)) + arr = np.asarray(d.array) + dp_arr = np.mean(arr, axis=(0, 1)) else: dp_arr = np.asarray(dp.array if hasattr(dp, "array") else dp) - im_bf_arr = np.mean(np.asarray(d.array), axis=(2, 3)) + arr = np.asarray(d.array) + im_bf_arr = np.mean(arr, axis=(2, 3)) self.dp_mean.append(np.asarray(dp_arr)) self.im_bf.append(np.asarray(im_bf_arr)) @@ -79,22 +120,18 @@ def preprocess( if plot_summary: tiles = [[(self.im_bf[i] / self.scales[i]), self.dp_mean[i]] for i in range(n)] titles = [[f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n)] - show_2d( - tiles, - titles=titles, - **plot_kwargs, - ) + show_2d(tiles, title=titles, **plot_kwargs) return self - - def diffraction_find_origin( + def diffraction_origin( self, origins=None, sigma=None, plot_origins: bool = True, plot_indices=None, - ): + **plot_kwargs: Any, + ) -> MAPED: """ Choose or automatically find the origin in diffraction space. @@ -104,26 +141,20 @@ def diffraction_find_origin( Optional manual origins. Can be: - a single (row, col) tuple, applied to all datasets - a list of (row, col) tuples of length n (one per dataset) - - a list of (row, col) tuples shorter than n, used for plot/inspection only (will error if not broadcastable) sigma Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. plot_origins If True, plot mean diffraction patterns with overlaid origin markers. plot_indices Optional indices to plot. If None, plots all datasets. + **plot_kwargs + Passed to show_2d. Stores ------ self.diffraction_origins : np.ndarray Array of shape (n, 2) with integer (row, col) origins. """ - import numpy as _np - - try: - from scipy.ndimage import gaussian_filter as _gaussian_filter - except Exception: # pragma: no cover - _gaussian_filter = None - n = len(self.datasets) if not hasattr(self, "dp_mean"): raise RuntimeError("Run preprocess() first so self.dp_mean exists.") @@ -137,119 +168,133 @@ def diffraction_find_origin( raise IndexError("plot_indices contains an out-of-range index.") if origins is None: - origins_arr = _np.zeros((n, 2), dtype=int) + origins_arr = np.zeros((n, 2), dtype=int) for i in range(n): - dp = _np.asarray(self.dp_mean[i]) + dp = np.asarray(self.dp_mean[i]) if sigma is not None and float(sigma) > 0: - if _gaussian_filter is None: - raise ImportError("scipy is required for sigma smoothing (gaussian_filter).") - dp_use = _gaussian_filter(dp.astype(float, copy=False), float(sigma)) + dp_use = gaussian_filter(dp.astype(float, copy=False), float(sigma), mode="nearest") else: dp_use = dp - ind = int(_np.argmax(dp_use)) - r, c = _np.unravel_index(ind, dp_use.shape) + r, c = np.unravel_index(int(np.argmax(dp_use)), dp_use.shape) origins_arr[i, 0] = int(r) origins_arr[i, 1] = int(c) else: if isinstance(origins, tuple) and len(origins) == 2: - origins_arr = _np.tile(_np.asarray(origins, dtype=int)[None, :], (n, 1)) + origins_arr = np.tile(np.asarray(origins, dtype=int)[None, :], (n, 1)) else: origins_list = list(origins) if len(origins_list) != n: raise ValueError("origins must be a single (row,col) tuple or a list of length n.") - origins_arr = _np.asarray(origins_list, dtype=int) + origins_arr = np.asarray(origins_list, dtype=int) if origins_arr.shape != (n, 2): raise ValueError("origins must have shape (n, 2) after conversion.") self.diffraction_origins = origins_arr if plot_origins: - dp_tiles = [[_np.asarray(self.dp_mean[i]) for i in plot_indices_list]] - titles = [[f"{i} - Mean Diffraction Pattern" for i in plot_indices_list]] - fig, axs = show_2d(dp_tiles, titles=titles, returnfig=True, **{}) - if not isinstance(axs, (list, _np.ndarray)): - axs = [axs] - axs_flat = _np.ravel(axs) + arrays = [np.asarray(self.dp_mean[i]) for i in plot_indices_list] + titles = [f"{i} - Mean Diffraction Pattern" for i in plot_indices_list] + fig, ax = show_2d(arrays, title=titles, returnfig=True, **plot_kwargs) + axs = np.ravel(np.asarray(ax, dtype=object)) for j, i in enumerate(plot_indices_list): - ax = axs_flat[j] r, c = self.diffraction_origins[i] - ax.plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) - return fig, axs + axs[j].plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) return self - def diffraction_align( self, - edge_blend = 16.0, - padding = None, - pad_val = 'min', - upsample_factor = 100, - weight_scale = 1/8, - plot_aligned = True, - linewidth = 2, - **kwargs, - ): + edge_blend: float = 16.0, + padding=None, + pad_val: str | float = "min", + upsample_factor: int = 100, + weight_scale: float = 1 / 8, + plot_aligned: bool = True, + **plot_kwargs: Any, + ) -> MAPED: """ - Refine the diffraction space origins, set padding, align images + Align mean diffraction patterns using weighted cross-correlation in Fourier space. + Parameters + ---------- + edge_blend + Tukey window edge taper (pixels). + padding + Passed to shift_images for plotting. + pad_val + Passed to shift_images for plotting. + upsample_factor + Subpixel upsampling factor for correlation peak estimation. + weight_scale + Radial weight falloff scale (fraction of mean DP size). + plot_aligned + If True, plot aligned mean diffraction patterns. + **plot_kwargs + Passed to show_2d when plotting. + + Stores + ------ + self.diffraction_shifts : np.ndarray + Array of shape (n, 2) with (row, col) shifts to align diffraction patterns. """ + if not hasattr(self, "dp_mean"): + raise RuntimeError("Run preprocess() first so self.dp_mean exists.") + if not hasattr(self, "diffraction_origins"): + raise RuntimeError("Run diffraction_origin() first so self.diffraction_origins exists.") + + H, W = np.asarray(self.dp_mean[0]).shape + + w = tukey(H, alpha=2.0 * float(edge_blend) / float(H))[:, None] * tukey( + W, alpha=2.0 * float(edge_blend) / float(W) + )[None, :] - # window function - from scipy.signal.windows import tukey - w = tukey(self.dp_mean[0].shape[0], alpha=2.0*edge_blend/self.dp_mean[0].shape[0])[:,None] * \ - tukey(self.dp_mean[0].shape[1], alpha=2.0*edge_blend/self.dp_mean[0].shape[1])[None,:] + r = np.fft.fftfreq(H, 1.0 / float(H))[:, None] + c = np.fft.fftfreq(W, 1.0 / float(W))[None, :] - # coordinates - r = np.fft.fftfreq(self.dp_mean[0].shape[0],1/self.dp_mean[0].shape[0])[:,None] - c = np.fft.fftfreq(self.dp_mean[0].shape[1],1/self.dp_mean[0].shape[1])[None,:] + n = len(self.dp_mean) + self.diffraction_shifts = np.zeros((n, 2), dtype=float) - # init - self.diffraction_shifts = np.zeros((len(self.dp_mean),2)) + G_ref = np.fft.fft2(w * np.asarray(self.dp_mean[0])) + xy0 = np.asarray(self.diffraction_origins[0], dtype=float) - # correlation alignment - G_ref = np.fft.fft2(w * self.dp_mean[0]) - xy0 = self.diffraction_origins[0] - for ind in range(1,len(self.dp_mean)): - G = np.fft.fft2(w * self.dp_mean[ind]) - xy = self.diffraction_origins[ind] + for ind in range(1, n): + G = np.fft.fft2(w * np.asarray(self.dp_mean[ind])) + xy = np.asarray(self.diffraction_origins[ind], dtype=float) - dr2 = (r - xy0[0] + xy[0])**2 \ - + (c - xy0[1] + xy[1])**2 - im_weight = np.clip(1 - np.sqrt(dr2)/np.mean(self.dp_mean[0].shape)/weight_scale, 0.0, 1.0) - im_weight = np.sin(im_weight*np.pi/2)**2 + dr2 = (r - xy0[0] + xy[0]) ** 2 + (c - xy0[1] + xy[1]) ** 2 + im_weight = np.clip( + 1.0 - np.sqrt(dr2) / float(np.mean((H, W))) / float(weight_scale), + 0.0, + 1.0, + ) + im_weight = np.sin(im_weight * np.pi / 2.0) ** 2 - shift, G_shift = weighted_cross_correlation_shift( + shift_rc, G_shift = weighted_cross_correlation_shift( im_ref=G_ref, im=G, - weight_real=im_weight*0+1.0, - upsample_factor = upsample_factor, - fft_input = True, - fft_output = True, - return_shifted_image = True, + weight_real=im_weight * 0.0 + 1.0, + upsample_factor=int(upsample_factor), + fft_input=True, + fft_output=True, + return_shifted_image=True, ) - self.diffraction_shifts[ind,:] = shift - - # update reference - G_ref = G_ref*(ind/(ind+1)) + G_shift/(ind+1) + self.diffraction_shifts[ind, :] = np.asarray(shift_rc, dtype=float) - # Center shifts - self.diffraction_shifts -= np.mean(self.diffraction_shifts,axis=0)[None,:] + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) - # Generate output image + self.diffraction_shifts -= np.mean(self.diffraction_shifts, axis=0)[None, :] if plot_aligned: im_aligned = shift_images( - images = self.dp_mean, - shifts_rc = self.diffraction_shifts, - edge_blend = edge_blend, - padding = padding, - pad_val = pad_val, - ) - show_2d( - im_aligned, - **kwargs, + images=self.dp_mean, + shifts_rc=self.diffraction_shifts, + edge_blend=float(edge_blend), + padding=padding, + pad_val=pad_val, ) + show_2d(im_aligned, **plot_kwargs) + + return self def real_space_align( @@ -266,16 +311,45 @@ def real_space_align( edge_sigma: float = 2.0, hanning_filter: bool = False, plot_aligned: bool = True, - **kwargs, - ): - import numpy as np - from scipy.ndimage import gaussian_filter, shift as ndi_shift - from scipy.signal import convolve2d - from scipy.signal.windows import tukey + **plot_kwargs: Any, + ) -> MAPED: + """ + Align real-space mean BF images using iterative average-reference correlation. - from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift - from quantem.core.visualization import show_2d + Parameters + ---------- + num_images + If provided, align only the first num_images images. + num_iter + Number of refinement iterations. + edge_blend + Used to set default correlation padding when max_shift is None. + padding + Passed to shift_images for plotting. + pad_val + Passed to shift_images for plotting. + upsample_factor + Subpixel upsampling factor for correlation peak estimation. + max_shift + Optional maximum shift constraint passed to weighted_cross_correlation_shift. + shift_method + Passed to shift_images for plotting ('bilinear' or 'fourier'). + edge_filter + If True, correlate on gradient magnitude instead of raw intensity. + edge_sigma + Gaussian sigma applied to gradients when edge_filter is True. + hanning_filter + If True, apply a Hanning window prior to FFT. + plot_aligned + If True, plot aligned mean BF images. + **plot_kwargs + Passed to show_2d when plotting. + Stores + ------ + self.real_space_shifts : np.ndarray + Array of shape (n_total, 2) with (row, col) shifts for aligned datasets. + """ if not hasattr(self, "im_bf"): raise RuntimeError("Run preprocess() first so self.im_bf exists.") if len(self.im_bf) == 0: @@ -317,16 +391,13 @@ def real_space_align( if w_h_sum <= 0: raise RuntimeError("hanning window sum is zero") - wx = None if edge_filter: wx = np.array( - [ - [-1.0, -2.0, -1.0], - [ 0.0, 0.0, 0.0], - [ 1.0, 2.0, 1.0], - ], + [[-1.0, -2.0, -1.0], [0.0, 0.0, 0.0], [1.0, 2.0, 1.0]], dtype=float, ) + else: + wx = None base_pad = np.zeros((n, Hp, Wp), dtype=float) for i in range(n): @@ -351,7 +422,7 @@ def real_space_align( for i in range(n): im_a = ndi_shift( base_pad[i], - shift=(float(shifts[i, 0]), float(shifts[i, 1])), + shift=(shifts[i, 0], shifts[i, 1]), order=1, mode="constant", cval=0.0, @@ -391,13 +462,12 @@ def real_space_align( edge_blend=float(edge_blend), padding=padding, pad_val=pad_val, - shift_method=str(shift_method), + shift_method=shift_method, ) - show_2d(im_aligned, **kwargs) + show_2d(im_aligned, **plot_kwargs) return self - def merge_datasets( self, real_space_padding=0, @@ -409,15 +479,46 @@ def merge_datasets( dtype=None, scale_output: bool = False, plot_result: bool = True, - **kwargs, - ): - import warnings + **plot_kwargs: Any, + ) -> Dataset4dstem: + """ + Merge aligned datasets into a single Dataset4dstem. - import numpy as np - from scipy.ndimage import shift as ndi_shift - from scipy.signal.windows import tukey - from tqdm import tqdm + Requires + -------- + self.real_space_shifts + From real_space_align(). + self.diffraction_shifts + From diffraction_align(). + Parameters + ---------- + real_space_padding + Output scan padding in pixels (adds border to scan grid). + real_space_edge_blend + Tukey taper width for scan-space interpolation weights. + diffraction_padding + Output diffraction padding in pixels (adds border around DPs). + diffraction_edge_blend + Tukey taper width for diffraction-space weights. + diffraction_pad_val + Pad value for diffraction padding ('min','max','mean','median' or float). + shift_method + Diffraction shift method: 'bilinear' or 'fourier'. + dtype + Output dtype. If None, uses parent dtype. + scale_output + If True and dtype is integer, scale to full dynamic range using global max. + plot_result + If True, plot merged BF and merged mean DP. + **plot_kwargs + Passed to show_2d. + + Returns + ------- + Dataset4dstem + Merged dataset. + """ if not hasattr(self, "real_space_shifts"): raise RuntimeError("Run real_space_align() first so self.real_space_shifts exists.") if not hasattr(self, "diffraction_shifts"): @@ -502,10 +603,10 @@ def merge_datasets( if method == "fourier": kr = np.fft.fftfreq(Hp)[:, None] kc = np.fft.fftfreq(Wp)[None, :] - ramps = [] Fw = np.fft.fft2(wdp_pad) + ramps: list[np.ndarray] = [] for i in range(n): - dr, dc = float(dp_shifts[i, 0]), float(dp_shifts[i, 1]) + dr, dc = dp_shifts[i, 0], dp_shifts[i, 1] ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) ramps.append(ramp) w_i = np.fft.ifft2(Fw * ramp).real @@ -514,17 +615,17 @@ def merge_datasets( for i in range(n): w_i = ndi_shift( wdp_pad, - shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), + shift=(dp_shifts[i, 0], dp_shifts[i, 1]), order=1, mode="constant", cval=0.0, prefilter=False, ) wdp_shifted[i] = np.clip(w_i, 0.0, 1.0) - ramps = None + ramps = [] - edge_w_dp = 1.0 - np.max(wdp_shifted, axis=0) - edge_w_dp = np.clip(edge_w_dp, 0.0, 1.0) + coverage = np.clip(np.sum(wdp_shifted, axis=0), 0.0, 1.0) + edge_w_dp = 1.0 - coverage merged = np.zeros((Rout, Cout, Hp, Wp), dtype=np.float64) @@ -535,25 +636,25 @@ def merge_datasets( den_tmp = np.zeros((Hp, Wp), dtype=np.float64) for ro in tqdm(range(Rout), desc="Merging (rows)"): - r_base = float(ro - real_space_padding) + r_base = ro - real_space_padding for co in range(Cout): - c_base = float(co - real_space_padding) + c_base = co - real_space_padding num_tmp.fill(0.0) den_tmp.fill(0.0) max_wi = 0.0 for i in range(n): - r_in = r_base - float(rs_shifts[i, 0]) - c_in = c_base - float(rs_shifts[i, 1]) + r_in = r_base - rs_shifts[i, 0] + c_in = c_base - rs_shifts[i, 1] r0 = int(np.floor(r_in)) c0 = int(np.floor(c_in)) if r0 < 0 or r0 >= Rs - 1 or c0 < 0 or c0 >= Cs - 1: continue - dr = float(r_in - r0) - dc = float(c_in - c0) + dr = r_in - r0 + dc = c_in - c0 w00 = (1.0 - dr) * (1.0 - dc) w10 = dr * (1.0 - dc) @@ -583,11 +684,12 @@ def merge_datasets( dp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = dp_local * w_dp if method == "fourier": - dp_shifted_tmp[:] = np.fft.ifft2(np.fft.fft2(dp_pad) * ramps[i]).real + ramp = ramps[i] + dp_shifted_tmp[:] = np.fft.ifft2(np.fft.fft2(dp_pad) * ramp).real else: dp_shifted_tmp[:] = ndi_shift( dp_pad, - shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), + shift=(dp_shifts[i, 0], dp_shifts[i, 1]), order=1, mode="constant", cval=0.0, @@ -617,38 +719,21 @@ def merge_datasets( dmin = float(info.min) dmax = float(info.max) - merged_f = merged # float64 + merged_f = merged if scale_output: peak = float(np.max(merged_f)) if peak <= 0.0: - scale = 1.0 merged_scaled = merged_f else: - scale = dmax / peak - merged_scaled = merged_f * scale + merged_scaled = merged_f * (dmax / peak) if np.issubdtype(dtype_out, np.unsignedinteger): - if float(np.min(merged_scaled)) < 0.0: - warnings.warn( - f"scale_output=True with unsigned dtype {dtype_out}: " - "negative values present; they will be clipped to 0.", - RuntimeWarning, - ) lo, hi = 0.0, dmax else: lo, hi = dmin, dmax - if float(np.min(merged_scaled)) < lo or float(np.max(merged_scaled)) > hi: - warnings.warn( - f"Output overflow for dtype {dtype_out} after scaling: " - f"data range [{float(np.min(merged_scaled))}, {float(np.max(merged_scaled))}] exceeds " - f"[{lo}, {hi}]. Values will be clipped.", - RuntimeWarning, - ) - merged_out = np.rint(np.clip(merged_scaled, lo, hi)).astype(dtype_out) - else: below = float(np.min(merged_f)) above = float(np.max(merged_f)) @@ -662,17 +747,15 @@ def merge_datasets( else: merged_out = merged.astype(dtype_out, copy=False) - dataset_merged = Dataset4dstem.from_array(array=merged_out) - dataset_merged.im_bf_merged = self.im_bf_merged dataset_merged.dp_mean_merged = self.dp_mean_merged if plot_result: show_2d( [[self.im_bf_merged, self.dp_mean_merged]], - titles=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], - **kwargs, + title=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], + **plot_kwargs, ) return dataset_merged @@ -683,13 +766,32 @@ def shift_images( shifts_rc, edge_blend: float = 8.0, padding=None, - pad_val=0.0, + pad_val: str | float = 0.0, shift_method: str = "bilinear", ): - import numpy as np - from scipy.ndimage import shift as ndi_shift - from scipy.signal.windows import tukey - + """ + Shift and blend a stack of 2D images into a common padded canvas. + + Parameters + ---------- + images + Sequence of (H, W) arrays. + shifts_rc + Array-like of shape (n, 2) with (row, col) shifts for each image. + edge_blend + Tukey taper width in pixels for image blending. + padding + Output padding. If None, set from max shift and edge_blend. + pad_val + Fill value outside support ('min','max','mean','median' or float). + shift_method + 'bilinear' or 'fourier'. + + Returns + ------- + np.ndarray + Blended image of shape (H + 2*padding, W + 2*padding). + """ images = [np.asarray(im, dtype=float) for im in images] if len(images) == 0: raise ValueError("images must be non-empty") @@ -707,17 +809,17 @@ def shift_images( s = pad_val.strip().lower() v = np.stack(images, axis=0).reshape(-1) if s == "min": - pad_val = float(np.min(v)) + pad_val_f = float(np.min(v)) elif s == "max": - pad_val = float(np.max(v)) + pad_val_f = float(np.max(v)) elif s == "mean": - pad_val = float(np.mean(v)) + pad_val_f = float(np.mean(v)) elif s == "median": - pad_val = float(np.median(v)) + pad_val_f = float(np.median(v)) else: raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") else: - pad_val = float(pad_val) + pad_val_f = float(pad_val) if padding is None: max_shift = float(np.max(np.abs(shifts_rc))) if shifts_rc.size else 0.0 @@ -749,7 +851,7 @@ def shift_images( kr = np.fft.fftfreq(Hp)[:, None] kc = np.fft.fftfreq(Wp)[None, :] for ind in range(len(images)): - dr, dc = float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1]) + dr, dc = shifts_rc[ind, 0], shifts_rc[ind, 1] ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) F = np.fft.fft2(stack[ind]) @@ -762,7 +864,7 @@ def shift_images( for ind in range(len(images)): stack[ind] = ndi_shift( stack[ind], - shift=(float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1])), + shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), order=1, mode="constant", cval=0.0, @@ -770,7 +872,7 @@ def shift_images( ) stack_w[ind] = ndi_shift( stack_w[ind], - shift=(float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1])), + shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), order=1, mode="constant", cval=0.0, @@ -778,11 +880,13 @@ def shift_images( ) stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) - # edge_w = 1.0 - np.clip(np.max(stack_w, axis=0), 0.0, 1.0) - edge_w = len(images) - np.sum(stack_w, axis=0) + edge_w = np.clip(1.0 - np.sum(stack_w, axis=0), 0.0, 1.0) - num = np.sum(stack, axis=0) + edge_w * pad_val + num = np.sum(stack, axis=0) + edge_w * pad_val_f den = np.sum(stack_w, axis=0) + edge_w - out = num / den + + out = np.empty_like(num) + np.divide(num, den, out=out, where=den != 0.0) + out[den == 0.0] = 0.0 return out From bf4b2b87f00ac847df94903252bd3187b31e7691 Mon Sep 17 00:00:00 2001 From: cophus Date: Mon, 16 Feb 2026 11:42:05 -0800 Subject: [PATCH 20/43] initial commit for model based refinment --- src/quantem/core/models/__init__.py | 29 +++ src/quantem/core/models/background.py | 78 ++++++ src/quantem/core/models/base.py | 241 +++++++++++++++++++ src/quantem/core/models/diffraction.py | 294 +++++++++++++++++++++++ src/quantem/diffraction/__init__.py | 1 + src/quantem/diffraction/model_fitting.py | 272 +++++++++++++++++++++ 6 files changed, 915 insertions(+) create mode 100644 src/quantem/core/models/__init__.py create mode 100644 src/quantem/core/models/background.py create mode 100644 src/quantem/core/models/base.py create mode 100644 src/quantem/core/models/diffraction.py create mode 100644 src/quantem/diffraction/model_fitting.py diff --git a/src/quantem/core/models/__init__.py b/src/quantem/core/models/__init__.py new file mode 100644 index 00000000..82d81d8e --- /dev/null +++ b/src/quantem/core/models/__init__.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from quantem.core.models.base import Component as Component +from quantem.core.models.base import Model as Model +from quantem.core.models.base import ModelContext as ModelContext +from quantem.core.models.base import OriginND as OriginND +from quantem.core.models.base import Overlay as Overlay +from quantem.core.models.base import Parameter as Parameter +from quantem.core.models.base import PreparedModel as PreparedModel + +from quantem.core.models.background import DCBackground as DCBackground +from quantem.core.models.background import GaussianBackground as GaussianBackground + +from quantem.core.models.diffraction import DiskTemplate as DiskTemplate +from quantem.core.models.diffraction import SyntheticDiskLattice as SyntheticDiskLattice + +__all__ = [ + "Parameter", + "Component", + "Overlay", + "OriginND", + "ModelContext", + "PreparedModel", + "Model", + "DCBackground", + "GaussianBackground", + "DiskTemplate", + "SyntheticDiskLattice", +] diff --git a/src/quantem/core/models/background.py b/src/quantem/core/models/background.py new file mode 100644 index 00000000..0f41f6a5 --- /dev/null +++ b/src/quantem/core/models/background.py @@ -0,0 +1,78 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +import torch + +from quantem.core.models.base import Component, ModelContext, Overlay, Parameter + +__all__ = ["DCBackground", "GaussianBackground"] + + +class DCBackground(Component): + def __init__( + self, + *, + intensity: float | tuple[float, float] | tuple[float, float, float | None], + name: str = "DCBackground", + ): + super().__init__(name=name) + self.p_intensity = Parameter(intensity, tags={"role": "background_dc", "name": name}) + + def prepare(self, ctx: ModelContext) -> Any: + pI = self.p_intensity + + @dataclass + class PreparedDCBackground: + name: str + + def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: + out += pI.value(x, device=ctx.device, dtype=ctx.dtype) + + def overlays(self) -> list[Overlay]: + return [] + + return PreparedDCBackground(name=self.name) + + +class GaussianBackground(Component): + def __init__( + self, + *, + sigma: float | tuple[float, float] | tuple[float, float, float | None], + intensity: float | tuple[float, float] | tuple[float, float, float | None], + origin_key: str = "origin", + name: str = "GaussianBackground", + ): + super().__init__(name=name) + self.origin_key = str(origin_key) + self.p_sigma = Parameter(sigma, tags={"role": "background_gaussian_sigma", "name": name}) + self.p_intensity = Parameter(intensity, tags={"role": "background_gaussian_intensity", "name": name}) + + def prepare(self, ctx: ModelContext) -> Any: + ok = self.origin_key + pS = self.p_sigma + pI = self.p_intensity + + rr = torch.arange(ctx.H, device=ctx.device, dtype=ctx.dtype)[:, None] + cc = torch.arange(ctx.W, device=ctx.device, dtype=ctx.dtype)[None, :] + + @dataclass + class PreparedGaussianBackground: + origin_key: str + + def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: + if self.origin_key not in ctx.origins: + return + r0, c0 = ctx.origins[self.origin_key] + sig = torch.clamp(pS.value(x, device=ctx.device, dtype=ctx.dtype), min=1e-6) + inten = pI.value(x, device=ctx.device, dtype=ctx.dtype) + dr = rr - r0 + dc = cc - c0 + out += inten * torch.exp(-0.5 * (dr * dr + dc * dc) / (sig * sig)) + + def overlays(self) -> list[Overlay]: + return [] + + return PreparedGaussianBackground(origin_key=ok) diff --git a/src/quantem/core/models/base.py b/src/quantem/core/models/base.py new file mode 100644 index 00000000..16c15840 --- /dev/null +++ b/src/quantem/core/models/base.py @@ -0,0 +1,241 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Iterable, Mapping, Sequence + +import numpy as np +import torch + +__all__ = [ + "Overlay", + "Parameter", + "ModelContext", + "Component", + "OriginND", + "Model", + "PreparedModel", +] + + +@dataclass(frozen=True) +class Overlay: + kind: str + data: dict[str, Any] + + +class Parameter: + def __init__( + self, + value: float | Sequence[float], + *, + lower_bound: float | None = None, + upper_bound: float | None = None, + tags: Mapping[str, Any] | None = None, + ): + v0, lb, ub = self._parse(value, lower_bound, upper_bound) + self.initial_value = float(v0) + self.lower_bound = float(lb) + self.upper_bound = float(ub) + self.tags: dict[str, Any] = dict(tags) if tags is not None else {} + self._index: int | None = None + + @staticmethod + def _parse( + value: float | Sequence[float], + lower_bound: float | None, + upper_bound: float | None, + ) -> tuple[float, float, float]: + if isinstance(value, (list, tuple, np.ndarray)): + seq = list(value) + if len(seq) == 2: + v0 = float(seq[0]) + dev = float(seq[1]) + return v0, v0 - dev, v0 + dev + if len(seq) == 3: + v0 = float(seq[0]) + lb = -np.inf if seq[1] is None else float(seq[1]) + ub = np.inf if seq[2] is None else float(seq[2]) + return v0, lb, ub + raise ValueError("Parameter sequences must have length 2 or 3.") + v0 = float(value) + lb = -np.inf if lower_bound is None else float(lower_bound) + ub = np.inf if upper_bound is None else float(upper_bound) + return v0, lb, ub + + def bind(self, index: int) -> None: + self._index = int(index) + + def value(self, x: torch.Tensor, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor: + if self._index is None: + return torch.as_tensor(self.initial_value, device=device, dtype=dtype).reshape(()) + return x[int(self._index)].to(device=device, dtype=dtype).reshape(()) + + +class ModelContext: + def __init__( + self, + *, + H: int, + W: int, + device: torch.device, + dtype: torch.dtype, + mask: torch.Tensor | None = None, + fields: Mapping[str, Any] | None = None, + ): + self.H = int(H) + self.W = int(W) + self.device = device + self.dtype = dtype + self.mask = mask + self.fields: dict[str, Any] = dict(fields) if fields is not None else {} + self.origins: dict[str, tuple[torch.Tensor, ...]] = {} + + def __getattr__(self, name: str) -> Any: + if name in self.fields: + return self.fields[name] + raise AttributeError(name) + + +class Component: + def __init__(self, *, name: str): + self.name = str(name) + + def parameters(self) -> list[Parameter]: + out: list[Parameter] = [] + + def add(obj: Any) -> None: + if obj is None: + return + if isinstance(obj, Parameter): + out.append(obj) + return + if isinstance(obj, (list, tuple)): + for z in obj: + add(z) + + for v in self.__dict__.values(): + add(v) + return out + + def prepare(self, ctx: ModelContext) -> Any: + raise NotImplementedError + + +class OriginND(Component): + def __init__(self, *, key: str, coords: Sequence[Parameter]): + super().__init__(name=f"Origin[{key}]") + self.key = str(key) + self.coords = list(coords) + + @classmethod + def from_row_col( + cls, + *, + key: str, + origin_row: float | Sequence[float], + origin_col: float | Sequence[float], + ) -> "OriginND": + p_row = Parameter(origin_row, tags={"role": "origin_row", "origin_key": str(key)}) + p_col = Parameter(origin_col, tags={"role": "origin_col", "origin_key": str(key)}) + return cls(key=key, coords=[p_row, p_col]) + + def prepare(self, ctx: ModelContext) -> Any: + key = self.key + p0 = self.coords[0] + p1 = self.coords[1] + + r0 = torch.as_tensor(p0.initial_value, device=ctx.device, dtype=ctx.dtype).reshape(()) + c0 = torch.as_tensor(p1.initial_value, device=ctx.device, dtype=ctx.dtype).reshape(()) + ctx.origins[key] = (r0, c0) + + class PreparedOriginND: + origin_key = key + + def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: + r = p0.value(x, device=ctx.device, dtype=ctx.dtype) + c = p1.value(x, device=ctx.device, dtype=ctx.dtype) + ctx.origins[key] = (r, c) + + def overlays(self) -> list[Overlay]: + r = float(p0.initial_value) + c = float(p1.initial_value) + return [ + Overlay( + kind="points_rc", + data={"r": np.array([r]), "c": np.array([c]), "marker": "x", "s": 80.0, "color": "tab:blue"}, + ) + ] + + return PreparedOriginND() + + +class Model: + def __init__(self) -> None: + self._components: list[Component] = [] + + def add(self, items: Iterable[Component]) -> "Model": + for obj in items: + if not isinstance(obj, Component): + raise TypeError("Model.add expects Component objects.") + self._components.append(obj) + return self + + def compile(self, ctx: ModelContext) -> "PreparedModel": + seen: set[int] = set() + params: list[Parameter] = [] + for c in self._components: + for p in c.parameters(): + if id(p) in seen: + continue + seen.add(id(p)) + params.append(p) + + for i, p in enumerate(params): + p.bind(i) + + x0 = torch.as_tensor([p.initial_value for p in params], device=ctx.device, dtype=ctx.dtype) + + prepared_components: list[Any] = [] + for c in self._components: + prepared_components.append(c.prepare(ctx)) + + return PreparedModel( + components=prepared_components, + ctx=ctx, + params=params, + x0=x0, + ) + + +class PreparedModel: + def __init__( + self, + *, + components: list[Any], + ctx: ModelContext, + params: list[Parameter], + x0: torch.Tensor, + ): + self.components = components + self.ctx = ctx + self.params = params + self.x0 = x0 + + def render(self, x: torch.Tensor) -> torch.Tensor: + out = torch.zeros((self.ctx.H, self.ctx.W), device=self.ctx.device, dtype=self.ctx.dtype) + for c in self.components: + if hasattr(c, "render"): + c.render(out, x, self.ctx) + return out + + def render_initial(self) -> torch.Tensor: + return self.render(self.x0) + + def overlays(self) -> list[Overlay]: + out: list[Overlay] = [] + for c in self.components: + if hasattr(c, "overlays"): + ov = c.overlays() + if ov: + out.extend(list(ov)) + return out diff --git a/src/quantem/core/models/diffraction.py b/src/quantem/core/models/diffraction.py new file mode 100644 index 00000000..ebf820bd --- /dev/null +++ b/src/quantem/core/models/diffraction.py @@ -0,0 +1,294 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Iterable + +import numpy as np +import torch + +from quantem.core.models.base import Component, ModelContext, Overlay, Parameter + +__all__ = ["DiskTemplate", "DiskAtOrigin", "SyntheticDiskLattice"] + + +def _as_tensor(x: Any, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor: + if torch.is_tensor(x): + return x.to(device=device, dtype=dtype) + return torch.as_tensor(x, device=device, dtype=dtype) + + +def _keep_center(r: float, c: float, H: int, W: int, boundary_px: float) -> bool: + b = float(boundary_px) + return (r >= b) and (r <= (H - 1) - b) and (c >= b) and (c <= (W - 1) - b) + + +def _place_patch_add(out: torch.Tensor, patch: torch.Tensor, center_r: float, center_c: float) -> None: + H, W = int(out.shape[-2]), int(out.shape[-1]) + ph, pw = int(patch.shape[-2]), int(patch.shape[-1]) + pr = ph // 2 + pc = pw // 2 + + r0 = int(np.rint(center_r)) - pr + c0 = int(np.rint(center_c)) - pc + r1 = r0 + ph + c1 = c0 + pw + + rr0 = max(0, r0) + cc0 = max(0, c0) + rr1 = min(H, r1) + cc1 = min(W, c1) + if rr1 <= rr0 or cc1 <= cc0: + return + + pr0 = rr0 - r0 + pc0 = cc0 - c0 + pr1 = pr0 + (rr1 - rr0) + pc1 = pc0 + (cc1 - cc0) + + out[rr0:rr1, cc0:cc1] += patch[pr0:pr1, pc0:pc1] + + +class DiskTemplate(Component): + def __init__( + self, + *, + name: str, + array: np.ndarray, + refine_all_pixels: bool = False, + place_at_origin: bool = False, + origin_key: str = "origin", + origin_intensity: float | tuple[float, float] | tuple[float, float, float | None] = 1.0, + ): + super().__init__(name=name) + arr = np.asarray(array, dtype=np.float32) + if arr.ndim != 2: + raise ValueError("DiskTemplate.array must be 2D.") + self.shape = (int(arr.shape[0]), int(arr.shape[1])) + self.refine_all_pixels = bool(refine_all_pixels) + self.place_at_origin = bool(place_at_origin) + self.origin_key = str(origin_key) + + self._array0 = arr + self.p_origin_intensity = Parameter(origin_intensity, tags={"role": "disk_origin_intensity", "name": name}) + + self.p_pixels: list[Parameter] = [] + if self.refine_all_pixels: + flat = arr.reshape(-1) + for i, v in enumerate(flat): + self.p_pixels.append(Parameter(float(v), tags={"role": "disk_pixel", "name": name, "pixel_index": int(i)})) + + @classmethod + def from_array( + cls, + *, + name: str, + array: np.ndarray, + refine_all_pixels: bool = False, + place_at_origin: bool = False, + origin_key: str = "origin", + origin_intensity: float | tuple[float, float] | tuple[float, float, float | None] = 1.0, + ) -> "DiskTemplate": + return cls( + name=name, + array=array, + refine_all_pixels=refine_all_pixels, + place_at_origin=place_at_origin, + origin_key=origin_key, + origin_intensity=origin_intensity, + ) + + def prepare(self, ctx: ModelContext) -> Any: + disk = self + ok = disk.origin_key + + if disk.refine_all_pixels: + idxs = [p._index for p in disk.p_pixels] + if any(i is None for i in idxs): + raise RuntimeError("DiskTemplate pixel parameters not bound.") + idx_t = torch.as_tensor([int(i) for i in idxs], device=ctx.device, dtype=torch.int64) + + def patch(x: torch.Tensor) -> torch.Tensor: + return x.index_select(0, idx_t).to(device=ctx.device, dtype=ctx.dtype).reshape(disk.shape[0], disk.shape[1]) + + else: + patch0 = _as_tensor(disk._array0, device=ctx.device, dtype=ctx.dtype) + + def patch(x: torch.Tensor) -> torch.Tensor: + return patch0 + + @dataclass + class PreparedDiskTemplate: + origin_key: str + place_at_origin: bool + + def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: + if not self.place_at_origin: + return + if self.origin_key not in ctx.origins: + return + r0, c0 = ctx.origins[self.origin_key] + r = float(r0.detach().cpu().item()) + c = float(c0.detach().cpu().item()) + inten = disk.p_origin_intensity.value(x, device=ctx.device, dtype=ctx.dtype) + _place_patch_add(out, patch(x) * inten, r, c) + + def overlays(self) -> list[Overlay]: + return [] + + return PreparedDiskTemplate(origin_key=ok, place_at_origin=disk.place_at_origin) + + +class SyntheticDiskLattice(Component): + def __init__( + self, + *, + name: str, + disk: DiskTemplate, + u_row: float | tuple[float, float] | tuple[float, float, float | None], + u_col: float | tuple[float, float] | tuple[float, float, float | None], + v_row: float | tuple[float, float] | tuple[float, float, float | None], + v_col: float | tuple[float, float] | tuple[float, float, float | None], + u_max: int = 0, + v_max: int = 0, + intensity_0: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + intensity_row: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + intensity_col: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + exclude_indices: Iterable[tuple[int, int]] | None = [], + boundary_px: float = 0.0, + origin_key: str = "origin", + ): + super().__init__(name=name) + self.disk = disk + self.origin_key = str(origin_key) + self.u_max = int(u_max) + self.v_max = int(v_max) + self.boundary_px = float(boundary_px) + + if exclude_indices is None: + self.exclude_indices = {(0, 0)} + else: + self.exclude_indices = {(int(a), int(b)) for (a, b) in exclude_indices} + + self.p_u_row = Parameter(u_row, tags={"role": "lattice_u_row", "name": name}) + self.p_u_col = Parameter(u_col, tags={"role": "lattice_u_col", "name": name}) + self.p_v_row = Parameter(v_row, tags={"role": "lattice_v_row", "name": name}) + self.p_v_col = Parameter(v_col, tags={"role": "lattice_v_col", "name": name}) + + self.p_intensity_0 = Parameter(intensity_0, tags={"role": "disk_intensity_0", "name": name}) + self.p_intensity_row = Parameter(intensity_row, tags={"role": "disk_intensity_row", "name": name}) + self.p_intensity_col = Parameter(intensity_col, tags={"role": "disk_intensity_col", "name": name}) + + def _uv_list(self) -> list[tuple[int, int]]: + uv: list[tuple[int, int]] = [] + for u in range(-self.u_max, self.u_max + 1): + for v in range(-self.v_max, self.v_max + 1): + if (u, v) in self.exclude_indices: + continue + uv.append((u, v)) + return uv + + def prepare(self, ctx: ModelContext) -> Any: + lat = self + ok = lat.origin_key + uv = lat._uv_list() + + if lat.disk.refine_all_pixels: + didxs = [p._index for p in lat.disk.p_pixels] + if any(i is None for i in didxs): + raise RuntimeError("DiskTemplate pixel parameters not bound.") + didx_t = torch.as_tensor([int(i) for i in didxs], device=ctx.device, dtype=torch.int64) + + def patch(x: torch.Tensor) -> torch.Tensor: + return x.index_select(0, didx_t).to(device=ctx.device, dtype=ctx.dtype).reshape(lat.disk.shape[0], lat.disk.shape[1]) + + else: + patch0 = _as_tensor(lat.disk._array0, device=ctx.device, dtype=ctx.dtype) + + def patch(x: torch.Tensor) -> torch.Tensor: + return patch0 + + r0_list: list[float] = [] + c0_list: list[float] = [] + if ok in ctx.origins: + or0, oc0 = ctx.origins[ok] + orow0 = float(or0.detach().cpu().item()) + ocol0 = float(oc0.detach().cpu().item()) + urow0 = float(lat.p_u_row.initial_value) + ucol0 = float(lat.p_u_col.initial_value) + vrow0 = float(lat.p_v_row.initial_value) + vcol0 = float(lat.p_v_col.initial_value) + for u, v in uv: + rr = orow0 + u * urow0 + v * vrow0 + cc = ocol0 + u * ucol0 + v * vcol0 + if _keep_center(rr, cc, ctx.H, ctx.W, lat.boundary_px): + r0_list.append(rr) + c0_list.append(cc) + + r0_np = np.asarray(r0_list, dtype=np.float32) + c0_np = np.asarray(c0_list, dtype=np.float32) + + @dataclass + class PreparedSyntheticDiskLattice: + origin_key: str + boundary_px: float + uv: list[tuple[int, int]] + r0: np.ndarray + c0: np.ndarray + + def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: + if self.origin_key not in ctx.origins: + return + if not self.uv: + return + + r_origin, c_origin = ctx.origins[self.origin_key] + r_origin = r_origin.reshape(()) + c_origin = c_origin.reshape(()) + + urow = lat.p_u_row.value(x, device=ctx.device, dtype=ctx.dtype) + ucol = lat.p_u_col.value(x, device=ctx.device, dtype=ctx.dtype) + vrow = lat.p_v_row.value(x, device=ctx.device, dtype=ctx.dtype) + vcol = lat.p_v_col.value(x, device=ctx.device, dtype=ctx.dtype) + + I0 = lat.p_intensity_0.value(x, device=ctx.device, dtype=ctx.dtype) + Ir = lat.p_intensity_row.value(x, device=ctx.device, dtype=ctx.dtype) + Ic = lat.p_intensity_col.value(x, device=ctx.device, dtype=ctx.dtype) + + uv_t = torch.as_tensor(self.uv, device=ctx.device, dtype=torch.int32) + uu = uv_t[:, 0].to(device=ctx.device, dtype=ctx.dtype) + vv = uv_t[:, 1].to(device=ctx.device, dtype=ctx.dtype) + + rr = r_origin + uu * urow + vv * vrow + cc = c_origin + uu * ucol + vv * vcol + + p = patch(x) + + H = int(ctx.H) + W = int(ctx.W) + for i in range(int(rr.numel())): + r = float(rr[i].detach().cpu().item()) + c = float(cc[i].detach().cpu().item()) + if not _keep_center(r, c, H, W, self.boundary_px): + continue + dr = rr[i] - r_origin + dc = cc[i] - c_origin + inten = torch.clamp(I0 + Ir * dr + Ic * dc, min=0.0) + _place_patch_add(out, p * inten, r, c) + + def overlays(self) -> list[Overlay]: + if self.r0.size == 0: + return [] + return [ + Overlay( + kind="points_rc", + data={"r": self.r0, "c": self.c0, "marker": "x", "s": 60.0, "color": "orange"}, + ) + ] + + return PreparedSyntheticDiskLattice( + origin_key=ok, + boundary_px=lat.boundary_px, + uv=uv, + r0=r0_np, + c0=c0_np, + ) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 2a79312b..a64a357e 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,3 +1,4 @@ from quantem.diffraction.polar import RDF as RDF from quantem.diffraction.strain_autocorrelation import StrainMapAutocorrelation as StrainMapAutocorrelation from quantem.diffraction.maped import MAPED as MAPED +from quantem.diffraction.model_fitting import ModelDiffraction as ModelDiffraction diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py new file mode 100644 index 00000000..84d9e5c8 --- /dev/null +++ b/src/quantem/diffraction/model_fitting.py @@ -0,0 +1,272 @@ +from __future__ import annotations + +from typing import Any + +import numpy as np +import torch +from scipy.ndimage import shift as ndi_shift +from scipy.signal.windows import tukey + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.datastructures.dataset4d import Dataset4d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.io.serialize import AutoSerialize +from quantem.core.models.base import Model, ModelContext, OriginND, Overlay, PreparedModel +from quantem.core.utils.imaging_utils import cross_correlation_shift +from quantem.core.visualization import show_2d + +__all__ = ["ModelDiffraction"] + + +def _to_numpy(x: Any) -> np.ndarray: + if isinstance(x, np.ndarray): + return x + if torch.is_tensor(x): + return x.detach().cpu().numpy() + return np.asarray(x) + + +class ModelDiffraction(AutoSerialize): + _token = object() + + def __init__(self, dataset: Any, _token: object | None = None): + if _token is not self._token: + raise RuntimeError("Use ModelDiffraction.from_dataset() or .from_file().") + super().__init__() + self.dataset = dataset + self.metadata: dict[str, Any] = {} + self.index_shape: tuple[int, ...] | None = None + self.image_ref: np.ndarray | None = None + self.preprocess_shifts: np.ndarray | None = None + self.model: Model | None = None + self.prepared: PreparedModel | None = None + self.fit_mask: np.ndarray | None = None + + @classmethod + def from_dataset(self, dataset: Dataset2d | Dataset3d | Dataset4d | Dataset4dstem | Any) -> "ModelDiffraction": + if isinstance(dataset, (Dataset2d, Dataset3d, Dataset4d, Dataset4dstem)): + return ModelDiffraction(dataset=dataset, _token=ModelDiffraction._token) + raise TypeError("from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance.") + + def preprocess( + self, + *, + align: bool = False, + edge_blend: float = 8.0, + upsample_factor: int = 32, + max_shift: float | None = None, + shift_order: int = 1, + ) -> "ModelDiffraction": + arr = np.asarray(self.dataset.array) + if arr.ndim < 2: + raise ValueError("dataset.array must have at least 2 dimensions.") + H, W = int(arr.shape[-2]), int(arr.shape[-1]) + self.index_shape = tuple(arr.shape[:-2]) + + stack = arr.reshape((-1, H, W)).astype(np.float32, copy=False) + n = int(stack.shape[0]) + + if (not align) or n <= 1: + self.image_ref = np.mean(stack, axis=0) + self.preprocess_shifts = None + self.metadata["preprocess"] = { + "align": bool(align), + "edge_blend": float(edge_blend), + "upsample_factor": int(upsample_factor), + "max_shift": None if max_shift is None else float(max_shift), + "shift_order": int(shift_order), + } + return self + + alpha_r = 0.0 if edge_blend <= 0 else min(1.0, 2.0 * float(edge_blend) / float(H)) + alpha_c = 0.0 if edge_blend <= 0 else min(1.0, 2.0 * float(edge_blend) / float(W)) + w = tukey(H, alpha=alpha_r)[:, None] * tukey(W, alpha=alpha_c)[None, :] + w = w.astype(np.float32, copy=False) + + shifts = np.zeros((n, 2), dtype=np.float32) + F_ref = np.fft.fft2(w * stack[0]) + + for i in range(1, n): + F_i = np.fft.fft2(w * stack[i]) + drc, F_shift = cross_correlation_shift( + F_ref, + F_i, + upsample_factor=int(upsample_factor), + max_shift=max_shift, + fft_input=True, + fft_output=True, + return_shifted_image=True, + ) + shifts[i, 0] = float(drc[0]) + shifts[i, 1] = float(drc[1]) + F_ref = F_ref * (i / (i + 1)) + F_shift / (i + 1) + + shifts -= np.mean(shifts, axis=0, keepdims=True) + + aligned = np.empty_like(stack, dtype=np.float32) + for i in range(n): + aligned[i] = ndi_shift( + stack[i], + shift=(float(shifts[i, 0]), float(shifts[i, 1])), + order=int(shift_order), + mode="nearest", + prefilter=False, + ) + + self.image_ref = np.mean(aligned, axis=0) + self.preprocess_shifts = shifts.reshape(self.index_shape + (2,)) + + self.metadata["preprocess"] = { + "align": bool(align), + "edge_blend": float(edge_blend), + "upsample_factor": int(upsample_factor), + "max_shift": None if max_shift is None else float(max_shift), + "shift_order": int(shift_order), + } + return self + + def _ensure_image_ref(self) -> None: + if self.image_ref is not None: + return + arr = np.asarray(self.dataset.array) + if arr.ndim < 2: + raise ValueError("dataset.array must have at least 2 dimensions.") + H, W = int(arr.shape[-2]), int(arr.shape[-1]) + self.index_shape = tuple(arr.shape[:-2]) + stack = arr.reshape((-1, H, W)).astype(np.float32, copy=False) + self.image_ref = np.mean(stack, axis=0) + self.preprocess_shifts = None + + def define_model( + self, + *, + origin_row: float | tuple[float, float] | tuple[float, float, float | None], + origin_col: float | tuple[float, float] | tuple[float, float, float | None], + components: list[Any], + device: torch.device | str | None = None, + dtype: torch.dtype | None = None, + mask: np.ndarray | torch.Tensor | None = None, + origin_key: str = "origin", + ) -> "ModelDiffraction": + self._ensure_image_ref() + if self.image_ref is None: + raise RuntimeError("image_ref not available.") + H, W = int(self.image_ref.shape[-2]), int(self.image_ref.shape[-1]) + + dev = torch.device(device) if device is not None else torch.device("cpu") + dt = dtype if dtype is not None else torch.float32 + + mask_t = None + if mask is not None: + if torch.is_tensor(mask): + mask_t = mask.to(device=dev, dtype=torch.float32) + else: + m = np.asarray(mask) + if m.shape != (H, W): + raise ValueError("mask must have shape (H, W).") + mask_t = torch.as_tensor(m.astype(np.float32, copy=False), device=dev) + + ctx = ModelContext(H=H, W=W, device=dev, dtype=dt, mask=mask_t) + + m = Model() + origin = OriginND.from_row_col(key=str(origin_key), origin_row=origin_row, origin_col=origin_col) + comps = [origin] + list(components) + m.add(comps) + + self.model = m + self.prepared = m.compile(ctx) + self.metadata["define_model"] = {"origin_key": str(origin_key), "device": str(dev), "dtype": str(dt)} + return self + + def _apply_overlays(self, ax: Any, overlays: list[Overlay]) -> None: + for ov in overlays: + d = dict(ov.data) + if ov.kind in {"points", "points_rc"} and ("r" in d) and ("c" in d): + r = np.asarray(d["r"]).ravel() + c = np.asarray(d["c"]).ravel() + s = float(d.get("s", 60.0)) + marker = d.get("marker", "x") + color = d.get("color", "orange") + ax.scatter(c, r, s=s, marker=marker, c=color) + continue + + def plot_mean_model( + self, + *, + power: float = 0.25, + returnfig: bool = False, + show_overlays: bool = True, + axsize: tuple[int, int] = (6, 6), + ) -> tuple[Any, Any] | None: + self._ensure_image_ref() + if self.image_ref is None: + raise RuntimeError("image_ref not available.") + if self.prepared is None: + raise RuntimeError("No model defined. Call .define_model(...) first.") + + ref = np.asarray(self.image_ref, dtype=np.float32) + init = _to_numpy(self.prepared.render_initial()).astype(np.float32, copy=False) + + refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) + initp = init if power == 1.0 else np.maximum(init, 0.0) ** float(power) + + vmin = float(np.min([refp.min(), initp.min()])) + vmax = float(np.max([refp.max(), initp.max()])) + + fig, ax = show_2d( + [refp, initp], + title=["image_ref", "model"], + cmap="gray", + cbar=False, + returnfig=True, + axsize=axsize, + norm="linear_minmax", + vmin=vmin, + vmax=vmax, + ) + + H, W = int(ref.shape[-2]), int(ref.shape[-1]) + + boundaries: list[float] = [] + for comp in getattr(self.prepared, "components", []): + b = getattr(comp, "boundary_px", None) + if b is not None: + boundaries.append(float(b)) + + inset = 0 + pad = 0 + if boundaries: + pos = [b for b in boundaries if b > 0.0] + neg = [b for b in boundaries if b < 0.0] + if pos: + inset = int(np.ceil(max(pos))) + if neg: + pad = int(np.ceil(-min(neg))) + + if isinstance(ax, np.ndarray): + axes = list(ax.ravel()) + elif isinstance(ax, (list, tuple)): + axes = list(ax) + else: + axes = [ax] + + x0 = (-pad + inset) + x1 = (W - 1) + pad - inset + y0 = (-pad + inset) + y1 = (H - 1) + pad - inset + + for a in axes[:2]: + a.set_xlim(x0, x1) + a.set_ylim(y1, y0) + + if show_overlays: + overlays = self.prepared.overlays() + if len(axes) >= 1: + self._apply_overlays(axes[0], overlays) + if len(axes) >= 2: + self._apply_overlays(axes[1], overlays) + + if returnfig: + return fig, ax + return None From 9bb0a56331ba95d0572f65061446e7fac1c90fe4 Mon Sep 17 00:00:00 2001 From: cophus Date: Mon, 16 Feb 2026 16:56:41 -0800 Subject: [PATCH 21/43] fitting model working! --- src/quantem/core/models/__init__.py | 18 +- src/quantem/core/models/background.py | 65 +-- src/quantem/core/models/base.py | 218 ++++------ src/quantem/core/models/diffraction.py | 489 +++++++++++++++-------- src/quantem/diffraction/model_fitting.py | 478 +++++++++++++++++----- 5 files changed, 830 insertions(+), 438 deletions(-) diff --git a/src/quantem/core/models/__init__.py b/src/quantem/core/models/__init__.py index 82d81d8e..7330baff 100644 --- a/src/quantem/core/models/__init__.py +++ b/src/quantem/core/models/__init__.py @@ -3,27 +3,27 @@ from quantem.core.models.base import Component as Component from quantem.core.models.base import Model as Model from quantem.core.models.base import ModelContext as ModelContext -from quantem.core.models.base import OriginND as OriginND from quantem.core.models.base import Overlay as Overlay from quantem.core.models.base import Parameter as Parameter from quantem.core.models.base import PreparedModel as PreparedModel -from quantem.core.models.background import DCBackground as DCBackground -from quantem.core.models.background import GaussianBackground as GaussianBackground - from quantem.core.models.diffraction import DiskTemplate as DiskTemplate +from quantem.core.models.diffraction import Origin2D as Origin2D from quantem.core.models.diffraction import SyntheticDiskLattice as SyntheticDiskLattice +from quantem.core.models.background import DCBackground as DCBackground +from quantem.core.models.background import GaussianBackground as GaussianBackground + __all__ = [ "Parameter", - "Component", "Overlay", - "OriginND", "ModelContext", - "PreparedModel", + "Component", "Model", - "DCBackground", - "GaussianBackground", + "PreparedModel", + "Origin2D", "DiskTemplate", "SyntheticDiskLattice", + "DCBackground", + "GaussianBackground", ] diff --git a/src/quantem/core/models/background.py b/src/quantem/core/models/background.py index 0f41f6a5..d0a9adff 100644 --- a/src/quantem/core/models/background.py +++ b/src/quantem/core/models/background.py @@ -1,78 +1,79 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Any +from typing import Any, Iterable import torch from quantem.core.models.base import Component, ModelContext, Overlay, Parameter - -__all__ = ["DCBackground", "GaussianBackground"] +from quantem.core.models.diffraction import _origin_indices class DCBackground(Component): def __init__( self, *, - intensity: float | tuple[float, float] | tuple[float, float, float | None], - name: str = "DCBackground", + intensity: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + name: str = "dc_background", ): super().__init__(name=name) - self.p_intensity = Parameter(intensity, tags={"role": "background_dc", "name": name}) + self.p_intensity = Parameter(intensity, lower_bound=0.0, tags={"role": "dc_bg"}) + + def parameters(self) -> list[Parameter]: + return [self.p_intensity] def prepare(self, ctx: ModelContext) -> Any: - pI = self.p_intensity + idx = self.p_intensity.index @dataclass - class PreparedDCBackground: - name: str - + class Prepared: def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - out += pI.value(x, device=ctx.device, dtype=ctx.dtype) + out.add_(x[idx]) - def overlays(self) -> list[Overlay]: + def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: return [] - return PreparedDCBackground(name=self.name) + return Prepared() class GaussianBackground(Component): def __init__( self, *, - sigma: float | tuple[float, float] | tuple[float, float, float | None], - intensity: float | tuple[float, float] | tuple[float, float, float | None], + sigma: float | tuple[float, float] | tuple[float, float, float | None] = (40.0, 5.0, None), + intensity: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, origin_key: str = "origin", - name: str = "GaussianBackground", + name: str = "gaussian_background", ): super().__init__(name=name) self.origin_key = str(origin_key) - self.p_sigma = Parameter(sigma, tags={"role": "background_gaussian_sigma", "name": name}) - self.p_intensity = Parameter(intensity, tags={"role": "background_gaussian_intensity", "name": name}) + self.p_sigma = Parameter(sigma, lower_bound=1e-6, upper_bound=None, tags={"role": "gauss_sigma"}) + self.p_intensity = Parameter(intensity, lower_bound=0.0, tags={"role": "gauss_int"}) + + def parameters(self) -> list[Parameter]: + return [self.p_sigma, self.p_intensity] def prepare(self, ctx: ModelContext) -> Any: - ok = self.origin_key - pS = self.p_sigma - pI = self.p_intensity + i_sig = self.p_sigma.index + i_int = self.p_intensity.index + r_idx, c_idx = _origin_indices(ctx, self.origin_key) rr = torch.arange(ctx.H, device=ctx.device, dtype=ctx.dtype)[:, None] cc = torch.arange(ctx.W, device=ctx.device, dtype=ctx.dtype)[None, :] @dataclass - class PreparedGaussianBackground: - origin_key: str - + class Prepared: def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - if self.origin_key not in ctx.origins: - return - r0, c0 = ctx.origins[self.origin_key] - sig = torch.clamp(pS.value(x, device=ctx.device, dtype=ctx.dtype), min=1e-6) - inten = pI.value(x, device=ctx.device, dtype=ctx.dtype) + sig = torch.clamp(x[i_sig], min=1e-6) + inten = x[i_int] + r0 = x[r_idx] + c0 = x[c_idx] dr = rr - r0 dc = cc - c0 - out += inten * torch.exp(-0.5 * (dr * dr + dc * dc) / (sig * sig)) + r2 = dr * dr + dc * dc + out.add_(inten * torch.exp(-0.5 * r2 / (sig * sig))) - def overlays(self) -> list[Overlay]: + def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: return [] - return PreparedGaussianBackground(origin_key=ok) + return Prepared() diff --git a/src/quantem/core/models/base.py b/src/quantem/core/models/base.py index 16c15840..7d453c59 100644 --- a/src/quantem/core/models/base.py +++ b/src/quantem/core/models/base.py @@ -6,16 +6,6 @@ import numpy as np import torch -__all__ = [ - "Overlay", - "Parameter", - "ModelContext", - "Component", - "OriginND", - "Model", - "PreparedModel", -] - @dataclass(frozen=True) class Overlay: @@ -32,43 +22,47 @@ def __init__( upper_bound: float | None = None, tags: Mapping[str, Any] | None = None, ): - v0, lb, ub = self._parse(value, lower_bound, upper_bound) - self.initial_value = float(v0) - self.lower_bound = float(lb) - self.upper_bound = float(ub) - self.tags: dict[str, Any] = dict(tags) if tags is not None else {} + self.tags: dict[str, Any] = {} if tags is None else dict(tags) + self.initial_value, self.lower_bound, self.upper_bound = self._parse_value( + value, lower_bound, upper_bound + ) self._index: int | None = None @staticmethod - def _parse( + def _parse_value( value: float | Sequence[float], lower_bound: float | None, upper_bound: float | None, - ) -> tuple[float, float, float]: + ) -> tuple[float, float | None, float | None]: if isinstance(value, (list, tuple, np.ndarray)): - seq = list(value) - if len(seq) == 2: - v0 = float(seq[0]) - dev = float(seq[1]) - return v0, v0 - dev, v0 + dev - if len(seq) == 3: - v0 = float(seq[0]) - lb = -np.inf if seq[1] is None else float(seq[1]) - ub = np.inf if seq[2] is None else float(seq[2]) + v = list(value) + if len(v) == 2: + v0 = float(v[0]) + dev = float(v[1]) + lb = v0 - dev if lower_bound is None else float(lower_bound) + ub = v0 + dev if upper_bound is None else float(upper_bound) + return v0, lb, ub + if len(v) == 3: + v0 = float(v[0]) + lb = None if v[1] is None else float(v[1]) + ub = None if v[2] is None else float(v[2]) + if lower_bound is not None: + lb = float(lower_bound) + if upper_bound is not None: + ub = float(upper_bound) return v0, lb, ub raise ValueError("Parameter sequences must have length 2 or 3.") v0 = float(value) - lb = -np.inf if lower_bound is None else float(lower_bound) - ub = np.inf if upper_bound is None else float(upper_bound) - return v0, lb, ub + return v0, lower_bound, upper_bound def bind(self, index: int) -> None: self._index = int(index) - def value(self, x: torch.Tensor, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor: + @property + def index(self) -> int: if self._index is None: - return torch.as_tensor(self.initial_value, device=device, dtype=dtype).reshape(()) - return x[int(self._index)].to(device=device, dtype=dtype).reshape(()) + raise RuntimeError("Parameter is not bound. Call Model.compile(...) first.") + return self._index class ModelContext: @@ -87,8 +81,7 @@ def __init__( self.device = device self.dtype = dtype self.mask = mask - self.fields: dict[str, Any] = dict(fields) if fields is not None else {} - self.origins: dict[str, tuple[torch.Tensor, ...]] = {} + self.fields: dict[str, Any] = {} if fields is None else dict(fields) def __getattr__(self, name: str) -> Any: if name in self.fields: @@ -101,76 +94,53 @@ def __init__(self, *, name: str): self.name = str(name) def parameters(self) -> list[Parameter]: - out: list[Parameter] = [] - - def add(obj: Any) -> None: - if obj is None: - return - if isinstance(obj, Parameter): - out.append(obj) - return - if isinstance(obj, (list, tuple)): - for z in obj: - add(z) - - for v in self.__dict__.values(): - add(v) - return out + return [] def prepare(self, ctx: ModelContext) -> Any: raise NotImplementedError -class OriginND(Component): - def __init__(self, *, key: str, coords: Sequence[Parameter]): - super().__init__(name=f"Origin[{key}]") - self.key = str(key) - self.coords = list(coords) - - @classmethod - def from_row_col( - cls, +class PreparedModel: + def __init__( + self, *, - key: str, - origin_row: float | Sequence[float], - origin_col: float | Sequence[float], - ) -> "OriginND": - p_row = Parameter(origin_row, tags={"role": "origin_row", "origin_key": str(key)}) - p_col = Parameter(origin_col, tags={"role": "origin_col", "origin_key": str(key)}) - return cls(key=key, coords=[p_row, p_col]) - - def prepare(self, ctx: ModelContext) -> Any: - key = self.key - p0 = self.coords[0] - p1 = self.coords[1] - - r0 = torch.as_tensor(p0.initial_value, device=ctx.device, dtype=ctx.dtype).reshape(()) - c0 = torch.as_tensor(p1.initial_value, device=ctx.device, dtype=ctx.dtype).reshape(()) - ctx.origins[key] = (r0, c0) - - class PreparedOriginND: - origin_key = key + components: list[Any], + ctx: ModelContext, + params: list[Parameter], + x0: torch.Tensor, + lb: torch.Tensor, + ub: torch.Tensor, + ): + self.components = components + self.ctx = ctx + self.params = params + self.x0 = x0 + self.lb = lb + self.ub = ub - def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - r = p0.value(x, device=ctx.device, dtype=ctx.dtype) - c = p1.value(x, device=ctx.device, dtype=ctx.dtype) - ctx.origins[key] = (r, c) + def render(self, x: torch.Tensor) -> torch.Tensor: + out = torch.zeros((self.ctx.H, self.ctx.W), device=self.ctx.device, dtype=self.ctx.dtype) + for c in self.components: + c.render(out, x, self.ctx) + return out - def overlays(self) -> list[Overlay]: - r = float(p0.initial_value) - c = float(p1.initial_value) - return [ - Overlay( - kind="points_rc", - data={"r": np.array([r]), "c": np.array([c]), "marker": "x", "s": 80.0, "color": "tab:blue"}, - ) - ] + def render_initial(self) -> torch.Tensor: + return self.render(self.x0) - return PreparedOriginND() + def overlays(self, x: torch.Tensor | None = None) -> list[Overlay]: + out: list[Overlay] = [] + for c in self.components: + fn = getattr(c, "overlays", None) + if fn is None: + continue + ov = fn(self.x0 if x is None else x, self.ctx) + if ov: + out.extend(list(ov)) + return out class Model: - def __init__(self) -> None: + def __init__(self): self._components: list[Component] = [] def add(self, items: Iterable[Component]) -> "Model": @@ -180,62 +150,30 @@ def add(self, items: Iterable[Component]) -> "Model": self._components.append(obj) return self - def compile(self, ctx: ModelContext) -> "PreparedModel": - seen: set[int] = set() + def parameter_list(self) -> list[Parameter]: params: list[Parameter] = [] for c in self._components: - for p in c.parameters(): - if id(p) in seen: - continue - seen.add(id(p)) - params.append(p) + params.extend(c.parameters()) + return params + def compile(self, ctx: ModelContext) -> PreparedModel: + params = self.parameter_list() for i, p in enumerate(params): p.bind(i) - x0 = torch.as_tensor([p.initial_value for p in params], device=ctx.device, dtype=ctx.dtype) + x0 = torch.zeros((len(params),), device=ctx.device, dtype=ctx.dtype) + lb = torch.full((len(params),), -torch.inf, device=ctx.device, dtype=ctx.dtype) + ub = torch.full((len(params),), torch.inf, device=ctx.device, dtype=ctx.dtype) + + for p in params: + x0[p.index] = torch.as_tensor(p.initial_value, device=ctx.device, dtype=ctx.dtype) + if p.lower_bound is not None: + lb[p.index] = torch.as_tensor(p.lower_bound, device=ctx.device, dtype=ctx.dtype) + if p.upper_bound is not None: + ub[p.index] = torch.as_tensor(p.upper_bound, device=ctx.device, dtype=ctx.dtype) prepared_components: list[Any] = [] for c in self._components: prepared_components.append(c.prepare(ctx)) - return PreparedModel( - components=prepared_components, - ctx=ctx, - params=params, - x0=x0, - ) - - -class PreparedModel: - def __init__( - self, - *, - components: list[Any], - ctx: ModelContext, - params: list[Parameter], - x0: torch.Tensor, - ): - self.components = components - self.ctx = ctx - self.params = params - self.x0 = x0 - - def render(self, x: torch.Tensor) -> torch.Tensor: - out = torch.zeros((self.ctx.H, self.ctx.W), device=self.ctx.device, dtype=self.ctx.dtype) - for c in self.components: - if hasattr(c, "render"): - c.render(out, x, self.ctx) - return out - - def render_initial(self) -> torch.Tensor: - return self.render(self.x0) - - def overlays(self) -> list[Overlay]: - out: list[Overlay] = [] - for c in self.components: - if hasattr(c, "overlays"): - ov = c.overlays() - if ov: - out.extend(list(ov)) - return out + return PreparedModel(components=prepared_components, ctx=ctx, params=params, x0=x0, lb=lb, ub=ub) diff --git a/src/quantem/core/models/diffraction.py b/src/quantem/core/models/diffraction.py index ebf820bd..8ecaf729 100644 --- a/src/quantem/core/models/diffraction.py +++ b/src/quantem/core/models/diffraction.py @@ -1,51 +1,115 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Any, Iterable +from typing import Any, Iterable, Sequence import numpy as np import torch from quantem.core.models.base import Component, ModelContext, Overlay, Parameter -__all__ = ["DiskTemplate", "DiskAtOrigin", "SyntheticDiskLattice"] - -def _as_tensor(x: Any, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor: +def _as_t(x: Any, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor: if torch.is_tensor(x): return x.to(device=device, dtype=dtype) return torch.as_tensor(x, device=device, dtype=dtype) -def _keep_center(r: float, c: float, H: int, W: int, boundary_px: float) -> bool: - b = float(boundary_px) - return (r >= b) and (r <= (H - 1) - b) and (c >= b) and (c <= (W - 1) - b) +def _splat_patch( + out: torch.Tensor, + *, + r0: torch.Tensor, + c0: torch.Tensor, + patch_vals: torch.Tensor, + dr: torch.Tensor, + dc: torch.Tensor, + scale: torch.Tensor, +) -> None: + H = out.shape[0] + W = out.shape[1] + + r = r0 + dr + c = c0 + dc + + r_base = torch.floor(r) + c_base = torch.floor(c) + + fr = r - r_base + fc = c - c_base + r0i = r_base.to(torch.long) + c0i = c_base.to(torch.long) -def _place_patch_add(out: torch.Tensor, patch: torch.Tensor, center_r: float, center_c: float) -> None: - H, W = int(out.shape[-2]), int(out.shape[-1]) - ph, pw = int(patch.shape[-2]), int(patch.shape[-1]) - pr = ph // 2 - pc = pw // 2 + w00 = (1.0 - fr) * (1.0 - fc) + w01 = (1.0 - fr) * fc + w10 = fr * (1.0 - fc) + w11 = fr * fc - r0 = int(np.rint(center_r)) - pr - c0 = int(np.rint(center_c)) - pc - r1 = r0 + ph - c1 = c0 + pw + v = patch_vals * scale - rr0 = max(0, r0) - cc0 = max(0, c0) - rr1 = min(H, r1) - cc1 = min(W, c1) - if rr1 <= rr0 or cc1 <= cc0: - return + def put(rr: torch.Tensor, cc: torch.Tensor, ww: torch.Tensor) -> None: + m = (rr >= 0) & (rr < H) & (cc >= 0) & (cc < W) + if torch.any(m): + out.index_put_((rr[m], cc[m]), (v[m] * ww[m]), accumulate=True) - pr0 = rr0 - r0 - pc0 = cc0 - c0 - pr1 = pr0 + (rr1 - rr0) - pc1 = pc0 + (cc1 - cc0) + put(r0i, c0i, w00) + put(r0i, c0i + 1, w01) + put(r0i + 1, c0i, w10) + put(r0i + 1, c0i + 1, w11) - out[rr0:rr1, cc0:cc1] += patch[pr0:pr1, pc0:pc1] + +def _origin_indices(ctx: ModelContext, origin_key: str) -> tuple[int, int]: + origins: dict[str, Any] = ctx.fields.get("origins", {}) + o = origins.get(origin_key) + if o is None or "row_param" not in o or "col_param" not in o: + raise RuntimeError(f"Origin '{origin_key}' not defined. Origin2D must be included first.") + return int(o["row_param"].index), int(o["col_param"].index) + + +class Origin2D(Component): + def __init__( + self, + *, + name: str = "origin", + origin_key: str = "origin", + row: float | tuple[float, float] | tuple[float, float, float | None] = (0.0, 0.0, None), + col: float | tuple[float, float] | tuple[float, float, float | None] = (0.0, 0.0, None), + ): + super().__init__(name=name) + self.origin_key = str(origin_key) + self.p_row = Parameter(row, tags={"role": "origin_row", "origin_key": self.origin_key}) + self.p_col = Parameter(col, tags={"role": "origin_col", "origin_key": self.origin_key}) + + def parameters(self) -> list[Parameter]: + return [self.p_row, self.p_col] + + def prepare(self, ctx: ModelContext) -> Any: + origins = ctx.fields.setdefault("origins", {}) + origins[self.origin_key] = {"row_param": self.p_row, "col_param": self.p_col} + + i_r = self.p_row.index + i_c = self.p_col.index + + @dataclass + class Prepared: + def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: + return + + def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: + return [ + Overlay( + kind="points_rc", + data={ + "r": x[i_r].detach(), + "c": x[i_c].detach(), + "marker": "+", + "s": 100.0, + "color": "dodgerblue", + }, + ) + ] + + return Prepared() class DiskTemplate(Component): @@ -56,26 +120,49 @@ def __init__( array: np.ndarray, refine_all_pixels: bool = False, place_at_origin: bool = False, + normalize: str = "none", origin_key: str = "origin", - origin_intensity: float | tuple[float, float] | tuple[float, float, float | None] = 1.0, + intensity: float | tuple[float, float] | tuple[float, float, float | None] = 1.0, ): super().__init__(name=name) - arr = np.asarray(array, dtype=np.float32) - if arr.ndim != 2: + + a = np.asarray(array, dtype=np.float32) + if a.ndim != 2: raise ValueError("DiskTemplate.array must be 2D.") - self.shape = (int(arr.shape[0]), int(arr.shape[1])) + + if normalize == "max": + mx = float(np.max(a)) + if mx > 0: + a = a / mx + elif normalize == "mean": + m = float(np.mean(a)) + if m != 0: + a = a / m + elif normalize == "none": + pass + else: + raise ValueError("normalize must be one of: 'none', 'max', 'mean'.") + + self.array = a self.refine_all_pixels = bool(refine_all_pixels) self.place_at_origin = bool(place_at_origin) self.origin_key = str(origin_key) - self._array0 = arr - self.p_origin_intensity = Parameter(origin_intensity, tags={"role": "disk_origin_intensity", "name": name}) - self.p_pixels: list[Parameter] = [] if self.refine_all_pixels: - flat = arr.reshape(-1) + flat = self.array.ravel() for i, v in enumerate(flat): - self.p_pixels.append(Parameter(float(v), tags={"role": "disk_pixel", "name": name, "pixel_index": int(i)})) + self.p_pixels.append( + Parameter( + float(v), + lower_bound=0.0, + tags={"role": "disk_pixel", "disk": self.name, "i": int(i)}, + ) + ) + + self.p_intensity: Parameter | None = None + if self.place_at_origin: + self.p_intensity = Parameter(intensity, lower_bound=0.0, tags={"role": "disk_intensity", "disk": self.name}) @classmethod def from_array( @@ -85,57 +172,79 @@ def from_array( array: np.ndarray, refine_all_pixels: bool = False, place_at_origin: bool = False, + normalize: str = "none", origin_key: str = "origin", - origin_intensity: float | tuple[float, float] | tuple[float, float, float | None] = 1.0, + intensity: float | tuple[float, float] | tuple[float, float, float | None] = 1.0, ) -> "DiskTemplate": return cls( name=name, array=array, refine_all_pixels=refine_all_pixels, place_at_origin=place_at_origin, + normalize=normalize, origin_key=origin_key, - origin_intensity=origin_intensity, + intensity=intensity, ) + def parameters(self) -> list[Parameter]: + out = list(self.p_pixels) + if self.p_intensity is not None: + out.append(self.p_intensity) + return out + def prepare(self, ctx: ModelContext) -> Any: - disk = self - ok = disk.origin_key + device = ctx.device + dtype = ctx.dtype - if disk.refine_all_pixels: - idxs = [p._index for p in disk.p_pixels] - if any(i is None for i in idxs): - raise RuntimeError("DiskTemplate pixel parameters not bound.") - idx_t = torch.as_tensor([int(i) for i in idxs], device=ctx.device, dtype=torch.int64) + Ht, Wt = self.array.shape + rr, cc = np.mgrid[0:Ht, 0:Wt] + rr = rr.astype(np.float32) - (Ht - 1) * 0.5 + cc = cc.astype(np.float32) - (Wt - 1) * 0.5 - def patch(x: torch.Tensor) -> torch.Tensor: - return x.index_select(0, idx_t).to(device=ctx.device, dtype=ctx.dtype).reshape(disk.shape[0], disk.shape[1]) + dr = _as_t(rr.ravel(), device=device, dtype=dtype) + dc = _as_t(cc.ravel(), device=device, dtype=dtype) + if self.refine_all_pixels: + pix_idx = torch.as_tensor([p.index for p in self.p_pixels], device=device, dtype=torch.long) + base_vals = None else: - patch0 = _as_tensor(disk._array0, device=ctx.device, dtype=ctx.dtype) - - def patch(x: torch.Tensor) -> torch.Tensor: - return patch0 + pix_idx = None + base_vals = _as_t(self.array.ravel(), device=device, dtype=dtype) + + ctx.fields.setdefault("disk_templates", {})[self.name] = { + "dr": dr, + "dc": dc, + "pix_idx": pix_idx, + "base": base_vals, + } + + i_int = None if self.p_intensity is None else self.p_intensity.index + if i_int is None: + r_idx = c_idx = None + else: + r_idx, c_idx = _origin_indices(ctx, self.origin_key) @dataclass - class PreparedDiskTemplate: - origin_key: str - place_at_origin: bool + class Prepared: + def patch(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if pix_idx is None: + vals = base_vals + else: + vals = x[pix_idx] + return vals, dr, dc def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - if not self.place_at_origin: + if i_int is None: return - if self.origin_key not in ctx.origins: - return - r0, c0 = ctx.origins[self.origin_key] - r = float(r0.detach().cpu().item()) - c = float(c0.detach().cpu().item()) - inten = disk.p_origin_intensity.value(x, device=ctx.device, dtype=ctx.dtype) - _place_patch_add(out, patch(x) * inten, r, c) + vals, drl, dcl = self.patch(x) + r0 = x[r_idx] + c0 = x[c_idx] + _splat_patch(out, r0=r0, c0=c0, patch_vals=vals, dr=drl, dc=dcl, scale=x[i_int]) - def overlays(self) -> list[Overlay]: + def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: return [] - return PreparedDiskTemplate(origin_key=ok, place_at_origin=disk.place_at_origin) + return Prepared() class SyntheticDiskLattice(Component): @@ -153,142 +262,188 @@ def __init__( intensity_0: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, intensity_row: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, intensity_col: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, - exclude_indices: Iterable[tuple[int, int]] | None = [], + per_disk_intensity: bool = False, + per_disk_slopes: bool = True, + center_intensity_0: float | tuple[float, float] | tuple[float, float, float | None] | None = None, + exclude_indices: Iterable[tuple[int, int]] | None = None, boundary_px: float = 0.0, origin_key: str = "origin", ): super().__init__(name=name) self.disk = disk - self.origin_key = str(origin_key) self.u_max = int(u_max) self.v_max = int(v_max) self.boundary_px = float(boundary_px) + self.origin_key = str(origin_key) + self.per_disk_intensity = bool(per_disk_intensity) + self.per_disk_slopes = bool(per_disk_slopes) if exclude_indices is None: - self.exclude_indices = {(0, 0)} + self.exclude_indices = {(0, 0)} if bool(getattr(disk, "place_at_origin", False)) else set() else: - self.exclude_indices = {(int(a), int(b)) for (a, b) in exclude_indices} - - self.p_u_row = Parameter(u_row, tags={"role": "lattice_u_row", "name": name}) - self.p_u_col = Parameter(u_col, tags={"role": "lattice_u_col", "name": name}) - self.p_v_row = Parameter(v_row, tags={"role": "lattice_v_row", "name": name}) - self.p_v_col = Parameter(v_col, tags={"role": "lattice_v_col", "name": name}) - - self.p_intensity_0 = Parameter(intensity_0, tags={"role": "disk_intensity_0", "name": name}) - self.p_intensity_row = Parameter(intensity_row, tags={"role": "disk_intensity_row", "name": name}) - self.p_intensity_col = Parameter(intensity_col, tags={"role": "disk_intensity_col", "name": name}) + self.exclude_indices = set(exclude_indices) - def _uv_list(self) -> list[tuple[int, int]]: uv: list[tuple[int, int]] = [] for u in range(-self.u_max, self.u_max + 1): for v in range(-self.v_max, self.v_max + 1): if (u, v) in self.exclude_indices: continue uv.append((u, v)) - return uv + self.uv_indices = uv + + self.p_u_row = Parameter(u_row, tags={"role": "lat_u_row"}) + self.p_u_col = Parameter(u_col, tags={"role": "lat_u_col"}) + self.p_v_row = Parameter(v_row, tags={"role": "lat_v_row"}) + self.p_v_col = Parameter(v_col, tags={"role": "lat_v_col"}) + self.p_i0 = None + self.p_ir = None + self.p_ic = None + self.p_i0_list: list[Parameter] = [] + self.p_ir_list: list[Parameter] = [] + self.p_ic_list: list[Parameter] = [] + if self.per_disk_intensity: + for u, v in self.uv_indices: + i0_val = center_intensity_0 if (center_intensity_0 is not None and (u, v) == (0, 0)) else intensity_0 + self.p_i0_list.append(Parameter(i0_val, lower_bound=0.0, tags={"role": "lat_int0", "u": u, "v": v})) + if self.per_disk_slopes: + self.p_ir_list.append(Parameter(intensity_row, tags={"role": "lat_int_row", "u": u, "v": v})) + self.p_ic_list.append(Parameter(intensity_col, tags={"role": "lat_int_col", "u": u, "v": v})) + else: + self.p_i0 = Parameter(intensity_0, lower_bound=0.0, tags={"role": "lat_int0"}) + self.p_ir = Parameter(intensity_row, tags={"role": "lat_int_row"}) + self.p_ic = Parameter(intensity_col, tags={"role": "lat_int_col"}) + + def parameters(self) -> list[Parameter]: + out = [self.p_u_row, self.p_u_col, self.p_v_row, self.p_v_col] + if self.per_disk_intensity: + out.extend(self.p_i0_list) + if self.per_disk_slopes: + out.extend(self.p_ir_list) + out.extend(self.p_ic_list) + else: + out.extend([self.p_i0, self.p_ir, self.p_ic]) + return out def prepare(self, ctx: ModelContext) -> Any: - lat = self - ok = lat.origin_key - uv = lat._uv_list() + dt = ctx.fields.get("disk_templates", {}) + if self.disk.name not in dt: + raise RuntimeError("DiskTemplate must be included before SyntheticDiskLattice in components.") + d = dt[self.disk.name] + dr = d["dr"] + dc = d["dc"] + pix_idx = d["pix_idx"] + base_vals = d["base"] + + r_idx, c_idx = _origin_indices(ctx, self.origin_key) + + i_ur = self.p_u_row.index + i_uc = self.p_u_col.index + i_vr = self.p_v_row.index + i_vc = self.p_v_col.index + if self.per_disk_intensity: + i_i0 = i_ir = i_ic = None + else: + i_i0 = self.p_i0.index + i_ir = self.p_ir.index + i_ic = self.p_ic.index + + uv = self.uv_indices + uv_t = torch.as_tensor(uv, device=ctx.device, dtype=torch.long) if uv else None + + boundary = torch.as_tensor(self.boundary_px, device=ctx.device, dtype=ctx.dtype) + + if self.per_disk_intensity: + i0_idx = torch.as_tensor([p.index for p in self.p_i0_list], device=ctx.device, dtype=torch.long) + if self.per_disk_slopes: + ir_idx = torch.as_tensor([p.index for p in self.p_ir_list], device=ctx.device, dtype=torch.long) + ic_idx = torch.as_tensor([p.index for p in self.p_ic_list], device=ctx.device, dtype=torch.long) + else: + ir_idx = ic_idx = None + else: + i0_idx = ir_idx = ic_idx = None + per_disk_intensity = self.per_disk_intensity + per_disk_slopes = self.per_disk_slopes - if lat.disk.refine_all_pixels: - didxs = [p._index for p in lat.disk.p_pixels] - if any(i is None for i in didxs): - raise RuntimeError("DiskTemplate pixel parameters not bound.") - didx_t = torch.as_tensor([int(i) for i in didxs], device=ctx.device, dtype=torch.int64) + @dataclass + class Prepared: + boundary_px: float = float(self.boundary_px) - def patch(x: torch.Tensor) -> torch.Tensor: - return x.index_select(0, didx_t).to(device=ctx.device, dtype=ctx.dtype).reshape(lat.disk.shape[0], lat.disk.shape[1]) + def _patch(self, x: torch.Tensor) -> torch.Tensor: + if pix_idx is None: + return base_vals + return x[pix_idx] - else: - patch0 = _as_tensor(lat.disk._array0, device=ctx.device, dtype=ctx.dtype) - - def patch(x: torch.Tensor) -> torch.Tensor: - return patch0 - - r0_list: list[float] = [] - c0_list: list[float] = [] - if ok in ctx.origins: - or0, oc0 = ctx.origins[ok] - orow0 = float(or0.detach().cpu().item()) - ocol0 = float(oc0.detach().cpu().item()) - urow0 = float(lat.p_u_row.initial_value) - ucol0 = float(lat.p_u_col.initial_value) - vrow0 = float(lat.p_v_row.initial_value) - vcol0 = float(lat.p_v_col.initial_value) - for u, v in uv: - rr = orow0 + u * urow0 + v * vrow0 - cc = ocol0 + u * ucol0 + v * vcol0 - if _keep_center(rr, cc, ctx.H, ctx.W, lat.boundary_px): - r0_list.append(rr) - c0_list.append(cc) - - r0_np = np.asarray(r0_list, dtype=np.float32) - c0_np = np.asarray(c0_list, dtype=np.float32) + def _centers(self, x: torch.Tensor, ctx: ModelContext) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if uv_t is None: + z = torch.empty((0,), device=ctx.device, dtype=ctx.dtype) + return z, z, z - @dataclass - class PreparedSyntheticDiskLattice: - origin_key: str - boundary_px: float - uv: list[tuple[int, int]] - r0: np.ndarray - c0: np.ndarray + r0 = x[r_idx] + c0 = x[c_idx] + + ur = x[i_ur] + uc = x[i_uc] + vr = x[i_vr] + vc = x[i_vc] + + u = uv_t[:, 0].to(ctx.dtype) + v = uv_t[:, 1].to(ctx.dtype) + + centers_r = r0 + u * ur + v * vr + centers_c = c0 + u * uc + v * vc + + keep = (centers_r >= boundary) & (centers_r <= (ctx.H - 1) - boundary) + keep &= (centers_c >= boundary) & (centers_c <= (ctx.W - 1) - boundary) + return centers_r[keep], centers_c[keep], keep def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - if self.origin_key not in ctx.origins: + if uv_t is None: return - if not self.uv: + + centers_r, centers_c, keep = self._centers(x, ctx) + if centers_r.numel() == 0: return - r_origin, c_origin = ctx.origins[self.origin_key] - r_origin = r_origin.reshape(()) - c_origin = c_origin.reshape(()) - - urow = lat.p_u_row.value(x, device=ctx.device, dtype=ctx.dtype) - ucol = lat.p_u_col.value(x, device=ctx.device, dtype=ctx.dtype) - vrow = lat.p_v_row.value(x, device=ctx.device, dtype=ctx.dtype) - vcol = lat.p_v_col.value(x, device=ctx.device, dtype=ctx.dtype) - - I0 = lat.p_intensity_0.value(x, device=ctx.device, dtype=ctx.dtype) - Ir = lat.p_intensity_row.value(x, device=ctx.device, dtype=ctx.dtype) - Ic = lat.p_intensity_col.value(x, device=ctx.device, dtype=ctx.dtype) - - uv_t = torch.as_tensor(self.uv, device=ctx.device, dtype=torch.int32) - uu = uv_t[:, 0].to(device=ctx.device, dtype=ctx.dtype) - vv = uv_t[:, 1].to(device=ctx.device, dtype=ctx.dtype) - - rr = r_origin + uu * urow + vv * vrow - cc = c_origin + uu * ucol + vv * vcol - - p = patch(x) - - H = int(ctx.H) - W = int(ctx.W) - for i in range(int(rr.numel())): - r = float(rr[i].detach().cpu().item()) - c = float(cc[i].detach().cpu().item()) - if not _keep_center(r, c, H, W, self.boundary_px): - continue - dr = rr[i] - r_origin - dc = cc[i] - c_origin - inten = torch.clamp(I0 + Ir * dr + Ic * dc, min=0.0) - _place_patch_add(out, p * inten, r, c) - - def overlays(self) -> list[Overlay]: - if self.r0.size == 0: + vals = self._patch(x) + + if per_disk_intensity: + keep_idx = torch.nonzero(keep, as_tuple=False).reshape(-1) + i0_keep = x[i0_idx[keep_idx]] + if per_disk_slopes: + ir_keep = x[ir_idx[keep_idx]] + ic_keep = x[ic_idx[keep_idx]] + for j, (rr0, cc0) in enumerate(zip(centers_r, centers_c)): + inten_local = torch.clamp(i0_keep[j] + ir_keep[j] * dr + ic_keep[j] * dc, min=0.0) + _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten_local) + else: + for j, (rr0, cc0) in enumerate(zip(centers_r, centers_c)): + inten_local = torch.clamp(i0_keep[j], min=0.0) + _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten_local) + else: + i0 = x[i_i0] + ir = x[i_ir] + ic = x[i_ic] + for rr0, cc0 in zip(centers_r, centers_c): + inten = torch.clamp(i0 + ir * rr0 + ic * cc0, min=0.0) + _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten) + + def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: + if uv_t is None: + return [] + centers_r, centers_c, _ = self._centers(x, ctx) + if centers_r.numel() == 0: return [] return [ Overlay( kind="points_rc", - data={"r": self.r0, "c": self.c0, "marker": "x", "s": 60.0, "color": "orange"}, + data={ + "r": centers_r.detach(), + "c": centers_c.detach(), + "marker": "x", + "s": 60.0, + "color": "orange", + }, ) ] - return PreparedSyntheticDiskLattice( - origin_key=ok, - boundary_px=lat.boundary_px, - uv=uv, - r0=r0_np, - c0=c0_np, - ) + return Prepared() diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 84d9e5c8..f382ab27 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -1,6 +1,7 @@ from __future__ import annotations -from typing import Any +import warnings +from typing import Any, Sequence import numpy as np import torch @@ -12,12 +13,11 @@ from quantem.core.datastructures.dataset4d import Dataset4d from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.models.base import Model, ModelContext, OriginND, Overlay, PreparedModel +from quantem.core.models.base import Model, ModelContext, Overlay, PreparedModel +from quantem.core.models.diffraction import Origin2D from quantem.core.utils.imaging_utils import cross_correlation_shift from quantem.core.visualization import show_2d -__all__ = ["ModelDiffraction"] - def _to_numpy(x: Any) -> np.ndarray: if isinstance(x, np.ndarray): @@ -36,17 +36,40 @@ def __init__(self, dataset: Any, _token: object | None = None): super().__init__() self.dataset = dataset self.metadata: dict[str, Any] = {} - self.index_shape: tuple[int, ...] | None = None self.image_ref: np.ndarray | None = None self.preprocess_shifts: np.ndarray | None = None + self.index_shape: tuple[int, ...] | None = None self.model: Model | None = None self.prepared: PreparedModel | None = None - self.fit_mask: np.ndarray | None = None + self.x_mean: torch.Tensor | None = None + self.x_defined: torch.Tensor | None = None + self.x_initial: torch.Tensor | None = None + self.mean_refined: bool = False + self.x_patterns: torch.Tensor | None = None + self.pattern_fit_losses: np.ndarray | None = None + self.pattern_fit_linear_indices: np.ndarray | None = None + self.pattern_fit_indices: list[tuple[int, ...]] | None = None + + @staticmethod + def _weak_softplus(x: torch.Tensor, *, scale: float) -> torch.Tensor: + s = torch.as_tensor(float(scale), device=x.device, dtype=x.dtype) + return torch.nn.functional.softplus(x / s) * s + + @classmethod + def _apply_intensity_transform( + cls, x: torch.Tensor, *, mode: str, weak_softplus_scale: float + ) -> torch.Tensor: + m = str(mode).lower() + if m == "none": + return x + if m == "weak_softplus": + return cls._weak_softplus(x, scale=weak_softplus_scale) + raise ValueError("intensity_transform must be one of: 'none', 'weak_softplus'.") @classmethod - def from_dataset(self, dataset: Dataset2d | Dataset3d | Dataset4d | Dataset4dstem | Any) -> "ModelDiffraction": + def from_dataset(cls, dataset: Dataset2d | Dataset3d | Dataset4d | Dataset4dstem | Any) -> "ModelDiffraction": if isinstance(dataset, (Dataset2d, Dataset3d, Dataset4d, Dataset4dstem)): - return ModelDiffraction(dataset=dataset, _token=ModelDiffraction._token) + return cls(dataset=dataset, _token=cls._token) raise TypeError("from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance.") def preprocess( @@ -61,22 +84,15 @@ def preprocess( arr = np.asarray(self.dataset.array) if arr.ndim < 2: raise ValueError("dataset.array must have at least 2 dimensions.") - H, W = int(arr.shape[-2]), int(arr.shape[-1]) + H, W = arr.shape[-2], arr.shape[-1] self.index_shape = tuple(arr.shape[:-2]) stack = arr.reshape((-1, H, W)).astype(np.float32, copy=False) - n = int(stack.shape[0]) + n = stack.shape[0] - if (not align) or n <= 1: + if not align or n <= 1: self.image_ref = np.mean(stack, axis=0) self.preprocess_shifts = None - self.metadata["preprocess"] = { - "align": bool(align), - "edge_blend": float(edge_blend), - "upsample_factor": int(upsample_factor), - "max_shift": None if max_shift is None else float(max_shift), - "shift_order": int(shift_order), - } return self alpha_r = 0.0 if edge_blend <= 0 else min(1.0, 2.0 * float(edge_blend) / float(H)) @@ -116,28 +132,8 @@ def preprocess( self.image_ref = np.mean(aligned, axis=0) self.preprocess_shifts = shifts.reshape(self.index_shape + (2,)) - - self.metadata["preprocess"] = { - "align": bool(align), - "edge_blend": float(edge_blend), - "upsample_factor": int(upsample_factor), - "max_shift": None if max_shift is None else float(max_shift), - "shift_order": int(shift_order), - } return self - def _ensure_image_ref(self) -> None: - if self.image_ref is not None: - return - arr = np.asarray(self.dataset.array) - if arr.ndim < 2: - raise ValueError("dataset.array must have at least 2 dimensions.") - H, W = int(arr.shape[-2]), int(arr.shape[-1]) - self.index_shape = tuple(arr.shape[:-2]) - stack = arr.reshape((-1, H, W)).astype(np.float32, copy=False) - self.image_ref = np.mean(stack, axis=0) - self.preprocess_shifts = None - def define_model( self, *, @@ -149,10 +145,12 @@ def define_model( mask: np.ndarray | torch.Tensor | None = None, origin_key: str = "origin", ) -> "ModelDiffraction": - self._ensure_image_ref() + if self.image_ref is None: + self.preprocess() + if self.image_ref is None: raise RuntimeError("image_ref not available.") - H, W = int(self.image_ref.shape[-2]), int(self.image_ref.shape[-1]) + H, W = int(self.image_ref.shape[0]), int(self.image_ref.shape[1]) dev = torch.device(device) if device is not None else torch.device("cpu") dt = dtype if dtype is not None else torch.float32 @@ -160,36 +158,346 @@ def define_model( mask_t = None if mask is not None: if torch.is_tensor(mask): - mask_t = mask.to(device=dev, dtype=torch.float32) + mask_t = mask.to(device=dev, dtype=dt) else: m = np.asarray(mask) if m.shape != (H, W): raise ValueError("mask must have shape (H, W).") - mask_t = torch.as_tensor(m.astype(np.float32, copy=False), device=dev) - - ctx = ModelContext(H=H, W=W, device=dev, dtype=dt, mask=mask_t) + mask_t = torch.as_tensor(m.astype(np.float32, copy=False), device=dev, dtype=dt) + ctx = ModelContext(H=H, W=W, device=dev, dtype=dt, mask=mask_t, fields={}) m = Model() - origin = OriginND.from_row_col(key=str(origin_key), origin_row=origin_row, origin_col=origin_col) - comps = [origin] + list(components) - m.add(comps) + m.add([Origin2D(origin_key=str(origin_key), row=origin_row, col=origin_col)]) + m.add(list(components)) self.model = m self.prepared = m.compile(ctx) - self.metadata["define_model"] = {"origin_key": str(origin_key), "device": str(dev), "dtype": str(dt)} + self.x_defined = self.prepared.x0.detach().clone() + self.x_initial = self.x_defined.detach().clone() + self.x_mean = self.x_initial.detach().clone() + self.mean_refined = False + self.x_patterns = None + self.pattern_fit_losses = None + self.pattern_fit_linear_indices = None + self.pattern_fit_indices = None + return self + + def _fit_target_image( + self, + *, + target: torch.Tensor, + x_start: torch.Tensor, + n_steps: int, + lr: float, + method: str, + power: float | None, + fit_disk_pixels: bool | None, + fit_only_disk_pixels: bool, + intensity_transform: str, + weak_softplus_scale: float, + progress: bool = False, + progress_desc: str | None = None, + ) -> tuple[torch.Tensor, float]: + if self.prepared is None: + raise RuntimeError("Call .define_model(...) first.") + + ctx = self.prepared.ctx + lb = self.prepared.lb + ub = self.prepared.ub + + x = x_start.detach().clone().to(device=ctx.device, dtype=ctx.dtype) + x.requires_grad_(True) + + if fit_disk_pixels is None: + fit_disk_pixels = any(p.tags.get("role") == "disk_pixel" for p in self.prepared.params) + + freeze = torch.zeros_like(x, dtype=torch.bool) + disk_mask = torch.zeros_like(x, dtype=torch.bool) + for p in self.prepared.params: + if p.tags.get("role") == "disk_pixel": + disk_mask[p.index] = True + + if fit_only_disk_pixels: + if not fit_disk_pixels: + raise ValueError("fit_only_disk_pixels=True requires fit_disk_pixels=True.") + freeze[:] = True + freeze[disk_mask] = False + elif not fit_disk_pixels: + freeze[disk_mask] = True + x_frozen = x.detach().clone() + + target_t = target.to(device=ctx.device, dtype=ctx.dtype) + target_t = self._apply_intensity_transform( + target_t, mode=intensity_transform, weak_softplus_scale=weak_softplus_scale + ) + if power is not None: + target_t = torch.clamp(target_t, min=0.0) ** float(power) + + def clamp_inplace() -> None: + with torch.no_grad(): + x.data = torch.max(torch.min(x.data, ub), lb) + if torch.any(freeze): + x.data[freeze] = x_frozen[freeze] + + def loss_fn() -> torch.Tensor: + clamp_inplace() + pred = self.prepared.render(x) + pred = self._apply_intensity_transform( + pred, mode=intensity_transform, weak_softplus_scale=weak_softplus_scale + ) + if power is not None: + pred = torch.clamp(pred, min=0.0) ** float(power) + if ctx.mask is not None: + m = ctx.mask + diff = (pred - target_t) * m + denom = torch.clamp(torch.sum(m), min=1.0) + return torch.sum(diff * diff) / denom + diff = pred - target_t + return torch.mean(diff * diff) + + if method == "adam": + opt = torch.optim.Adam([x], lr=lr) + step_iter: Any = range(int(n_steps)) + if progress: + try: + from tqdm.auto import trange + + step_iter = trange(int(n_steps), desc=progress_desc or "Refining", leave=False) + except Exception: + warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) + for _ in step_iter: + opt.zero_grad(set_to_none=True) + loss = loss_fn() + loss.backward() + opt.step() + clamp_inplace() + elif method == "lbfgs": + opt = torch.optim.LBFGS([x], lr=lr, max_iter=int(n_steps), line_search_fn="strong_wolfe") + + def closure() -> torch.Tensor: + opt.zero_grad(set_to_none=True) + loss = loss_fn() + loss.backward() + return loss + + opt.step(closure) + clamp_inplace() + else: + raise ValueError("method must be one of: 'lbfgs', 'adam'.") + + with torch.no_grad(): + final_loss = float(loss_fn().detach().cpu()) + return x.detach().clone(), final_loss + + def _resolve_pattern_indices(self, indices: Any, n: int, index_shape: tuple[int, ...]) -> np.ndarray: + if indices is None: + out = np.arange(n, dtype=np.int64) + elif isinstance(indices, (int, np.integer)): + out = np.asarray([int(indices)], dtype=np.int64) + elif isinstance(indices, slice): + out = np.arange(n, dtype=np.int64)[indices] + elif isinstance(indices, tuple) and all(isinstance(i, (int, np.integer)) for i in indices): + if len(index_shape) == 0: + if len(indices) != 0: + raise ValueError("indices tuple must be empty for single-pattern datasets.") + out = np.asarray([0], dtype=np.int64) + else: + out = np.asarray([np.ravel_multi_index(tuple(int(i) for i in indices), index_shape)], dtype=np.int64) + elif isinstance(indices, tuple): + if len(index_shape) == 0: + raise ValueError("slice tuple indices are not valid for single-pattern datasets.") + grid = np.arange(n, dtype=np.int64).reshape(index_shape) + out = np.asarray(grid[indices], dtype=np.int64).ravel() + elif isinstance(indices, np.ndarray) and indices.dtype == np.bool_: + if indices.shape != index_shape: + raise ValueError(f"Boolean mask must have shape {index_shape}.") + out = np.flatnonzero(indices.ravel()).astype(np.int64, copy=False) + elif isinstance(indices, Sequence) and not isinstance(indices, (str, bytes)): + seq = list(indices) + if len(seq) == 0: + out = np.asarray([], dtype=np.int64) + elif isinstance(seq[0], (tuple, list, np.ndarray)): + if len(index_shape) == 0: + raise ValueError("multi-index selection is not valid for single-pattern datasets.") + out = np.asarray( + [np.ravel_multi_index(tuple(int(j) for j in i), index_shape) for i in seq], + dtype=np.int64, + ) + else: + out = np.asarray([int(i) for i in seq], dtype=np.int64) + else: + raise TypeError("Unsupported indices type for fit_all_patterns.") + + if out.ndim != 1: + out = out.ravel() + if np.any(out < 0) or np.any(out >= n): + raise IndexError(f"indices must be in [0, {n - 1}].") + return out + + def refine_mean_model( + self, + *, + n_steps: int = 50, + lr: float = 1e-3, + method: str = "adam", + power: float | None = 1.0, + fit_disk_pixels: bool | None = None, + fit_only_disk_pixels: bool = False, + warmup_disk_steps: int = 0, + overwrite_initial: bool = True, + intensity_transform: str = "none", + weak_softplus_scale: float = 1e-3, + progress: bool = False, + ) -> "ModelDiffraction": + method = str(method).lower() + + if self.image_ref is None: + self.preprocess() + if self.image_ref is None or self.prepared is None or self.x_mean is None or self.x_initial is None: + raise RuntimeError("Call .define_model(...) first.") + + ctx = self.prepared.ctx + target = torch.as_tensor(self.image_ref, device=ctx.device, dtype=ctx.dtype) + x_start = self.x_initial if overwrite_initial else self.x_mean + if int(warmup_disk_steps) > 0: + x_start, _ = self._fit_target_image( + target=target, + x_start=x_start, + n_steps=int(warmup_disk_steps), + lr=float(lr), + method=method, + power=power, + fit_disk_pixels=True, + fit_only_disk_pixels=True, + intensity_transform=intensity_transform, + weak_softplus_scale=float(weak_softplus_scale), + progress=bool(progress), + progress_desc="Refine mean model (disk warmup)", + ) + x_fit, _ = self._fit_target_image( + target=target, + x_start=x_start, + n_steps=int(n_steps), + lr=float(lr), + method=method, + power=power, + fit_disk_pixels=fit_disk_pixels, + fit_only_disk_pixels=bool(fit_only_disk_pixels), + intensity_transform=intensity_transform, + weak_softplus_scale=float(weak_softplus_scale), + progress=bool(progress), + progress_desc="Refine mean model", + ) + + self.x_mean = x_fit + self.mean_refined = True + if overwrite_initial: + self.x_initial = x_fit.detach().clone() + return self + + def fit_all_patterns( + self, + *, + indices: Any = None, + use_refined_init: bool = True, + strict_refined_init: bool = False, + n_steps: int = 50, + lr: float = 1e-3, + method: str = "adam", + power: float | None = 1.0, + fit_disk_pixels: bool | None = None, + fit_only_disk_pixels: bool = False, + intensity_transform: str = "none", + weak_softplus_scale: float = 1e-3, + progress: bool = False, + ) -> "ModelDiffraction": + method = str(method).lower() + + if self.prepared is None or self.x_defined is None or self.x_initial is None: + raise RuntimeError("Call .define_model(...) first.") + + arr = np.asarray(self.dataset.array) + if arr.ndim < 2: + raise ValueError("dataset.array must have at least 2 dimensions.") + H, W = arr.shape[-2], arr.shape[-1] + index_shape = tuple(arr.shape[:-2]) + stack = arr.reshape((-1, H, W)).astype(np.float32, copy=False) + n = int(stack.shape[0]) + + linear = self._resolve_pattern_indices(indices=indices, n=n, index_shape=index_shape) + if linear.size == 0: + raise ValueError("No patterns selected for fitting.") + + if use_refined_init: + if not self.mean_refined: + msg = "fit_all_patterns(use_refined_init=True) was requested before refine_mean_model()." + if strict_refined_init: + raise RuntimeError(msg) + warnings.warn(f"{msg} Falling back to defined initial parameters.", stacklevel=2) + x_seed = self.x_defined + else: + x_seed = self.x_initial + else: + x_seed = self.x_defined + + n_sel = int(linear.size) + x_fit_all = torch.empty( + (n_sel, self.x_defined.numel()), + device=self.prepared.ctx.device, + dtype=self.prepared.ctx.dtype, + ) + losses = np.empty((n_sel,), dtype=np.float32) + + pat_iter: Any = enumerate(linear) + if progress: + try: + from tqdm.auto import tqdm + + pat_iter = enumerate(tqdm(linear, desc="Fit patterns", leave=False)) + except Exception: + warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) + + for j, i_lin in pat_iter: + target = torch.as_tensor(stack[int(i_lin)], device=self.prepared.ctx.device, dtype=self.prepared.ctx.dtype) + x_fit, loss = self._fit_target_image( + target=target, + x_start=x_seed, + n_steps=int(n_steps), + lr=float(lr), + method=method, + power=power, + fit_disk_pixels=fit_disk_pixels, + fit_only_disk_pixels=bool(fit_only_disk_pixels), + intensity_transform=intensity_transform, + weak_softplus_scale=float(weak_softplus_scale), + progress=False, + ) + x_fit_all[j] = x_fit + losses[j] = float(loss) + + self.x_patterns = x_fit_all + self.pattern_fit_losses = losses + self.pattern_fit_linear_indices = linear + if len(index_shape) == 0: + self.pattern_fit_indices = [tuple() for _ in linear] + else: + self.pattern_fit_indices = [tuple(int(k) for k in np.unravel_index(int(i), index_shape)) for i in linear] return self def _apply_overlays(self, ax: Any, overlays: list[Overlay]) -> None: for ov in overlays: - d = dict(ov.data) - if ov.kind in {"points", "points_rc"} and ("r" in d) and ("c" in d): - r = np.asarray(d["r"]).ravel() - c = np.asarray(d["c"]).ravel() - s = float(d.get("s", 60.0)) - marker = d.get("marker", "x") - color = d.get("color", "orange") - ax.scatter(c, r, s=s, marker=marker, c=color) + if ov.kind != "points_rc": continue + d = dict(ov.data) + r = _to_numpy(d["r"]).ravel() + c = _to_numpy(d["c"]).ravel() + ax.scatter( + c, + r, + s=float(d.get("s", 60.0)), + marker=d.get("marker", "x"), + color=d.get("color", "orange"), + ) def plot_mean_model( self, @@ -199,51 +507,46 @@ def plot_mean_model( show_overlays: bool = True, axsize: tuple[int, int] = (6, 6), ) -> tuple[Any, Any] | None: - self._ensure_image_ref() if self.image_ref is None: - raise RuntimeError("image_ref not available.") - if self.prepared is None: - raise RuntimeError("No model defined. Call .define_model(...) first.") + self.preprocess() + if self.image_ref is None or self.prepared is None: + raise RuntimeError("Call .define_model(...) first.") + if self.x_mean is None: + self.x_mean = self.prepared.x0.detach().clone() ref = np.asarray(self.image_ref, dtype=np.float32) - init = _to_numpy(self.prepared.render_initial()).astype(np.float32, copy=False) + mod = _to_numpy(self.prepared.render(self.x_mean)).astype(np.float32, copy=False) refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) - initp = init if power == 1.0 else np.maximum(init, 0.0) ** float(power) + modp = mod if power == 1.0 else np.maximum(mod, 0.0) ** float(power) - vmin = float(np.min([refp.min(), initp.min()])) - vmax = float(np.max([refp.max(), initp.max()])) + vmin = float(min(refp.min(), modp.min())) + vmax = float(max(refp.max(), modp.max())) fig, ax = show_2d( - [refp, initp], + [refp, modp], title=["image_ref", "model"], cmap="gray", cbar=False, returnfig=True, axsize=axsize, - norm="linear_minmax", vmin=vmin, vmax=vmax, ) - H, W = int(ref.shape[-2]), int(ref.shape[-1]) - - boundaries: list[float] = [] - for comp in getattr(self.prepared, "components", []): - b = getattr(comp, "boundary_px", None) + H, W = ref.shape[-2], ref.shape[-1] + pad = 0 + boundaries = [] + for c in getattr(self.prepared, "components", []): + b = getattr(c, "boundary_px", None) if b is not None: boundaries.append(float(b)) - - inset = 0 - pad = 0 if boundaries: - pos = [b for b in boundaries if b > 0.0] - neg = [b for b in boundaries if b < 0.0] - if pos: - inset = int(np.ceil(max(pos))) - if neg: - pad = int(np.ceil(-min(neg))) + min_b = float(np.min(boundaries)) + if min_b < 0.0: + pad = int(np.ceil(-min_b)) + axes: list[Any] if isinstance(ax, np.ndarray): axes = list(ax.ravel()) elif isinstance(ax, (list, tuple)): @@ -251,21 +554,16 @@ def plot_mean_model( else: axes = [ax] - x0 = (-pad + inset) - x1 = (W - 1) + pad - inset - y0 = (-pad + inset) - y1 = (H - 1) + pad - inset - for a in axes[:2]: - a.set_xlim(x0, x1) - a.set_ylim(y1, y0) + a.set_xlim(-pad, (W - 1) + pad) + a.set_ylim((H - 1) + pad, -pad) if show_overlays: - overlays = self.prepared.overlays() + ovs = self.prepared.overlays(self.x_mean) if len(axes) >= 1: - self._apply_overlays(axes[0], overlays) + self._apply_overlays(axes[0], ovs) if len(axes) >= 2: - self._apply_overlays(axes[1], overlays) + self._apply_overlays(axes[1], ovs) if returnfig: return fig, ax From 8cc81321491093ce7a19b11275f320923d7a73f7 Mon Sep 17 00:00:00 2001 From: cophus Date: Mon, 16 Feb 2026 20:18:27 -0800 Subject: [PATCH 22/43] adding docstrings --- src/quantem/core/models/background.py | 28 ++++ src/quantem/core/models/base.py | 94 +++++++++++ src/quantem/core/models/diffraction.py | 119 ++++++++++++++ src/quantem/diffraction/model_fitting.py | 191 +++++++++++++++++++++++ 4 files changed, 432 insertions(+) diff --git a/src/quantem/core/models/background.py b/src/quantem/core/models/background.py index d0a9adff..f0231a43 100644 --- a/src/quantem/core/models/background.py +++ b/src/quantem/core/models/background.py @@ -1,5 +1,7 @@ from __future__ import annotations +"""Background components for diffraction model fitting.""" + from dataclasses import dataclass from typing import Any, Iterable @@ -10,6 +12,17 @@ class DCBackground(Component): + """ + Uniform additive background with nonnegative intensity. + + Parameters + ---------- + intensity + Constant background intensity parameter specification. + name + Component name. + """ + def __init__( self, *, @@ -37,6 +50,21 @@ def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: class GaussianBackground(Component): + """ + Radial Gaussian background centered at a named model origin. + + Parameters + ---------- + sigma + Gaussian width parameter specification (constrained to `>= 1e-6`). + intensity + Nonnegative Gaussian amplitude parameter specification. + origin_key + Name of the origin to use from `ModelContext.fields["origins"]`. + name + Component name. + """ + def __init__( self, *, diff --git a/src/quantem/core/models/base.py b/src/quantem/core/models/base.py index 7d453c59..53a4c0aa 100644 --- a/src/quantem/core/models/base.py +++ b/src/quantem/core/models/base.py @@ -1,5 +1,7 @@ from __future__ import annotations +"""Composable model primitives for diffraction-style forward models.""" + from dataclasses import dataclass from typing import Any, Iterable, Mapping, Sequence @@ -9,11 +11,34 @@ @dataclass(frozen=True) class Overlay: + """Lightweight plotting overlay emitted by prepared components.""" + kind: str data: dict[str, Any] class Parameter: + """ + Scalar model parameter with optional bounds and metadata tags. + + Parameters + ---------- + value + Initial parameter specification. Supported forms: + - scalar: `v0` + - 2-sequence: `(v0, dev)` interpreted as bounds `(v0-dev, v0+dev)` + - 3-sequence: `(v0, lb, ub)` where either bound can be None + lower_bound, upper_bound + Optional explicit overrides for lower/upper bound. + tags + Optional metadata dictionary used by higher-level fitting code for + grouping/freezing/selecting parameters. + + Notes + ----- + Parameter indices are assigned during `Model.compile(...)`. + """ + def __init__( self, value: float | Sequence[float], @@ -66,6 +91,22 @@ def index(self) -> int: class ModelContext: + """ + Execution context shared by components during preparation and rendering. + + Parameters + ---------- + H, W + Output image height and width. + device, dtype + Torch device and dtype for parameter tensors/rendering. + mask + Optional per-pixel mask used by fitting loss functions. + fields + Mutable shared dictionary used by components to publish/consume + prepared state (for example origins or template metadata). + """ + def __init__( self, *, @@ -90,6 +131,14 @@ def __getattr__(self, name: str) -> Any: class Component: + """ + Abstract base class for additive model components. + + Subclasses implement: + - `parameters()` to expose fit parameters + - `prepare(ctx)` to build prepared render objects + """ + def __init__(self, *, name: str): self.name = str(name) @@ -101,6 +150,17 @@ def prepare(self, ctx: ModelContext) -> Any: class PreparedModel: + """ + Compiled model bundle with bound parameter vectors and render helpers. + + Attributes + ---------- + x0, lb, ub + Initial parameter vector and bound vectors, all on `ctx.device`. + components + Prepared component objects called in sequence during `render`. + """ + def __init__( self, *, @@ -119,15 +179,18 @@ def __init__( self.ub = ub def render(self, x: torch.Tensor) -> torch.Tensor: + """Render a model image for parameter vector ``x``.""" out = torch.zeros((self.ctx.H, self.ctx.W), device=self.ctx.device, dtype=self.ctx.dtype) for c in self.components: c.render(out, x, self.ctx) return out def render_initial(self) -> torch.Tensor: + """Render the model using the compile-time initial parameter vector.""" return self.render(self.x0) def overlays(self, x: torch.Tensor | None = None) -> list[Overlay]: + """Collect component overlays for plotting/debugging.""" out: list[Overlay] = [] for c in self.components: fn = getattr(c, "overlays", None) @@ -140,10 +203,27 @@ def overlays(self, x: torch.Tensor | None = None) -> list[Overlay]: class Model: + """ + Container for additive components that compiles to a `PreparedModel`. + + Notes + ----- + Component order defines render order and can also define dependency order + when components share prepared state through `ModelContext.fields`. + """ + def __init__(self): self._components: list[Component] = [] def add(self, items: Iterable[Component]) -> "Model": + """ + Append components to the model in render order. + + Parameters + ---------- + items + Iterable of `Component` instances. + """ for obj in items: if not isinstance(obj, Component): raise TypeError("Model.add expects Component objects.") @@ -151,12 +231,26 @@ def add(self, items: Iterable[Component]) -> "Model": return self def parameter_list(self) -> list[Parameter]: + """Return the flattened parameter list across all components.""" params: list[Parameter] = [] for c in self._components: params.extend(c.parameters()) return params def compile(self, ctx: ModelContext) -> PreparedModel: + """ + Bind parameter indices and prepare components for fast rendering. + + Parameters + ---------- + ctx + Model execution context. + + Returns + ------- + PreparedModel + Compiled model with `x0`, bounds, and prepared component state. + """ params = self.parameter_list() for i, p in enumerate(params): p.bind(i) diff --git a/src/quantem/core/models/diffraction.py b/src/quantem/core/models/diffraction.py index 8ecaf729..c324ecca 100644 --- a/src/quantem/core/models/diffraction.py +++ b/src/quantem/core/models/diffraction.py @@ -1,5 +1,7 @@ from __future__ import annotations +"""Diffraction-specific model components and low-level rendering helpers.""" + from dataclasses import dataclass from typing import Any, Iterable, Sequence @@ -10,6 +12,16 @@ def _as_t(x: Any, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor: + """ + Convert values to tensors on the requested device/dtype. + + Parameters + ---------- + x + Input scalar/array/tensor. + device, dtype + Target torch device and dtype. + """ if torch.is_tensor(x): return x.to(device=device, dtype=dtype) return torch.as_tensor(x, device=device, dtype=dtype) @@ -25,6 +37,22 @@ def _splat_patch( dc: torch.Tensor, scale: torch.Tensor, ) -> None: + """ + Render a shifted patch into ``out`` with bilinear interpolation. + + Parameters + ---------- + out + Destination image `(H, W)` updated in-place. + r0, c0 + Patch center coordinates in output-image row/column space. + patch_vals + Flattened patch intensities. + dr, dc + Flattened local row/column offsets for each patch sample. + scale + Scalar or vector multiplier applied to `patch_vals`. + """ H = out.shape[0] W = out.shape[1] @@ -59,6 +87,16 @@ def put(rr: torch.Tensor, cc: torch.Tensor, ww: torch.Tensor) -> None: def _origin_indices(ctx: ModelContext, origin_key: str) -> tuple[int, int]: + """ + Resolve origin parameter indices from ``ModelContext``. + + Parameters + ---------- + ctx + Model context with an `origins` registry in `ctx.fields`. + origin_key + Origin name to resolve. + """ origins: dict[str, Any] = ctx.fields.get("origins", {}) o = origins.get(origin_key) if o is None or "row_param" not in o or "col_param" not in o: @@ -67,6 +105,19 @@ def _origin_indices(ctx: ModelContext, origin_key: str) -> tuple[int, int]: class Origin2D(Component): + """ + Named 2D origin component that exposes row/column fit parameters. + + Parameters + ---------- + name + Component name. + origin_key + Key used to publish this origin into `ctx.fields["origins"]`. + row, col + Parameter specifications for origin row/column. + """ + def __init__( self, *, @@ -113,6 +164,33 @@ def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: class DiskTemplate(Component): + """ + Template patch component used for central disks and lattice motifs. + + Parameters + ---------- + name + Template name used for registry lookup by dependent components. + array + 2D template image. + refine_all_pixels + If True, every template pixel is exposed as a fit parameter. + place_at_origin + If True, render this template once at the named origin with a + separate nonnegative intensity parameter. + normalize + Optional array normalization mode: `"none"`, `"max"`, `"mean"`. + origin_key + Origin key used when `place_at_origin=True`. + intensity + Intensity parameter specification used when `place_at_origin=True`. + + Notes + ----- + The template is also published into `ctx.fields["disk_templates"]` so + lattice components can reuse its prepared sampling offsets. + """ + def __init__( self, *, @@ -248,6 +326,47 @@ def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: class SyntheticDiskLattice(Component): + """ + Lattice of shifted ``DiskTemplate`` patches around a shared origin. + + Parameters + ---------- + name + Component name. + disk + `DiskTemplate` used as the motif for each lattice spot. + u_row, u_col, v_row, v_col + Basis vector parameter specifications in row/column coordinates. + u_max, v_max + Inclusive lattice index extents. + intensity_0, intensity_row, intensity_col + Intensity parameter specifications. Interpretation depends on + `per_disk_intensity` and `per_disk_slopes`. + per_disk_intensity + If True, allocate independent base intensities per lattice disk. + per_disk_slopes + If True and `per_disk_intensity=True`, also allocate per-disk local + slope parameters for template-local coordinates. + center_intensity_0 + Optional override for the `(u, v) == (0, 0)` disk base intensity. + exclude_indices + Optional set/list of lattice indices to skip. + boundary_px + Keep only lattice centers within `[boundary_px, size-1-boundary_px]`. + origin_key + Origin key used for center placement. + + Notes + ----- + Intensity models: + - shared mode (`per_disk_intensity=False`): + `max(i0 + ir*row_center + ic*col_center, 0)` + - per-disk scalar mode (`per_disk_intensity=True`, `per_disk_slopes=False`): + `max(i0_i, 0)` + - per-disk local affine mode (`per_disk_intensity=True`, `per_disk_slopes=True`): + `max(i0_i + ir_i*dr + ic_i*dc, 0)` where `dr/dc` are template-local offsets. + """ + def __init__( self, *, diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index f382ab27..69dda4ca 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -1,5 +1,7 @@ from __future__ import annotations +"""High-level diffraction model fitting workflow utilities.""" + import warnings from typing import Any, Sequence @@ -20,6 +22,7 @@ def _to_numpy(x: Any) -> np.ndarray: + """Convert arrays/tensors to NumPy arrays.""" if isinstance(x, np.ndarray): return x if torch.is_tensor(x): @@ -28,6 +31,31 @@ def _to_numpy(x: Any) -> np.ndarray: class ModelDiffraction(AutoSerialize): + """ + End-to-end helper for defining and fitting additive diffraction forward models. + + This class wraps a diffraction dataset, builds an average reference image + (`image_ref`), compiles a composable component model, and provides optimization + routines for: + - fitting to the mean reference image, + - fitting selected individual diffraction patterns. + + Features + -------- + - Build a mean reference image with optional stack alignment. + - Define a composable model from origin/background/template/lattice components. + - Refine a mean model with Adam or L-BFGS. + - Fit all or selected patterns with optional progress bars. + - Plot reference/model comparisons and component overlays. + + Typical workflow + ---------------- + >>> md = ModelDiffraction.from_dataset(ds).preprocess().define_model(...) + >>> md.refine_mean_model(...) + >>> md.fit_all_patterns(...) + >>> md.plot_mean_model(...) + """ + _token = object() def __init__(self, dataset: Any, _token: object | None = None): @@ -68,6 +96,19 @@ def _apply_intensity_transform( @classmethod def from_dataset(cls, dataset: Dataset2d | Dataset3d | Dataset4d | Dataset4dstem | Any) -> "ModelDiffraction": + """ + Construct a ModelDiffraction object from a QuantEM dataset container. + + Parameters + ---------- + dataset + Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance. + + Returns + ------- + ModelDiffraction + New model-fitting helper bound to the provided dataset. + """ if isinstance(dataset, (Dataset2d, Dataset3d, Dataset4d, Dataset4dstem)): return cls(dataset=dataset, _token=cls._token) raise TypeError("from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance.") @@ -81,6 +122,34 @@ def preprocess( max_shift: float | None = None, shift_order: int = 1, ) -> "ModelDiffraction": + """ + Precompute the mean reference image used for model fitting. + + Parameters + ---------- + align + If True, align the flattened pattern stack before averaging. + edge_blend + Tukey edge taper width (pixels) used for robust FFT alignment. + upsample_factor + Sub-pixel alignment upsampling factor for cross-correlation shift. + max_shift + Optional maximum shift magnitude during alignment. + shift_order + Interpolation order used when applying shifts to patterns. + + Returns + ------- + ModelDiffraction + Returns self. + + Notes + ----- + - `dataset.array` is interpreted as `(..., H, W)`, where leading dimensions + are flattened into a pattern stack. + - The computed stack-average is stored in `self.image_ref`. + - If `align=False`, preprocessing is a direct mean over stack elements. + """ arr = np.asarray(self.dataset.array) if arr.ndim < 2: raise ValueError("dataset.array must have at least 2 dimensions.") @@ -145,6 +214,43 @@ def define_model( mask: np.ndarray | torch.Tensor | None = None, origin_key: str = "origin", ) -> "ModelDiffraction": + """ + Define and compile a diffraction model against `image_ref`. + + Parameters + ---------- + origin_row, origin_col + Initial origin parameter specification. Supported forms are: + - scalar: fixed initial value with no explicit bounds + - `(value, deviation)`: symmetric bounds `(value - deviation, value + deviation)` + - `(value, lower_bound, upper_bound)`: explicit bounds + components + Sequence of model components (e.g. `DiskTemplate`, backgrounds, lattice). + Components are rendered additively in the provided order. + device + Torch device used for compiled parameters and rendering. + dtype + Torch dtype used for compiled parameters and rendering. + mask + Optional `(H, W)` mask for weighted loss during optimization. + origin_key + Name used to register/retrieve the origin component in context fields. + + Returns + ------- + ModelDiffraction + Returns self with compiled model state. + + Notes + ----- + - If `image_ref` is missing, `preprocess()` is run automatically. + - `Origin2D` is inserted automatically before user components. + - Component dependency ordering still matters for shared context fields: + for example, `DiskTemplate` should appear before `SyntheticDiskLattice` + when the lattice references that template. + - This method resets fit state (`x_defined`, `x_initial`, `x_mean`, + `x_patterns`, and pattern-fit metadata). + """ if self.image_ref is None: self.preprocess() @@ -349,6 +455,43 @@ def refine_mean_model( weak_softplus_scale: float = 1e-3, progress: bool = False, ) -> "ModelDiffraction": + """ + Refine model parameters against the mean reference image. + + Parameters + ---------- + n_steps + Number of optimization steps/iterations for the main phase. + lr + Optimizer learning rate. + method + Optimizer name: `"adam"` or `"lbfgs"`. + power + Optional power-law transform applied to both target and prediction + before computing MSE loss. + fit_disk_pixels + Controls whether `disk_pixel` parameters are trainable. If None, + inferred from model parameters. + fit_only_disk_pixels + If True, freeze all non-disk parameters. + warmup_disk_steps + Optional number of disk-only warmup steps run before the main phase. + overwrite_initial + If True, store refined parameters as new initial parameters for + subsequent per-pattern fitting. + intensity_transform + Optional intensity transform (`"none"` or `"weak_softplus"`) applied + before the power transform and loss evaluation. + weak_softplus_scale + Scale parameter used when `intensity_transform="weak_softplus"`. + progress + If True, show a tqdm progress bar (when tqdm is available). + + Returns + ------- + ModelDiffraction + Returns self with updated `x_mean` (and optionally `x_initial`). + """ method = str(method).lower() if self.image_ref is None: @@ -411,6 +554,35 @@ def fit_all_patterns( weak_softplus_scale: float = 1e-3, progress: bool = False, ) -> "ModelDiffraction": + """ + Fit selected diffraction patterns using the compiled model. + + Parameters + ---------- + indices + Pattern selector. Supported forms include None (all patterns), + integer, slice, tuple indexing, list of linear indices, list of + multi-indices, or boolean mask shaped like scan dimensions. + use_refined_init + If True, initialize per-pattern fitting from `x_initial`. + strict_refined_init + If True, raise when refined init is requested before mean refinement. + If False, emit warning and fall back to defined initialization. + n_steps, lr, method, power, fit_disk_pixels, fit_only_disk_pixels + Optimization settings analogous to `refine_mean_model`. + intensity_transform, weak_softplus_scale + Prediction/target intensity transform options. + progress + If True, show a tqdm progress bar over selected patterns. + + Returns + ------- + ModelDiffraction + Returns self with: + - `x_patterns`: fitted parameter vectors `(n_selected, n_params)` + - `pattern_fit_losses`: per-pattern final losses + - index bookkeeping for selected patterns. + """ method = str(method).lower() if self.prepared is None or self.x_defined is None or self.x_initial is None: @@ -507,6 +679,25 @@ def plot_mean_model( show_overlays: bool = True, axsize: tuple[int, int] = (6, 6), ) -> tuple[Any, Any] | None: + """ + Plot `image_ref` and the current mean-model prediction side-by-side. + + Parameters + ---------- + power + Display transform exponent applied to both reference and model images. + returnfig + If True, return `(fig, ax)` from `show_2d`. + show_overlays + If True, draw component overlays (e.g., origin and lattice markers). + axsize + Base axis size passed to the plotting helper. + + Returns + ------- + tuple[Any, Any] | None + `(fig, ax)` if `returnfig=True`, else None. + """ if self.image_ref is None: self.preprocess() if self.image_ref is None or self.prepared is None: From 7dd327a7b8483a06aae478d1cfd1491633bb3c3c Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 19 Feb 2026 19:20:44 -0800 Subject: [PATCH 23/43] fixing CoM behaviour --- src/quantem/core/models/diffraction.py | 1 + src/quantem/diffraction/model_fitting.py | 84 ++++++++++++++++++++++++ 2 files changed, 85 insertions(+) diff --git a/src/quantem/core/models/diffraction.py b/src/quantem/core/models/diffraction.py index c324ecca..7b6d00ae 100644 --- a/src/quantem/core/models/diffraction.py +++ b/src/quantem/core/models/diffraction.py @@ -294,6 +294,7 @@ def prepare(self, ctx: ModelContext) -> Any: "dc": dc, "pix_idx": pix_idx, "base": base_vals, + "shape": (int(Ht), int(Wt)), } i_int = None if self.p_intensity is None else self.p_intensity.index diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 69dda4ca..3362d60c 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -7,6 +7,7 @@ import numpy as np import torch +import torch.nn.functional as F from scipy.ndimage import shift as ndi_shift from scipy.signal.windows import tukey @@ -299,6 +300,8 @@ def _fit_target_image( power: float | None, fit_disk_pixels: bool | None, fit_only_disk_pixels: bool, + enforce_disk_max_one: bool, + enforce_disk_center_of_mass: bool, intensity_transform: str, weak_softplus_scale: float, progress: bool = False, @@ -323,6 +326,40 @@ def _fit_target_image( if p.tags.get("role") == "disk_pixel": disk_mask[p.index] = True + # Cache template groups for optional per-step projection constraints. + disk_templates = self.prepared.ctx.fields.get("disk_templates", {}) + disk_param_groups: list[dict[str, Any]] = [] + if enforce_disk_max_one or enforce_disk_center_of_mass: + grouped: dict[str, list[tuple[int, int]]] = {} + for p in self.prepared.params: + if p.tags.get("role") != "disk_pixel": + continue + name = str(p.tags.get("disk")) + i_flat = int(p.tags.get("i")) + grouped.setdefault(name, []).append((i_flat, int(p.index))) + + for name, pairs in grouped.items(): + if name not in disk_templates: + continue + dmeta = disk_templates[name] + shape = dmeta.get("shape", None) + if shape is None: + continue + Ht, Wt = int(shape[0]), int(shape[1]) + order = sorted(pairs, key=lambda t: t[0]) + flat_i = torch.as_tensor([t[0] for t in order], device=ctx.device, dtype=torch.long) + p_idx = torch.as_tensor([t[1] for t in order], device=ctx.device, dtype=torch.long) + dr = dmeta["dr"][flat_i] + dc = dmeta["dc"][flat_i] + disk_param_groups.append( + { + "param_idx": p_idx, + "shape": (Ht, Wt), + "dr": dr, + "dc": dc, + } + ) + if fit_only_disk_pixels: if not fit_disk_pixels: raise ValueError("fit_only_disk_pixels=True requires fit_disk_pixels=True.") @@ -344,6 +381,43 @@ def clamp_inplace() -> None: x.data = torch.max(torch.min(x.data, ub), lb) if torch.any(freeze): x.data[freeze] = x_frozen[freeze] + if (enforce_disk_max_one or enforce_disk_center_of_mass) and disk_param_groups: + eps = torch.as_tensor(1e-12, device=ctx.device, dtype=ctx.dtype) + for g in disk_param_groups: + p_idx = g["param_idx"] + if torch.all(freeze[p_idx]): + continue + vals = x.data[p_idx] + vals = torch.clamp(vals, min=0.0) + + if enforce_disk_center_of_mass: + mass = torch.sum(vals) + if mass > eps: + r_com = torch.sum(vals * g["dr"]) / mass + c_com = torch.sum(vals * g["dc"]) / mass + Ht, Wt = g["shape"] + img = vals.reshape(Ht, Wt)[None, None, :, :] + yy = torch.linspace(-1.0, 1.0, Ht, device=ctx.device, dtype=ctx.dtype) + xx = torch.linspace(-1.0, 1.0, Wt, device=ctx.device, dtype=ctx.dtype) + gy, gx = torch.meshgrid(yy, xx, indexing="ij") + sx = gx + (2.0 * c_com / max(Wt - 1, 1)) + sy = gy + (2.0 * r_com / max(Ht - 1, 1)) + grid = torch.stack((sx, sy), dim=-1)[None, :, :, :] + img_shift = F.grid_sample( + img, + grid, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + vals = torch.clamp(img_shift[0, 0].reshape(-1), min=0.0) + + if enforce_disk_max_one: + vmax = torch.max(vals) + if vmax > eps: + vals = vals / vmax + + x.data[p_idx] = vals def loss_fn() -> torch.Tensor: clamp_inplace() @@ -449,6 +523,8 @@ def refine_mean_model( power: float | None = 1.0, fit_disk_pixels: bool | None = None, fit_only_disk_pixels: bool = False, + enforce_disk_max_one: bool = True, + enforce_disk_center_of_mass: bool = True, warmup_disk_steps: int = 0, overwrite_initial: bool = True, intensity_transform: str = "none", @@ -512,6 +588,8 @@ def refine_mean_model( power=power, fit_disk_pixels=True, fit_only_disk_pixels=True, + enforce_disk_max_one=bool(enforce_disk_max_one), + enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=bool(progress), @@ -526,6 +604,8 @@ def refine_mean_model( power=power, fit_disk_pixels=fit_disk_pixels, fit_only_disk_pixels=bool(fit_only_disk_pixels), + enforce_disk_max_one=bool(enforce_disk_max_one), + enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=bool(progress), @@ -550,6 +630,8 @@ def fit_all_patterns( power: float | None = 1.0, fit_disk_pixels: bool | None = None, fit_only_disk_pixels: bool = False, + enforce_disk_max_one: bool = True, + enforce_disk_center_of_mass: bool = True, intensity_transform: str = "none", weak_softplus_scale: float = 1e-3, progress: bool = False, @@ -640,6 +722,8 @@ def fit_all_patterns( power=power, fit_disk_pixels=fit_disk_pixels, fit_only_disk_pixels=bool(fit_only_disk_pixels), + enforce_disk_max_one=bool(enforce_disk_max_one), + enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=False, From 9bfaa7f02c3ac7f49f85c94494dc764cd909799d Mon Sep 17 00:00:00 2001 From: cophus Date: Fri, 20 Feb 2026 11:45:48 -0800 Subject: [PATCH 24/43] updates --- src/quantem/core/fitting/__init__.py | 29 +++ .../core/{models => fitting}/background.py | 4 +- src/quantem/core/{models => fitting}/base.py | 0 .../core/{models => fitting}/diffraction.py | 152 +++++++++++---- src/quantem/core/models/__init__.py | 29 --- src/quantem/diffraction/model_fitting.py | 182 +++++++++--------- 6 files changed, 242 insertions(+), 154 deletions(-) create mode 100644 src/quantem/core/fitting/__init__.py rename src/quantem/core/{models => fitting}/background.py (95%) rename src/quantem/core/{models => fitting}/base.py (100%) rename src/quantem/core/{models => fitting}/diffraction.py (71%) delete mode 100644 src/quantem/core/models/__init__.py diff --git a/src/quantem/core/fitting/__init__.py b/src/quantem/core/fitting/__init__.py new file mode 100644 index 00000000..5aa67459 --- /dev/null +++ b/src/quantem/core/fitting/__init__.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from quantem.core.fitting.base import Component as Component +from quantem.core.fitting.base import Model as Model +from quantem.core.fitting.base import ModelContext as ModelContext +from quantem.core.fitting.base import Overlay as Overlay +from quantem.core.fitting.base import Parameter as Parameter +from quantem.core.fitting.base import PreparedModel as PreparedModel + +from quantem.core.fitting.diffraction import DiskTemplate as DiskTemplate +from quantem.core.fitting.diffraction import Origin2D as Origin2D +from quantem.core.fitting.diffraction import SyntheticDiskLattice as SyntheticDiskLattice + +from quantem.core.fitting.background import DCBackground as DCBackground +from quantem.core.fitting.background import GaussianBackground as GaussianBackground + +__all__ = [ + "Parameter", + "Overlay", + "ModelContext", + "Component", + "Model", + "PreparedModel", + "Origin2D", + "DiskTemplate", + "SyntheticDiskLattice", + "DCBackground", + "GaussianBackground", +] diff --git a/src/quantem/core/models/background.py b/src/quantem/core/fitting/background.py similarity index 95% rename from src/quantem/core/models/background.py rename to src/quantem/core/fitting/background.py index f0231a43..2db88616 100644 --- a/src/quantem/core/models/background.py +++ b/src/quantem/core/fitting/background.py @@ -7,8 +7,8 @@ import torch -from quantem.core.models.base import Component, ModelContext, Overlay, Parameter -from quantem.core.models.diffraction import _origin_indices +from quantem.core.fitting.base import Component, ModelContext, Overlay, Parameter +from quantem.core.fitting.diffraction import _origin_indices class DCBackground(Component): diff --git a/src/quantem/core/models/base.py b/src/quantem/core/fitting/base.py similarity index 100% rename from src/quantem/core/models/base.py rename to src/quantem/core/fitting/base.py diff --git a/src/quantem/core/models/diffraction.py b/src/quantem/core/fitting/diffraction.py similarity index 71% rename from src/quantem/core/models/diffraction.py rename to src/quantem/core/fitting/diffraction.py index 7b6d00ae..78a45c14 100644 --- a/src/quantem/core/models/diffraction.py +++ b/src/quantem/core/fitting/diffraction.py @@ -8,7 +8,7 @@ import numpy as np import torch -from quantem.core.models.base import Component, ModelContext, Overlay, Parameter +from quantem.core.fitting.base import Component, ModelContext, Overlay, Parameter def _as_t(x: Any, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor: @@ -342,12 +342,19 @@ class SyntheticDiskLattice(Component): Inclusive lattice index extents. intensity_0, intensity_row, intensity_col Intensity parameter specifications. Interpretation depends on - `per_disk_intensity` and `per_disk_slopes`. + `per_disk_intensity` and intensity-order settings. + intensity_row_row, intensity_col_col, intensity_row_col + Optional quadratic intensity terms for order-2 models. per_disk_intensity If True, allocate independent base intensities per lattice disk. per_disk_slopes - If True and `per_disk_intensity=True`, also allocate per-disk local - slope parameters for template-local coordinates. + Backward-compatible switch controlling whether linear terms are included + when `max_intensity_order` is not explicitly provided. + max_intensity_order + Maximum polynomial order used by this component (`0`, `1`, or `2`). + default_pattern_intensity_order + Default active intensity order used during rendering unless overridden + in `ctx.fields["lattice_intensity_order_override"]`. center_intensity_0 Optional override for the `(u, v) == (0, 0)` disk base intensity. exclude_indices @@ -362,10 +369,12 @@ class SyntheticDiskLattice(Component): Intensity models: - shared mode (`per_disk_intensity=False`): `max(i0 + ir*row_center + ic*col_center, 0)` - - per-disk scalar mode (`per_disk_intensity=True`, `per_disk_slopes=False`): + - per-disk scalar mode (`per_disk_intensity=True`, order=0): `max(i0_i, 0)` - - per-disk local affine mode (`per_disk_intensity=True`, `per_disk_slopes=True`): + - per-disk local affine mode (`per_disk_intensity=True`, order=1): `max(i0_i + ir_i*dr + ic_i*dc, 0)` where `dr/dc` are template-local offsets. + - per-disk local quadratic mode (`per_disk_intensity=True`, order=2): + `max(i0_i + ir_i*dr + ic_i*dc + irr_i*dr^2 + icc_i*dc^2 + irc_i*dr*dc, 0)`. """ def __init__( @@ -382,8 +391,13 @@ def __init__( intensity_0: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, intensity_row: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, intensity_col: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + intensity_row_row: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + intensity_col_col: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + intensity_row_col: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, per_disk_intensity: bool = False, per_disk_slopes: bool = True, + max_intensity_order: int | None = None, + default_pattern_intensity_order: int | None = None, center_intensity_0: float | tuple[float, float] | tuple[float, float, float | None] | None = None, exclude_indices: Iterable[tuple[int, int]] | None = None, boundary_px: float = 0.0, @@ -396,7 +410,18 @@ def __init__( self.boundary_px = float(boundary_px) self.origin_key = str(origin_key) self.per_disk_intensity = bool(per_disk_intensity) - self.per_disk_slopes = bool(per_disk_slopes) + if max_intensity_order is None: + self.max_intensity_order = 1 if bool(per_disk_slopes) else 0 + else: + self.max_intensity_order = int(max_intensity_order) + if self.max_intensity_order < 0 or self.max_intensity_order > 2: + raise ValueError("max_intensity_order must be 0, 1, or 2.") + if default_pattern_intensity_order is None: + self.default_pattern_intensity_order = int(self.max_intensity_order) + else: + self.default_pattern_intensity_order = int(default_pattern_intensity_order) + if self.default_pattern_intensity_order < 0 or self.default_pattern_intensity_order > self.max_intensity_order: + raise ValueError("default_pattern_intensity_order must be in [0, max_intensity_order].") if exclude_indices is None: self.exclude_indices = {(0, 0)} if bool(getattr(disk, "place_at_origin", False)) else set() @@ -421,27 +446,57 @@ def __init__( self.p_i0_list: list[Parameter] = [] self.p_ir_list: list[Parameter] = [] self.p_ic_list: list[Parameter] = [] + self.p_irr_list: list[Parameter] = [] + self.p_icc_list: list[Parameter] = [] + self.p_irc_list: list[Parameter] = [] if self.per_disk_intensity: for u, v in self.uv_indices: i0_val = center_intensity_0 if (center_intensity_0 is not None and (u, v) == (0, 0)) else intensity_0 - self.p_i0_list.append(Parameter(i0_val, lower_bound=0.0, tags={"role": "lat_int0", "u": u, "v": v})) - if self.per_disk_slopes: - self.p_ir_list.append(Parameter(intensity_row, tags={"role": "lat_int_row", "u": u, "v": v})) - self.p_ic_list.append(Parameter(intensity_col, tags={"role": "lat_int_col", "u": u, "v": v})) + self.p_i0_list.append( + Parameter(i0_val, lower_bound=0.0, tags={"role": "lat_int0", "u": u, "v": v, "intensity_order": 0}) + ) + if self.max_intensity_order >= 1: + self.p_ir_list.append( + Parameter(intensity_row, tags={"role": "lat_int_row", "u": u, "v": v, "intensity_order": 1}) + ) + self.p_ic_list.append( + Parameter(intensity_col, tags={"role": "lat_int_col", "u": u, "v": v, "intensity_order": 1}) + ) + if self.max_intensity_order >= 2: + self.p_irr_list.append( + Parameter(intensity_row_row, tags={"role": "lat_int_rr", "u": u, "v": v, "intensity_order": 2}) + ) + self.p_icc_list.append( + Parameter(intensity_col_col, tags={"role": "lat_int_cc", "u": u, "v": v, "intensity_order": 2}) + ) + self.p_irc_list.append( + Parameter(intensity_row_col, tags={"role": "lat_int_rc", "u": u, "v": v, "intensity_order": 2}) + ) else: - self.p_i0 = Parameter(intensity_0, lower_bound=0.0, tags={"role": "lat_int0"}) - self.p_ir = Parameter(intensity_row, tags={"role": "lat_int_row"}) - self.p_ic = Parameter(intensity_col, tags={"role": "lat_int_col"}) + self.p_i0 = Parameter(intensity_0, lower_bound=0.0, tags={"role": "lat_int0", "intensity_order": 0}) + self.p_ir = Parameter(intensity_row, tags={"role": "lat_int_row", "intensity_order": 1}) + self.p_ic = Parameter(intensity_col, tags={"role": "lat_int_col", "intensity_order": 1}) + self.p_irr = Parameter(intensity_row_row, tags={"role": "lat_int_rr", "intensity_order": 2}) + self.p_icc = Parameter(intensity_col_col, tags={"role": "lat_int_cc", "intensity_order": 2}) + self.p_irc = Parameter(intensity_row_col, tags={"role": "lat_int_rc", "intensity_order": 2}) def parameters(self) -> list[Parameter]: out = [self.p_u_row, self.p_u_col, self.p_v_row, self.p_v_col] if self.per_disk_intensity: out.extend(self.p_i0_list) - if self.per_disk_slopes: + if self.max_intensity_order >= 1: out.extend(self.p_ir_list) out.extend(self.p_ic_list) + if self.max_intensity_order >= 2: + out.extend(self.p_irr_list) + out.extend(self.p_icc_list) + out.extend(self.p_irc_list) else: - out.extend([self.p_i0, self.p_ir, self.p_ic]) + out.append(self.p_i0) + if self.max_intensity_order >= 1: + out.extend([self.p_ir, self.p_ic]) + if self.max_intensity_order >= 2: + out.extend([self.p_irr, self.p_icc, self.p_irc]) return out def prepare(self, ctx: ModelContext) -> Any: @@ -461,11 +516,14 @@ def prepare(self, ctx: ModelContext) -> Any: i_vr = self.p_v_row.index i_vc = self.p_v_col.index if self.per_disk_intensity: - i_i0 = i_ir = i_ic = None + i_i0 = i_ir = i_ic = i_irr = i_icc = i_irc = None else: i_i0 = self.p_i0.index - i_ir = self.p_ir.index - i_ic = self.p_ic.index + i_ir = None if self.max_intensity_order < 1 else self.p_ir.index + i_ic = None if self.max_intensity_order < 1 else self.p_ic.index + i_irr = None if self.max_intensity_order < 2 else self.p_irr.index + i_icc = None if self.max_intensity_order < 2 else self.p_icc.index + i_irc = None if self.max_intensity_order < 2 else self.p_irc.index uv = self.uv_indices uv_t = torch.as_tensor(uv, device=ctx.device, dtype=torch.long) if uv else None @@ -474,15 +532,23 @@ def prepare(self, ctx: ModelContext) -> Any: if self.per_disk_intensity: i0_idx = torch.as_tensor([p.index for p in self.p_i0_list], device=ctx.device, dtype=torch.long) - if self.per_disk_slopes: + if self.max_intensity_order >= 1: ir_idx = torch.as_tensor([p.index for p in self.p_ir_list], device=ctx.device, dtype=torch.long) ic_idx = torch.as_tensor([p.index for p in self.p_ic_list], device=ctx.device, dtype=torch.long) else: ir_idx = ic_idx = None + if self.max_intensity_order >= 2: + irr_idx = torch.as_tensor([p.index for p in self.p_irr_list], device=ctx.device, dtype=torch.long) + icc_idx = torch.as_tensor([p.index for p in self.p_icc_list], device=ctx.device, dtype=torch.long) + irc_idx = torch.as_tensor([p.index for p in self.p_irc_list], device=ctx.device, dtype=torch.long) + else: + irr_idx = icc_idx = irc_idx = None else: - i0_idx = ir_idx = ic_idx = None + i0_idx = ir_idx = ic_idx = irr_idx = icc_idx = irc_idx = None per_disk_intensity = self.per_disk_intensity - per_disk_slopes = self.per_disk_slopes + max_intensity_order = self.max_intensity_order + default_pattern_intensity_order = self.default_pattern_intensity_order + comp_name = self.name @dataclass class Prepared: @@ -525,26 +591,46 @@ def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: return vals = self._patch(x) + dr2 = dr * dr + dc2 = dc * dc + drdc = dr * dc + + order_override = ctx.fields.get("lattice_intensity_order_override", None) + if isinstance(order_override, dict): + active_order = int(order_override.get(comp_name, default_pattern_intensity_order)) + elif order_override is None: + active_order = int(default_pattern_intensity_order) + else: + active_order = int(order_override) + active_order = max(0, min(active_order, int(max_intensity_order))) if per_disk_intensity: keep_idx = torch.nonzero(keep, as_tuple=False).reshape(-1) i0_keep = x[i0_idx[keep_idx]] - if per_disk_slopes: + if active_order >= 1: ir_keep = x[ir_idx[keep_idx]] ic_keep = x[ic_idx[keep_idx]] - for j, (rr0, cc0) in enumerate(zip(centers_r, centers_c)): - inten_local = torch.clamp(i0_keep[j] + ir_keep[j] * dr + ic_keep[j] * dc, min=0.0) - _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten_local) - else: - for j, (rr0, cc0) in enumerate(zip(centers_r, centers_c)): - inten_local = torch.clamp(i0_keep[j], min=0.0) - _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten_local) + if active_order >= 2: + irr_keep = x[irr_idx[keep_idx]] + icc_keep = x[icc_idx[keep_idx]] + irc_keep = x[irc_idx[keep_idx]] + for j, (rr0, cc0) in enumerate(zip(centers_r, centers_c)): + inten_local = i0_keep[j] + if active_order >= 1: + inten_local = inten_local + ir_keep[j] * dr + ic_keep[j] * dc + if active_order >= 2: + inten_local = inten_local + irr_keep[j] * dr2 + icc_keep[j] * dc2 + irc_keep[j] * drdc + inten_local = torch.clamp(inten_local, min=0.0) + _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten_local) else: i0 = x[i_i0] - ir = x[i_ir] - ic = x[i_ic] for rr0, cc0 in zip(centers_r, centers_c): - inten = torch.clamp(i0 + ir * rr0 + ic * cc0, min=0.0) + inten = i0 + if active_order >= 1: + inten = inten + x[i_ir] * rr0 + x[i_ic] * cc0 + if active_order >= 2: + inten = inten + x[i_irr] * rr0 * rr0 + x[i_icc] * cc0 * cc0 + x[i_irc] * rr0 * cc0 + inten = torch.clamp(inten, min=0.0) _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten) def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: diff --git a/src/quantem/core/models/__init__.py b/src/quantem/core/models/__init__.py deleted file mode 100644 index 7330baff..00000000 --- a/src/quantem/core/models/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import annotations - -from quantem.core.models.base import Component as Component -from quantem.core.models.base import Model as Model -from quantem.core.models.base import ModelContext as ModelContext -from quantem.core.models.base import Overlay as Overlay -from quantem.core.models.base import Parameter as Parameter -from quantem.core.models.base import PreparedModel as PreparedModel - -from quantem.core.models.diffraction import DiskTemplate as DiskTemplate -from quantem.core.models.diffraction import Origin2D as Origin2D -from quantem.core.models.diffraction import SyntheticDiskLattice as SyntheticDiskLattice - -from quantem.core.models.background import DCBackground as DCBackground -from quantem.core.models.background import GaussianBackground as GaussianBackground - -__all__ = [ - "Parameter", - "Overlay", - "ModelContext", - "Component", - "Model", - "PreparedModel", - "Origin2D", - "DiskTemplate", - "SyntheticDiskLattice", - "DCBackground", - "GaussianBackground", -] diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 3362d60c..5ed35999 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -7,7 +7,6 @@ import numpy as np import torch -import torch.nn.functional as F from scipy.ndimage import shift as ndi_shift from scipy.signal.windows import tukey @@ -16,8 +15,8 @@ from quantem.core.datastructures.dataset4d import Dataset4d from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.models.base import Model, ModelContext, Overlay, PreparedModel -from quantem.core.models.diffraction import Origin2D +from quantem.core.fitting.base import Model, ModelContext, Overlay, PreparedModel +from quantem.core.fitting.diffraction import Origin2D from quantem.core.utils.imaging_utils import cross_correlation_shift from quantem.core.visualization import show_2d @@ -300,8 +299,9 @@ def _fit_target_image( power: float | None, fit_disk_pixels: bool | None, fit_only_disk_pixels: bool, - enforce_disk_max_one: bool, + intensity_order: int | None, enforce_disk_center_of_mass: bool, + normalize_disk_template_max: bool, intensity_transform: str, weak_softplus_scale: float, progress: bool = False, @@ -325,11 +325,15 @@ def _fit_target_image( for p in self.prepared.params: if p.tags.get("role") == "disk_pixel": disk_mask[p.index] = True + if intensity_order is not None and str(p.tags.get("role", "")).startswith("lat_int"): + p_ord = int(p.tags.get("intensity_order", 0)) + if p_ord > int(intensity_order): + freeze[p.index] = True - # Cache template groups for optional per-step projection constraints. - disk_templates = self.prepared.ctx.fields.get("disk_templates", {}) + # Group disk pixel parameters by disk template for optional projection. disk_param_groups: list[dict[str, Any]] = [] - if enforce_disk_max_one or enforce_disk_center_of_mass: + if enforce_disk_center_of_mass or normalize_disk_template_max: + disk_templates = self.prepared.ctx.fields.get("disk_templates", {}) grouped: dict[str, list[tuple[int, int]]] = {} for p in self.prepared.params: if p.tags.get("role") != "disk_pixel": @@ -337,28 +341,18 @@ def _fit_target_image( name = str(p.tags.get("disk")) i_flat = int(p.tags.get("i")) grouped.setdefault(name, []).append((i_flat, int(p.index))) - for name, pairs in grouped.items(): - if name not in disk_templates: - continue - dmeta = disk_templates[name] - shape = dmeta.get("shape", None) - if shape is None: + dmeta = disk_templates.get(name) + if dmeta is None: continue - Ht, Wt = int(shape[0]), int(shape[1]) order = sorted(pairs, key=lambda t: t[0]) - flat_i = torch.as_tensor([t[0] for t in order], device=ctx.device, dtype=torch.long) p_idx = torch.as_tensor([t[1] for t in order], device=ctx.device, dtype=torch.long) - dr = dmeta["dr"][flat_i] - dc = dmeta["dc"][flat_i] - disk_param_groups.append( - { - "param_idx": p_idx, - "shape": (Ht, Wt), - "dr": dr, - "dc": dc, - } - ) + g: dict[str, Any] = {"param_idx": p_idx} + if enforce_disk_center_of_mass: + flat_i = torch.as_tensor([t[0] for t in order], device=ctx.device, dtype=torch.long) + g["dr"] = dmeta["dr"][flat_i] + g["dc"] = dmeta["dc"][flat_i] + disk_param_groups.append(g) if fit_only_disk_pixels: if not fit_disk_pixels: @@ -369,6 +363,10 @@ def _fit_target_image( freeze[disk_mask] = True x_frozen = x.detach().clone() + old_order_override = ctx.fields.get("lattice_intensity_order_override", None) + if intensity_order is not None: + ctx.fields["lattice_intensity_order_override"] = int(intensity_order) + target_t = target.to(device=ctx.device, dtype=ctx.dtype) target_t = self._apply_intensity_transform( target_t, mode=intensity_transform, weak_softplus_scale=weak_softplus_scale @@ -381,42 +379,33 @@ def clamp_inplace() -> None: x.data = torch.max(torch.min(x.data, ub), lb) if torch.any(freeze): x.data[freeze] = x_frozen[freeze] - if (enforce_disk_max_one or enforce_disk_center_of_mass) and disk_param_groups: + if (enforce_disk_center_of_mass or normalize_disk_template_max) and disk_param_groups: eps = torch.as_tensor(1e-12, device=ctx.device, dtype=ctx.dtype) for g in disk_param_groups: p_idx = g["param_idx"] if torch.all(freeze[p_idx]): continue - vals = x.data[p_idx] - vals = torch.clamp(vals, min=0.0) - + vals = torch.clamp(x.data[p_idx], min=0.0) if enforce_disk_center_of_mass: - mass = torch.sum(vals) - if mass > eps: - r_com = torch.sum(vals * g["dr"]) / mass - c_com = torch.sum(vals * g["dc"]) / mass - Ht, Wt = g["shape"] - img = vals.reshape(Ht, Wt)[None, None, :, :] - yy = torch.linspace(-1.0, 1.0, Ht, device=ctx.device, dtype=ctx.dtype) - xx = torch.linspace(-1.0, 1.0, Wt, device=ctx.device, dtype=ctx.dtype) - gy, gx = torch.meshgrid(yy, xx, indexing="ij") - sx = gx + (2.0 * c_com / max(Wt - 1, 1)) - sy = gy + (2.0 * r_com / max(Ht - 1, 1)) - grid = torch.stack((sx, sy), dim=-1)[None, :, :, :] - img_shift = F.grid_sample( - img, - grid, - mode="bilinear", - padding_mode="zeros", - align_corners=True, - ) - vals = torch.clamp(img_shift[0, 0].reshape(-1), min=0.0) - - if enforce_disk_max_one: + dr = g["dr"] + dc = g["dc"] + # Project template moments toward zero COM by iterative multiplicative reweighting. + for _ in range(12): + mass = torch.sum(vals) + if mass <= eps: + break + r_com = torch.sum(vals * dr) / mass + c_com = torch.sum(vals * dc) / mass + if torch.abs(r_com) <= 1e-5 and torch.abs(c_com) <= 1e-5: + break + var_r = torch.sum(vals * dr * dr) / mass + eps + var_c = torch.sum(vals * dc * dc) / mass + eps + vals = vals * torch.exp(-(r_com / var_r) * dr - (c_com / var_c) * dc) + vals = torch.clamp(vals, min=0.0) + if normalize_disk_template_max: vmax = torch.max(vals) if vmax > eps: vals = vals / vmax - x.data[p_idx] = vals def loss_fn() -> torch.Tensor: @@ -435,39 +424,46 @@ def loss_fn() -> torch.Tensor: diff = pred - target_t return torch.mean(diff * diff) - if method == "adam": - opt = torch.optim.Adam([x], lr=lr) - step_iter: Any = range(int(n_steps)) - if progress: - try: - from tqdm.auto import trange - - step_iter = trange(int(n_steps), desc=progress_desc or "Refining", leave=False) - except Exception: - warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) - for _ in step_iter: - opt.zero_grad(set_to_none=True) - loss = loss_fn() - loss.backward() - opt.step() + try: + if method == "adam": + opt = torch.optim.Adam([x], lr=lr) + step_iter: Any = range(int(n_steps)) + if progress: + try: + from tqdm.auto import trange + + step_iter = trange(int(n_steps), desc=progress_desc or "Refining", leave=False) + except Exception: + warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) + for _ in step_iter: + opt.zero_grad(set_to_none=True) + loss = loss_fn() + loss.backward() + opt.step() + clamp_inplace() + elif method == "lbfgs": + opt = torch.optim.LBFGS([x], lr=lr, max_iter=int(n_steps), line_search_fn="strong_wolfe") + + def closure() -> torch.Tensor: + opt.zero_grad(set_to_none=True) + loss = loss_fn() + loss.backward() + return loss + + opt.step(closure) clamp_inplace() - elif method == "lbfgs": - opt = torch.optim.LBFGS([x], lr=lr, max_iter=int(n_steps), line_search_fn="strong_wolfe") - - def closure() -> torch.Tensor: - opt.zero_grad(set_to_none=True) - loss = loss_fn() - loss.backward() - return loss - - opt.step(closure) - clamp_inplace() - else: - raise ValueError("method must be one of: 'lbfgs', 'adam'.") + else: + raise ValueError("method must be one of: 'lbfgs', 'adam'.") - with torch.no_grad(): - final_loss = float(loss_fn().detach().cpu()) - return x.detach().clone(), final_loss + with torch.no_grad(): + final_loss = float(loss_fn().detach().cpu()) + return x.detach().clone(), final_loss + finally: + if intensity_order is not None: + if old_order_override is None: + ctx.fields.pop("lattice_intensity_order_override", None) + else: + ctx.fields["lattice_intensity_order_override"] = old_order_override def _resolve_pattern_indices(self, indices: Any, n: int, index_shape: tuple[int, ...]) -> np.ndarray: if indices is None: @@ -523,8 +519,9 @@ def refine_mean_model( power: float | None = 1.0, fit_disk_pixels: bool | None = None, fit_only_disk_pixels: bool = False, - enforce_disk_max_one: bool = True, + intensity_order: int = 0, enforce_disk_center_of_mass: bool = True, + normalize_disk_template_max: bool = True, warmup_disk_steps: int = 0, overwrite_initial: bool = True, intensity_transform: str = "none", @@ -588,8 +585,9 @@ def refine_mean_model( power=power, fit_disk_pixels=True, fit_only_disk_pixels=True, - enforce_disk_max_one=bool(enforce_disk_max_one), + intensity_order=int(intensity_order), enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), + normalize_disk_template_max=bool(normalize_disk_template_max), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=bool(progress), @@ -604,8 +602,9 @@ def refine_mean_model( power=power, fit_disk_pixels=fit_disk_pixels, fit_only_disk_pixels=bool(fit_only_disk_pixels), - enforce_disk_max_one=bool(enforce_disk_max_one), + intensity_order=int(intensity_order), enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), + normalize_disk_template_max=bool(normalize_disk_template_max), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=bool(progress), @@ -630,8 +629,9 @@ def fit_all_patterns( power: float | None = 1.0, fit_disk_pixels: bool | None = None, fit_only_disk_pixels: bool = False, - enforce_disk_max_one: bool = True, + intensity_order: int | None = None, enforce_disk_center_of_mass: bool = True, + normalize_disk_template_max: bool = False, intensity_transform: str = "none", weak_softplus_scale: float = 1e-3, progress: bool = False, @@ -722,8 +722,9 @@ def fit_all_patterns( power=power, fit_disk_pixels=fit_disk_pixels, fit_only_disk_pixels=bool(fit_only_disk_pixels), - enforce_disk_max_one=bool(enforce_disk_max_one), + intensity_order=None if intensity_order is None else int(intensity_order), enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), + normalize_disk_template_max=bool(normalize_disk_template_max), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=False, @@ -830,8 +831,9 @@ def plot_mean_model( axes = [ax] for a in axes[:2]: - a.set_xlim(-pad, (W - 1) + pad) - a.set_ylim((H - 1) + pad, -pad) + # Match imshow's pixel-edge convention so overlay markers land on pixel centers. + a.set_xlim(-0.5 - pad, (W - 0.5) + pad) + a.set_ylim((H - 0.5) + pad, -0.5 - pad) if show_overlays: ovs = self.prepared.overlays(self.x_mean) From 73d12366dfbe50ea2d7f8c27705870175d45a4a2 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 22 Feb 2026 09:25:50 -0800 Subject: [PATCH 25/43] slight tweaks --- src/quantem/diffraction/model_fitting.py | 57 +++++++++++++++++++++--- 1 file changed, 51 insertions(+), 6 deletions(-) diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 5ed35999..e7728997 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -302,6 +302,8 @@ def _fit_target_image( intensity_order: int | None, enforce_disk_center_of_mass: bool, normalize_disk_template_max: bool, + template_binary_weight: float, + template_binary_power: float, intensity_transform: str, weak_softplus_scale: float, progress: bool = False, @@ -332,7 +334,11 @@ def _fit_target_image( # Group disk pixel parameters by disk template for optional projection. disk_param_groups: list[dict[str, Any]] = [] - if enforce_disk_center_of_mass or normalize_disk_template_max: + if ( + enforce_disk_center_of_mass + or normalize_disk_template_max + or float(template_binary_weight) > 0.0 + ): disk_templates = self.prepared.ctx.fields.get("disk_templates", {}) grouped: dict[str, list[tuple[int, int]]] = {} for p in self.prepared.params: @@ -348,6 +354,9 @@ def _fit_target_image( order = sorted(pairs, key=lambda t: t[0]) p_idx = torch.as_tensor([t[1] for t in order], device=ctx.device, dtype=torch.long) g: dict[str, Any] = {"param_idx": p_idx} + shape = dmeta.get("shape", None) + if shape is not None: + g["shape"] = (int(shape[0]), int(shape[1])) if enforce_disk_center_of_mass: flat_i = torch.as_tensor([t[0] for t in order], device=ctx.device, dtype=torch.long) g["dr"] = dmeta["dr"][flat_i] @@ -379,7 +388,10 @@ def clamp_inplace() -> None: x.data = torch.max(torch.min(x.data, ub), lb) if torch.any(freeze): x.data[freeze] = x_frozen[freeze] - if (enforce_disk_center_of_mass or normalize_disk_template_max) and disk_param_groups: + if ( + enforce_disk_center_of_mass + or normalize_disk_template_max + ) and disk_param_groups: eps = torch.as_tensor(1e-12, device=ctx.device, dtype=ctx.dtype) for g in disk_param_groups: p_idx = g["param_idx"] @@ -420,9 +432,32 @@ def loss_fn() -> torch.Tensor: m = ctx.mask diff = (pred - target_t) * m denom = torch.clamp(torch.sum(m), min=1.0) - return torch.sum(diff * diff) / denom - diff = pred - target_t - return torch.mean(diff * diff) + loss = torch.sum(diff * diff) / denom + else: + diff = pred - target_t + loss = torch.mean(diff * diff) + + if float(template_binary_weight) > 0.0 and disk_param_groups: + bp = torch.as_tensor(0.0, device=ctx.device, dtype=ctx.dtype) + n_bp = 0 + eps = torch.as_tensor(1e-12, device=ctx.device, dtype=ctx.dtype) + pwr = float(template_binary_power) + for g in disk_param_groups: + p_idx = g["param_idx"] + vals = torch.clamp(x[p_idx], min=0.0) + vmax = torch.max(vals) + if vmax <= eps: + continue + vals_n = vals / (vmax + eps) + core = vals_n * (1.0 - vals_n) + if pwr != 1.0: + core = torch.pow(core + eps, pwr) + bp = bp + torch.mean(core) + n_bp += 1 + if n_bp > 0: + loss = loss + float(template_binary_weight) * (bp / n_bp) + + return loss try: if method == "adam": @@ -521,7 +556,9 @@ def refine_mean_model( fit_only_disk_pixels: bool = False, intensity_order: int = 0, enforce_disk_center_of_mass: bool = True, - normalize_disk_template_max: bool = True, + normalize_disk_template_max: bool = False, + template_binary_weight: float = 0.0, + template_binary_power: float = 1.0, warmup_disk_steps: int = 0, overwrite_initial: bool = True, intensity_transform: str = "none", @@ -588,6 +625,8 @@ def refine_mean_model( intensity_order=int(intensity_order), enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), normalize_disk_template_max=bool(normalize_disk_template_max), + template_binary_weight=float(template_binary_weight), + template_binary_power=float(template_binary_power), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=bool(progress), @@ -605,6 +644,8 @@ def refine_mean_model( intensity_order=int(intensity_order), enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), normalize_disk_template_max=bool(normalize_disk_template_max), + template_binary_weight=float(template_binary_weight), + template_binary_power=float(template_binary_power), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=bool(progress), @@ -632,6 +673,8 @@ def fit_all_patterns( intensity_order: int | None = None, enforce_disk_center_of_mass: bool = True, normalize_disk_template_max: bool = False, + template_binary_weight: float = 0.0, + template_binary_power: float = 1.0, intensity_transform: str = "none", weak_softplus_scale: float = 1e-3, progress: bool = False, @@ -725,6 +768,8 @@ def fit_all_patterns( intensity_order=None if intensity_order is None else int(intensity_order), enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), normalize_disk_template_max=bool(normalize_disk_template_max), + template_binary_weight=float(template_binary_weight), + template_binary_power=float(template_binary_power), intensity_transform=intensity_transform, weak_softplus_scale=float(weak_softplus_scale), progress=False, From 57563fbadb986e71cdd1d5e86f4269c302802eab Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Feb 2026 16:00:11 -0800 Subject: [PATCH 26/43] dataset4dstem.from_file consistent with read_4dstem args --- src/quantem/core/datastructures/dataset4dstem.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index dd92b914..ed66f6a2 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -1,3 +1,4 @@ +from os import PathLike from typing import Any, Self import matplotlib.pyplot as plt @@ -8,7 +9,6 @@ from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset4d import Dataset4d from quantem.core.datastructures.polar4dstem import dataset4dstem_polar_transform - from quantem.core.utils.validators import ensure_valid_array from quantem.core.visualization import show_2d from quantem.core.visualization.visualization_utils import ScalebarConfig @@ -74,7 +74,7 @@ def __init__( _token : object | None, optional Token to prevent direct instantiation, by default None """ - mdata_keys_4dstem = ["q_to_r_rotation_ccw_deg", 'q_transpose', "ellipticity"] + mdata_keys_4dstem = ["q_to_r_rotation_ccw_deg", "q_transpose", "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): metadata[k] = None @@ -93,13 +93,13 @@ def __init__( self._virtual_detectors = {} # Store detector information for regeneration @classmethod - def from_file(cls, file_path: str, file_type: str) -> "Dataset4dstem": + def from_file(cls, file_path: str | PathLike, file_type: str | None = None) -> "Dataset4dstem": """ Create a new Dataset4dstem from a file. Parameters ---------- - file_path : str + file_path : str | PathLike Path to the data file file_type : str The type of file reader needed. See rosettasciio for supported formats @@ -754,5 +754,4 @@ def median_filter_masked_pixels(self, mask: np.ndarray, kernel_width: int = 3): self.array[:, :, x_min:x_max, y_min:y_max], axis=(2, 3) ) - - polar_transform = dataset4dstem_polar_transform \ No newline at end of file + polar_transform = dataset4dstem_polar_transform From b2f5253165eab2aa6c67c785434bb4b2ad1fc4b8 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Thu, 26 Feb 2026 14:08:41 -0800 Subject: [PATCH 27/43] refactor in progress, base and rendering largely done, working not perfect --- src/quantem/core/fitting/__init__.py | 6 - src/quantem/core/fitting/background.py | 146 ++-- src/quantem/core/fitting/base.py | 293 +------ src/quantem/core/fitting/diffraction.py | 774 ++++++------------ src/quantem/diffraction/model_fitting.py | 994 ++++++----------------- 5 files changed, 605 insertions(+), 1608 deletions(-) diff --git a/src/quantem/core/fitting/__init__.py b/src/quantem/core/fitting/__init__.py index 5aa67459..f7d3c3e3 100644 --- a/src/quantem/core/fitting/__init__.py +++ b/src/quantem/core/fitting/__init__.py @@ -3,12 +3,9 @@ from quantem.core.fitting.base import Component as Component from quantem.core.fitting.base import Model as Model from quantem.core.fitting.base import ModelContext as ModelContext -from quantem.core.fitting.base import Overlay as Overlay from quantem.core.fitting.base import Parameter as Parameter -from quantem.core.fitting.base import PreparedModel as PreparedModel from quantem.core.fitting.diffraction import DiskTemplate as DiskTemplate -from quantem.core.fitting.diffraction import Origin2D as Origin2D from quantem.core.fitting.diffraction import SyntheticDiskLattice as SyntheticDiskLattice from quantem.core.fitting.background import DCBackground as DCBackground @@ -16,12 +13,9 @@ __all__ = [ "Parameter", - "Overlay", "ModelContext", "Component", "Model", - "PreparedModel", - "Origin2D", "DiskTemplate", "SyntheticDiskLattice", "DCBackground", diff --git a/src/quantem/core/fitting/background.py b/src/quantem/core/fitting/background.py index 2db88616..7c50ea42 100644 --- a/src/quantem/core/fitting/background.py +++ b/src/quantem/core/fitting/background.py @@ -1,107 +1,87 @@ from __future__ import annotations -"""Background components for diffraction model fitting.""" - -from dataclasses import dataclass -from typing import Any, Iterable +from typing import Sequence, cast +import numpy as np import torch +from torch import nn + +from quantem.core.fitting.base import RenderComponent, RenderContext +from quantem.core.fitting.diffraction import OriginND + -from quantem.core.fitting.base import Component, ModelContext, Overlay, Parameter -from quantem.core.fitting.diffraction import _origin_indices +def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) -> float: + if isinstance(value, (list, tuple, np.ndarray)): + if len(value) == 0: + raise ValueError(f"{name} cannot be empty.") + if value[0] is None: + raise ValueError(f"{name} initial value cannot be None.") + return float(value[0]) + return float(cast(float | int, value)) -class DCBackground(Component): - """ - Uniform additive background with nonnegative intensity. +def _softplus_pos(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: + return torch.nn.functional.softplus(x) + eps - Parameters - ---------- - intensity - Constant background intensity parameter specification. - name - Component name. - """ +def _relu_pos(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: + return torch.nn.functional.relu(x) + eps + + +class DCBackground(RenderComponent): def __init__( self, *, - intensity: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + intensity: float | int | Sequence[float | int | None] = 0.0, name: str = "dc_background", ): - super().__init__(name=name) - self.p_intensity = Parameter(intensity, lower_bound=0.0, tags={"role": "dc_bg"}) - - def parameters(self) -> list[Parameter]: - return [self.p_intensity] - - def prepare(self, ctx: ModelContext) -> Any: - idx = self.p_intensity.index - - @dataclass - class Prepared: - def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - out.add_(x[idx]) - - def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: - return [] - - return Prepared() - + super().__init__() + self.name = str(name) + self.intensity_raw = nn.Parameter( + torch.tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) + ) -class GaussianBackground(Component): - """ - Radial Gaussian background centered at a named model origin. + def forward(self, ctx: RenderContext) -> torch.Tensor: + inten = _relu_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + # inten = _softplus_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + return torch.ones(ctx.shape, device=ctx.device, dtype=ctx.dtype) * inten - Parameters - ---------- - sigma - Gaussian width parameter specification (constrained to `>= 1e-6`). - intensity - Nonnegative Gaussian amplitude parameter specification. - origin_key - Name of the origin to use from `ModelContext.fields["origins"]`. - name - Component name. - """ +class GaussianBackground(RenderComponent): def __init__( self, *, - sigma: float | tuple[float, float] | tuple[float, float, float | None] = (40.0, 5.0, None), - intensity: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + sigma: float | int | Sequence[float | int | None] = (40.0, 5.0, None), + intensity: float | int | Sequence[float | int | None] = 0.0, + origin: OriginND | None = None, origin_key: str = "origin", name: str = "gaussian_background", ): - super().__init__(name=name) + super().__init__() + self.name = str(name) + self.origin = origin self.origin_key = str(origin_key) - self.p_sigma = Parameter(sigma, lower_bound=1e-6, upper_bound=None, tags={"role": "gauss_sigma"}) - self.p_intensity = Parameter(intensity, lower_bound=0.0, tags={"role": "gauss_int"}) - - def parameters(self) -> list[Parameter]: - return [self.p_sigma, self.p_intensity] - - def prepare(self, ctx: ModelContext) -> Any: - i_sig = self.p_sigma.index - i_int = self.p_intensity.index - r_idx, c_idx = _origin_indices(ctx, self.origin_key) - - rr = torch.arange(ctx.H, device=ctx.device, dtype=ctx.dtype)[:, None] - cc = torch.arange(ctx.W, device=ctx.device, dtype=ctx.dtype)[None, :] - - @dataclass - class Prepared: - def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - sig = torch.clamp(x[i_sig], min=1e-6) - inten = x[i_int] - r0 = x[r_idx] - c0 = x[c_idx] - dr = rr - r0 - dc = cc - c0 - r2 = dr * dr + dc * dc - out.add_(inten * torch.exp(-0.5 * r2 / (sig * sig))) - - def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: - return [] - - return Prepared() + self.sigma_raw = nn.Parameter( + torch.tensor(_parse_init(sigma, name="sigma"), dtype=torch.float32) + ) + self.intensity_raw = nn.Parameter( + torch.tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) + ) + + def set_origin(self, origin: OriginND) -> None: + self.origin = origin + + def forward(self, ctx: RenderContext) -> torch.Tensor: + if self.origin is None: + raise RuntimeError("GaussianBackground requires an OriginND instance.") + + rr = torch.arange(ctx.shape[0], device=ctx.device, dtype=ctx.dtype)[:, None] + cc = torch.arange(ctx.shape[1], device=ctx.device, dtype=ctx.dtype)[None, :] + r0, c0 = self.origin.coords[0], self.origin.coords[1] + + sigma = _relu_pos(self.sigma_raw.to(device=ctx.device, dtype=ctx.dtype), eps=1e-6) + # sigma = _softplus_pos(self.sigma_raw.to(device=ctx.device, dtype=ctx.dtype), eps=1e-6) + inten = _relu_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + # inten = _softplus_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + r2 = (rr - r0) ** 2 + (cc - c0) ** 2 + return inten * torch.exp(-0.5 * r2 / (sigma * sigma)) diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index 53a4c0aa..c37ccef9 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -1,273 +1,54 @@ from __future__ import annotations -"""Composable model primitives for diffraction-style forward models.""" +from dataclasses import dataclass, field +from typing import Any, cast -from dataclasses import dataclass -from typing import Any, Iterable, Mapping, Sequence - -import numpy as np import torch +from torch import nn -@dataclass(frozen=True) -class Overlay: - """Lightweight plotting overlay emitted by prepared components.""" - - kind: str - data: dict[str, Any] - - -class Parameter: - """ - Scalar model parameter with optional bounds and metadata tags. - - Parameters - ---------- - value - Initial parameter specification. Supported forms: - - scalar: `v0` - - 2-sequence: `(v0, dev)` interpreted as bounds `(v0-dev, v0+dev)` - - 3-sequence: `(v0, lb, ub)` where either bound can be None - lower_bound, upper_bound - Optional explicit overrides for lower/upper bound. - tags - Optional metadata dictionary used by higher-level fitting code for - grouping/freezing/selecting parameters. - - Notes - ----- - Parameter indices are assigned during `Model.compile(...)`. - """ - - def __init__( - self, - value: float | Sequence[float], - *, - lower_bound: float | None = None, - upper_bound: float | None = None, - tags: Mapping[str, Any] | None = None, - ): - self.tags: dict[str, Any] = {} if tags is None else dict(tags) - self.initial_value, self.lower_bound, self.upper_bound = self._parse_value( - value, lower_bound, upper_bound - ) - self._index: int | None = None - - @staticmethod - def _parse_value( - value: float | Sequence[float], - lower_bound: float | None, - upper_bound: float | None, - ) -> tuple[float, float | None, float | None]: - if isinstance(value, (list, tuple, np.ndarray)): - v = list(value) - if len(v) == 2: - v0 = float(v[0]) - dev = float(v[1]) - lb = v0 - dev if lower_bound is None else float(lower_bound) - ub = v0 + dev if upper_bound is None else float(upper_bound) - return v0, lb, ub - if len(v) == 3: - v0 = float(v[0]) - lb = None if v[1] is None else float(v[1]) - ub = None if v[2] is None else float(v[2]) - if lower_bound is not None: - lb = float(lower_bound) - if upper_bound is not None: - ub = float(upper_bound) - return v0, lb, ub - raise ValueError("Parameter sequences must have length 2 or 3.") - v0 = float(value) - return v0, lower_bound, upper_bound - - def bind(self, index: int) -> None: - self._index = int(index) - - @property - def index(self) -> int: - if self._index is None: - raise RuntimeError("Parameter is not bound. Call Model.compile(...) first.") - return self._index - - -class ModelContext: - """ - Execution context shared by components during preparation and rendering. - - Parameters - ---------- - H, W - Output image height and width. - device, dtype - Torch device and dtype for parameter tensors/rendering. - mask - Optional per-pixel mask used by fitting loss functions. - fields - Mutable shared dictionary used by components to publish/consume - prepared state (for example origins or template metadata). - """ - - def __init__( - self, - *, - H: int, - W: int, - device: torch.device, - dtype: torch.dtype, - mask: torch.Tensor | None = None, - fields: Mapping[str, Any] | None = None, - ): - self.H = int(H) - self.W = int(W) - self.device = device - self.dtype = dtype - self.mask = mask - self.fields: dict[str, Any] = {} if fields is None else dict(fields) - - def __getattr__(self, name: str) -> Any: - if name in self.fields: - return self.fields[name] - raise AttributeError(name) - - -class Component: - """ - Abstract base class for additive model components. +@dataclass +class RenderContext: + shape: tuple[int, ...] + device: torch.device + dtype: torch.dtype + mask: torch.Tensor | None = None + fields: dict[str, Any] = field(default_factory=dict) - Subclasses implement: - - `parameters()` to expose fit parameters - - `prepare(ctx)` to build prepared render objects - """ - def __init__(self, *, name: str): - self.name = str(name) - - def parameters(self) -> list[Parameter]: - return [] - - def prepare(self, ctx: ModelContext) -> Any: +class RenderComponent(nn.Module): + def forward(self, ctx: RenderContext) -> torch.Tensor: raise NotImplementedError + def constraint_loss(self, ctx: RenderContext) -> torch.Tensor: + return torch.zeros((), device=ctx.device, dtype=ctx.dtype) -class PreparedModel: - """ - Compiled model bundle with bound parameter vectors and render helpers. - - Attributes - ---------- - x0, lb, ub - Initial parameter vector and bound vectors, all on `ctx.device`. - components - Prepared component objects called in sequence during `render`. - """ - def __init__( - self, - *, - components: list[Any], - ctx: ModelContext, - params: list[Parameter], - x0: torch.Tensor, - lb: torch.Tensor, - ub: torch.Tensor, - ): - self.components = components - self.ctx = ctx - self.params = params - self.x0 = x0 - self.lb = lb - self.ub = ub +class AdditiveRenderModel(nn.Module): + def __init__(self, *, origin: nn.Module, components: list[RenderComponent]): + super().__init__() + self.origin = origin + self.components = nn.ModuleList(components) - def render(self, x: torch.Tensor) -> torch.Tensor: - """Render a model image for parameter vector ``x``.""" - out = torch.zeros((self.ctx.H, self.ctx.W), device=self.ctx.device, dtype=self.ctx.dtype) - for c in self.components: - c.render(out, x, self.ctx) + def forward(self, ctx: RenderContext) -> torch.Tensor: + if len(self.components) == 0: + return torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) + first = cast(RenderComponent, self.components[0]) + out = first(ctx) + for module in self.components[1:]: + component = cast(RenderComponent, module) + out = out + component(ctx) return out - def render_initial(self) -> torch.Tensor: - """Render the model using the compile-time initial parameter vector.""" - return self.render(self.x0) - - def overlays(self, x: torch.Tensor | None = None) -> list[Overlay]: - """Collect component overlays for plotting/debugging.""" - out: list[Overlay] = [] - for c in self.components: - fn = getattr(c, "overlays", None) - if fn is None: - continue - ov = fn(self.x0 if x is None else x, self.ctx) - if ov: - out.extend(list(ov)) - return out - - -class Model: - """ - Container for additive components that compiles to a `PreparedModel`. - - Notes - ----- - Component order defines render order and can also define dependency order - when components share prepared state through `ModelContext.fields`. - """ - - def __init__(self): - self._components: list[Component] = [] - - def add(self, items: Iterable[Component]) -> "Model": - """ - Append components to the model in render order. - - Parameters - ---------- - items - Iterable of `Component` instances. - """ - for obj in items: - if not isinstance(obj, Component): - raise TypeError("Model.add expects Component objects.") - self._components.append(obj) - return self - - def parameter_list(self) -> list[Parameter]: - """Return the flattened parameter list across all components.""" - params: list[Parameter] = [] - for c in self._components: - params.extend(c.parameters()) - return params - - def compile(self, ctx: ModelContext) -> PreparedModel: - """ - Bind parameter indices and prepare components for fast rendering. - - Parameters - ---------- - ctx - Model execution context. - - Returns - ------- - PreparedModel - Compiled model with `x0`, bounds, and prepared component state. - """ - params = self.parameter_list() - for i, p in enumerate(params): - p.bind(i) - - x0 = torch.zeros((len(params),), device=ctx.device, dtype=ctx.dtype) - lb = torch.full((len(params),), -torch.inf, device=ctx.device, dtype=ctx.dtype) - ub = torch.full((len(params),), torch.inf, device=ctx.device, dtype=ctx.dtype) - - for p in params: - x0[p.index] = torch.as_tensor(p.initial_value, device=ctx.device, dtype=ctx.dtype) - if p.lower_bound is not None: - lb[p.index] = torch.as_tensor(p.lower_bound, device=ctx.device, dtype=ctx.dtype) - if p.upper_bound is not None: - ub[p.index] = torch.as_tensor(p.upper_bound, device=ctx.device, dtype=ctx.dtype) + def total_constraint_loss(self, ctx: RenderContext) -> torch.Tensor: + loss = torch.zeros((), device=ctx.device, dtype=ctx.dtype) + for module in self.components: + component = cast(RenderComponent, module) + loss = loss + component.constraint_loss(ctx) + return loss - prepared_components: list[Any] = [] - for c in self._components: - prepared_components.append(c.prepare(ctx)) - return PreparedModel(components=prepared_components, ctx=ctx, params=params, x0=x0, lb=lb, ub=ub) +Component = RenderComponent +ModelContext = RenderContext +Model = AdditiveRenderModel +Parameter = nn.Parameter diff --git a/src/quantem/core/fitting/diffraction.py b/src/quantem/core/fitting/diffraction.py index 78a45c14..13224632 100644 --- a/src/quantem/core/fitting/diffraction.py +++ b/src/quantem/core/fitting/diffraction.py @@ -1,30 +1,30 @@ from __future__ import annotations -"""Diffraction-specific model components and low-level rendering helpers.""" - -from dataclasses import dataclass -from typing import Any, Iterable, Sequence +from typing import Iterable, Sequence, cast import numpy as np import torch +from torch import nn + +from quantem.core.fitting.base import RenderComponent, RenderContext + -from quantem.core.fitting.base import Component, ModelContext, Overlay, Parameter +def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) -> float: + if isinstance(value, (list, tuple, np.ndarray)): + if len(value) == 0: + raise ValueError(f"{name} cannot be empty.") + if value[0] is None: + raise ValueError(f"{name} initial value cannot be None.") + return float(value[0]) + return float(cast(float | int, value)) -def _as_t(x: Any, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor: - """ - Convert values to tensors on the requested device/dtype. +def _softplus_pos(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: + return torch.nn.functional.softplus(x) + eps - Parameters - ---------- - x - Input scalar/array/tensor. - device, dtype - Target torch device and dtype. - """ - if torch.is_tensor(x): - return x.to(device=device, dtype=dtype) - return torch.as_tensor(x, device=device, dtype=dtype) + +def _relu_pos(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: + return torch.nn.functional.relu(x) + eps def _splat_patch( @@ -37,34 +37,14 @@ def _splat_patch( dc: torch.Tensor, scale: torch.Tensor, ) -> None: - """ - Render a shifted patch into ``out`` with bilinear interpolation. - - Parameters - ---------- - out - Destination image `(H, W)` updated in-place. - r0, c0 - Patch center coordinates in output-image row/column space. - patch_vals - Flattened patch intensities. - dr, dc - Flattened local row/column offsets for each patch sample. - scale - Scalar or vector multiplier applied to `patch_vals`. - """ - H = out.shape[0] - W = out.shape[1] - + h, w = out.shape r = r0 + dr c = c0 + dc r_base = torch.floor(r) c_base = torch.floor(c) - fr = r - r_base fc = c - c_base - r0i = r_base.to(torch.long) c0i = c_base.to(torch.long) @@ -72,13 +52,12 @@ def _splat_patch( w01 = (1.0 - fr) * fc w10 = fr * (1.0 - fc) w11 = fr * fc - v = patch_vals * scale def put(rr: torch.Tensor, cc: torch.Tensor, ww: torch.Tensor) -> None: - m = (rr >= 0) & (rr < H) & (cc >= 0) & (cc < W) - if torch.any(m): - out.index_put_((rr[m], cc[m]), (v[m] * ww[m]), accumulate=True) + keep = (rr >= 0) & (rr < h) & (cc >= 0) & (cc < w) + if torch.any(keep): + out.index_put_((rr[keep], cc[keep]), v[keep] * ww[keep], accumulate=True) put(r0i, c0i, w00) put(r0i, c0i + 1, w01) @@ -86,111 +65,18 @@ def put(rr: torch.Tensor, cc: torch.Tensor, ww: torch.Tensor) -> None: put(r0i + 1, c0i + 1, w11) -def _origin_indices(ctx: ModelContext, origin_key: str) -> tuple[int, int]: - """ - Resolve origin parameter indices from ``ModelContext``. - - Parameters - ---------- - ctx - Model context with an `origins` registry in `ctx.fields`. - origin_key - Origin name to resolve. - """ - origins: dict[str, Any] = ctx.fields.get("origins", {}) - o = origins.get(origin_key) - if o is None or "row_param" not in o or "col_param" not in o: - raise RuntimeError(f"Origin '{origin_key}' not defined. Origin2D must be included first.") - return int(o["row_param"].index), int(o["col_param"].index) - - -class Origin2D(Component): - """ - Named 2D origin component that exposes row/column fit parameters. - - Parameters - ---------- - name - Component name. - origin_key - Key used to publish this origin into `ctx.fields["origins"]`. - row, col - Parameter specifications for origin row/column. - """ +class OriginND(nn.Module): + def __init__(self, *, ndim: int, init: Sequence[float]): + super().__init__() + if int(ndim) <= 0: + raise ValueError("ndim must be >= 1.") + if len(init) != int(ndim): + raise ValueError("init length must match ndim.") + self.ndim = int(ndim) + self.coords = nn.Parameter(torch.as_tensor(init, dtype=torch.float32).reshape(self.ndim)) - def __init__( - self, - *, - name: str = "origin", - origin_key: str = "origin", - row: float | tuple[float, float] | tuple[float, float, float | None] = (0.0, 0.0, None), - col: float | tuple[float, float] | tuple[float, float, float | None] = (0.0, 0.0, None), - ): - super().__init__(name=name) - self.origin_key = str(origin_key) - self.p_row = Parameter(row, tags={"role": "origin_row", "origin_key": self.origin_key}) - self.p_col = Parameter(col, tags={"role": "origin_col", "origin_key": self.origin_key}) - - def parameters(self) -> list[Parameter]: - return [self.p_row, self.p_col] - - def prepare(self, ctx: ModelContext) -> Any: - origins = ctx.fields.setdefault("origins", {}) - origins[self.origin_key] = {"row_param": self.p_row, "col_param": self.p_col} - - i_r = self.p_row.index - i_c = self.p_col.index - - @dataclass - class Prepared: - def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - return - - def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: - return [ - Overlay( - kind="points_rc", - data={ - "r": x[i_r].detach(), - "c": x[i_c].detach(), - "marker": "+", - "s": 100.0, - "color": "dodgerblue", - }, - ) - ] - - return Prepared() - - -class DiskTemplate(Component): - """ - Template patch component used for central disks and lattice motifs. - - Parameters - ---------- - name - Template name used for registry lookup by dependent components. - array - 2D template image. - refine_all_pixels - If True, every template pixel is exposed as a fit parameter. - place_at_origin - If True, render this template once at the named origin with a - separate nonnegative intensity parameter. - normalize - Optional array normalization mode: `"none"`, `"max"`, `"mean"`. - origin_key - Origin key used when `place_at_origin=True`. - intensity - Intensity parameter specification used when `place_at_origin=True`. - - Notes - ----- - The template is also published into `ctx.fields["disk_templates"]` so - lattice components can reuse its prepared sampling offsets. - """ +class DiskTemplate(RenderComponent): def __init__( self, *, @@ -199,48 +85,47 @@ def __init__( refine_all_pixels: bool = False, place_at_origin: bool = False, normalize: str = "none", + origin: OriginND | None = None, origin_key: str = "origin", - intensity: float | tuple[float, float] | tuple[float, float, float | None] = 1.0, + intensity: float | Sequence[float] = 1.0, ): - super().__init__(name=name) + super().__init__() + self.name = str(name) + self.refine_all_pixels = bool(refine_all_pixels) + self.place_at_origin = bool(place_at_origin) + self.origin = origin + self.origin_key = str(origin_key) a = np.asarray(array, dtype=np.float32) if a.ndim != 2: raise ValueError("DiskTemplate.array must be 2D.") - if normalize == "max": - mx = float(np.max(a)) - if mx > 0: - a = a / mx + s = float(np.max(a)) + if s > 0.0: + a = a / s elif normalize == "mean": - m = float(np.mean(a)) - if m != 0: - a = a / m - elif normalize == "none": - pass - else: + s = float(np.mean(a)) + if s != 0.0: + a = a / s + elif normalize != "none": raise ValueError("normalize must be one of: 'none', 'max', 'mean'.") - self.array = a - self.refine_all_pixels = bool(refine_all_pixels) - self.place_at_origin = bool(place_at_origin) - self.origin_key = str(origin_key) - - self.p_pixels: list[Parameter] = [] - if self.refine_all_pixels: - flat = self.array.ravel() - for i, v in enumerate(flat): - self.p_pixels.append( - Parameter( - float(v), - lower_bound=0.0, - tags={"role": "disk_pixel", "disk": self.name, "i": int(i)}, - ) - ) + template = torch.as_tensor(a, dtype=torch.float32) + self.template_raw = nn.Parameter(template.clone(), requires_grad=self.refine_all_pixels) - self.p_intensity: Parameter | None = None if self.place_at_origin: - self.p_intensity = Parameter(intensity, lower_bound=0.0, tags={"role": "disk_intensity", "disk": self.name}) + self.intensity_raw = nn.Parameter( + torch.as_tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) + ) + else: + self.intensity_raw = None + + ht, wt = int(template.shape[0]), int(template.shape[1]) + rr, cc = np.mgrid[0:ht, 0:wt] + rr = rr.astype(np.float32) - (ht - 1) * 0.5 + cc = cc.astype(np.float32) - (wt - 1) * 0.5 + self.register_buffer("dr", torch.as_tensor(rr.ravel(), dtype=torch.float32)) + self.register_buffer("dc", torch.as_tensor(cc.ravel(), dtype=torch.float32)) @classmethod def from_array( @@ -251,8 +136,9 @@ def from_array( refine_all_pixels: bool = False, place_at_origin: bool = False, normalize: str = "none", + origin: OriginND | None = None, origin_key: str = "origin", - intensity: float | tuple[float, float] | tuple[float, float, float | None] = 1.0, + intensity: float | Sequence[float] = 1.0, ) -> "DiskTemplate": return cls( name=name, @@ -260,396 +146,234 @@ def from_array( refine_all_pixels=refine_all_pixels, place_at_origin=place_at_origin, normalize=normalize, + origin=origin, origin_key=origin_key, intensity=intensity, ) - def parameters(self) -> list[Parameter]: - out = list(self.p_pixels) - if self.p_intensity is not None: - out.append(self.p_intensity) - return out - - def prepare(self, ctx: ModelContext) -> Any: - device = ctx.device - dtype = ctx.dtype - - Ht, Wt = self.array.shape - rr, cc = np.mgrid[0:Ht, 0:Wt] - rr = rr.astype(np.float32) - (Ht - 1) * 0.5 - cc = cc.astype(np.float32) - (Wt - 1) * 0.5 - - dr = _as_t(rr.ravel(), device=device, dtype=dtype) - dc = _as_t(cc.ravel(), device=device, dtype=dtype) + def set_origin(self, origin: OriginND) -> None: + self.origin = origin + def patch_values(self) -> torch.Tensor: if self.refine_all_pixels: - pix_idx = torch.as_tensor([p.index for p in self.p_pixels], device=device, dtype=torch.long) - base_vals = None - else: - pix_idx = None - base_vals = _as_t(self.array.ravel(), device=device, dtype=dtype) - - ctx.fields.setdefault("disk_templates", {})[self.name] = { - "dr": dr, - "dc": dc, - "pix_idx": pix_idx, - "base": base_vals, - "shape": (int(Ht), int(Wt)), - } - - i_int = None if self.p_intensity is None else self.p_intensity.index - if i_int is None: - r_idx = c_idx = None - else: - r_idx, c_idx = _origin_indices(ctx, self.origin_key) - - @dataclass - class Prepared: - def patch(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - if pix_idx is None: - vals = base_vals - else: - vals = x[pix_idx] - return vals, dr, dc - - def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - if i_int is None: - return - vals, drl, dcl = self.patch(x) - r0 = x[r_idx] - c0 = x[c_idx] - _splat_patch(out, r0=r0, c0=c0, patch_vals=vals, dr=drl, dc=dcl, scale=x[i_int]) - - def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: - return [] - - return Prepared() - - -class SyntheticDiskLattice(Component): - """ - Lattice of shifted ``DiskTemplate`` patches around a shared origin. - - Parameters - ---------- - name - Component name. - disk - `DiskTemplate` used as the motif for each lattice spot. - u_row, u_col, v_row, v_col - Basis vector parameter specifications in row/column coordinates. - u_max, v_max - Inclusive lattice index extents. - intensity_0, intensity_row, intensity_col - Intensity parameter specifications. Interpretation depends on - `per_disk_intensity` and intensity-order settings. - intensity_row_row, intensity_col_col, intensity_row_col - Optional quadratic intensity terms for order-2 models. - per_disk_intensity - If True, allocate independent base intensities per lattice disk. - per_disk_slopes - Backward-compatible switch controlling whether linear terms are included - when `max_intensity_order` is not explicitly provided. - max_intensity_order - Maximum polynomial order used by this component (`0`, `1`, or `2`). - default_pattern_intensity_order - Default active intensity order used during rendering unless overridden - in `ctx.fields["lattice_intensity_order_override"]`. - center_intensity_0 - Optional override for the `(u, v) == (0, 0)` disk base intensity. - exclude_indices - Optional set/list of lattice indices to skip. - boundary_px - Keep only lattice centers within `[boundary_px, size-1-boundary_px]`. - origin_key - Origin key used for center placement. - - Notes - ----- - Intensity models: - - shared mode (`per_disk_intensity=False`): - `max(i0 + ir*row_center + ic*col_center, 0)` - - per-disk scalar mode (`per_disk_intensity=True`, order=0): - `max(i0_i, 0)` - - per-disk local affine mode (`per_disk_intensity=True`, order=1): - `max(i0_i + ir_i*dr + ic_i*dc, 0)` where `dr/dc` are template-local offsets. - - per-disk local quadratic mode (`per_disk_intensity=True`, order=2): - `max(i0_i + ir_i*dr + ic_i*dc + irr_i*dr^2 + icc_i*dc^2 + irc_i*dr*dc, 0)`. - """ + return _relu_pos(self.template_raw).reshape(-1) + # return _softplus_pos(self.template_raw).reshape(-1) + return self.template_raw.reshape(-1) + + def patch_offsets(self) -> tuple[torch.Tensor, torch.Tensor]: + return cast(torch.Tensor, self.dr), cast(torch.Tensor, self.dc) + + def add_patch( + self, out: torch.Tensor, *, r0: torch.Tensor, c0: torch.Tensor, scale: torch.Tensor + ) -> None: + vals = self.patch_values().to(device=out.device, dtype=out.dtype) + dr = cast(torch.Tensor, self.dr).to(device=out.device, dtype=out.dtype) + dc = cast(torch.Tensor, self.dc).to(device=out.device, dtype=out.dtype) + _splat_patch(out, r0=r0, c0=c0, patch_vals=vals, dr=dr, dc=dc, scale=scale) + + def forward(self, ctx: RenderContext) -> torch.Tensor: + out = torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) + if not self.place_at_origin: + return out + if self.origin is None: + raise RuntimeError( + "DiskTemplate with place_at_origin=True requires an OriginND instance." + ) + r0, c0 = self.origin.coords[0], self.origin.coords[1] + if self.intensity_raw is None: + raise RuntimeError("DiskTemplate intensity parameter is missing.") + scale = _relu_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + # scale = _softplus_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + self.add_patch(out, r0=r0, c0=c0, scale=scale) + return out + +class SyntheticDiskLattice(RenderComponent): def __init__( self, *, name: str, disk: DiskTemplate, - u_row: float | tuple[float, float] | tuple[float, float, float | None], - u_col: float | tuple[float, float] | tuple[float, float, float | None], - v_row: float | tuple[float, float] | tuple[float, float, float | None], - v_col: float | tuple[float, float] | tuple[float, float, float | None], + u_row: float | Sequence[float], + u_col: float | Sequence[float], + v_row: float | Sequence[float], + v_col: float | Sequence[float], u_max: int = 0, v_max: int = 0, - intensity_0: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, - intensity_row: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, - intensity_col: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, - intensity_row_row: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, - intensity_col_col: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, - intensity_row_col: float | tuple[float, float] | tuple[float, float, float | None] = 0.0, + intensity_0: float | Sequence[float] = 0.0, + intensity_row: float | Sequence[float] = 0.0, + intensity_col: float | Sequence[float] = 0.0, + intensity_row_row: float | Sequence[float] = 0.0, + intensity_col_col: float | Sequence[float] = 0.0, + intensity_row_col: float | Sequence[float] = 0.0, per_disk_intensity: bool = False, per_disk_slopes: bool = True, max_intensity_order: int | None = None, default_pattern_intensity_order: int | None = None, - center_intensity_0: float | tuple[float, float] | tuple[float, float, float | None] | None = None, + center_intensity_0: float | Sequence[float] | None = None, exclude_indices: Iterable[tuple[int, int]] | None = None, boundary_px: float = 0.0, + origin: OriginND | None = None, origin_key: str = "origin", ): - super().__init__(name=name) + super().__init__() + self.name = str(name) self.disk = disk + self.origin = origin + self.origin_key = str(origin_key) + self.per_disk_intensity = bool(per_disk_intensity) self.u_max = int(u_max) self.v_max = int(v_max) self.boundary_px = float(boundary_px) - self.origin_key = str(origin_key) - self.per_disk_intensity = bool(per_disk_intensity) + if max_intensity_order is None: - self.max_intensity_order = 1 if bool(per_disk_slopes) else 0 - else: - self.max_intensity_order = int(max_intensity_order) + max_intensity_order = 1 if bool(per_disk_slopes) else 0 + self.max_intensity_order = int(max_intensity_order) if self.max_intensity_order < 0 or self.max_intensity_order > 2: raise ValueError("max_intensity_order must be 0, 1, or 2.") + if default_pattern_intensity_order is None: - self.default_pattern_intensity_order = int(self.max_intensity_order) - else: - self.default_pattern_intensity_order = int(default_pattern_intensity_order) - if self.default_pattern_intensity_order < 0 or self.default_pattern_intensity_order > self.max_intensity_order: - raise ValueError("default_pattern_intensity_order must be in [0, max_intensity_order].") + default_pattern_intensity_order = self.max_intensity_order + self.default_pattern_intensity_order = int(default_pattern_intensity_order) + + self.u_row = nn.Parameter( + torch.tensor(_parse_init(u_row, name="u_row"), dtype=torch.float32) + ) + self.u_col = nn.Parameter( + torch.tensor(_parse_init(u_col, name="u_col"), dtype=torch.float32) + ) + self.v_row = nn.Parameter( + torch.tensor(_parse_init(v_row, name="v_row"), dtype=torch.float32) + ) + self.v_col = nn.Parameter( + torch.tensor(_parse_init(v_col, name="v_col"), dtype=torch.float32) + ) if exclude_indices is None: - self.exclude_indices = {(0, 0)} if bool(getattr(disk, "place_at_origin", False)) else set() + exclude = {(0, 0)} if bool(getattr(disk, "place_at_origin", False)) else set() else: - self.exclude_indices = set(exclude_indices) - + exclude = set(exclude_indices) uv: list[tuple[int, int]] = [] for u in range(-self.u_max, self.u_max + 1): for v in range(-self.v_max, self.v_max + 1): - if (u, v) in self.exclude_indices: - continue - uv.append((u, v)) - self.uv_indices = uv - - self.p_u_row = Parameter(u_row, tags={"role": "lat_u_row"}) - self.p_u_col = Parameter(u_col, tags={"role": "lat_u_col"}) - self.p_v_row = Parameter(v_row, tags={"role": "lat_v_row"}) - self.p_v_col = Parameter(v_col, tags={"role": "lat_v_col"}) - self.p_i0 = None - self.p_ir = None - self.p_ic = None - self.p_i0_list: list[Parameter] = [] - self.p_ir_list: list[Parameter] = [] - self.p_ic_list: list[Parameter] = [] - self.p_irr_list: list[Parameter] = [] - self.p_icc_list: list[Parameter] = [] - self.p_irc_list: list[Parameter] = [] - if self.per_disk_intensity: - for u, v in self.uv_indices: - i0_val = center_intensity_0 if (center_intensity_0 is not None and (u, v) == (0, 0)) else intensity_0 - self.p_i0_list.append( - Parameter(i0_val, lower_bound=0.0, tags={"role": "lat_int0", "u": u, "v": v, "intensity_order": 0}) - ) - if self.max_intensity_order >= 1: - self.p_ir_list.append( - Parameter(intensity_row, tags={"role": "lat_int_row", "u": u, "v": v, "intensity_order": 1}) - ) - self.p_ic_list.append( - Parameter(intensity_col, tags={"role": "lat_int_col", "u": u, "v": v, "intensity_order": 1}) - ) - if self.max_intensity_order >= 2: - self.p_irr_list.append( - Parameter(intensity_row_row, tags={"role": "lat_int_rr", "u": u, "v": v, "intensity_order": 2}) - ) - self.p_icc_list.append( - Parameter(intensity_col_col, tags={"role": "lat_int_cc", "u": u, "v": v, "intensity_order": 2}) - ) - self.p_irc_list.append( - Parameter(intensity_row_col, tags={"role": "lat_int_rc", "u": u, "v": v, "intensity_order": 2}) - ) - else: - self.p_i0 = Parameter(intensity_0, lower_bound=0.0, tags={"role": "lat_int0", "intensity_order": 0}) - self.p_ir = Parameter(intensity_row, tags={"role": "lat_int_row", "intensity_order": 1}) - self.p_ic = Parameter(intensity_col, tags={"role": "lat_int_col", "intensity_order": 1}) - self.p_irr = Parameter(intensity_row_row, tags={"role": "lat_int_rr", "intensity_order": 2}) - self.p_icc = Parameter(intensity_col_col, tags={"role": "lat_int_cc", "intensity_order": 2}) - self.p_irc = Parameter(intensity_row_col, tags={"role": "lat_int_rc", "intensity_order": 2}) - - def parameters(self) -> list[Parameter]: - out = [self.p_u_row, self.p_u_col, self.p_v_row, self.p_v_col] - if self.per_disk_intensity: - out.extend(self.p_i0_list) - if self.max_intensity_order >= 1: - out.extend(self.p_ir_list) - out.extend(self.p_ic_list) - if self.max_intensity_order >= 2: - out.extend(self.p_irr_list) - out.extend(self.p_icc_list) - out.extend(self.p_irc_list) - else: - out.append(self.p_i0) - if self.max_intensity_order >= 1: - out.extend([self.p_ir, self.p_ic]) - if self.max_intensity_order >= 2: - out.extend([self.p_irr, self.p_icc, self.p_irc]) - return out - - def prepare(self, ctx: ModelContext) -> Any: - dt = ctx.fields.get("disk_templates", {}) - if self.disk.name not in dt: - raise RuntimeError("DiskTemplate must be included before SyntheticDiskLattice in components.") - d = dt[self.disk.name] - dr = d["dr"] - dc = d["dc"] - pix_idx = d["pix_idx"] - base_vals = d["base"] - - r_idx, c_idx = _origin_indices(ctx, self.origin_key) - - i_ur = self.p_u_row.index - i_uc = self.p_u_col.index - i_vr = self.p_v_row.index - i_vc = self.p_v_col.index - if self.per_disk_intensity: - i_i0 = i_ir = i_ic = i_irr = i_icc = i_irc = None - else: - i_i0 = self.p_i0.index - i_ir = None if self.max_intensity_order < 1 else self.p_ir.index - i_ic = None if self.max_intensity_order < 1 else self.p_ic.index - i_irr = None if self.max_intensity_order < 2 else self.p_irr.index - i_icc = None if self.max_intensity_order < 2 else self.p_icc.index - i_irc = None if self.max_intensity_order < 2 else self.p_irc.index - - uv = self.uv_indices - uv_t = torch.as_tensor(uv, device=ctx.device, dtype=torch.long) if uv else None - - boundary = torch.as_tensor(self.boundary_px, device=ctx.device, dtype=ctx.dtype) + if (u, v) not in exclude: + uv.append((u, v)) + uv_t = ( + torch.as_tensor(uv, dtype=torch.long) if uv else torch.zeros((0, 2), dtype=torch.long) + ) + self.register_buffer("uv_indices", uv_t) + + n_uv = int(uv_t.shape[0]) + i0_init = _parse_init(intensity_0, name="intensity_0") + i0_center = ( + i0_init + if center_intensity_0 is None + else _parse_init(center_intensity_0, name="center_intensity_0") + ) + ir_init = _parse_init(intensity_row, name="intensity_row") + ic_init = _parse_init(intensity_col, name="intensity_col") + irr_init = _parse_init(intensity_row_row, name="intensity_row_row") + icc_init = _parse_init(intensity_col_col, name="intensity_col_col") + irc_init = _parse_init(intensity_row_col, name="intensity_row_col") if self.per_disk_intensity: - i0_idx = torch.as_tensor([p.index for p in self.p_i0_list], device=ctx.device, dtype=torch.long) + i0_values = torch.full((n_uv,), float(i0_init), dtype=torch.float32) + if n_uv > 0: + center_mask = (uv_t[:, 0] == 0) & (uv_t[:, 1] == 0) + i0_values[center_mask] = float(i0_center) + self.i0_raw = nn.Parameter(i0_values) if self.max_intensity_order >= 1: - ir_idx = torch.as_tensor([p.index for p in self.p_ir_list], device=ctx.device, dtype=torch.long) - ic_idx = torch.as_tensor([p.index for p in self.p_ic_list], device=ctx.device, dtype=torch.long) + self.ir = nn.Parameter(torch.full((n_uv,), float(ir_init), dtype=torch.float32)) + self.ic = nn.Parameter(torch.full((n_uv,), float(ic_init), dtype=torch.float32)) else: - ir_idx = ic_idx = None + self.ir = None + self.ic = None if self.max_intensity_order >= 2: - irr_idx = torch.as_tensor([p.index for p in self.p_irr_list], device=ctx.device, dtype=torch.long) - icc_idx = torch.as_tensor([p.index for p in self.p_icc_list], device=ctx.device, dtype=torch.long) - irc_idx = torch.as_tensor([p.index for p in self.p_irc_list], device=ctx.device, dtype=torch.long) + self.irr = nn.Parameter(torch.full((n_uv,), float(irr_init), dtype=torch.float32)) + self.icc = nn.Parameter(torch.full((n_uv,), float(icc_init), dtype=torch.float32)) + self.irc = nn.Parameter(torch.full((n_uv,), float(irc_init), dtype=torch.float32)) else: - irr_idx = icc_idx = irc_idx = None + self.irr = None + self.icc = None + self.irc = None else: - i0_idx = ir_idx = ic_idx = irr_idx = icc_idx = irc_idx = None - per_disk_intensity = self.per_disk_intensity - max_intensity_order = self.max_intensity_order - default_pattern_intensity_order = self.default_pattern_intensity_order - comp_name = self.name - - @dataclass - class Prepared: - boundary_px: float = float(self.boundary_px) - - def _patch(self, x: torch.Tensor) -> torch.Tensor: - if pix_idx is None: - return base_vals - return x[pix_idx] - - def _centers(self, x: torch.Tensor, ctx: ModelContext) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - if uv_t is None: - z = torch.empty((0,), device=ctx.device, dtype=ctx.dtype) - return z, z, z - - r0 = x[r_idx] - c0 = x[c_idx] - - ur = x[i_ur] - uc = x[i_uc] - vr = x[i_vr] - vc = x[i_vc] - - u = uv_t[:, 0].to(ctx.dtype) - v = uv_t[:, 1].to(ctx.dtype) - - centers_r = r0 + u * ur + v * vr - centers_c = c0 + u * uc + v * vc - - keep = (centers_r >= boundary) & (centers_r <= (ctx.H - 1) - boundary) - keep &= (centers_c >= boundary) & (centers_c <= (ctx.W - 1) - boundary) - return centers_r[keep], centers_c[keep], keep - - def render(self, out: torch.Tensor, x: torch.Tensor, ctx: ModelContext) -> None: - if uv_t is None: - return - - centers_r, centers_c, keep = self._centers(x, ctx) - if centers_r.numel() == 0: - return - - vals = self._patch(x) - dr2 = dr * dr - dc2 = dc * dc - drdc = dr * dc - - order_override = ctx.fields.get("lattice_intensity_order_override", None) - if isinstance(order_override, dict): - active_order = int(order_override.get(comp_name, default_pattern_intensity_order)) - elif order_override is None: - active_order = int(default_pattern_intensity_order) - else: - active_order = int(order_override) - active_order = max(0, min(active_order, int(max_intensity_order))) - - if per_disk_intensity: - keep_idx = torch.nonzero(keep, as_tuple=False).reshape(-1) - i0_keep = x[i0_idx[keep_idx]] - if active_order >= 1: - ir_keep = x[ir_idx[keep_idx]] - ic_keep = x[ic_idx[keep_idx]] - if active_order >= 2: - irr_keep = x[irr_idx[keep_idx]] - icc_keep = x[icc_idx[keep_idx]] - irc_keep = x[irc_idx[keep_idx]] - for j, (rr0, cc0) in enumerate(zip(centers_r, centers_c)): - inten_local = i0_keep[j] - if active_order >= 1: - inten_local = inten_local + ir_keep[j] * dr + ic_keep[j] * dc - if active_order >= 2: - inten_local = inten_local + irr_keep[j] * dr2 + icc_keep[j] * dc2 + irc_keep[j] * drdc - inten_local = torch.clamp(inten_local, min=0.0) - _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten_local) - else: - i0 = x[i_i0] - for rr0, cc0 in zip(centers_r, centers_c): - inten = i0 - if active_order >= 1: - inten = inten + x[i_ir] * rr0 + x[i_ic] * cc0 - if active_order >= 2: - inten = inten + x[i_irr] * rr0 * rr0 + x[i_icc] * cc0 * cc0 + x[i_irc] * rr0 * cc0 - inten = torch.clamp(inten, min=0.0) - _splat_patch(out, r0=rr0, c0=cc0, patch_vals=vals, dr=dr, dc=dc, scale=inten) - - def overlays(self, x: torch.Tensor, ctx: ModelContext) -> Iterable[Overlay]: - if uv_t is None: - return [] - centers_r, centers_c, _ = self._centers(x, ctx) - if centers_r.numel() == 0: - return [] - return [ - Overlay( - kind="points_rc", - data={ - "r": centers_r.detach(), - "c": centers_c.detach(), - "marker": "x", - "s": 60.0, - "color": "orange", - }, + self.i0_raw = nn.Parameter(torch.tensor(i0_init, dtype=torch.float32)) + self.ir = nn.Parameter(torch.tensor(ir_init, dtype=torch.float32)) + self.ic = nn.Parameter(torch.tensor(ic_init, dtype=torch.float32)) + self.irr = nn.Parameter(torch.tensor(irr_init, dtype=torch.float32)) + self.icc = nn.Parameter(torch.tensor(icc_init, dtype=torch.float32)) + self.irc = nn.Parameter(torch.tensor(irc_init, dtype=torch.float32)) + + def set_origin(self, origin: OriginND) -> None: + self.origin = origin + + def forward(self, ctx: RenderContext) -> torch.Tensor: + if self.origin is None: + raise RuntimeError("SyntheticDiskLattice requires an OriginND instance.") + + out = torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) + uv_indices = cast(torch.Tensor, self.uv_indices) + if torch.numel(uv_indices) == 0: + return out + + uv = torch.as_tensor(uv_indices, device=ctx.device) + u = uv[:, 0].to(dtype=ctx.dtype) + v = uv[:, 1].to(dtype=ctx.dtype) + r0, c0 = self.origin.coords[0], self.origin.coords[1] + centers_r = r0 + u * self.u_row + v * self.v_row + centers_c = c0 + u * self.u_col + v * self.v_col + + b = torch.as_tensor(self.boundary_px, device=ctx.device, dtype=ctx.dtype) + keep = (centers_r >= b) & (centers_r <= (ctx.shape[0] - 1) - b) + keep = keep & (centers_c >= b) & (centers_c <= (ctx.shape[1] - 1) - b) + keep_idx = torch.nonzero(keep, as_tuple=False).reshape(-1) + if keep_idx.numel() == 0: + return out + + active_order = int( + ctx.fields.get( + "lattice_intensity_order_override", self.default_pattern_intensity_order + ) + ) + active_order = max(0, min(active_order, self.max_intensity_order)) + + dr, dc = self.disk.patch_offsets() + dr = dr.to(device=ctx.device, dtype=ctx.dtype) + dc = dc.to(device=ctx.device, dtype=ctx.dtype) + dr2 = dr * dr + dc2 = dc * dc + drdc = dr * dc + + for j in keep_idx: + rr0 = centers_r[j] + cc0 = centers_c[j] + + if self.per_disk_intensity: + inten = _relu_pos(self.i0_raw[j]) + # inten = _softplus_pos(self.i0_raw[j]) + if active_order >= 1 and self.ir is not None and self.ic is not None: + inten = inten + self.ir[j] * dr + self.ic[j] * dc + if ( + active_order >= 2 + and self.irr is not None + and self.icc is not None + and self.irc is not None + ): + inten = inten + self.irr[j] * dr2 + self.icc[j] * dc2 + self.irc[j] * drdc + inten = torch.clamp(inten, min=0.0) + else: + inten = _relu_pos(self.i0_raw) + # inten = _softplus_pos(self.i0_raw) + if active_order >= 1: + assert self.ir is not None and self.ic is not None + inten = inten + self.ir * rr0 + self.ic * cc0 + if active_order >= 2: + assert self.irr is not None and self.icc is not None and self.irc is not None + inten = ( + inten + self.irr * rr0 * rr0 + self.icc * cc0 * cc0 + self.irc * rr0 * cc0 ) - ] + inten = torch.clamp(inten, min=0.0) - return Prepared() + self.disk.add_patch(out, r0=rr0, c0=cc0, scale=inten) + + return out diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index e7728997..10477d4f 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -1,117 +1,75 @@ from __future__ import annotations -"""High-level diffraction model fitting workflow utilities.""" - import warnings -from typing import Any, Sequence +from typing import Any, Sequence, cast import numpy as np import torch from scipy.ndimage import shift as ndi_shift from scipy.signal.windows import tukey +from torch.nn.utils import parameters_to_vector -from quantem.core.datastructures.dataset2d import Dataset2d -from quantem.core.datastructures.dataset3d import Dataset3d -from quantem.core.datastructures.dataset4d import Dataset4d -from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.datastructures import Dataset2d, Dataset3d, Dataset4d, Dataset4dstem +from quantem.core.fitting.base import AdditiveRenderModel, RenderComponent, RenderContext +from quantem.core.fitting.diffraction import OriginND from quantem.core.io.serialize import AutoSerialize -from quantem.core.fitting.base import Model, ModelContext, Overlay, PreparedModel -from quantem.core.fitting.diffraction import Origin2D +from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.imaging_utils import cross_correlation_shift +from quantem.core.utils.utils import to_numpy from quantem.core.visualization import show_2d -def _to_numpy(x: Any) -> np.ndarray: - """Convert arrays/tensors to NumPy arrays.""" - if isinstance(x, np.ndarray): - return x - if torch.is_tensor(x): - return x.detach().cpu().numpy() - return np.asarray(x) - - -class ModelDiffraction(AutoSerialize): - """ - End-to-end helper for defining and fitting additive diffraction forward models. - - This class wraps a diffraction dataset, builds an average reference image - (`image_ref`), compiles a composable component model, and provides optimization - routines for: - - fitting to the mean reference image, - - fitting selected individual diffraction patterns. - - Features - -------- - - Build a mean reference image with optional stack alignment. - - Define a composable model from origin/background/template/lattice components. - - Refine a mean model with Adam or L-BFGS. - - Fit all or selected patterns with optional progress bars. - - Plot reference/model comparisons and component overlays. - - Typical workflow - ---------------- - >>> md = ModelDiffraction.from_dataset(ds).preprocess().define_model(...) - >>> md.refine_mean_model(...) - >>> md.fit_all_patterns(...) - >>> md.plot_mean_model(...) - """ +def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) -> float: + if isinstance(value, (list, tuple, np.ndarray)): + if len(value) == 0: + raise ValueError(f"{name} cannot be empty.") + if value[0] is None: + raise ValueError(f"{name} initial value cannot be None.") + return float(value[0]) + return float(cast(float | int, value)) + +class ModelDiffraction(OptimizerMixin, AutoSerialize): _token = object() + DEFAULT_LR = 1e-3 + DEFAULT_OPTIMIZER_TYPE = "adamw" def __init__(self, dataset: Any, _token: object | None = None): if _token is not self._token: raise RuntimeError("Use ModelDiffraction.from_dataset() or .from_file().") - super().__init__() + AutoSerialize.__init__(self) + OptimizerMixin.__init__(self) self.dataset = dataset self.metadata: dict[str, Any] = {} self.image_ref: np.ndarray | None = None self.preprocess_shifts: np.ndarray | None = None self.index_shape: tuple[int, ...] | None = None - self.model: Model | None = None - self.prepared: PreparedModel | None = None - self.x_mean: torch.Tensor | None = None + + self.ctx: RenderContext | None = None + self.model: AdditiveRenderModel | None = None + self.target_mean: torch.Tensor | None = None + self.x_defined: torch.Tensor | None = None self.x_initial: torch.Tensor | None = None + self.x_mean: torch.Tensor | None = None self.mean_refined: bool = False - self.x_patterns: torch.Tensor | None = None - self.pattern_fit_losses: np.ndarray | None = None - self.pattern_fit_linear_indices: np.ndarray | None = None - self.pattern_fit_indices: list[tuple[int, ...]] | None = None - - @staticmethod - def _weak_softplus(x: torch.Tensor, *, scale: float) -> torch.Tensor: - s = torch.as_tensor(float(scale), device=x.device, dtype=x.dtype) - return torch.nn.functional.softplus(x / s) * s - - @classmethod - def _apply_intensity_transform( - cls, x: torch.Tensor, *, mode: str, weak_softplus_scale: float - ) -> torch.Tensor: - m = str(mode).lower() - if m == "none": - return x - if m == "weak_softplus": - return cls._weak_softplus(x, scale=weak_softplus_scale) - raise ValueError("intensity_transform must be one of: 'none', 'weak_softplus'.") + self.mean_fit_history: list[float] = [] + self.mean_fit_lrs: list[float] = [] @classmethod - def from_dataset(cls, dataset: Dataset2d | Dataset3d | Dataset4d | Dataset4dstem | Any) -> "ModelDiffraction": - """ - Construct a ModelDiffraction object from a QuantEM dataset container. - - Parameters - ---------- - dataset - Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance. - - Returns - ------- - ModelDiffraction - New model-fitting helper bound to the provided dataset. - """ + def from_dataset( + cls, dataset: Dataset2d | Dataset3d | Dataset4d | Dataset4dstem | Any + ) -> "ModelDiffraction": if isinstance(dataset, (Dataset2d, Dataset3d, Dataset4d, Dataset4dstem)): return cls(dataset=dataset, _token=cls._token) - raise TypeError("from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance.") + raise TypeError( + "from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance." + ) + + def get_optimization_parameters(self) -> Any: + if self.model is None: + return [] + return self.model.parameters() def preprocess( self, @@ -122,73 +80,44 @@ def preprocess( max_shift: float | None = None, shift_order: int = 1, ) -> "ModelDiffraction": - """ - Precompute the mean reference image used for model fitting. - - Parameters - ---------- - align - If True, align the flattened pattern stack before averaging. - edge_blend - Tukey edge taper width (pixels) used for robust FFT alignment. - upsample_factor - Sub-pixel alignment upsampling factor for cross-correlation shift. - max_shift - Optional maximum shift magnitude during alignment. - shift_order - Interpolation order used when applying shifts to patterns. - - Returns - ------- - ModelDiffraction - Returns self. - - Notes - ----- - - `dataset.array` is interpreted as `(..., H, W)`, where leading dimensions - are flattened into a pattern stack. - - The computed stack-average is stored in `self.image_ref`. - - If `align=False`, preprocessing is a direct mean over stack elements. - """ arr = np.asarray(self.dataset.array) if arr.ndim < 2: raise ValueError("dataset.array must have at least 2 dimensions.") - H, W = arr.shape[-2], arr.shape[-1] + h, w = arr.shape[-2], arr.shape[-1] self.index_shape = tuple(arr.shape[:-2]) - stack = arr.reshape((-1, H, W)).astype(np.float32, copy=False) + stack = arr.reshape((-1, h, w)).astype(np.float32, copy=False) n = stack.shape[0] - if not align or n <= 1: self.image_ref = np.mean(stack, axis=0) self.preprocess_shifts = None return self - alpha_r = 0.0 if edge_blend <= 0 else min(1.0, 2.0 * float(edge_blend) / float(H)) - alpha_c = 0.0 if edge_blend <= 0 else min(1.0, 2.0 * float(edge_blend) / float(W)) - w = tukey(H, alpha=alpha_r)[:, None] * tukey(W, alpha=alpha_c)[None, :] - w = w.astype(np.float32, copy=False) + alpha_r = 0.0 if edge_blend <= 0 else min(1.0, 2.0 * float(edge_blend) / float(h)) + alpha_c = 0.0 if edge_blend <= 0 else min(1.0, 2.0 * float(edge_blend) / float(w)) + window = tukey(h, alpha=alpha_r)[:, None] * tukey(w, alpha=alpha_c)[None, :] + window = window.astype(np.float32, copy=False) shifts = np.zeros((n, 2), dtype=np.float32) - F_ref = np.fft.fft2(w * stack[0]) - + fft_ref = np.fft.fft2(window * stack[0]) for i in range(1, n): - F_i = np.fft.fft2(w * stack[i]) - drc, F_shift = cross_correlation_shift( - F_ref, - F_i, + fft_i = np.fft.fft2(window * stack[i]) + drc, fft_shift = cross_correlation_shift( + fft_ref, + fft_i, upsample_factor=int(upsample_factor), max_shift=max_shift, fft_input=True, fft_output=True, return_shifted_image=True, ) + if not isinstance(drc, (list, tuple, np.ndarray)) or len(drc) < 2: + raise RuntimeError("cross_correlation_shift returned an invalid shift vector.") shifts[i, 0] = float(drc[0]) shifts[i, 1] = float(drc[1]) - F_ref = F_ref * (i / (i + 1)) + F_shift / (i + 1) + fft_ref = fft_ref * (i / (i + 1)) + fft_shift / (i + 1) shifts -= np.mean(shifts, axis=0, keepdims=True) - aligned = np.empty_like(stack, dtype=np.float32) for i in range(n): aligned[i] = ndi_shift( @@ -206,646 +135,268 @@ def preprocess( def define_model( self, *, - origin_row: float | tuple[float, float] | tuple[float, float, float | None], - origin_col: float | tuple[float, float] | tuple[float, float, float | None], - components: list[Any], + origin_row: float | Sequence[float], + origin_col: float | Sequence[float], + components: list[RenderComponent], device: torch.device | str | None = None, dtype: torch.dtype | None = None, mask: np.ndarray | torch.Tensor | None = None, origin_key: str = "origin", ) -> "ModelDiffraction": - """ - Define and compile a diffraction model against `image_ref`. - - Parameters - ---------- - origin_row, origin_col - Initial origin parameter specification. Supported forms are: - - scalar: fixed initial value with no explicit bounds - - `(value, deviation)`: symmetric bounds `(value - deviation, value + deviation)` - - `(value, lower_bound, upper_bound)`: explicit bounds - components - Sequence of model components (e.g. `DiskTemplate`, backgrounds, lattice). - Components are rendered additively in the provided order. - device - Torch device used for compiled parameters and rendering. - dtype - Torch dtype used for compiled parameters and rendering. - mask - Optional `(H, W)` mask for weighted loss during optimization. - origin_key - Name used to register/retrieve the origin component in context fields. - - Returns - ------- - ModelDiffraction - Returns self with compiled model state. - - Notes - ----- - - If `image_ref` is missing, `preprocess()` is run automatically. - - `Origin2D` is inserted automatically before user components. - - Component dependency ordering still matters for shared context fields: - for example, `DiskTemplate` should appear before `SyntheticDiskLattice` - when the lattice references that template. - - This method resets fit state (`x_defined`, `x_initial`, `x_mean`, - `x_patterns`, and pattern-fit metadata). - """ if self.image_ref is None: self.preprocess() - if self.image_ref is None: raise RuntimeError("image_ref not available.") - H, W = int(self.image_ref.shape[0]), int(self.image_ref.shape[1]) + h, w = int(self.image_ref.shape[0]), int(self.image_ref.shape[1]) dev = torch.device(device) if device is not None else torch.device("cpu") dt = dtype if dtype is not None else torch.float32 mask_t = None if mask is not None: - if torch.is_tensor(mask): - mask_t = mask.to(device=dev, dtype=dt) - else: - m = np.asarray(mask) - if m.shape != (H, W): - raise ValueError("mask must have shape (H, W).") - mask_t = torch.as_tensor(m.astype(np.float32, copy=False), device=dev, dtype=dt) - - ctx = ModelContext(H=H, W=W, device=dev, dtype=dt, mask=mask_t, fields={}) - m = Model() - m.add([Origin2D(origin_key=str(origin_key), row=origin_row, col=origin_col)]) - m.add(list(components)) - - self.model = m - self.prepared = m.compile(ctx) - self.x_defined = self.prepared.x0.detach().clone() - self.x_initial = self.x_defined.detach().clone() - self.x_mean = self.x_initial.detach().clone() + mask_t = ( + mask.to(device=dev, dtype=dt) + if torch.is_tensor(mask) + else torch.as_tensor(mask, device=dev, dtype=dt) + ) + if tuple(mask_t.shape) != (h, w): + raise ValueError("mask must have shape (H, W).") + + origin = OriginND( + ndim=2, + init=[ + _parse_init(origin_row, name="origin_row"), + _parse_init(origin_col, name="origin_col"), + ], + ) + origin._quantem_origin_key = str(origin_key) # type: ignore[attr-defined] + + for component in components: + if hasattr(component, "set_origin"): + component.set_origin(origin) # type: ignore[misc] + elif hasattr(component, "origin") and getattr(component, "origin") is None: + component.origin = origin # type: ignore[attr-defined] + + self.model = AdditiveRenderModel(origin=origin, components=list(components)).to( + device=dev, dtype=dt + ) + self.ctx = RenderContext(shape=(h, w), device=dev, dtype=dt, mask=mask_t, fields={}) + self.target_mean = torch.as_tensor(self.image_ref, device=dev, dtype=dt) + + x0 = parameters_to_vector(self.model.parameters()).detach().clone() + self.x_defined = x0.detach().clone() + self.x_initial = x0.detach().clone() + self.x_mean = x0.detach().clone() self.mean_refined = False - self.x_patterns = None - self.pattern_fit_losses = None - self.pattern_fit_linear_indices = None - self.pattern_fit_indices = None + self.mean_fit_history = [] + self.mean_fit_lrs = [] + self.remove_optimizer() return self - def _fit_target_image( + def fit_mean_diffraction_pattern( self, *, - target: torch.Tensor, - x_start: torch.Tensor, - n_steps: int, - lr: float, - method: str, - power: float | None, - fit_disk_pixels: bool | None, - fit_only_disk_pixels: bool, - intensity_order: int | None, - enforce_disk_center_of_mass: bool, - normalize_disk_template_max: bool, - template_binary_weight: float, - template_binary_power: float, - intensity_transform: str, - weak_softplus_scale: float, + n_steps: int = 200, + optimizer_params: dict | None = None, + scheduler_params: dict | None = None, + constraint_weight: float = 1.0, + overwrite_initial: bool = True, progress: bool = False, - progress_desc: str | None = None, - ) -> tuple[torch.Tensor, float]: - if self.prepared is None: + **kwargs: Any, + ) -> "ModelDiffraction": + if self.model is None or self.ctx is None or self.target_mean is None: raise RuntimeError("Call .define_model(...) first.") - ctx = self.prepared.ctx - lb = self.prepared.lb - ub = self.prepared.ub - - x = x_start.detach().clone().to(device=ctx.device, dtype=ctx.dtype) - x.requires_grad_(True) - - if fit_disk_pixels is None: - fit_disk_pixels = any(p.tags.get("role") == "disk_pixel" for p in self.prepared.params) - - freeze = torch.zeros_like(x, dtype=torch.bool) - disk_mask = torch.zeros_like(x, dtype=torch.bool) - for p in self.prepared.params: - if p.tags.get("role") == "disk_pixel": - disk_mask[p.index] = True - if intensity_order is not None and str(p.tags.get("role", "")).startswith("lat_int"): - p_ord = int(p.tags.get("intensity_order", 0)) - if p_ord > int(intensity_order): - freeze[p.index] = True - - # Group disk pixel parameters by disk template for optional projection. - disk_param_groups: list[dict[str, Any]] = [] - if ( - enforce_disk_center_of_mass - or normalize_disk_template_max - or float(template_binary_weight) > 0.0 - ): - disk_templates = self.prepared.ctx.fields.get("disk_templates", {}) - grouped: dict[str, list[tuple[int, int]]] = {} - for p in self.prepared.params: - if p.tags.get("role") != "disk_pixel": - continue - name = str(p.tags.get("disk")) - i_flat = int(p.tags.get("i")) - grouped.setdefault(name, []).append((i_flat, int(p.index))) - for name, pairs in grouped.items(): - dmeta = disk_templates.get(name) - if dmeta is None: - continue - order = sorted(pairs, key=lambda t: t[0]) - p_idx = torch.as_tensor([t[1] for t in order], device=ctx.device, dtype=torch.long) - g: dict[str, Any] = {"param_idx": p_idx} - shape = dmeta.get("shape", None) - if shape is not None: - g["shape"] = (int(shape[0]), int(shape[1])) - if enforce_disk_center_of_mass: - flat_i = torch.as_tensor([t[0] for t in order], device=ctx.device, dtype=torch.long) - g["dr"] = dmeta["dr"][flat_i] - g["dc"] = dmeta["dc"][flat_i] - disk_param_groups.append(g) - - if fit_only_disk_pixels: - if not fit_disk_pixels: - raise ValueError("fit_only_disk_pixels=True requires fit_disk_pixels=True.") - freeze[:] = True - freeze[disk_mask] = False - elif not fit_disk_pixels: - freeze[disk_mask] = True - x_frozen = x.detach().clone() - - old_order_override = ctx.fields.get("lattice_intensity_order_override", None) - if intensity_order is not None: - ctx.fields["lattice_intensity_order_override"] = int(intensity_order) - - target_t = target.to(device=ctx.device, dtype=ctx.dtype) - target_t = self._apply_intensity_transform( - target_t, mode=intensity_transform, weak_softplus_scale=weak_softplus_scale - ) - if power is not None: - target_t = torch.clamp(target_t, min=0.0) ** float(power) - - def clamp_inplace() -> None: - with torch.no_grad(): - x.data = torch.max(torch.min(x.data, ub), lb) - if torch.any(freeze): - x.data[freeze] = x_frozen[freeze] - if ( - enforce_disk_center_of_mass - or normalize_disk_template_max - ) and disk_param_groups: - eps = torch.as_tensor(1e-12, device=ctx.device, dtype=ctx.dtype) - for g in disk_param_groups: - p_idx = g["param_idx"] - if torch.all(freeze[p_idx]): - continue - vals = torch.clamp(x.data[p_idx], min=0.0) - if enforce_disk_center_of_mass: - dr = g["dr"] - dc = g["dc"] - # Project template moments toward zero COM by iterative multiplicative reweighting. - for _ in range(12): - mass = torch.sum(vals) - if mass <= eps: - break - r_com = torch.sum(vals * dr) / mass - c_com = torch.sum(vals * dc) / mass - if torch.abs(r_com) <= 1e-5 and torch.abs(c_com) <= 1e-5: - break - var_r = torch.sum(vals * dr * dr) / mass + eps - var_c = torch.sum(vals * dc * dc) / mass + eps - vals = vals * torch.exp(-(r_com / var_r) * dr - (c_com / var_c) * dc) - vals = torch.clamp(vals, min=0.0) - if normalize_disk_template_max: - vmax = torch.max(vals) - if vmax > eps: - vals = vals / vmax - x.data[p_idx] = vals - - def loss_fn() -> torch.Tensor: - clamp_inplace() - pred = self.prepared.render(x) - pred = self._apply_intensity_transform( - pred, mode=intensity_transform, weak_softplus_scale=weak_softplus_scale - ) - if power is not None: - pred = torch.clamp(pred, min=0.0) ** float(power) - if ctx.mask is not None: - m = ctx.mask - diff = (pred - target_t) * m - denom = torch.clamp(torch.sum(m), min=1.0) - loss = torch.sum(diff * diff) / denom - else: - diff = pred - target_t - loss = torch.mean(diff * diff) - - if float(template_binary_weight) > 0.0 and disk_param_groups: - bp = torch.as_tensor(0.0, device=ctx.device, dtype=ctx.dtype) - n_bp = 0 - eps = torch.as_tensor(1e-12, device=ctx.device, dtype=ctx.dtype) - pwr = float(template_binary_power) - for g in disk_param_groups: - p_idx = g["param_idx"] - vals = torch.clamp(x[p_idx], min=0.0) - vmax = torch.max(vals) - if vmax <= eps: - continue - vals_n = vals / (vmax + eps) - core = vals_n * (1.0 - vals_n) - if pwr != 1.0: - core = torch.pow(core + eps, pwr) - bp = bp + torch.mean(core) - n_bp += 1 - if n_bp > 0: - loss = loss + float(template_binary_weight) * (bp / n_bp) - - return loss - - try: - if method == "adam": - opt = torch.optim.Adam([x], lr=lr) - step_iter: Any = range(int(n_steps)) - if progress: - try: - from tqdm.auto import trange - - step_iter = trange(int(n_steps), desc=progress_desc or "Refining", leave=False) - except Exception: - warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) - for _ in step_iter: - opt.zero_grad(set_to_none=True) - loss = loss_fn() - loss.backward() - opt.step() - clamp_inplace() - elif method == "lbfgs": - opt = torch.optim.LBFGS([x], lr=lr, max_iter=int(n_steps), line_search_fn="strong_wolfe") - - def closure() -> torch.Tensor: - opt.zero_grad(set_to_none=True) - loss = loss_fn() - loss.backward() - return loss - - opt.step(closure) - clamp_inplace() - else: - raise ValueError("method must be one of: 'lbfgs', 'adam'.") - - with torch.no_grad(): - final_loss = float(loss_fn().detach().cpu()) - return x.detach().clone(), final_loss - finally: - if intensity_order is not None: - if old_order_override is None: - ctx.fields.pop("lattice_intensity_order_override", None) - else: - ctx.fields["lattice_intensity_order_override"] = old_order_override - - def _resolve_pattern_indices(self, indices: Any, n: int, index_shape: tuple[int, ...]) -> np.ndarray: - if indices is None: - out = np.arange(n, dtype=np.int64) - elif isinstance(indices, (int, np.integer)): - out = np.asarray([int(indices)], dtype=np.int64) - elif isinstance(indices, slice): - out = np.arange(n, dtype=np.int64)[indices] - elif isinstance(indices, tuple) and all(isinstance(i, (int, np.integer)) for i in indices): - if len(index_shape) == 0: - if len(indices) != 0: - raise ValueError("indices tuple must be empty for single-pattern datasets.") - out = np.asarray([0], dtype=np.int64) + optimizer_rebuilt = False + if optimizer_params is not None: + self.set_optimizer(optimizer_params) + optimizer_rebuilt = True + elif self.optimizer is None: + if self.optimizer_params: + self.set_optimizer(self.optimizer_params) else: - out = np.asarray([np.ravel_multi_index(tuple(int(i) for i in indices), index_shape)], dtype=np.int64) - elif isinstance(indices, tuple): - if len(index_shape) == 0: - raise ValueError("slice tuple indices are not valid for single-pattern datasets.") - grid = np.arange(n, dtype=np.int64).reshape(index_shape) - out = np.asarray(grid[indices], dtype=np.int64).ravel() - elif isinstance(indices, np.ndarray) and indices.dtype == np.bool_: - if indices.shape != index_shape: - raise ValueError(f"Boolean mask must have shape {index_shape}.") - out = np.flatnonzero(indices.ravel()).astype(np.int64, copy=False) - elif isinstance(indices, Sequence) and not isinstance(indices, (str, bytes)): - seq = list(indices) - if len(seq) == 0: - out = np.asarray([], dtype=np.int64) - elif isinstance(seq[0], (tuple, list, np.ndarray)): - if len(index_shape) == 0: - raise ValueError("multi-index selection is not valid for single-pattern datasets.") - out = np.asarray( - [np.ravel_multi_index(tuple(int(j) for j in i), index_shape) for i in seq], - dtype=np.int64, - ) - else: - out = np.asarray([int(i) for i in seq], dtype=np.int64) - else: - raise TypeError("Unsupported indices type for fit_all_patterns.") + self.set_optimizer({"type": self.DEFAULT_OPTIMIZER_TYPE, "lr": self.DEFAULT_LR}) + optimizer_rebuilt = True - if out.ndim != 1: - out = out.ravel() - if np.any(out < 0) or np.any(out >= n): - raise IndexError(f"indices must be in [0, {n - 1}].") - return out + if scheduler_params is not None: + self.set_scheduler(scheduler_params, num_iter=int(n_steps)) + elif self.scheduler is None and self.scheduler_params: + self.set_scheduler(self.scheduler_params, num_iter=int(n_steps)) + elif optimizer_rebuilt and self.scheduler is not None and self.optimizer is not None: + self.scheduler.optimizer = self.optimizer - def refine_mean_model( - self, - *, - n_steps: int = 50, - lr: float = 1e-3, - method: str = "adam", - power: float | None = 1.0, - fit_disk_pixels: bool | None = None, - fit_only_disk_pixels: bool = False, - intensity_order: int = 0, - enforce_disk_center_of_mass: bool = True, - normalize_disk_template_max: bool = False, - template_binary_weight: float = 0.0, - template_binary_power: float = 1.0, - warmup_disk_steps: int = 0, - overwrite_initial: bool = True, - intensity_transform: str = "none", - weak_softplus_scale: float = 1e-3, - progress: bool = False, - ) -> "ModelDiffraction": - """ - Refine model parameters against the mean reference image. - - Parameters - ---------- - n_steps - Number of optimization steps/iterations for the main phase. - lr - Optimizer learning rate. - method - Optimizer name: `"adam"` or `"lbfgs"`. - power - Optional power-law transform applied to both target and prediction - before computing MSE loss. - fit_disk_pixels - Controls whether `disk_pixel` parameters are trainable. If None, - inferred from model parameters. - fit_only_disk_pixels - If True, freeze all non-disk parameters. - warmup_disk_steps - Optional number of disk-only warmup steps run before the main phase. - overwrite_initial - If True, store refined parameters as new initial parameters for - subsequent per-pattern fitting. - intensity_transform - Optional intensity transform (`"none"` or `"weak_softplus"`) applied - before the power transform and loss evaluation. - weak_softplus_scale - Scale parameter used when `intensity_transform="weak_softplus"`. - progress - If True, show a tqdm progress bar (when tqdm is available). - - Returns - ------- - ModelDiffraction - Returns self with updated `x_mean` (and optionally `x_initial`). - """ - method = str(method).lower() + iterator: Any = range(int(n_steps)) + if progress: + try: + from tqdm.auto import trange - if self.image_ref is None: - self.preprocess() - if self.image_ref is None or self.prepared is None or self.x_mean is None or self.x_initial is None: - raise RuntimeError("Call .define_model(...) first.") + iterator = trange(int(n_steps), desc="Fit mean diffraction", leave=False) + except Exception: + warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) - ctx = self.prepared.ctx - target = torch.as_tensor(self.image_ref, device=ctx.device, dtype=ctx.dtype) - x_start = self.x_initial if overwrite_initial else self.x_mean - if int(warmup_disk_steps) > 0: - x_start, _ = self._fit_target_image( - target=target, - x_start=x_start, - n_steps=int(warmup_disk_steps), - lr=float(lr), - method=method, - power=power, - fit_disk_pixels=True, - fit_only_disk_pixels=True, - intensity_order=int(intensity_order), - enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), - normalize_disk_template_max=bool(normalize_disk_template_max), - template_binary_weight=float(template_binary_weight), - template_binary_power=float(template_binary_power), - intensity_transform=intensity_transform, - weak_softplus_scale=float(weak_softplus_scale), - progress=bool(progress), - progress_desc="Refine mean model (disk warmup)", + self.mean_fit_history = [] + self.mean_fit_lrs = [] + for _ in iterator: + self.zero_optimizer_grad() + pred = self.model(self.ctx) + if self.ctx.mask is not None: + diff = (pred - self.target_mean) * self.ctx.mask + denom = torch.clamp(torch.sum(self.ctx.mask), min=1.0) + loss_data = torch.sum(diff * diff) / denom + else: + loss_data = torch.mean((pred - self.target_mean) ** 2) + loss = loss_data + float(constraint_weight) * self.model.total_constraint_loss( + self.ctx ) - x_fit, _ = self._fit_target_image( - target=target, - x_start=x_start, - n_steps=int(n_steps), - lr=float(lr), - method=method, - power=power, - fit_disk_pixels=fit_disk_pixels, - fit_only_disk_pixels=bool(fit_only_disk_pixels), - intensity_order=int(intensity_order), - enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), - normalize_disk_template_max=bool(normalize_disk_template_max), - template_binary_weight=float(template_binary_weight), - template_binary_power=float(template_binary_power), - intensity_transform=intensity_transform, - weak_softplus_scale=float(weak_softplus_scale), - progress=bool(progress), - progress_desc="Refine mean model", - ) - + loss.backward() + self.step_optimizer() + loss_value = float(loss.detach().cpu()) + self.step_scheduler(loss_value) + self.mean_fit_history.append(loss_value) + self.mean_fit_lrs.append(float(self.get_current_lr())) + + x_fit = parameters_to_vector(self.model.parameters()).detach().clone() self.x_mean = x_fit self.mean_refined = True if overwrite_initial: self.x_initial = x_fit.detach().clone() return self - def fit_all_patterns( - self, - *, - indices: Any = None, - use_refined_init: bool = True, - strict_refined_init: bool = False, - n_steps: int = 50, - lr: float = 1e-3, - method: str = "adam", - power: float | None = 1.0, - fit_disk_pixels: bool | None = None, - fit_only_disk_pixels: bool = False, - intensity_order: int | None = None, - enforce_disk_center_of_mass: bool = True, - normalize_disk_template_max: bool = False, - template_binary_weight: float = 0.0, - template_binary_power: float = 1.0, - intensity_transform: str = "none", - weak_softplus_scale: float = 1e-3, - progress: bool = False, - ) -> "ModelDiffraction": - """ - Fit selected diffraction patterns using the compiled model. - - Parameters - ---------- - indices - Pattern selector. Supported forms include None (all patterns), - integer, slice, tuple indexing, list of linear indices, list of - multi-indices, or boolean mask shaped like scan dimensions. - use_refined_init - If True, initialize per-pattern fitting from `x_initial`. - strict_refined_init - If True, raise when refined init is requested before mean refinement. - If False, emit warning and fall back to defined initialization. - n_steps, lr, method, power, fit_disk_pixels, fit_only_disk_pixels - Optimization settings analogous to `refine_mean_model`. - intensity_transform, weak_softplus_scale - Prediction/target intensity transform options. - progress - If True, show a tqdm progress bar over selected patterns. - - Returns - ------- - ModelDiffraction - Returns self with: - - `x_patterns`: fitted parameter vectors `(n_selected, n_params)` - - `pattern_fit_losses`: per-pattern final losses - - index bookkeeping for selected patterns. - """ - method = str(method).lower() - - if self.prepared is None or self.x_defined is None or self.x_initial is None: - raise RuntimeError("Call .define_model(...) first.") + def plot_losses( + self, figax: tuple[Any, Any] | None = None, plot_lrs: bool = True + ) -> tuple[Any, Any]: + import matplotlib.pyplot as plt - arr = np.asarray(self.dataset.array) - if arr.ndim < 2: - raise ValueError("dataset.array must have at least 2 dimensions.") - H, W = arr.shape[-2], arr.shape[-1] - index_shape = tuple(arr.shape[:-2]) - stack = arr.reshape((-1, H, W)).astype(np.float32, copy=False) - n = int(stack.shape[0]) - - linear = self._resolve_pattern_indices(indices=indices, n=n, index_shape=index_shape) - if linear.size == 0: - raise ValueError("No patterns selected for fitting.") - - if use_refined_init: - if not self.mean_refined: - msg = "fit_all_patterns(use_refined_init=True) was requested before refine_mean_model()." - if strict_refined_init: - raise RuntimeError(msg) - warnings.warn(f"{msg} Falling back to defined initial parameters.", stacklevel=2) - x_seed = self.x_defined - else: - x_seed = self.x_initial + if figax is None: + fig, ax = plt.subplots() else: - x_seed = self.x_defined + fig, ax = figax + + losses = np.asarray(self.mean_fit_history, dtype=np.float64) + if losses.size == 0: + ax.text( + 0.5, + 0.5, + "No fit history available", + ha="center", + va="center", + transform=ax.transAxes, + ) + ax.set_xlabel("Iterations") + ax.set_ylabel("Loss") + if figax is None: + plt.tight_layout() + plt.show() + return fig, ax - n_sel = int(linear.size) - x_fit_all = torch.empty( - (n_sel, self.x_defined.numel()), - device=self.prepared.ctx.device, - dtype=self.prepared.ctx.dtype, - ) - losses = np.empty((n_sel,), dtype=np.float32) + iters = np.arange(losses.size) + lines: list[Any] = [] + lines.extend(ax.semilogy(iters, losses, c="k", lw=2, label="loss")) + ax.set_xlabel("Iterations") + ax.set_ylabel("Loss", color="k") + ax.tick_params(axis="y", which="both", colors="k") + ax.spines["left"].set_color("k") + ax.set_xbound(-2, max(1, int(iters.max())) + 2) + + lrs = np.asarray(self.mean_fit_lrs, dtype=np.float64) + if plot_lrs and lrs.size > 0: + if lrs.size == losses.size and not np.allclose(lrs, lrs[0]): + ax_lr = ax.twinx() + ax.set_zorder(2) + ax_lr.set_zorder(1) + ax.patch.set_visible(False) + ax_lr.spines["left"].set_visible(False) + lines.extend( + ax_lr.semilogy( + np.arange(lrs.size), lrs, c="tab:blue", lw=2, ls="--", label="LR" + ) + ) + ax_lr.set_ylabel("LR", color="tab:blue") + ax_lr.tick_params(axis="y", which="both", colors="tab:blue") + ax_lr.spines["right"].set_color("tab:blue") + else: + ax.set_title(f"LR: {float(lrs[-1]):.2e}", fontsize=10) - pat_iter: Any = enumerate(linear) - if progress: - try: - from tqdm.auto import tqdm + labels = [line.get_label() for line in lines] + if len(labels) > 1: + ax.legend(lines, labels, loc="upper right") - pat_iter = enumerate(tqdm(linear, desc="Fit patterns", leave=False)) - except Exception: - warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) + if figax is None: + plt.tight_layout() + plt.show() + return fig, ax - for j, i_lin in pat_iter: - target = torch.as_tensor(stack[int(i_lin)], device=self.prepared.ctx.device, dtype=self.prepared.ctx.dtype) - x_fit, loss = self._fit_target_image( - target=target, - x_start=x_seed, - n_steps=int(n_steps), - lr=float(lr), - method=method, - power=power, - fit_disk_pixels=fit_disk_pixels, - fit_only_disk_pixels=bool(fit_only_disk_pixels), - intensity_order=None if intensity_order is None else int(intensity_order), - enforce_disk_center_of_mass=bool(enforce_disk_center_of_mass), - normalize_disk_template_max=bool(normalize_disk_template_max), - template_binary_weight=float(template_binary_weight), - template_binary_power=float(template_binary_power), - intensity_transform=intensity_transform, - weak_softplus_scale=float(weak_softplus_scale), - progress=False, - ) - x_fit_all[j] = x_fit - losses[j] = float(loss) - - self.x_patterns = x_fit_all - self.pattern_fit_losses = losses - self.pattern_fit_linear_indices = linear - if len(index_shape) == 0: - self.pattern_fit_indices = [tuple() for _ in linear] - else: - self.pattern_fit_indices = [tuple(int(k) for k in np.unravel_index(int(i), index_shape)) for i in linear] - return self + def visualize( + self, *, power: float = 0.25, cbar: bool = False, axsize: tuple[int, int] = (6, 6) + ) -> tuple[Any, Any]: + import matplotlib.pyplot as plt + from matplotlib import gridspec - def _apply_overlays(self, ax: Any, overlays: list[Overlay]) -> None: - for ov in overlays: - if ov.kind != "points_rc": - continue - d = dict(ov.data) - r = _to_numpy(d["r"]).ravel() - c = _to_numpy(d["c"]).ravel() - ax.scatter( - c, - r, - s=float(d.get("s", 60.0)), - marker=d.get("marker", "x"), - color=d.get("color", "orange"), + if self.image_ref is None: + self.preprocess() + if self.image_ref is None or self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + + fig = plt.figure(figsize=(12, 7)) + gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2], hspace=0.3) + ax_top = fig.add_subplot(gs[0]) + self.plot_losses(figax=(fig, ax_top), plot_lrs=True) + + ref = np.asarray(self.image_ref, dtype=np.float32) + pred = to_numpy(self.model.forward(self.ctx)).astype(np.float32, copy=False) + refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) + predp = pred if power == 1.0 else np.maximum(pred, 0.0) ** float(power) + vmin = float(min(refp.min(), predp.min())) + vmax = float(max(refp.max(), predp.max())) + + gs_bot = gridspec.GridSpecFromSubplotSpec(1, 2, subplot_spec=gs[1], wspace=0.15) + axs = np.array( + [fig.add_subplot(gs_bot[0, 0]), fig.add_subplot(gs_bot[0, 1])], dtype=object + ) + show_2d( + [refp, predp], + figax=(fig, axs), + title=["image_ref", "model"], + cmap="gray", + cbar=bool(cbar), + returnfig=False, + axsize=axsize, + vmin=vmin, + vmax=vmax, + ) + + if len(self.mean_fit_history) > 0: + fig.suptitle( + f"Final loss: {self.mean_fit_history[-1]:.3e} | Iters: {len(self.mean_fit_history)}", + fontsize=13, + y=0.98, ) + plt.show() + return fig, axs def plot_mean_model( self, *, power: float = 0.25, returnfig: bool = False, - show_overlays: bool = True, axsize: tuple[int, int] = (6, 6), + **_: Any, ) -> tuple[Any, Any] | None: - """ - Plot `image_ref` and the current mean-model prediction side-by-side. - - Parameters - ---------- - power - Display transform exponent applied to both reference and model images. - returnfig - If True, return `(fig, ax)` from `show_2d`. - show_overlays - If True, draw component overlays (e.g., origin and lattice markers). - axsize - Base axis size passed to the plotting helper. - - Returns - ------- - tuple[Any, Any] | None - `(fig, ax)` if `returnfig=True`, else None. - """ if self.image_ref is None: self.preprocess() - if self.image_ref is None or self.prepared is None: + if self.image_ref is None or self.model is None or self.ctx is None: raise RuntimeError("Call .define_model(...) first.") - if self.x_mean is None: - self.x_mean = self.prepared.x0.detach().clone() ref = np.asarray(self.image_ref, dtype=np.float32) - mod = _to_numpy(self.prepared.render(self.x_mean)).astype(np.float32, copy=False) + pred = self.model.forward(self.ctx).detach().cpu().numpy() refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) - modp = mod if power == 1.0 else np.maximum(mod, 0.0) ** float(power) - - vmin = float(min(refp.min(), modp.min())) - vmax = float(max(refp.max(), modp.max())) + predp = pred if power == 1.0 else np.maximum(pred, 0.0) ** float(power) + vmin = float(min(refp.min(), predp.min())) + vmax = float(max(refp.max(), predp.max())) fig, ax = show_2d( - [refp, modp], + [refp, predp], title=["image_ref", "model"], cmap="gray", cbar=False, @@ -854,39 +405,6 @@ def plot_mean_model( vmin=vmin, vmax=vmax, ) - - H, W = ref.shape[-2], ref.shape[-1] - pad = 0 - boundaries = [] - for c in getattr(self.prepared, "components", []): - b = getattr(c, "boundary_px", None) - if b is not None: - boundaries.append(float(b)) - if boundaries: - min_b = float(np.min(boundaries)) - if min_b < 0.0: - pad = int(np.ceil(-min_b)) - - axes: list[Any] - if isinstance(ax, np.ndarray): - axes = list(ax.ravel()) - elif isinstance(ax, (list, tuple)): - axes = list(ax) - else: - axes = [ax] - - for a in axes[:2]: - # Match imshow's pixel-edge convention so overlay markers land on pixel centers. - a.set_xlim(-0.5 - pad, (W - 0.5) + pad) - a.set_ylim((H - 0.5) + pad, -0.5 - pad) - - if show_overlays: - ovs = self.prepared.overlays(self.x_mean) - if len(axes) >= 1: - self._apply_overlays(axes[0], ovs) - if len(axes) >= 2: - self._apply_overlays(axes[1], ovs) - if returnfig: return fig, ax return None From c7f32a564c366ffa91e193d448336fe153041fdc Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Fri, 27 Feb 2026 13:49:12 -0800 Subject: [PATCH 28/43] adding state saving to ModelDiffraction --- src/quantem/diffraction/model_fitting.py | 119 +++++++++++++++++++---- 1 file changed, 99 insertions(+), 20 deletions(-) diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 10477d4f..81c3af66 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -1,13 +1,13 @@ from __future__ import annotations import warnings -from typing import Any, Sequence, cast +from typing import Any, Literal, Sequence, cast import numpy as np import torch from scipy.ndimage import shift as ndi_shift from scipy.signal.windows import tukey -from torch.nn.utils import parameters_to_vector +from torch.nn.utils import parameters_to_vector, vector_to_parameters from quantem.core.datastructures import Dataset2d, Dataset3d, Dataset4d, Dataset4dstem from quantem.core.fitting.base import AdditiveRenderModel, RenderComponent, RenderContext @@ -15,7 +15,6 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.imaging_utils import cross_correlation_shift -from quantem.core.utils.utils import to_numpy from quantem.core.visualization import show_2d @@ -49,9 +48,9 @@ def __init__(self, dataset: Any, _token: object | None = None): self.model: AdditiveRenderModel | None = None self.target_mean: torch.Tensor | None = None - self.x_defined: torch.Tensor | None = None - self.x_initial: torch.Tensor | None = None - self.x_mean: torch.Tensor | None = None + self.state_initialized: torch.Tensor | None = None + self.state_mean_refined: torch.Tensor | None = None + self.state_current: torch.Tensor | None = None self.mean_refined: bool = False self.mean_fit_history: list[float] = [] self.mean_fit_lrs: list[float] = [] @@ -71,6 +70,82 @@ def get_optimization_parameters(self) -> Any: return [] return self.model.parameters() + def _get_model_state_vector(self) -> torch.Tensor: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + return parameters_to_vector(self.model.parameters()).detach().clone() + + def _load_model_state_vector(self, state: torch.Tensor) -> None: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + dst = next(self.model.parameters(), None) + if dst is None: + raise RuntimeError("Model has no parameters.") + vec = state.detach().clone().to(device=dst.device, dtype=dst.dtype) + vector_to_parameters(vec, self.model.parameters()) + + def _clear_histories(self) -> None: + for name in ("mean_fit_history", "mean_fit_lrs", "fit_history", "fit_lrs"): + if hasattr(self, name): + val = getattr(self, name) + if isinstance(val, list): + val.clear() + + def _render_state_array(self, state: torch.Tensor) -> np.ndarray: + if self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + live = self._get_model_state_vector() + try: + self._load_model_state_vector(state) + arr = self.model(self.ctx).cpu().detach().numpy() + # arr = to_numpy(self.model(self.ctx)).astype(np.float32, copy=False) + finally: + self._load_model_state_vector(live) + return arr + + @property + def render_initialized(self) -> np.ndarray: + if self.state_initialized is None: + raise RuntimeError("initialized state is unavailable. Call .define_model(...) first.") + return self._render_state_array(self.state_initialized) + + @property + def render_mean_refined(self) -> np.ndarray: + if self.state_mean_refined is None: + raise RuntimeError( + "mean_refined state is unavailable. Run .fit_mean_diffraction_pattern(...) first." + ) + return self._render_state_array(self.state_mean_refined) + + @property + def render_current(self) -> np.ndarray: + if self.state_current is None: + raise RuntimeError("current state is unavailable. Call .define_model(...) first.") + return self._render_state_array(self.state_current) + + def reset( + self, reset_to: Literal["initialized", "mean_refined"] = "mean_refined" + ) -> "ModelDiffraction": + if reset_to == "initialized": + state = self.state_initialized + if state is None: + raise RuntimeError( + "initialized state is unavailable. Call .define_model(...) first." + ) + elif reset_to == "mean_refined": + state = self.state_mean_refined + if state is None: + raise RuntimeError( + "mean_refined state is unavailable. Run .fit_mean_diffraction_pattern(...) first." + ) + else: + raise ValueError("reset_to must be 'initialized' or 'mean_refined'.") + + self._load_model_state_vector(state) + self.state_current = self._get_model_state_vector() + self._clear_histories() + return self + def preprocess( self, *, @@ -184,12 +259,11 @@ def define_model( self.target_mean = torch.as_tensor(self.image_ref, device=dev, dtype=dt) x0 = parameters_to_vector(self.model.parameters()).detach().clone() - self.x_defined = x0.detach().clone() - self.x_initial = x0.detach().clone() - self.x_mean = x0.detach().clone() + self.state_initialized = x0.detach().clone() + self.state_current = x0.detach().clone() + self.state_mean_refined = None self.mean_refined = False - self.mean_fit_history = [] - self.mean_fit_lrs = [] + self._clear_histories() self.remove_optimizer() return self @@ -197,15 +271,23 @@ def fit_mean_diffraction_pattern( self, *, n_steps: int = 200, + reset: bool | Literal["initialized", "mean_refined"] = False, optimizer_params: dict | None = None, scheduler_params: dict | None = None, constraint_weight: float = 1.0, - overwrite_initial: bool = True, progress: bool = False, **kwargs: Any, ) -> "ModelDiffraction": if self.model is None or self.ctx is None or self.target_mean is None: raise RuntimeError("Call .define_model(...) first.") + if reset is True: + self.reset("initialized") + elif isinstance(reset, str): + if reset not in ("initialized", "mean_refined"): + raise ValueError("reset must be False, True, 'initialized', or 'mean_refined'.") + self.reset(reset_to=cast(Literal["initialized", "mean_refined"], reset)) + elif reset not in (False,): + raise ValueError("reset must be False, True, 'initialized', or 'mean_refined'.") optimizer_rebuilt = False if optimizer_params is not None: @@ -234,8 +316,6 @@ def fit_mean_diffraction_pattern( except Exception: warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) - self.mean_fit_history = [] - self.mean_fit_lrs = [] for _ in iterator: self.zero_optimizer_grad() pred = self.model(self.ctx) @@ -255,11 +335,10 @@ def fit_mean_diffraction_pattern( self.mean_fit_history.append(loss_value) self.mean_fit_lrs.append(float(self.get_current_lr())) - x_fit = parameters_to_vector(self.model.parameters()).detach().clone() - self.x_mean = x_fit + x_fit = self._get_model_state_vector() + self.state_current = x_fit.detach().clone() + self.state_mean_refined = x_fit.detach().clone() self.mean_refined = True - if overwrite_initial: - self.x_initial = x_fit.detach().clone() return self def plot_losses( @@ -343,7 +422,7 @@ def visualize( self.plot_losses(figax=(fig, ax_top), plot_lrs=True) ref = np.asarray(self.image_ref, dtype=np.float32) - pred = to_numpy(self.model.forward(self.ctx)).astype(np.float32, copy=False) + pred = self.render_current refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) predp = pred if power == 1.0 else np.maximum(pred, 0.0) ** float(power) vmin = float(min(refp.min(), predp.min())) @@ -388,7 +467,7 @@ def plot_mean_model( raise RuntimeError("Call .define_model(...) first.") ref = np.asarray(self.image_ref, dtype=np.float32) - pred = self.model.forward(self.ctx).detach().cpu().numpy() + pred = self.render_current refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) predp = pred if power == 1.0 else np.maximum(pred, 0.0) ** float(power) From 2a9d6403b3c2b9cc0bbfa8c983e87aa0efc36eb3 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Fri, 27 Feb 2026 13:58:21 -0800 Subject: [PATCH 29/43] switching to state_dict saving --- src/quantem/diffraction/model_fitting.py | 54 ++++++++++++------------ 1 file changed, 26 insertions(+), 28 deletions(-) diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 81c3af66..7fdd43e4 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -7,7 +7,6 @@ import torch from scipy.ndimage import shift as ndi_shift from scipy.signal.windows import tukey -from torch.nn.utils import parameters_to_vector, vector_to_parameters from quantem.core.datastructures import Dataset2d, Dataset3d, Dataset4d, Dataset4dstem from quantem.core.fitting.base import AdditiveRenderModel, RenderComponent, RenderContext @@ -48,9 +47,9 @@ def __init__(self, dataset: Any, _token: object | None = None): self.model: AdditiveRenderModel | None = None self.target_mean: torch.Tensor | None = None - self.state_initialized: torch.Tensor | None = None - self.state_mean_refined: torch.Tensor | None = None - self.state_current: torch.Tensor | None = None + self.state_initialized: dict[str, torch.Tensor] | None = None + self.state_mean_refined: dict[str, torch.Tensor] | None = None + self.state_current: dict[str, torch.Tensor] | None = None self.mean_refined: bool = False self.mean_fit_history: list[float] = [] self.mean_fit_lrs: list[float] = [] @@ -70,19 +69,18 @@ def get_optimization_parameters(self) -> Any: return [] return self.model.parameters() - def _get_model_state_vector(self) -> torch.Tensor: + def _clone_state_dict(self, state: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + return {k: v.detach().clone() for k, v in state.items()} + + def _get_model_state_dict_copy(self) -> dict[str, torch.Tensor]: if self.model is None: raise RuntimeError("Call .define_model(...) first.") - return parameters_to_vector(self.model.parameters()).detach().clone() + return self._clone_state_dict(self.model.state_dict()) - def _load_model_state_vector(self, state: torch.Tensor) -> None: + def _load_model_state_dict_copy(self, state: dict[str, torch.Tensor]) -> None: if self.model is None: raise RuntimeError("Call .define_model(...) first.") - dst = next(self.model.parameters(), None) - if dst is None: - raise RuntimeError("Model has no parameters.") - vec = state.detach().clone().to(device=dst.device, dtype=dst.dtype) - vector_to_parameters(vec, self.model.parameters()) + self.model.load_state_dict(self._clone_state_dict(state), strict=True) def _clear_histories(self) -> None: for name in ("mean_fit_history", "mean_fit_lrs", "fit_history", "fit_lrs"): @@ -91,16 +89,15 @@ def _clear_histories(self) -> None: if isinstance(val, list): val.clear() - def _render_state_array(self, state: torch.Tensor) -> np.ndarray: + def _render_state_array(self, state: dict[str, torch.Tensor]) -> np.ndarray: if self.model is None or self.ctx is None: raise RuntimeError("Call .define_model(...) first.") - live = self._get_model_state_vector() + live = self._get_model_state_dict_copy() try: - self._load_model_state_vector(state) + self._load_model_state_dict_copy(state) arr = self.model(self.ctx).cpu().detach().numpy() - # arr = to_numpy(self.model(self.ctx)).astype(np.float32, copy=False) finally: - self._load_model_state_vector(live) + self._load_model_state_dict_copy(live) return arr @property @@ -119,9 +116,10 @@ def render_mean_refined(self) -> np.ndarray: @property def render_current(self) -> np.ndarray: - if self.state_current is None: - raise RuntimeError("current state is unavailable. Call .define_model(...) first.") - return self._render_state_array(self.state_current) + if self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + # Current render follows live module parameters by design. + return self.model(self.ctx).cpu().detach().numpy() def reset( self, reset_to: Literal["initialized", "mean_refined"] = "mean_refined" @@ -141,8 +139,8 @@ def reset( else: raise ValueError("reset_to must be 'initialized' or 'mean_refined'.") - self._load_model_state_vector(state) - self.state_current = self._get_model_state_vector() + self._load_model_state_dict_copy(state) + self.state_current = self._get_model_state_dict_copy() self._clear_histories() return self @@ -258,9 +256,9 @@ def define_model( self.ctx = RenderContext(shape=(h, w), device=dev, dtype=dt, mask=mask_t, fields={}) self.target_mean = torch.as_tensor(self.image_ref, device=dev, dtype=dt) - x0 = parameters_to_vector(self.model.parameters()).detach().clone() - self.state_initialized = x0.detach().clone() - self.state_current = x0.detach().clone() + s0 = self._get_model_state_dict_copy() + self.state_initialized = s0 + self.state_current = self._clone_state_dict(s0) self.state_mean_refined = None self.mean_refined = False self._clear_histories() @@ -335,9 +333,9 @@ def fit_mean_diffraction_pattern( self.mean_fit_history.append(loss_value) self.mean_fit_lrs.append(float(self.get_current_lr())) - x_fit = self._get_model_state_vector() - self.state_current = x_fit.detach().clone() - self.state_mean_refined = x_fit.detach().clone() + s_fit = self._get_model_state_dict_copy() + self.state_current = s_fit + self.state_mean_refined = self._clone_state_dict(s_fit) self.mean_refined = True return self From a9b4eaabffa0d4e1d613383f939e25b4262cbb13 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Fri, 27 Feb 2026 14:43:58 -0800 Subject: [PATCH 30/43] first version of FitBase --- src/quantem/core/fitting/base.py | 103 +++++++++++++++++++++++ src/quantem/diffraction/model_fitting.py | 97 ++++++++++----------- 2 files changed, 148 insertions(+), 52 deletions(-) diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index c37ccef9..fa3dbf25 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -1,10 +1,14 @@ from __future__ import annotations +from abc import abstractmethod from dataclasses import dataclass, field from typing import Any, cast import torch from torch import nn +from tqdm.auto import tqdm + +from quantem.core.ml.optimizer_mixin import OptimizerMixin @dataclass @@ -48,6 +52,105 @@ def total_constraint_loss(self, ctx: RenderContext) -> torch.Tensor: return loss +@dataclass +class FitResult: + losses: list[float] + lrs: list[float] + final_loss: float + num_steps: int + metrics: dict[str, list[float]] = field(default_factory=dict) + + +class FitBase(OptimizerMixin): + DEFAULT_LR = 1e-2 + DEFAULT_OPTIMIZER_TYPE = "adam" + + def __init__(self): + super().__init__() + self.fit_history_by_run: dict[str, FitResult] = {} + + @abstractmethod + def _forward_for_fit(self, *, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: + raise NotImplementedError + + @abstractmethod + def _fidelity_loss( + self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any + ) -> torch.Tensor: + raise NotImplementedError + + @abstractmethod + def _soft_losses( + self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any + ) -> dict[str, torch.Tensor]: + return {} + + def fit_render( + self, + *, + target: torch.Tensor, + n_steps: int, + optimizer_params: dict | None = None, + scheduler_params: dict | None = None, + progress: bool = False, + run_key: str = "default", + **kwargs: Any, + ) -> FitResult: + optimizer_rebuilt = False + if optimizer_params is not None: + self.set_optimizer(optimizer_params) + optimizer_rebuilt = True + elif self.optimizer is None: + if self.optimizer_params: + self.set_optimizer(self.optimizer_params) + else: + self.set_optimizer( + { + "type": getattr(self, "DEFAULT_OPTIMIZER_TYPE", "adamw"), + "lr": float(getattr(self, "DEFAULT_LR", self.DEFAULT_LR)), + } + ) + optimizer_rebuilt = True + + if scheduler_params is not None: + self.set_scheduler(scheduler_params, num_iter=int(n_steps)) + elif self.scheduler is None and self.scheduler_params: + self.set_scheduler(self.scheduler_params, num_iter=int(n_steps)) + elif optimizer_rebuilt and self.scheduler is not None and self.optimizer is not None: + self.scheduler.optimizer = self.optimizer + + pbar = tqdm(range(int(n_steps)), desc="Fit render", disable=not progress) + + losses: list[float] = [] + lrs: list[float] = [] + metrics: dict[str, list[float]] = {} + for _ in pbar: + self.zero_optimizer_grad() + pred = self._forward_for_fit(target=target, **kwargs) + fidelity_loss = self._fidelity_loss(pred, target, **kwargs) + soft_losses = self._soft_losses(pred, target, **kwargs) + total_loss = fidelity_loss + for k, v in soft_losses.items(): + metrics.setdefault(k, []).append(float(v.detach().cpu())) + total_loss = total_loss + v + total_loss.backward() + self.step_optimizer() + total_loss_value = float(total_loss.detach().cpu()) + self.step_scheduler(total_loss_value) + losses.append(total_loss_value) + lrs.append(float(self.get_current_lr())) + + result = FitResult( + losses=losses, + lrs=lrs, + final_loss=(losses[-1] if losses else float("nan")), + num_steps=int(n_steps), + metrics=metrics, + ) + self.fit_history_by_run[str(run_key)] = result + return result + + Component = RenderComponent ModelContext = RenderContext Model = AdditiveRenderModel diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 7fdd43e4..61fbef82 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -1,6 +1,5 @@ from __future__ import annotations -import warnings from typing import Any, Literal, Sequence, cast import numpy as np @@ -9,10 +8,14 @@ from scipy.signal.windows import tukey from quantem.core.datastructures import Dataset2d, Dataset3d, Dataset4d, Dataset4dstem -from quantem.core.fitting.base import AdditiveRenderModel, RenderComponent, RenderContext +from quantem.core.fitting.base import ( + AdditiveRenderModel, + FitBase, + RenderComponent, + RenderContext, +) from quantem.core.fitting.diffraction import OriginND from quantem.core.io.serialize import AutoSerialize -from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.imaging_utils import cross_correlation_shift from quantem.core.visualization import show_2d @@ -27,16 +30,16 @@ def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) return float(cast(float | int, value)) -class ModelDiffraction(OptimizerMixin, AutoSerialize): +class ModelDiffraction(FitBase, AutoSerialize): _token = object() - DEFAULT_LR = 1e-3 - DEFAULT_OPTIMIZER_TYPE = "adamw" + DEFAULT_LR = 5e-2 + DEFAULT_OPTIMIZER_TYPE = "adam" def __init__(self, dataset: Any, _token: object | None = None): if _token is not self._token: raise RuntimeError("Use ModelDiffraction.from_dataset() or .from_file().") AutoSerialize.__init__(self) - OptimizerMixin.__init__(self) + FitBase.__init__(self) self.dataset = dataset self.metadata: dict[str, Any] = {} self.image_ref: np.ndarray | None = None @@ -69,6 +72,30 @@ def get_optimization_parameters(self) -> Any: return [] return self.model.parameters() + def _forward_for_fit(self, *, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: + if self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + return self.model(self.ctx) + + def _fidelity_loss( + self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any + ) -> torch.Tensor: + if self.ctx is not None and self.ctx.mask is not None: + diff = (pred - target) * self.ctx.mask + denom = torch.clamp(torch.sum(self.ctx.mask), min=1.0) + return torch.sum(diff * diff) / denom + return torch.mean((pred - target) ** 2) + + def _soft_constraints( + self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any + ) -> dict[str, torch.Tensor]: + if self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + constraint_weight = float(kwargs.get("constraint_weight", 1.0)) + if constraint_weight == 0.0: + return {} + return {"constraint": constraint_weight * self.model.total_constraint_loss(self.ctx)} + def _clone_state_dict(self, state: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: return {k: v.detach().clone() for k, v in state.items()} @@ -287,51 +314,17 @@ def fit_mean_diffraction_pattern( elif reset not in (False,): raise ValueError("reset must be False, True, 'initialized', or 'mean_refined'.") - optimizer_rebuilt = False - if optimizer_params is not None: - self.set_optimizer(optimizer_params) - optimizer_rebuilt = True - elif self.optimizer is None: - if self.optimizer_params: - self.set_optimizer(self.optimizer_params) - else: - self.set_optimizer({"type": self.DEFAULT_OPTIMIZER_TYPE, "lr": self.DEFAULT_LR}) - optimizer_rebuilt = True - - if scheduler_params is not None: - self.set_scheduler(scheduler_params, num_iter=int(n_steps)) - elif self.scheduler is None and self.scheduler_params: - self.set_scheduler(self.scheduler_params, num_iter=int(n_steps)) - elif optimizer_rebuilt and self.scheduler is not None and self.optimizer is not None: - self.scheduler.optimizer = self.optimizer - - iterator: Any = range(int(n_steps)) - if progress: - try: - from tqdm.auto import trange - - iterator = trange(int(n_steps), desc="Fit mean diffraction", leave=False) - except Exception: - warnings.warn("progress=True requested but tqdm is unavailable.", stacklevel=2) - - for _ in iterator: - self.zero_optimizer_grad() - pred = self.model(self.ctx) - if self.ctx.mask is not None: - diff = (pred - self.target_mean) * self.ctx.mask - denom = torch.clamp(torch.sum(self.ctx.mask), min=1.0) - loss_data = torch.sum(diff * diff) / denom - else: - loss_data = torch.mean((pred - self.target_mean) ** 2) - loss = loss_data + float(constraint_weight) * self.model.total_constraint_loss( - self.ctx - ) - loss.backward() - self.step_optimizer() - loss_value = float(loss.detach().cpu()) - self.step_scheduler(loss_value) - self.mean_fit_history.append(loss_value) - self.mean_fit_lrs.append(float(self.get_current_lr())) + fit_result = self.fit_render( + target=self.target_mean, + n_steps=int(n_steps), + optimizer_params=optimizer_params, + scheduler_params=scheduler_params, + progress=bool(progress), + run_key="mean", + constraint_weight=float(constraint_weight), + ) + self.mean_fit_history.extend(fit_result.losses) + self.mean_fit_lrs.extend(fit_result.lrs) s_fit = self._get_model_state_dict_copy() self.state_current = s_fit From b79e32c855a910349e1005cb65348e27ef2d321a Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Fri, 27 Feb 2026 16:29:05 -0800 Subject: [PATCH 31/43] cleaning up FitBase and ModelDiffraction --- src/quantem/core/fitting/__init__.py | 2 + src/quantem/core/fitting/background.py | 20 +--- src/quantem/core/fitting/base.py | 116 ++++++++++++++++------- src/quantem/core/fitting/diffraction.py | 33 +------ src/quantem/diffraction/model_fitting.py | 79 ++++----------- 5 files changed, 108 insertions(+), 142 deletions(-) diff --git a/src/quantem/core/fitting/__init__.py b/src/quantem/core/fitting/__init__.py index f7d3c3e3..4dae1661 100644 --- a/src/quantem/core/fitting/__init__.py +++ b/src/quantem/core/fitting/__init__.py @@ -3,6 +3,7 @@ from quantem.core.fitting.base import Component as Component from quantem.core.fitting.base import Model as Model from quantem.core.fitting.base import ModelContext as ModelContext +from quantem.core.fitting.base import OriginND as OriginND from quantem.core.fitting.base import Parameter as Parameter from quantem.core.fitting.diffraction import DiskTemplate as DiskTemplate @@ -16,6 +17,7 @@ "ModelContext", "Component", "Model", + "OriginND", "DiskTemplate", "SyntheticDiskLattice", "DCBackground", diff --git a/src/quantem/core/fitting/background.py b/src/quantem/core/fitting/background.py index 7c50ea42..7a15d37b 100644 --- a/src/quantem/core/fitting/background.py +++ b/src/quantem/core/fitting/background.py @@ -6,8 +6,7 @@ import torch from torch import nn -from quantem.core.fitting.base import RenderComponent, RenderContext -from quantem.core.fitting.diffraction import OriginND +from quantem.core.fitting.base import OriginND, RenderComponent, RenderContext def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) -> float: @@ -20,14 +19,6 @@ def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) return float(cast(float | int, value)) -def _softplus_pos(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: - return torch.nn.functional.softplus(x) + eps - - -def _relu_pos(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: - return torch.nn.functional.relu(x) + eps - - class DCBackground(RenderComponent): def __init__( self, @@ -42,8 +33,7 @@ def __init__( ) def forward(self, ctx: RenderContext) -> torch.Tensor: - inten = _relu_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) - # inten = _softplus_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + inten = torch.clamp(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype), min=0.0) return torch.ones(ctx.shape, device=ctx.device, dtype=ctx.dtype) * inten @@ -79,9 +69,7 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: cc = torch.arange(ctx.shape[1], device=ctx.device, dtype=ctx.dtype)[None, :] r0, c0 = self.origin.coords[0], self.origin.coords[1] - sigma = _relu_pos(self.sigma_raw.to(device=ctx.device, dtype=ctx.dtype), eps=1e-6) - # sigma = _softplus_pos(self.sigma_raw.to(device=ctx.device, dtype=ctx.dtype), eps=1e-6) - inten = _relu_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) - # inten = _softplus_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + sigma = torch.clamp(self.sigma_raw.to(device=ctx.device, dtype=ctx.dtype), min=1e-6) + inten = torch.clamp(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype), min=0.0) r2 = (rr - r0) ** 2 + (cc - c0) ** 2 return inten * torch.exp(-0.5 * r2 / (sigma * sigma)) diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index fa3dbf25..d919a499 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -1,8 +1,7 @@ from __future__ import annotations -from abc import abstractmethod from dataclasses import dataclass, field -from typing import Any, cast +from typing import Any, Sequence, cast import torch from torch import nn @@ -20,6 +19,17 @@ class RenderContext: fields: dict[str, Any] = field(default_factory=dict) +class OriginND(nn.Module): + def __init__(self, *, ndim: int, init: Sequence[float]): + super().__init__() + if int(ndim) <= 0: + raise ValueError("ndim must be >= 1.") + if len(init) != int(ndim): + raise ValueError("init length must match ndim.") + self.ndim = int(ndim) + self.coords = nn.Parameter(torch.as_tensor(init, dtype=torch.float32).reshape(self.ndim)) + + class RenderComponent(nn.Module): def forward(self, ctx: RenderContext) -> torch.Tensor: raise NotImplementedError @@ -37,10 +47,8 @@ def __init__(self, *, origin: nn.Module, components: list[RenderComponent]): def forward(self, ctx: RenderContext) -> torch.Tensor: if len(self.components) == 0: return torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) - first = cast(RenderComponent, self.components[0]) - out = first(ctx) - for module in self.components[1:]: - component = cast(RenderComponent, module) + out = self.components[0](ctx) + for component in self.components[1:]: out = out + component(ctx) return out @@ -67,29 +75,62 @@ class FitBase(OptimizerMixin): def __init__(self): super().__init__() - self.fit_history_by_run: dict[str, FitResult] = {} + self.model: AdditiveRenderModel | None = None + self.ctx: RenderContext | None = None + self.fit_history: dict[str, FitResult] = {} + self.state_initialized: dict[str, torch.Tensor] | None = None + + def get_optimization_parameters(self) -> Any: + if self.model is None: + return [] + return self.model.parameters() + + def _clone_state_dict(self, state: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + return {k: v.detach().clone() for k, v in state.items()} + + def _get_model_state_dict_copy(self) -> dict[str, torch.Tensor]: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + return self._clone_state_dict(self.model.state_dict()) + + def _load_model_state_dict_copy(self, state: dict[str, torch.Tensor]) -> None: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + self.model.load_state_dict(self._clone_state_dict(state), strict=True) + + @property + def state_current(self) -> dict[str, torch.Tensor] | None: + if self.model is None: + return None + return self._get_model_state_dict_copy() + + def _clear_fit_history_all(self) -> None: + self.fit_history.clear() + + def _clear_fit_history_run(self, run_key: str) -> None: + self.fit_history.pop(str(run_key), None) - @abstractmethod def _forward_for_fit(self, *, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: - raise NotImplementedError + if self.model is None or self.ctx is None: + raise RuntimeError("Model and context are not defined for fitting.") + return self.model(self.ctx) - @abstractmethod - def _fidelity_loss( - self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any - ) -> torch.Tensor: + def _data_loss(self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: raise NotImplementedError - @abstractmethod - def _soft_losses( + def _constraint_loss( self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any - ) -> dict[str, torch.Tensor]: - return {} + ) -> torch.Tensor: + if self.model is None or self.ctx is None: + raise RuntimeError("Model and context are not defined for fitting.") + return self.model.total_constraint_loss(self.ctx) def fit_render( self, *, target: torch.Tensor, n_steps: int, + constraint_weight: float = 1.0, optimizer_params: dict | None = None, scheduler_params: dict | None = None, progress: bool = False, @@ -112,27 +153,24 @@ def fit_render( ) optimizer_rebuilt = True + n_steps = int(n_steps) if scheduler_params is not None: - self.set_scheduler(scheduler_params, num_iter=int(n_steps)) + self.set_scheduler(scheduler_params, num_iter=n_steps) elif self.scheduler is None and self.scheduler_params: - self.set_scheduler(self.scheduler_params, num_iter=int(n_steps)) + self.set_scheduler(self.scheduler_params, num_iter=n_steps) elif optimizer_rebuilt and self.scheduler is not None and self.optimizer is not None: self.scheduler.optimizer = self.optimizer - pbar = tqdm(range(int(n_steps)), desc="Fit render", disable=not progress) + pbar = tqdm(range(n_steps), desc="Fit render", disable=not progress) losses: list[float] = [] lrs: list[float] = [] - metrics: dict[str, list[float]] = {} for _ in pbar: self.zero_optimizer_grad() pred = self._forward_for_fit(target=target, **kwargs) - fidelity_loss = self._fidelity_loss(pred, target, **kwargs) - soft_losses = self._soft_losses(pred, target, **kwargs) - total_loss = fidelity_loss - for k, v in soft_losses.items(): - metrics.setdefault(k, []).append(float(v.detach().cpu())) - total_loss = total_loss + v + data_loss = self._data_loss(pred, target, **kwargs) + constraint_loss = self._constraint_loss(pred, target, **kwargs) + total_loss = data_loss + constraint_weight * constraint_loss total_loss.backward() self.step_optimizer() total_loss_value = float(total_loss.detach().cpu()) @@ -140,14 +178,22 @@ def fit_render( losses.append(total_loss_value) lrs.append(float(self.get_current_lr())) - result = FitResult( - losses=losses, - lrs=lrs, - final_loss=(losses[-1] if losses else float("nan")), - num_steps=int(n_steps), - metrics=metrics, - ) - self.fit_history_by_run[str(run_key)] = result + key = str(run_key) + if key in self.fit_history: + prev = self.fit_history[key] + prev.losses.extend(losses) + prev.lrs.extend(lrs) + prev.final_loss = prev.losses[-1] if prev.losses else float("nan") + prev.num_steps = len(prev.losses) + result = prev + else: + result = FitResult( + losses=losses, + lrs=lrs, + final_loss=(losses[-1] if losses else float("nan")), + num_steps=n_steps, + ) + self.fit_history[key] = result return result diff --git a/src/quantem/core/fitting/diffraction.py b/src/quantem/core/fitting/diffraction.py index 13224632..0872ffb5 100644 --- a/src/quantem/core/fitting/diffraction.py +++ b/src/quantem/core/fitting/diffraction.py @@ -6,7 +6,7 @@ import torch from torch import nn -from quantem.core.fitting.base import RenderComponent, RenderContext +from quantem.core.fitting.base import OriginND, RenderComponent, RenderContext def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) -> float: @@ -19,14 +19,6 @@ def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) return float(cast(float | int, value)) -def _softplus_pos(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: - return torch.nn.functional.softplus(x) + eps - - -def _relu_pos(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: - return torch.nn.functional.relu(x) + eps - - def _splat_patch( out: torch.Tensor, *, @@ -65,17 +57,6 @@ def put(rr: torch.Tensor, cc: torch.Tensor, ww: torch.Tensor) -> None: put(r0i + 1, c0i + 1, w11) -class OriginND(nn.Module): - def __init__(self, *, ndim: int, init: Sequence[float]): - super().__init__() - if int(ndim) <= 0: - raise ValueError("ndim must be >= 1.") - if len(init) != int(ndim): - raise ValueError("init length must match ndim.") - self.ndim = int(ndim) - self.coords = nn.Parameter(torch.as_tensor(init, dtype=torch.float32).reshape(self.ndim)) - - class DiskTemplate(RenderComponent): def __init__( self, @@ -156,8 +137,7 @@ def set_origin(self, origin: OriginND) -> None: def patch_values(self) -> torch.Tensor: if self.refine_all_pixels: - return _relu_pos(self.template_raw).reshape(-1) - # return _softplus_pos(self.template_raw).reshape(-1) + return torch.clamp(self.template_raw, min=0.0).reshape(-1) return self.template_raw.reshape(-1) def patch_offsets(self) -> tuple[torch.Tensor, torch.Tensor]: @@ -182,8 +162,7 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: r0, c0 = self.origin.coords[0], self.origin.coords[1] if self.intensity_raw is None: raise RuntimeError("DiskTemplate intensity parameter is missing.") - scale = _relu_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) - # scale = _softplus_pos(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype)) + scale = torch.clamp(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype), min=0.0) self.add_patch(out, r0=r0, c0=c0, scale=scale) return out @@ -349,8 +328,7 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: cc0 = centers_c[j] if self.per_disk_intensity: - inten = _relu_pos(self.i0_raw[j]) - # inten = _softplus_pos(self.i0_raw[j]) + inten = torch.clamp(self.i0_raw[j], min=0.0) if active_order >= 1 and self.ir is not None and self.ic is not None: inten = inten + self.ir[j] * dr + self.ic[j] * dc if ( @@ -362,8 +340,7 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: inten = inten + self.irr[j] * dr2 + self.icc[j] * dc2 + self.irc[j] * drdc inten = torch.clamp(inten, min=0.0) else: - inten = _relu_pos(self.i0_raw) - # inten = _softplus_pos(self.i0_raw) + inten = torch.clamp(self.i0_raw, min=0.0) if active_order >= 1: assert self.ir is not None and self.ic is not None inten = inten + self.ir * rr0 + self.ic * cc0 diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 61fbef82..8b6df7c3 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -11,10 +11,10 @@ from quantem.core.fitting.base import ( AdditiveRenderModel, FitBase, + OriginND, RenderComponent, RenderContext, ) -from quantem.core.fitting.diffraction import OriginND from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.imaging_utils import cross_correlation_shift from quantem.core.visualization import show_2d @@ -46,16 +46,10 @@ def __init__(self, dataset: Any, _token: object | None = None): self.preprocess_shifts: np.ndarray | None = None self.index_shape: tuple[int, ...] | None = None - self.ctx: RenderContext | None = None - self.model: AdditiveRenderModel | None = None self.target_mean: torch.Tensor | None = None - self.state_initialized: dict[str, torch.Tensor] | None = None self.state_mean_refined: dict[str, torch.Tensor] | None = None - self.state_current: dict[str, torch.Tensor] | None = None self.mean_refined: bool = False - self.mean_fit_history: list[float] = [] - self.mean_fit_lrs: list[float] = [] @classmethod def from_dataset( @@ -67,55 +61,13 @@ def from_dataset( "from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance." ) - def get_optimization_parameters(self) -> Any: - if self.model is None: - return [] - return self.model.parameters() - - def _forward_for_fit(self, *, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: - if self.model is None or self.ctx is None: - raise RuntimeError("Call .define_model(...) first.") - return self.model(self.ctx) - - def _fidelity_loss( - self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any - ) -> torch.Tensor: + def _data_loss(self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: if self.ctx is not None and self.ctx.mask is not None: diff = (pred - target) * self.ctx.mask denom = torch.clamp(torch.sum(self.ctx.mask), min=1.0) return torch.sum(diff * diff) / denom return torch.mean((pred - target) ** 2) - def _soft_constraints( - self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any - ) -> dict[str, torch.Tensor]: - if self.model is None or self.ctx is None: - raise RuntimeError("Call .define_model(...) first.") - constraint_weight = float(kwargs.get("constraint_weight", 1.0)) - if constraint_weight == 0.0: - return {} - return {"constraint": constraint_weight * self.model.total_constraint_loss(self.ctx)} - - def _clone_state_dict(self, state: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: - return {k: v.detach().clone() for k, v in state.items()} - - def _get_model_state_dict_copy(self) -> dict[str, torch.Tensor]: - if self.model is None: - raise RuntimeError("Call .define_model(...) first.") - return self._clone_state_dict(self.model.state_dict()) - - def _load_model_state_dict_copy(self, state: dict[str, torch.Tensor]) -> None: - if self.model is None: - raise RuntimeError("Call .define_model(...) first.") - self.model.load_state_dict(self._clone_state_dict(state), strict=True) - - def _clear_histories(self) -> None: - for name in ("mean_fit_history", "mean_fit_lrs", "fit_history", "fit_lrs"): - if hasattr(self, name): - val = getattr(self, name) - if isinstance(val, list): - val.clear() - def _render_state_array(self, state: dict[str, torch.Tensor]) -> np.ndarray: if self.model is None or self.ctx is None: raise RuntimeError("Call .define_model(...) first.") @@ -157,18 +109,21 @@ def reset( raise RuntimeError( "initialized state is unavailable. Call .define_model(...) first." ) + self._clear_fit_history_all() elif reset_to == "mean_refined": state = self.state_mean_refined if state is None: raise RuntimeError( "mean_refined state is unavailable. Run .fit_mean_diffraction_pattern(...) first." ) + mean_hist = self.fit_history.get("mean") + self._clear_fit_history_all() + if mean_hist is not None: + self.fit_history["mean"] = mean_hist else: raise ValueError("reset_to must be 'initialized' or 'mean_refined'.") self._load_model_state_dict_copy(state) - self.state_current = self._get_model_state_dict_copy() - self._clear_histories() return self def preprocess( @@ -285,10 +240,9 @@ def define_model( s0 = self._get_model_state_dict_copy() self.state_initialized = s0 - self.state_current = self._clone_state_dict(s0) self.state_mean_refined = None self.mean_refined = False - self._clear_histories() + self._clear_fit_history_all() self.remove_optimizer() return self @@ -314,20 +268,17 @@ def fit_mean_diffraction_pattern( elif reset not in (False,): raise ValueError("reset must be False, True, 'initialized', or 'mean_refined'.") - fit_result = self.fit_render( + self.fit_render( target=self.target_mean, n_steps=int(n_steps), + constraint_weight=float(constraint_weight), optimizer_params=optimizer_params, scheduler_params=scheduler_params, progress=bool(progress), run_key="mean", - constraint_weight=float(constraint_weight), ) - self.mean_fit_history.extend(fit_result.losses) - self.mean_fit_lrs.extend(fit_result.lrs) s_fit = self._get_model_state_dict_copy() - self.state_current = s_fit self.state_mean_refined = self._clone_state_dict(s_fit) self.mean_refined = True return self @@ -342,7 +293,8 @@ def plot_losses( else: fig, ax = figax - losses = np.asarray(self.mean_fit_history, dtype=np.float64) + mean_hist = self.fit_history.get("mean") + losses = np.asarray([] if mean_hist is None else mean_hist.losses, dtype=np.float64) if losses.size == 0: ax.text( 0.5, @@ -368,7 +320,7 @@ def plot_losses( ax.spines["left"].set_color("k") ax.set_xbound(-2, max(1, int(iters.max())) + 2) - lrs = np.asarray(self.mean_fit_lrs, dtype=np.float64) + lrs = np.asarray([] if mean_hist is None else mean_hist.lrs, dtype=np.float64) if plot_lrs and lrs.size > 0: if lrs.size == losses.size and not np.allclose(lrs, lrs[0]): ax_lr = ax.twinx() @@ -435,9 +387,10 @@ def visualize( vmax=vmax, ) - if len(self.mean_fit_history) > 0: + mean_hist = self.fit_history.get("mean") + if mean_hist is not None and len(mean_hist.losses) > 0: fig.suptitle( - f"Final loss: {self.mean_fit_history[-1]:.3e} | Iters: {len(self.mean_fit_history)}", + f"Final loss: {mean_hist.losses[-1]:.3e} | Iters: {len(mean_hist.losses)}", fontsize=13, y=0.98, ) From e0aeb0bfe1fe33a889c77ec64425d202fa2c5695 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Fri, 27 Feb 2026 16:43:11 -0800 Subject: [PATCH 32/43] moving more stuff to FitBase --- src/quantem/core/fitting/base.py | 53 ++++++++++++++++++++++-- src/quantem/diffraction/model_fitting.py | 34 +-------------- 2 files changed, 51 insertions(+), 36 deletions(-) diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index d919a499..4b60fa5c 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -1,8 +1,9 @@ from __future__ import annotations from dataclasses import dataclass, field -from typing import Any, Sequence, cast +from typing import Any, Literal, Self, Sequence, cast +import numpy as np import torch from torch import nn from tqdm.auto import tqdm @@ -79,6 +80,7 @@ def __init__(self): self.ctx: RenderContext | None = None self.fit_history: dict[str, FitResult] = {} self.state_initialized: dict[str, torch.Tensor] | None = None + self.loss_fn = torch.nn.MSELoss(reduction="mean") def get_optimization_parameters(self) -> Any: if self.model is None: @@ -110,13 +112,56 @@ def _clear_fit_history_all(self) -> None: def _clear_fit_history_run(self, run_key: str) -> None: self.fit_history.pop(str(run_key), None) + def _render_state_array(self, state: dict[str, torch.Tensor]) -> np.ndarray: + if self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + live = self._get_model_state_dict_copy() + try: + self._load_model_state_dict_copy(state) + arr = self.model(self.ctx).detach().cpu().numpy() + finally: + self._load_model_state_dict_copy(live) + return arr + + @property + def render_initialized(self) -> np.ndarray: + if self.state_initialized is None: + raise RuntimeError("initialized state is unavailable. Call .define_model(...) first.") + return self._render_state_array(self.state_initialized) + + @property + def render_current(self) -> np.ndarray: + if self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + return self.model(self.ctx).detach().cpu().numpy() + + def reset( + self, + reset_to: Literal["initialized"] = "initialized", + ) -> Self: + if reset_to != "initialized": + raise ValueError("FitBase.reset only supports reset_to='initialized'.") + if self.state_initialized is None: + raise RuntimeError("initialized state is unavailable. Call .define_model(...) first.") + self._load_model_state_dict_copy(self.state_initialized) + self._clear_fit_history_all() + return self + def _forward_for_fit(self, *, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: if self.model is None or self.ctx is None: raise RuntimeError("Model and context are not defined for fitting.") return self.model(self.ctx) - def _data_loss(self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: - raise NotImplementedError + def _fidelity_loss( + self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any + ) -> torch.Tensor: + if self.ctx is not None and self.ctx.mask is not None: + # TODO -- use loss modules (currently implemented in tomo branch) + # and update them to allow for masking at module level + diff = (pred - target) * self.ctx.mask + denom = torch.clamp(torch.sum(self.ctx.mask), min=1.0) + return torch.sum(diff * diff) / denom + return self.loss_fn(pred, target) def _constraint_loss( self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any @@ -168,7 +213,7 @@ def fit_render( for _ in pbar: self.zero_optimizer_grad() pred = self._forward_for_fit(target=target, **kwargs) - data_loss = self._data_loss(pred, target, **kwargs) + data_loss = self._fidelity_loss(pred, target, **kwargs) constraint_loss = self._constraint_loss(pred, target, **kwargs) total_loss = data_loss + constraint_weight * constraint_loss total_loss.backward() diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 8b6df7c3..1cbef162 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -61,30 +61,6 @@ def from_dataset( "from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance." ) - def _data_loss(self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: - if self.ctx is not None and self.ctx.mask is not None: - diff = (pred - target) * self.ctx.mask - denom = torch.clamp(torch.sum(self.ctx.mask), min=1.0) - return torch.sum(diff * diff) / denom - return torch.mean((pred - target) ** 2) - - def _render_state_array(self, state: dict[str, torch.Tensor]) -> np.ndarray: - if self.model is None or self.ctx is None: - raise RuntimeError("Call .define_model(...) first.") - live = self._get_model_state_dict_copy() - try: - self._load_model_state_dict_copy(state) - arr = self.model(self.ctx).cpu().detach().numpy() - finally: - self._load_model_state_dict_copy(live) - return arr - - @property - def render_initialized(self) -> np.ndarray: - if self.state_initialized is None: - raise RuntimeError("initialized state is unavailable. Call .define_model(...) first.") - return self._render_state_array(self.state_initialized) - @property def render_mean_refined(self) -> np.ndarray: if self.state_mean_refined is None: @@ -93,15 +69,9 @@ def render_mean_refined(self) -> np.ndarray: ) return self._render_state_array(self.state_mean_refined) - @property - def render_current(self) -> np.ndarray: - if self.model is None or self.ctx is None: - raise RuntimeError("Call .define_model(...) first.") - # Current render follows live module parameters by design. - return self.model(self.ctx).cpu().detach().numpy() - def reset( - self, reset_to: Literal["initialized", "mean_refined"] = "mean_refined" + self, + reset_to: Literal["initialized", "mean_refined"] = "mean_refined", ) -> "ModelDiffraction": if reset_to == "initialized": state = self.state_initialized From 2c67dfebebce89abe14c25603b7ba94787da6ced Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Fri, 27 Feb 2026 16:54:11 -0800 Subject: [PATCH 33/43] reorganizing classes -- no functional change --- src/quantem/core/fitting/__init__.py | 18 ++-- src/quantem/core/fitting/base.py | 115 ++++++++++++----------- src/quantem/diffraction/model_fitting.py | 78 +++++++-------- 3 files changed, 108 insertions(+), 103 deletions(-) diff --git a/src/quantem/core/fitting/__init__.py b/src/quantem/core/fitting/__init__.py index 4dae1661..1bb92620 100644 --- a/src/quantem/core/fitting/__init__.py +++ b/src/quantem/core/fitting/__init__.py @@ -1,25 +1,21 @@ -from __future__ import annotations - +from quantem.core.fitting.background import DCBackground as DCBackground +from quantem.core.fitting.background import GaussianBackground as GaussianBackground from quantem.core.fitting.base import Component as Component from quantem.core.fitting.base import Model as Model from quantem.core.fitting.base import ModelContext as ModelContext from quantem.core.fitting.base import OriginND as OriginND from quantem.core.fitting.base import Parameter as Parameter - from quantem.core.fitting.diffraction import DiskTemplate as DiskTemplate from quantem.core.fitting.diffraction import SyntheticDiskLattice as SyntheticDiskLattice -from quantem.core.fitting.background import DCBackground as DCBackground -from quantem.core.fitting.background import GaussianBackground as GaussianBackground - __all__ = [ - "Parameter", - "ModelContext", "Component", + "DCBackground", + "DiskTemplate", + "GaussianBackground", "Model", + "ModelContext", "OriginND", - "DiskTemplate", + "Parameter", "SyntheticDiskLattice", - "DCBackground", - "GaussianBackground", ] diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index 4b60fa5c..dfce2e7b 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -76,53 +76,28 @@ class FitBase(OptimizerMixin): def __init__(self): super().__init__() + # Core wiring + self.loss_fn = torch.nn.MSELoss(reduction="mean") self.model: AdditiveRenderModel | None = None self.ctx: RenderContext | None = None - self.fit_history: dict[str, FitResult] = {} + + # State/checkpoints self.state_initialized: dict[str, torch.Tensor] | None = None - self.loss_fn = torch.nn.MSELoss(reduction="mean") + + # Histories/results + self.fit_history: dict[str, FitResult] = {} def get_optimization_parameters(self) -> Any: if self.model is None: return [] return self.model.parameters() - def _clone_state_dict(self, state: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: - return {k: v.detach().clone() for k, v in state.items()} - - def _get_model_state_dict_copy(self) -> dict[str, torch.Tensor]: - if self.model is None: - raise RuntimeError("Call .define_model(...) first.") - return self._clone_state_dict(self.model.state_dict()) - - def _load_model_state_dict_copy(self, state: dict[str, torch.Tensor]) -> None: - if self.model is None: - raise RuntimeError("Call .define_model(...) first.") - self.model.load_state_dict(self._clone_state_dict(state), strict=True) - @property def state_current(self) -> dict[str, torch.Tensor] | None: if self.model is None: return None return self._get_model_state_dict_copy() - def _clear_fit_history_all(self) -> None: - self.fit_history.clear() - - def _clear_fit_history_run(self, run_key: str) -> None: - self.fit_history.pop(str(run_key), None) - - def _render_state_array(self, state: dict[str, torch.Tensor]) -> np.ndarray: - if self.model is None or self.ctx is None: - raise RuntimeError("Call .define_model(...) first.") - live = self._get_model_state_dict_copy() - try: - self._load_model_state_dict_copy(state) - arr = self.model(self.ctx).detach().cpu().numpy() - finally: - self._load_model_state_dict_copy(live) - return arr - @property def render_initialized(self) -> np.ndarray: if self.state_initialized is None: @@ -147,29 +122,6 @@ def reset( self._clear_fit_history_all() return self - def _forward_for_fit(self, *, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: - if self.model is None or self.ctx is None: - raise RuntimeError("Model and context are not defined for fitting.") - return self.model(self.ctx) - - def _fidelity_loss( - self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any - ) -> torch.Tensor: - if self.ctx is not None and self.ctx.mask is not None: - # TODO -- use loss modules (currently implemented in tomo branch) - # and update them to allow for masking at module level - diff = (pred - target) * self.ctx.mask - denom = torch.clamp(torch.sum(self.ctx.mask), min=1.0) - return torch.sum(diff * diff) / denom - return self.loss_fn(pred, target) - - def _constraint_loss( - self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any - ) -> torch.Tensor: - if self.model is None or self.ctx is None: - raise RuntimeError("Model and context are not defined for fitting.") - return self.model.total_constraint_loss(self.ctx) - def fit_render( self, *, @@ -241,6 +193,59 @@ def fit_render( self.fit_history[key] = result return result + def _clone_state_dict(self, state: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + return {k: v.detach().clone() for k, v in state.items()} + + def _get_model_state_dict_copy(self) -> dict[str, torch.Tensor]: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + return self._clone_state_dict(self.model.state_dict()) + + def _load_model_state_dict_copy(self, state: dict[str, torch.Tensor]) -> None: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + self.model.load_state_dict(self._clone_state_dict(state), strict=True) + + def _clear_fit_history_all(self) -> None: + self.fit_history.clear() + + def _clear_fit_history_run(self, run_key: str) -> None: + self.fit_history.pop(str(run_key), None) + + def _render_state_array(self, state: dict[str, torch.Tensor]) -> np.ndarray: + if self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + live = self._get_model_state_dict_copy() + try: + self._load_model_state_dict_copy(state) + arr = self.model(self.ctx).detach().cpu().numpy() + finally: + self._load_model_state_dict_copy(live) + return arr + + def _forward_for_fit(self, *, target: torch.Tensor, **kwargs: Any) -> torch.Tensor: + if self.model is None or self.ctx is None: + raise RuntimeError("Model and context are not defined for fitting.") + return self.model(self.ctx) + + def _fidelity_loss( + self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any + ) -> torch.Tensor: + if self.ctx is not None and self.ctx.mask is not None: + # TODO -- use loss modules (currently implemented in tomo branch) + # and update them to allow for masking at module level + diff = (pred - target) * self.ctx.mask + denom = torch.clamp(torch.sum(self.ctx.mask), min=1.0) + return torch.sum(diff * diff) / denom + return self.loss_fn(pred, target) + + def _constraint_loss( + self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any + ) -> torch.Tensor: + if self.model is None or self.ctx is None: + raise RuntimeError("Model and context are not defined for fitting.") + return self.model.total_constraint_loss(self.ctx) + Component = RenderComponent ModelContext = RenderContext diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 1cbef162..465bfdb8 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -40,17 +40,21 @@ def __init__(self, dataset: Any, _token: object | None = None): raise RuntimeError("Use ModelDiffraction.from_dataset() or .from_file().") AutoSerialize.__init__(self) FitBase.__init__(self) + + # Dataset/input references self.dataset = dataset - self.metadata: dict[str, Any] = {} self.image_ref: np.ndarray | None = None self.preprocess_shifts: np.ndarray | None = None self.index_shape: tuple[int, ...] | None = None - self.target_mean: torch.Tensor | None = None + # Diffraction-specific state/checkpoints self.state_mean_refined: dict[str, torch.Tensor] | None = None self.mean_refined: bool = False + # Misc metadata + self.metadata: dict[str, Any] = {} + @classmethod def from_dataset( cls, dataset: Dataset2d | Dataset3d | Dataset4d | Dataset4dstem | Any @@ -61,41 +65,6 @@ def from_dataset( "from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance." ) - @property - def render_mean_refined(self) -> np.ndarray: - if self.state_mean_refined is None: - raise RuntimeError( - "mean_refined state is unavailable. Run .fit_mean_diffraction_pattern(...) first." - ) - return self._render_state_array(self.state_mean_refined) - - def reset( - self, - reset_to: Literal["initialized", "mean_refined"] = "mean_refined", - ) -> "ModelDiffraction": - if reset_to == "initialized": - state = self.state_initialized - if state is None: - raise RuntimeError( - "initialized state is unavailable. Call .define_model(...) first." - ) - self._clear_fit_history_all() - elif reset_to == "mean_refined": - state = self.state_mean_refined - if state is None: - raise RuntimeError( - "mean_refined state is unavailable. Run .fit_mean_diffraction_pattern(...) first." - ) - mean_hist = self.fit_history.get("mean") - self._clear_fit_history_all() - if mean_hist is not None: - self.fit_history["mean"] = mean_hist - else: - raise ValueError("reset_to must be 'initialized' or 'mean_refined'.") - - self._load_model_state_dict_copy(state) - return self - def preprocess( self, *, @@ -253,6 +222,41 @@ def fit_mean_diffraction_pattern( self.mean_refined = True return self + def reset( + self, + reset_to: Literal["initialized", "mean_refined"] = "mean_refined", + ) -> "ModelDiffraction": + if reset_to == "initialized": + state = self.state_initialized + if state is None: + raise RuntimeError( + "initialized state is unavailable. Call .define_model(...) first." + ) + self._clear_fit_history_all() + elif reset_to == "mean_refined": + state = self.state_mean_refined + if state is None: + raise RuntimeError( + "mean_refined state is unavailable. Run .fit_mean_diffraction_pattern(...) first." + ) + mean_hist = self.fit_history.get("mean") + self._clear_fit_history_all() + if mean_hist is not None: + self.fit_history["mean"] = mean_hist + else: + raise ValueError("reset_to must be 'initialized' or 'mean_refined'.") + + self._load_model_state_dict_copy(state) + return self + + @property + def render_mean_refined(self) -> np.ndarray: + if self.state_mean_refined is None: + raise RuntimeError( + "mean_refined state is unavailable. Run .fit_mean_diffraction_pattern(...) first." + ) + return self._render_state_array(self.state_mean_refined) + def plot_losses( self, figax: tuple[Any, Any] | None = None, plot_lrs: bool = True ) -> tuple[Any, Any]: From fc283c7cfb4962cdffb5c34e3dbb6635a2f24914 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Fri, 27 Feb 2026 17:05:25 -0800 Subject: [PATCH 34/43] splitting off ModelDiffractionVisualizations into separate file --- src/quantem/diffraction/model_fitting.py | 153 +--------------- .../model_fitting_visualizations.py | 165 ++++++++++++++++++ 2 files changed, 167 insertions(+), 151 deletions(-) create mode 100644 src/quantem/diffraction/model_fitting_visualizations.py diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 465bfdb8..d5c87477 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -17,7 +17,7 @@ ) from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.imaging_utils import cross_correlation_shift -from quantem.core.visualization import show_2d +from quantem.diffraction.model_fitting_visualizations import ModelDiffractionVisualizations def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) -> float: @@ -30,7 +30,7 @@ def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) return float(cast(float | int, value)) -class ModelDiffraction(FitBase, AutoSerialize): +class ModelDiffraction(ModelDiffractionVisualizations, FitBase, AutoSerialize): _token = object() DEFAULT_LR = 5e-2 DEFAULT_OPTIMIZER_TYPE = "adam" @@ -256,152 +256,3 @@ def render_mean_refined(self) -> np.ndarray: "mean_refined state is unavailable. Run .fit_mean_diffraction_pattern(...) first." ) return self._render_state_array(self.state_mean_refined) - - def plot_losses( - self, figax: tuple[Any, Any] | None = None, plot_lrs: bool = True - ) -> tuple[Any, Any]: - import matplotlib.pyplot as plt - - if figax is None: - fig, ax = plt.subplots() - else: - fig, ax = figax - - mean_hist = self.fit_history.get("mean") - losses = np.asarray([] if mean_hist is None else mean_hist.losses, dtype=np.float64) - if losses.size == 0: - ax.text( - 0.5, - 0.5, - "No fit history available", - ha="center", - va="center", - transform=ax.transAxes, - ) - ax.set_xlabel("Iterations") - ax.set_ylabel("Loss") - if figax is None: - plt.tight_layout() - plt.show() - return fig, ax - - iters = np.arange(losses.size) - lines: list[Any] = [] - lines.extend(ax.semilogy(iters, losses, c="k", lw=2, label="loss")) - ax.set_xlabel("Iterations") - ax.set_ylabel("Loss", color="k") - ax.tick_params(axis="y", which="both", colors="k") - ax.spines["left"].set_color("k") - ax.set_xbound(-2, max(1, int(iters.max())) + 2) - - lrs = np.asarray([] if mean_hist is None else mean_hist.lrs, dtype=np.float64) - if plot_lrs and lrs.size > 0: - if lrs.size == losses.size and not np.allclose(lrs, lrs[0]): - ax_lr = ax.twinx() - ax.set_zorder(2) - ax_lr.set_zorder(1) - ax.patch.set_visible(False) - ax_lr.spines["left"].set_visible(False) - lines.extend( - ax_lr.semilogy( - np.arange(lrs.size), lrs, c="tab:blue", lw=2, ls="--", label="LR" - ) - ) - ax_lr.set_ylabel("LR", color="tab:blue") - ax_lr.tick_params(axis="y", which="both", colors="tab:blue") - ax_lr.spines["right"].set_color("tab:blue") - else: - ax.set_title(f"LR: {float(lrs[-1]):.2e}", fontsize=10) - - labels = [line.get_label() for line in lines] - if len(labels) > 1: - ax.legend(lines, labels, loc="upper right") - - if figax is None: - plt.tight_layout() - plt.show() - return fig, ax - - def visualize( - self, *, power: float = 0.25, cbar: bool = False, axsize: tuple[int, int] = (6, 6) - ) -> tuple[Any, Any]: - import matplotlib.pyplot as plt - from matplotlib import gridspec - - if self.image_ref is None: - self.preprocess() - if self.image_ref is None or self.model is None or self.ctx is None: - raise RuntimeError("Call .define_model(...) first.") - - fig = plt.figure(figsize=(12, 7)) - gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2], hspace=0.3) - ax_top = fig.add_subplot(gs[0]) - self.plot_losses(figax=(fig, ax_top), plot_lrs=True) - - ref = np.asarray(self.image_ref, dtype=np.float32) - pred = self.render_current - refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) - predp = pred if power == 1.0 else np.maximum(pred, 0.0) ** float(power) - vmin = float(min(refp.min(), predp.min())) - vmax = float(max(refp.max(), predp.max())) - - gs_bot = gridspec.GridSpecFromSubplotSpec(1, 2, subplot_spec=gs[1], wspace=0.15) - axs = np.array( - [fig.add_subplot(gs_bot[0, 0]), fig.add_subplot(gs_bot[0, 1])], dtype=object - ) - show_2d( - [refp, predp], - figax=(fig, axs), - title=["image_ref", "model"], - cmap="gray", - cbar=bool(cbar), - returnfig=False, - axsize=axsize, - vmin=vmin, - vmax=vmax, - ) - - mean_hist = self.fit_history.get("mean") - if mean_hist is not None and len(mean_hist.losses) > 0: - fig.suptitle( - f"Final loss: {mean_hist.losses[-1]:.3e} | Iters: {len(mean_hist.losses)}", - fontsize=13, - y=0.98, - ) - plt.show() - return fig, axs - - def plot_mean_model( - self, - *, - power: float = 0.25, - returnfig: bool = False, - axsize: tuple[int, int] = (6, 6), - **_: Any, - ) -> tuple[Any, Any] | None: - if self.image_ref is None: - self.preprocess() - if self.image_ref is None or self.model is None or self.ctx is None: - raise RuntimeError("Call .define_model(...) first.") - - ref = np.asarray(self.image_ref, dtype=np.float32) - pred = self.render_current - - refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) - predp = pred if power == 1.0 else np.maximum(pred, 0.0) ** float(power) - vmin = float(min(refp.min(), predp.min())) - vmax = float(max(refp.max(), predp.max())) - - fig, ax = show_2d( - [refp, predp], - title=["image_ref", "model"], - cmap="gray", - cbar=False, - returnfig=True, - axsize=axsize, - vmin=vmin, - vmax=vmax, - ) - if returnfig: - return fig, ax - return None diff --git a/src/quantem/diffraction/model_fitting_visualizations.py b/src/quantem/diffraction/model_fitting_visualizations.py new file mode 100644 index 00000000..9bd26ef1 --- /dev/null +++ b/src/quantem/diffraction/model_fitting_visualizations.py @@ -0,0 +1,165 @@ +from typing import TYPE_CHECKING, Any, cast + +import numpy as np +from matplotlib import gridspec +from matplotlib import pyplot as plt + +from quantem.core.visualization import show_2d + +if TYPE_CHECKING: + from quantem.diffraction.model_fitting import ModelDiffraction + + +class ModelDiffractionVisualizations: + def plot_losses( + self, figax: tuple[Any, Any] | None = None, plot_lrs: bool = True + ) -> tuple[Any, Any]: + md = cast("ModelDiffraction", self) + + if figax is None: + fig, ax = plt.subplots() + else: + fig, ax = figax + + mean_hist = md.fit_history.get("mean") + losses = np.asarray([] if mean_hist is None else mean_hist.losses, dtype=np.float64) + if losses.size == 0: + ax.text( + 0.5, + 0.5, + "No fit history available", + ha="center", + va="center", + transform=ax.transAxes, + ) + ax.set_xlabel("Iterations") + ax.set_ylabel("Loss") + if figax is None: + plt.tight_layout() + plt.show() + return fig, ax + + iters = np.arange(losses.size) + lines: list[Any] = [] + lines.extend(ax.semilogy(iters, losses, c="k", lw=2, label="loss")) + ax.set_xlabel("Iterations") + ax.set_ylabel("Loss", color="k") + ax.tick_params(axis="y", which="both", colors="k") + ax.spines["left"].set_color("k") + ax.set_xbound(-2, max(1, int(iters.max())) + 2) + + lrs = np.asarray([] if mean_hist is None else mean_hist.lrs, dtype=np.float64) + if plot_lrs and lrs.size > 0: + if lrs.size == losses.size and not np.allclose(lrs, lrs[0]): + ax_lr = ax.twinx() + ax.set_zorder(2) + ax_lr.set_zorder(1) + ax.patch.set_visible(False) + ax_lr.spines["left"].set_visible(False) + lines.extend( + ax_lr.semilogy( + np.arange(lrs.size), lrs, c="tab:blue", lw=2, ls="--", label="LR" + ) + ) + ax_lr.set_ylabel("LR", color="tab:blue") + ax_lr.tick_params(axis="y", which="both", colors="tab:blue") + ax_lr.spines["right"].set_color("tab:blue") + else: + ax.set_title(f"LR: {float(lrs[-1]):.2e}", fontsize=10) + + labels = [line.get_label() for line in lines] + if len(labels) > 1: + ax.legend(lines, labels, loc="upper right") + + if figax is None: + plt.tight_layout() + plt.show() + return fig, ax + + def visualize( + self, + *, + power: float = 0.25, + cbar: bool = False, + axsize: tuple[int, int] = (6, 6), + ) -> tuple[Any, Any]: + md = cast("ModelDiffraction", self) + + if md.image_ref is None: + md.preprocess() + if md.image_ref is None or md.model is None or md.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + + fig = plt.figure(figsize=(12, 7)) + gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2], hspace=0.3) + ax_top = fig.add_subplot(gs[0]) + md.plot_losses(figax=(fig, ax_top), plot_lrs=True) + + ref = np.asarray(md.image_ref, dtype=np.float32) + pred = md.render_current + refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) + predp = pred if power == 1.0 else np.maximum(pred, 0.0) ** float(power) + vmin = float(min(refp.min(), predp.min())) + vmax = float(max(refp.max(), predp.max())) + + gs_bot = gridspec.GridSpecFromSubplotSpec(1, 2, subplot_spec=gs[1], wspace=0.15) + axs = np.array( + [fig.add_subplot(gs_bot[0, 0]), fig.add_subplot(gs_bot[0, 1])], dtype=object + ) + show_2d( + [refp, predp], + figax=(fig, axs), + title=["image_ref", "model"], + cmap="gray", + cbar=bool(cbar), + returnfig=False, + axsize=axsize, + vmin=vmin, + vmax=vmax, + ) + + mean_hist = md.fit_history.get("mean") + if mean_hist is not None and len(mean_hist.losses) > 0: + fig.suptitle( + f"Final loss: {mean_hist.losses[-1]:.3e} | Iters: {len(mean_hist.losses)}", + fontsize=13, + y=0.98, + ) + plt.show() + return fig, axs + + def plot_mean_model( + self, + *, + power: float = 0.25, + returnfig: bool = False, + axsize: tuple[int, int] = (6, 6), + **_: Any, + ) -> tuple[Any, Any] | None: + md = cast("ModelDiffraction", self) + if md.image_ref is None: + md.preprocess() + if md.image_ref is None or md.model is None or md.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + + ref = np.asarray(md.image_ref, dtype=np.float32) + pred = md.render_current + + refp = ref if power == 1.0 else np.maximum(ref, 0.0) ** float(power) + predp = pred if power == 1.0 else np.maximum(pred, 0.0) ** float(power) + vmin = float(min(refp.min(), predp.min())) + vmax = float(max(refp.max(), predp.max())) + + fig, ax = show_2d( + [refp, predp], + title=["image_ref", "model"], + cmap="gray", + cbar=False, + returnfig=True, + axsize=axsize, + vmin=vmin, + vmax=vmax, + ) + if returnfig: + return fig, ax + return None From 59aba0861a28f54d138d0f6ccb4a5c29376a53b6 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 15:00:02 -0800 Subject: [PATCH 35/43] adding hard constraints like force_center for DiskTemplate --- src/quantem/core/fitting/background.py | 8 +- src/quantem/core/fitting/base.py | 163 ++++++++++++++++++++++- src/quantem/core/fitting/diffraction.py | 87 +++++++++++- src/quantem/diffraction/model_fitting.py | 78 ++++++++++- 4 files changed, 324 insertions(+), 12 deletions(-) diff --git a/src/quantem/core/fitting/background.py b/src/quantem/core/fitting/background.py index 7a15d37b..47333a29 100644 --- a/src/quantem/core/fitting/background.py +++ b/src/quantem/core/fitting/background.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Sequence, cast +from typing import Any, Sequence, cast import numpy as np import torch @@ -25,12 +25,15 @@ def __init__( *, intensity: float | int | Sequence[float | int | None] = 0.0, name: str = "dc_background", + constraint_params: dict[str, Any] | None = None, ): super().__init__() self.name = str(name) self.intensity_raw = nn.Parameter( torch.tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) ) + if constraint_params is not None: + self.apply_constraint_params(constraint_params, strict=True) def forward(self, ctx: RenderContext) -> torch.Tensor: inten = torch.clamp(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype), min=0.0) @@ -46,6 +49,7 @@ def __init__( origin: OriginND | None = None, origin_key: str = "origin", name: str = "gaussian_background", + constraint_params: dict[str, Any] | None = None, ): super().__init__() self.name = str(name) @@ -57,6 +61,8 @@ def __init__( self.intensity_raw = nn.Parameter( torch.tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) ) + if constraint_params is not None: + self.apply_constraint_params(constraint_params, strict=True) def set_origin(self, origin: OriginND) -> None: self.origin = origin diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index dfce2e7b..9217ddf6 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -32,10 +32,91 @@ def __init__(self, *, ndim: int, init: Sequence[float]): class RenderComponent(nn.Module): + DEFAULT_HARD_CONSTRAINTS: dict[str, Any] = {} + DEFAULT_SOFT_CONSTRAINTS: dict[str, Any] = {} + + def __init__(self) -> None: + super().__init__() + self.hard_constraints: dict[str, Any] = dict(self.DEFAULT_HARD_CONSTRAINTS) + self.soft_constraints: dict[str, Any] = dict(self.DEFAULT_SOFT_CONSTRAINTS) + + def _set_constraints( + self, + current: dict[str, Any], + defaults: dict[str, Any], + constraints: dict[str, Any], + *, + strict: bool, + ) -> None: + if strict: + unknown = [k for k in constraints if k not in defaults] + if unknown: + keys = ", ".join(str(k) for k in unknown) + raise KeyError(f"Unknown constraint keys: {keys}") + current.update(constraints) + + def set_hard_constraints(self, constraints: dict[str, Any], strict: bool = True) -> None: + self._set_constraints( + self.hard_constraints, self.DEFAULT_HARD_CONSTRAINTS, constraints, strict=strict + ) + + def set_soft_constraints(self, constraints: dict[str, Any], strict: bool = True) -> None: + self._set_constraints( + self.soft_constraints, self.DEFAULT_SOFT_CONSTRAINTS, constraints, strict=strict + ) + + def apply_constraint_params(self, params: dict[str, Any], strict: bool = True) -> None: + if not isinstance(params, dict): + raise TypeError("constraint params must be a dict.") + if "hard" in params or "soft" in params: + hard = params.get("hard") + soft = params.get("soft") + if hard is not None: + if not isinstance(hard, dict): + raise TypeError("constraint params 'hard' value must be a dict.") + self.set_hard_constraints(hard, strict=strict) + if soft is not None: + if not isinstance(soft, dict): + raise TypeError("constraint params 'soft' value must be a dict.") + self.set_soft_constraints(soft, strict=strict) + return + + hard_updates: dict[str, Any] = {} + soft_updates: dict[str, Any] = {} + unknown: dict[str, Any] = {} + for k, v in params.items(): + if k in self.DEFAULT_HARD_CONSTRAINTS: + hard_updates[k] = v + elif k in self.DEFAULT_SOFT_CONSTRAINTS: + soft_updates[k] = v + else: + unknown[k] = v + + if unknown and strict: + keys = ", ".join(str(k) for k in unknown.keys()) + raise KeyError(f"Unknown constraint keys for {self.__class__.__name__}: {keys}") + if unknown: + soft_updates.update(unknown) + if hard_updates: + self.set_hard_constraints(hard_updates, strict=strict) + if soft_updates: + self.set_soft_constraints(soft_updates, strict=strict) + + def effective_soft_constraints(self, params: dict[str, Any] | None = None) -> dict[str, Any]: + effective = dict(self.soft_constraints) + if isinstance(params, dict): + effective.update(params) + return effective + + def enforce_hard_constraints(self, ctx: RenderContext) -> None: + return None + def forward(self, ctx: RenderContext) -> torch.Tensor: raise NotImplementedError - def constraint_loss(self, ctx: RenderContext) -> torch.Tensor: + def constraint_loss( + self, ctx: RenderContext, params: dict[str, Any] | None = None + ) -> torch.Tensor: return torch.zeros((), device=ctx.device, dtype=ctx.dtype) @@ -53,11 +134,65 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: out = out + component(ctx) return out - def total_constraint_loss(self, ctx: RenderContext) -> torch.Tensor: - loss = torch.zeros((), device=ctx.device, dtype=ctx.dtype) + def _component_constraint_name(self, component: RenderComponent, idx: int) -> str: + name = getattr(component, "name", None) + if isinstance(name, str) and name: + return name + class_name = component.__class__.__name__ + if class_name: + return class_name + return f"component_{idx}" + + def apply_constraint_params( + self, constraint_params: dict[str, Any], strict: bool = True + ) -> None: + if not isinstance(constraint_params, dict): + raise TypeError("constraint_params must be a dict.") + source = constraint_params.get("components") + component_map = source if isinstance(source, dict) else constraint_params + for target, params in component_map.items(): + if not isinstance(params, dict): + if strict: + raise TypeError(f"Constraint params for '{target}' must be a dict.") + continue + target_str = str(target) + name_matches: list[RenderComponent] = [] + class_matches: list[RenderComponent] = [] + for idx, module in enumerate(self.components): + component = cast(RenderComponent, module) + if self._component_constraint_name(component, idx) == target_str: + name_matches.append(component) + if component.__class__.__name__ == target_str: + class_matches.append(component) + targets = name_matches if name_matches else class_matches + if not targets: + if strict: + raise KeyError(f"No matching component for constraint target '{target_str}'.") + continue + for component in targets: + component.apply_constraint_params(params, strict=strict) + + def apply_hard_constraints(self, ctx: RenderContext) -> None: for module in self.components: component = cast(RenderComponent, module) - loss = loss + component.constraint_loss(ctx) + component.enforce_hard_constraints(ctx) + + def total_constraint_loss( + self, ctx: RenderContext, constraint_params: dict[str, Any] | None = None + ) -> torch.Tensor: + loss = torch.zeros((), device=ctx.device, dtype=ctx.dtype) + per_component = None + if isinstance(constraint_params, dict): + comp_cfg = constraint_params.get("components") + if isinstance(comp_cfg, dict): + per_component = comp_cfg + for idx, module in enumerate(self.components): + component = cast(RenderComponent, module) + resolved_name = self._component_constraint_name(component, idx) + component_params = per_component.get(resolved_name) if per_component else None + if not isinstance(component_params, dict): + component_params = None + loss = loss + component.constraint_loss(ctx, params=component_params) return loss @@ -128,12 +263,18 @@ def fit_render( target: torch.Tensor, n_steps: int, constraint_weight: float = 1.0, + constraint_params: dict[str, Any] | None = None, optimizer_params: dict | None = None, scheduler_params: dict | None = None, progress: bool = False, run_key: str = "default", **kwargs: Any, ) -> FitResult: + if self.model is None or self.ctx is None: + raise RuntimeError("Model and context are not defined for fitting.") + if constraint_params is not None: + self.model.apply_constraint_params(constraint_params, strict=True) + optimizer_rebuilt = False if optimizer_params is not None: self.set_optimizer(optimizer_params) @@ -166,10 +307,13 @@ def fit_render( self.zero_optimizer_grad() pred = self._forward_for_fit(target=target, **kwargs) data_loss = self._fidelity_loss(pred, target, **kwargs) - constraint_loss = self._constraint_loss(pred, target, **kwargs) + constraint_loss = self._constraint_loss(pred, target, constraint_params=None, **kwargs) total_loss = data_loss + constraint_weight * constraint_loss total_loss.backward() self.step_optimizer() + if self.model is None or self.ctx is None: + raise RuntimeError("Model and context are not defined for fitting.") + self.model.apply_hard_constraints(self.ctx) total_loss_value = float(total_loss.detach().cpu()) self.step_scheduler(total_loss_value) losses.append(total_loss_value) @@ -240,11 +384,16 @@ def _fidelity_loss( return self.loss_fn(pred, target) def _constraint_loss( - self, pred: torch.Tensor, target: torch.Tensor, **kwargs: Any + self, + pred: torch.Tensor, + target: torch.Tensor, + *, + constraint_params: dict[str, Any] | None = None, + **kwargs: Any, ) -> torch.Tensor: if self.model is None or self.ctx is None: raise RuntimeError("Model and context are not defined for fitting.") - return self.model.total_constraint_loss(self.ctx) + return self.model.total_constraint_loss(self.ctx, constraint_params=constraint_params) Component = RenderComponent diff --git a/src/quantem/core/fitting/diffraction.py b/src/quantem/core/fitting/diffraction.py index 0872ffb5..204b8803 100644 --- a/src/quantem/core/fitting/diffraction.py +++ b/src/quantem/core/fitting/diffraction.py @@ -1,9 +1,10 @@ from __future__ import annotations -from typing import Iterable, Sequence, cast +from typing import Any, Iterable, Sequence, cast import numpy as np import torch +import torch.nn.functional as F from torch import nn from quantem.core.fitting.base import OriginND, RenderComponent, RenderContext @@ -58,6 +59,12 @@ def put(rr: torch.Tensor, cc: torch.Tensor, ww: torch.Tensor) -> None: class DiskTemplate(RenderComponent): + DEFAULT_HARD_CONSTRAINTS: dict[str, bool] = { + "force_center": False, + "force_positive": True, + } + DEFAULT_SOFT_CONSTRAINTS: dict[str, float] = {"tv_weight": 0.0} + def __init__( self, *, @@ -69,6 +76,7 @@ def __init__( origin: OriginND | None = None, origin_key: str = "origin", intensity: float | Sequence[float] = 1.0, + constraint_params: dict[str, Any] | None = None, ): super().__init__() self.name = str(name) @@ -107,6 +115,10 @@ def __init__( cc = cc.astype(np.float32) - (wt - 1) * 0.5 self.register_buffer("dr", torch.as_tensor(rr.ravel(), dtype=torch.float32)) self.register_buffer("dc", torch.as_tensor(cc.ravel(), dtype=torch.float32)) + if constraint_params is not None: + self.apply_constraint_params(constraint_params, strict=True) + if bool(self.hard_constraints.get("force_positive", False)): + self._enforce_positivity() @classmethod def from_array( @@ -120,6 +132,7 @@ def from_array( origin: OriginND | None = None, origin_key: str = "origin", intensity: float | Sequence[float] = 1.0, + constraint_params: dict[str, Any] | None = None, ) -> "DiskTemplate": return cls( name=name, @@ -130,14 +143,13 @@ def from_array( origin=origin, origin_key=origin_key, intensity=intensity, + constraint_params=constraint_params, ) def set_origin(self, origin: OriginND) -> None: self.origin = origin def patch_values(self) -> torch.Tensor: - if self.refine_all_pixels: - return torch.clamp(self.template_raw, min=0.0).reshape(-1) return self.template_raw.reshape(-1) def patch_offsets(self) -> tuple[torch.Tensor, torch.Tensor]: @@ -166,6 +178,72 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: self.add_patch(out, r0=r0, c0=c0, scale=scale) return out + def _center_disk(self) -> None: + with torch.no_grad(): + template = self.template_raw + h, w = int(template.shape[0]), int(template.shape[1]) + weights = torch.clamp(template, min=0.0) + mass = torch.sum(weights) + if float(mass.detach().cpu()) <= 1e-12: + return + rr = torch.arange(h, device=template.device, dtype=template.dtype)[:, None] + cc = torch.arange(w, device=template.device, dtype=template.dtype)[None, :] + com_r = torch.sum(weights * rr) / mass + com_c = torch.sum(weights * cc) / mass + target_r = torch.as_tensor((h - 1) * 0.5, device=template.device, dtype=template.dtype) + target_c = torch.as_tensor((w - 1) * 0.5, device=template.device, dtype=template.dtype) + shift_r = target_r - com_r + shift_c = target_c - com_c + denom_h = max(h - 1, 1) + denom_w = max(w - 1, 1) + ty = -2.0 * shift_r / float(denom_h) + tx = -2.0 * shift_c / float(denom_w) + theta = torch.as_tensor( + [[1.0, 0.0, tx], [0.0, 1.0, ty]], + device=template.device, + dtype=template.dtype, + )[None, ...] + src = template[None, None, :, :] + grid = F.affine_grid(theta, [1, 1, h, w], align_corners=True) + shifted = F.grid_sample( + src, + grid, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + )[0, 0] + self.template_raw.copy_(shifted) + + def _enforce_positivity(self) -> None: + with torch.no_grad(): + self.template_raw.clamp_(min=0.0) + + def enforce_hard_constraints(self, ctx: RenderContext) -> None: + if bool(self.hard_constraints.get("force_center", False)): + self._center_disk() + if bool(self.hard_constraints.get("force_positive", False)): + self._enforce_positivity() + + def constraint_loss( + self, ctx: RenderContext, params: dict[str, object] | None = None + ) -> torch.Tensor: + cfg = self.effective_soft_constraints(cast(dict[str, object] | None, params)) + tv_weight = float(cfg.get("tv_weight", 0.0)) + if tv_weight <= 0.0: + return torch.zeros((), device=ctx.device, dtype=ctx.dtype) + template = self.template_raw.to(device=ctx.device, dtype=ctx.dtype) + tv_r = ( + torch.mean(torch.abs(template[1:, :] - template[:-1, :])) + if template.shape[0] > 1 + else torch.zeros((), device=ctx.device, dtype=ctx.dtype) + ) + tv_c = ( + torch.mean(torch.abs(template[:, 1:] - template[:, :-1])) + if template.shape[1] > 1 + else torch.zeros((), device=ctx.device, dtype=ctx.dtype) + ) + return torch.as_tensor(tv_weight, device=ctx.device, dtype=ctx.dtype) * (tv_r + tv_c) + class SyntheticDiskLattice(RenderComponent): def __init__( @@ -194,6 +272,7 @@ def __init__( boundary_px: float = 0.0, origin: OriginND | None = None, origin_key: str = "origin", + constraint_params: dict[str, Any] | None = None, ): super().__init__() self.name = str(name) @@ -282,6 +361,8 @@ def __init__( self.irr = nn.Parameter(torch.tensor(irr_init, dtype=torch.float32)) self.icc = nn.Parameter(torch.tensor(icc_init, dtype=torch.float32)) self.irc = nn.Parameter(torch.tensor(irc_init, dtype=torch.float32)) + if constraint_params is not None: + self.apply_constraint_params(constraint_params, strict=True) def set_origin(self, origin: OriginND) -> None: self.origin = origin diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index d5c87477..5578e25f 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -65,6 +65,81 @@ def from_dataset( "from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance." ) + def _resolve_component_by_name(self, name: str) -> RenderComponent: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + target = str(name) + for idx, module in enumerate(self.model.components): + component = cast(RenderComponent, module) + resolved_name = self.model._component_constraint_name(component, idx) + if resolved_name == target: + return component + raise KeyError(f"Component not found: {target}") + + def get_component(self, name: str) -> RenderComponent: + return self._resolve_component_by_name(name) + + def _render_disk_template_component( + self, component: RenderComponent, ctx: RenderContext + ) -> torch.Tensor: + from quantem.core.fitting.diffraction import DiskTemplate + + if not isinstance(component, DiskTemplate): + raise TypeError("Component is not a DiskTemplate.") + out = torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) + r0 = torch.as_tensor((ctx.shape[0] - 1) * 0.5, device=ctx.device, dtype=ctx.dtype) + c0 = torch.as_tensor((ctx.shape[1] - 1) * 0.5, device=ctx.device, dtype=ctx.dtype) + scale = torch.as_tensor(1.0, device=ctx.device, dtype=ctx.dtype) + component.add_patch(out, r0=r0, c0=c0, scale=scale) + return out + + def get_rendered_component( + self, name: str, apply_hard_constraints: bool = False + ) -> np.ndarray: + if self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + component = self._resolve_component_by_name(name) + if apply_hard_constraints: + component.enforce_hard_constraints(self.ctx) + from quantem.core.fitting.diffraction import DiskTemplate + + if isinstance(component, DiskTemplate): + rendered = self._render_disk_template_component(component, self.ctx) + else: + rendered = component(self.ctx) + return rendered.detach().cpu().numpy() + + def get_rendered_disk_template( + self, apply_hard_constraints: bool = True, name: str | None = None + ) -> np.ndarray: + if self.ctx is None or self.model is None: + raise RuntimeError("Call .define_model(...) first.") + from quantem.core.fitting.diffraction import DiskTemplate + + if name is not None: + component = self._resolve_component_by_name(name) + if not isinstance(component, DiskTemplate): + raise TypeError(f"Component '{name}' is not a DiskTemplate.") + if apply_hard_constraints: + component.enforce_hard_constraints(self.ctx) + return component.template_raw.detach().cpu().numpy() + matches = [m for m in self.model.components if isinstance(m, DiskTemplate)] + if len(matches) == 0: + raise RuntimeError("No DiskTemplate components found.") + if len(matches) > 1: + raise RuntimeError("Multiple DiskTemplate components found; pass name explicitly.") + disk = matches[0] + if apply_hard_constraints: + disk.enforce_hard_constraints(self.ctx) + return disk.template_raw.detach().cpu().numpy() + + def get_component_constraints(self, name: str) -> dict[str, dict[str, Any]]: + component = self._resolve_component_by_name(name) + return { + "hard": dict(component.hard_constraints), + "soft": dict(component.soft_constraints), + } + def preprocess( self, *, @@ -193,8 +268,8 @@ def fit_mean_diffraction_pattern( optimizer_params: dict | None = None, scheduler_params: dict | None = None, constraint_weight: float = 1.0, + constraint_params: dict[str, Any] | None = None, progress: bool = False, - **kwargs: Any, ) -> "ModelDiffraction": if self.model is None or self.ctx is None or self.target_mean is None: raise RuntimeError("Call .define_model(...) first.") @@ -211,6 +286,7 @@ def fit_mean_diffraction_pattern( target=self.target_mean, n_steps=int(n_steps), constraint_weight=float(constraint_weight), + constraint_params=constraint_params, optimizer_params=optimizer_params, scheduler_params=scheduler_params, progress=bool(progress), From 7443869cf8398de07a8d88094d016f98a62c7941 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 15:50:38 -0800 Subject: [PATCH 36/43] adding docstrings and cleaning --- src/quantem/core/fitting/base.py | 62 +++++++--- src/quantem/core/fitting/diffraction.py | 24 +--- src/quantem/diffraction/model_fitting.py | 138 +++++++++++++++++------ 3 files changed, 151 insertions(+), 73 deletions(-) diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index 9217ddf6..898ad90a 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -177,22 +177,11 @@ def apply_hard_constraints(self, ctx: RenderContext) -> None: component = cast(RenderComponent, module) component.enforce_hard_constraints(ctx) - def total_constraint_loss( - self, ctx: RenderContext, constraint_params: dict[str, Any] | None = None - ) -> torch.Tensor: + def total_constraint_loss(self, ctx: RenderContext) -> torch.Tensor: loss = torch.zeros((), device=ctx.device, dtype=ctx.dtype) - per_component = None - if isinstance(constraint_params, dict): - comp_cfg = constraint_params.get("components") - if isinstance(comp_cfg, dict): - per_component = comp_cfg - for idx, module in enumerate(self.components): + for module in self.components: component = cast(RenderComponent, module) - resolved_name = self._component_constraint_name(component, idx) - component_params = per_component.get(resolved_name) if per_component else None - if not isinstance(component_params, dict): - component_params = None - loss = loss + component.constraint_loss(ctx, params=component_params) + loss = loss + component.constraint_loss(ctx) return loss @@ -270,6 +259,45 @@ def fit_render( run_key: str = "default", **kwargs: Any, ) -> FitResult: + """ + Fit model parameters to a target render. + + Parameters + ---------- + target : torch.Tensor + Target tensor to fit. + n_steps : int + Number of optimization steps. + constraint_weight : float, optional + Multiplier applied to the summed soft-constraint loss. + constraint_params : dict[str, Any] | None, optional + Optional constraint updates applied once to matching components before + optimization starts. If ``None``, existing component constraints are reused. + optimizer_params : dict | None, optional + Optimizer configuration override for this call. + scheduler_params : dict | None, optional + Scheduler configuration override for this call. + progress : bool, optional + If ``True``, display a progress bar. + run_key : str, optional + History key used to store/append fit metrics. + **kwargs : Any + Forwarded to internal forward/loss hooks. + + Returns + ------- + FitResult + Fit history and final loss metadata for this run key. + + Raises + ------ + RuntimeError + If model/context are undefined. + + Notes + ----- + Hard constraints are applied after each optimizer step. + """ if self.model is None or self.ctx is None: raise RuntimeError("Model and context are not defined for fitting.") if constraint_params is not None: @@ -307,7 +335,7 @@ def fit_render( self.zero_optimizer_grad() pred = self._forward_for_fit(target=target, **kwargs) data_loss = self._fidelity_loss(pred, target, **kwargs) - constraint_loss = self._constraint_loss(pred, target, constraint_params=None, **kwargs) + constraint_loss = self._constraint_loss(pred, target, **kwargs) total_loss = data_loss + constraint_weight * constraint_loss total_loss.backward() self.step_optimizer() @@ -387,13 +415,11 @@ def _constraint_loss( self, pred: torch.Tensor, target: torch.Tensor, - *, - constraint_params: dict[str, Any] | None = None, **kwargs: Any, ) -> torch.Tensor: if self.model is None or self.ctx is None: raise RuntimeError("Model and context are not defined for fitting.") - return self.model.total_constraint_loss(self.ctx, constraint_params=constraint_params) + return self.model.total_constraint_loss(self.ctx) Component = RenderComponent diff --git a/src/quantem/core/fitting/diffraction.py b/src/quantem/core/fitting/diffraction.py index 204b8803..90070106 100644 --- a/src/quantem/core/fitting/diffraction.py +++ b/src/quantem/core/fitting/diffraction.py @@ -71,7 +71,6 @@ def __init__( name: str, array: np.ndarray, refine_all_pixels: bool = False, - place_at_origin: bool = False, normalize: str = "none", origin: OriginND | None = None, origin_key: str = "origin", @@ -81,7 +80,6 @@ def __init__( super().__init__() self.name = str(name) self.refine_all_pixels = bool(refine_all_pixels) - self.place_at_origin = bool(place_at_origin) self.origin = origin self.origin_key = str(origin_key) @@ -102,13 +100,6 @@ def __init__( template = torch.as_tensor(a, dtype=torch.float32) self.template_raw = nn.Parameter(template.clone(), requires_grad=self.refine_all_pixels) - if self.place_at_origin: - self.intensity_raw = nn.Parameter( - torch.as_tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) - ) - else: - self.intensity_raw = None - ht, wt = int(template.shape[0]), int(template.shape[1]) rr, cc = np.mgrid[0:ht, 0:wt] rr = rr.astype(np.float32) - (ht - 1) * 0.5 @@ -127,7 +118,6 @@ def from_array( name: str, array: np.ndarray, refine_all_pixels: bool = False, - place_at_origin: bool = False, normalize: str = "none", origin: OriginND | None = None, origin_key: str = "origin", @@ -138,7 +128,6 @@ def from_array( name=name, array=array, refine_all_pixels=refine_all_pixels, - place_at_origin=place_at_origin, normalize=normalize, origin=origin, origin_key=origin_key, @@ -165,17 +154,10 @@ def add_patch( def forward(self, ctx: RenderContext) -> torch.Tensor: out = torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) - if not self.place_at_origin: - return out if self.origin is None: - raise RuntimeError( - "DiskTemplate with place_at_origin=True requires an OriginND instance." - ) + raise RuntimeError("DiskTemplate.forward() requires an OriginND instance.") r0, c0 = self.origin.coords[0], self.origin.coords[1] - if self.intensity_raw is None: - raise RuntimeError("DiskTemplate intensity parameter is missing.") - scale = torch.clamp(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype), min=0.0) - self.add_patch(out, r0=r0, c0=c0, scale=scale) + self.add_patch(out, r0=r0, c0=c0, scale=torch.tensor(1.0)) # scale learned directly return out def _center_disk(self) -> None: @@ -308,7 +290,7 @@ def __init__( ) if exclude_indices is None: - exclude = {(0, 0)} if bool(getattr(disk, "place_at_origin", False)) else set() + exclude = set() else: exclude = set(exclude_indices) uv: list[tuple[int, int]] = [] diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 5578e25f..9fd487e0 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -15,6 +15,7 @@ RenderComponent, RenderContext, ) +from quantem.core.fitting.diffraction import DiskTemplate from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.imaging_utils import cross_correlation_shift from quantem.diffraction.model_fitting_visualizations import ModelDiffractionVisualizations @@ -77,60 +78,92 @@ def _resolve_component_by_name(self, name: str) -> RenderComponent: raise KeyError(f"Component not found: {target}") def get_component(self, name: str) -> RenderComponent: + """ + Return a live model component by resolved name. + + Parameters + ---------- + name : str + Resolved component name. + + Returns + ------- + RenderComponent + The live component object. + + Raises + ------ + RuntimeError + If the model is not defined. + KeyError + If no component matches ``name``. + """ return self._resolve_component_by_name(name) - def _render_disk_template_component( - self, component: RenderComponent, ctx: RenderContext - ) -> torch.Tensor: - from quantem.core.fitting.diffraction import DiskTemplate - - if not isinstance(component, DiskTemplate): - raise TypeError("Component is not a DiskTemplate.") - out = torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) - r0 = torch.as_tensor((ctx.shape[0] - 1) * 0.5, device=ctx.device, dtype=ctx.dtype) - c0 = torch.as_tensor((ctx.shape[1] - 1) * 0.5, device=ctx.device, dtype=ctx.dtype) - scale = torch.as_tensor(1.0, device=ctx.device, dtype=ctx.dtype) - component.add_patch(out, r0=r0, c0=c0, scale=scale) - return out - - def get_rendered_component( - self, name: str, apply_hard_constraints: bool = False - ) -> np.ndarray: + def get_rendered_component(self, name: str) -> np.ndarray: + """ + Render a component and return a NumPy array. + + Parameters + ---------- + name : str + Resolved component name. + + Returns + ------- + np.ndarray + Rendered component image. + + Raises + ------ + RuntimeError + If model/context are not defined. + KeyError + If no component matches ``name``. + """ if self.ctx is None: raise RuntimeError("Call .define_model(...) first.") + ctx = self.ctx component = self._resolve_component_by_name(name) - if apply_hard_constraints: - component.enforce_hard_constraints(self.ctx) - from quantem.core.fitting.diffraction import DiskTemplate - - if isinstance(component, DiskTemplate): - rendered = self._render_disk_template_component(component, self.ctx) - else: - rendered = component(self.ctx) + rendered = component(ctx) return rendered.detach().cpu().numpy() - def get_rendered_disk_template( - self, apply_hard_constraints: bool = True, name: str | None = None - ) -> np.ndarray: + def get_rendered_disk_template(self, name: str | None = None) -> np.ndarray: + """ + Return a DiskTemplate patch as a numpy array--not rendered onto the full frame. + + Parameters + ---------- + name : str | None, optional + DiskTemplate component name. If omitted, requires exactly one DiskTemplate. + + Returns + ------- + np.ndarray + Template-sized array from ``template_raw``. + + Raises + ------ + RuntimeError + If model/context are not defined, no DiskTemplate exists, or multiple + DiskTemplates exist when ``name`` is omitted. + TypeError + If a named component exists but is not a DiskTemplate. + """ if self.ctx is None or self.model is None: raise RuntimeError("Call .define_model(...) first.") - from quantem.core.fitting.diffraction import DiskTemplate if name is not None: component = self._resolve_component_by_name(name) if not isinstance(component, DiskTemplate): raise TypeError(f"Component '{name}' is not a DiskTemplate.") - if apply_hard_constraints: - component.enforce_hard_constraints(self.ctx) return component.template_raw.detach().cpu().numpy() matches = [m for m in self.model.components if isinstance(m, DiskTemplate)] if len(matches) == 0: raise RuntimeError("No DiskTemplate components found.") if len(matches) > 1: raise RuntimeError("Multiple DiskTemplate components found; pass name explicitly.") - disk = matches[0] - if apply_hard_constraints: - disk.enforce_hard_constraints(self.ctx) + disk = cast(DiskTemplate, matches[0]) return disk.template_raw.detach().cpu().numpy() def get_component_constraints(self, name: str) -> dict[str, dict[str, Any]]: @@ -271,6 +304,43 @@ def fit_mean_diffraction_pattern( constraint_params: dict[str, Any] | None = None, progress: bool = False, ) -> "ModelDiffraction": + """ + Fit the mean diffraction pattern. + + Parameters + ---------- + n_steps : int, optional + Number of optimization steps. + reset : bool | Literal["initialized", "mean_refined"], optional + Reset behavior before fitting. + optimizer_params : dict | None, optional + Optimizer override for this fit call. + scheduler_params : dict | None, optional + Scheduler override for this fit call. + constraint_weight : float, optional + Global multiplier for soft-constraint loss. + constraint_params : dict[str, Any] | None, optional + Optional constraint updates applied once to components before fitting. + If ``None``, previously assigned constraints are reused. + progress : bool, optional + If ``True``, show progress bar. + + Returns + ------- + ModelDiffraction + Self, with updated fit state and history. + + Raises + ------ + RuntimeError + If model/context/target are not defined. + ValueError + If ``reset`` has an unsupported value. + + Notes + ----- + Constraint assignments persist on components across fit calls. + """ if self.model is None or self.ctx is None or self.target_mean is None: raise RuntimeError("Call .define_model(...) first.") if reset is True: From 136f626f0a182875e1526b4f787327f9433f56f0 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 20:42:05 -0800 Subject: [PATCH 37/43] adding visualizations and overlays --- src/quantem/diffraction/model_fitting.py | 80 +++++- .../model_fitting_visualizations.py | 264 +++++++++++++++++- 2 files changed, 342 insertions(+), 2 deletions(-) diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 9fd487e0..12f8235d 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -15,7 +15,7 @@ RenderComponent, RenderContext, ) -from quantem.core.fitting.diffraction import DiskTemplate +from quantem.core.fitting.diffraction import DiskTemplate, SyntheticDiskLattice from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.imaging_utils import cross_correlation_shift from quantem.diffraction.model_fitting_visualizations import ModelDiffractionVisualizations @@ -66,6 +66,18 @@ def from_dataset( "from_dataset expects a Dataset2d, Dataset3d, Dataset4d, or Dataset4dstem instance." ) + @property + def components(self) -> torch.nn.ModuleList: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + return self.model.components + + @property + def component_names(self) -> list[str]: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + return [cast(str, c.name) for c in self.components] + def _resolve_component_by_name(self, name: str) -> RenderComponent: if self.model is None: raise RuntimeError("Call .define_model(...) first.") @@ -173,6 +185,72 @@ def get_component_constraints(self, name: str) -> dict[str, dict[str, Any]]: "soft": dict(component.soft_constraints), } + def get_overlay_coordinates(self) -> tuple[np.ndarray, np.ndarray]: + """ + Return origin and lattice disk-center coordinates for overlay plotting. + + Parameters + ---------- + None + + Returns + ------- + origin_rc : np.ndarray + Origin coordinate array with shape ``(2,)`` as ``(row, col)``. + disk_centers_rc : np.ndarray + Disk-center array with shape ``(N, 2)`` as ``(row, col)``. + + Raises + ------ + RuntimeError + If model/context are not defined. + + Notes + ----- + Coordinates are computed from current model parameters without mutating state. + Boundary filtering matches ``SyntheticDiskLattice.forward`` behavior. + """ + if self.model is None or self.ctx is None: + raise RuntimeError("Call .define_model(...) first.") + + with torch.no_grad(): + origin = cast(OriginND, self.model.origin) + origin_rc = origin.coords[:2].detach().cpu().numpy().astype(np.float32, copy=False) + + centers: list[np.ndarray] = [] + for module in self.model.components: + component = cast(RenderComponent, module) + if not isinstance(component, SyntheticDiskLattice): + continue + if component.origin is None: + continue + uv_indices = cast(torch.Tensor, component.uv_indices) + if torch.numel(uv_indices) == 0: + continue + + uv = torch.as_tensor(uv_indices, device=self.ctx.device) + u = uv[:, 0].to(dtype=self.ctx.dtype) + v = uv[:, 1].to(dtype=self.ctx.dtype) + r0, c0 = component.origin.coords[0], component.origin.coords[1] + centers_r = r0 + u * component.u_row + v * component.v_row + centers_c = c0 + u * component.u_col + v * component.v_col + + b = torch.as_tensor( + component.boundary_px, device=self.ctx.device, dtype=self.ctx.dtype + ) + keep = (centers_r >= b) & (centers_r <= (self.ctx.shape[0] - 1) - b) + keep = keep & (centers_c >= b) & (centers_c <= (self.ctx.shape[1] - 1) - b) + if torch.any(keep): + rc = torch.stack((centers_r[keep], centers_c[keep]), dim=1) + centers.append(rc.detach().cpu().numpy().astype(np.float32, copy=False)) + + if centers: + disk_centers_rc = np.concatenate(centers, axis=0) + else: + disk_centers_rc = np.zeros((0, 2), dtype=np.float32) + + return origin_rc, disk_centers_rc + def preprocess( self, *, diff --git a/src/quantem/diffraction/model_fitting_visualizations.py b/src/quantem/diffraction/model_fitting_visualizations.py index 9bd26ef1..95245aa1 100644 --- a/src/quantem/diffraction/model_fitting_visualizations.py +++ b/src/quantem/diffraction/model_fitting_visualizations.py @@ -1,4 +1,4 @@ -from typing import TYPE_CHECKING, Any, cast +from typing import TYPE_CHECKING, Any, Literal, cast import numpy as np from matplotlib import gridspec @@ -11,6 +11,65 @@ class ModelDiffractionVisualizations: + def _plot_overlays( + self, + ax: Any, + origin_rc: np.ndarray, + disk_centers_rc: np.ndarray, + *, + overlay_origin: bool = True, + overlay_disks: bool = True, + origin_marker_kwargs: dict[str, Any] | None = None, + disk_marker_kwargs: dict[str, Any] | None = None, + ) -> None: + """ + Plot origin and disk-center overlays on an axis. + + Parameters + ---------- + ax : Any + Matplotlib axis receiving overlays. + origin_rc : np.ndarray + Origin coordinate as ``(row, col)``. + disk_centers_rc : np.ndarray + Disk centers as ``(N, 2)`` in ``(row, col)`` order. + overlay_origin : bool, optional + If ``True``, plot origin marker. + overlay_disks : bool, optional + If ``True``, plot disk-center markers. + origin_marker_kwargs : dict[str, Any] | None, optional + Matplotlib kwargs merged onto origin marker defaults. + disk_marker_kwargs : dict[str, Any] | None, optional + Matplotlib kwargs merged onto disk marker defaults. + + Returns + ------- + None + """ + if overlay_origin and origin_rc.shape == (2,): + kw_origin = { + "marker": "+", + "color": "red", + "markersize": 10, + "markeredgewidth": 1.5, + "linestyle": "None", + } + if origin_marker_kwargs is not None: + kw_origin.update(origin_marker_kwargs) + ax.plot(float(origin_rc[1]), float(origin_rc[0]), **kw_origin) + + if overlay_disks and disk_centers_rc.ndim == 2 and disk_centers_rc.shape[0] > 0: + kw_disks = { + "marker": "x", + "color": "cyan", + "markersize": 5, + "markeredgewidth": 1.0, + "linestyle": "None", + } + if disk_marker_kwargs is not None: + kw_disks.update(disk_marker_kwargs) + ax.plot(disk_centers_rc[:, 1], disk_centers_rc[:, 0], **kw_disks) + def plot_losses( self, figax: tuple[Any, Any] | None = None, plot_lrs: bool = True ) -> tuple[Any, Any]: @@ -82,7 +141,49 @@ def visualize( power: float = 0.25, cbar: bool = False, axsize: tuple[int, int] = (6, 6), + overlay: bool = True, + overlay_origin: bool = True, + overlay_disks: bool = True, + overlay_on: Literal["model", "both"] = "model", + origin_marker_kwargs: dict[str, Any] | None = None, + disk_marker_kwargs: dict[str, Any] | None = None, ) -> tuple[Any, Any]: + """ + Visualize fit losses with reference/model image panels. + + Parameters + ---------- + power : float, optional + Power-law display scaling. + cbar : bool, optional + If ``True``, draw colorbars. + axsize : tuple[int, int], optional + Axis size passed through to ``show_2d``. + overlay : bool, optional + If ``True``, draw coordinate overlays. + overlay_origin : bool, optional + If ``True``, include origin marker in overlays. + overlay_disks : bool, optional + If ``True``, include disk-center markers in overlays. + overlay_on : {"model", "both"}, optional + Which image panel(s) receive overlays. + origin_marker_kwargs : dict[str, Any] | None, optional + Marker kwargs override for origin marker. + disk_marker_kwargs : dict[str, Any] | None, optional + Marker kwargs override for disk-center markers. + + Returns + ------- + tuple[Any, Any] + ``(fig, axs)`` for further editing. + + Raises + ------ + RuntimeError + If model/context are not defined. + ValueError + If ``overlay_on`` is invalid. + """ md = cast("ModelDiffraction", self) if md.image_ref is None: @@ -118,6 +219,22 @@ def visualize( vmax=vmax, ) + if overlay: + if overlay_on not in ("model", "both"): + raise ValueError("overlay_on must be 'model' or 'both'.") + origin_rc, disk_centers_rc = md.get_overlay_coordinates() + axes = [axs[1]] if overlay_on == "model" else [axs[0], axs[1]] + for ax in axes: + self._plot_overlays( + ax, + origin_rc, + disk_centers_rc, + overlay_origin=overlay_origin, + overlay_disks=overlay_disks, + origin_marker_kwargs=origin_marker_kwargs, + disk_marker_kwargs=disk_marker_kwargs, + ) + mean_hist = md.fit_history.get("mean") if mean_hist is not None and len(mean_hist.losses) > 0: fig.suptitle( @@ -134,8 +251,52 @@ def plot_mean_model( power: float = 0.25, returnfig: bool = False, axsize: tuple[int, int] = (6, 6), + overlay: bool = True, + overlay_origin: bool = True, + overlay_disks: bool = True, + overlay_on: Literal["model", "both"] = "model", + origin_marker_kwargs: dict[str, Any] | None = None, + disk_marker_kwargs: dict[str, Any] | None = None, **_: Any, ) -> tuple[Any, Any] | None: + """ + Plot reference and model mean diffraction images. + + Parameters + ---------- + power : float, optional + Power-law display scaling. + returnfig : bool, optional + If ``True``, return ``(fig, ax)``. + axsize : tuple[int, int], optional + Axis size passed through to ``show_2d``. + overlay : bool, optional + If ``True``, draw coordinate overlays. + overlay_origin : bool, optional + If ``True``, include origin marker in overlays. + overlay_disks : bool, optional + If ``True``, include disk-center markers in overlays. + overlay_on : {"model", "both"}, optional + Which image panel(s) receive overlays. + origin_marker_kwargs : dict[str, Any] | None, optional + Marker kwargs override for origin marker. + disk_marker_kwargs : dict[str, Any] | None, optional + Marker kwargs override for disk-center markers. + **_ : Any + Ignored extra kwargs for backward compatibility. + + Returns + ------- + tuple[Any, Any] | None + Figure/axes tuple when ``returnfig=True``; otherwise ``None``. + + Raises + ------ + RuntimeError + If model/context are not defined. + ValueError + If ``overlay_on`` is invalid. + """ md = cast("ModelDiffraction", self) if md.image_ref is None: md.preprocess() @@ -160,6 +321,107 @@ def plot_mean_model( vmin=vmin, vmax=vmax, ) + if overlay: + if overlay_on not in ("model", "both"): + raise ValueError("overlay_on must be 'model' or 'both'.") + origin_rc, disk_centers_rc = md.get_overlay_coordinates() + axs_arr = np.asarray(ax, dtype=object).reshape(-1) + axes = [axs_arr[1]] if overlay_on == "model" else [axs_arr[0], axs_arr[1]] + for a in axes: + self._plot_overlays( + a, + origin_rc, + disk_centers_rc, + overlay_origin=overlay_origin, + overlay_disks=overlay_disks, + origin_marker_kwargs=origin_marker_kwargs, + disk_marker_kwargs=disk_marker_kwargs, + ) + if returnfig: + return fig, ax + return None + + def visualize_components( + self, + components: str | list[str], + *, + power: float = 0.25, + cbar: bool = False, + axsize: tuple[int, int] = (6, 6), + returnfig: bool = False, + overlay: bool = True, + overlay_origin: bool = True, + overlay_disks: bool = True, + origin_marker_kwargs: dict[str, Any] | None = None, + disk_marker_kwargs: dict[str, Any] | None = None, + ) -> tuple[Any, Any] | None: + """ + Render and display one or more named components. + + Parameters + ---------- + components : str | list[str] + Component name or list of component names. + power : float, optional + Power-law display scaling. + cbar : bool, optional + If ``True``, draw colorbars. + axsize : tuple[int, int], optional + Axis size passed through to ``show_2d``. + returnfig : bool, optional + If ``True``, return ``(fig, ax)``. + overlay : bool, optional + If ``True``, draw coordinate overlays on all component panels. + overlay_origin : bool, optional + If ``True``, include origin marker in overlays. + overlay_disks : bool, optional + If ``True``, include disk-center markers in overlays. + origin_marker_kwargs : dict[str, Any] | None, optional + Marker kwargs override for origin marker. + disk_marker_kwargs : dict[str, Any] | None, optional + Marker kwargs override for disk-center markers. + + Returns + ------- + tuple[Any, Any] | None + Figure/axes tuple when ``returnfig=True``; otherwise ``None``. + """ + md = cast("ModelDiffraction", self) + names = [components] if isinstance(components, str) else list(components) + if len(names) == 0: + raise ValueError("components must contain at least one component name.") + + rendered = [md.get_rendered_component(name) for name in names] + rendered_scaled = [ + arr + if power == 1.0 + else np.maximum(np.asarray(arr, dtype=np.float32), 0.0) ** float(power) + for arr in rendered + ] + + fig, ax = show_2d( + rendered_scaled, + title=names, + cmap="gray", + cbar=bool(cbar), + returnfig=True, + axsize=axsize, + ) + + if overlay: + origin_rc, disk_centers_rc = md.get_overlay_coordinates() + axs_arr = np.asarray(ax, dtype=object).reshape(-1) + for a in axs_arr: + self._plot_overlays( + a, + origin_rc, + disk_centers_rc, + overlay_origin=overlay_origin, + overlay_disks=overlay_disks, + origin_marker_kwargs=origin_marker_kwargs, + disk_marker_kwargs=disk_marker_kwargs, + ) + if returnfig: return fig, ax return None From 6429e255e576e17622a54537b1fc59d4a10e98a0 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 21:45:35 -0800 Subject: [PATCH 38/43] adding turning on/off individual components and parameters --- src/quantem/core/fitting/base.py | 202 ++++++++++++++++++++++- src/quantem/diffraction/model_fitting.py | 82 +++++++-- 2 files changed, 271 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index 898ad90a..f4136cdd 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -214,7 +214,7 @@ def __init__(self): def get_optimization_parameters(self) -> Any: if self.model is None: return [] - return self.model.parameters() + return [p for p in self.model.parameters() if p.requires_grad] @property def state_current(self) -> dict[str, torch.Tensor] | None: @@ -246,6 +246,164 @@ def reset( self._clear_fit_history_all() return self + def set_component_trainable( + self, component_name: str, enabled: bool, rebuild_optimizer: bool = True + ) -> None: + """ + Enable or disable optimization for all parameters in one component. + + Parameters + ---------- + component_name : str + Resolved component name. + enabled : bool + If ``True``, mark component parameters trainable. + rebuild_optimizer : bool, optional + If ``True``, rebuild optimizer param groups after toggling. + + Returns + ------- + None + + Raises + ------ + RuntimeError + If the model is not defined. + KeyError + If ``component_name`` is unknown. + + Notes + ----- + When rebuilding, the optimizer is reconstructed from stored optimizer + parameters if available, otherwise inferred from the current optimizer + type and learning rate, else defaults. Scheduler state is cleared + predictably by setting scheduler type to ``"none"``. + """ + component = self._resolve_component_by_name(component_name) + for _, param in component.named_parameters(recurse=True): + param.requires_grad_(bool(enabled)) + if rebuild_optimizer: + self._rebuild_optimizer_after_trainability_change() + + def set_parameter_trainable( + self, + component_name: str, + parameter_name: str, + enabled: bool, + rebuild_optimizer: bool = True, + ) -> None: + """ + Enable or disable optimization for one component parameter. + + Parameters + ---------- + component_name : str + Resolved component name. + parameter_name : str + Parameter name from ``component.named_parameters()``. + enabled : bool + If ``True``, mark parameter trainable. + rebuild_optimizer : bool, optional + If ``True``, rebuild optimizer param groups after toggling. + + Returns + ------- + None + + Raises + ------ + RuntimeError + If the model is not defined. + KeyError + If ``component_name`` or ``parameter_name`` is unknown. + + Notes + ----- + When rebuilding, scheduler state is cleared by setting scheduler type + to ``"none"``. + """ + component = self._resolve_component_by_name(component_name) + params = dict(component.named_parameters(recurse=True)) + if parameter_name not in params: + known = ", ".join(sorted(params.keys())) + raise KeyError( + f"Parameter '{parameter_name}' not found in component '{component_name}'. " + f"Known parameters: {known}" + ) + params[parameter_name].requires_grad_(bool(enabled)) + if rebuild_optimizer: + self._rebuild_optimizer_after_trainability_change() + + def set_parameters_trainable( + self, + component_name: str, + parameter_names: list[str], + enabled: bool, + rebuild_optimizer: bool = True, + ) -> None: + """ + Enable or disable optimization for multiple component parameters. + + Parameters + ---------- + component_name : str + Resolved component name. + parameter_names : list[str] + Parameter names from ``component.named_parameters()``. + enabled : bool + If ``True``, mark parameters trainable. + rebuild_optimizer : bool, optional + If ``True``, rebuild optimizer param groups after toggling. + + Returns + ------- + None + + Raises + ------ + RuntimeError + If the model is not defined. + KeyError + If any parameter name is unknown. + """ + component = self._resolve_component_by_name(component_name) + params = dict(component.named_parameters(recurse=True)) + missing = [name for name in parameter_names if name not in params] + if missing: + known = ", ".join(sorted(params.keys())) + raise KeyError( + f"Unknown parameters for component '{component_name}': {', '.join(missing)}. " + f"Known parameters: {known}" + ) + for name in parameter_names: + params[name].requires_grad_(bool(enabled)) + if rebuild_optimizer: + self._rebuild_optimizer_after_trainability_change() + + def get_component_trainable(self, component_name: str) -> dict[str, bool]: + """ + Return trainability flags for one component's parameters. + + Parameters + ---------- + component_name : str + Resolved component name. + + Returns + ------- + dict[str, bool] + Mapping of parameter name to ``requires_grad``. + + Raises + ------ + RuntimeError + If the model is not defined. + KeyError + If ``component_name`` is unknown. + """ + component = self._resolve_component_by_name(component_name) + return {name: bool(param.requires_grad) for name, param in component.named_parameters()} + def fit_render( self, *, @@ -365,6 +523,48 @@ def fit_render( self.fit_history[key] = result return result + def _resolve_component_by_name(self, component_name: str) -> RenderComponent: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + target = str(component_name) + for idx, module in enumerate(self.model.components): + component = cast(RenderComponent, module) + resolved_name = self.model._component_constraint_name(component, idx) + if resolved_name == target: + return component + raise KeyError(f"Component not found: {target}") + + def _infer_optimizer_rebuild_params(self) -> dict[str, Any]: + if self.optimizer_params: + return dict(self.optimizer_params) + if self.optimizer is not None: + opt_type: str | type[torch.optim.Optimizer] + if isinstance(self.optimizer, torch.optim.AdamW): + opt_type = "adamw" + elif isinstance(self.optimizer, torch.optim.Adam): + opt_type = "adam" + elif isinstance(self.optimizer, torch.optim.SGD): + opt_type = "sgd" + else: + opt_type = type(self.optimizer) + lr = float( + self.optimizer.param_groups[0].get( + "lr", getattr(self, "DEFAULT_LR", self.DEFAULT_LR) + ) + ) + return {"type": opt_type, "lr": lr} + return { + "type": getattr(self, "DEFAULT_OPTIMIZER_TYPE", self.DEFAULT_OPTIMIZER_TYPE), + "lr": float(getattr(self, "DEFAULT_LR", self.DEFAULT_LR)), + } + + def _rebuild_optimizer_after_trainability_change(self) -> None: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + rebuild_params = self._infer_optimizer_rebuild_params() + self.set_optimizer(rebuild_params) + self.set_scheduler({"type": "none"}) + def _clone_state_dict(self, state: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: return {k: v.detach().clone() for k, v in state.items()} diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 12f8235d..991653e9 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -78,17 +78,6 @@ def component_names(self) -> list[str]: raise RuntimeError("Call .define_model(...) first.") return [cast(str, c.name) for c in self.components] - def _resolve_component_by_name(self, name: str) -> RenderComponent: - if self.model is None: - raise RuntimeError("Call .define_model(...) first.") - target = str(name) - for idx, module in enumerate(self.model.components): - component = cast(RenderComponent, module) - resolved_name = self.model._component_constraint_name(component, idx) - if resolved_name == target: - return component - raise KeyError(f"Component not found: {target}") - def get_component(self, name: str) -> RenderComponent: """ Return a live model component by resolved name. @@ -178,6 +167,75 @@ def get_rendered_disk_template(self, name: str | None = None) -> np.ndarray: disk = cast(DiskTemplate, matches[0]) return disk.template_raw.detach().cpu().numpy() + def set_disk_template_trainable( + self, enabled: bool, name: str | None = None, rebuild_optimizer: bool = True + ) -> None: + """ + Toggle DiskTemplate ``template_raw`` trainability. + + Parameters + ---------- + enabled : bool + If ``True``, enable optimization of ``template_raw``. + name : str | None, optional + DiskTemplate component name. If ``None``, applies to all DiskTemplate + components in the current model. + rebuild_optimizer : bool, optional + If ``True``, rebuild optimizer param groups after toggling. + + Returns + ------- + None + + Raises + ------ + KeyError + If ``name`` does not match any component. + RuntimeError + If model is not defined or no DiskTemplate components are found. + TypeError + If ``name`` resolves to a non-DiskTemplate component. + + Notes + ----- + This toggles only ``template_raw.requires_grad``. Other DiskTemplate + parameters (for example ``intensity_raw``) are unchanged. When + ``rebuild_optimizer=True``, optimizer param groups are rebuilt to match + current ``requires_grad`` flags. + """ + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + + if name is not None: + component = self._resolve_component_by_name(name) + if not isinstance(component, DiskTemplate): + raise TypeError(f"Component '{name}' is not a DiskTemplate.") + self.set_parameter_trainable( + name, + "template_raw", + enabled=enabled, + rebuild_optimizer=rebuild_optimizer, + ) + return + + disk_names: list[str] = [] + for idx, module in enumerate(self.model.components): + component = cast(RenderComponent, module) + if isinstance(component, DiskTemplate): + disk_names.append(self.model._component_constraint_name(component, idx)) + if len(disk_names) == 0: + raise RuntimeError("No DiskTemplate components found.") + + for disk_name in disk_names: + self.set_parameter_trainable( + disk_name, + "template_raw", + enabled=enabled, + rebuild_optimizer=False, + ) + if rebuild_optimizer: + self._rebuild_optimizer_after_trainability_change() + def get_component_constraints(self, name: str) -> dict[str, dict[str, Any]]: component = self._resolve_component_by_name(name) return { @@ -380,7 +438,7 @@ def fit_mean_diffraction_pattern( scheduler_params: dict | None = None, constraint_weight: float = 1.0, constraint_params: dict[str, Any] | None = None, - progress: bool = False, + progress: bool = True, ) -> "ModelDiffraction": """ Fit the mean diffraction pattern. From 55886091bb3572d508775b4f1d2f232a2a4d4e7e Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 22:22:38 -0800 Subject: [PATCH 39/43] fixing center disk duplication and a couple viz bugs --- src/quantem/core/fitting/diffraction.py | 126 ++++++++++++++++-- .../model_fitting_visualizations.py | 40 +++--- 2 files changed, 136 insertions(+), 30 deletions(-) diff --git a/src/quantem/core/fitting/diffraction.py b/src/quantem/core/fitting/diffraction.py index 90070106..a38c358d 100644 --- a/src/quantem/core/fitting/diffraction.py +++ b/src/quantem/core/fitting/diffraction.py @@ -77,11 +77,36 @@ def __init__( intensity: float | Sequence[float] = 1.0, constraint_params: dict[str, Any] | None = None, ): + """ + Build a disk template renderer centered at the shared origin. + + Parameters + ---------- + intensity : float | Sequence[float], optional + Trainable scalar amplitude applied to the rendered template. + + Returns + ------- + None + + Raises + ------ + ValueError + If ``array`` is not 2D or if ``normalize`` is unsupported. + + Notes + ----- + ``template_raw`` controls template shape and ``intensity_raw`` controls + center-disk amplitude. + """ super().__init__() self.name = str(name) self.refine_all_pixels = bool(refine_all_pixels) self.origin = origin self.origin_key = str(origin_key) + self.intensity_raw = nn.Parameter( + torch.tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) + ) a = np.asarray(array, dtype=np.float32) if a.ndim != 2: @@ -138,6 +163,11 @@ def from_array( def set_origin(self, origin: OriginND) -> None: self.origin = origin + def set_intensity(self, value: float | int) -> None: + """Assign ``intensity_raw`` in-place.""" + with torch.no_grad(): + self.intensity_raw.copy_(torch.as_tensor(float(value), dtype=self.intensity_raw.dtype)) + def patch_values(self) -> torch.Tensor: return self.template_raw.reshape(-1) @@ -153,11 +183,25 @@ def add_patch( _splat_patch(out, r0=r0, c0=c0, patch_vals=vals, dr=dr, dc=dc, scale=scale) def forward(self, ctx: RenderContext) -> torch.Tensor: + """ + Render template at origin with scalar amplitude ``intensity_raw``. + + Parameters + ---------- + ctx : RenderContext + Rendering context. + + Returns + ------- + torch.Tensor + Rendered center disk image. + """ out = torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) if self.origin is None: raise RuntimeError("DiskTemplate.forward() requires an OriginND instance.") r0, c0 = self.origin.coords[0], self.origin.coords[1] - self.add_patch(out, r0=r0, c0=c0, scale=torch.tensor(1.0)) # scale learned directly + scale = self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype) + self.add_patch(out, r0=r0, c0=c0, scale=scale) return out def _center_disk(self) -> None: @@ -199,6 +243,7 @@ def _center_disk(self) -> None: def _enforce_positivity(self) -> None: with torch.no_grad(): self.template_raw.clamp_(min=0.0) + self.intensity_raw.clamp_(min=0.0) def enforce_hard_constraints(self, ctx: RenderContext) -> None: if bool(self.hard_constraints.get("force_center", False)): @@ -228,6 +273,10 @@ def constraint_loss( class SyntheticDiskLattice(RenderComponent): + DEFAULT_HARD_CONSTRAINTS: dict[str, bool] = { + "force_positive_intensity": True, + } + def __init__( self, *, @@ -256,6 +305,41 @@ def __init__( origin_key: str = "origin", constraint_params: dict[str, Any] | None = None, ): + """ + Build a synthetic disk lattice renderer. + + Parameters + ---------- + intensity_0 : float | Sequence[float], optional + Baseline intensity for included lattice disks. + center_intensity_0 : float | Sequence[float] | None, optional + Optional center-disk baseline used only when ``(0, 0)`` is included + in the lattice index set. + exclude_indices : Iterable[tuple[int, int]] | None, optional + Lattice indices excluded from rendering. By default, ``(0, 0)`` is + excluded. To include center explicitly, pass ``exclude_indices`` that + does not contain ``(0, 0)``. + + Returns + ------- + None + + Raises + ------ + ValueError + If ``center_intensity_0`` is provided with center included while + ``per_disk_intensity=False``. + + Notes + ----- + Center-intensity routing is explicit: + - Center excluded (default): ``center_intensity_0`` maps to + ``disk.intensity_raw``. + - Center included with ``per_disk_intensity=True``: + ``center_intensity_0`` maps to lattice center ``i0_raw`` entry. + In this case ``disk.intensity_raw`` is set to ``0`` to avoid + duplicate center ownership when disk is rendered standalone. + """ super().__init__() self.name = str(name) self.disk = disk @@ -289,10 +373,13 @@ def __init__( torch.tensor(_parse_init(v_col, name="v_col"), dtype=torch.float32) ) - if exclude_indices is None: - exclude = set() - else: - exclude = set(exclude_indices) + exclude = {(0, 0)} if exclude_indices is None else set(exclude_indices) + center_included = (0, 0) not in exclude + if center_intensity_0 is not None and center_included and not self.per_disk_intensity: + raise ValueError( + "center_intensity_0 with center included requires per_disk_intensity=True, " + "or exclude (0,0) and use DiskTemplate intensity ownership." + ) uv: list[tuple[int, int]] = [] for u in range(-self.u_max, self.u_max + 1): for v in range(-self.v_max, self.v_max + 1): @@ -310,6 +397,8 @@ def __init__( if center_intensity_0 is None else _parse_init(center_intensity_0, name="center_intensity_0") ) + if center_intensity_0 is not None and not center_included: + self.disk.set_intensity(float(i0_center)) ir_init = _parse_init(intensity_row, name="intensity_row") ic_init = _parse_init(intensity_col, name="intensity_col") irr_init = _parse_init(intensity_row_row, name="intensity_row_row") @@ -322,6 +411,8 @@ def __init__( center_mask = (uv_t[:, 0] == 0) & (uv_t[:, 1] == 0) i0_values[center_mask] = float(i0_center) self.i0_raw = nn.Parameter(i0_values) + if center_intensity_0 is not None and center_included: + self.disk.set_intensity(0.0) if self.max_intensity_order >= 1: self.ir = nn.Parameter(torch.full((n_uv,), float(ir_init), dtype=torch.float32)) self.ic = nn.Parameter(torch.full((n_uv,), float(ic_init), dtype=torch.float32)) @@ -345,10 +436,29 @@ def __init__( self.irc = nn.Parameter(torch.tensor(irc_init, dtype=torch.float32)) if constraint_params is not None: self.apply_constraint_params(constraint_params, strict=True) + if bool(self.hard_constraints.get("force_positive_intensity", False)): + self._enforce_positive_intensity_params() def set_origin(self, origin: OriginND) -> None: self.origin = origin + def _enforce_positive_intensity_params(self) -> None: + """ + Project base intensity parameter(s) to nonnegative values. + + Notes + ----- + Positivity is enforced as a hard projection after optimizer steps. + The forward path intentionally avoids clamp-based dead gradients. + Only ``i0_raw`` is projected; slope terms remain unconstrained. + """ + with torch.no_grad(): + self.i0_raw.clamp_(min=0.0) + + def enforce_hard_constraints(self, ctx: RenderContext) -> None: + if bool(self.hard_constraints.get("force_positive_intensity", False)): + self._enforce_positive_intensity_params() + def forward(self, ctx: RenderContext) -> torch.Tensor: if self.origin is None: raise RuntimeError("SyntheticDiskLattice requires an OriginND instance.") @@ -391,7 +501,7 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: cc0 = centers_c[j] if self.per_disk_intensity: - inten = torch.clamp(self.i0_raw[j], min=0.0) + inten = self.i0_raw[j] if active_order >= 1 and self.ir is not None and self.ic is not None: inten = inten + self.ir[j] * dr + self.ic[j] * dc if ( @@ -401,9 +511,8 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: and self.irc is not None ): inten = inten + self.irr[j] * dr2 + self.icc[j] * dc2 + self.irc[j] * drdc - inten = torch.clamp(inten, min=0.0) else: - inten = torch.clamp(self.i0_raw, min=0.0) + inten = self.i0_raw if active_order >= 1: assert self.ir is not None and self.ic is not None inten = inten + self.ir * rr0 + self.ic * cc0 @@ -412,7 +521,6 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: inten = ( inten + self.irr * rr0 * rr0 + self.icc * cc0 * cc0 + self.irc * rr0 * cc0 ) - inten = torch.clamp(inten, min=0.0) self.disk.add_patch(out, r0=rr0, c0=cc0, scale=inten) diff --git a/src/quantem/diffraction/model_fitting_visualizations.py b/src/quantem/diffraction/model_fitting_visualizations.py index 95245aa1..7be1f470 100644 --- a/src/quantem/diffraction/model_fitting_visualizations.py +++ b/src/quantem/diffraction/model_fitting_visualizations.py @@ -356,12 +356,13 @@ def visualize_components( disk_marker_kwargs: dict[str, Any] | None = None, ) -> tuple[Any, Any] | None: """ - Render and display one or more named components. + Render and display a summed component image. Parameters ---------- components : str | list[str] - Component name or list of component names. + Component name or list of component names. Multiple names are + composited by summing component renders. power : float, optional Power-law display scaling. cbar : bool, optional @@ -391,17 +392,16 @@ def visualize_components( if len(names) == 0: raise ValueError("components must contain at least one component name.") - rendered = [md.get_rendered_component(name) for name in names] - rendered_scaled = [ - arr - if power == 1.0 - else np.maximum(np.asarray(arr, dtype=np.float32), 0.0) ** float(power) - for arr in rendered + rendered = [ + np.asarray(md.get_rendered_component(name), dtype=np.float32) for name in names ] + summed = np.sum(np.stack(rendered, axis=0), axis=0) + summed_scaled = summed if power == 1.0 else np.maximum(summed, 0.0) ** float(power) + title = names[0] if len(names) == 1 else " + ".join(names) fig, ax = show_2d( - rendered_scaled, - title=names, + summed_scaled, + title=title, cmap="gray", cbar=bool(cbar), returnfig=True, @@ -410,17 +410,15 @@ def visualize_components( if overlay: origin_rc, disk_centers_rc = md.get_overlay_coordinates() - axs_arr = np.asarray(ax, dtype=object).reshape(-1) - for a in axs_arr: - self._plot_overlays( - a, - origin_rc, - disk_centers_rc, - overlay_origin=overlay_origin, - overlay_disks=overlay_disks, - origin_marker_kwargs=origin_marker_kwargs, - disk_marker_kwargs=disk_marker_kwargs, - ) + self._plot_overlays( + ax, + origin_rc, + disk_centers_rc, + overlay_origin=overlay_origin, + overlay_disks=overlay_disks, + origin_marker_kwargs=origin_marker_kwargs, + disk_marker_kwargs=disk_marker_kwargs, + ) if returnfig: return fig, ax From 1fbc76c9589a3ced07ba55e1bfcf2797d31d93ad Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 22:41:02 -0800 Subject: [PATCH 40/43] adding back parameter bounds --- src/quantem/core/fitting/background.py | 36 +++--- src/quantem/core/fitting/base.py | 153 +++++++++++++++++++++++- src/quantem/core/fitting/diffraction.py | 130 ++++++++++++++------ 3 files changed, 260 insertions(+), 59 deletions(-) diff --git a/src/quantem/core/fitting/background.py b/src/quantem/core/fitting/background.py index 47333a29..31878dec 100644 --- a/src/quantem/core/fitting/background.py +++ b/src/quantem/core/fitting/background.py @@ -1,24 +1,13 @@ from __future__ import annotations -from typing import Any, Sequence, cast +from typing import Any, Sequence -import numpy as np import torch from torch import nn from quantem.core.fitting.base import OriginND, RenderComponent, RenderContext -def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) -> float: - if isinstance(value, (list, tuple, np.ndarray)): - if len(value) == 0: - raise ValueError(f"{name} cannot be empty.") - if value[0] is None: - raise ValueError(f"{name} initial value cannot be None.") - return float(value[0]) - return float(cast(float | int, value)) - - class DCBackground(RenderComponent): def __init__( self, @@ -29,9 +18,12 @@ def __init__( ): super().__init__() self.name = str(name) - self.intensity_raw = nn.Parameter( - torch.tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) + intensity_init, intensity_lo, intensity_hi = self.parse_bounded_init( + intensity, name="intensity" ) + self.intensity_raw = nn.Parameter(torch.tensor(intensity_init, dtype=torch.float32)) + if intensity_lo is not None or intensity_hi is not None: + self.register_parameter_bounds("intensity_raw", intensity_lo, intensity_hi) if constraint_params is not None: self.apply_constraint_params(constraint_params, strict=True) @@ -40,7 +32,7 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: return torch.ones(ctx.shape, device=ctx.device, dtype=ctx.dtype) * inten -class GaussianBackground(RenderComponent): +class GaussianBackground(RenderComponent): # TODO this should be N dimensional by default def __init__( self, *, @@ -55,12 +47,16 @@ def __init__( self.name = str(name) self.origin = origin self.origin_key = str(origin_key) - self.sigma_raw = nn.Parameter( - torch.tensor(_parse_init(sigma, name="sigma"), dtype=torch.float32) - ) - self.intensity_raw = nn.Parameter( - torch.tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) + sigma_init, sigma_lo, sigma_hi = self.parse_bounded_init(sigma, name="sigma") + intensity_init, intensity_lo, intensity_hi = self.parse_bounded_init( + intensity, name="intensity" ) + self.sigma_raw = nn.Parameter(torch.tensor(sigma_init, dtype=torch.float32)) + if sigma_lo is not None or sigma_hi is not None: + self.register_parameter_bounds("sigma_raw", sigma_lo, sigma_hi) + self.intensity_raw = nn.Parameter(torch.tensor(intensity_init, dtype=torch.float32)) + if intensity_lo is not None or intensity_hi is not None: + self.register_parameter_bounds("intensity_raw", intensity_lo, intensity_hi) if constraint_params is not None: self.apply_constraint_params(constraint_params, strict=True) diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index f4136cdd..29933e14 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -11,6 +11,65 @@ from quantem.core.ml.optimizer_mixin import OptimizerMixin +def parse_bounded_init( + value: float | int | Sequence[float | int | None], *, name: str +) -> tuple[float, float | None, float | None]: + """ + Parse a scalar or bounded initializer specification. + + Parameters + ---------- + value : float | int | Sequence[float | int | None] + Accepted forms: + - ``x`` -> init ``x`` with no bounds. + - ``(x0, delta)`` -> init ``x0`` with bounds ``[x0-|delta|, x0+|delta|]``. + - ``(x0, lo, hi)`` -> init ``x0`` with explicit bounds. + name : str + Parameter name used in error messages. + + Returns + ------- + tuple[float, float | None, float | None] + Parsed ``(init, lo, hi)``. + + Raises + ------ + ValueError + If the sequence form is invalid, contains required ``None`` entries, + has invalid ordering, or ``init`` lies outside explicit bounds. + """ + if not isinstance(value, (list, tuple, np.ndarray)): + x = float(cast(float | int, value)) + return x, None, None + + seq = list(value) + if len(seq) == 0: + raise ValueError(f"{name} cannot be empty.") + if seq[0] is None: + raise ValueError(f"{name} initial value cannot be None.") + x0 = float(cast(float | int, seq[0])) + + if len(seq) == 1: + return x0, None, None + if len(seq) == 2: + if seq[1] is None: + raise ValueError(f"{name} delta cannot be None.") + delta = abs(float(cast(float | int, seq[1]))) + return x0, x0 - delta, x0 + delta + if len(seq) == 3: + if seq[1] is None or seq[2] is None: + raise ValueError(f"{name} bounds cannot contain None.") + lo = float(cast(float | int, seq[1])) + hi = float(cast(float | int, seq[2])) + if lo > hi: + raise ValueError(f"{name} has invalid bounds: lo ({lo}) > hi ({hi}).") + if x0 < lo or x0 > hi: + raise ValueError(f"{name} initial value {x0} is outside bounds [{lo}, {hi}].") + return x0, lo, hi + + raise ValueError(f"{name} must be scalar, (x0, delta), or (x0, lo, hi).") + + @dataclass class RenderContext: shape: tuple[int, ...] @@ -39,6 +98,98 @@ def __init__(self) -> None: super().__init__() self.hard_constraints: dict[str, Any] = dict(self.DEFAULT_HARD_CONSTRAINTS) self.soft_constraints: dict[str, Any] = dict(self.DEFAULT_SOFT_CONSTRAINTS) + self.parameter_bounds: dict[str, tuple[float | None, float | None]] = {} + + @staticmethod + def parse_bounded_init( + value: float | int | Sequence[float | int | None], *, name: str + ) -> tuple[float, float | None, float | None]: + """ + Parse bounded initializer forms into ``(init, lo, hi)``. + + Parameters + ---------- + value : float | int | Sequence[float | int | None] + Scalar, ``(x0, delta)``, or ``(x0, lo, hi)``. + name : str + Parameter name used in error messages. + + Returns + ------- + tuple[float, float | None, float | None] + Parsed ``(init, lo, hi)``. + """ + return parse_bounded_init(value, name=name) + + def register_parameter_bounds( + self, parameter_name: str, lo: float | None, hi: float | None + ) -> None: + """ + Register hard bounds for a trainable parameter. + + Parameters + ---------- + parameter_name : str + Name of an ``nn.Parameter`` attribute on this component. + lo : float | None + Lower bound, or ``None`` for unbounded lower side. + hi : float | None + Upper bound, or ``None`` for unbounded upper side. + + Returns + ------- + None + + Raises + ------ + ValueError + If ``lo > hi``. + """ + if lo is not None and hi is not None and float(lo) > float(hi): + raise ValueError(f"Invalid bounds for {parameter_name}: lo ({lo}) > hi ({hi}).") + self.parameter_bounds[str(parameter_name)] = ( + None if lo is None else float(lo), + None if hi is None else float(hi), + ) + + def _enforce_parameter_bounds(self) -> None: + """ + Clamp registered parameters in-place to configured bounds. + + Returns + ------- + None + + Raises + ------ + AttributeError + If a registered parameter attribute is missing. + TypeError + If a registered attribute is not an ``nn.Parameter``. + """ + if not self.parameter_bounds: + return + with torch.no_grad(): + for param_name, (lo, hi) in self.parameter_bounds.items(): + if not hasattr(self, param_name): + raise AttributeError( + f"Parameter '{param_name}' is not an attribute of {self.__class__.__name__}." + ) + param = getattr(self, param_name) + if not isinstance(param, nn.Parameter): + raise TypeError( + f"Attribute '{param_name}' on {self.__class__.__name__} is not an nn.Parameter." + ) + if lo is None and hi is None: + continue + if lo is None: + assert hi is not None + param.clamp_(max=float(hi)) + elif hi is None: + assert lo is not None + param.clamp_(min=float(lo)) + else: + param.clamp_(min=float(lo), max=float(hi)) def _set_constraints( self, @@ -109,7 +260,7 @@ def effective_soft_constraints(self, params: dict[str, Any] | None = None) -> di return effective def enforce_hard_constraints(self, ctx: RenderContext) -> None: - return None + self._enforce_parameter_bounds() def forward(self, ctx: RenderContext) -> torch.Tensor: raise NotImplementedError diff --git a/src/quantem/core/fitting/diffraction.py b/src/quantem/core/fitting/diffraction.py index a38c358d..30934bf5 100644 --- a/src/quantem/core/fitting/diffraction.py +++ b/src/quantem/core/fitting/diffraction.py @@ -10,16 +10,6 @@ from quantem.core.fitting.base import OriginND, RenderComponent, RenderContext -def _parse_init(value: float | int | Sequence[float | int | None], *, name: str) -> float: - if isinstance(value, (list, tuple, np.ndarray)): - if len(value) == 0: - raise ValueError(f"{name} cannot be empty.") - if value[0] is None: - raise ValueError(f"{name} initial value cannot be None.") - return float(value[0]) - return float(cast(float | int, value)) - - def _splat_patch( out: torch.Tensor, *, @@ -84,6 +74,7 @@ def __init__( ---------- intensity : float | Sequence[float], optional Trainable scalar amplitude applied to the rendered template. + Accepts ``x``, ``(x0, delta)``, or ``(x0, lo, hi)``. Returns ------- @@ -104,9 +95,12 @@ def __init__( self.refine_all_pixels = bool(refine_all_pixels) self.origin = origin self.origin_key = str(origin_key) - self.intensity_raw = nn.Parameter( - torch.tensor(_parse_init(intensity, name="intensity"), dtype=torch.float32) + intensity_init, intensity_lo, intensity_hi = self.parse_bounded_init( + intensity, name="intensity" ) + self.intensity_raw = nn.Parameter(torch.tensor(intensity_init, dtype=torch.float32)) + if intensity_lo is not None or intensity_hi is not None: + self.register_parameter_bounds("intensity_raw", intensity_lo, intensity_hi) a = np.asarray(array, dtype=np.float32) if a.ndim != 2: @@ -250,6 +244,7 @@ def enforce_hard_constraints(self, ctx: RenderContext) -> None: self._center_disk() if bool(self.hard_constraints.get("force_positive", False)): self._enforce_positivity() + super().enforce_hard_constraints(ctx) def constraint_loss( self, ctx: RenderContext, params: dict[str, object] | None = None @@ -310,11 +305,19 @@ def __init__( Parameters ---------- + u_row, u_col, v_row, v_col : float | Sequence[float | int | None] + Lattice basis parameters. Accept ``x``, ``(x0, delta)``, or + ``(x0, lo, hi)``. intensity_0 : float | Sequence[float], optional - Baseline intensity for included lattice disks. + Baseline intensity for included lattice disks. Accepts ``x``, + ``(x0, delta)``, or ``(x0, lo, hi)``. + intensity_row, intensity_col, intensity_row_row, intensity_col_col, intensity_row_col : + float | Sequence[float | int | None] + Intensity polynomial parameters. Accept ``x``, ``(x0, delta)``, or + ``(x0, lo, hi)``. center_intensity_0 : float | Sequence[float] | None, optional - Optional center-disk baseline used only when ``(0, 0)`` is included - in the lattice index set. + Optional center-disk baseline. Accepts ``x``, ``(x0, delta)``, or + ``(x0, lo, hi)`` and routes by center ownership rules. exclude_indices : Iterable[tuple[int, int]] | None, optional Lattice indices excluded from rendering. By default, ``(0, 0)`` is excluded. To include center explicitly, pass ``exclude_indices`` that @@ -360,18 +363,22 @@ def __init__( default_pattern_intensity_order = self.max_intensity_order self.default_pattern_intensity_order = int(default_pattern_intensity_order) - self.u_row = nn.Parameter( - torch.tensor(_parse_init(u_row, name="u_row"), dtype=torch.float32) - ) - self.u_col = nn.Parameter( - torch.tensor(_parse_init(u_col, name="u_col"), dtype=torch.float32) - ) - self.v_row = nn.Parameter( - torch.tensor(_parse_init(v_row, name="v_row"), dtype=torch.float32) - ) - self.v_col = nn.Parameter( - torch.tensor(_parse_init(v_col, name="v_col"), dtype=torch.float32) - ) + u_row_init, u_row_lo, u_row_hi = self.parse_bounded_init(u_row, name="u_row") + u_col_init, u_col_lo, u_col_hi = self.parse_bounded_init(u_col, name="u_col") + v_row_init, v_row_lo, v_row_hi = self.parse_bounded_init(v_row, name="v_row") + v_col_init, v_col_lo, v_col_hi = self.parse_bounded_init(v_col, name="v_col") + self.u_row = nn.Parameter(torch.tensor(u_row_init, dtype=torch.float32)) + if u_row_lo is not None or u_row_hi is not None: + self.register_parameter_bounds("u_row", u_row_lo, u_row_hi) + self.u_col = nn.Parameter(torch.tensor(u_col_init, dtype=torch.float32)) + if u_col_lo is not None or u_col_hi is not None: + self.register_parameter_bounds("u_col", u_col_lo, u_col_hi) + self.v_row = nn.Parameter(torch.tensor(v_row_init, dtype=torch.float32)) + if v_row_lo is not None or v_row_hi is not None: + self.register_parameter_bounds("v_row", v_row_lo, v_row_hi) + self.v_col = nn.Parameter(torch.tensor(v_col_init, dtype=torch.float32)) + if v_col_lo is not None or v_col_hi is not None: + self.register_parameter_bounds("v_col", v_col_lo, v_col_hi) exclude = {(0, 0)} if exclude_indices is None else set(exclude_indices) center_included = (0, 0) not in exclude @@ -391,31 +398,51 @@ def __init__( self.register_buffer("uv_indices", uv_t) n_uv = int(uv_t.shape[0]) - i0_init = _parse_init(intensity_0, name="intensity_0") - i0_center = ( - i0_init - if center_intensity_0 is None - else _parse_init(center_intensity_0, name="center_intensity_0") - ) + i0_init, i0_lo, i0_hi = self.parse_bounded_init(intensity_0, name="intensity_0") + if center_intensity_0 is None: + i0_center, i0_center_lo, i0_center_hi = i0_init, None, None + else: + i0_center, i0_center_lo, i0_center_hi = self.parse_bounded_init( + center_intensity_0, name="center_intensity_0" + ) if center_intensity_0 is not None and not center_included: self.disk.set_intensity(float(i0_center)) - ir_init = _parse_init(intensity_row, name="intensity_row") - ic_init = _parse_init(intensity_col, name="intensity_col") - irr_init = _parse_init(intensity_row_row, name="intensity_row_row") - icc_init = _parse_init(intensity_col_col, name="intensity_col_col") - irc_init = _parse_init(intensity_row_col, name="intensity_row_col") + if i0_center_lo is not None or i0_center_hi is not None: + self.disk.register_parameter_bounds("intensity_raw", i0_center_lo, i0_center_hi) + ir_init, ir_lo, ir_hi = self.parse_bounded_init(intensity_row, name="intensity_row") + ic_init, ic_lo, ic_hi = self.parse_bounded_init(intensity_col, name="intensity_col") + irr_init, irr_lo, irr_hi = self.parse_bounded_init( + intensity_row_row, name="intensity_row_row" + ) + icc_init, icc_lo, icc_hi = self.parse_bounded_init( + intensity_col_col, name="intensity_col_col" + ) + irc_init, irc_lo, irc_hi = self.parse_bounded_init( + intensity_row_col, name="intensity_row_col" + ) + self._center_i0_bounds: tuple[float | None, float | None] | None = None + self._center_i0_index: int | None = None if self.per_disk_intensity: i0_values = torch.full((n_uv,), float(i0_init), dtype=torch.float32) if n_uv > 0: center_mask = (uv_t[:, 0] == 0) & (uv_t[:, 1] == 0) i0_values[center_mask] = float(i0_center) + if center_intensity_0 is not None and bool(torch.any(center_mask)): + self._center_i0_index = int(torch.nonzero(center_mask, as_tuple=False)[0, 0]) + self._center_i0_bounds = (i0_center_lo, i0_center_hi) self.i0_raw = nn.Parameter(i0_values) + if i0_lo is not None or i0_hi is not None: + self.register_parameter_bounds("i0_raw", i0_lo, i0_hi) if center_intensity_0 is not None and center_included: self.disk.set_intensity(0.0) if self.max_intensity_order >= 1: self.ir = nn.Parameter(torch.full((n_uv,), float(ir_init), dtype=torch.float32)) self.ic = nn.Parameter(torch.full((n_uv,), float(ic_init), dtype=torch.float32)) + if ir_lo is not None or ir_hi is not None: + self.register_parameter_bounds("ir", ir_lo, ir_hi) + if ic_lo is not None or ic_hi is not None: + self.register_parameter_bounds("ic", ic_lo, ic_hi) else: self.ir = None self.ic = None @@ -423,17 +450,35 @@ def __init__( self.irr = nn.Parameter(torch.full((n_uv,), float(irr_init), dtype=torch.float32)) self.icc = nn.Parameter(torch.full((n_uv,), float(icc_init), dtype=torch.float32)) self.irc = nn.Parameter(torch.full((n_uv,), float(irc_init), dtype=torch.float32)) + if irr_lo is not None or irr_hi is not None: + self.register_parameter_bounds("irr", irr_lo, irr_hi) + if icc_lo is not None or icc_hi is not None: + self.register_parameter_bounds("icc", icc_lo, icc_hi) + if irc_lo is not None or irc_hi is not None: + self.register_parameter_bounds("irc", irc_lo, irc_hi) else: self.irr = None self.icc = None self.irc = None else: self.i0_raw = nn.Parameter(torch.tensor(i0_init, dtype=torch.float32)) + if i0_lo is not None or i0_hi is not None: + self.register_parameter_bounds("i0_raw", i0_lo, i0_hi) self.ir = nn.Parameter(torch.tensor(ir_init, dtype=torch.float32)) + if ir_lo is not None or ir_hi is not None: + self.register_parameter_bounds("ir", ir_lo, ir_hi) self.ic = nn.Parameter(torch.tensor(ic_init, dtype=torch.float32)) + if ic_lo is not None or ic_hi is not None: + self.register_parameter_bounds("ic", ic_lo, ic_hi) self.irr = nn.Parameter(torch.tensor(irr_init, dtype=torch.float32)) + if irr_lo is not None or irr_hi is not None: + self.register_parameter_bounds("irr", irr_lo, irr_hi) self.icc = nn.Parameter(torch.tensor(icc_init, dtype=torch.float32)) + if icc_lo is not None or icc_hi is not None: + self.register_parameter_bounds("icc", icc_lo, icc_hi) self.irc = nn.Parameter(torch.tensor(irc_init, dtype=torch.float32)) + if irc_lo is not None or irc_hi is not None: + self.register_parameter_bounds("irc", irc_lo, irc_hi) if constraint_params is not None: self.apply_constraint_params(constraint_params, strict=True) if bool(self.hard_constraints.get("force_positive_intensity", False)): @@ -458,6 +503,15 @@ def _enforce_positive_intensity_params(self) -> None: def enforce_hard_constraints(self, ctx: RenderContext) -> None: if bool(self.hard_constraints.get("force_positive_intensity", False)): self._enforce_positive_intensity_params() + if self._center_i0_bounds is not None and self._center_i0_index is not None: + with torch.no_grad(): + lo, hi = self._center_i0_bounds + idx = self._center_i0_index + if lo is not None: + self.i0_raw[idx].clamp_(min=float(lo)) + if hi is not None: + self.i0_raw[idx].clamp_(max=float(hi)) + super().enforce_hard_constraints(ctx) def forward(self, ctx: RenderContext) -> torch.Tensor: if self.origin is None: From 20b2c58f2c26b1e5c271dca7e5216033df4f2762 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 22:48:17 -0800 Subject: [PATCH 41/43] updating colormaps --- .../model_fitting_visualizations.py | 37 ++++++++++--------- 1 file changed, 20 insertions(+), 17 deletions(-) diff --git a/src/quantem/diffraction/model_fitting_visualizations.py b/src/quantem/diffraction/model_fitting_visualizations.py index 7be1f470..6dfb4f62 100644 --- a/src/quantem/diffraction/model_fitting_visualizations.py +++ b/src/quantem/diffraction/model_fitting_visualizations.py @@ -4,6 +4,7 @@ from matplotlib import gridspec from matplotlib import pyplot as plt +from quantem.core import config from quantem.core.visualization import show_2d if TYPE_CHECKING: @@ -46,12 +47,13 @@ def _plot_overlays( ------- None """ + colors = config.get("viz.colors.set") if overlay_origin and origin_rc.shape == (2,): kw_origin = { "marker": "+", - "color": "red", + "color": colors[0], "markersize": 10, - "markeredgewidth": 1.5, + "markeredgewidth": 3, "linestyle": "None", } if origin_marker_kwargs is not None: @@ -61,9 +63,9 @@ def _plot_overlays( if overlay_disks and disk_centers_rc.ndim == 2 and disk_centers_rc.shape[0] > 0: kw_disks = { "marker": "x", - "color": "cyan", + "color": colors[1], "markersize": 5, - "markeredgewidth": 1.0, + "markeredgewidth": 2.0, "linestyle": "None", } if disk_marker_kwargs is not None: @@ -74,6 +76,9 @@ def plot_losses( self, figax: tuple[Any, Any] | None = None, plot_lrs: bool = True ) -> tuple[Any, Any]: md = cast("ModelDiffraction", self) + colors = config.get("viz.colors.set") + loss_color = "k" + lr_color = colors[8] if figax is None: fig, ax = plt.subplots() @@ -100,11 +105,11 @@ def plot_losses( iters = np.arange(losses.size) lines: list[Any] = [] - lines.extend(ax.semilogy(iters, losses, c="k", lw=2, label="loss")) + lines.extend(ax.semilogy(iters, losses, c=loss_color, lw=2, label="loss")) ax.set_xlabel("Iterations") - ax.set_ylabel("Loss", color="k") - ax.tick_params(axis="y", which="both", colors="k") - ax.spines["left"].set_color("k") + ax.set_ylabel("Loss", color=loss_color) + ax.tick_params(axis="y", which="both", colors=loss_color) + ax.spines["left"].set_color(loss_color) ax.set_xbound(-2, max(1, int(iters.max())) + 2) lrs = np.asarray([] if mean_hist is None else mean_hist.lrs, dtype=np.float64) @@ -116,13 +121,11 @@ def plot_losses( ax.patch.set_visible(False) ax_lr.spines["left"].set_visible(False) lines.extend( - ax_lr.semilogy( - np.arange(lrs.size), lrs, c="tab:blue", lw=2, ls="--", label="LR" - ) + ax_lr.semilogy(np.arange(lrs.size), lrs, c=lr_color, lw=2, ls="--", label="LR") ) - ax_lr.set_ylabel("LR", color="tab:blue") - ax_lr.tick_params(axis="y", which="both", colors="tab:blue") - ax_lr.spines["right"].set_color("tab:blue") + ax_lr.set_ylabel("LR", color=lr_color) + ax_lr.tick_params(axis="y", which="both", colors=lr_color) + ax_lr.spines["right"].set_color(lr_color) else: ax.set_title(f"LR: {float(lrs[-1]):.2e}", fontsize=10) @@ -211,7 +214,7 @@ def visualize( [refp, predp], figax=(fig, axs), title=["image_ref", "model"], - cmap="gray", + cmap=config.get("viz.cmap"), cbar=bool(cbar), returnfig=False, axsize=axsize, @@ -314,7 +317,7 @@ def plot_mean_model( fig, ax = show_2d( [refp, predp], title=["image_ref", "model"], - cmap="gray", + cmap=config.get("viz.cmap"), cbar=False, returnfig=True, axsize=axsize, @@ -402,7 +405,7 @@ def visualize_components( fig, ax = show_2d( summed_scaled, title=title, - cmap="gray", + cmap=config.get("viz.cmap"), cbar=bool(cbar), returnfig=True, axsize=axsize, From f68742ea93e735c4b76f0365631eea81c91f5bba Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 22:58:21 -0800 Subject: [PATCH 42/43] more consistent hard constraints of ranges --- src/quantem/core/fitting/background.py | 53 ++++++++++++++++++++---- src/quantem/diffraction/model_fitting.py | 5 ++- 2 files changed, 48 insertions(+), 10 deletions(-) diff --git a/src/quantem/core/fitting/background.py b/src/quantem/core/fitting/background.py index 31878dec..000d4a01 100644 --- a/src/quantem/core/fitting/background.py +++ b/src/quantem/core/fitting/background.py @@ -16,19 +16,36 @@ def __init__( name: str = "dc_background", constraint_params: dict[str, Any] | None = None, ): + """ + Build a constant background component. + + Notes + ----- + Validity is enforced via hard constraints/parameter bounds. Forward + intentionally avoids hard clamps for gradient flow. + """ super().__init__() self.name = str(name) intensity_init, intensity_lo, intensity_hi = self.parse_bounded_init( intensity, name="intensity" ) self.intensity_raw = nn.Parameter(torch.tensor(intensity_init, dtype=torch.float32)) - if intensity_lo is not None or intensity_hi is not None: - self.register_parameter_bounds("intensity_raw", intensity_lo, intensity_hi) + bounded_lo = 0.0 if intensity_lo is None else max(float(intensity_lo), 0.0) + self.register_parameter_bounds("intensity_raw", bounded_lo, intensity_hi) if constraint_params is not None: self.apply_constraint_params(constraint_params, strict=True) + self._enforce_parameter_bounds() def forward(self, ctx: RenderContext) -> torch.Tensor: - inten = torch.clamp(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype), min=0.0) + """ + Render constant background from raw trainable intensity. + + Notes + ----- + Validity is enforced via hard constraints/parameter bounds, not via + forward-time hard clamps. + """ + inten = self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype) return torch.ones(ctx.shape, device=ctx.device, dtype=ctx.dtype) * inten @@ -43,6 +60,15 @@ def __init__( name: str = "gaussian_background", constraint_params: dict[str, Any] | None = None, ): + """ + Build a Gaussian background component centered at origin. + + Notes + ----- + ``sigma_raw`` and ``intensity_raw`` validity is enforced via hard + constraints/parameter bounds. Forward intentionally avoids hard clamps + for gradient flow. + """ super().__init__() self.name = str(name) self.origin = origin @@ -52,18 +78,27 @@ def __init__( intensity, name="intensity" ) self.sigma_raw = nn.Parameter(torch.tensor(sigma_init, dtype=torch.float32)) - if sigma_lo is not None or sigma_hi is not None: - self.register_parameter_bounds("sigma_raw", sigma_lo, sigma_hi) + sigma_bounded_lo = 1e-6 if sigma_lo is None else max(float(sigma_lo), 1e-6) + self.register_parameter_bounds("sigma_raw", sigma_bounded_lo, sigma_hi) self.intensity_raw = nn.Parameter(torch.tensor(intensity_init, dtype=torch.float32)) - if intensity_lo is not None or intensity_hi is not None: - self.register_parameter_bounds("intensity_raw", intensity_lo, intensity_hi) + intensity_bounded_lo = 0.0 if intensity_lo is None else max(float(intensity_lo), 0.0) + self.register_parameter_bounds("intensity_raw", intensity_bounded_lo, intensity_hi) if constraint_params is not None: self.apply_constraint_params(constraint_params, strict=True) + self._enforce_parameter_bounds() def set_origin(self, origin: OriginND) -> None: self.origin = origin def forward(self, ctx: RenderContext) -> torch.Tensor: + """ + Render Gaussian background from raw trainable parameters. + + Notes + ----- + Validity is enforced via hard constraints/parameter bounds, not via + forward-time hard clamps. + """ if self.origin is None: raise RuntimeError("GaussianBackground requires an OriginND instance.") @@ -71,7 +106,7 @@ def forward(self, ctx: RenderContext) -> torch.Tensor: cc = torch.arange(ctx.shape[1], device=ctx.device, dtype=ctx.dtype)[None, :] r0, c0 = self.origin.coords[0], self.origin.coords[1] - sigma = torch.clamp(self.sigma_raw.to(device=ctx.device, dtype=ctx.dtype), min=1e-6) - inten = torch.clamp(self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype), min=0.0) + sigma = self.sigma_raw.to(device=ctx.device, dtype=ctx.dtype) + inten = self.intensity_raw.to(device=ctx.device, dtype=ctx.dtype) r2 = (rr - r0) ** 2 + (cc - c0) ** 2 return inten * torch.exp(-0.5 * r2 / (sigma * sigma)) diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 991653e9..70928022 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -76,7 +76,10 @@ def components(self) -> torch.nn.ModuleList: def component_names(self) -> list[str]: if self.model is None: raise RuntimeError("Call .define_model(...) first.") - return [cast(str, c.name) for c in self.components] + return [ + self.model._component_constraint_name(cast(RenderComponent, module), idx) + for idx, module in enumerate(self.model.components) + ] def get_component(self, name: str) -> RenderComponent: """ From b787564507e39e50c8c8537740d8fde2ae878385 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Mar 2026 23:05:56 -0800 Subject: [PATCH 43/43] cleaning up naming of Components --- src/quantem/core/fitting/base.py | 41 +++++++++++++++++++++--- src/quantem/diffraction/model_fitting.py | 19 +++-------- 2 files changed, 42 insertions(+), 18 deletions(-) diff --git a/src/quantem/core/fitting/base.py b/src/quantem/core/fitting/base.py index 29933e14..b21e1126 100644 --- a/src/quantem/core/fitting/base.py +++ b/src/quantem/core/fitting/base.py @@ -674,16 +674,49 @@ def fit_render( self.fit_history[key] = result return result - def _resolve_component_by_name(self, component_name: str) -> RenderComponent: + def _iter_named_components(self) -> list[tuple[str, RenderComponent]]: + """ + Return canonical component names paired with components. + + Returns + ------- + list[tuple[str, RenderComponent]] + ``(name, component)`` entries using the model's canonical naming + rule. Names fall back to class-name/index behavior when ``.name`` is + missing. + + Raises + ------ + RuntimeError + If the model is not defined. + """ if self.model is None: raise RuntimeError("Call .define_model(...) first.") - target = str(component_name) + entries: list[tuple[str, RenderComponent]] = [] for idx, module in enumerate(self.model.components): component = cast(RenderComponent, module) - resolved_name = self.model._component_constraint_name(component, idx) + name = self.model._component_constraint_name(component, idx) + entries.append((name, component)) + return entries + + def get_component_names(self) -> list[str]: + """ + Return canonical component names. + + Returns + ------- + list[str] + Canonical component names. + """ + return [name for name, _ in self._iter_named_components()] + + def _resolve_component_by_name(self, component_name: str) -> RenderComponent: + target = str(component_name) + for resolved_name, component in self._iter_named_components(): if resolved_name == target: return component - raise KeyError(f"Component not found: {target}") + known = ", ".join(self.get_component_names()) + raise KeyError(f"Component not found: {target}. Known components: {known}") def _infer_optimizer_rebuild_params(self) -> dict[str, Any]: if self.optimizer_params: diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py index 70928022..55cddfcb 100644 --- a/src/quantem/diffraction/model_fitting.py +++ b/src/quantem/diffraction/model_fitting.py @@ -72,15 +72,6 @@ def components(self) -> torch.nn.ModuleList: raise RuntimeError("Call .define_model(...) first.") return self.model.components - @property - def component_names(self) -> list[str]: - if self.model is None: - raise RuntimeError("Call .define_model(...) first.") - return [ - self.model._component_constraint_name(cast(RenderComponent, module), idx) - for idx, module in enumerate(self.model.components) - ] - def get_component(self, name: str) -> RenderComponent: """ Return a live model component by resolved name. @@ -221,11 +212,11 @@ def set_disk_template_trainable( ) return - disk_names: list[str] = [] - for idx, module in enumerate(self.model.components): - component = cast(RenderComponent, module) - if isinstance(component, DiskTemplate): - disk_names.append(self.model._component_constraint_name(component, idx)) + disk_names = [ + component_name + for component_name, component in self._iter_named_components() + if isinstance(component, DiskTemplate) + ] if len(disk_names) == 0: raise RuntimeError("No DiskTemplate components found.")