diff --git a/pyproject.toml b/pyproject.toml index 5ca6787b..4f08e141 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -82,4 +82,5 @@ dev = [ "pre-commit>=4.2.0", "ruff>=0.11.5", "tomli>=2.2.1", + "hdf5plugin>=6.0.0", ] diff --git a/src/quantem/__init__.py b/src/quantem/__init__.py index b9aa54b4..b5099b9a 100644 --- a/src/quantem/__init__.py +++ b/src/quantem/__init__.py @@ -8,6 +8,7 @@ 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 __version__ = version("quantem") 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/dataset.py b/src/quantem/core/datastructures/dataset.py index f01e68f6..53bd5b01 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -164,6 +164,11 @@ 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 @@ -305,6 +310,35 @@ 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/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 28328636..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 @@ -7,6 +8,7 @@ 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 @@ -72,7 +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_ccw_deg", "q_transpose", "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): metadata[k] = None @@ -91,13 +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 @@ -751,3 +753,5 @@ 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 diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py new file mode 100644 index 00000000..6619af5c --- /dev/null +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -0,0 +1,237 @@ +import numpy as np +from numpy.typing import NDArray +from typing import Any, TYPE_CHECKING +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).""" + + 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 = np.asarray(array) + if array.ndim != 4: + raise ValueError("Polar4dstem.from_array expects a 4D array.") + if origin is None: + origin = np.zeros(4, dtype=float) + if sampling is None: + sampling = np.ones(4, dtype=float) + 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 _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 | 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, + 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: + 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 + 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=origin_row_f, + origin_col=origin_col_f, + 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, + ) + 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, n_phi, n_r), dtype=result_dtype) + for iy in range(scan_y): + for ix in range(scan_x): + dp = self.array[iy, ix] + out[iy, ix] = map_coordinates( + dp, + coords, + order=1, + mode="constant", + cval=0.0, + ) + 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], + "deg", + self.units[-1], + ] + metadata = dict(self.metadata) + metadata.update( + { + "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", + 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, + ) diff --git a/src/quantem/core/fitting/__init__.py b/src/quantem/core/fitting/__init__.py new file mode 100644 index 00000000..1bb92620 --- /dev/null +++ b/src/quantem/core/fitting/__init__.py @@ -0,0 +1,21 @@ +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 + +__all__ = [ + "Component", + "DCBackground", + "DiskTemplate", + "GaussianBackground", + "Model", + "ModelContext", + "OriginND", + "Parameter", + "SyntheticDiskLattice", +] diff --git a/src/quantem/core/fitting/background.py b/src/quantem/core/fitting/background.py new file mode 100644 index 00000000..000d4a01 --- /dev/null +++ b/src/quantem/core/fitting/background.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +from typing import Any, Sequence + +import torch +from torch import nn + +from quantem.core.fitting.base import OriginND, RenderComponent, RenderContext + + +class DCBackground(RenderComponent): + def __init__( + self, + *, + intensity: float | int | Sequence[float | int | None] = 0.0, + 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)) + 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: + """ + 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 + + +class GaussianBackground(RenderComponent): # TODO this should be N dimensional by default + def __init__( + self, + *, + 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", + 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 + self.origin_key = str(origin_key) + 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)) + 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)) + 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.") + + 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 = 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/core/fitting/base.py b/src/quantem/core/fitting/base.py new file mode 100644 index 00000000..b21e1126 --- /dev/null +++ b/src/quantem/core/fitting/base.py @@ -0,0 +1,812 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Literal, Self, Sequence, cast + +import numpy as np +import torch +from torch import nn +from tqdm.auto import tqdm + +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, ...] + device: torch.device + dtype: torch.dtype + mask: torch.Tensor | None = None + 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): + 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) + 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, + 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: + self._enforce_parameter_bounds() + + def forward(self, ctx: RenderContext) -> torch.Tensor: + raise NotImplementedError + + def constraint_loss( + self, ctx: RenderContext, params: dict[str, Any] | None = None + ) -> torch.Tensor: + return torch.zeros((), device=ctx.device, dtype=ctx.dtype) + + +class AdditiveRenderModel(nn.Module): + def __init__(self, *, origin: nn.Module, components: list[RenderComponent]): + super().__init__() + self.origin = origin + self.components = nn.ModuleList(components) + + def forward(self, ctx: RenderContext) -> torch.Tensor: + if len(self.components) == 0: + return torch.zeros(ctx.shape, device=ctx.device, dtype=ctx.dtype) + out = self.components[0](ctx) + for component in self.components[1:]: + out = out + component(ctx) + return out + + 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) + component.enforce_hard_constraints(ctx) + + 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 + + +@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__() + # Core wiring + self.loss_fn = torch.nn.MSELoss(reduction="mean") + self.model: AdditiveRenderModel | None = None + self.ctx: RenderContext | None = None + + # State/checkpoints + self.state_initialized: dict[str, torch.Tensor] | None = None + + # Histories/results + self.fit_history: dict[str, FitResult] = {} + + def get_optimization_parameters(self) -> Any: + if self.model is None: + return [] + return [p for p in self.model.parameters() if p.requires_grad] + + @property + def state_current(self) -> dict[str, torch.Tensor] | None: + if self.model is None: + return None + return self._get_model_state_dict_copy() + + @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 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, + *, + 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: + """ + 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: + self.model.apply_constraint_params(constraint_params, strict=True) + + 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 + + n_steps = int(n_steps) + if scheduler_params is not None: + 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=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(n_steps), desc="Fit render", disable=not progress) + + losses: list[float] = [] + lrs: list[float] = [] + for _ in pbar: + 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) + 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) + lrs.append(float(self.get_current_lr())) + + 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 + + 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.") + entries: list[tuple[str, RenderComponent]] = [] + for idx, module in enumerate(self.model.components): + component = cast(RenderComponent, module) + 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 + 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: + 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()} + + 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 +Model = AdditiveRenderModel +Parameter = nn.Parameter diff --git a/src/quantem/core/fitting/diffraction.py b/src/quantem/core/fitting/diffraction.py new file mode 100644 index 00000000..30934bf5 --- /dev/null +++ b/src/quantem/core/fitting/diffraction.py @@ -0,0 +1,581 @@ +from __future__ import annotations + +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 + + +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, 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) + + w00 = (1.0 - fr) * (1.0 - fc) + 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: + 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) + put(r0i + 1, c0i, w10) + put(r0i + 1, c0i + 1, w11) + + +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, + *, + name: str, + array: np.ndarray, + refine_all_pixels: bool = False, + normalize: str = "none", + origin: OriginND | None = None, + origin_key: str = "origin", + 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. + Accepts ``x``, ``(x0, delta)``, or ``(x0, lo, hi)``. + + 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) + 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: + raise ValueError("DiskTemplate.array must be 2D.") + if normalize == "max": + s = float(np.max(a)) + if s > 0.0: + a = a / s + elif normalize == "mean": + 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'.") + + template = torch.as_tensor(a, dtype=torch.float32) + self.template_raw = nn.Parameter(template.clone(), requires_grad=self.refine_all_pixels) + + 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)) + 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( + cls, + *, + name: str, + array: np.ndarray, + refine_all_pixels: bool = False, + normalize: str = "none", + 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, + array=array, + refine_all_pixels=refine_all_pixels, + normalize=normalize, + origin=origin, + origin_key=origin_key, + intensity=intensity, + constraint_params=constraint_params, + ) + + 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) + + 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: + """ + 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] + 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: + 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) + self.intensity_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() + super().enforce_hard_constraints(ctx) + + 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): + DEFAULT_HARD_CONSTRAINTS: dict[str, bool] = { + "force_positive_intensity": True, + } + + def __init__( + self, + *, + name: str, + disk: DiskTemplate, + 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 | 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 | 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", + constraint_params: dict[str, Any] | None = None, + ): + """ + Build a synthetic disk lattice renderer. + + 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. 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. 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 + 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 + 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) + + if max_intensity_order is None: + 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: + default_pattern_intensity_order = self.max_intensity_order + self.default_pattern_intensity_order = int(default_pattern_intensity_order) + + 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 + 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): + 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, 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)) + 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 + if self.max_intensity_order >= 2: + 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)): + 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() + 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: + 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 = 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 + else: + 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 + 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 + ) + + self.disk.add_patch(out, r0=rr0, c0=cc0, scale=inten) + + return out 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"] diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index d352a051..a4585ad2 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -6,16 +6,22 @@ import numpy as np import torch from numpy.typing import NDArray -from scipy.ndimage import gaussian_filter +from scipy.ndimage import gaussian_filter, map_coordinates 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: @@ -24,27 +30,55 @@ 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) + 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])) ) - return xp.real(kern_row @ F @ kern_col) + 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( @@ -55,7 +89,6 @@ 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. @@ -67,98 +100,78 @@ 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 = (x0 + dx) % cc.shape[0] - y0 = (y0 + dy) % cc.shape[1] + x0 = np.round((float(x0) + float(dx)) * 2.0) / 2.0 + y0 = np.round((float(y0) + float(dy)) * 2.0) / 2.0 - if upsample_factor <= 1: - shifts = (x0, y0) - else: - # Local DFT upsampling - - local = dft_upsample(cc, upsample_factor, (x0, y0), device=device) - peak = np.unravel_index(xp.argmax(local), local.shape) + xy_shift = np.array([x0, y0], dtype=float) - 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 @@ -175,7 +188,6 @@ 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 @@ -197,12 +209,10 @@ 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)] @@ -210,14 +220,11 @@ 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 @@ -242,7 +249,6 @@ 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) @@ -253,26 +259,19 @@ 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 = dx.item() @@ -282,7 +281,6 @@ 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) @@ -311,13 +309,9 @@ 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] ) @@ -325,28 +319,125 @@ 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 +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, + fft_output: bool = False, + return_shifted_image: bool = False, +): + """ + Weighted peak selection + DFT subpixel refinement for Fourier cross-correlation. + + 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`. + + Returns + ------- + shift_rc : tuple[float, float] + (d_row, d_col) shift to apply to `im` to align it to `im_ref`. + shifted : ndarray (optional) + If return_shifted=True: shifted image. If fft_output=True returns FFT (corner-centered), + else returns real-space image. + """ + 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: + 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 + + 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_image: + return shift_rc + + if im is None: + 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)) + + 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 + + if fft_output: + return shift_rc, F_im_shifted + return shift_rc, np.fft.ifft2(F_im_shifted).real + + def bilinear_kde( xa: NDArray, ya: NDArray, @@ -361,32 +452,6 @@ 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) @@ -416,14 +481,12 @@ 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)) @@ -455,23 +518,7 @@ 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 @@ -497,10 +544,7 @@ 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 @@ -512,20 +556,7 @@ 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 @@ -536,13 +567,9 @@ 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 @@ -556,22 +583,6 @@ 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 @@ -601,12 +612,10 @@ 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 ) @@ -636,45 +645,14 @@ 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) @@ -698,11 +676,9 @@ 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 ) @@ -725,20 +701,6 @@ 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 @@ -759,10 +721,8 @@ 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 ) @@ -841,9 +801,7 @@ 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()) @@ -855,7 +813,6 @@ 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(), @@ -884,7 +841,6 @@ 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]: @@ -962,18 +918,6 @@ 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 @@ -1013,10 +957,10 @@ 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 @@ -1165,3 +1109,67 @@ def radially_project_fourier_tensor( array_1d = array_1d[0] return q_bins_out, array_1d + + +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) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index e69de29b..a64a357e 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -0,0 +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/maped.py b/src/quantem/diffraction/maped.py new file mode 100644 index 00000000..3b5154c0 --- /dev/null +++ b/src/quantem/diffraction/maped.py @@ -0,0 +1,892 @@ +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.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.") + super().__init__() + self.datasets = datasets + self.metadata: dict[str, Any] = {} + + @classmethod + 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] = [] + 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 not ds_list: + 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: + """ + 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) + 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: list[np.ndarray] = [] + self.im_bf: list[np.ndarray] = [] + + 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: + 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) + + 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)) + + 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, title=titles, **plot_kwargs) + + return self + + 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. + + 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) + 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. + """ + 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: + dp_use = gaussian_filter(dp.astype(float, copy=False), float(sigma), mode="nearest") + else: + dp_use = dp + 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)) + 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: + 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): + r, c = self.diffraction_origins[i] + axs[j].plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) + + return self + + def diffraction_align( + self, + 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: + """ + 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, :] + + r = np.fft.fftfreq(H, 1.0 / float(H))[:, None] + c = np.fft.fftfreq(W, 1.0 / float(W))[None, :] + + n = len(self.dp_mean) + self.diffraction_shifts = np.zeros((n, 2), dtype=float) + + G_ref = np.fft.fft2(w * np.asarray(self.dp_mean[0])) + xy0 = np.asarray(self.diffraction_origins[0], dtype=float) + + 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( + 1.0 - 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_rc, G_shift = weighted_cross_correlation_shift( + im_ref=G_ref, + im=G, + 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, :] = np.asarray(shift_rc, dtype=float) + + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) + + 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=float(edge_blend), + padding=padding, + pad_val=pad_val, + ) + show_2d(im_aligned, **plot_kwargs) + + return self + + + def real_space_align( + 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, + **plot_kwargs: Any, + ) -> MAPED: + """ + Align real-space mean BF images using iterative average-reference correlation. + + 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: + 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") + + 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, + ) + else: + wx = None + + 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=(shifts[i, 0], 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=shift_method, + ) + show_2d(im_aligned, **plot_kwargs) + + return self + + def merge_datasets( + 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", + dtype=None, + scale_output: bool = False, + plot_result: bool = True, + **plot_kwargs: Any, + ) -> Dataset4dstem: + """ + Merge aligned datasets into a single Dataset4dstem. + + 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"): + raise RuntimeError("Run diffraction_align() first so self.diffraction_shifts exists.") + + arrays = [np.asarray(d.array) for d in self.datasets] + n = len(arrays) + if n == 0: + raise RuntimeError("No datasets found in self.datasets.") + + Rs, Cs, H, W = arrays[0].shape + for a in arrays: + if a.shape != (Rs, Cs, 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) + 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).") + + 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) + 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'.") + + 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) + + 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) + + dp_means = [np.mean(a, axis=(0, 1), dtype=np.float64) for a in arrays] + v = np.stack(dp_means, axis=0).reshape(-1) + + if isinstance(diffraction_pad_val, str): + s = diffraction_pad_val.strip().lower() + 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) + + 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, :] + Fw = np.fft.fft2(wdp_pad) + ramps: list[np.ndarray] = [] + for i in range(n): + 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 + wdp_shifted[i] = np.clip(w_i, 0.0, 1.0) + else: + for i in range(n): + w_i = ndi_shift( + wdp_pad, + 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 = [] + + 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) + + 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 = ro - real_space_padding + for co in range(Cout): + 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 - 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 = r_in - r0 + dc = 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": + 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=(dp_shifts[i, 0], 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 + + 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 + + if scale_output: + peak = float(np.max(merged_f)) + if peak <= 0.0: + merged_scaled = merged_f + else: + merged_scaled = merged_f * (dmax / peak) + + if np.issubdtype(dtype_out, np.unsignedinteger): + lo, hi = 0.0, dmax + else: + lo, hi = dmin, dmax + + 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(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]], + title=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], + **plot_kwargs, + ) + + return dataset_merged + + +def shift_images( + images, + shifts_rc, + edge_blend: float = 8.0, + padding=None, + pad_val: str | float = 0.0, + shift_method: str = "bilinear", +): + """ + 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") + + 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_f = float(np.min(v)) + elif s == "max": + pad_val_f = float(np.max(v)) + elif s == "mean": + pad_val_f = float(np.mean(v)) + elif s == "median": + 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_f = 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 = shifts_rc[ind, 0], 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=(shifts_rc[ind, 0], shifts_rc[ind, 1]), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = ndi_shift( + stack_w[ind], + shift=(shifts_rc[ind, 0], 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 = np.clip(1.0 - np.sum(stack_w, axis=0), 0.0, 1.0) + + num = np.sum(stack, axis=0) + edge_w * pad_val_f + den = np.sum(stack_w, axis=0) + edge_w + + out = np.empty_like(num) + np.divide(num, den, out=out, where=den != 0.0) + out[den == 0.0] = 0.0 + + return out diff --git a/src/quantem/diffraction/model_fitting.py b/src/quantem/diffraction/model_fitting.py new file mode 100644 index 00000000..55cddfcb --- /dev/null +++ b/src/quantem/diffraction/model_fitting.py @@ -0,0 +1,534 @@ +from __future__ import annotations + +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 quantem.core.datastructures import Dataset2d, Dataset3d, Dataset4d, Dataset4dstem +from quantem.core.fitting.base import ( + AdditiveRenderModel, + FitBase, + OriginND, + RenderComponent, + RenderContext, +) +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 + + +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(ModelDiffractionVisualizations, FitBase, AutoSerialize): + _token = object() + 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) + FitBase.__init__(self) + + # Dataset/input references + self.dataset = dataset + 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 + ) -> "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." + ) + + @property + def components(self) -> torch.nn.ModuleList: + if self.model is None: + raise RuntimeError("Call .define_model(...) first.") + return self.model.components + + 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 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) + rendered = component(ctx) + return rendered.detach().cpu().numpy() + + 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.") + + 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.") + 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 = 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 = [ + 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.") + + 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 { + "hard": dict(component.hard_constraints), + "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, + *, + 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 = 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 = 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)) + 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) + fft_ref = np.fft.fft2(window * stack[0]) + for i in range(1, n): + 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]) + 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( + 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,)) + return self + + def define_model( + self, + *, + 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": + 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]) + 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: + 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) + + s0 = self._get_model_state_dict_copy() + self.state_initialized = s0 + self.state_mean_refined = None + self.mean_refined = False + self._clear_fit_history_all() + self.remove_optimizer() + return self + + 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, + constraint_params: dict[str, Any] | None = None, + progress: bool = True, + ) -> "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: + 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'.") + + self.fit_render( + 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), + run_key="mean", + ) + + s_fit = self._get_model_state_dict_copy() + self.state_mean_refined = self._clone_state_dict(s_fit) + 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) diff --git a/src/quantem/diffraction/model_fitting_visualizations.py b/src/quantem/diffraction/model_fitting_visualizations.py new file mode 100644 index 00000000..6dfb4f62 --- /dev/null +++ b/src/quantem/diffraction/model_fitting_visualizations.py @@ -0,0 +1,428 @@ +from typing import TYPE_CHECKING, Any, Literal, cast + +import numpy as np +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: + from quantem.diffraction.model_fitting import ModelDiffraction + + +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 + """ + colors = config.get("viz.colors.set") + if overlay_origin and origin_rc.shape == (2,): + kw_origin = { + "marker": "+", + "color": colors[0], + "markersize": 10, + "markeredgewidth": 3, + "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": colors[1], + "markersize": 5, + "markeredgewidth": 2.0, + "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]: + 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() + 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=loss_color, lw=2, label="loss")) + ax.set_xlabel("Iterations") + 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) + 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=lr_color, lw=2, ls="--", label="LR") + ) + 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) + + 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), + 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: + 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=config.get("viz.cmap"), + cbar=bool(cbar), + returnfig=False, + axsize=axsize, + 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() + 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( + 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), + 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() + 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=config.get("viz.cmap"), + cbar=False, + returnfig=True, + axsize=axsize, + 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 a summed component image. + + Parameters + ---------- + components : str | list[str] + 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 + 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 = [ + 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( + summed_scaled, + title=title, + cmap=config.get("viz.cmap"), + cbar=bool(cbar), + returnfig=True, + axsize=axsize, + ) + + if overlay: + origin_rc, disk_centers_rc = md.get_overlay_coordinates() + 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 + return None diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py new file mode 100644 index 00000000..7e87eff3 --- /dev/null +++ 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.") diff --git a/src/quantem/diffraction/strain_autocorrelation.py b/src/quantem/diffraction/strain_autocorrelation.py new file mode 100644 index 00000000..a084b32d --- /dev/null +++ b/src/quantem/diffraction/strain_autocorrelation.py @@ -0,0 +1,1173 @@ +from __future__ import annotations + +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 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 StrainMapAutocorrelation(AutoSerialize): + _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 StrainMapAutocorrelation.from_dataset() or StrainMapAutocorrelation.from_array() 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 + + 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_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) + + @classmethod + def from_array(cls, array: NDArray, *, name: str = "strain_map_autocorrelation") -> "StrainMapAutocorrelation": + arr = ensure_valid_array(array) + if arr.ndim != 4: + raise ValueError( + "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=array, _token=cls._token) + + def diffraction_mask( + self, + threshold=None, + edge_blend=64.0, + 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.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, + skip=None, + plot_transform: bool = True, + cropping_factor: float = 0.25, + gamma: float = 0.5, + **plot_kwargs: Any, + ) -> "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_norm + if mode_norm == "gamma": + self.metadata["gamma"] = gamma + + qrow_unit = self.dataset.units[2] + qcol_unit = self.dataset.units[3] + + if qrow_unit in {"A", "Å"}: + qrow_sampling_ang = self.dataset.sampling[2] + elif qrow_unit == "mrad": + 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 = self.dataset.sampling[3] + elif qcol_unit == "mrad": + 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 * self.dataset.shape[2]), + 1.0 / (qcol_sampling_ang * self.dataset.shape[3]), + ), + dtype=float, + ) + + if qrow_unit == "pixels" and qcol_unit == "pixels": + self.metadata["real_units"] = "1/pixels" + else: + self.metadata["real_units"] = r"$\mathrm{\AA}$" + + parent_rot = self.dataset.metadata.get("q_to_r_rotation_ccw_deg", None) + parent_tr = self.dataset.metadata.get("q_transpose", 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 + + 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, + ) + + 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( + "StrainMapPatterson.preprocess: setting q_to_r_rotation_ccw_deg=0.0 and q_transpose=False.", + UserWarning, + ) + + 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: + 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( + im, + origin=(im.shape[0] // 2, im.shape[1] // 2), + sampling=(1.0, 1.0), + units=(qrow_unit, qcol_unit), + signal_units="intensity", + ) + + im_plot = self.transform.array + if self.metadata["q_transpose"]: + im_plot = im_plot.T + + self.transform_rotated = Dataset2d.from_array( + rotate_image( + im_plot, + 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=(self.metadata["real_units"], self.metadata["real_units"]), + 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, + 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 = np.mean(self.metadata["sampling_real"]) + units = self.metadata.get("real_units", r"$\mathrm{\AA}$") + + 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) + + 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(np.nanargmax(tmp), tmp.shape) + vmin = 0.0 + vmax = im0[i0] + + defaults = dict( + vmin=vmin, + vmax=vmax, + title=("Original Transform", "Rotated Transform"), + scalebar=ScalebarConfig( + 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) + + 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_gaussian: bool = True, + refine_dft: bool = False, + refine_radius_px: float = 2.0, + upsample: int = 16, + gaussian_maxfev: int = 100, + plot: bool = True, + cropping_factor: float = 0.25, + **plot_kwargs: Any, + ) -> "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 = 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) + + 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_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) + _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_gaussian: bool = True, + refine_dft: bool = False, + refine_radius_px: float = 2.0, + upsample: int = 16, + gaussian_maxfev: int = 100, + progressbar: bool = True, + ) -> "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).") + + 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_fit", + signal_units="pixels", + ) + self.v_fit = Dataset3d.from_shape( + (scan_r, scan_c, 2), + name="v_fit", + signal_units="pixels", + ) + + mode = self.metadata.get("mode", "linear").lower() + if mode == "gamma": + g = self.metadata["gamma"] + + 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) + + 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 + + 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', '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_peak_fit.array[r, c, :] = u_fit_abs + self.v_peak_fit.array[r, c, :] = v_fit_abs + + 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 + + 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, + 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 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] + + 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: + 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))) + + vmin = -vlim + vmax = vlim + + cm = plt.get_cmap(cmap).copy() + cm.set_bad(color="black") + + 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) + + 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 = 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, + ): + 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 = 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) + 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 = 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) + + self.strain_trans = Dataset4d.from_shape( + (scan_r, scan_c, 2, 2), + name="transformation matrix", + signal_units="fractional", + ) + + 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 + + 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="RdBu_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 * ref_u_v[0] + self.v_ref * ref_u_v[1] + ref_angle = np.arctan2(ref_vec[1], ref_vec[0]) + else: + ref_angle = np.deg2rad(ref_angle_degrees) + + angle = ref_angle + np.deg2rad(self.metadata["q_to_r_rotation_ccw_deg"]) + c = np.cos(angle) + s = 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 + + 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, + ) + ax[3].set_title("Rotation", fontsize=title_fs) + + 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: + 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 nice * (10.0**exp) + + +def _apply_center_crop_limits(ax: Any, shape: tuple[int, int], cropping_factor: float) -> None: + if cropping_factor >= 1.0: + return + if not (0.0 < cropping_factor <= 1.0): + raise ValueError("cropping_factor must be in (0, 1].") + + 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) + + 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) + + +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 = v[0], v[1] + + if transpose: + dr, dc = dc, dr + + theta = np.deg2rad(rotation_ccw_deg) + ct = np.cos(theta) + st = 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 = v[0], v[1] + + theta = np.deg2rad(rotation_ccw_deg) + ct = np.cos(theta) + st = 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 = 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") + + _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 = 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=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 + 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 np.clip(delta, -1.0, 1.0) + + +def _refine_peak_subpixel( + im: NDArray, + *, + r_guess: float, + c_guess: float, + radius_px: float = 2.0, +) -> 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(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 r_guess, c_guess + + 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] + 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] + dc = _parabolic_vertex_delta(row[0], row[1], row[2]) + else: + dc = 0.0 + + return r_peak + dr, c_peak + dc + + +def _refine_peak_subpixel_dft( + im: NDArray, + *, + r0: float, + c0: float, + upsample: int, +) -> tuple[float, float]: + if upsample <= 1: + return r0, c0 + + im = np.asarray(im, dtype=float) + F = np.fft.fft2(im) + + up = upsample + du = int(np.ceil(1.5 * up)) + + patch = dft_upsample(F, up=up, shift=(r0, c0), device="cpu") + patch = np.asarray(patch, dtype=float) + + i0, j0 = np.unravel_index(np.argmax(patch), patch.shape) + + if 0 < i0 < patch.shape[0] - 1: + col = patch[i0 - 1 : i0 + 2, j0] + 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] + dj = _parabolic_vertex_delta(row[0], row[1], row[2]) + else: + dj = 0.0 + + dr = (i0 - du + di) / up + dc = (j0 - du + dj) / up + + return r0 + dr, c0 + dc + + +def _refine_lattice_vectors( + im: NDArray, + *, + u_rc: NDArray, + v_rc: NDArray, + radius_px: float = 2.0, + 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 = 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 + + 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 + vec[0] + c_guess = c_center + vec[1] + + r_par, c_par, amp_par = _parabolic_peak_rc_amp(r_guess=r_guess, c_guess=c_guess) + + 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: + 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((r_fit, c_fit, amp, sig, bg), dtype=float) + + return _refine_one(u_rc), _refine_one(v_rc) diff --git a/tests/core/utils/test_imaging_utils.py b/tests/core/utils/test_imaging_utils.py new file mode 100644 index 00000000..bc3989d6 --- /dev/null +++ b/tests/core/utils/test_imaging_utils.py @@ -0,0 +1,127 @@ +""" +Tests for imaging utilities in quantem.core.utils.imaging_utils +""" + +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, weighted_cross_correlation_shift + + +@pytest.fixture +def spot_image(): + + 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) + +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) diff --git a/uv.lock 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