diff --git a/ImageAnalysis/image_analysis/algorithms/axis_interpolation.py b/ImageAnalysis/image_analysis/algorithms/axis_interpolation.py index 6edd56331..6c72cad9e 100644 --- a/ImageAnalysis/image_analysis/algorithms/axis_interpolation.py +++ b/ImageAnalysis/image_analysis/algorithms/axis_interpolation.py @@ -90,15 +90,19 @@ def interpolate_image_axis( ) physical_max = calib_max - # Create uniform physical axis + # Compute original bin widths + dE = np.gradient(pixel_to_physical) + + # Uniform axis uniform_axis = np.linspace(physical_min, physical_max, num_points) - # Interpolate - if axis == 1: # Horizontal (typical for energy dispersion) - # Interpolate each row + if axis == 1: output = np.zeros((image.shape[0], num_points), dtype=image.dtype) + for i in range(image.shape[0]): - output[i] = np.interp(uniform_axis, pixel_to_physical, image[i, :]) + density = image[i, :] / dE + density_interp = np.interp(uniform_axis, pixel_to_physical, density) + output[i] = density_interp logger.debug( f"Interpolated {image.shape[0]} rows from {image.shape[1]} to {num_points} points" ) diff --git a/ImageAnalysis/image_analysis/base.py b/ImageAnalysis/image_analysis/base.py index 81899cb4a..5c44d0ae0 100644 --- a/ImageAnalysis/image_analysis/base.py +++ b/ImageAnalysis/image_analysis/base.py @@ -59,20 +59,28 @@ def __init__(self, **config): pass def analyze_image( - self, image: Union[Array1D, Array2D], auxiliary_data: Optional[dict] = None + self, + image: Union[Array1D, Array2D, list[Array1D], list[Array2D]], + auxiliary_data: Optional[dict] = None, ) -> ImageAnalyzerResult: - """Calculate metrics from an image. + """Calculate metrics from an image or list of images. This function should be implemented by each device's ImageAnalyzer subclass, to run on an image from that device (obviously). Should take full-size (i.e. uncropped) image. + For multi-device analyzers (e.g. multi-camera diagnostics), a list of + arrays may be passed — one per device. The analyzer is responsible for + combining them as needed and returning a single result. + Parameters ---------- - image : 2d array (e.g. MxN) or standard y vs x data (eg. Nx2) - auxiliary_data : dict - Additional data used by the image image_analyzer for this image, such as + image : Union[Array1D, Array2D, list[Array1D], list[Array2D]] + A single 2D array (e.g. MxN), a single 1D dataset (e.g. Nx2), + or a list of such arrays for multi-device analysis. + auxiliary_data : dict, optional + Additional data used by the image analyzer for this image, such as image range. Returns @@ -84,16 +92,23 @@ def analyze_image( raise NotImplementedError() def analyze_image_file( - self, image_filepath: Path, auxiliary_data: Optional[dict] = None + self, + image_filepath: Union[Path, list[Path]], + auxiliary_data: Optional[dict] = None, ) -> ImageAnalyzerResult: """ - Method to enable the use of a file path rather than Array2D. + Method to enable the use of a file path (or list of paths) rather than arrays. + + For multi-device analyzers, a list of file paths can be passed — one + per device. The paths are loaded via :meth:`load_image` and the + resulting array(s) are passed to :meth:`analyze_image`. Parameters ---------- - image_filepath : Path - auxiliary_data : dict - Additional data used by the image image_analyzer for this image, such as + image_filepath : Union[Path, list[Path]] + A single file path or a list of file paths for multi-device analysis. + auxiliary_data : dict, optional + Additional data used by the image analyzer for this image, such as image range. Returns @@ -106,23 +121,34 @@ def analyze_image_file( return self.analyze_image(image, auxiliary_data) - def load_image(self, file_path: Path) -> Union[Array1D, Array2D]: + def load_image( + self, file_path: Union[Path, list[Path]] + ) -> Union[Array1D, Array2D, list[Union[Array1D, Array2D]]]: """ - Load an image from a path. + Load an image from a path, or multiple images from a list of paths. - By default, the read_imaq_png function is used. - For file types not directly supported by this method, e.g. .himg files from a - Haso device type, this method be implemented in that device's ImageAnalyzer - subclass. + When given a single path, loads and returns a single array using + :func:`read_imaq_image` (or subclass override). + + When given a list of paths, recursively calls ``self.load_image`` on + each path and returns a list of arrays. This means subclasses that + override ``load_image`` for custom file formats (e.g. ``.himg``) + automatically get list support for free. Parameters ---------- - file_path : Path + file_path : Union[Path, list[Path]] + A single file path or a list of file paths for multi-device loading. Returns ------- - image : Union[Array1D,Array2D] + Union[Array1D, Array2D, list[Union[Array1D, Array2D]]] + A single array for a single path, or a list of arrays for a list + of paths. """ + if isinstance(file_path, list): + return [self.load_image(p) for p in file_path] + image = read_imaq_image(file_path) return image diff --git a/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/170925DnnVarianTrj.txt b/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/170925DnnVarianTrj.txt new file mode 100644 index 000000000..8ad44694c --- /dev/null +++ b/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/170925DnnVarianTrj.txt @@ -0,0 +1,693 @@ +kinetic energy [MeV] momentum [MeV/c] total energy [MeV] \gamma \beta orbit radius [m] bending angle [dgr] bending angle at screen [dgr] incident angle [dgr] drift1 [m] drift2 [m] total path [m] screen [m] Bpeak [T] B.m [T.m] effective length [m] MagRds [m] side logic x conv fct rms y conv fct rms momentum rsl [%/mrad] side screen [m] front screen [m] +9999.489014 10000.000000 10000.000013 19569.507649 1.000000 33.356410 0.671816 0.671816 0.053831 0.691770 0.333112 2.716000 0.006203 1.000000 0.391118 0.391118 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0.041154 0.188533 0.286677 1.000000 1.000000 1.000000 1.000000 0.812873 0.812873 +5.510722 6.000000 6.021721 11.784211 0.996393 0.100557 130.062765 108.051403 23.361543 0.597290 0.022329 2.109300 0.818163 0.199031 0.037752 0.189682 0.293621 1.000000 1.000000 1.000000 1.000000 0.818163 0.818163 +5.012688 5.500000 5.523687 10.809584 0.995712 0.101302 130.788547 107.253348 23.249435 0.590262 0.019658 2.099600 0.824151 0.181103 0.034351 0.189680 0.300614 1.000000 1.000000 1.000000 1.000000 0.824151 0.824151 +4.515045 5.000000 5.026044 9.835721 0.994818 0.101940 131.588597 106.334987 23.156600 0.582624 0.017033 2.088900 0.830900 0.163609 0.030962 0.189243 0.308218 1.000000 1.000000 1.000000 1.000000 0.830900 0.830900 +4.017921 4.500000 4.528920 8.862874 0.993614 0.102656 132.501492 105.259186 22.994867 0.573928 0.014223 2.076800 0.838711 0.146220 0.027584 0.188650 0.316905 1.000000 1.000000 1.000000 1.000000 0.838711 0.838711 +3.521509 4.000000 4.032508 7.891419 0.991939 0.103112 133.492647 103.932171 22.934947 0.564505 0.011592 2.063200 0.847597 0.129398 0.024211 0.187104 0.326324 1.000000 1.000000 1.000000 1.000000 0.847597 0.847597 +3.026107 3.500000 3.537106 6.921943 0.989509 0.103352 134.585848 102.207179 22.953135 0.554011 0.009058 2.047500 0.857945 0.112962 0.020834 0.184431 0.336868 1.000000 1.000000 1.000000 1.000000 0.857945 0.857945 +2.532210 3.000000 3.043209 5.955410 0.985802 0.103144 135.873719 100.151152 23.047091 0.542095 0.006640 2.029100 0.870469 0.097019 0.017499 0.180365 0.348941 1.000000 1.000000 1.000000 1.000000 0.870469 0.870469 +2.040691 2.500000 2.551690 4.993531 0.979743 0.102686 137.277528 97.278610 23.335208 0.528257 0.004422 2.007100 0.885578 0.081210 0.014165 0.174421 0.362879 1.000000 1.000000 1.000000 1.000000 0.885578 0.885578 +1.553249 2.000000 2.064248 4.039632 0.968876 0.102603 138.775843 92.938356 23.651247 0.510938 0.002448 1.979900 0.904507 0.065021 0.010827 0.166514 0.380342 1.000000 1.000000 1.000000 1.000000 0.904507 0.904507 diff --git a/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/250101lanexCalib.txt b/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/250101lanexCalib.txt new file mode 100644 index 000000000..1af86ba89 --- /dev/null +++ b/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/250101lanexCalib.txt @@ -0,0 +1,15 @@ +setting number FOV slope FOV offset vignette 4 vignette 2 vignette 0 sensitivity 2 sensitivity 1 sensitivity 0 full width full height +1 0.7315 22.753 2.17E-12 -1.76E-06 1 1.81E-05 0.00116156 0.01691097 512 384 +2 0.9121 24.762 3.83E-12 -3.64E-06 1 0.0000247 0.00146 0.0174 512 384 +3 0.1896 0.982 2.85E-12 -1.41E-06 1 0.000000338 -0.0000936 0.03011273 512 512 +4 0.5721 7.7876 2.55E-12 -9.29E-07 1 1.57E-05 -0.000705303 0.05496356 512 384 +5 0.592 6.9601 5.42E-14 -4.24E-07 1 1.83E-06 -0.0000549 -0.0000543 1288 964 +6 0.7866 10.824 -4.00E-13 -6.31E-07 1 5.03E-06 0.000150642 -0.00088443 1384 1032 +7 0.9865 27.171 5.66E-12 -3.12E-06 1 7.95E-06 0.000496924 -0.003981836 692 516 +8 0.9865 27.171 -6.00E-13 -7.80E-07 1 7.95E-06 4.97E-04 -0.003981836 1384 1032 +9 0.7866 10.824 2.47E-12 -2.52E-06 1 5.03E-06 0.000150642 -0.00088443 692 516 +10 0.61 6.12 2.54317E-13 -9.22E-07 1 1.5379E-05 2.2023E-04 1.8637E-02 724 542 +11 0.596 7.81 1.59310E-13 -9.28E-06 1.00000E+00 7.40600E-06 3.16210E-04 9.08630E-03 1440 1080 +12 1 27 0 0 1 0 0 3.13 1288 728 +13 0.9865 27.171 2.50E-12 -3.20E-06 1 7.95E-06 4.97E-04 -0.003981836 1384 1032 +14 0.9865 27.171 1.20E-12 -2.00E-06 1 7.95E-06 4.97E-04 -0.003981836 1384 1032 diff --git a/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/260224DnnVarianCam.txt b/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/260224DnnVarianCam.txt new file mode 100644 index 000000000..37b100cf7 --- /dev/null +++ b/ImageAnalysis/image_analysis/data/HTT-MagSpce-Calibrations/260224DnnVarianCam.txt @@ -0,0 +1,6 @@ +cam number FOV [mm] Left edge [mm] ROI Y offset ROI height ROI X offset ROI width Y center pixel Y Start Y End X Start X End rot [deg] set screen name sensitivity +1 109.97 169.4 170 600 0 1384 305 1 600 1 1357 0 6 back HTT-C23_1_MagSpec1 0.36974 +2 355.73 521.52 320 256 0 1384 139 45 230 370 1370 0 8 back HTT-C23_2_MagSpec2 0.36974 +3 368.62 416.98 340 256 0 1384 134 45 225 140 750 0.2 13 back HTT-C23_3_MagSpec3 0.244 +4 438.32 813.62 420 256 0 1384 96 21 170 26 1384 0.1 14 back HTT-C23_4_MagSpec4 0.36974 +5 207.57 -96.7 0 542 0 724 157 31 490 221 500 0 10 front HTT-VHEE_Lanex 1.21 diff --git a/ImageAnalysis/image_analysis/offline_analyzers/magspec_manual_calib_analyzer.py b/ImageAnalysis/image_analysis/offline_analyzers/magspec_manual_calib_analyzer.py index 63e03d828..3915d4c71 100644 --- a/ImageAnalysis/image_analysis/offline_analyzers/magspec_manual_calib_analyzer.py +++ b/ImageAnalysis/image_analysis/offline_analyzers/magspec_manual_calib_analyzer.py @@ -3,14 +3,44 @@ Provides analysis for magnetic spectrometer images with pixel-to-energy conversion using device-specific calibrations. + +Supported YAML config format (under ``analysis:``): + +.. code-block:: yaml + + analysis: + energy_range: [76.2, 201.3] + num_energy_points: 1000 + calibration: + kind: dnn_axis + camera_calibration_file: "magspec_code/calibrations/260224DnnVarianCam.txt" + trajectory_calibration_file: "magspec_code/calibrations/170925DnnVarianTrj.txt" + lanex_calibration_file: "magspec_code/calibrations/250101lanexCalib.txt" + camera_number: 1 + magnetic_field_t: 0.2005 + +Polynomial calibration example: + +.. code-block:: yaml + + analysis: + energy_range: [20, 150] + num_energy_points: 500 + calibration: + kind: polynomial + coeffs: [89.368, 0.01836, 1.14e-06] """ +import csv +import png import numpy as np import logging -from typing import Optional, Tuple, Dict, List, Callable +from typing import Optional, Tuple, Dict, List, Annotated, Union, Literal, Any from pathlib import Path -from dataclasses import dataclass +from abc import ABC, abstractmethod import matplotlib.pyplot as plt +from scipy.interpolate import CubicSpline +from pydantic import BaseModel, Field, field_validator, model_validator, ConfigDict from image_analysis.tools.rendering import base_render_image from image_analysis.offline_analyzers.beam_analyzer import BeamAnalyzer @@ -20,381 +50,528 @@ logger = logging.getLogger(__name__) -@dataclass -class MagSpecCalibration: +# ------------------------------------------------------------------ +# Helpers +# ------------------------------------------------------------------ + + +def _read_tab_delimited_rows(file_path: str) -> List[Dict[str, str]]: + """Read a tab-delimited text file with a header row into a list of dicts.""" + resolved = Path(file_path) + if not resolved.is_absolute(): + if not resolved.exists(): + resolved = Path(__file__).parent.parent / resolved + with resolved.open(newline="") as f: + return list(csv.DictReader(f, delimiter="\t")) + + +# ------------------------------------------------------------------ +# Energy calibration strategy +# ------------------------------------------------------------------ + + +class EnergyCalibration(ABC): + """Interface for mapping image column index to energy.""" + + @abstractmethod + def build_axis(self, image_width: int) -> np.ndarray: + """Return energy axis values corresponding to image columns.""" + ... + + def get_charge_factor(self) -> Optional[float]: + """Return counts-to-fC factor if available, else None.""" + return None + + +class PolynomialCalibration(EnergyCalibration, BaseModel): + """Polynomial energy calibration: E(pixel) = c0 + c1*x + c2*x^2 + ... + + YAML example:: + + calibration: + kind: polynomial + coeffs: [89.368, 0.01836, 1.14e-06] """ - Calibration specification for pixel-to-energy mapping. - Supports three calibration types: - - polynomial: Uses polynomial coefficients - - array: Uses pre-computed array (from file or inline) - - callable: Uses custom function + kind: Literal["polynomial"] = "polynomial" + coeffs: List[float] + + @field_validator("coeffs") + def _check_coeffs(cls, v: List[float]) -> List[float]: + if not v: + raise ValueError("coeffs must contain at least one element") + return v + + def build_axis(self, image_width: int) -> np.ndarray: + """Evaluate polynomial E(pixel) for each column index.""" + pixel = np.arange(image_width, dtype=float) + return sum(c * pixel**i for i, c in enumerate(self.coeffs)) + + +class ArrayCalibration(EnergyCalibration, BaseModel): + """Calibration from a precomputed axis array (inline list or ``.npy`` file). + + YAML example:: + + calibration: + kind: array + file: "path/to/energy_axis.npy" """ - type: str # "polynomial", "array", or "callable" - coeffs: Optional[List[float]] = None # For polynomial - values: Optional[np.ndarray] = None # For inline array - file: Optional[str] = None # For array from file - function: Optional[Callable[[np.ndarray], np.ndarray]] = None # For callable + kind: Literal["array"] = "array" + values: Optional[List[float]] = None + file: Optional[str] = None + + @model_validator(mode="after") + def _require_source(self): + if self.values is None and self.file is None: + raise ValueError("array calibration requires 'values' or 'file'") + return self + + def build_axis(self, image_width: int) -> np.ndarray: + """Load axis from file or inline list and validate against image width.""" + if self.file is not None: + cal_path = Path(self.file) + if not cal_path.is_absolute() and not cal_path.exists(): + cal_path = Path(__file__).parent.parent / cal_path + axis = np.load(cal_path) + else: + axis = np.asarray(self.values, dtype=float) - def __post_init__(self): - """Validate calibration configuration.""" - if self.type not in ["polynomial", "array", "callable"]: + if len(axis) != image_width: raise ValueError( - f"Calibration type must be 'polynomial', 'array', or 'callable', " - f"got '{self.type}'" + f"Calibration array length ({len(axis)}) " + f"doesn't match image width ({image_width})" ) + return axis - if self.type == "polynomial" and self.coeffs is None: - raise ValueError("Polynomial calibration requires 'coeffs'") - if self.type == "array" and self.values is None and self.file is None: - raise ValueError("Array calibration requires 'values' or 'file'") +class DnnAxisCalibration(EnergyCalibration, BaseModel): + """MATLAB-style DNN magspec calibration (camera + trajectory files). - if self.type == "callable" and self.function is None: - raise ValueError("Callable calibration requires 'function'") + On construction the tab-delimited calibration files are read and the + internal arrays are populated. If ``lanex_calibration_file`` is + provided, a counts-to-fC charge factor is also computed. + YAML example:: -@dataclass -class MagSpecConfig: - """ - Configuration for magnetic spectrometer analysis. - - Parameters - ---------- - mag_field : str - Magnetic field setting identifier (e.g., "825mT") - calibration : MagSpecCalibration - Pixel-to-energy calibration specification (always in canonical orientation) - energy_range : tuple of (float, float) - Energy range in MeV (min, max) - num_energy_points : int, default=500 - Number of points in uniform energy grid - flip_horizontal : bool, default=False - Flip raw image horizontally to align with canonical calibration. - Set to True if camera is mounted backwards relative to calibration. - flip_vertical : bool, default=False - Flip raw image vertically to align with canonical calibration. - Set to True if camera is mounted upside-down. - - Notes - ----- - The calibration defines the canonical orientation (energy increasing - left-to-right). Flip parameters correct for camera mounting, ensuring - the raw image aligns with this canonical orientation before interpolation. - The output is always in canonical orientation. + calibration: + kind: dnn_axis + camera_calibration_file: "magspec_code/calibrations/260224DnnVarianCam.txt" + trajectory_calibration_file: "magspec_code/calibrations/170925DnnVarianTrj.txt" + lanex_calibration_file: "magspec_code/calibrations/250101lanexCalib.txt" + camera_number: 1 + magnetic_field_t: 0.2005 """ - mag_field: str - calibration: MagSpecCalibration - energy_range: Tuple[float, float] - num_energy_points: int = 500 - flip_horizontal: bool = False - flip_vertical: bool = False - - @classmethod - def for_uc_hiresmag( - cls, - mag_field: str = "825mT", - energy_range: Optional[Tuple[float, float]] = None, - num_energy_points: int = 500, - flip_horizontal: bool = False, - flip_vertical: bool = False, - ) -> "MagSpecConfig": - """ - Preset configuration for UC_HiResMagCam device. + model_config = ConfigDict(arbitrary_types_allowed=True) + + kind: Literal["dnn_axis"] = "dnn_axis" + camera_calibration_file: str + trajectory_calibration_file: str + camera_number: int = Field(..., ge=1, le=4) + magnetic_field_t: float = 1.0 + lanex_calibration_file: Optional[str] = None + + # Populated during model_post_init + _fov_mm: float = 0.0 + _roi_width_px: int = 0 + _left_edge_mm: float = 0.0 + _x_start_px: int = 0 + _x_end_px: int = 0 + _traj_screen_mm: np.ndarray = np.empty(0) + _traj_momentum: np.ndarray = np.empty(0) + _charge_factor: Optional[float] = None + + def model_post_init(self, __context: Any) -> None: + """Load calibration data from text files after Pydantic validation.""" + cam_rows = _read_tab_delimited_rows(self.camera_calibration_file) + trj_rows = _read_tab_delimited_rows(self.trajectory_calibration_file) + + # Find camera row + cam_row = None + for row in cam_rows: + if int(float(row["cam number"])) == self.camera_number: + cam_row = row + break + if cam_row is None: + raise ValueError( + f"Camera number {self.camera_number} not found in " + f"'{self.camera_calibration_file}'" + ) - Parameters - ---------- - mag_field : str, default="825mT" - Magnetic field setting. Options: "800mT", "825mT" - energy_range : tuple of (float, float), optional - Energy range in MeV. Defaults to (20, 150) - num_energy_points : int, default=500 - Number of points in uniform energy grid - flip_horizontal : bool, default=False - Whether to flip image left-right - flip_vertical : bool, default=False - Whether to flip image up-down - - Returns - ------- - MagSpecConfig - Configuration for UC_HiResMagCam - - Raises - ------ - ValueError - If mag_field is not recognized - """ - # Calibration coefficients for UC_HiResMagCam - calibrations = { - "800mT": [8.66599527e01, 1.78007126e-02, 1.10546749e-06], - "825mT": [8.93681013e01, 1.83568540e-02, 1.14012869e-06], - } + self._fov_mm = float(cam_row["FOV [mm]"]) + self._roi_width_px = int(float(cam_row["ROI width"])) + self._left_edge_mm = float(cam_row["Left edge [mm]"]) + self._x_start_px = int(float(cam_row["X Start"])) + self._x_end_px = int(float(cam_row["X End"])) + + # Validate indices + if self._roi_width_px <= 0: + raise ValueError("ROI width must be > 0") + if self._x_start_px < 1 or self._x_end_px < self._x_start_px: + raise ValueError("X Start / X End must be 1-based with X End >= X Start") + if self._x_end_px > self._roi_width_px: + raise ValueError( + f"X End ({self._x_end_px}) exceeds ROI width ({self._roi_width_px})" + ) + + # Split trajectory by front/side + split_idx = None + for i, row in enumerate(trj_rows): + if float(row["side logic"]) == 1.0: + split_idx = i + break + if split_idx is None: + raise ValueError("No 'side logic == 1' split in trajectory file") + + if self.camera_number in (1, 2): + trj_subset = trj_rows[:split_idx] + screen_col = "front screen [m]" + else: + trj_subset = trj_rows[split_idx:] + screen_col = "side screen [m]" - if mag_field not in calibrations: + self._traj_screen_mm = 1000.0 * np.array( + [float(r[screen_col]) for r in trj_subset], dtype=float + ) + self._traj_momentum = np.array( + [float(r["momentum [MeV/c]"]) for r in trj_subset], dtype=float + ) + + # Charge calibration from lanex file (if provided) + if self.lanex_calibration_file is not None: + self._charge_factor = self._compute_charge_factor(cam_row) + + def _compute_charge_factor(self, cam_row: Dict[str, str]) -> float: + """Compute counts-to-fC factor from LANEX calibration table. + + Mirrors MATLAB ``fDnnLanex -> fLanexClbOutV01 + fLanexClbV02``. + """ + lanex_rows = _read_tab_delimited_rows(self.lanex_calibration_file) + + set_n = int(float(cam_row["set"])) + screen = str(cam_row.get("screen", "")).strip().lower() + cam_fov = float(cam_row["FOV [mm]"]) + sensitivity = float(cam_row.get("sensitivity", 1.0)) + if sensitivity == 0.0: + raise ValueError("Camera sensitivity cannot be zero") + + # Find lanex row by setting number + lanex_row = None + for row in lanex_rows: + if "setting number" in row and int(float(row["setting number"])) == set_n: + lanex_row = row + break + if lanex_row is None: + idx = set_n - 1 + if idx < 0 or idx >= len(lanex_rows): + raise ValueError(f"LANEX set {set_n} not available") + lanex_row = lanex_rows[idx] + + fov_slope = float(lanex_row["FOV slope"]) + fov_offset = float(lanex_row["FOV offset"]) + sense2 = float(lanex_row["sensitivity 2"]) + sense1 = float(lanex_row["sensitivity 1"]) + sense0 = float(lanex_row["sensitivity 0"]) + + z = (cam_fov - fov_offset) / fov_slope + als_ratio = sense2 * z**2 + sense1 * z + sense0 + screen_factor = 1.0 if screen == "back" else 1.98 + c2c = screen_factor * als_ratio / 146.0 + c2c /= sensitivity + return c2c + + def build_axis(self, image_width: int) -> np.ndarray: + """Build MATLAB-style DNN energy axis for the current image width.""" + expected_width = self._x_end_px - self._x_start_px + 1 + if expected_width != image_width: raise ValueError( - f"Mag field '{mag_field}' not available for UC_HiResMagCam. " - f"Available: {list(calibrations.keys())}" + f"DNN calibration ROI width ({expected_width}) doesn't match " + f"image width ({image_width}). Check ROI and X Start/X End." ) - return cls( - mag_field=mag_field, - calibration=MagSpecCalibration( - type="polynomial", coeffs=calibrations[mag_field] - ), - energy_range=energy_range or (20, 150), - num_energy_points=num_energy_points, - flip_horizontal=flip_horizontal, - flip_vertical=flip_vertical, + dx_mm = self._fov_mm / self._roi_width_px + x_mm_full = self._left_edge_mm - dx_mm * np.arange( + self._roi_width_px, dtype=float ) + x_mm = x_mm_full[self._x_start_px - 1 : self._x_end_px] + order = np.argsort(self._traj_screen_mm) + spline = CubicSpline( + self._traj_screen_mm[order], + self._traj_momentum[order], + extrapolate=False, + ) + momentum = spline(x_mm) -class MagSpecManualCalibAnalyzer(BeamAnalyzer): - """ - Analyzer for magnetic spectrometer with device-specific calibrations. + if np.any(~np.isfinite(momentum)): + raise ValueError( + "DNN interpolation produced non-finite momentum values. " + "Check screen range overlap between camera and trajectory." + ) + + return self.magnetic_field_t * momentum + + @property + def expected_image_width(self) -> int: + """Expected image width based on X Start / X End from camera calibration.""" + return self._x_end_px - self._x_start_px + 1 + + def get_charge_factor(self) -> Optional[float]: + """Return counts-to-fC conversion factor, or None if no lanex file.""" + return self._charge_factor + + +# Discriminated union of calibration types +CalibrationSpec = Annotated[ + Union[PolynomialCalibration, ArrayCalibration, DnnAxisCalibration], + Field(discriminator="kind"), +] - Supports multiple calibration formats through MagSpecConfig: - - Polynomial coefficients - - Pre-computed arrays (from file or inline) - - Custom calibration functions + +# ------------------------------------------------------------------ +# Top-level magspec config +# ------------------------------------------------------------------ + + +class MagSpecAnalyzerConfig(BaseModel): + """Configuration parsed from ``camera_config.analysis``. + + YAML shape:: + + analysis: + energy_range: [76.2, 201.3] + num_energy_points: 1000 + calibration: + kind: dnn_axis + ... """ - def __init__( - self, - camera_config_name: str, - magspec_config: Optional[MagSpecConfig] = None, - ): - """ - Initialize magnetic spectrometer analyzer. + model_config = ConfigDict(arbitrary_types_allowed=True) - Parameters - ---------- - camera_config_name : str - Camera configuration name (loaded from config system) - magspec_config : MagSpecConfig, optional - Magnetic spectrometer configuration. If None, attempts to use - UC_HiResMagCam preset with default settings. - - Raises - ------ - ValueError - If magspec_config is None and camera is not UC_HiResMagCam - """ - super().__init__(camera_config_name) + calibration: CalibrationSpec + energy_range: Tuple[float, float] + num_energy_points: int = 500 - # Use provided config or create default for UC_HiResMagCam - if magspec_config is None: - if self.camera_name == "UC_HiResMagCam": - self.magspec_config = MagSpecConfig.for_uc_hiresmag() - logger.info("Using default UC_HiResMagCam configuration") - else: - raise ValueError( - f"magspec_config required for camera '{self.camera_name}'. " - f"Use MagSpecConfig.for_uc_hiresmag() or create custom config." - ) - else: - self.magspec_config = magspec_config + @field_validator("energy_range") + def _check_range(cls, v: Tuple[float, float]) -> Tuple[float, float]: + if len(v) != 2: + raise ValueError("energy_range must be a 2-element tuple") + if v[1] <= v[0]: + raise ValueError("energy_range max must exceed min") + return v - logger.info( - f"Initialized {self.camera_name} with mag_field={self.magspec_config.mag_field}, " - f"energy_range={self.magspec_config.energy_range} MeV" - ) - def _build_energy_calibration(self, image: np.ndarray) -> np.ndarray: - """ - Build pixel-to-energy mapping for current configuration. +# ------------------------------------------------------------------ +# Analyzer +# ------------------------------------------------------------------ - Parameters - ---------- - image : np.ndarray - Image to build calibration for (used to get width) - Returns - ------- - np.ndarray - Array mapping pixel index to energy value - """ - cal = self.magspec_config.calibration +class MagSpecManualCalibAnalyzer(BeamAnalyzer): + """Magnetic spectrometer analyzer with energy calibration. - # Get image width - image_width = image.shape[1] + Reads configuration from ``camera_config.analysis`` and supports + multiple calibration formats via the ``kind`` discriminator: - # Create pixel axis - pixel_axis = np.arange(image_width) + - ``polynomial`` — polynomial pixel-to-energy mapping + - ``array`` — precomputed axis from file or inline list + - ``dnn_axis`` — MATLAB-era DNN camera + trajectory calibration - if cal.type == "polynomial": - # Polynomial calibration: sum(coeffs[i] * x^i) - pixel_to_energy = np.zeros_like(pixel_axis, dtype=float) - for i, coeff in enumerate(cal.coeffs): - pixel_to_energy += coeff * np.power(pixel_axis, i) - logger.debug( - f"Built polynomial calibration with {len(cal.coeffs)} coefficients" - ) + Image flips are handled by the camera-level ``transforms`` config, + not by the magspec config. Charge calibration (counts → fC) is + automatically applied when ``lanex_calibration_file`` is present + in a ``dnn_axis`` calibration block. + """ - elif cal.type == "array": - # Pre-computed array calibration - if cal.file is not None: - # Load from file - cal_path = Path(cal.file) - if not cal_path.is_absolute(): - # Try to resolve relative to package - cal_path = Path(__file__).parent.parent / cal_path - pixel_to_energy = np.load(cal_path) - logger.debug(f"Loaded calibration array from {cal_path}") - else: - # Use inline array - pixel_to_energy = cal.values - logger.debug("Using inline calibration array") - - # Validate length - if len(pixel_to_energy) != image_width: - raise ValueError( - f"Calibration array length ({len(pixel_to_energy)}) " - f"doesn't match image width ({image_width})" - ) + def __init__(self, camera_config_name: str): + super().__init__(camera_config_name) - elif cal.type == "callable": - # Custom function calibration - pixel_to_energy = cal.function(pixel_axis) - logger.debug("Applied callable calibration function") + analysis = self.camera_config.analysis + if not analysis or "calibration" not in analysis: + raise ValueError( + f"Camera '{self.camera_name}' requires an 'analysis' section " + f"with at least 'calibration' and 'energy_range'." + ) - else: - raise ValueError(f"Unknown calibration type: {cal.type}") + # Accept both `analysis.magspec.calibration` (legacy) and + # `analysis.calibration` (preferred flat format). + raw = ( + analysis.get("magspec", analysis) + if isinstance(analysis.get("magspec"), dict) + else analysis + ) - return pixel_to_energy + self.magspec_config = MagSpecAnalyzerConfig.model_validate(raw) + + logger.info( + "Initialized %s magspec analyzer: energy_range=%s MeV, calibration=%s", + self.camera_name, + self.magspec_config.energy_range, + self.magspec_config.calibration.kind, + ) def analyze_image( self, image: np.ndarray, auxiliary_data: Optional[Dict] = None ) -> ImageAnalyzerResult: + """Analyze magnetic spectrometer image with energy calibration. + + Processing pipeline: + 1. Standard preprocessing (background, ROI, transforms) via BeamAnalyzer + 2. Optional charge calibration (if lanex file was provided) + 3. Pixel-to-energy axis mapping + 4. Resampling onto uniform energy grid + 5. Spectrum metrics (peak energy, mean energy, total charge) """ - Analyze magnetic spectrometer image with energy calibration. - - Parameters - ---------- - image : np.ndarray - Image array to analyze - auxiliary_data : dict, optional - Additional data (unused) - - Returns - ------- - ImageAnalyzerResult - Analysis result with energy-calibrated image and metadata - - Notes - ----- - The calibration is always defined in canonical orientation (increasing - left-to-right). The flip parameters correct for camera mounting: - - flip_horizontal: Camera is mounted backwards relative to calibration - - flip_vertical: Camera is mounted upside-down - - Flips are applied to the raw image BEFORE interpolation to align it - with the canonical calibration. The output is always in canonical - orientation. - """ - # Standard processing from BeamAnalyzer initial_result: ImageAnalyzerResult = super().analyze_image( image=image, auxiliary_data=auxiliary_data ) - - # Get processed image final_image = initial_result.processed_image.copy() - # Apply flips to align image with canonical calibration orientation - # These correct for camera mounting - NOT post-processing effects - if self.magspec_config.flip_horizontal: - final_image = final_image[:, ::-1] - logger.debug("Applied horizontal flip to align with canonical calibration") + cal = self.magspec_config.calibration + + # check if magnetic field is being passed through analyze_image, + # overriding the config value in the DNN calibration (if present) + BField_key = "HTT-MagTeslameter-DTM141 Field" + aux_mag_field = auxiliary_data.get(BField_key, None) if auxiliary_data else None + + if isinstance(cal, DnnAxisCalibration) and aux_mag_field is not None: + logger.info( + "Overriding DNN calibration magnetic field with auxiliary value: " + "config=%s T, auxiliary=%s T", + cal.magnetic_field_t, + aux_mag_field, + ) + cal.magnetic_field_t = aux_mag_field - if self.magspec_config.flip_vertical: - final_image = final_image[::-1, :] - logger.debug("Applied vertical flip to align with canonical calibration") + # Charge calibration (if available) + charge_factor = cal.get_charge_factor() + if charge_factor is not None: + final_image = final_image * charge_factor - # Build energy calibration (always canonical/increasing) - pixel_to_energy = self._build_energy_calibration(image=final_image) + # Build pixel-to-energy axis + pixel_to_energy = cal.build_axis(image_width=final_image.shape[1]) - # Apply energy axis interpolation - # Both image and calibration are now in canonical orientation + # Resample to uniform energy grid interp_image, energy_axis = interpolate_image_axis( final_image, pixel_to_energy, - axis=1, # Horizontal = energy + axis=1, num_points=self.magspec_config.num_energy_points, physical_min=self.magspec_config.energy_range[0], physical_max=self.magspec_config.energy_range[1], ) - # Compute analysis metrics + # Compute metrics scalars = self._compute_metrics(interp_image, energy_axis) - # Create result result = ImageAnalyzerResult( data_type="2d", processed_image=interp_image, scalars=scalars, metadata={ "energy_units": "MeV", - "mag_field": self.magspec_config.mag_field, - "flip_horizontal": self.magspec_config.flip_horizontal, - "flip_vertical": self.magspec_config.flip_vertical, + "calibration_kind": cal.kind, + "charge_calibrated": charge_factor is not None, + "charge_factor_fc_per_count": charge_factor, }, ) - - result.render_data = { - "energy_axis": energy_axis, - } + result.render_data = {"energy_axis": energy_axis} + + file_path = auxiliary_data.get("file_path") if auxiliary_data else None + if file_path is not None: + try: + self._save_calibrated_outputs(result, Path(file_path)) + except Exception as e: + logger.warning( + "Failed to save calibrated outputs for %s: %s", file_path, e + ) return result + @staticmethod def _compute_metrics( - self, image: np.ndarray, energy_axis: np.ndarray + image: np.ndarray, energy_axis: np.ndarray ) -> Dict[str, float]: - """ - Compute energy spectrum metrics. + """Compute energy spectrum metrics from calibrated image.""" + spectrum = np.sum(image, axis=0) + total = np.sum(image) - Parameters - ---------- - image : np.ndarray - Energy-calibrated image - energy_axis : np.ndarray - Energy axis values in MeV - - Returns - ------- - dict - Dictionary of scalar metrics - """ - # Vertical projection (sum over vertical to get energy spectrum) - energy_spectrum = np.sum(image, axis=0) - - # Find peak energy - if len(energy_spectrum) > 0 and np.max(energy_spectrum) > 0: - peak_idx = np.argmax(energy_spectrum) - peak_energy = energy_axis[peak_idx] + if len(spectrum) > 0 and np.max(spectrum) > 0: + peak_energy = energy_axis[np.argmax(spectrum)] else: peak_energy = np.nan - # Total charge (integrated signal) - total_charge = np.sum(image) - - # Mean energy (weighted by intensity) - if total_charge > 0: - mean_energy = np.sum(energy_axis * energy_spectrum) / np.sum( - energy_spectrum - ) + if total > 0: + mean_energy = np.sum(energy_axis * spectrum) / np.sum(spectrum) else: mean_energy = np.nan - metrics = { + return { "peak_energy_MeV": peak_energy, "mean_energy_MeV": mean_energy, - "total_charge_au": total_charge, - "max_intensity": np.max(image), + "total_charge_au": total, + "max_intensity": float(np.max(image)), } - return metrics + def _save_calibrated_outputs( + self, result: ImageAnalyzerResult, file_path: Path + ) -> None: + """Save calibrated image and spectrum derived from *result*. + + Called automatically from :meth:`analyze_image` when ``file_path`` is + present in ``auxiliary_data``. Two sibling directories are created + next to the camera's raw-data folder (``file_path.parent.parent``): + + ``{camera_name}-interp/`` + 16-bit PNG where each pixel value equals the charge-calibrated + intensity divided by the energy bin width (units: fC / MeV). + + ``{camera_name}-interpSpec/`` + Two-column TSV: ``Energy [MeV]`` and ``Charge Density [pC/MeV]`` + (vertical integral of the calibrated image, converted fC → pC, + divided by the energy bin width). + + Parameters + ---------- + result : ImageAnalyzerResult + Result from :meth:`analyze_image`. + file_path : Path + Path to the source image file (as supplied via ``auxiliary_data``). + """ + image = result.processed_image + energy_axis = np.asarray(result.render_data["energy_axis"], dtype=float) + + output_dir = file_path.parent.parent + file_stem = file_path.stem + + # --- interp: 16-bit PNG with units atto_C/MeV per pixel --- + interp_dir = output_dir / f"{self.camera_name}-interp" + interp_dir.mkdir(parents=True, exist_ok=True) + # scale image by 1000 to convert fC/MeV to aC/MeV, then clip and convert to uint16 for PNG + image_uint16 = np.clip(image * 1000, 0, 65535).astype(np.uint16) + with open(interp_dir / f"{file_stem}.png", "wb") as f: + png.Writer( + width=image_uint16.shape[1], + height=image_uint16.shape[0], + bitdepth=16, + greyscale=True, + ).write(f, image_uint16) + logger.info("Saved calibrated image to %s", interp_dir / f"{file_stem}.png") + + # --- interpSpec: energy / charge-density TSV --- + spec_dir = output_dir / f"{self.camera_name}-interpSpec" + spec_dir.mkdir(parents=True, exist_ok=True) + spectrum_pC_per_MeV = np.sum(image, axis=0) / 1000.0 + data = np.column_stack([energy_axis, spectrum_pC_per_MeV]) + np.savetxt( + str(spec_dir / f"{file_stem}.tsv"), + data, + delimiter="\t", + header="Energy [MeV]\tCharge Density [pC/MeV]", + comments="", + ) + logger.info("Saved spectrum to %s", spec_dir / f"{file_stem}.tsv") @staticmethod def render_image( @@ -406,32 +583,7 @@ def render_image( dpi: int = 150, ax: Optional[plt.Axes] = None, ) -> Tuple[plt.Figure, plt.Axes]: - """ - Custom render function for energy-calibrated spectra. - - Parameters - ---------- - result : ImageAnalyzerResult - Analysis result containing energy-calibrated image - vmin : float, optional - Minimum value for colormap - vmax : float, optional - Maximum value for colormap - cmap : str, default="plasma" - Colormap name - figsize : tuple, default=(4, 4) - Figure size in inches - dpi : int, default=150 - Figure DPI - ax : matplotlib.axes.Axes, optional - Axes to plot on. If None, creates new figure - - Returns - ------- - tuple of (Figure, Axes) - The matplotlib figure and axes objects - """ - # Base rendering + """Render energy-calibrated spectrum with energy axis labels.""" fig, ax = base_render_image( result=result, vmin=vmin, @@ -442,17 +594,13 @@ def render_image( ax=ax, ) - # Get energy axis from render_data energy_axis = result.render_data.get("energy_axis") - if energy_axis is not None: - # Update tick labels to show energy values (not pixels) num_ticks = 5 - tick_positions = np.linspace(0, len(energy_axis) - 1, num_ticks) - tick_labels = [f"{energy_axis[int(pos)]:.1f}" for pos in tick_positions] - - ax.set_xticks(tick_positions) - ax.set_xticklabels(tick_labels) + positions = np.linspace(0, len(energy_axis) - 1, num_ticks) + labels = [f"{energy_axis[int(p)]:.1f}" for p in positions] + ax.set_xticks(positions) + ax.set_xticklabels(labels) ax.set_xlabel("Energy (MeV)") ax.set_ylabel("Vertical Position (pixels)") diff --git a/ImageAnalysis/image_analysis/processing/array2d/__init__.py b/ImageAnalysis/image_analysis/processing/array2d/__init__.py index 1edc9a016..6bbfca1f8 100644 --- a/ImageAnalysis/image_analysis/processing/array2d/__init__.py +++ b/ImageAnalysis/image_analysis/processing/array2d/__init__.py @@ -19,9 +19,11 @@ TransformConfig, ThresholdingConfig, CircularMaskConfig, + VignetteConfig, BackgroundMethod, ThresholdMethod, ThresholdMode, + VignetteMethod, ) # backgroudn operations @@ -61,6 +63,14 @@ # Thresholding operations from .thresholding import apply_threshold +# Vignette operations +from .vignette import ( + build_radial_vignette_map, + load_vignette_map_from_file, + apply_vignette_map, + apply_vignette_config, +) + # Pipeline from .pipeline import ( apply_camera_processing_pipeline, @@ -71,6 +81,7 @@ # Registry for dynamic function lookup PROCESSING_FUNCTIONS = { "crosshair_masking": apply_crosshair_masking, + "vignette": apply_vignette_config, "roi_cropping": apply_roi_cropping, "circular_masking": apply_circular_mask, "gaussian_filter": apply_gaussian_filter, @@ -100,6 +111,11 @@ "apply_transform_config", # Thresholding operations "apply_threshold", + # Vignette operations + "build_radial_vignette_map", + "load_vignette_map_from_file", + "apply_vignette_map", + "apply_vignette_config", # Unified processing pipeline "apply_camera_processing_pipeline", "create_background_manager_from_config", @@ -113,9 +129,11 @@ "TransformConfig", "ThresholdingConfig", "CircularMaskConfig", + "VignetteConfig", "BackgroundMethod", "ThresholdMethod", "ThresholdMode", + "VignetteMethod", # Function registry "PROCESSING_FUNCTIONS", ] diff --git a/ImageAnalysis/image_analysis/processing/array2d/config_models.py b/ImageAnalysis/image_analysis/processing/array2d/config_models.py index 0397e119e..c63d9694c 100644 --- a/ImageAnalysis/image_analysis/processing/array2d/config_models.py +++ b/ImageAnalysis/image_analysis/processing/array2d/config_models.py @@ -6,7 +6,7 @@ All models use Pydantic for validation and automatic YAML/JSON serialization. """ -from pydantic import BaseModel, Field, field_validator, ConfigDict +from pydantic import BaseModel, Field, field_validator, model_validator, ConfigDict from typing import Any, Optional, Tuple, List, Union, Dict from pathlib import Path from enum import Enum @@ -330,6 +330,89 @@ def validate_center_coordinates(cls, v): return v +class VignetteMethod(str, Enum): + """Supported vignette correction methods.""" + + RADIAL_POLYNOMIAL = "radial_polynomial" + MAP_FILE = "map_file" + + +class VignetteConfig(BaseModel): + """ + Configuration for vignette correction. + + Supports a MATLAB-compatible radial polynomial model using full-sensor + coordinates and saved-image offsets, plus optional map-file correction. + + Attributes + ---------- + enabled : bool + Whether vignette correction is enabled. + method : VignetteMethod + Vignette correction method. + full_width : Optional[int] + Full sensor width in pixels (required for radial_polynomial). + full_height : Optional[int] + Full sensor height in pixels (required for radial_polynomial). + x_offset : int + Saved image x-offset in full-sensor pixels. + y_offset : int + Saved image y-offset in full-sensor pixels. + vgnt4 : float + 4th-order radial vignette coefficient. + vgnt2 : float + 2nd-order radial vignette coefficient. + vgnt0 : float + 0th-order radial vignette coefficient. + min_model_value : float + Minimum allowed attenuation model value to avoid divide-by-zero. + map_file_path : Optional[Union[str, Path]] + Path to precomputed vignette correction map (.npy) for map_file method. + """ + + enabled: bool = Field(False, description="Whether vignette correction is enabled") + method: VignetteMethod = Field( + VignetteMethod.RADIAL_POLYNOMIAL, description="Vignette correction method" + ) + + full_width: Optional[int] = Field( + None, gt=0, description="Full sensor width in pixels" + ) + full_height: Optional[int] = Field( + None, gt=0, description="Full sensor height in pixels" + ) + x_offset: int = Field(0, ge=0, description="Saved image x-offset in full sensor") + y_offset: int = Field(0, ge=0, description="Saved image y-offset in full sensor") + + vgnt4: float = Field(0.0, description="4th-order vignette coefficient") + vgnt2: float = Field(0.0, description="2nd-order vignette coefficient") + vgnt0: float = Field(1.0, description="0th-order vignette coefficient") + min_model_value: float = Field( + 1e-9, gt=0.0, description="Minimum attenuation model value" + ) + + map_file_path: Optional[Union[str, Path]] = Field( + None, description="Path to .npy vignette map when method is map_file" + ) + + @model_validator(mode="after") + def validate_method_requirements(self): + """Validate required fields for the selected vignette method when enabled.""" + if not self.enabled: + return self + + if self.method == VignetteMethod.MAP_FILE and self.map_file_path is None: + raise ValueError("map_file_path required when method is 'map_file'") + + if self.method == VignetteMethod.RADIAL_POLYNOMIAL: + if self.full_width is None or self.full_height is None: + raise ValueError( + "full_width/full_height required when method is 'radial_polynomial'" + ) + + return self + + class ThresholdMethod(str, Enum): """Supported thresholding methods.""" @@ -448,6 +531,7 @@ class ProcessingStepType(str, Enum): """Available processing step types for the pipeline.""" BACKGROUND = "background" + VIGNETTE = "vignette" CROSSHAIR_MASKING = "crosshair_masking" ROI = "roi" CIRCULAR_MASK = "circular_mask" @@ -476,6 +560,7 @@ class PipelineConfig(BaseModel): steps: List[ProcessingStepType] = Field( default_factory=lambda: [ ProcessingStepType.BACKGROUND, + ProcessingStepType.VIGNETTE, ProcessingStepType.CROSSHAIR_MASKING, ProcessingStepType.ROI, ProcessingStepType.CIRCULAR_MASK, @@ -512,6 +597,8 @@ class CameraConfig(BaseModel): Crosshair masking configuration. circular_mask : Optional[CircularMaskConfig] Circular masking configuration. + vignette : Optional[VignetteConfig] + Vignette correction configuration. thresholding : Optional[ThresholdingConfig] Image thresholding configuration. filtering : Optional[FilteringConfig] @@ -542,6 +629,9 @@ class CameraConfig(BaseModel): circular_mask: Optional[CircularMaskConfig] = Field( None, description="Circular masking settings" ) + vignette: Optional[VignetteConfig] = Field( + None, description="Vignette correction settings" + ) thresholding: Optional[ThresholdingConfig] = Field( None, description="Image thresholding settings" ) @@ -592,6 +682,7 @@ def get_processing_configs(self) -> Dict[str, BaseModel]: "background": self.background, "crosshair_masking": self.crosshair_masking, "circular_mask": self.circular_mask, + "vignette": self.vignette, "thresholding": self.thresholding, "filtering": self.filtering, "normalization": self.normalization, diff --git a/ImageAnalysis/image_analysis/processing/array2d/pipeline.py b/ImageAnalysis/image_analysis/processing/array2d/pipeline.py index 61bf0d7e6..e2b7f076c 100644 --- a/ImageAnalysis/image_analysis/processing/array2d/pipeline.py +++ b/ImageAnalysis/image_analysis/processing/array2d/pipeline.py @@ -20,6 +20,7 @@ from .transforms import apply_transform_config from .thresholding import apply_threshold from .normalization import apply_normalization +from .vignette import apply_vignette_config from ...utils import ensure_float64_processing logger = logging.getLogger(__name__) @@ -40,6 +41,13 @@ def _apply_roi_step(image: Array2D, camera_config: CameraConfig) -> Array2D: return image +def _apply_vignette_step(image: Array2D, camera_config: CameraConfig) -> Array2D: + """Apply vignette correction if configured.""" + if camera_config.vignette and camera_config.vignette.enabled: + return apply_vignette_config(image, camera_config.vignette) + return image + + def _apply_circular_mask_step(image: Array2D, camera_config: CameraConfig) -> Array2D: """Apply circular masking if configured.""" if camera_config.circular_mask and camera_config.circular_mask.enabled: @@ -84,6 +92,7 @@ def _apply_normalization_step(image: Array2D, camera_config: CameraConfig) -> Ar # Step registry mapping step types to their execution functions STEP_REGISTRY: Dict[ProcessingStepType, Callable[[Array2D, CameraConfig], Array2D]] = { ProcessingStepType.CROSSHAIR_MASKING: _apply_crosshair_step, + ProcessingStepType.VIGNETTE: _apply_vignette_step, ProcessingStepType.ROI: _apply_roi_step, ProcessingStepType.CIRCULAR_MASK: _apply_circular_mask_step, ProcessingStepType.THRESHOLDING: _apply_thresholding_step, diff --git a/ImageAnalysis/image_analysis/processing/array2d/vignette.py b/ImageAnalysis/image_analysis/processing/array2d/vignette.py new file mode 100644 index 000000000..0d2cb128a --- /dev/null +++ b/ImageAnalysis/image_analysis/processing/array2d/vignette.py @@ -0,0 +1,168 @@ +"""Vignette correction utilities for 2D image processing.""" + +from pathlib import Path +import numpy as np + +from ...types import Array2D +from .config_models import VignetteConfig, VignetteMethod + + +def build_radial_vignette_map( + image_shape: tuple[int, int], + full_width: int, + full_height: int, + x_offset: int = 0, + y_offset: int = 0, + vgnt4: float = 0.0, + vgnt2: float = 0.0, + vgnt0: float = 1.0, + min_model_value: float = 1e-9, +) -> Array2D: + """ + Build a MATLAB-compatible radial vignette correction map. + + The coordinate convention mirrors the original MATLAB implementation: + centered coordinate = pixel_index_1_based - full_size/2 + 0.5 + + Parameters + ---------- + image_shape : tuple[int, int] + Shape of the saved image `(height, width)`. + full_width : int + Full sensor width in pixels. + full_height : int + Full sensor height in pixels. + x_offset : int, default=0 + Saved image x-offset in full-sensor pixels. + y_offset : int, default=0 + Saved image y-offset in full-sensor pixels. + vgnt4 : float, default=0.0 + 4th-order radial coefficient. + vgnt2 : float, default=0.0 + 2nd-order radial coefficient. + vgnt0 : float, default=1.0 + 0th-order radial coefficient. + min_model_value : float, default=1e-9 + Lower bound on attenuation model before inversion. + + Returns + ------- + Array2D + Multiplicative correction map with shape `image_shape`. + """ + height, width = image_shape + + x_full_1_based = x_offset + np.arange(width, dtype=float) + 1.0 + y_full_1_based = y_offset + np.arange(height, dtype=float) + 1.0 + + x_centered = x_full_1_based - (full_width / 2.0) + 0.5 + y_centered = y_full_1_based - (full_height / 2.0) + 0.5 + xx, yy = np.meshgrid(x_centered, y_centered) + radius = np.sqrt(xx**2 + yy**2) + + attenuation = vgnt4 * radius**4 + vgnt2 * radius**2 + vgnt0 + attenuation = np.maximum(attenuation, min_model_value) + return 1.0 / attenuation + + +def _resolve_vignette_map_path(map_file_path: str) -> Path: + """Resolve vignette map path, with package-root fallback for relatives.""" + path = Path(map_file_path) + if path.is_absolute(): + return path + if path.exists(): + return path + return Path(__file__).resolve().parents[2] / path + + +def load_vignette_map_from_file( + map_file_path: str, + image_shape: tuple[int, int], + full_width: int | None = None, + full_height: int | None = None, + x_offset: int = 0, + y_offset: int = 0, +) -> Array2D: + """ + Load a vignette map from file and adapt it to image shape. + + Supports either: + - direct map matching saved image shape, or + - full-sensor map cropped using full dimensions and offsets. + """ + map_path = _resolve_vignette_map_path(map_file_path) + vignette_map = np.load(map_path) + + if vignette_map.ndim != 2: + raise ValueError( + f"Vignette map must be 2D, got shape {vignette_map.shape} from {map_path}" + ) + + image_h, image_w = image_shape + if vignette_map.shape == (image_h, image_w): + return vignette_map + + if ( + full_width is not None + and full_height is not None + and vignette_map.shape == (full_height, full_width) + ): + y_end = y_offset + image_h + x_end = x_offset + image_w + if y_end > full_height or x_end > full_width: + raise ValueError( + "Saved image offsets + shape exceed full-sensor map dimensions" + ) + return vignette_map[y_offset:y_end, x_offset:x_end] + + raise ValueError( + "Vignette map shape mismatch. " + f"Map shape={vignette_map.shape}, image shape={(image_h, image_w)}. " + "Provide either an image-shaped map, or a full-sensor map with " + "full_width/full_height and offsets." + ) + + +def apply_vignette_map(image: Array2D, vignette_map: Array2D) -> Array2D: + """Apply a multiplicative vignette correction map.""" + if image.shape != vignette_map.shape: + raise ValueError( + f"Vignette map shape {vignette_map.shape} does not match image shape {image.shape}" + ) + return image * vignette_map + + +def apply_vignette_config(image: Array2D, config: VignetteConfig) -> Array2D: + """ + Apply vignette correction according to configuration. + + Returns the original image unchanged when correction is disabled. + """ + if not config.enabled: + return image + + if config.method == VignetteMethod.RADIAL_POLYNOMIAL: + vignette_map = build_radial_vignette_map( + image_shape=image.shape, + full_width=int(config.full_width), + full_height=int(config.full_height), + x_offset=config.x_offset, + y_offset=config.y_offset, + vgnt4=config.vgnt4, + vgnt2=config.vgnt2, + vgnt0=config.vgnt0, + min_model_value=config.min_model_value, + ) + elif config.method == VignetteMethod.MAP_FILE: + vignette_map = load_vignette_map_from_file( + map_file_path=str(config.map_file_path), + image_shape=image.shape, + full_width=config.full_width, + full_height=config.full_height, + x_offset=config.x_offset, + y_offset=config.y_offset, + ) + else: + raise ValueError(f"Unsupported vignette method: {config.method}") + + return apply_vignette_map(image, vignette_map) diff --git a/ImageAnalysis/tests/algorithms/test_vignette_processing.py b/ImageAnalysis/tests/algorithms/test_vignette_processing.py new file mode 100644 index 000000000..a3c7eeecc --- /dev/null +++ b/ImageAnalysis/tests/algorithms/test_vignette_processing.py @@ -0,0 +1,71 @@ +"""Tests for array2d vignette correction.""" + +import numpy as np + +from image_analysis.processing.array2d.config_models import ( + CameraConfig, + PipelineConfig, + ProcessingStepType, + VignetteConfig, + VignetteMethod, +) +from image_analysis.processing.array2d.pipeline import apply_camera_processing_pipeline +from image_analysis.processing.array2d.vignette import ( + build_radial_vignette_map, + apply_vignette_config, +) + + +def test_radial_vignette_constant_model(): + """Radial vignette map should be uniform when only vgnt0 is used.""" + image = np.ones((4, 5), dtype=float) + cfg = VignetteConfig( + enabled=True, + method=VignetteMethod.RADIAL_POLYNOMIAL, + full_width=5, + full_height=4, + x_offset=0, + y_offset=0, + vgnt4=0.0, + vgnt2=0.0, + vgnt0=2.0, + ) + + corrected = apply_vignette_config(image, cfg) + np.testing.assert_allclose(corrected, 0.5 * image) + + +def test_pipeline_applies_vignette_step(): + """Pipeline should apply vignette when VIGNETTE step is enabled.""" + image = np.full((3, 3), 10.0, dtype=float) + camera_cfg = CameraConfig( + name="test_cam", + vignette=VignetteConfig( + enabled=True, + method=VignetteMethod.RADIAL_POLYNOMIAL, + full_width=3, + full_height=3, + vgnt4=0.0, + vgnt2=0.0, + vgnt0=2.0, + ), + pipeline=PipelineConfig(steps=[ProcessingStepType.VIGNETTE]), + ) + + out = apply_camera_processing_pipeline(image, camera_cfg, background_manager=None) + np.testing.assert_allclose(out, 5.0 * np.ones_like(image)) + + +def test_radial_map_matches_image_shape(): + """Radial map shape should match the provided image shape.""" + vignette_map = build_radial_vignette_map( + image_shape=(7, 9), + full_width=20, + full_height=10, + x_offset=3, + y_offset=1, + vgnt4=1e-12, + vgnt2=1e-6, + vgnt0=1.0, + ) + assert vignette_map.shape == (7, 9) diff --git a/docs/image_analysis/examples/HTT_magspec_analysis_tests.ipynb b/docs/image_analysis/examples/HTT_magspec_analysis_tests.ipynb new file mode 100644 index 000000000..9e1daf206 --- /dev/null +++ b/docs/image_analysis/examples/HTT_magspec_analysis_tests.ipynb @@ -0,0 +1,375 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HTT Magspec analysis \n", + "\n", + "Magspec analysis originally written in Matlab by Kei Nakamura has been translated into python and integrated in this image analysis package. This example shows the basic usage and a benchmark against the original code\n", + "\n", + "This example " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# define paths to find configs and set up logging\n", + "from geecs_data_utils.scan_data import ScanPaths, ScanData\n", + "from geecs_data_utils.config_roots import image_analysis_config\n", + "from matplotlib import pyplot as plt\n", + "from pathlib import Path\n", + "import logging\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "from image_analysis.offline_analyzers.magspec_manual_calib_analyzer import (\n", + " MagSpecManualCalibAnalyzer,\n", + ")\n", + "\n", + "logging.getLogger(\"image_analysis\").setLevel(logging.WARNING)\n", + "logging.getLogger(\"geecs_data_utils\").setLevel(logging.WARNING)\n", + "\n", + "\n", + "logging.basicConfig(\n", + " level=logging.INFO,\n", + " format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + ")\n", + "\n", + "\n", + "image_analysis_config.set_base_dir(ScanPaths.paths_config.image_analysis_configs_path)\n", + "\n", + "# calibrations are stored in the repo, provide their path\n", + "img_analysis_dir = Path(os.getcwd()).parents[2] / \"ImageAnalysis\" / \"image_analysis\"\n", + "magspec_calibrations_dir = img_analysis_dir / \"data\" / \"HTT-MagSpce-Calibrations\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we set up and construct magspec analyzers for two of the HTT magspec cameras. Note, we explicitly defined some background and bfield settings after loading the config. These settings 'scan' specific and need to be controlled/adapted in downstream applications" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-26 13:58:21,879 - image_analysis.algorithms.axis_interpolation - WARNING - Requested physical_min (16.200) below calibration range (159.824). Clipping to calibration minimum.\n", + "2026-03-26 13:58:21,880 - image_analysis.algorithms.axis_interpolation - WARNING - Requested physical_max (501.300) above calibration range (430.426). Clipping to calibration maximum.\n", + "2026-03-26 13:58:22,080 - image_analysis.algorithms.axis_interpolation - WARNING - Requested physical_min (10.200) below calibration range (70.293). Clipping to calibration minimum.\n", + "2026-03-26 13:58:22,081 - image_analysis.algorithms.axis_interpolation - WARNING - Requested physical_max (501.300) above calibration range (159.605). Clipping to calibration maximum.\n" + ] + } + ], + "source": [ + "from image_analysis.processing.array2d import config_models as cfg\n", + "\n", + "scan_number = 16\n", + "bkg_scan = 13\n", + "shot = 3\n", + "\n", + "sd = ScanData.from_date(\n", + " year=2026,\n", + " month=3,\n", + " day=23,\n", + " number=scan_number,\n", + " experiment=\"Thomson\",\n", + " append_paths=True,\n", + ")\n", + "\n", + "bkg = ScanData.from_date(\n", + " year=2026, month=3, day=23, number=bkg_scan, experiment=\"Thomson\", append_paths=True\n", + ")\n", + "\n", + "# this was looked up in the sfile. Note, this can be defined in the config file, but we\n", + "# explicitly set it here for the example\n", + "bfield = {\"HTT-MagTeslameter-DTM141 Field\": 0.4265}\n", + "\n", + "\n", + "# setup for constrcuting magcam1 analyzer\n", + "dev_name_1 = \"HTT-C23_1_MagSpec1\"\n", + "config_name_1 = \"HTT-MagCam1\"\n", + "file_path_1 = sd.data_frame[f\"{dev_name_1}_expected_path\"][shot - 1]\n", + "bkg_path_1 = bkg.data_frame[f\"{dev_name_1}_expected_path\"][3]\n", + "\n", + "analyzer_1 = MagSpecManualCalibAnalyzer(camera_config_name=config_name_1)\n", + "\n", + "# overwrite the default background config\n", + "new_bkg_1 = cfg.BackgroundConfig()\n", + "new_bkg_1.method = \"from_file\"\n", + "new_bkg_1.file_path = bkg_path_1\n", + "new_bkg_1.additional_constant = 0.9\n", + "analyzer_1.update_config(background=new_bkg_1)\n", + "\n", + "# construct the analyzer for magcam1\n", + "res_1 = analyzer_1.analyze_image_file(image_filepath=file_path_1, auxiliary_data=bfield)\n", + "\n", + "# setup for constrcuting magcam2 analyzer\n", + "dev_name_2 = \"HTT-C23_2_MagSpec2\"\n", + "config_name_2 = \"HTT-MagCam2\"\n", + "file_path_2 = sd.data_frame[f\"{dev_name_2}_expected_path\"][shot - 1]\n", + "bkg_path_2 = bkg.data_frame[f\"{dev_name_2}_expected_path\"][3]\n", + "\n", + "new_bkg_2 = cfg.BackgroundConfig()\n", + "new_bkg_2.method = \"from_file\"\n", + "new_bkg_2.file_path = bkg_path_2\n", + "\n", + "analyzer_2 = MagSpecManualCalibAnalyzer(camera_config_name=config_name_2)\n", + "new_bkg_2 = cfg.BackgroundConfig()\n", + "new_bkg_2.method = \"from_file\"\n", + "new_bkg_2.file_path = bkg_path_2\n", + "new_bkg_2.additional_constant = 1.7\n", + "analyzer_2.update_config(background=new_bkg_2)\n", + "res_2 = analyzer_2.analyze_image_file(image_filepath=file_path_2, auxiliary_data=bfield)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize the energy interpolated, charge calibrated images" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-26 13:58:24,029 - image_analysis.algorithms.axis_interpolation - WARNING - Requested physical_min (16.200) below calibration range (159.824). Clipping to calibration minimum.\n", + "2026-03-26 13:58:24,029 - image_analysis.algorithms.axis_interpolation - WARNING - Requested physical_max (501.300) above calibration range (430.426). Clipping to calibration maximum.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-26 13:58:24,413 - image_analysis.algorithms.axis_interpolation - WARNING - Requested physical_min (10.200) below calibration range (70.293). Clipping to calibration minimum.\n", + "2026-03-26 13:58:24,414 - image_analysis.algorithms.axis_interpolation - WARNING - Requested physical_max (501.300) above calibration range (159.605). Clipping to calibration maximum.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(186, 1000)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = analyzer_1.analyze_image_file(file_path_1)\n", + "analyzer_1.visualize(res)\n", + "res.processed_image.shape\n", + "\n", + "res = analyzer_2.analyze_image_file(file_path_2)\n", + "analyzer_2.visualize(res)\n", + "res.processed_image.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the full analysis pipeline for HTT-MagCam1 and 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, stitch the integrated results together the results from these two cameras and compare against the original result. Note, the background used for the python analysis was simply a single image from the background scan (not averaged) and there was no further 'lowpass' filtering on the python analyzed result. Overall, near perfect agreement." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sn = str(scan_number).rjust(3, \"0\")\n", + "shot_str = str(shot).rjust(3, \"0\")\n", + "analyzed_data_path = (\n", + " sd.paths.get_analysis_folder() / f\"MgspcHtt/Scan{sn}_MgspcHttSpec_{shot_str}.txt\"\n", + ")\n", + "df = pd.read_csv(analyzed_data_path, sep=\"\\t\")\n", + "\n", + "signal_calib_1 = np.sum(res_1.processed_image, axis=0) / 1000\n", + "energy_axis_1 = res_1.render_data[\"energy_axis\"]\n", + "dE = np.diff(energy_axis_1)\n", + "E_center_1 = 0.5 * (energy_axis_1[:-1] + energy_axis_1[1:])\n", + "\n", + "signal_calib_2 = np.sum(res_2.processed_image, axis=0) / 1000\n", + "energy_axis_2 = res_2.render_data[\"energy_axis\"]\n", + "dE = np.diff(energy_axis_2)\n", + "E_center_2 = 0.5 * (energy_axis_2[:-1] + energy_axis_2[1:])\n", + "\n", + "E_combined = np.concatenate([E_center_1, E_center_2])\n", + "signal_combined = np.concatenate([signal_calib_1[:-1], signal_calib_2[:-1]])\n", + "\n", + "idx = np.argsort(E_combined)\n", + "\n", + "plt.figure(figsize=(7, 4.5))\n", + "\n", + "plt.plot(E_combined[idx], signal_combined[idx], lw=2, label=\"Python analyzed spectrum\")\n", + "plt.plot(df.iloc[:, 0], df.iloc[:, 2], lw=2, label=\"Original Matlab data\")\n", + "\n", + "plt.xlabel(\"Energy [MeV]\")\n", + "plt.ylabel(\"Charge density [pC/MeV]\")\n", + "\n", + "plt.grid(alpha=0.3)\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can directly inspect the calculation of the energy axis for a a given camera number and magnetic field. These values can be compared against existing saved data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from image_analysis.offline_analyzers.magspec_manual_calib_analyzer import (\n", + " DnnAxisCalibration,\n", + ")\n", + "from pathlib import Path\n", + "\n", + "camera_calibration_file = magspec_calibrations_dir / \"260224DnnVarianCam.txt\"\n", + "trajectory_calibration_file = magspec_calibrations_dir / \"170925DnnVarianTrj.txt\"\n", + "\n", + "# Build calibration object directly (Pydantic model — reads files on construction)\n", + "dnn_cal = DnnAxisCalibration(\n", + " camera_calibration_file=str(camera_calibration_file),\n", + " trajectory_calibration_file=str(trajectory_calibration_file),\n", + " camera_number=2,\n", + " magnetic_field_t=0.2003,\n", + ")\n", + "\n", + "# Build pixel->energy axis\n", + "image_width = dnn_cal._x_end_px - dnn_cal._x_start_px + 1\n", + "energy_axis = dnn_cal.build_axis(image_width=image_width)\n", + "\n", + "print(f\"axis length: {len(energy_axis)}\")\n", + "print(f\"energy min/max: {energy_axis.min():.3f}, {energy_axis.max():.3f} MeV\")\n", + "print(f\"first/last: {energy_axis[0]:.3f}, {energy_axis[-1]:.3f} MeV\")\n", + "energy_axis[:10] # quick peek" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (geecs-plugins, poetry)", + "language": "python", + "name": "geecs-plugins-py310" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}