From 0a3e8972816047694a47b34cd94be78392e89810 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?No=C3=A9mie=20Bonnet?= Date: Fri, 25 Oct 2024 13:06:03 +0200 Subject: [PATCH] New types of datasets supported for Delmic HDF5 format New supported formats: intensity, hyperspectral, angle-resolved, E-k, time-resolved. --- doc/user_guide/supported_formats/delmic.rst | 5 +- rsciio/delmic/_api.py | 830 ++++++++++++++- .../delmic/sparc-angle-resolved-angles.npy | Bin 0 -> 2176 bytes .../delmic/sparc-angle-resolved-channels.npy | Bin 0 -> 2176 bytes .../data/delmic/sparc-angle-resolved-data.npy | Bin 0 -> 3145856 bytes .../tests/data/delmic/sparc-angle-resolved.h5 | Bin 0 -> 904335 bytes .../tests/data/delmic/sparc-e-k-channels.npy | Bin 0 -> 1208 bytes rsciio/tests/data/delmic/sparc-e-k-data.npy | Bin 0 -> 259328 bytes .../data/delmic/sparc-e-k-wavelengths.npy | Bin 0 -> 1408 bytes rsciio/tests/data/delmic/sparc-e-k.h5 | Bin 0 -> 401453 bytes .../data/delmic/sparc-hyperspectral-data.npy | Bin 0 -> 1664 bytes .../sparc-hyperspectral-wavelengths.npy | Bin 0 -> 1152 bytes .../tests/data/delmic/sparc-hyperspectral.h5 | Bin 0 -> 194401 bytes rsciio/tests/data/delmic/sparc-intensity.h5 | Bin 0 -> 401712 bytes .../delmic/sparc-streak-camera-angles.npy | Bin 0 -> 1208 bytes .../data/delmic/sparc-streak-camera-data.npy | Bin 0 -> 1572992 bytes .../delmic/sparc-streak-camera-timelist.npy | Bin 0 -> 2176 bytes .../sparc-streak-camera-wavelengths.npy | Bin 0 -> 2176 bytes .../tests/data/delmic/sparc-streak-camera.h5 | Bin 0 -> 690430 bytes .../delmic/sparc-time-correlator-data.npy | Bin 0 -> 1572992 bytes .../delmic/sparc-time-correlator-timelist.npy | Bin 0 -> 524416 bytes .../data/delmic/sparc-time-correlator.h5 | Bin 0 -> 1649734 bytes rsciio/tests/test_delmic.py | 945 ++++++++---------- 23 files changed, 1191 insertions(+), 589 deletions(-) create mode 100644 rsciio/tests/data/delmic/sparc-angle-resolved-angles.npy create mode 100644 rsciio/tests/data/delmic/sparc-angle-resolved-channels.npy create mode 100644 rsciio/tests/data/delmic/sparc-angle-resolved-data.npy create mode 100644 rsciio/tests/data/delmic/sparc-angle-resolved.h5 create mode 100644 rsciio/tests/data/delmic/sparc-e-k-channels.npy create mode 100644 rsciio/tests/data/delmic/sparc-e-k-data.npy create mode 100644 rsciio/tests/data/delmic/sparc-e-k-wavelengths.npy create mode 100644 rsciio/tests/data/delmic/sparc-e-k.h5 create mode 100644 rsciio/tests/data/delmic/sparc-hyperspectral-data.npy create mode 100644 rsciio/tests/data/delmic/sparc-hyperspectral-wavelengths.npy create mode 100644 rsciio/tests/data/delmic/sparc-hyperspectral.h5 create mode 100644 rsciio/tests/data/delmic/sparc-intensity.h5 create mode 100644 rsciio/tests/data/delmic/sparc-streak-camera-angles.npy create mode 100644 rsciio/tests/data/delmic/sparc-streak-camera-data.npy create mode 100644 rsciio/tests/data/delmic/sparc-streak-camera-timelist.npy create mode 100644 rsciio/tests/data/delmic/sparc-streak-camera-wavelengths.npy create mode 100644 rsciio/tests/data/delmic/sparc-streak-camera.h5 create mode 100644 rsciio/tests/data/delmic/sparc-time-correlator-data.npy create mode 100644 rsciio/tests/data/delmic/sparc-time-correlator-timelist.npy create mode 100644 rsciio/tests/data/delmic/sparc-time-correlator.h5 diff --git a/doc/user_guide/supported_formats/delmic.rst b/doc/user_guide/supported_formats/delmic.rst index 60ef92d1..3bbeab65 100644 --- a/doc/user_guide/supported_formats/delmic.rst +++ b/doc/user_guide/supported_formats/delmic.rst @@ -3,7 +3,7 @@ Delmic HDF5 ----------- -RosettaScIO can only read hyperspectral cathodoluminescence .h5 datasets from Delmic. The file reading will be implemented step by step for the various Delmic data formats. +RosettaScIO can only read cathodoluminescence .h5 datasets from Delmic containing a single stream. All the imaging modes are supported as long as only one cathodoluminescence stream is contained in the file. The current supported CL data formats currently include: intensity, hyperspectral, angle-resolved, time-resolved decay trace, time-resolved g(2), time resolved streak camera, and energy-momentum. The file import as a stack of hyperspy datasets will be implemented in the next iteration. .. Note:: To read the cathodoluminescence .h5 datasets in `HyperSpy `_, use the @@ -15,6 +15,9 @@ RosettaScIO can only read hyperspectral cathodoluminescence .h5 datasets from De >>> import hyperspy.api as hs >>> hs.load("filename.h5", reader="Delmic") +For now only the CL dataset is opened with hyperspy, the data extraction as a stack of datasets is the next planned step. + + API functions """"""""""""" diff --git a/rsciio/delmic/_api.py b/rsciio/delmic/_api.py index 077084c4..b2ae56ce 100644 --- a/rsciio/delmic/_api.py +++ b/rsciio/delmic/_api.py @@ -1,14 +1,640 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 26 16:38:00 2024. + +@author: noemiebonnet +""" + import h5py import numpy as np from rsciio._docstrings import FILENAME_DOC, LAZY_DOC, RETURNS_DOC +def count_acquisitions(filename): + """Count the number of first-level groups in an HDF5 file.""" + try: + with h5py.File(filename, "r") as hdf: + # Count the number of first-level groups + group_count = sum( + 1 for item in hdf.values() if isinstance(item, h5py.Group) + ) + return group_count + except FileNotFoundError: + raise FileNotFoundError(f"The file '{filename}' does not exist.") + except IOError: + raise IOError(f"Cannot open the file '{filename}'.") + + +def read_image_type(Acq): + """ + Retrieve the image type from the 'PhysicalData' group. + + Access the 'PhysicalData' group within an acquisition object to retrieve + the associated image type. + """ + if Acq is None: + raise TypeError( + "The input 'Acq' is None. A valid acquisition object is required." + ) + + if "PhysicalData" in Acq: + PhysData = Acq.get("PhysicalData") + if PhysData is not None: + Title = PhysData.get("Title") + if Title is not None: + try: + Title_str = Title[()].decode("utf-8") + except (AttributeError, TypeError, UnicodeDecodeError) as e: + Title_str = f"Decoding error: {e}" + else: + Title_str = "Title dataset missing" + else: + Title_str = "PhysicalData group missing" + else: + Title_str = "PhysicalData group missing" + + return Title_str + + +def is_AR(filename): + """ + Check if the HDF5 file has 'Angle-resolved' dataset type. + + This function verifies whether any acquisition in the provided HDF5 + file has the "Angle-resolved" type. + """ + try: + with h5py.File(filename, "r") as hdf: + num_acq = count_acquisitions(filename) + + for i in range(num_acq): + Acquisition = "Acquisition" + str(i) + Acq = hdf.get(Acquisition) + + if ( + Acq is not None and + read_image_type(Acq) == "Angle-resolved" + ): + return True # Early return if AR type is found + + except IOError as e: + raise IOError(f"Unable to open the file: {filename}. Error: {e}") + + # Return False if no "Angle-resolved" image type is found + return False + + +def load_image(Image): + """Extract a 2D image from a 5D dataset.""" + # Check if Image is a h5py dataset + if not isinstance(Image, h5py._hl.dataset.Dataset): + raise TypeError("The input 'Image' must be a h5 dataset.") + + # Check if Image has 5 dimensions + if Image.ndim != 5: + raise ValueError("The input 'Image' must be a 5D h5 dataset.") + + # Check for the expected dimensions + if Image.shape[3] < 1 or Image.shape[4] < 1: + raise ValueError( + "The input 'Image' does not have the expected dimensions." + ) + + # Extract and transpose the data + data = Image[0, 0, 0, :, :] + + return data + + +def load_hyperspectral(Image): + """Extract a 3D hyperspectral dataset from a 5D dataset.""" + # Check if Image is a h5py dataset + if not isinstance(Image, h5py._hl.dataset.Dataset): + raise TypeError("The input 'Image' must be a h5 dataset.") + + # Check if Image has 5 dimensions + if Image.ndim != 5: + raise ValueError("The input 'Image' must be a 5D h5 dataset.") + + # Check for the expected dimensions + if Image.shape[0] < 1 or Image.shape[3] < 1 or Image.shape[4] < 1: + raise ValueError( + "The input 'Image' does not have the expected dimensions." + ) + + # Extract and transpose the data + data = Image[:, 0, 0, :, :].transpose(1, 2, 0) + + return data + + +def load_streak_camera_image(Image): + """Extract a a 4D streak camera image from a 5D dataset.""" + # Check if Image is a h5py dataset + if not isinstance(Image, h5py._hl.dataset.Dataset): + raise TypeError("The input 'Image' must be a h5 dataset.") + + # Check if Image has 5 dimensions + if Image.ndim != 5: + raise ValueError("The input 'Image' must be a 5D h5 dataset.") + + # Check for the expected dimensions + if ( + Image.shape[0] < 1 + or Image.shape[1] < 1 + or Image.shape[3] < 1 + or Image.shape[4] < 1 + ): + raise ValueError( + "The input 'Image' does not have the expected dimensions." + ) + + # Extract and transpose the data + data = Image[:, :, 0, :, :].transpose(2, 3, 1, 0) + # .transpose(3, 2, 0, 1) + + return data + + +def load_AR_spectrum(Image): + """Extract an Angle-Resolved Spectrum (aka E-k) from a 5D dataset.""" + # Check if Image is a h5py dataset + if not isinstance(Image, h5py._hl.dataset.Dataset): + raise TypeError("The input 'Image' must be a h5 dataset.") + + # Check if Image has 5 dimensions + if Image.ndim != 5: + raise ValueError("The input 'Image' must be a 5D h5 dataset.") + + # Check for the expected dimensions + if ( + Image.shape[0] < 1 + or Image.shape[1] < 1 + or Image.shape[3] < 1 + or Image.shape[4] < 1 + ): + raise ValueError( + "The input 'Image' does not have the expected dimensions." + ) + + # Extract and transpose the data + data = Image[:, :, 0, :, :].transpose(2, 3, 1, 0) + + return data + + +def load_temporal_trace(Image): + """Extract a decay trace or g(2) curve from a 5D dataset.""" + # Check if Image is a h5py dataset + if not isinstance(Image, h5py._hl.dataset.Dataset): + raise TypeError("The input 'Image' must be a h5 dataset.") + + # Check if Image has 5 dimensions + if Image.ndim != 5: + raise ValueError("The input 'Image' must be a 5D h5 dataset.") + + # Check for the expected dimensions + if Image.shape[1] < 1 or Image.shape[3] < 1 or Image.shape[4] < 1: + raise ValueError( + "The input 'Image' does not have the expected dimensions." + ) + + # Extract and transpose the data + data = Image[0, :, 0, :, :].transpose(1, 2, 0) + + return data + + +def get_unit_prefix(number): + """Return the SI prefix for the given number based on its magnitude.""" + if 1e-15 < np.abs(number) < 1e-12: + prefix = "f" + if 1e-12 < np.abs(number) < 1e-9: + prefix = "p" + elif 1e-9 < np.abs(number) < 1e-6: + prefix = "n" + elif 1e-6 < np.abs(number) < 1e-3: + prefix = "µ" + elif 1e-3 < np.abs(number) < 1: + prefix = "m" + else: + prefix = "" + return prefix + + +def unit_factor(prefix): + """Return the multiplication factor for a SI unit prefix.""" + prefix_to_factor = {"f": 1e15, "p": 1e12, "n": 1e9, "µ": 1e6, "m": 1e3} + + return prefix_to_factor.get(prefix, 1) + + +def make_axes(Acq): + """Create a dictionary for the axes of a dataset.""" + ImgData = Acq.get("ImageData") + Image = ImgData.get("Image") + axes = [] + + # Dynamically detect the presence of "T" or "A" in the data + if "DimensionScaleT" in ImgData: + time_axis = ("T", "DimensionScaleT", "TOffset") + elif "DimensionScaleA" in ImgData: + time_axis = ("A", "DimensionScaleA", "AOffset") + else: + time_axis = None + + # List of possible axes with corresponding scale and offset names, + # reordered to X, Y, C (Wavelength), A (Angle) + axis_info = [ + ("X", "DimensionScaleX", "XOffset"), # X axis + ("Y", "DimensionScaleY", "YOffset"), # Y axis + ("C", "DimensionScaleC", "COffset"), # C axis for Wavelength + time_axis, # T axis for Time or A axis for Angle + ] + + # Iterate over the axes, handling them appropriately + for i, axis in enumerate(axis_info): + if axis is None: # Skip if no time or angle axis is detected + continue + axis_name, scale_key, offset_key = axis + if scale_key in ImgData: + Scale = np.array(ImgData.get(scale_key)) + + if axis_name in ["C", "T"]: + scale_value = np.mean( + Scale + ) + prefix = get_unit_prefix( + scale_value + ) + elif axis_name == "A": + scale_value = None # No scale value needed for "A" axis + prefix = "" # No prefix needed for "A" axis + else: + try: + scale_value = float(Scale) + prefix = get_unit_prefix(scale_value) + except (TypeError, ValueError) as e: + raise TypeError( + f"Expected a numeric value for '{scale_key}', " + f"got {type(Scale)} instead." + ) from e + + if scale_value is not None and not (1e-15 < scale_value < 1): + raise ValueError( + f"Scale value {scale_value} for axis {axis_name}" + f"is outside of expected ranges." + ) + + # Handle the special cases for the C, T, and A axes + if axis_name == "C": + axis_dict = { + "name": "Wavelength", + "axis": Scale * unit_factor(prefix), # Full axis loaded + "units": prefix + "m", # Adjusted unit prefix + "navigate": False, # No navigation for the C axis + } + elif axis_name == "T": + axis_dict = { + "name": "Time", + "axis": Scale * unit_factor(prefix), # Full axis loaded + "units": prefix + "s", # Adjusted unit prefix + "navigate": False, # No navigation for the T axis + } + elif axis_name == "A": + ScaleA = np.arange(0, Image.shape[1]) + axis_dict = { + "name": "Angle", + "axis": ScaleA, # Full axis loaded, no scaling needed + "units": "", # Unit for Angle + "navigate": False, # No navigation for the A axis + } + else: + axis_dict = { + "name": axis_name, + "size": Image.shape[ + -(i + 1) + ], + "offset": float(np.array(ImgData.get(offset_key))) + * unit_factor(prefix), + "scale": scale_value * unit_factor(prefix), + "units": prefix + "m", + "navigate": True, + } + axes.append(axis_dict) + + # Reorder axes to X, Y, Wavelength, Angle + axes_ordered = [] + for axis_name in ["Y", "X", "Time", "Angle", "Wavelength"]: + for axis in axes: + if axis["name"] == axis_name: + axes_ordered.append(axis) + break + + return axes_ordered + + +def make_metadata(Acq): + """Create a metadata dictionary for a given acquisition (Acq).""" + metadata = { + "Signal": { + "quantity": "", # Placeholder, to be filled with actual data + "signal_type": "", # Placeholder, to be filled with actual data + "signal_origin": "", # Placeholder, to be filled with actual data + } + } + if "quantity" in Acq: + metadata["Signal"]["quantity"] = Acq["quantity"] + else: + metadata["Signal"]["quantity"] = "Intensity (counts)" # Default value + + if "signal_type" in Acq: + metadata["Signal"]["signal_type"] = Acq["signal_type"] + else: + metadata["Signal"]["signal_type"] = "" # Default value + + if "signal_origin" in Acq: + metadata["Signal"]["signal_origin"] = Acq["signal_origin"] + else: + metadata["Signal"]["signal_origin"] = "" # Default value + + return metadata + + +def make_original_metadata(Acq): + """Create a dictionary for the original metadata of a dataset.""" + ImgData = Acq.get("ImageData") + Image = ImgData.get("Image") + original_metadata = {} + + # Extract the shape of the image data + shape = Image.shape + # Determine the number of dimensions and handle accordingly + if len(shape) == 5: + C, second_axis, Z, Y, X = shape + else: + raise ValueError( + f"Unexpected image shape: 5 dimensions," + f"got {len(shape)}" + ) + + # Identify whether the second axis is Time (T) or Angle (A) + if "DimensionScaleT" in ImgData: + time_or_angle = "T" + + elif "DimensionScaleA" in ImgData: + time_or_angle = "A" + else: + time_or_angle = "T" # T is the default value for the array + + # Map the axis sizes to the corresponding metadata dictionary keys + axis_mapping = { + "X": X, # X axis + "Y": Y, # Y axis + "Z": Z, # Z axis + "C": C, # C axis (Wavelength) + } + + if time_or_angle: + axis_mapping[time_or_angle[0]] = second_axis + + # Fill the original_metadata dictionary with sizes + original_metadata.update(axis_mapping) + + # time_or_angle[0]: second_axis + return original_metadata + + +def make_original_AR_metadata(Acq, data): + """Create a dictionary of the original metadata of an AR image dataset.""" + original_metadata = {} + + # Extract the shape of the image data + shape = data.shape + # Determine the number of dimensions and handle accordingly + if len(shape) == 4: + Y, X, A, Z = shape + else: + raise ValueError( + f"Unexpected image shape: 5 dimensions," + f"got {len(shape)}" + ) + + # Map the axis sizes to the corresponding metadata dictionary keys + axis_mapping = { + "X": X, # X axis + "Y": Y, # Y axis + "Z": Z, # Z axis + "A": A, # C axis (Wavelength) + } + + # Fill the original_metadata dictionary with sizes + original_metadata.update(axis_mapping) + + # time_or_angle[0]: second_axis + return original_metadata + + +def load_AR(filename): + """Load angle-resolved (AR) image data from the specified HDF5 file.""" + try: + with h5py.File(filename, "r") as hdf: + num_acq = count_acquisitions(filename) + + # Initialize dimensions for data array + x, y, a, b = None, None, None, None + data_shape_identified = False + + for i in range(min(3, num_acq)): + Acquisition = "Acquisition" + str(i) + + if Acquisition in hdf: + Acq = hdf.get(Acquisition) + ImgData = Acq.get("ImageData") + Image = ImgData.get("Image") + + img_type = read_image_type(Acq) + + if img_type == "Secondary electrons concurrent": + x, y = Image[0, 0, 0, :, :].transpose().shape + + elif img_type == "Secondary electrons survey": + continue # Skip this image type + + elif img_type == "Angle-resolved": + a, b = Image[0, 0, 0, :, :].transpose().shape + data_shape_identified = True + break # Exit once dimensions are identified + + else: + raise ValueError(f"Unknown data type: {img_type}") + else: + raise ValueError( + f"Acquisition '{Acquisition}' is missing or invalid." + ) + + # Ensure that dimensions have been identified + if ( + not data_shape_identified + or x is None + or y is None + or a is None + or b is None + ): + raise ValueError( + "Could not determine data shape from initial acquisitions." + ) + + # Initialize the data array + data = np.empty((x, y, a, b)) + + # Reset the indices for data population + j, k = 0, 0 + + # Second loop: Populate the data array with AR image data + for i in range(num_acq): + Acquisition = "Acquisition" + str(i) + + if Acquisition in hdf: + Acq = hdf.get(Acquisition) + img_type = read_image_type(Acq) + if img_type == "Angle-resolved": + ImgData = Acq.get("ImageData") + Image = ImgData.get("Image") + + data[j, k, :, :] = Image[0, 0, 0, :, :].transpose() + + j += 1 + if j == x: + k += 1 + j = 0 + + # Check if the array is completely filled + if j == 0 and k == y: + data = data[::-1, :, :, :] + return data.transpose(1, 0, 3, 2) + + elif img_type == "Secondary electrons concurrent": + continue + + elif img_type == "Secondary electrons survey": + continue + + elif img_type == "Anchor region": + continue + + else: + raise ValueError( + f"Acquisition '{Acquisition}' is not an " + f"AR dataset, found: {read_image_type(Acq)}" + ) + else: + raise ValueError( + f"Acquisition '{Acquisition}' is missing or invalid." + ) + + data = data[::-1, :, :, :] + return data.transpose(1, 0, 3, 2) + + except IOError as e: + raise IOError(f"Failed to load file '{filename}'. Error: {e}") + + +def load_AR_axes(hdf, acquisition, AR=True): + """Load the axes information for an angle-resolved dataset.""" + axes = [] + + # Load X and Y axes from SE concurrent image + se_concurrent_acquisition = ( + "Acquisition1" + ) + SE_Acq = hdf.get(se_concurrent_acquisition) + if SE_Acq: + SE_ImgData = SE_Acq.get("ImageData") + SE_Image = SE_ImgData.get("Image") + + # Ensure SE Image shape is compatible by taking the last two dimensions + if len(SE_Image.shape) >= 2: + Y, X = SE_Image.shape[-2:] + else: + raise ValueError( + "SE Image shape is invalid, expected at least 2 dimensions." + ) + + axis_info_se = [ + ("X", "DimensionScaleX", "XOffset"), + ("Y", "DimensionScaleY", "YOffset"), + ] + + for i, (axis_name, scale_key, offset_key) in enumerate(axis_info_se): + Scale = np.array(SE_ImgData.get(scale_key)) + if Scale.ndim == 0: # Check if Scale is a scalar + scale_value = float(Scale) + else: # Handle Scale as an array + scale_value = Scale[0] + + prefix = get_unit_prefix(scale_value) + + axis_dict = { + "name": axis_name, + "size": SE_Image.shape[ + -(i + 1) + ], # Using the last two dimensions for X and Y + "offset": float(np.array(SE_ImgData.get(offset_key))) + * unit_factor(prefix), + "scale": scale_value * unit_factor(prefix), + "units": prefix + "m", + "navigate": True, + } + axes.append(axis_dict) + + # Load C and A axes from the angle-resolved image + ar_acquisition = acquisition + AR_Acq = hdf.get(ar_acquisition) + if AR_Acq: + AR_ImgData = AR_Acq.get("ImageData") + AR_Image = AR_ImgData.get("Image") + + axis_info_ar = [ + ("C", "DimensionScaleC", "COffset"), # C axis for Wavelength + ("A", "DimensionScaleA", "AOffset"), # A axis for Angle + ] + + for axis_name, scale_key, offset_key in axis_info_ar: + ScaleC = np.arange(0, AR_Image.shape[3]) + ScaleA = np.arange(0, AR_Image.shape[4]) + + if axis_name == "C": + axis_dict = { + "name": "C", + "axis": ScaleC, # Use full data array for Wavelength + "units": "", # Appropriate units for Wavelength + "navigate": False, + } + elif axis_name == "A": + axis_dict = { + "name": "Angle", + "axis": ScaleA, # Use full data array for Angle + "units": "", # Appropriate units for Angle + "navigate": False, + } + axes.append(axis_dict) + + # Reorder axes to X, Y, C, A + axes_ordered = [] + for axis_name in ["Y", "X", "C", "Angle"]: + for axis in axes: + if axis["name"] == axis_name: + axes_ordered.append(axis) + break + return axes_ordered + + def file_reader(filename, lazy=False): """ Read a Delmic hdf5 hyperspectral image. - Parameters ---------- %s @@ -16,65 +642,163 @@ def file_reader(filename, lazy=False): %s """ - hdf = h5py.File(filename, "r") + try: + with h5py.File(filename, "r") as hdf: + # num_acq = count_acquisitions( + # filename + # ) this number will be used in future versions supporting + # multiple acquisition loading. + i = 2 + # Acquisition 2 generally contains the CL data, supporting + # multiple acquisition numbers will be supported in the + # next version. + Acquisition = "Acquisition" + str(i) - Acquisition2 = hdf.get("Acquisition2") - Acquisition2_ImageData = Acquisition2.get("ImageData") - Acquisition2_ImageData_Image = Acquisition2_ImageData.get("Image") + Acq = hdf.get(Acquisition) + if Acq is None: + raise ValueError( + f"Acquisition {Acquisition} not found in file." + ) - Acquisition2_ImageData_DimensionScaleC = Acquisition2_ImageData.get( - "DimensionScaleC" - ) - Acquisition2_ImageData_DimensionScaleX = Acquisition2_ImageData.get( - "DimensionScaleX" - ) - Acquisition2_ImageData_DimensionScaleY = Acquisition2_ImageData.get( - "DimensionScaleY" - ) + ImgData = Acq.get("ImageData") + Image = ImgData.get("Image") - DATA = Acquisition2_ImageData_Image[:, 0, 0, :, :] - data = DATA.transpose() - del DATA - - axes = [ - { - "name": "X", - "size": data.shape[0], - "offset": 0, - "scale": float(np.array(Acquisition2_ImageData_DimensionScaleX)), - "units": "µm", - "navigate": True, - }, - { - "name": "Y", - "size": data.shape[1], - "offset": 0, - "scale": float(np.array(Acquisition2_ImageData_DimensionScaleY)), - "units": "µm", - "navigate": True, - }, - { - "name": "Wavelength", - "axis": np.array(Acquisition2_ImageData_DimensionScaleC), - "units": "nm", - "navigate": False, - }, - ] + dim = np.array(Image.shape) + C, T, Z, Y, X = dim - metadata = {"signal": {"signal_type": "", "quantity": "Intensity (counts)"}} + AR = is_AR(filename) - original_metadata = dict(DimensionScaleX="182", DimensionScaleY="132") + if dim.sum() < 5: + raise ValueError("Image dimensions are too small.") - spim = { - "data": data, - "axes": axes, - "metadata": metadata, - "original_metadata": original_metadata, - } + if dim.sum() == 5: + if C == 1 and T == 1 and Z == 1 and Y == 1 and X == 1: + data = load_image(Image) + img_type = read_image_type(Acq) + axes = make_axes(Acq) + metadata = make_metadata(Acq) + original_metadata = make_original_metadata(Acq) + if img_type == "Secondary electrons survey": + pass # Add additional processing if needed + elif img_type == "Secondary electrons concurrent": + pass # Add additional processing if needed + else: + raise ValueError( + f"Unknown data type: {img_type}," + f"loaded as an image." + ) - return [ - spim, - ] + elif dim.sum() > 5: + if AR: + data = load_AR(filename) + axes = load_AR_axes( + hdf, Acquisition, AR=True + ) # Custom function to handle AR axes + metadata = make_metadata(Acq) + original_metadata = make_original_AR_metadata(Acq, data) + else: + if C > 1: + if T > 1: + img_type = read_image_type(Acq) + if img_type == "Temporal Spectrum": + data = load_streak_camera_image(Image) + axes = make_axes(Acq) + metadata = make_metadata(Acq) + original_metadata = make_original_metadata(Acq) + elif img_type == "AR Spectrum": + data = load_AR_spectrum(Image) + axes = make_axes(Acq) + metadata = make_metadata(Acq) + original_metadata = make_original_metadata(Acq) + elif img_type.startswith("Spectrum"): + data = load_hyperspectral( + Image + ) + axes = make_axes(Acq) + metadata = make_metadata(Acq) + original_metadata = make_original_metadata(Acq) + else: + raise ValueError( + f"Unknown data type: {img_type}," + f"loaded as a temporal spectrum." + ) + elif T == 1: + data = load_hyperspectral(Image) + img_type = read_image_type(Acq) + axes = make_axes(Acq) + metadata = make_metadata(Acq) + original_metadata = make_original_metadata(Acq) + if ( + img_type.startswith("Spectrum") or + img_type == ( + "Large Area Spectrum with Spectrometer" + ) + ): + pass # Add additional processing if needed + else: + raise ValueError( + f"Unknown data type: {img_type}," + f"loaded as a hyperspectral dataset." + ) + elif C == 1: + if T > 1: + data = load_temporal_trace(Image) + img_type = read_image_type(Acq) + axes = make_axes(Acq) + metadata = make_metadata(Acq) + original_metadata = make_original_metadata(Acq) + if img_type == "Time Correlator": + pass # Add additional processing if needed + else: + raise ValueError( + f"Unknown data type: {img_type}," + f"loaded as a temporal trace." + ) + elif T == 1: + img_type = read_image_type(Acq) + if img_type == "CL intensity": + data = load_image(Image) + axes = make_axes(Acq) + metadata = make_metadata(Acq) + original_metadata = make_original_metadata(Acq) + elif img_type in [ + "Secondary electrons survey", + "Secondary electrons concurrent", + ]: + data = load_image(Image) + axes = make_axes(Acq) + metadata = make_metadata(Acq) + original_metadata = make_original_metadata(Acq) + elif img_type == "Angle-resolved": + raise ValueError( + "Angle-resolved image type detected," + "use specific AR loading tool." + ) + else: + raise ValueError( + f"Unknown data type: {img_type}," + f"loaded as an image." + ) + elif Z != 1: + raise ValueError( + "Invalid format: Z dimension should be 1." + ) + else: + raise ValueError("Invalid format.") + + spim = { + "data": data, + "axes": axes, + "metadata": metadata, + "original_metadata": original_metadata, + } + + return [ + spim, + ] + + except IOError as e: + raise IOError(f"Failed to load file '{filename}'. 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-testfile_dir = (Path(__file__).parent / "data" / "delmic").resolve() -testfile_hyperspectral_path = (testfile_dir / "test_hyperspectral.h5").resolve() - - -ref = np.array( - [ - 3.76316345e-07, - 3.77196136e-07, - 3.78075897e-07, - 3.78955627e-07, - 3.79835419e-07, - 3.80715179e-07, - 3.81594910e-07, - 3.82474640e-07, - 3.83354401e-07, - 3.84234161e-07, - 3.85113861e-07, - 3.85993622e-07, - 3.86873322e-07, - 3.87753052e-07, - 3.88632782e-07, - 3.89512482e-07, - 3.90392181e-07, - 3.91271881e-07, - 3.92151611e-07, - 3.93031311e-07, - 3.93910950e-07, - 3.94790680e-07, - 3.95670319e-07, - 3.96549988e-07, - 3.97429657e-07, - 3.98309296e-07, - 3.99188934e-07, - 4.00068573e-07, - 4.00948212e-07, - 4.01827820e-07, - 4.02707428e-07, - 4.03587036e-07, - 4.04466644e-07, - 4.05346191e-07, - 4.06225800e-07, - 4.07105347e-07, - 4.07984894e-07, - 4.08864471e-07, - 4.09743988e-07, - 4.10623474e-07, - 4.11502991e-07, - 4.12382507e-07, - 4.13261993e-07, - 4.14141479e-07, - 4.15020966e-07, - 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7.98719360e-07, - 7.99573059e-07, - 8.00426819e-07, - 8.01280334e-07, - 8.02133789e-07, - 8.02987183e-07, - 8.03840393e-07, - 8.04693542e-07, - 8.05546509e-07, - 8.06399475e-07, - 8.07252258e-07, - 8.08104919e-07, - 8.08957520e-07, - 8.09809998e-07, - 8.10662354e-07, - 8.11514648e-07, - 8.12366882e-07, - 8.13218872e-07, - 8.14070923e-07, - 8.14922729e-07, - 8.15774414e-07, - 8.16626038e-07, - 8.17477539e-07, - 8.18328918e-07, - 8.19180237e-07, - 8.20031433e-07, - 8.20882507e-07, - ] -) - - -def test_read(): +testfile_dir = ( + Path(__file__).parent / "data" / "delmic" +).resolve() +testfile_intensity_path = ( + testfile_dir / "sparc-intensity.h5" +).resolve() +testfile_hyperspectral_path = ( + testfile_dir / "sparc-hyperspectral.h5" +).resolve() +testfile_hyperspectral_data_path = ( + testfile_dir / "sparc-hyperspectral-data.npy" +).resolve() +testfile_hyperspectral_wavelengths_path = ( + testfile_dir / "sparc-hyperspectral-wavelengths.npy" +).resolve() +testfile_temporaltrace_path = ( + testfile_dir / "sparc-time-correlator.h5" +).resolve() +testfile_temporaltrace_data_path = ( + testfile_dir / "sparc-time-correlator-data.npy" +).resolve() +testfile_temporaltrace_timelist_path = ( + testfile_dir / "sparc-time-correlator-timelist.npy" +).resolve() +testfile_streakcamera_path = ( + testfile_dir / "sparc-streak-camera.h5" +).resolve() +testfile_streakcamera_data_path = ( + testfile_dir / "sparc-streak-camera-data.npy" +).resolve() +testfile_streakcamera_timelist_path = ( + testfile_dir / "sparc-streak-camera-timelist.npy" +).resolve() +testfile_streakcamera_wavelengths_path = ( + testfile_dir / "sparc-streak-camera-wavelengths.npy" +).resolve() +testfile_ek_path = ( + testfile_dir / "sparc-e-k.h5" +).resolve() +testfile_ek_data_path = ( + testfile_dir / "sparc-e-k-data.npy" +).resolve() +testfile_ek_channels_path = ( + testfile_dir / "sparc-e-k-channels.npy" +).resolve() +testfile_ek_wavelengths_path = ( + testfile_dir / "sparc-e-k-wavelengths.npy" +).resolve() +testfile_AR_path = ( + testfile_dir / "sparc-angle-resolved.h5" +).resolve() +testfile_AR_data_path = ( + testfile_dir / "sparc-angle-resolved-data.npy" +).resolve() +testfile_AR_angles_path = ( + testfile_dir / "sparc-angle-resolved-angles.npy" +).resolve() +testfile_AR_channels_path = ( + testfile_dir / "sparc-angle-resolved-channels.npy" +).resolve() + + +# Intensity dataset +def test_read_data_intensity(): + """Test reading data for a CL intensity dataset.""" + s = hs.load(testfile_intensity_path, reader="Delmic") + + x = np.array([27147, 28907]) + y = np.array([26964, 27695]) + np.testing.assert_allclose(s.data[0], x) + np.testing.assert_allclose(s.data[1], y) + + +def test_read_axes_intensity(): + """Test reading axes for a CL intensity dataset.""" + s = hs.load(testfile_intensity_path, reader="Delmic") + + # test X axis + assert s.axes_manager[0].name == "X" + assert s.axes_manager[0].units == "nm" + + np.testing.assert_allclose(s.axes_manager[0].scale, 200) + np.testing.assert_allclose(s.axes_manager[0].offset, 0.0) + np.testing.assert_allclose(s.axes_manager[0].size, 2) + + # test Y axis + assert s.axes_manager[1].name == "Y" + assert s.axes_manager[1].units == "nm" + + np.testing.assert_allclose(s.axes_manager[1].scale, 200) + np.testing.assert_allclose(s.axes_manager[1].offset, 0.0) + np.testing.assert_allclose(s.axes_manager[1].size, 2) + + +def test_read_metadata_intensity(): + """Test reading metadata for a CL intensity dataset.""" + s = hs.load(testfile_intensity_path, reader="Delmic") + + assert s.metadata["General"]["title"] == "" + assert s.metadata["Signal"]["quantity"] == "Intensity (counts)" + assert s.metadata["Signal"]["signal_origin"] == "" + assert ( + s.metadata["Signal"]["signal_type"] == "" + ) # to be modified in case of lumispy + + +def test_read_original_metadata_intensity(): + """Test reading original metadata for a CL intensity dataset.""" + s = hs.load(testfile_intensity_path, reader="Delmic") + + assert s.original_metadata["C"] == 1 + assert s.original_metadata["T"] == 1 + assert s.original_metadata["X"] == 2 + assert s.original_metadata["Y"] == 2 + assert s.original_metadata["Z"] == 1 + + +# Hyperspectral dataset +def test_read_data_hyperspectral(): + """Test reading data for a CL hyperspectral dataset.""" + s = hs.load(testfile_hyperspectral_path, reader="Delmic") + data = np.load(testfile_hyperspectral_data_path) + + np.testing.assert_allclose(s.data, data) + + +def test_read_axes_hyperspectral(): + """Test reading axes for a CL hyperspectral dataset.""" s = hs.load(testfile_hyperspectral_path, reader="Delmic") + ref = np.load(testfile_hyperspectral_wavelengths_path) - np.testing.assert_allclose(s.axes_manager[0].scale, 0.00010759749808557962) - np.testing.assert_allclose(s.axes_manager[0].offset, 0) - np.testing.assert_allclose(s.axes_manager[0].size, 4) + np.testing.assert_allclose(s.axes_manager[0].scale, 963.862901465797) + np.testing.assert_allclose(s.axes_manager[0].offset, -411.20439112265274) + np.testing.assert_allclose(s.axes_manager[0].size, 2) - np.testing.assert_allclose(s.axes_manager[1].scale, 0.00010759749808557962) - np.testing.assert_allclose(s.axes_manager[1].offset, 0) - np.testing.assert_allclose(s.axes_manager[1].size, 4) + np.testing.assert_allclose(s.axes_manager[1].scale, 963.862901465797) + np.testing.assert_allclose(s.axes_manager[1].offset, 362.48947994366983) + np.testing.assert_allclose(s.axes_manager[1].size, 3) np.testing.assert_allclose(s.axes_manager[2].axis, ref) - assert s.axes_manager[1].name == "X" - assert s.axes_manager[1].units == "µm" + assert s.axes_manager[0].name == "X" + assert s.axes_manager[0].units == "nm" + + assert s.axes_manager[1].name == "Y" + assert s.axes_manager[1].units == "nm" + + assert s.axes_manager[2].name == "Wavelength" + assert s.axes_manager[2].units == "nm" + + +def test_read_metadata_hyperspectral(): + """Test reading metadata for a CL hyperspectral dataset.""" + s = hs.load(testfile_hyperspectral_path, reader="Delmic") + + assert s.metadata["General"]["title"] == "" + assert s.metadata["Signal"]["quantity"] == "Intensity (counts)" + assert s.metadata["Signal"]["signal_origin"] == "" + assert ( + s.metadata["Signal"]["signal_type"] == "" + ) # to be modified in case of lumispy + + +def test_read_original_metadata_hyperspectral(): + """Test reading original metadata for a CL hyperspectral dataset.""" + s = hs.load(testfile_hyperspectral_path, reader="Delmic") + + assert s.original_metadata["C"] == 128 + assert s.original_metadata["T"] == 1 + assert s.original_metadata["X"] == 2 + assert s.original_metadata["Y"] == 3 + assert s.original_metadata["Z"] == 1 + + +# Time-resolved dataset +def test_read_data_temporaltrace(): + """Test reading data for a CL decay trace or g(2) datasets.""" + s = hs.load(testfile_temporaltrace_path, reader="Delmic") + data = np.load(testfile_temporaltrace_data_path) + + np.testing.assert_allclose(s.data, data) + + +def test_read_axes_temporaltrace(): + """Test reading axes for a CL decay trace or g(2) datasets.""" + s = hs.load(testfile_temporaltrace_path, reader="Delmic") + ref = np.load(testfile_temporaltrace_timelist_path) + + np.testing.assert_allclose(s.axes_manager[0].scale, 917.1372563963987) + np.testing.assert_allclose(s.axes_manager[0].offset, -314.999999999998) + np.testing.assert_allclose(s.axes_manager[0].size, 2) + + np.testing.assert_allclose(s.axes_manager[1].scale, 917.1372563963987) + np.testing.assert_allclose(s.axes_manager[1].offset, 350.00000000000034) + np.testing.assert_allclose(s.axes_manager[1].size, 3) + + np.testing.assert_allclose(s.axes_manager[2].axis, ref) + + assert s.axes_manager[0].name == "X" + assert s.axes_manager[0].units == "nm" + + assert s.axes_manager[1].name == "Y" + assert s.axes_manager[1].units == "nm" + + assert s.axes_manager[2].name == "Time" + assert s.axes_manager[2].units == "ns" + + +def test_read_metadata_temporaltrace(): + """Test reading metadata for a CL decay trace or g(2) datasets.""" + s = hs.load(testfile_temporaltrace_path, reader="Delmic") + + assert s.metadata["General"]["title"] == "" + assert s.metadata["Signal"]["quantity"] == "Intensity (counts)" + assert s.metadata["Signal"]["signal_origin"] == "" + assert ( + s.metadata["Signal"]["signal_type"] == "" + ) # to be modified in case of lumispy + + +def test_read_original_metadata_temporaltrace(): + """Test reading original metadata for a CL decay trace or g(2) datasets.""" + s = hs.load(testfile_temporaltrace_path, reader="Delmic") + + assert s.original_metadata["C"] == 1 + assert s.original_metadata["T"] == 65536 + assert s.original_metadata["X"] == 2 + assert s.original_metadata["Y"] == 3 + assert s.original_metadata["Z"] == 1 - assert s.axes_manager[0].name == "Y" + +# Streak camera dataset +def test_read_data_streakcamera(): + """Test reading data for a CL streak camera dataset.""" + s = hs.load(testfile_streakcamera_path, reader="Delmic") + data = np.load(testfile_streakcamera_data_path) + + np.testing.assert_allclose(s.data, data) + + +def test_read_axes_streakcamera(): + """Test reading axes for a CL streak camera dataset.""" + s = hs.load(testfile_streakcamera_path, reader="Delmic") + ref_t = np.load(testfile_streakcamera_timelist_path) + ref_w = np.load(testfile_streakcamera_wavelengths_path) + + np.testing.assert_allclose(s.axes_manager[0].scale, 3.1073798455896666) + np.testing.assert_allclose(s.axes_manager[0].offset, -2.411301009767636) + np.testing.assert_allclose(s.axes_manager[0].size, 3) + + np.testing.assert_allclose(s.axes_manager[1].scale, 3.1073798455896666) + np.testing.assert_allclose(s.axes_manager[1].offset, 0.573579891248638) + np.testing.assert_allclose(s.axes_manager[1].size, 2) + + np.testing.assert_allclose(s.axes_manager[3].axis, ref_t) + np.testing.assert_allclose(s.axes_manager[2].axis, ref_w) + + assert s.axes_manager[0].name == "X" assert s.axes_manager[0].units == "µm" + assert s.axes_manager[1].name == "Y" + assert s.axes_manager[1].units == "µm" + + assert s.axes_manager[3].name == "Time" + assert s.axes_manager[3].units == "s" + + assert s.axes_manager[2].name == "Wavelength" assert s.axes_manager[2].units == "nm" + + +def test_read_metadata_streakcamera(): + """Test reading metadata for a CL streack camera dataset.""" + s = hs.load(testfile_streakcamera_path, reader="Delmic") + + assert s.metadata["General"]["title"] == "" + assert s.metadata["Signal"]["quantity"] == "Intensity (counts)" + assert s.metadata["Signal"]["signal_origin"] == "" + assert ( + s.metadata["Signal"]["signal_type"] == "" + ) # to be modified in case of lumispy + + +def test_read_original_metadata_streakcamera(): + """Test reading original metadata for a CL streak camera dataset.""" + s = hs.load(testfile_streakcamera_path, reader="Delmic") + + assert s.original_metadata["C"] == 256 + assert s.original_metadata["T"] == 256 + assert s.original_metadata["X"] == 3 + assert s.original_metadata["Y"] == 2 + assert s.original_metadata["Z"] == 1 + + +# E-k dataset +def test_read_data_ek(): + """Test reading data for a CL AR Spectrum (E-k) dataset.""" + s = hs.load(testfile_ek_path, reader="Delmic") + data = np.load(testfile_ek_data_path) + + np.testing.assert_allclose(s.data, data) + + +def test_read_axes_ek(): + """Test reading axes for a CL AR Spectrum (E-k) dataset.""" + s = hs.load(testfile_ek_path, reader="Delmic") + ref_a = np.load(testfile_ek_channels_path) + ref_w = np.load(testfile_ek_wavelengths_path) + + np.testing.assert_allclose(s.axes_manager[0].scale, 299.0099783670874) + np.testing.assert_allclose(s.axes_manager[0].offset, -184.58470506959597) + np.testing.assert_allclose(s.axes_manager[0].size, 3) + + np.testing.assert_allclose(s.axes_manager[1].scale, 299.0099783670874) + np.testing.assert_allclose(s.axes_manager[1].offset, 83.36029719057113) + np.testing.assert_allclose(s.axes_manager[1].size, 2) + + np.testing.assert_allclose(s.axes_manager[3].axis, ref_a) + np.testing.assert_allclose(s.axes_manager[2].axis, ref_w) + + assert s.axes_manager[0].name == "X" + assert s.axes_manager[0].units == "nm" + + assert s.axes_manager[1].name == "Y" + assert s.axes_manager[1].units == "nm" + assert s.axes_manager[2].name == "Wavelength" + assert s.axes_manager[2].units == "nm" + + assert s.axes_manager[3].name == "Angle" + assert s.axes_manager[3].units == "" + + +def test_read_metadata_ek(): + """Test reading metadata for a CL AR Spectrum (E-k) dataset.""" + s = hs.load(testfile_ek_path, reader="Delmic") + + assert s.metadata["General"]["title"] == "" + assert s.metadata["Signal"]["quantity"] == "Intensity (counts)" + assert s.metadata["Signal"]["signal_origin"] == "" + assert ( + s.metadata["Signal"]["signal_type"] == "" + ) # to be modified in case of lumispy + + +def test_read_original_metadata_ek(): + """Test reading original metadata for a CL AR Spectrum (E-k) dataset.""" + s = hs.load(testfile_ek_path, reader="Delmic") + + assert s.original_metadata["C"] == 160 + assert s.original_metadata["A"] == 135 + assert s.original_metadata["X"] == 3 + assert s.original_metadata["Y"] == 2 + assert s.original_metadata["Z"] == 1 + + +# Angle-resolved dataset +def test_read_data_AR(): + """Test reading data for a CL AR dataset.""" + s = hs.load(testfile_AR_path, reader="Delmic") + data = np.load(testfile_AR_data_path) + + np.testing.assert_allclose(s.data, data) + + +def test_read_axes_AR(): + """Test reading axes for a CL AR dataset.""" + s = hs.load(testfile_AR_path, reader="Delmic") + ref_a = np.load(testfile_AR_angles_path) + ref_w = np.load(testfile_AR_channels_path) + + np.testing.assert_allclose(s.axes_manager[0].scale, 963.8629014657938) + np.testing.assert_allclose(s.axes_manager[0].offset, -403.5624697252699) + np.testing.assert_allclose(s.axes_manager[0].size, 2) + + np.testing.assert_allclose(s.axes_manager[1].scale, 963.8629014657942) + np.testing.assert_allclose(s.axes_manager[1].offset, 393.0571655331861) + np.testing.assert_allclose(s.axes_manager[1].size, 3) + + np.testing.assert_allclose(s.axes_manager[2].axis, ref_a) + np.testing.assert_allclose(s.axes_manager[3].axis, ref_w) + + assert s.axes_manager[0].name == "X" + assert s.axes_manager[0].units == "nm" + + assert s.axes_manager[1].name == "Y" + assert s.axes_manager[1].units == "nm" + + assert s.axes_manager[3].name == "C" + assert s.axes_manager[3].units == "" + + assert s.axes_manager[2].name == "Angle" + assert s.axes_manager[2].units == "" + + +def test_read_metadata_AR(): + """Test reading metadata for a CL AR dataset.""" + s = hs.load(testfile_AR_path, reader="Delmic") + + assert s.metadata["General"]["title"] == "" + assert s.metadata["Signal"]["quantity"] == "Intensity (counts)" + assert s.metadata["Signal"]["signal_origin"] == "" + assert ( + s.metadata["Signal"]["signal_type"] == "" + ) # to be modified in case of lumispy + + +def test_read_original_metadata_AR(): + """Test reading original metadata for a CL AR dataset.""" + s = hs.load(testfile_AR_path, reader="Delmic") + + assert s.original_metadata["A"] == 256 + assert s.original_metadata["X"] == 2 + assert s.original_metadata["Y"] == 3 + assert s.original_metadata["Z"] == 256

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