from __future__ import division, print_function, absolute_import import h5py import numpy as np import numbers import datetime import dateutil.parser import torch as t from contextlib import contextmanager __all__ = ['get_entry_info', 'get_sample_info', 'get_wavelength', 'get_detector_geometry', 'get_mask', 'get_dark', 'get_data', 'get_ptycho_translations', 'create_cxi', 'add_entry_info', 'add_sample_info', 'add_source', 'add_detector', 'add_mask', 'add_dark', 'add_data', 'add_ptycho_translations'] # # # I will put here some thoughts about how to load data into this program. # # # The reconstructions should have the ability to generate datasets. # So you could write a reconstruction engine and then it would be # able to simulate data directly in the engine for you to use as a # reconstruction # # I don't even think there needs to be a loading tool for loading cxi files # because there isn't really a better method beyond just loading the # file into an h5py object. This file could host the simple cxi file # browser, perhaps. But I think the reality is that we need individual # loaders for each kind of experiment. Perhaps we could put some basic # reuseable tools for inspecting cxi-type h5 files in this file. # # # Then, there can be some more sophisticated tools that load data for # specific use cases that are common - loading data for a 2D CDI experiment, # loading data for a 2D Ptycho experiment, loading data for Bragg Ptycho in # 3D, loading data for a 3D CDI experiment, etc. # # # Perhaps one good way to package this is for the kind of data associated # with any particular experiment to have it's own kind of dataset or view. # So there would be a "2D Ptychography" data viewer, which would contain # all the measured data that comes from a 2D ptychography experiment. # The specialized functions would plop out these data viewers, and the # reconstruction classes could be designed around a particular kind of # viewer with the most general kind just requiring a generic data viewer. # # Data viewers could have simple tools like the ability to send themselves # to the GPU, CPU, change the datatype, etc. I think the most generic thing # is as a subclass of the torch Data objects, where they would for each slice # return the index, a set of defining parameters (translation, angle, energy, # whatever), and a diffraction pattern. They would also have a "setup" # attribute, or "metadata", or whatever you'd want to call it, that contain # the various fixed experimental parameters (energy, distance, etc.) # # And I think the cxi visualizer should really go into it's own script, # because it's not a reuseable component. # # # Functions to inspect the basic attributes of a cxi file represented as an # h5 file object # def get_entry_info(cxi_file): """Returns a dictionary with the basic metadata from the cxi file's entry_1 attribute String type metadata is read out as a string, and datetime metadata is converted to python datetime objects if the string is properly formatted. Args: cxi_file (h5py.File) : a file object to be read Returns: dict : A dictionary with basic metadata defined in the cxi file """ e1 = cxi_file['entry_1'] metadata_attrs = ['title', 'experiment_identifier', 'experiment_description', 'program_name'] metadata = {attr: str(e1[attr][()].decode()) for attr in metadata_attrs if attr in e1} datetime_attrs = ['start_time', 'end_time'] for attr in datetime_attrs: if attr in e1: try: metadata[attr] = dateutil.parser.parse(str(e1[attr][()].decode())) except ValueError: metadata[attr] = str(e1[attr][()].decode()) return metadata def get_sample_info(cxi_file): """Returns a dictionary with the basic metadata from the cxi file's entry_1/sample_1 attribute Args: cxi_file (h5py.File) : a file object to be read Returns: dict : A dictionary with basic metadata from the sample defined in the cxi file """ if 'entry_1/sample_1' not in cxi_file: return None s1 = cxi_file['entry_1/sample_1'] metadata_attrs = ['name','description','unit_cell_group'] metadata = {} for attr in metadata_attrs: # Somehow different ways of saving can lead to different ways to # decode it here, so we try both if attr in s1: try: metadata[attr] = str(s1[attr][()].decode()) except AttributeError as e: metadata[attr] = str(np.array(s1[attr][:])[0].decode()) float_attrs = ['concentration', 'mass', 'temperature', 'thickness', 'unit_cell_volume'] for attr in float_attrs: if attr in s1: metadata[attr] = np.float32(s1[attr][()]) if 'unit_cell' in s1: metadata['unit_cell'] = np.array(s1['unit_cell']).astype(np.float32) if 'geometry_1/orientation' in s1: orient = np.array(s1['geometry_1/orientation']).astype(np.float32) xvec = orient[:3] / np.linalg.norm(orient[:3]) yvec = orient[3:] / np.linalg.norm(orient[3:]) metadata['orientation'] = np.array([xvec,yvec, np.cross(xvec,yvec)]) if 'geometry_1/surface_normal' in s1: snorm = np.array(s1['geometry_1/surface_normal']).astype(np.float32) xvec = np.cross(np.array([0.,1.,0.]), snorm) xvec /= np.linalg.norm(xvec) yvec = np.cross(snorm, xvec) yvec /= np.linalg.norm(yvec) metadata['orientation'] = np.array([xvec, yvec, snorm]) # Check if the metadata is empty if metadata == {}: metadata = None return metadata def get_wavelength(cxi_file): """Returns the wavelength of the source defined in the cxi file object, in m Args: cxi_file (h5py.File) : a file object to be read Returns: np.float32 : The wavelength of the source defined in the cxi file """ i1 = cxi_file['entry_1/instrument_1'] if 'source_1/wavelength' in i1: wavelength = np.float32(i1['source_1/wavelength']) elif 'source_1/energy' in i1: energy = np.float32(i1['source_1/energy']) wavelength = 1.9864459e-25 / energy else: raise KeyError('Neither Wavelength or Energy Defined in provided .cxi File') return wavelength def get_detector_geometry(cxi_file): """Returns a standardized description of the detector geometry defined in the cxi file object It makes intelligent assumptions based on the definitions in the cxi file definition. The standardized description of the geometry that it outputs includes the sample to detector distance, the corner location of the detector, and the basis vectors defining the detector. It can only handle detectors defined as rectangular grids of pixels. The distance and corner_location values are technically overdetermining the detector location, but for many experiments (particularly transmission experiments), the distance is needed and the exact corner location is not. If the corner location is not reported in the cxi file, no attempt will be made to calculate it. Args: cxi_file (h5py.File) : a file object to be read Returns: distance (np.float32) : The sample to detector distance, in m basis_vectors (np.array) : The basis vectors for the detector corner_location (np.array) : The location of the (0,0) pixel in the detector """ i1 = cxi_file['entry_1/instrument_1'] d1 = i1['detector_1'] if 'detector_1/basis_vectors' in i1: basis_vectors = np.array(d1['basis_vectors']) if basis_vectors.shape == (2,3): basis_vectors = basis_vectors.T else: # This whole thing just to account for all the ways people can # implicitly define the x or y pixel size for a detector. I've # seen too many of these in the wild, unfortunately... try: x_pixel_size = np.float32(d1['x_pixel_size']) except: x_pixel_size = None try: y_pixel_size = np.float32(d1['y_pixel_size']) except: y_pixel_size = None if x_pixel_size is None and y_pixel_size is not None: x_pixel_size = y_pixel_size elif x_pixel_size is not None and y_pixel_size is None: y_pixel_size = x_pixel_size if x_pixel_size is None and y_pixel_size is None: raise KeyError('Detector pixel size not defined in file.') basis_vectors = np.array([[0,-y_pixel_size,0], [-x_pixel_size,0,0]]).transpose() try: distance = np.float32(d1['distance']) except: distance = None try: corner_position = np.array(d1['corner_position']) except: corner_position = None # Don't pretend to calculate corner position from distance if it's # if it's not defined, but do calculate distance from corner position # if distance is not defined. If neither is defined, then raise # an error. if distance is None and corner_position is not None: detector_normal = np.cross(basis_vectors[:,0], basis_vectors[:,1]) detector_normal /= np.linalg.norm(detector_normal) distance = np.linalg.norm(np.dot(corner_position, detector_normal)) if distance is None and corner_position is not None: raise KeyError('Neither sample to detector distance or corner position is defined in file.') return distance, basis_vectors, corner_position def get_mask(cxi_file): """Returns the detector mask defined in the cxi file object This function converts from the format specified in the cxi file definition to a simple on/off mask, where a value of 1 defines a good pixel (on) and a value of 0 defines a bad pixel (off). If any bit is set in the mask at all, it will be defined as a bad pixel, with the exception of pixels marked exactly as 0x00001000, which is defined to mean that the pixel has signal above the background. These pixels are treated as on pixels Args: cxi_file (h5py.File) : a file object to be read Returns: np.array : An array storing the mask from the cxi file """ i1 = cxi_file['entry_1/instrument_1'] if 'detector_1/mask' in i1: mask = np.array(i1['detector_1/mask']).astype(np.uint32) mask_on = np.equal(mask,np.uint32(0)) mask_has_signal = np.equal(mask,np.uint32(0x00001000)) return np.logical_or(mask_on,mask_has_signal).astype(np.uint8) else: return None def get_dark(cxi_file): """Returns an array with a dark image to use for initialization of a background model This looks for a set of dark images at entry_1/instrument_1/detector_1/data_dark. If the darks exist, it will return the mean of the array along all axes but the last two. That is, if the dark image is a single image, it will return that image. If it is a stack of images, it will return the mean along the stack axis. If the darks do not exist, it will return None Args: cxi_file (h5py.File) : a file object to be read Returns: np.array : An array storing the dark image """ i1 = cxi_file['entry_1/instrument_1'] if 'detector_1/data_dark' in i1: darks = np.array(i1['detector_1/data_dark']) dims = tuple(range(len(darks.shape) - 2)) darks = np.nanmean(darks,axis=dims) else: darks = None return darks def get_data(cxi_file, cut_zeroes = True): """Returns an array with the full stack of detector data defined in the cxi file object This function will make sure to check all the various places that it's okay to store the data in, to ensure that it can find the data regardless of whether the creator of the .cxi file has remembered to link the data to all the required locations. It will return the data array in whatever shape it's defined in. It will also read out the axes attribute of the data into a list of strings Args: cxi_file (h5py.File) : a file object to be read Returns: np.array : An array storing the data defined in the cxi file list : A list of the axes defined in the axes attribute, if any """ # Possible locations for the data # # entry_1/detector_1/data if 'entry_1/data_1/data' in cxi_file: pull_from = 'entry_1/data_1/data' elif 'entry_1/instrument_1/detector_1/data' in cxi_file: pull_from = 'entry_1/instrument_1/detector_1/data' else: raise KeyError('Data is not defined within cxi file') data = np.array(cxi_file[pull_from]).astype(np.float32) if cut_zeroes: data[data < 0] = 0 if 'axes' in cxi_file[pull_from].attrs: axes = str(cxi_file[pull_from].attrs['axes'].decode()).split(':') axes = [axis.strip().lower() for axis in axes] else: axes = None return data, axes def get_ptycho_translations(cxi_file): """Gets an array of x,y,z translations, if such an array has been defined in the file It negates the translations, because the CXI file format is designed to specify translations of the samples and the CDTools code specifies translations of the optics. Args: cxi_file (h5py.File) : a file object to be read Returns: np.array : An array storing the translations defined in the cxi file list : A list of the axes defined in the axes attribute, if any """ if 'entry_1/data_1/translation' in cxi_file: pull_from = 'entry_1/data_1/translation' elif 'entry_1/sample_1/geometry_1/translation' in cxi_file: pull_from = 'entry_1/sample_1/geometry_1/translation' elif 'entry_1/instrument_1/detector_1/translation' in cxi_file: pull_from = 'entry_1/instrument_1/detector_1/translation' else: raise KeyError('Translations are not defined within cxi file') translations = -np.array(cxi_file[pull_from]).astype(np.float32) return translations # # It might be useful to make some helper functions to help write cxi files # def create_cxi(filename): """Creates a new cxi file with a single entry group Args: filename (str) : The path at which to create the file """ file_obj = h5py.File(filename,'w') file_obj.create_dataset('cxi_version', data=160) file_obj.create_dataset('number_of_entries',data=1) e1f = file_obj.create_group('entry_1') return file_obj def add_entry_info(cxi_file, metadata): """Adds a dictionary of entry metadata to the entry_1 group of a cxi file object Args: cxi_file (h5py.File) : The file to add the info to metadata (dict) : A dictionary containing all the metadata to be stored """ # Just the string and datetime types should be relevant but all are # included in case the cxi spec becomes more permissive for key, value in metadata.items(): if isinstance(value,(str,bytes)): cxi_file['entry_1'][key] = np.string_(value) elif isinstance(value, datetime.datetime): cxi_file['entry_1'][key] = np.string_(value.isoformat()) elif isinstance(value, numbers.Number): si[key] = value elif isinstance(value, (np.ndarray,list,tuple)): s1.create_dataset(key, data=np.asarray(value)) elif isinstance(value, t.Tensor): asnumpy = value.detach().cpu().numpy() cxi_file['entry_1'].create_dataset(key, data=asnumpy) def add_sample_info(cxi_file, metadata): """Adds a dictionary of entry metadata to the entry_1/sample_1 group of a cxi file object This function will create the sample_1 attribute if it doesn't already exist Args: cxi_file (h5py.File) : The file to add the info to metadata (dict) : A dictionary containing all the metadata to be stored """ if 'entry_1/sample_1' not in cxi_file: cxi_file['entry_1'].create_group('sample_1') s1 = cxi_file['entry_1/sample_1'] if 'orientation' in metadata: if 'geometry_1' not in s1: s1.create_group('geometry_1') # Only store the part of this matrix as defined in the CXI file spec s1['geometry_1'].create_dataset('orientation', data=metadata['orientation'].ravel()[:6]) for key, value in metadata.items(): if key == 'orientation': continue # this is a special case if isinstance(value,(str,bytes)): s1[key] = np.string_(value) elif isinstance(value, datetime.datetime): s1[key] = np.string_(value.isoformat()) elif isinstance(value, numbers.Number): s1[key] = value elif isinstance(value, (np.ndarray,list,tuple)): s1.create_dataset(key, data=np.asarray(value)) elif isinstance(value, t.Tensor): asnumpy = value.detach().cpu().numpy() s1.create_dataset(key, data=asnumpy) def add_source(cxi_file, wavelength): """Adds the entry_1/source_1 group to a cxi file object It stores the energy and wavelength attributes in the source_1 group, given a wavelength to define them from. Args: cxi_file (h5py.File) : The file to add the source to wavelength (float) : The wavelength of light """ if 'entry_1/instrument_1' not in cxi_file: cxi_file['entry_1'].create_group('instrument_1') i1 = cxi_file['entry_1/instrument_1'] if 'source_1' not in i1: i1.create_group('source_1') s1 = i1['source_1'] s1['wavelength'] = np.float32(wavelength) s1['energy'] = np.float32(1.9864459e-25 / wavelength) def add_detector(cxi_file, distance, basis, corner=None): """Adds the entry_1/instrument_1/detector_1 group to a cxi file object It will define all the relevant parameters - distance, pixel size, detector basis, and corner position (if relevant) based on the provided information Args: cxi_file (h5py.File) : The file to add the detector to distance (float) : The sample to detector distance basis (array_like) : The detector basis corner (array_like) : Optional, the corner position of the detector """ if 'entry_1/instrument_1' not in cxi_file: cxi_file['entry_1'].create_group('instrument_1') i1 = cxi_file['entry_1/instrument_1'] if 'detector_1' not in i1: i1.create_group('detector_1') d1 = i1['detector_1'] d1['distance'] = np.float32(distance) d1['x_pixel_size'] = np.linalg.norm(basis[:,1]) d1['y_pixel_size'] = np.linalg.norm(basis[:,0]) if isinstance(basis, t.Tensor): basis = basis.detach().cpu().numpy() d1.create_dataset('basis_vectors', data=basis) if corner is not None: if isinstance(corner, t.Tensor): corner = corner.detach().cpu().numpy() d1.create_dataset('corner_position',data=corner) def add_mask(cxi_file, mask): """Adds the specified mask to the cxi file It places the mask into the mask dataset under entry_1/instrument_1/detector_1. The internal mask is defined simply as a 1 for an "on" pixel and a 0 for an "off" pixel, and the saved mask is exactly the opposite. This is simpler than the most general mask allowed by the cxi file format but it captures the distinction between pixels to be used and pixels not to be used. Args: cxi_file (h5py.File) : The file to add the mask to mask (array_like) : The mask to save out to the file """ if 'entry_1/instrument_1' not in cxi_file: cxi_file['entry_1'].create_group('instrument_1') i1 = cxi_file['entry_1/instrument_1'] if 'detector_1' not in i1: i1.create_group('detector_1') d1 = i1['detector_1'] if isinstance(mask, t.Tensor): mask = mask.detach().cpu().numpy() mask_to_save = np.zeros(mask.shape).astype(np.uint32) mask_to_save[mask == 0] = 1 d1.create_dataset('mask',data=mask_to_save) def add_dark(cxi_file, dark): """Adds the specified dark image to a cxi file It places the dark image data into the data_dark dataset under entry_1/instrument_1/detector_1. Args: cxi_file (h5py.File) : The file to add the mask to dark (array_like) : The dark image(s) to save out to the file """ if 'entry_1/instrument_1' not in cxi_file: cxi_file['entry_1'].create_group('instrument_1') i1 = cxi_file['entry_1/instrument_1'] if 'detector_1' not in i1: i1.create_group('detector_1') d1 = i1['detector_1'] if isinstance(dark, t.Tensor): dark = dark.detach().cpu().numpy() d1.create_dataset('data_dark',data=dark) def add_data(cxi_file, data, axes=None): """Adds the specified data to the cxi file It will add the data unchanged to the file, placing it in two spots: 1) The entry_1/instrument_1/detector_1/data path 2) A softlink at entry_1/data_1/data Args: cxi_file (h5py.File) : The file to add the data to data (array_like) : The data to be saved axes (list) : Optional, a list of axis names to be saved in the axes attribute """ if 'entry_1/data_1' not in cxi_file: cxi_file['entry_1'].create_group('data_1') data1 = cxi_file['entry_1/data_1'] if 'entry_1/instrument_1' not in cxi_file: cxi_file['entry_1'].create_group('instrument_1') i1 = cxi_file['entry_1/instrument_1'] if 'detector_1' not in i1: i1.create_group('detector_1') det1 = i1['detector_1'] if isinstance(data, t.Tensor): data = data.detach().cpu().numpy() det1.create_dataset('data', data=data) data1['data'] = h5py.SoftLink('/entry_1/instrument_1/detector_1/data') if axes is not None: if isinstance(axes, list): axes_str = ':'.join(axes) else: axes_str = str(axes) det1['data'].attrs['axes'] = np.string_(axes_str) def add_ptycho_translations(cxi_file, translations): """Adds the specified translations to the cxi file It will add the translations to the file, negating them to conform to the standard in cxi files that the translations refer to the object's translation. It will generally store them in 3 places: 1) The entry_1/sample_1/geometry_1/translation path 2) A softlink at entry_1/data_1/translation 3) A softlink at entry_1/instrument_1/detector_1/translation Args: cxi_file (h5py.File) : The file to add the translations to translations (array_like) : The translations to be saved """ if 'entry_1/sample_1' not in cxi_file: cxi_file['entry_1'].create_group('sample_1') s1 = cxi_file['entry_1/sample_1'] if 'geometry_1' not in s1: s1.create_group('geometry_1') g1 = s1['geometry_1'] if 'entry_1/data_1' not in cxi_file: cxi_file['entry_1'].create_group('data_1') data1 = cxi_file['entry_1/data_1'] if 'entry_1/instrument_1' not in cxi_file: cxi_file['entry_1'].create_group('instrument_1') i1 = cxi_file['entry_1/instrument_1'] if 'detector_1' not in i1: i1.create_group('detector_1') det1 = i1['detector_1'] if isinstance(translations, t.Tensor): translations = translations.detach().cpu().numpy() # accounting for the different definition between cxi files and # CDTools translations = -translations g1.create_dataset('translation', data=translations) data1['translation'] = h5py.SoftLink('/entry_1/sample_1/geometry_1/translation') det1['translation'] = h5py.SoftLink('/entry_1/sample_1/geometry_1/translation')