Source code for

This module provides an interface to the Berkeley EMD file format.

See for more details.

* It fully supports v0.2 for reading and writing

General users:
    Use the simplified emd.emdReader() function to load the data and meta
    data as a python dictionary.

Advanced users and developers:
    Access the file internals through the emd.fileDEMD() class.


from pathlib import Path
import datetime

import numpy as np
import h5py

[docs]class fileEMD: """Class to represent Berkeley EMD files. Implemented for spec 0.2 using the recommended layout for metadata. Attributes ---------- file_name : str The name of the file file_path : pathlib.Path A pathlib.Path object for the open file file_hdl : h5py.File The h5py file handle which provides direct access to the underlying h5py file structure. version : tuple A 2-tuple of the major and minor EMD version as ints. data : h5py.Group A link to the data group in the EMD file. microscope : h5py.Group A link to the microscope EMD group with metadata. sample : h5py.Group A link to the sample EMD group with metadata. user : h5py.Group A link to the user EMD group with metadata. comments : h5py.Group A link to the comments in this EMD file. A time stamp is included with the comment. list_emds : list A list of the EMD groups in the file. Examples -------- Open an Berkeley EMD file using the *advanced* interface. See the emdReader function for a more convenient way to load the data. We show how to list the available data sets, load a 3D data set and plot the first image. >> from import emd >> with emd.fileEMD('filename.emd') as emd1: >> [print( for dataGroup in emd1.list_emds] # print all available EMD datasets >> data1, dims1 = emd1.get_emdgroup(0) # load the first full data array and dimension information """ def __init__(self, filename, readonly=True): """Init opening/creating the file. Parameters ---------- filename : str or pathlib.Path or file object The EMD file to open. readonly : bool, default True Set to False to allow writing to the file. """ # necessary declarations in case something goes bad self.file_hdl = None # convenience handles to access the data in the emd file, everything can as well be accessed using the file_hdl self.version = None = None self.microscope = None self.sample = None self.user = None self.comments = None self.list_emds = [] # list of HDF5 groups with emd_data_type type 1 if hasattr(filename, 'read'): try: self.file_path = Path( self.file_name = except AttributeError: self.file_path = None self.file_name = None else: # check filename type, change to pathlib.Path if isinstance(filename, str): filename = Path(filename) elif isinstance(filename, Path): pass else: raise TypeError('Filename is supposed to be a string or pathlib.Path or file object') self.file_path = filename self.file_name = # try opening the file if readonly: try: self.file_hdl = h5py.File(filename, 'r') except: print('Error opening file for readonly: "{}"'.format(filename)) raise else: try: self.file_hdl = h5py.File(filename, 'a') except: print('Error opening file for read/write: "{}"'.format(filename)) raise # if we got a working file if self.file_hdl: # check version information if 'version_major' in self.file_hdl.attrs and 'version_minor' in self.file_hdl.attrs: # read version information self.version = (self.file_hdl.attrs['version_major'], self.file_hdl.attrs['version_minor']) # compare to implementation if not self.version == (0, 2): print('WARNING: You are reading a version {}.{} EMD file, ' 'this implementation assumes version 0.2!'.format(self.version[0], self.version[1])) else: # set version information if not readonly: self.file_hdl.attrs['version_major'] = 0 self.file_hdl.attrs['version_minor'] = 2 # check for data group if 'data' not in self.file_hdl: if not readonly: = self.file_hdl.create_group('data') else: = self.file_hdl['data'] # check for data group if 'microscope' not in self.file_hdl: if not readonly: self.microscope = self.file_hdl.create_group('microscope') else: self.microscope = self.file_hdl['microscope'] # check for data group if 'sample' not in self.file_hdl: if not readonly: self.sample = self.file_hdl.create_group('sample') else: self.sample = self.file_hdl['sample'] # check for data group if 'user' not in self.file_hdl: if not readonly: self.user = self.file_hdl.create_group('user') else: self.user = self.file_hdl['user'] # check for data group if 'comments' not in self.file_hdl: if not readonly: self.comments = self.file_hdl.create_group('comments') else: self.comments = self.file_hdl['comments'] # find emd_data_type groups in the file self.list_emds = self.find_emdgroups(self.file_hdl) def __del__(self): """Destructor for EMD file object. """ # close the file # if(not self.file_hdl.closed): self.file_hdl.close() def __enter__(self): """Implement python's with statement """ return self def __exit__(self, exception_type, exception_value, traceback): """Implement python's with statement and close the file via __del__() """ self.__del__() return None
[docs] def find_emdgroups(self, parent): """Find all emd_data_type groups within the group parent and return a list of references to their HDF5 groups. Parameters ---------- parent: h5py.Group Handle to the parent group. Returns ------- : list A list of h5py.Group handles to children groups being emd_data_type groups. """ emds0 = [] # recursive function to run and retrieve groups with emd_group_type set to 1 def proc_group(group, emds): # take a look at each item in the group for item in group: # check if group if group.get(item, getclass=True) == h5py.Group: item = group.get(item) # check if emd_group_type if 'emd_group_type' in item.attrs and 'data' in item.keys(): if item.attrs['emd_group_type'] == 1: emds.append(item) # process subgroups proc_group(item, emds) # run proc_group(parent, emds0) return emds0
[docs] def get_emddims(self, group): """Get the emdtype dimensions saved in in group. Parameters ---------- group: h5py.Group Reference to the emdtype HDF5 group. Returns ------- : tuple List of dimension vectors plus labels and units. """ # get the dims dims = [] for ii in range(group['data'].ndim): dim = group['dim{}'.format(ii + 1)] # save them as (vector, name, units) if 'name' in dim.attrs: if isinstance(dim.attrs['name'], np.ndarray): name = dim.attrs['name'][0] else: name = dim.attrs['name'] else: name = 'dim{}'.format(ii + 1) if 'units' in dim.attrs: if isinstance(dim.attrs['units'], np.ndarray): units = dim.attrs['units'][0] else: units = dim.attrs['units'] else: units = 'pixels' # Handle bytes objects by decoding them to strings # If something goes wrong, the original attribute is left as-is try: if isinstance(name, bytes): name = name.decode('utf-8') if isinstance(units, bytes): units = units.decode('utf-8') except: pass dims.append((dim[:], name, units)) dims = tuple(dims) return dims
[docs] def get_emdgroup(self, group): """Get the emd data saved in the requested group. Note ____ The memmap keyword has been removed. Please use get_memmap(). Parameters ---------- group: or int Reference to the HDF5 group to load. If int is used then the item corresponding to self.list_emds is loaded Returns ------- : tuple or None None or tuple containing: : np.ndarray The data of the emdtype group. : list List of [0] dimension vectors, [1] labels and [2] units. """ # check input if not isinstance(group, h5py.Group): if isinstance(group, int): try: group = self.list_emds[group] except IndexError: print('group does not exist') return else: raise TypeError('group needs to refer to a valid HDF5 group!') if 'emd_group_type' not in group.attrs: raise TypeError('group is not a emd_group_type group!') if not group.attrs['emd_group_type'] == 1: raise TypeError('group is not a emd_group_type group!') # retrieve data and dims try: data = group['data'][:] dims = self.get_emddims(group) return data, dims except: # if something goes wrong, return None print('Content of "{}" does not seem to be in emd specified shape'.format( return None
[docs] def get_memmap(self, group): """ Opens a new fileEMD object and returns the requested EMD group. This prevents the file from being closed. The original HDF5 file is left open. The file pointer is lost, but it will be closed once the EMD group is deleted or removed. See also get_emdgroup(). Parameters ---------- group: or int Reference to the HDF5 group to load. If int is used then the item corresponding to self.list_emds is loaded """ f = h5py.File(self.file_hdl.filename, 'r') # check input if not isinstance(group, h5py.Group): if isinstance(group, int): try: group = self.list_emds[group] except IndexError: print('group does not exist') return else: raise TypeError('group needs to refer to a valid HDF5 group!') data = f[ + '/data'] return data, self.get_emddims(group)
[docs] def write_dim(self, label, dim, parent): """Auxiliary function to write a dim dataset to parent. Input is not checked for sanity, so handle exceptions in call. Parameters ---------- label: str Label for dataset, usually dim1, dim2, dimN. dim: tuple Tuple containing (data, name, units). parent: h5py.Group HDF5 handle to parent group. Returns ------- : h5py.Group HDF5 dataset handle referencing this dim. """ try: dset = parent.create_dataset(label, data=dim[0]) dset.attrs['name'] = np.string_(dim[1]) dset.attrs['units'] = np.string_(dim[2]) except: raise RuntimeError('Error during writing dim dataset') return dset
[docs] def put_emdgroup(self, label, data, dims, parent=None, overwrite=False, **kwargs): """Put an emdtype dataset into the EMD file. Parameters ---------- label: str Label for the emdtype group containing the dataset. data: np.ndarray Numpy array containing the data. dims: tuple Tuple containing the necessary dims as ((vec, name, units), (vec, name, units), ...) parent: h5py.Group or None Parent for the emdtype group, if None it will be written to /data. overwrite: bool Set to force overwriting entry in EMD file. **kwargs: various Keyword arguments to be passed to h5py.create_dataset(), e.g. for compression. Returns ------- : h5py.Group or None Group referencing this emdtype dataset or None if failed. """ # check input if not isinstance(label, str): raise TypeError('label needs to be string!') if not isinstance(data, np.ndarray): raise TypeError('data needs to be a numpy.ndarray!') try: assert len(dims) == len(data.shape) for i in range(len(dims)): assert len(dims[i]) == 3 assert dims[i][0].shape[0] == data.shape[i] except: raise TypeError('Something wrong with the provided dims') # write stuff to HDF5 # create group try: if parent: if label in parent: if overwrite: print('overwriting "{}" in "{}"'.format(label, del parent[label] else: print('"{}" already exists in "{}"'.format(label, raise RuntimeError('"{}" already exists in "{}"'.format(label, grp = parent.create_group(label) else: if label in if overwrite: print('overwriting "{}" in "{}"'.format(label, del[label] else: print('"{}" already exists in "{}"'.format(label, raise RuntimeError('"{}" already exists in "{}"'.format(label, grp = # add attribute grp.attrs['emd_group_type'] = 1 # create dataset _ = grp.create_dataset('data', data=data, **kwargs) # create dim datasets for i in range(len(dims)): self.write_dim('dim{}'.format(i + 1), dims[i], grp) # update emds list self.list_emds = self.find_emdgroups(self.file_hdl) return grp except: print('Something went wrong trying to write the dataset.') return None
[docs] def put_comment(self, msg, timestamp=None): """Create a comment in the EMD file. If timestamp already exists, the msg is appended to existing comment. Parameters ---------- msg: str String of the message to save. timestamp: str/None Timestamp used as the key, defaults to the current UTC time. """ # check input if not isinstance(msg, str): raise TypeError('msg needs to be a string!') # create timestamp if missing if not timestamp: timestamp = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S (UTC)') else: # try to convert given timestamp to string try: timestamp = str(timestamp) except: raise # write comment if timestamp in self.comments.attrs: # append to existing self.comments.attrs[timestamp] += np.string_('\n' + msg) else: # create new entry self.comments.attrs[timestamp] = np.string_(msg)
[docs]def defaultDims(data, pixel_size=None): """ A helper function that can generate a properly setup dim tuple with default values to allow quick writing of EMD files without the need to create these dim vectors manually. Parameters ---------- data : ndarray The data that will be written to the EMD file. This is used to get the number of dims and their shape pixel_size : tuple, optional A tuple of pixel sizes. Must have same length as the number of dimensions of data. Returns ------- dims: list A properly formatted tuple of dim vectors used as input to emd.emdFile.put_emdgroup() """ if isinstance(pixel_size, np.ndarray): raise Exception('pixel_size must be a tuple') num = data.ndim if not pixel_size: pixel_size = (1,) * num if len(pixel_size) != data.ndim: raise ValueError('pixel_size and data dimensions must match') dims = [] for ii in range(num): curDim = [np.linspace(0, data.shape[ii] - 1, data.shape[ii]) * pixel_size[ii], 'dim{}'.format(ii+1), 'unit{}'.format(ii+1)] dims.append(curDim) return dims
[docs]def emdReader(filename, dsetNum=0): """ A simple helper function to read in the data and metadata in a structured format similar to the other ncempy readers. Note ---- Note fully implemented yet. Work in progress. Parameters ---------- filename : str or pathlib.Path The path to the file as a string. dsetNum : int The index of the data set to load. Returns ------- : dict Data and metadata as a dictionary similar to other ncempy readers. Example ------- Simply load all data and metadata from a data set in an EMD Velox file >> import as nio >> emd0 = nio.emd.emdReader('filename.emd', dsetNum = 0) """ with fileEMD(filename, readonly=True) as emd0: d, dims = emd0.get_emdgroup(dsetNum) # memmap must be false. File is closed out = {'data': d, 'filename': filename, 'pixelSize': []} # Get the group name name = emd0.list_emds[dsetNum].name.split('/')[-1] out['name'] = name for dim in dims: try: d = dim[0][1] - dim[0][0] except: d = 0 out['pixelSize'].append(d) out['pixelUnit'] = [aa[2] for aa in dims] out['pixelName'] = [aa[1] for aa in dims] return out
[docs]def emdWriter(filename, data, pixel_size=None, overwrite=False): """ Simple method to write data to a file formatted as an EMD v0.2. The only possible metadata to write is the pixel size for each dimension. Use the emd.fileEMD() class for more complex operations. The file must not already exist. Parameters ---------- filename : str or pathlib.Path The path and file name to write the data to. data : ndarray The data as an ndarray to write to the file. pixel_size : tuple A tuple with the same length as the number of dims in data. len(pixel_size) == data.ndim overwrite : boolean If file exists, overwrite it. """ if isinstance(filename, str): filename = Path(filename) if pixel_size: try: assert len(pixel_size) == data.ndim except ValueError: raise ValueError('pixel_size length must match the number of dimensions of data.') # Delete the file if it exists and overwrite is true if filename.exists() and overwrite: filename.unlink() if not filename.exists(): with fileEMD(filename, readonly=False) as emd0: # Setup the dims for the EMD file. Pixel size is set to 1 by default for each dimension dims0 = defaultDims(data, pixel_size=pixel_size) # Write the data to he emd file. emd0.put_emdgroup('converted', data, dims0) else: raise FileExistsError