- Loading and saving data
- Loading files: the load function
- Saving data to files
- Supported formats
>>> s = hs.load("lena.jpg")
Note for python programmers: the data is stored in a numpy array
data attribute, but you will not
normally need to access it there.)
HyperSpy will try to guess the most likely data type for the corresponding
file. However, you can force it to read the data as a particular data type by
signal keyword, which has to be one of:
>>> s = hs.load("filename", signal = "EELS")
Some file formats store some extra information about the data, which can be
stored in “attributes”. If HyperSpy manages to read some extra information
about the data it stores it in ~.signal.BaseSignal.original_metadata
attribute. Also, it is possible that other information will be mapped by
HyperSpy to a standard location where it can be used by some standard routines,
To print the content of the parameters simply:
metadata can be exported to text files
export() method, e.g.:
New in version 1.0: load_to_memory argument.
Some file readers support accessing the data without reading it to memory. This feature can be useful when analysing large files. To load a file without loading it to memory simply set load_to_memory to False e.g.
>>> s = hs.load("filename.hdf5", load_to_memory=False)
However, note that as of v1.0 HyperSpy cannot efficiently use this feature to operate on big data files. Only hdf5, blockfile and EMD currently support not reading to memory.
Rather than loading files individually, several files can be loaded with a single command. This can be done by passing a list of filenames to the load functions, e.g.:
>>> s = hs.load(["file1.hdf5", "file2.hdf5"])
or by using shell-style wildcards
By default HyperSpy will return a list of all the files loaded. Alternatively, HyperSpy can stack the data of the files contain data with exactly the same dimensions. If this is not the case an error is raised.
It is also possible to load multiple files with a single command without stacking them by passing the stack=False argument to the load function, in which case the function will return a list of objects, e.g.:
>>> ls CL1.raw CL1.rpl~ CL2.rpl CL3.rpl CL4.rpl LL3.raw shift_map- SI3.npy CL1.rpl CL2.raw CL3.raw CL4.raw hdf5/ LL3.rpl >>> s = hs.load('*.rpl') >>> s [<EELSSpectrum, title: CL1, dimensions: (64, 64, 1024)>, <EELSSpectrum, title: CL2, dimensions: (64, 64, 1024)>, <EELSSpectrum, title: CL3, dimensions: (64, 64, 1024)>, <EELSSpectrum, title: CL4, dimensions: (64, 64, 1024)>, <EELSSpectrum, title: LL3, dimensions: (64, 64, 1024)>] >>> s = hs.load('*.rpl', stack=True) >>> s <EELSSpectrum, title: mva, dimensions: (5, 64, 64, 1024)>
To save data to a file use the
save() method. The
first argument is the filename and the format is defined by the filename
extension. If the filename does not contain the extension the default format
(HDF5) is used. For example, if the
BaseSignal that you want to write to a file, the
following will write the data to a file called
spectrum.hdf5 in the
default HDF5 format:
If instead you want to save in the Ripple write instead:
Some formats take extra arguments. See the relevant subsection of Supported formats for more information.
Here is a summary of the different formats that are currently supported by HyperSpy.
|FEI’s emi and ser||Yes||No|
|DENS heater log||Yes||No|
|EMD (Berkley Labs)||Yes||Yes|
This is the default format and it is the only one that guarantees that no information will be lost in the writing process and that supports saving data of arbitrary dimensions. It is based on the HDF5 open standard. The HDF5 file format is supported by many applications.
Note that only HDF5 files written by HyperSpy are supported
New in version 0.8.
It is also possible to save more complex structures (i.e. lists, tuples and signals) in
metadata of the signal. Please note that in order to increase
saving efficiency and speed, if possible, the inner-most structures are
converted to numpy arrays when saved. This procedure homogenizes any types of
the objects inside, most notably casting numbers as strings if any other
strings are present:
>>> # before saving: >>> somelist [1, 2.0, 'a name'] >>> # after saving: ['1', '2.0', 'a name']
The change of type is done using numpy “safe” rules, so no information is lost, as numbers are represented to full machine precision.
This feature is particularly useful when using
get_lines_intensity() (see get lines
>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum() >>> s.metadata.Sample.intensities = s.get_lines_intensity() >>> s.save('EDS_spectrum.hdf5') >>> s_new = hs.load('EDS_spectrum.hdf5') >>> s_new.metadata.Sample.intensities [<BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: Al_Ka at 1.49 keV, dimensions: (|)>, <BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: C_Ka at 0.28 keV, dimensions: (|)>, <BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: Cu_La at 0.93 keV, dimensions: (|)>, <BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: Mn_La at 0.63 keV, dimensions: (|)>, <BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: Zr_La at 2.04 keV, dimensions: (|)>]
This was the default format in HyperSpy’s predecessor, EELSLab, but it has been superseeded by HDF5 in HyperSpy. We provide only reading capabilities but we do not support writing to this format.
Note that only NetCDF files written by EELSLab are supported.
To use this format a python netcdf interface must be installed manually because it is not installed by default when using the automatic installers.
This is a format widely used for tomographic data. Our implementation is based on this specification. We also partly support FEI’s custom header. We do not provide writing features for this format, but, as it is an an open format, we may implement this feature in the future on demand.
This open standard format is widely used to exchange single spectrum data, but it does not support multidimensional data. It can be used to exchange single spectra with Gatan’s Digital Micrograph.
For the MSA format the msa_format argument is used to specify whether the energy axis should also be saved with the data. The default, ‘Y’ omits the energy axis in the file. The alternative, ‘XY’, saves a second column with the calibrated energy data. It is possible to personalise the separator with the separator keyword.
However, if a different separator is chosen the resulting file will not comply with the MSA/EMSA standard and HyperSpy and other software may not be able to read it.
The default encoding is latin-1. It is possible to set a different encoding using the encoding argument, e.g.:
>>> s.save('file.msa', encoding = 'utf8')
This open standard format is widely used to exchange multidimensional data. However, it only supports data of up to three dimensions. It can be used to exchange data with Bruker and Lispix. Used in combination with the ImportRPL Digital Micrograph plugin it is very useful for exporting data to Gatan’s Digital Micrograph.
The default encoding is latin-1. It is possible to set a different encoding using the encoding argument, e.g.:
>>> s.save('file.rpl', encoding = 'utf8')
HyperSpy is able to read and write data too all the image formats supported by the Python Image Library (PIL). This includes png, pdf, gif etc.
It is important to note that these image formats only support 8-bit files, and therefore have an insufficient dynamic range for most scientific applications. It is therefore highly discouraged to use any general image format (with the exception of TIFF which uses another library) to store data for analysis purposes.
HyperSpy can read and write 2D and 3D TIFF files using using Christoph Gohlke’s tifffile library. In particular it supports reading and writing of TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH, and FluoView files. Most of these are uncompressed or losslessly compressed 2**(0 to 6) bit integer,16, 32 and 64-bit float, grayscale and RGB(A) images, which are commonly used in bio-scientific imaging. See the library webpage for more details.
Currently HyperSpy has limited support for reading and saving the TIFF tags. However, the way that HyperSpy reads and saves the scale and the units of tiff files is compatible with ImageJ/Fiji and Gatan Digital Micrograph softwares. HyperSpy can also import the scale and the units from tiff files saved using FEI and Zeiss SEM softwares.
>>> # Force read image resolution using the x_resolution, y_resolution and >>> # the resolution_unit of the tiff tags. Be aware, that most of the >>> # software doesn't (properly) use these tags when saving tiff files. >>> s = hs.load('file.tif', force_read_resolution=True)
HyperSpy can also read and save custom tags through Christoph Gohlke’s tifffile library. See the library webpage for more details.
>>> # Saving the string 'Random metadata' in a custom tag (ID 65000) >>> extratag = [(65000, 's', 1, "Random metadata", False)] >>> s.save('file.tif', extratags=extratag) >>> # Saving the string 'Random metadata' from a custom tag (ID 65000) >>> s2 = hs.load('file.tif') >>> s2.original_metadata['Number_65000'] b'Random metadata'
HyperSpy can read both dm3 and dm4 files but the reading features are not complete (and probably they will be unless Gatan releases the specifications of the format). That said, we understand that this is an important feature and if loading a particular Digital Micrograph file fails for you, please report it as an issue in the issues tracker to make us aware of the problem.
HyperSpy can read
emi files but the reading features are not
complete (and probably they will be unless FEI releases the specifications of
the format). That said we know that this is an important feature and if loading
a particular ser or emi file fails for you, please report it as an issue in the
issues tracker to make us aware of the
HyperSpy (unlike TIA) can read data directly from the
.ser files. However,
by doing so, the information that is stored in the emi file is lost.
Therefore strongly reccommend to load using the
.emi file instead.
When reading an
.emi file if there are several
.ser files associated
with it, all of them will be read and returned as a list.
SEMPER is a fully portable system of programs for image processing, particularly suitable for applications in electron microscopy developed by Owen Saxton (see DOI: 10.1016/S0304-3991(79)80044-3 for more information).The unf format is a binary format with an extensive header for up to 3 dimensional data. HyperSpy can read and write unf-files and will try to convert the data into a fitting BaseSignal subclass, based on the information stored in the label. Currently version 7 of the format should be fully supported.
HyperSpy can read and write the blockfile format from NanoMegas ASTAR software. It is used to store a series of diffraction patterns from scanning precession electron difraction (SPED) measurements, with a limited set of metadata. The header of the blockfile contains information about centering and distortions of the diffraction patterns, but is not applied to the signal during reading. Blockfiles only support data values of type np.uint8 (integers in range 0-255).
While Blockfiles are supported, it is a proprietary format, and future versions of the format might therefore not be readable. Complete interoperability with the official software can neither be guaranteed.
Blockfiles are by default loaded into memory, but can instead be loaded in a “copy-on-write” manner using numpy.memmap . This behavior can be controlled by the arguments load_to_memory and mmap_mode. For valid values for mmap_mode, see the documentation for numpy.memmap.
Examples of ways of loading:
>>> hs.load('file.blo') # Default loading, equivalent to the next line >>> hs.load('file.blo', load_to_memory=True) # Load directly to memory >>> # Default memmap loading: >>> hs.load('file.blo', load_to_memory=False, mmap_mode='c') >>> # Loads data read only: >>> hs.load('file.blo', load_to_memory=False, mmap_mode='r') >>> # Loads data read/write: >>> hs.load('file.blo', load_to_memory=False, mmap_mode='r+')
By loading the data read/write, any changes to the original data array will be
written to disk. The data is written when the original data array is deleted,
HyperSpy can read heater log format for DENS solution’s heating holder. The format stores all the captured data for each timestamp, together with a small header in a plain-text format. The reader extracts the measured temperature along the time axis, as well as the date and calibration constants stored in the header.
HyperSpy can read “hypermaps” saved with Bruker’s Esprit v1.x or v2.x in bcf hybrid (virtual file system/container with xml and binary data, optionally compressed) format. Most bcf import functionality is implemented. Both high-resolution 16-bit SEM images and hyperspectral EDX data can be retrieved simultaneously.
Note that Bruker Esprit uses a similar format for EBSD data, but it is not currently supported by HyperSpy.
select_type: One of (‘spectrum’, ‘image’). If specified just selected type of data is returned. (default None)
index: index of dataset in bcf v2 files, which can hold few datasets (delaut 0)
downsample: the downsample ratio of hyperspectral array (hight and width only), can be integer >=1, where ‘1’ results in no downsampling (default 1). The underlying method of downsampling is unchangable: sum. Differently than block_reduce from skimage.measure it is memory efficient (does not creates intermediate arrays, works inplace).
cutoff_at_kV: if set (can be int of float >= 0) can be used either to crop or enlarge energy (or channels) range at max values. (default None)
Example of loading reduced (downsampled, and with energy range cropped) “spectrum only” data from bcf (original shape: 80keV EDS range (4096 channels), 100x75 pixels):
>>> hs.load("sample80kv.bcf", select_type='spectrum', downsample=2, cutoff_at_kV=10) <EDSSEMSpectrum, title: EDX, dimensions: (50, 38|595)>
load the same file without extra arguments:
>>> hs.load("sample80kv.bcf") [<Image, title: BSE, dimensions: (|100, 75)>, <Image, title: SE, dimensions: (|100, 75)>, <EDSSEMSpectrum, title: EDX, dimensions: (100, 75|1095)>]
The loaded array energy dimention can by forced to be larger than the data recorded by setting the ‘cutoff_at_kV’ kwarg to higher value:
>>> hs.load("sample80kv.bcf", cutoff_at_kV=80) [<Image, title: BSE, dimensions: (|100, 75)>, <Image, title: SE, dimensions: (|100, 75)>, <EDSSEMSpectrum, title: EDX, dimensions: (100, 75|4096)>]
Note that setting downsample to >1 currently locks out using sem imagery as navigator in the plotting.
EMD stands for “Electron Microscopy Dataset.” It is a subset of the open source HDF5 wrapper format. N-dimensional data arrays of any standard type can be stored in an HDF5 file, as well as tags and other metadata. The EMD format was developed at Lawrence Berkeley National Lab (see http://emdatasets.lbl.gov/ for more information). NOT to be confused with the FEI EMD format which was developed later and has a different structure.