Loading and saving data

Loading files: the load function

HyperSpy can read and write to multiple formats (see Supported formats). To load data use the load() command. For example, to load the image ascent.jpg you can type:

>>> s = hs.load("ascent.jpg")

If the loading was successful, the variable s contains a generic BaseSignal, a Signal1D or an Signal2D.


Note for python programmers: the data is stored in a numpy array in the 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 providing the signal keyword, which has to be one of: spectrum, image or EELS, e.g.:

>>> 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 the 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, the metadata attribute.

To print the content of the parameters simply:

>>> s.metadata

The original_metadata and metadata can be exported to text files using the export() method, e.g.:

>>> s.original_metadata.export('parameters')

Deprecated since version 1.2: memmap_dir and load_to_memory load() keyword arguments. Use lazy instead of load_to_memory. lazy makes memmap_dir unnecessary.

Almost all file readers support accessing the data without reading it to memory (see Supported formats for a list). This feature can be useful when analysing large files. To load a file without loading it to memory simply set lazy to True e.g.:

>>> s = hs.load("filename.hspy", lazy=True)

More details on lazy evaluation support in Working with big data.

Loading multiple files

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.hspy", "file2.hspy"])

or by using shell-style wildcards

New in version 1.2.0: stack multi-signal files

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. If each file contains multiple (N) signals, N stacks will be created. Here, the numbers of signals per file must also match, or an error will be 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)>

Saving data to files

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 (HSpy - HyperSpy’s HDF5 Specification) is used. For example, if the s variable contains the BaseSignal that you want to write to a file, the following will write the data to a file called spectrum.hspy in the default HSpy - HyperSpy’s HDF5 Specification format:

>>> s.save('spectrum')

If instead you want to save in the Ripple write instead:

>>> s.save('spectrum.rpl')

Some formats take extra arguments. See the relevant subsection of Supported formats for more information.

Supported formats

Here is a summary of the different formats that are currently supported by HyperSpy. The “lazy” column specifies if lazy evaluation is supported.

Supported file formats
Format Read Write lazy
Gatan’s dm3 Yes No Yes
Gatan’s dm4 Yes No Yes
FEI’s emi and ser Yes No Yes
HDF5 Yes Yes Yes
Image: jpg Yes Yes Yes
TIFF Yes Yes Yes
MRC Yes No Yes
NetCDF Yes No No
Ripple Yes Yes Yes
SEMPER unf Yes Yes Yes
Blockfile Yes Yes Yes
DENS heater log Yes No No
Bruker’s bcf Yes No Yes
EMD (Berkley Labs) Yes Yes Yes
Protochips log Yes No No
EDAX .spc and .spd Yes No Yes

HSpy - HyperSpy’s HDF5 Specification

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. Part of the specification is documented in Metadata structure.

New in version 1.2: Enable saving HSpy files with the .hspy extension. Preveously only the .hdf5 extension was recognised.

Changed in version 1.3: The default extension for the HyperSpy HDF5 specification is now .hspy. The option to change the default is no longer present in preferences.

Only loading of HDF5 files following the HyperSpy specification are supported. Usually their extension is .hspy extension, but older versions of HyperSpy would save them with the .hdf5 extension. Both extensions are recognised by HyperSpy since version 1.2. However, HyperSpy versions older than 1.2 won’t recognise the .hspy extension. To workaround the issue when using old HyperSpy installations simply change the extension manually to .hdf5 or save directly the file using this extension by explicitly adding it to the filename e.g.:

>>> s = hs.signals.BaseSignal([0])
>>> s.save('test.hdf5')

New in version 0.8: Saving list, tuples and signals present in metadata.

When saving to hspy, all supported objects in the signal’s metadata is stored. This includes lists, tuples and signals. 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 intensity):

>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum()
>>> s.metadata.Sample.intensities = s.get_lines_intensity()
>>> s.save('EDS_spectrum.hspy')

>>> s_new = hs.load('EDS_spectrum.hspy')
>>> 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: (|)>]

New in version 1.3.1: chunks keyword argument

By default, the data is saved in chunks that are optimised to contain at least one full signal. It is possible to customise the chunk shape using the chunks keyword. For example, to save the data with (20, 20, 256) chunks instead of the default (7, 7, 2048) chunks for this signal:

Note that currently it is not possible to pass different customised chunk shapes to all signals and arrays contained in a signal and its metadata. Therefore, the value of chunks provided on saving will be applied to all arrays contained in the signal.

By passing True to chunks the chunk shape is guessed using h5py’s guess_chunks function what, for large signal spaces usually leads to smaller chunks as guess_chunks does not impose the constrain of storing at least one signal per chunks. For example, for the signal in the example above passing chunks=True results in (7, 7, 256) chunks.

Extra saving arguments

compression: One of None, ‘gzip’, ‘szip’, ‘lzf’.

‘gzip’ is the default


This was the default format in HyperSpy’s predecessor, EELSLab, but it has been superseded 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 open format, we may implement this feature in the future on demand.

For mrc files load takes the mmap_mode keyword argument enabling loading the file using a different mode (default is copy-on-write) . However, note that lazy loading does not support in-place writing (i.e lazy loading and the “r+” mode are incompatible).


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.


If several spectra are loaded and stacked (hs.load('pattern', stack_signals=True) the calibration read from the first spectrum and applied to all other spectra.

Extra saving arguments

For the MSA format the 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')

For mrc files load takes the mmap_mode keyword argument enabling loading the file using a different mode (default is copy-on-write) . However, note that lazy loading does not support in-place writing (i.e lazy loading and the “r+” mode are incompatible).


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 software. HyperSpy can also import the scale and the units from tiff files saved using FEI and Zeiss SEM software.

>>> # 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'

Gatan Digital Micrograph

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.

Extra loading arguments

optimize: bool, default is True. During loading, the data is replaced by its optimized copy to speed up operations, e. g. iteration over navigation axes. The cost of this speed improvement is to double the memory requirement during data loading.


HyperSpy can read both .spd (spectrum image) and .spc (single spectra) files from the EDAX TEAM software. If reading an .spd file, the calibration of the spectrum image is loaded from the corresponding .ipr and .spc files stored in the same directory, or from specific files indicated by the user. If these calibration files are not available, the data from the .spd file will still be loaded, but with no spatial or energy calibration. If elemental information has been defined in the spectrum image, those elements will automatically be added to the signal loaded by HyperSpy.

Currently, loading an EDAX TEAM spectrum or spectrum image will load an EDSSEMSpectrum Signal. If support for TEM EDS data is needed, please open an issue in the issues tracker to alert the developers of the need.

For further reference, file specifications for the formats are available publicly available from EDAX and are on Github (.spc, .spd, and .ipr).

FEI TIA ser and emi

HyperSpy can read ser and 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 problem.

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 recommend 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 unf binary format

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 diffraction (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 in a “copy-on-write” manner using numpy.memmap . For blockfiles load takes the mmap_mode keyword argument enabling loading the file using a different mode. However, note that lazy loading does not support in-place writing (i.e lazy loading and the “r+” mode are incompatible).

DENS heater log

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.

Bruker composite file

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.

BCF can look as all inclusive format, however it does not save some key EDX parameters: any of dead/live/real times, FWHM at Mn_Ka line. However, real time for whole map is calculated from pixelAverage, lineAverage, pixelTime, lineCounter and map height parameters.

Note that Bruker Esprit uses a similar format for EBSD data, but it is not currently supported by HyperSpy.

Extra loading arguments

select_type: one of (None, ‘spectrum’, ‘image’). If specified, only the corresponding type of data, either spectrum or image, is returned. By default (None), all data are loaded.

index: one of (None, int, “all”). Allow to select the index of the dataset in the bcf file, which can contains several datasets. Default None value result in loading the first dataset. When set to ‘all’, all available datasets will be loaded and returned as separate signals.

downsample: the downsample ratio of hyperspectral array (height and width only), can be integer >=1, where ‘1’ results in no downsampling (default 1). The underlying method of downsampling is unchangeable: 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 or 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 dimension 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 Electron Microscopy Datasets (HDF5)

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.com/ for more information). NOT to be confused with the FEI EMD format which was developed later and has a different structure.

Protochips log

HyperSpy can read heater, biasing and gas cell log files for Protochips holder. The format stores all the captured data together with a small header in a csv file. The reader extracts the measured quantity (e. g. temperature, pressure, current, voltage) along the time axis, as well as the notes saved during the experiment. The reader returns a list of signal with each signal corresponding to a quantity. Since there is a small fluctuation in the step of the time axis, the reader assumes that the step is constant and takes its mean, which is a good approximation. Further release of HyperSpy will read the time axis more precisely by supporting non-linear axis.

Reading data generated by HyperSpy using other software packages

The following scripts may help reading data generated by HyperSpy using other software packages.

ImportRPL Digital Micrograph plugin

This Digital Micrograph plugin is designed to import Ripple files into Digital Micrograph. It is used to ease data transit between DigitalMicrograph and HyperSpy without losing the calibration using the extra keywords that HyperSpy adds to the standard format.

When executed it will ask for 2 files:

  1. The riple file with the data format and calibrations
  2. The data itself in raw format.

If a file with the same name and path as the riple file exits with raw or bin extension it is opened directly without prompting

ImportRPL was written by Luiz Fernando Zagonel.

Download ImportRPL

readHyperSpyH5 MATLAB Plugin

This MATLAB script is designed to import HyperSpy’s saved HDF5 files (.hspy extension). Like the Digital Micrograph script above, it is used to easily transfer data from HyperSpy to MATLAB, while retaining spatial calibration information.

Download readHyperSpyH5 from its Github repository.