Loading and saving data ¶

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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 lena.jpg you can type:

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

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

Note

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 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')

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.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)>

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 (HDF5) 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.hdf5 in the default HDF5 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.

Supported file formats¶
Format	Read	Write
Gatan’s dm3	Yes	No
Gatan’s dm4	Yes	No
FEI’s emi and ser	Yes	No
HDF5	Yes	Yes
Image: jpg..	Yes	Yes
TIFF	Yes	Yes
MRC	Yes	No
EMSA/MSA	Yes	Yes
NetCDF	Yes	No
Ripple	Yes	Yes

HDF5 ¶

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 intensity):

>>> 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
[<Signal, title: X-ray line intensity of EDS SEM Spectrum: Al_Ka at 1.49 keV, dimensions: (|)>,
 <Signal, title: X-ray line intensity of EDS SEM Spectrum: C_Ka at 0.28 keV, dimensions: (|)>,
 <Signal, title: X-ray line intensity of EDS SEM Spectrum: Cu_La at 0.93 keV, dimensions: (|)>,
 <Signal, title: X-ray line intensity of EDS SEM Spectrum: Mn_La at 0.63 keV, dimensions: (|)>,
 <Signal, title: X-ray line intensity of EDS SEM Spectrum: Zr_La at 2.04 keV, dimensions: (|)>]

Extra saving arguments ¶

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

‘gzip’ is the default

NetCDF ¶

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.

MRC ¶

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.

EMSA/MSA ¶

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.

Extra saving arguments ¶

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.

Warning

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')

Ripple ¶

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')

Images ¶

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.

TIFF ¶

Since version 4.1 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 cannot read the TIFF tags.

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.

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 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.