Getting started *************** .. _importing_hyperspy-label: Starting HyperSpy ----------------- HyperSpy is a `Python `_ library for multi-dimensional data analysis. HyperSpy's API can be imported as any other Python library as follows: .. code-block:: python >>> import hyperspy.api as hs The most common way of using HyperSpy is interactively using interactive computing package `IPython `_. In all operating systems (OS) you can start IPython by opening a system terminal and executing ``ipython``, optionally followed by the frontend. In most cases, **the most agreeable way** to work with HyperSpy interactively is using the `Jupyter Notebook `_ (previously known as the IPython Notebook), which can be started as follows: .. code-block:: bash $ jupyter notebook Some may find it more convenient to start Jupyter/IPython from the `file manager context menu `_ or by `double-clicking a notebook file `_. Typically you will need to `set up IPython for interactive plotting with matplotlib `_ using the ``%matplotlib`` magic *before executing any plotting command*. So, typically, after starting IPython, you can import HyperSpy and set up interactive matplotlib plotting by executing the following two lines in the IPython terminal: .. code-block:: python In [1]: %matplotlib qt In [2]: import hyperspy.api as hs We also fully support the wx backend. Other backends are supported for plotting but some features such as navigation sliders may be missing. .. warning:: When using the qt4 backend in Python 2 the matplotlib magic must be executed after importing hyperspy and qt must be the default hyperspy backend. .. NOTE:: When running in a headless system it is necessary to set the matplotlib backend appropiately to avoid a `cannot connect to X server` error, for example as follows: .. code-block:: python In [1]: import matplotlib In [2]: matplotlib.rcParams["backend"] = "Agg" In [3]: import hyperspy.api as hs This documentation assumes that numpy and matplotlib are also imported as follows: >>> import numpy as np >>> import matplotlib.pyplot as plt .. warning:: Starting HyperSpy using the ``hyperspy`` starting script and the ``%hyperspy`` IPython magic is now deprecated and will be removed in Hyperspy 1.0. The IPython magic does not work with IPython 4 and above. .. _starting_hyperspy-label: Getting help ------------ When using IPython, the documentation (docstring in Python jargon) can be accessed by adding a question mark to the name of a function. e.g.: .. code-block:: python >>> hs? >>> hs.load? >>> hs.signals? This syntax is a shortcut to the standard way one of displaying the help associated to a given functions (docstring in Python jargon) and it is one of the many features of `IPython `_, which is the interactive python shell that HyperSpy uses under the hood. Please note that the documentation of the code is a work in progress, so not all the objects are documented yet. Up-to-date documentation is always available in `the HyperSpy website. `_ Autocompletion -------------- Another useful `IPython `_ feature is the autocompletion of commands and filenames using the tab and arrow keys. It is highly recommended to read the `Ipython documentation `_ (specially their `Getting started `_ section) for many more useful features that will boost your efficiency when working with HyperSpy/Python interactively. Loading data ------------ Once hyperspy is running, to load from a supported file format (see :ref:`supported-formats`) simply type: .. code-block:: python >>> s = hs.load("filename") .. HINT:: The load function returns an object that contains data read from the file. We assign this object to the variable ``s`` but you can choose any (valid) variable name you like. for the filename, don't forget to include the quotation marks and the file extension. If no argument is passed to the load function, a window will be raised that allows to select a single file through your OS file manager, e.g.: .. code-block:: python >>> # This raises the load user interface >>> s = hs.load() It is also possible to load multiple files at once or even stack multiple files. For more details read :ref:`loading_files` "Loading" data from a numpy array --------------------------------- HyperSpy can operate on any numpy array by assigning it to a Signal class. This is useful e.g. for loading data stored in a format that is not yet supported by HyperSpy—supposing that they can be read with another Python library—or to explore numpy arrays generated by other Python libraries. Simply select the most appropiate signal from the :py:mod:`~.signals` module and create a new instance by passing a numpy array to the constructor e.g. .. code-block:: python >>> my_np_array = np.random.random((10,20,100)) >>> s = hs.signals.Signal1D(my_np_array) >>> s The numpy array is stored in the :py:attr:`~.signal.BaseSignal.data` attribute of the signal class. The navigation and signal dimensions ------------------------------------ In HyperSpy the data is interpreted as a signal array and, therefore, the data axes are not equivalent. HyperSpy distiguises between *signal* and *navigation* axes and most functions operate on the *signal* axes and iterate on the *navigation* axes. For example, an EELS spectrum image (i.e. a 2D array of spectra) has three dimensions X, Y and energy-loss. In HyperSpy, X and Y are the *navigation* dimensions an the energy-loss is the *signal* dimension. To make this distinction more explicit the representation of the object includes a separator ``|`` between the navigaton and signal dimensions e.g. In Hyperpsy a spectrum image has signal dimension 1 and navigation dimension 2 and is stored in the Signal1D subclass. .. code-block:: python >>> s = hs.signals.Signal1D(np.zeros((10, 20, 30))) >>> s An image stack has signal dimension 2 and navigation dimension 1 and is stored in the Signal2D subclass. .. code-block:: python >>> im = hs.signals.Signal2D(np.zeros((30, 10, 20))) >>> im Note the HyperSpy rearranges the axes position to match the following pattern: (navigatons axis 0,..., navigation axis n|signal axis 0,..., signal axis n). This is the order used for :ref:`indexing the Signal class `. .. _Setting_axis_properties: Setting axis properties ----------------------- The axes are managed and stored by the :py:class:`~.axes.AxesManager` class that is stored in the :py:attr:`~.signal.BaseSignal.axes_manager` attribute of the signal class. The indidual axes can be accessed by indexing the AxesManager e.g. .. code-block:: python >>> s = hs.signals.Signal1D(np.random.random((10, 20 , 100))) >>> s >>> s.axes_manager , |)> >>> s.axes_manager[0] The axis properties can be set by setting the :py:class:`~.axes.DataAxis` attributes e.g. .. code-block:: python >>> s.axes_manager[0].name = "X" >>> s.axes_manager[0] Once the name of an axis has been defined it is possible to request it by its name e.g.: .. code-block:: python >>> s.axes_manager["X"] >>> s.axes_manager["X"].scale = 0.2 >>> s.axes_manager["X"].units = nm >>> s.axes_manager["X"].offset = 100 It is also possible to set the axes properties using a GUI by calling the :py:meth:`~.axes.AxesManager.gui` method of the :py:class:`~.axes.AxesManager`. .. _saving: Saving Files ------------ The data can be saved to several file formats. The format is specified by the extension of the filename. .. code-block:: python >>> # load the data >>> d = hs.load("example.tif") >>> # save the data as a tiff >>> d.save("example_processed.tif") >>> # save the data as a png >>> d.save("example_processed.png") >>> # save the data as an hdf5 file >>> d.save("example_processed.hdf5") Some file formats are much better at maintaining the information about how you processed your data. The preferred format in HyperSpy is hdf5, the hierarchical data format. This format keeps the most information possible. There are optional flags that may be passed to the save function. See :ref:`saving_files` for more details. Accessing and setting the metadata ---------------------------------- When loading a file HyperSpy stores all metadata in the Signal :py:attr:`~.signal.BaseSignal.original_metadata` attribute. In addition, some of those metadata and any new metadata generated by HyperSpy are stored in :py:attr:`~.signal.BaseSignal.metadata` attribute. .. code-block:: python >>> s = hs.load("NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217.msa") >>> s.metadata ├── original_filename = NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217.msa ├── record_by = spectrum ├── signal_origin = ├── signal_type = EELS └── title = NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217 >>> s.original_metadata ├── DATATYPE = XY ├── DATE = ├── FORMAT = EMSA/MAS Spectral Data File ├── NCOLUMNS = 1.0 ├── NPOINTS = 1340.0 ├── OFFSET = 120.0003 ├── OWNER = eelsdatabase.net ├── SIGNALTYPE = ELS ├── TIME = ├── TITLE = NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217 ├── VERSION = 1.0 ├── XPERCHAN = 0.5 ├── XUNITS = eV └── YUNITS = >>> s.set_microscope_parameters(100, 10, 20) >>> s.metadata ├── TEM │ ├── EELS │ │ └── collection_angle = 20 │ ├── beam_energy = 100 │ └── convergence_angle = 10 ├── original_filename = NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217.msa ├── record_by = spectrum ├── signal_origin = ├── signal_type = EELS └── title = NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217 >>> s.metadata.TEM.microscope = "STEM VG" >>> s.metadata ├── TEM │ ├── EELS │ │ └── collection_angle = 20 │ ├── beam_energy = 100 │ ├── convergence_angle = 10 │ └── microscope = STEM VG ├── original_filename = NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217.msa ├── record_by = spectrum ├── signal_origin = ├── signal_type = EELS └── title = NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217 .. _configuring-hyperspy-label: Configuring HyperSpy -------------------- The behaviour of HyperSpy can be customised using the :py:class:`~.defaults_parser.Preferences` class. The easiest way to do it is by calling the :meth:`gui` method: .. code-block:: python >>> hs.preferences.gui() This command should raise the Preferences user interface: .. _preferences_image: .. figure:: images/preferences.png :align: center Preferences user interface.