Getting started *************** .. _importing_hyperspy-label: Starting Python in Windows ---------------------------- If you used the bundle installation you should be able to use the context menus to get started. Right-click on the folder containing the data you wish to analyse and select "Jupyter notebook here" or "Jupyter qtconsole here". We recommend the former, since notebooks have many advantages over convetional consoles, as will be illustrated in later sections. The examples in some later sections assume Notebook operation. A new tab should appear in your default browser listing the files in the selected folder. To start a python notebook choose "Python 3" in the "New" drop-down menu at the top right of the page. Another new tab will open which is your Notebook. Starting Python in Linux and MacOS ------------------------------------ You can start IPython by opening a system terminal and executing ``ipython``, (optionally followed by the "frontend": "qtconsole" for example). However, 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 Linux users may find it more convenient to start Jupyter/IPython from the `file manager context menu `_ . In either OS you can also start by `double-clicking a notebook file `_ if one already exists. Starting HyperSpy in the notebook (or terminal) ----------------------------------------------- Typically you will need to `set up IPython for interactive plotting with matplotlib `_ using ``%matplotlib`` (which is known as a 'Jupyter 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 (In these docs we normally use the general Python prompt symbol ``>>>`` but you will probably see ``In [1]:`` etc.): .. code-block:: python >>> %matplotlib qt >>> import hyperspy.api as hs Note that to execute lines of code in the notebook you must press ``Shift+Return``. (For details about notebooks and their functionality try the help menu in the notebook). Next, import two useful modules: numpy and matplotlib.pyplot, as follows: .. code-block:: python >>> import numpy as np >>> import matplotlib.pyplot as plt The rest of the documentation will assume you have done this. Now you are ready to load your data (see below). **Notes for experienced users:** 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 >>> import matplotlib >>> matplotlib.rcParams["backend"] = "Agg" >>> import hyperspy.api as hs 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 BaseSignal 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. .. _example-data-label: Loading example data and data from online databases ---------------------------------------------------- HyperSpy is distributed with some example data that can be found in `hs.datasets.example_signals`. The following example plots one of the example signals: .. code-block:: python >>> hs.datasets.example_signals.EDS_TEM_Spectrum().plot() .. _eelsdb-label: .. versionadded:: 1.0 :py:func:`~.misc.eels.eelsdb.eelsdb` function. The :py:func:`~.misc.eels.eelsdb.eelsdb` function in `hs.datasets` can directly load spectra from `The EELS Database `_. For example, the following loads all the boron trioxide spectra currently available in the database: .. code-block:: python >>> hs.datasets.eelsdb(formula="B2O3") [, ] 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 BaseSignal 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 BaseSignal :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_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_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_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. .. _logger-label: Messages log ------------ .. versionadded:: 1.0 HyperSpy writes messages to the `Python logger `_. The deafault log level is "WARNING", meaning that only warnings and more severe event messages will be displayed. The default can be set in the :ref:`preferences `. Alternatively, it can be set using :py:func:`~.logger.set_log_level` e.g.: .. code-block:: python >>> import hyperspy.api as hs >>> hs.set_log_level('INFO') >>> hs.load(r'my_file.dm3') INFO:hyperspy.io_plugins.digital_micrograph:DM version: 3 INFO:hyperspy.io_plugins.digital_micrograph:size 4796607 B INFO:hyperspy.io_plugins.digital_micrograph:Is file Little endian? True INFO:hyperspy.io_plugins.digital_micrograph:Total tags in root group: 15