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 conventional 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. It also assumes that you have installed at least one of HyperSpy's GUI packages: `jupyter widgets GUI `_ and the `traitsui GUI `_. By default, HyperSpy warns the user if one of the GUI packages is not installed. These warnings can be turned off using the :py:class:`~.defaults_parser.Preferences` GUI (see :ref:`here ` for more information) or programmatically as follows: .. code-block:: python >>> import hyperspy.api as hs >>> hs.preferences.GUIs.warn_if_guis_are_missing = False >>> hs.preferences.save() Now you are ready to load your data (see below). .. versionchanged:: v1.3 HyperSpy works with all matplotlib backends, including the nbagg backend that enables interactive plotting embedded in the jupyter notebook. .. 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 appropriate 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() .. versionadded:: 1.4 :py:mod:`~.datasets.artificial_data` There are also artificial datasets, which are made to resemble real experimental data. .. code-block:: python >>> s = hs.datasets.artificial_data.get_core_loss_eels_signal() >>> s.plot() .. _eelsdb-label: 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 distinguishes 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 and the energy-loss is the *signal* dimension. To make this distinction more explicit the representation of the object includes a separator ``|`` between the navigation and signal dimensions e.g. In HyperSpy 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 that HyperSpy rearranges the axes when compared to the array order. The following few paragraphs explain how and why it does it. Depending how the array is arranged, some axes are faster to iterate than others. Consider an example of a book as the dataset in question. It is trivially simple to look at letters in a line, and then lines down the page, and finally pages in the whole book. However if your words are written vertically, it can be inconvenient to read top-down (the lines are still horizontal, it's just the meaning that's vertical!). It's very time-consuming if every letter is on a different page, and for every word you have to turn 5-6 pages. Exactly the same idea applies here - in order to iterate through the data (most often for plotting, but applies for any other operation too), you want to keep it ordered for "fast access". In Python (more explicitly `numpy`) the "fast axes order" is C order (also called row-major order). This means that the **last** axis of a numpy array is fastest to iterate over (i.e. the lines in the book). An alternative ordering convention is F order (column-major), where it is the reverse - the first axis of an array is the fastest to iterate over. In both cases, the further an axis is from the `fast axis` the slower it is to iterate over it. In the book analogy you could think, for example, think about reading the first lines of all pages, then the second and so on. When data is acquired sequentially it is usually stored in acquisition order. When a dataset is loaded, HyperSpy generally stores it in memory in the same order, which is good for the computer. However, HyperSpy will reorder and classify the axes to make it easier for humans. Let's imagine a single numpy array that contains pictures of a scene acquired with different exposure times on different days. In numpy the array dimensions are ``(D, E, Y, X)``. This order makes it fast to iterate over the images in the order in which they were acquired. From a human point of view, this dataset is just a collection of images, so HyperSpy first classifies the image axes (``X`` and ``Y``) as `signal axes` and the remaining axes the `navigation axes`. Then it reverses the order of each sets of axes because many humans are used to get the ``X`` axis first and, more generally the axes in acquisition order from left to right. So, the same axes in HyperSpy are displayed like this: ``(E, D | X, Y)``. Extending this to arbitrary dimensions, by default, we reverse the numpy axes, chop it into two chunks (signal and navigation), and then swap those chunks, at least when printing. As an example: .. code-block:: bash (a1, a2, a3, a4, a5, a6) # original (numpy) (a6, a5, a4, a3, a2, a1) # reverse (a6, a5) (a4, a3, a2, a1) # chop (a4, a3, a2, a1) (a6, a5) # swap (HyperSpy) In the background, HyperSpy also takes care of storing the data in memory in a "machine-friendly" way, so that iterating over the navigation axes is always fast. .. _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 individual 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` .. code-block:: python >>> s.axes_manager.gui() .. _axes_manager_gui_image: .. figure:: images/axes_manager_gui_ipywidgets.png :align: center AxesManager ipywidgets GUI. or the :py:class:`~.axes.DataAxis`, e.g: .. code-block:: python >>> s.axes_manager["X"].gui() .. _data_axis_gui_image: .. figure:: images/data_axis_gui_ipywidgets.png :align: center DataAxis ipywidgets GUI. To simply change the "current position" (i.e. the indices of the navigation axes) you could use the navigation sliders: .. code-block:: python >>> s.axes_manager.gui_navigation_sliders() .. _navigation_sliders_image: .. figure:: images/axes_manager_navigation_sliders_ipywidgets.png :align: center Navigation sliders ipywidgets GUI. Alternatively, the "current position" can be changed programmatically by directly accessing ``indices`` attribute of a Signal's :py:class:`~.axes.AxesManager`. This is particularly useful if trying to set a specific location with which to initialize a model's parameters to sensible values before preforming a fit over an entire spectrum image. The ``indices`` must be provided as a tuple, with the same length as the number of navigation dimensions: .. code-block:: python >>> s.axes_manager.indices = (5, 4) .. _quantity_and_converting_units: Using quantity and converting units ------------------------------------------- The scale and the offset of each axis can be set and retrieved as quantity. .. code-block:: python >>> s = hs.signals.Signal1D(np.arange(10)) >>> s.axes_manager[0].scale_as_quantity 1.0 dimensionless >>> s.axes_manager[0].scale_as_quantity = '2.5 µm' >>> s.axes_manager Name | size | index | offset | scale | units ================ | ====== | ====== | ======= | ======= | ====== ---------------- | ------ | ------ | ------- | ------- | ------ | 10 | | 0 | 2.5 | µm >>> s.axes_manager[0].offset_as_quantity = '2.5 nm' Name | size | index | offset | scale | units ================ | ====== | ====== | ======= | ======= | ====== ---------------- | ------ | ------ | ------- | ------- | ------ | 10 | | 2.5 | 2.5e+03 | nm Internally, HyperSpy uses the `pint `_ library to manage the scale and offset quantities. The ``scale_as_quantity`` and ``offset_as_quantity`` attributes return pint object: .. code-block:: python >>> q = s.axes_manager[0].offset_as_quantity >>> type(q) # q is a pint quantity object pint.quantity.build_quantity_class..Quantity >>> q 2.5 nanometer The ``convert_units`` method of the :py:class:`~.axes.AxesManager` converts units, which by default (no parameters provided) converts all axis units to an optimal units to avoid using too large or small number. Each axis can also be converted individually using the ``convert_to_units`` method of the :py:class:`~.axes.DataAxis`: .. code-block:: python >>> axis = hs.hyperspy.axes.DataAxis(size=10, scale=0.1, offset=10, units='mm') >>> axis.scale_as_quantity 0.1 millimeter >>> axis.convert_to_units('µm') >>> axis.scale_as_quantity 100.0 micrometer .. _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 hspy file >>> d.save("example_processed.hspy") Some file formats are much better at maintaining the information about how you processed your data. The preferred format in HyperSpy is hspy, which is based on the HDF5 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 if one of the hyperspy gui packages are installed and enabled: .. _preferences_image: .. figure:: images/preferences.png :align: center Preferences user interface. .. versionadded:: 1.3 Possibility to enable/disable GUIs in the It is also possible to set the preferences programmatically. For example, to disable the traitsui GUI elements and save the changes to disk: .. code-block:: python >>> hs.preferences.GUIs.enable_traitsui_gui = False >>> hs.preferences.save() .. versionchanged:: 1.3 The following items were removed from preferences: ``General.default_export_format``, ``General.lazy``, ``Model.default_fitter``, ``Machine_learning.multiple_files``, ``Machine_learning.same_window``, ``Plot.default_style_to_compare_spectra``, ``Plot.plot_on_load``, ``Plot.pylab_inline``, ``EELS.fine_structure_width``, ``EELS.fine_structure_active``, ``EELS.fine_structure_smoothing``, ``EELS.synchronize_cl_with_ll``, ``EELS.preedge_safe_window_width``, ``EELS.min_distance_between_edges_for_fine_structure``. .. _logger-label: Messages log ------------ HyperSpy writes messages to the `Python logger `_. The default 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