hyperspy.signal module

class hyperspy.signal.BaseSetMetadataItems(signal): Bases: HasTraits

class hyperspy.signal.BaseSignal(data, **kwds)

Bases: FancySlicing, MVA, MVATools

Create a Signal from a numpy array.

Parameters:

data (numpy.ndarray) – The signal data. It can be an array of any dimensions.
axes ([dict/axes], optional) – List of either dictionaries or axes objects to define the axes (see the documentation of the AxesManager class for more details).
attributes (dict, optional) – A dictionary whose items are stored as attributes.
metadata (dict, optional) – A dictionary containing a set of parameters that will to stores in the metadata attribute. Some parameters might be mandatory in some cases.
original_metadata (dict, optional) – A dictionary containing a set of parameters that will to stores in the original_metadata attribute. It typically contains all the parameters that has been imported from the original data file.
ragged (bool or None, optional) – Define whether the signal is ragged or not. Overwrite the ragged value in the attributes dictionary. If None, it does nothing. Default is None.

property T: The transpose of the signal, with signal and navigation spaces swapped. Enables calling transpose() with the default parameters as a property of a Signal.

_check_navigation_mask(mask)

Check the shape of the navigation mask.

Parameters:: mask (numpy array or BaseSignal.) – Mask to check the shape.
Raises:: ValueError – If shape doesn’t match the shape of the navigation dimension.
Return type:: None.

_check_signal_mask(mask)

Check the shape of the signal mask.

Parameters:: mask (numpy array or BaseSignal.) – Mask to check the shape.
Raises:: ValueError – If shape doesn’t match the shape of the signal dimension.
Return type:: None.

_cycle_signal()

Cycles over the signal data.

It is faster than using the signal iterator.

Warning! could produce a infinite loop.

property _data_aligned_with_axes: Returns a view of data with is axes aligned with the Signal axes.

_deepcopy_with_new_data(data=None, copy_variance=False, copy_navigator=False, copy_learning_results=False)

Returns a deepcopy of itself replacing the data.

This method has an advantage over the default copy.deepcopy() in that it does not copy the data, which can save memory.

Parameters:

data (None or numpy.ndarray) –
copy_variance (bool) – Whether to copy the variance of the signal to the new copy
copy_navigator (bool) – Whether to copy the navigator of the signal to the new copy
copy_learning_results (bool) – Whether to copy the learning_results of the signal to the new copy

Returns:

ns – The newly copied signal

Return type:

BaseSignal (or subclass)

_get_navigation_signal(data=None, dtype=None)

Return a signal with the same axes as the navigation space.

Parameters:

data (None or numpy.ndarray, optional) – If None, the resulting Signal data is an array of the same dtype as the current one filled with zeros. If a numpy array, the array must have the correct dimensions.
dtype (numpy.dtype, optional) – The desired data-type for the data array when data is None, e.g., numpy.int8. The default is the data type of the current signal data.

_get_signal_signal(data=None, dtype=None)

Return a signal with the same axes as the signal space.

Parameters:

data (None or numpy.ndarray, optional) – If None, the resulting Signal data is an array of the same dtype as the current one filled with zeros. If a numpy array, the array must have the correct dimensions.
dtype (numpy.dtype, optional) – The desired data-type for the data array when data is None, e.g., numpy.int8. The default is the data type of the current signal data.

_get_undefined_axes_list(ragged=False): Returns default list of axes construct from the data array shape.

_iterate_signal(iterpath=None)

Iterates over the signal data. It is faster than using the signal iterator, because it avoids making deepcopy of metadata and other attributes.

Parameters:: iterpath (None or str or iterable) – Any valid iterpath supported by the axes_manager.
Return type:: numpy array when iterating over the navigation space

_load_dictionary(file_data_dict)

Load data from dictionary.

Parameters:

file_data_dict (dict) –

A dictionary containing at least a ‘data’ keyword with an array of arbitrary dimensions. Additionally the dictionary can contain the following items:

data: the signal data. It can be an array of any dimensions.
axes: a dictionary to define the axes (see the documentation of the AxesManager class for more details).
attributes: a dictionary whose items are stored as attributes.
metadata: a dictionary containing a set of parameters that will to stores in the metadata attribute. Some parameters might be mandatory in some cases.
original_metadata: a dictionary containing a set of parameters that will to stores in the original_metadata attribute. It typically contains all the parameters that has been imported from the original data file.
ragged: a bool, defining whether the signal is ragged or not. Overwrite the attributes[‘ragged’] entry

_map_all(function, inplace=True, **kwargs): The function has to have either ‘axis’ or ‘axes’ keyword argument, and hence support operating on the full dataset efficiently.

_to_dictionary(add_learning_results=True, add_models=False, add_original_metadata=True)

Returns a dictionary that can be used to recreate the signal.

All items but data are copies.

Parameters:

add_learning_results (bool, optional) – Whether or not to include any multivariate learning results in the outputted dictionary. Default is True.
add_models (bool, optional) – Whether or not to include any models in the outputted dictionary. Default is False
add_original_metadata (bool) – Whether or not to include the original_medata in the outputted dictionary. Default is True.

Returns:

dic – The dictionary that can be used to recreate the signal

Return type:

dict

_unfold(steady_axes, unfolded_axis)

Modify the shape of the data by specifying the axes whose dimension do not change and the axis over which the remaining axes will be unfolded

Parameters:

steady_axes (list) – The indices of the axes which dimensions do not change
unfolded_axis (int) – The index of the axis over which all the rest of the axes (except the steady axes) will be unfolded

See also

fold

Notes

WARNING: this private function does not modify the signal subclass and it is intended for internal use only. To unfold use the public unfold(), unfold_navigation_space(), unfold_signal_space() instead. It doesn’t make sense to perform an unfolding when dim < 2

add_gaussian_noise(std, random_state=None)

Add Gaussian noise to the data.

The operation is performed in-place (i.e. the data of the signal is modified). This method requires the signal to have a float data type, otherwise it will raise a TypeError.

Parameters:

std (float) – The standard deviation of the Gaussian noise.
random_state (None or int or Generator instance, default None) – Seed for the random generator.

Note

This method uses numpy.random.normal() (or dask.array.random.normal() for lazy signals) to generate the noise.

add_marker(marker, plot_on_signal=True, plot_marker=True, permanent=False, plot_signal=True, render_figure=True)

Add one or several markers to the signal or navigator plot and plot the signal, if not yet plotted (by default)

Parameters:

marker (hyperspy.drawing.marker object or iterable) – The marker or iterable (list, tuple, …) of markers to add. See the Markers section in the User Guide if you want to add a large number of markers as an iterable, since this will be much faster. For signals with navigation dimensions, the markers can be made to change for different navigation indices. See the examples for info.
plot_on_signal (bool) – If True (default), add the marker to the signal. If False, add the marker to the navigator
plot_marker (bool) – If True (default), plot the marker.
permanent (bool) – If False (default), the marker will only appear in the current plot. If True, the marker will be added to the metadata.Markers list, and be plotted with plot(plot_markers=True). If the signal is saved as a HyperSpy HDF5 file, the markers will be stored in the HDF5 signal and be restored when the file is loaded.

Examples

>>> import skimage
>>> im = hs.signals.Signal2D(skimage.data.camera())
>>> m = hs.markers.rectangle(x1=150, y1=100, x2=400,
>>>                                  y2=400, color='red')
>>> im.add_marker(m)

Adding to a 1D signal, where the point will change when the navigation index is changed:

>>> rng = np.random.default_rng(1)
>>> s = hs.signals.Signal1D(rng.random((3, 100)))
>>> marker = hs.markers.point((19, 10, 60), (0.2, 0.5, 0.9))
>>> s.add_marker(marker, permanent=True, plot_marker=True)

Add permanent marker:

>>> rng = np.random.default_rng(1)
>>> s = hs.signals.Signal2D(rng.random((100, 100)))
>>> marker = hs.markers.point(50, 60, color='red')
>>> s.add_marker(marker, permanent=True, plot_marker=True)

Add permanent marker to signal with 2 navigation dimensions. The signal has navigation dimensions (3, 2), as the dimensions gets flipped compared to the output from numpy.random.random(). To add a vertical line marker which changes for different navigation indices, the list used to make the marker must be a nested list: 2 lists with 3 elements each (2 x 3):

>>> rng = np.random.default_rng(1)
>>> s = hs.signals.Signal1D(rng.random((2, 3, 10)))
>>> marker = hs.markers.vertical_line([[1, 3, 5], [2, 4, 6]])
>>> s.add_marker(marker, permanent=True)

Add permanent marker which changes with navigation position, and do not add it to a current plot:

>>> rng = np.random.default_rng(1)
>>> s = hs.signals.Signal2D(rng.integers(10, size=(3, 100, 100)))
>>> marker = hs.markers.point((10, 30, 50), (30, 50, 60), color='red')
>>> s.add_marker(marker, permanent=True, plot_marker=False)
>>> s.plot(plot_markers=True) 

Removing a permanent marker:

>>> rng = np.random.default_rng(1)
>>> s = hs.signals.Signal2D(rng.integers(10, size=(100, 100)))
>>> marker = hs.markers.point(10, 60, color='red')
>>> marker.name = "point_marker"
>>> s.add_marker(marker, permanent=True)
>>> del s.metadata.Markers.point_marker

Adding many markers as a list:

>>> rng = np.random.default_rng(1)
>>> s = hs.signals.Signal2D(rng.integers(10, size=(100, 100)))
>>> marker_list = []
>>> for i in range(100):
>>>     marker = hs.markers.point(random()*100, random()*100, color='red')
>>>     marker_list.append(marker)
>>> s.add_marker(marker_list, permanent=True)

add_poissonian_noise(keep_dtype=True, random_state=None)

Add Poissonian noise to the data.

This method works in-place. The resulting data type is int64. If this is different from the original data type then a warning is added to the log.

Parameters:

keep_dtype (bool, default True) – If True, keep the original data type of the signal data. For example, if the data type was initially 'float64', the result of the operation (usually 'int64') will be converted to 'float64'.
random_state (None or int or Generator instance, default None) – Seed for the random generator.

Note

This method uses numpy.random.poisson() (or dask.array.random.poisson() for lazy signals) to generate the Poissonian noise.

apply_apodization(window='hann', hann_order=None, tukey_alpha=0.5, inplace=False)

Apply an apodization window to a Signal.

Parameters:

window (str, optional) – Select between {'hann' (default), 'hamming', or 'tukey'}
hann_order (None or int, optional) – Only used if window='hann' If integer n is provided, a Hann window of n-th order will be used. If None, a first order Hann window is used. Higher orders result in more homogeneous intensity distribution.
tukey_alpha (float, optional) –
Only used if window='tukey' (default is 0.5). From the documentation of scipy.signal.windows.tukey():
- Shape parameter of the Tukey window, representing the fraction of the window inside the cosine tapered region. If zero, the Tukey window is equivalent to a rectangular window. If one, the Tukey window is equivalent to a Hann window.
inplace (bool, optional) – If True, the apodization is applied in place, i.e. the signal data will be substituted by the apodized one (default is False).

Returns:

out – If inplace=False, returns the apodized signal of the same type as the provided Signal.

Return type:

BaseSignal (or subclasses), optional

Examples

>>> import hyperspy.api as hs
>>> holo = hs.datasets.example_signals.object_hologram()
>>> holo.apply_apodization('tukey', tukey_alpha=0.1).plot()

as_lazy(copy_variance=True, copy_navigator=True, copy_learning_results=True)

Create a copy of the given Signal as a LazySignal.

Parameters:

copy_variance (bool) – Whether or not to copy the variance from the original Signal to the new lazy version. Default is True.
copy_navigator (bool) – Whether or not to copy the navigator from the original Signal to the new lazy version. Default is True.
copy_learning_results (bool) – Whether to copy the learning_results from the original signal to the new lazy version. Default is True.

Returns:

res – The same signal, converted to be lazy

Return type:

LazySignal

as_signal1D(spectral_axis, out=None, optimize=True)

Return the Signal as a spectrum.

The chosen spectral axis is moved to the last index in the array and the data is made contiguous for efficient iteration over spectra. By default, the method ensures the data is stored optimally, hence often making a copy of the data. See transpose() for a more general method with more options.

Parameters:

spectral_axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
optimize (bool) – If True, the location of the data in memory is optimised for the fastest iteration over the navigation axes. This operation can cause a peak of memory usage and requires considerable processing times for large datasets and/or low specification hardware. See the Transposing (changing signal spaces) section of the HyperSpy user guide for more information. When operating on lazy signals, if True, the chunks are optimised for the new axes configuration.

Examples

>>> img = hs.signals.Signal2D(np.ones((3, 4, 5, 6)))
>>> img
<Signal2D, title: , dimensions: (4, 3|6, 5)>
>>> img.as_signal1D(-1+1j)
<Signal1D, title: , dimensions: (6, 5, 4|3)>
>>> img.as_signal1D(0)
<Signal1D, title: , dimensions: (6, 5, 3|4)>

as_signal2D(image_axes, out=None, optimize=True)

Convert a signal to image ( Signal2D).

The chosen image axes are moved to the last indices in the array and the data is made contiguous for efficient iteration over images.

Parameters:

image_axes (tuple (of int, str or DataAxis)) – Select the image axes. Note that the order of the axes matters and it is given in the “natural” i.e. X, Y, Z… order.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
optimize (bool) – If True, the location of the data in memory is optimised for the fastest iteration over the navigation axes. This operation can cause a peak of memory usage and requires considerable processing times for large datasets and/or low specification hardware. See the Transposing (changing signal spaces) section of the HyperSpy user guide for more information. When operating on lazy signals, if True, the chunks are optimised for the new axes configuration.

Raises:

DataDimensionError – When data.ndim < 2

Examples

>>> s = hs.signals.Signal1D(np.ones((2,3,4,5)))
>>> s
<Signal1D, title: , dimensions: (4, 3, 2, 5)>
>>> s.as_signal2D((0,1))
<Signal2D, title: , dimensions: (5, 2, 4, 3)>

>>> s.to_signal2D((1,2))
<Signal2D, title: , dimensions: (4, 5, 3, 2)>

change_dtype(dtype, rechunk=True)

Change the data type of a Signal.

Parameters:

dtype (str or numpy.dtype) – Typecode string or data-type to which the Signal’s data array is cast. In addition to all the standard numpy Data type objects (dtype), HyperSpy supports four extra dtypes for RGB images: 'rgb8', 'rgba8', 'rgb16', and 'rgba16'. Changing from and to any rgb(a) dtype is more constrained than most other dtype conversions. To change to an rgb(a) dtype, the signal_dimension must be 1, and its size should be 3 (for rgb) or 4 (for rgba) dtypes. The original dtype should be uint8 or uint16 if converting to rgb(a)8 or rgb(a))16, and the navigation_dimension should be at least 2. After conversion, the signal_dimension becomes 2. The dtype of images with original dtype rgb(a)8 or rgb(a)16 can only be changed to uint8 or uint16, and the signal_dimension becomes 1.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Examples

>>> s = hs.signals.Signal1D([1,2,3,4,5])
>>> s.data
array([1, 2, 3, 4, 5])
>>> s.change_dtype('float')
>>> s.data
array([ 1.,  2.,  3.,  4.,  5.])

copy(): Return a “shallow copy” of this Signal using the standard library’s copy() function. Note: this will return a copy of the signal, but it will not duplicate the underlying data in memory, and both Signals will reference the same data.

See also

deepcopy()

crop(axis, start=None, end=None, convert_units=False)

Crops the data in a given axis. The range is given in pixels.

Parameters:

axis (int or str) – Specify the data axis in which to perform the cropping operation. The axis can be specified using the index of the axis in axes_manager or the axis name.
start (int, float, or None) – The beginning of the cropping interval. If type is int, the value is taken as the axis index. If type is float the index is calculated using the axis calibration. If start/end is None the method crops from/to the low/high end of the axis.
end (int, float, or None) – The end of the cropping interval. If type is int, the value is taken as the axis index. If type is float the index is calculated using the axis calibration. If start/end is None the method crops from/to the low/high end of the axis.
convert_units (bool) – Default is False. If True, convert the units using the convert_units() method of the AxesManager. If False, does nothing.

property data: The underlying data structure as a numpy.ndarray (or dask.array.Array, if the Signal is lazy).

deepcopy(): Return a “deep copy” of this Signal using the standard library’s deepcopy() function. Note: this means the underlying data structure will be duplicated in memory.

See also

copy()

derivative(axis, order=1, out=None, **kwargs)

Calculate the numerical derivative along the given axis, with respect to the calibrated units of that axis.

For a function \(y = f(x)\) and two consecutive values \(x_1\) and \(x_2\):

\[\frac{df(x)}{dx} = \frac{y(x_2)-y(x_1)}{x_2-x_1}\]

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
order (int) – The order of the derivative.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
**kwargs (dict) – All extra keyword arguments are passed to numpy.gradient()

Returns:

der – Note that the size of the data on the given axis decreases by the given order. i.e. if axis is "x" and order is 2, if the x dimension is N, then der’s x dimension is N - 2.

Return type:

BaseSignal

Notes

This function uses numpy.gradient to perform the derivative. See its documentation for implementation details.

See also

integrate1D, integrate_simpson

diff(axis, order=1, out=None, rechunk=True)

Returns a signal with the n-th order discrete difference along given axis. i.e. it calculates the difference between consecutive values in the given axis: out[n] = a[n+1] - a[n]. See numpy.diff() for more details.

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
order (int) – The order of the discrete difference.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – Note that the size of the data on the given axis decreases by the given order. i.e. if axis is "x" and order is 2, the x dimension is N, der’s x dimension is N - 2.

Return type:

BaseSignal (or subclasses) or None

Note

If you intend to calculate the numerical derivative, please use the proper derivative() function instead. To avoid erroneous misuse of the diff function as derivative, it raises an error when when working with a non-uniform axis.

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.diff(-1).data.shape
(64,64,1023)

estimate_poissonian_noise_variance(expected_value=None, gain_factor=None, gain_offset=None, correlation_factor=None)

Estimate the Poissonian noise variance of the signal.

The variance is stored in the metadata.Signal.Noise_properties.variance attribute.

The Poissonian noise variance is equal to the expected value. With the default arguments, this method simply sets the variance attribute to the given expected_value. However, more generally (although then the noise is not strictly Poissonian), the variance may be proportional to the expected value. Moreover, when the noise is a mixture of white (Gaussian) and Poissonian noise, the variance is described by the following linear model:

\[\mathrm{Var}[X] = (a * \mathrm{E}[X] + b) * c\]

Where a is the gain_factor, b is the gain_offset (the Gaussian noise variance) and c the correlation_factor. The correlation factor accounts for correlation of adjacent signal elements that can be modeled as a convolution with a Gaussian point spread function.

Parameters:

expected_value (None or BaseSignal (or subclasses)) – If None, the signal data is taken as the expected value. Note that this may be inaccurate where the value of data is small.
gain_factor (None or float) – a in the above equation. Must be positive. If None, take the value from metadata.Signal.Noise_properties.Variance_linear_model if defined. Otherwise, suppose pure Poissonian noise (i.e. gain_factor=1). If not None, the value is stored in metadata.Signal.Noise_properties.Variance_linear_model.
gain_offset (None or float) – b in the above equation. Must be positive. If None, take the value from metadata.Signal.Noise_properties.Variance_linear_model if defined. Otherwise, suppose pure Poissonian noise (i.e. gain_offset=0). If not None, the value is stored in metadata.Signal.Noise_properties.Variance_linear_model.
correlation_factor (None or float) – c in the above equation. Must be positive. If None, take the value from metadata.Signal.Noise_properties.Variance_linear_model if defined. Otherwise, suppose pure Poissonian noise (i.e. correlation_factor=1). If not None, the value is stored in metadata.Signal.Noise_properties.Variance_linear_model.

fft(shift=False, apodization=False, real_fft_only=False, **kwargs)

Compute the discrete Fourier Transform.

This function computes the discrete Fourier Transform over the signal axes by means of the Fast Fourier Transform (FFT) as implemented in numpy.

Parameters:

shift (bool, optional) – If True, the origin of FFT will be shifted to the centre (default is False).
apodization (bool or str) –
Apply an apodization window before calculating the FFT in order to suppress streaks. Valid string values are {'hann' or 'hamming' or 'tukey'} If True or 'hann', applies a Hann window. If 'hamming' or 'tukey', applies Hamming or Tukey windows, respectively (default is False).
real_fft_only (bool, default False) – If True and data is real-valued, uses numpy.fft.rfftn() instead of numpy.fft.fftn()
**kwargs (dict) – other keyword arguments are described in numpy.fft.fftn()

Returns:

s – A Signal containing the result of the FFT algorithm

Return type:

ComplexSignal

Raises:

NotImplementedError – If performing FFT along a non-uniform axis.

Examples

>>> import skimage
>>> im = hs.signals.Signal2D(skimage.data.camera())
>>> im.fft()
<ComplexSignal2D, title: FFT of , dimensions: (|512, 512)>

>>> # Use following to plot power spectrum of `im`:
>>> im.fft(shift=True, apodization=True).plot(power_spectrum=True)

Note

Requires a uniform axis. For further information see the documentation of numpy.fft.fftn()

fold(): If the signal was previously unfolded, fold it back

get_current_signal(auto_title=True, auto_filename=True, as_numpy=False)

Returns the data at the current coordinates as a BaseSignal subclass.

The signal subclass is the same as that of the current object. All the axes navigation attributes are set to False.

Parameters:

auto_title (bool) – If True, the current indices (in parentheses) are appended to the title, separated by a space, otherwise the title of the signal is used unchanged.
auto_filename (bool) – If True and tmp_parameters.filename is defined (which is always the case when the Signal has been read from a file), the filename stored in the metadata is modified by appending an underscore and the current indices in parentheses.
as_numpy (bool or None) – Only with cupy array. If True, return the current signal as numpy array, otherwise return as cupy array.

Returns:

cs – The data at the current coordinates as a Signal

Return type:

BaseSignal (or subclass)

Examples

>>> im = hs.signals.Signal2D(np.zeros((2,3, 32,32)))
>>> im
<Signal2D, title: , dimensions: (3, 2, 32, 32)>
>>> im.axes_manager.indices = 2,1
>>> im.get_current_signal()
<Signal2D, title:  (2, 1), dimensions: (32, 32)>

get_dimensions_from_data(): Get the dimension parameters from the Signal’s underlying data. Useful when the data structure was externally modified, or when the spectrum image was not loaded from a file

get_histogram(bins='fd', range_bins=None, max_num_bins=250, out=None, **kwargs)

Return a histogram of the signal data.

More sophisticated algorithms for determining the bins can be used by passing a string as the bins argument. Other than the 'blocks' and 'knuth' methods, the available algorithms are the same as numpy.histogram().

Note: The lazy version of the algorithm only supports "scott" and "fd" as a string argument for bins.

Parameters:

bins (int or sequence of scalars or str, default "fd") –
If bins is an int, it defines the number of equal-width bins in the given range. If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths.

If bins is a string from the list below, will use the method chosen to calculate the optimal bin width and consequently the number of bins (see Notes for more detail on the estimators) from the data that falls within the requested range. While the bin width will be optimal for the actual data in the range, the number of bins will be computed to fill the entire range, including the empty portions. For visualisation, using the ‘auto’ option is suggested. Weighted data is not supported for automated bin size selection.

’auto’
Maximum of the ‘sturges’ and ‘fd’ estimators. Provides good all around performance.

’fd’ (Freedman Diaconis Estimator)
Robust (resilient to outliers) estimator that takes into account data variability and data size.

’doane’
An improved version of Sturges’ estimator that works better with non-normal datasets.

’scott’
Less robust estimator that that takes into account data variability and data size.

’stone’
Estimator based on leave-one-out cross-validation estimate of the integrated squared error. Can be regarded as a generalization of Scott’s rule.

’rice’
Estimator does not take variability into account, only data size. Commonly overestimates number of bins required.

’sturges’
R’s default method, only accounts for data size. Only optimal for gaussian data and underestimates number of bins for large non-gaussian datasets.

’sqrt’
Square root (of data size) estimator, used by Excel and other programs for its speed and simplicity.

’knuth’
Knuth’s rule is a fixed-width, Bayesian approach to determining the optimal bin width of a histogram.

’blocks’
Determination of optimal adaptive-width histogram bins using the Bayesian Blocks algorithm.
range_bins (tuple or None, optional) – the minimum and maximum range for the histogram. If range_bins is None, (x.min(), x.max()) will be used.
max_num_bins (int, default 250) – When estimating the bins using one of the str methods, the number of bins is capped by this number to avoid a MemoryError being raised by numpy.histogram().
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.
**kwargs – other keyword arguments (weight and density) are described in numpy.histogram().

Returns:

hist_spec – A 1D spectrum instance containing the histogram.

Return type:

Signal1D

See also

print_summary_statistics
numpy.histogram()
dask.histogram()

Examples

>>> s = hs.signals.Signal1D(np.random.normal(size=(10, 100)))
>>> # Plot the data histogram
>>> s.get_histogram().plot()
>>> # Plot the histogram of the signal at the current coordinates
>>> s.get_current_signal().get_histogram().plot()

get_noise_variance()

Get the noise variance of the signal, if set.

Equivalent to s.metadata.Signal.Noise_properties.variance.

Parameters:: None –
Returns:: variance – Noise variance of the signal, if set. Otherwise returns None.
Return type:: None or float or BaseSignal (or subclasses)

ifft(shift=None, return_real=True, **kwargs)

Compute the inverse discrete Fourier Transform.

This function computes the real part of the inverse of the discrete Fourier Transform over the signal axes by means of the Fast Fourier Transform (FFT) as implemented in numpy.

Parameters:

shift (bool or None, optional) – If None, the shift option will be set to the original status of the FFT using the value in metadata. If no FFT entry is present in metadata, the parameter will be set to False. If True, the origin of the FFT will be shifted to the centre. If False, the origin will be kept at (0, 0) (default is None).
return_real (bool, default True) – If True, returns only the real part of the inverse FFT. If False, returns all parts.
**kwargs (dict) – other keyword arguments are described in numpy.fft.ifftn()

Returns:

s – A Signal containing the result of the inverse FFT algorithm

Return type:

BaseSignal (or subclasses)

Raises:

NotImplementedError – If performing IFFT along a non-uniform axis.

Examples

>>> import skimage
>>> im = hs.signals.Signal2D(skimage.data.camera())
>>> imfft = im.fft()
>>> imfft.ifft()
<Signal2D, title: real(iFFT of FFT of ), dimensions: (|512, 512)>

Note

Requires a uniform axis. For further information see the documentation of numpy.fft.ifftn()

indexmax(axis, out=None, rechunk=True)

Returns a signal with the index of the maximum along an axis.

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the indices of the maximum along the specified axis. Note: the data dtype is always int.

Return type:

BaseSignal (or subclasses)

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.indexmax(-1).data.shape
(64,64)

indexmin(axis, out=None, rechunk=True)

Returns a signal with the index of the minimum along an axis.

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the indices of the minimum along the specified axis. Note: the data dtype is always int.

Return type:

BaseSignal (or subclasses)

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.indexmin(-1).data.shape
(64,64)

integrate1D(axis, out=None)

Integrate the signal over the given axis.

The integration is performed using Simpson’s rule if axis.is_binned is False and simple summation over the given axis if True (along binned axes, the detector already provides integrated counts per bin).

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.

Returns:

s – A new Signal containing the integral of the provided Signal along the specified axis.

Return type:

BaseSignal (or subclasses)

See also

integrate_simpson, derivative

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.integrate1D(-1).data.shape
(64,64)

integrate_simpson(axis, out=None)

Calculate the integral of a Signal along an axis using Simpson’s rule.

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.

Returns:

s – A new Signal containing the integral of the provided Signal along the specified axis.

Return type:

BaseSignal (or subclasses)

See also

derivative, integrate1D

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.integrate_simpson(-1).data.shape
(64,64)

property is_rgb: Whether or not this signal is an RGB dtype.

property is_rgba: Whether or not this signal is an RGB + alpha channel dtype.

property is_rgbx: Whether or not this signal is either an RGB or RGB + alpha channel dtype.

map(function, show_progressbar=None, parallel=None, max_workers=None, inplace=True, ragged=None, output_signal_size=None, output_dtype=None, lazy_output=None, **kwargs)

Apply a function to the signal data at all the navigation coordinates.

The function must operate on numpy arrays. It is applied to the data at each navigation coordinate pixel-py-pixel. Any extra keyword arguments are passed to the function. The keywords can take different values at different coordinates. If the function takes an axis or axes argument, the function is assumed to be vectorized and the signal axes are assigned to axis or axes. Otherwise, the signal is iterated over the navigation axes and a progress bar is displayed to monitor the progress.

In general, only navigation axes (order, calibration, and number) are guaranteed to be preserved.

Parameters:

function (function) – Any function that can be applied to the signal. This function should not alter any mutable input arguments or input data. So do not do operations which alter the input, without copying it first. For example, instead of doing image *= mask, rather do image = image * mask. Likewise, do not do image[5, 5] = 10 directly on the input data or arguments, but make a copy of it first. For example via image = copy.deepcopy(image).
show_progressbar (None or bool) – If True, display a progress bar. If None, the default from the preferences settings is used.
parallel (None or bool) – If True, perform computation in parallel using multithreading. If None, the default from the preferences settings is used. The number of threads is controlled by the max_workers argument.
inplace (bool, default True) – If True, the data is replaced by the result. Otherwise a new Signal with the results is returned.
ragged (None or bool, default None) – Indicates if the results for each navigation pixel are of identical shape (and/or numpy arrays to begin with). If None, the output signal will be ragged only if the original signal is ragged.
output_signal_size (None, tuple) – Since the size and dtype of the signal dimension of the output signal can be different from the input signal, this output signal size must be calculated somehow. If both output_signal_size and output_dtype is None, this is automatically determined. However, if for some reason this is not working correctly, this can be specified via output_signal_size and output_dtype. The most common reason for this failing is due to the signal size being different for different navigation positions. If this is the case, use ragged=True. None is default.
output_dtype (None, NumPy dtype) – See docstring for output_signal_size for more information. Default None.
lazy_output (None or bool) – If True, the output will be returned as a lazy signal. This means the calculation itself will be delayed until either compute() is used, or the signal is stored as a file. If False, the output will be returned as a non-lazy signal, this means the outputs will be calculated directly, and loaded into memory. If None the output will be lazy if the input signal is lazy, and non-lazy if the input signal is non-lazy.
max_workers (None or int) – Maximum number of threads used when parallel=True. If None, defaults to min(32, os.cpu_count()).
**kwargs (dict) – All extra keyword arguments are passed to the provided function

Notes

If the function results do not have identical shapes, the result is an array of navigation shape, where each element corresponds to the result of the function (of arbitrary object type), called a “ragged array”. As such, most functions are not able to operate on the result and the data should be used directly.

This method is similar to Python’s map() that can also be utilized with a BaseSignal instance for similar purposes. However, this method has the advantage of being faster because it iterates the underlying numpy data array instead of the BaseSignal.

Examples

Apply a Gaussian filter to all the images in the dataset. The sigma parameter is constant:

>>> import scipy.ndimage
>>> im = hs.signals.Signal2D(np.random.random((10, 64, 64)))
>>> im.map(scipy.ndimage.gaussian_filter, sigma=2.5)

Apply a Gaussian filter to all the images in the dataset. The signal parameter is variable:

>>> im = hs.signals.Signal2D(np.random.random((10, 64, 64)))
>>> sigmas = hs.signals.BaseSignal(np.linspace(2, 5, 10)).T
>>> im.map(scipy.ndimage.gaussian_filter, sigma=sigmas)

Rotate the two signal dimensions, with different amount as a function of navigation index. Delay the calculation by getting the output lazily. The calculation is then done using the compute method.

>>> from scipy.ndimage import rotate
>>> s = hs.signals.Signal2D(np.random.random((5, 4, 40, 40)))
>>> s_angle = hs.signals.BaseSignal(np.linspace(0, 90, 20).reshape(5, 4)).T
>>> s.map(rotate, angle=s_angle, reshape=False, lazy_output=True)
>>> s.compute()

Rotate the two signal dimensions, with different amount as a function of navigation index. In addition, the output is returned as a new signal, instead of replacing the old signal.

>>> s = hs.signals.Signal2D(np.random.random((5, 4, 40, 40)))
>>> s_angle = hs.signals.BaseSignal(np.linspace(0, 90, 20).reshape(5, 4)).T
>>> s_rot = s.map(rotate, angle=s_angle, reshape=False, inplace=False)

Note

Currently requires a uniform axis.

max(axis=None, out=None, rechunk=True)

Returns a signal with the maximum of the signal along at least one axis.

Parameters:

axis (int, str, DataAxis, tuple (of DataAxis) or None) – Either one on its own, or many axes in a tuple can be passed. In both cases the axes can be passed directly, or specified using the index in axes_manager or the name of the axis. Any duplicates are removed. If None, the operation is performed over all navigation axes (default).
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the maximum of the provided Signal over the specified axes

Return type:

BaseSignal (or subclasses)

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.max(-1).data.shape
(64,64)

mean(axis=None, out=None, rechunk=True)

Returns a signal with the average of the signal along at least one axis.

Parameters:

axis (int, str, DataAxis, tuple (of DataAxis) or None) – Either one on its own, or many axes in a tuple can be passed. In both cases the axes can be passed directly, or specified using the index in axes_manager or the name of the axis. Any duplicates are removed. If None, the operation is performed over all navigation axes (default).
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the mean of the provided Signal over the specified axes

Return type:

BaseSignal (or subclasses)

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.mean(-1).data.shape
(64,64)

property metadata: The metadata of the signal.

min(axis=None, out=None, rechunk=True)

Returns a signal with the minimum of the signal along at least one axis.

Parameters:

axis (int, str, DataAxis, tuple (of DataAxis) or None) – Either one on its own, or many axes in a tuple can be passed. In both cases the axes can be passed directly, or specified using the index in axes_manager or the name of the axis. Any duplicates are removed. If None, the operation is performed over all navigation axes (default).
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the minimum of the provided Signal over the specified axes

Return type:

BaseSignal (or subclasses)

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.min(-1).data.shape
(64,64)

nanmax(axis=None, out=None, rechunk=True): Identical to max(), except ignores missing (NaN) values. See that method’s documentation for details.

nanmean(axis=None, out=None, rechunk=True): Identical to mean(), except ignores missing (NaN) values. See that method’s documentation for details.

nanmin(axis=None, out=None, rechunk=True): Identical to min(), except ignores missing (NaN) values. See that method’s documentation for details.

nanstd(axis=None, out=None, rechunk=True): Identical to std(), except ignores missing (NaN) values. See that method’s documentation for details.

nansum(axis=None, out=None, rechunk=True): Identical to sum(), except ignores missing (NaN) values. See that method’s documentation for details.

nanvar(axis=None, out=None, rechunk=True): Identical to var(), except ignores missing (NaN) values. See that method’s documentation for details.

property original_metadata: The original metadata of the signal.

plot(navigator='auto', axes_manager=None, plot_markers=True, **kwargs)

Plot the signal at the current coordinates.

For multidimensional datasets an optional figure, the “navigator”, with a cursor to navigate that data is raised. In any case it is possible to navigate the data using the sliders. Currently only signals with signal_dimension equal to 0, 1 and 2 can be plotted.

Parameters:

navigator (str, None, or BaseSignal (or subclass). Allowed string values are 'auto', 'slider', and 'spectrum'.) –
If 'auto':
- If navigation_dimension > 0, a navigator is provided to explore the data.
- If navigation_dimension is 1 and the signal is an image the navigator is a sum spectrum obtained by integrating over the signal axes (the image).
- If navigation_dimension is 1 and the signal is a spectrum the navigator is an image obtained by stacking all the spectra in the dataset horizontally.
- If navigation_dimension is > 1, the navigator is a sum image obtained by integrating the data over the signal axes.
- Additionally, if navigation_dimension > 2, a window with one slider per axis is raised to navigate the data.
- For example, if the dataset consists of 3 navigation axes X, Y, Z and one signal axis, E, the default navigator will be an image obtained by integrating the data over E at the current Z index and a window with sliders for the X, Y, and Z axes will be raised. Notice that changing the Z-axis index changes the navigator in this case.
- For lazy signals, the navigator will be calculated using the compute_navigator() method.
If 'slider':
- If navigation dimension > 0 a window with one slider per axis is raised to navigate the data.
If 'spectrum':
- If navigation_dimension > 0 the navigator is always a spectrum obtained by integrating the data over all other axes.
- Not supported for lazy signals, the 'auto' option will be used instead.
If None, no navigator will be provided.

Alternatively a BaseSignal (or subclass) instance can be provided. The navigation or signal shape must match the navigation shape of the signal to plot or the navigation_shape + signal_shape must be equal to the navigator_shape of the current object (for a dynamic navigator). If the signal dtype is RGB or RGBA this parameter has no effect and the value is always set to 'slider'.
axes_manager (None or AxesManager) – If None, the signal’s axes_manager attribute is used.
plot_markers (bool, default True) – Plot markers added using s.add_marker(marker, permanent=True). Note, a large number of markers might lead to very slow plotting.
navigator_kwds (dict) – Only for image navigator, additional keyword arguments for matplotlib.pyplot.imshow().
norm (str, optional) – The function used to normalize the data prior to plotting. Allowable strings are: 'auto', 'linear', 'log'. (default value is 'auto'). If 'auto', intensity is plotted on a linear scale except when power_spectrum=True (only for complex signals).
autoscale (str) – The string must contain any combination of the ‘x’ and ‘v’ characters. If ‘x’ or ‘v’ (for values) are in the string, the corresponding horizontal or vertical axis limits are set to their maxima and the axis limits will reset when the data or the navigation indices are changed. Default is ‘v’.
**kwargs (dict) – Only when plotting an image: additional (optional) keyword arguments for matplotlib.pyplot.imshow().

print_summary_statistics(formatter='%.3g', rechunk=True)

Prints the five-number summary statistics of the data, the mean, and the standard deviation.

Prints the mean, standard deviation (std), maximum (max), minimum (min), first quartile (Q1), median, and third quartile. nans are removed from the calculations.

Parameters:

formatter (str) – The number formatter to use for the output
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

See also

get_histogram

rebin(new_shape=None, scale=None, crop=True, dtype=None, out=None)

Rebin the signal into a smaller or larger shape, based on linear interpolation. Specify either new_shape or scale. Scale of 1 means no binning and scale less than one results in up-sampling.

Parameters:

new_shape (list (of floats or integer) or None) – For each dimension specify the new_shape. This will internally be converted into a scale parameter.
scale (list (of floats or integer) or None) – For each dimension, specify the new:old pixel ratio, e.g. a ratio of 1 is no binning and a ratio of 2 means that each pixel in the new spectrum is twice the size of the pixels in the old spectrum. The length of the list should match the dimension of the Signal’s underlying data array. Note : Only one of `scale` or `new_shape` should be specified, otherwise the function will not run
crop (bool) – Whether or not to crop the resulting rebinned data (default is True). When binning by a non-integer number of pixels it is likely that the final row in each dimension will contain fewer than the full quota to fill one pixel. For example, a 5*5 array binned by 2.1 will produce two rows containing 2.1 pixels and one row containing only 0.8 pixels. Selection of crop=True or crop=False determines whether or not this “black” line is cropped from the final binned array or not. Please note that if ``crop=False`` is used, the final row in each dimension may appear black if a fractional number of pixels are left over. It can be removed but has been left to preserve total counts before and after binning.
dtype ({None, numpy.dtype, "same"}) – Specify the dtype of the output. If None, the dtype will be determined by the behaviour of numpy.sum(), if “same”, the dtype will be kept the same. Default is None.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.

Returns:

s – The resulting cropped signal.

Return type:

BaseSignal (or subclass)

Raises:

NotImplementedError – If trying to rebin over a non-uniform axis.

Examples

>>> spectrum = hs.signals.EDSTEMSpectrum(np.ones([4, 4, 10]))
>>> spectrum.data[1, 2, 9] = 5
>>> print(spectrum)
<EDXTEMSpectrum, title: dimensions: (4, 4|10)>
>>> print ('Sum = ', sum(sum(sum(spectrum.data))))
Sum = 164.0

>>> scale = [2, 2, 5]
>>> test = spectrum.rebin(scale)
>>> print(test)
<EDSTEMSpectrum, title: dimensions (2, 2|2)>
>>> print('Sum = ', sum(sum(sum(test.data))))
Sum =  164.0

>>> s = hs.signals.Signal1D(np.ones((2, 5, 10), dtype=np.uint8)
>>> print(s)
<Signal1D, title: , dimensions: (5, 2|10)>
>>> print(s.data.dtype)
uint8

Use dtype=np.unit16 to specify a dtype

>>> s2 = s.rebin(scale=(5, 2, 1), dtype=np.uint16)
>>> print(s2.data.dtype)
uint16

Use dtype=”same” to keep the same dtype

>>> s3 = s.rebin(scale=(5, 2, 1), dtype="same")
>>> print(s3.data.dtype)
uint8

By default dtype=None, the dtype is determined by the behaviour of numpy.sum, in this case, unsigned integer of the same precision as the platform interger

>>> s4 = s.rebin(scale=(5, 2, 1))
>>> print(s4.data.dtype)
uint64

rollaxis(axis, to_axis, optimize=False)

Roll the specified axis backwards, until it lies in a given position.

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name. The axis to roll backwards. The positions of the other axes do not change relative to one another.
to_axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name. The axis is rolled until it lies before this other axis.
optimize (bool) – If True, the location of the data in memory is optimised for the fastest iteration over the navigation axes. This operation can cause a peak of memory usage and requires considerable processing times for large datasets and/or low specification hardware. See the Transposing (changing signal spaces) section of the HyperSpy user guide for more information. When operating on lazy signals, if True, the chunks are optimised for the new axes configuration.

Returns:

s – Output signal.

Return type:

BaseSignal (or subclass)

See also

numpy.roll(), swap_axes

Examples

>>> s = hs.signals.Signal1D(np.ones((5,4,3,6)))
>>> s
<Signal1D, title: , dimensions: (3, 4, 5, 6)>
>>> s.rollaxis(3, 1)
<Signal1D, title: , dimensions: (3, 4, 5, 6)>
>>> s.rollaxis(2,0)
<Signal1D, title: , dimensions: (5, 3, 4, 6)>

save(filename=None, overwrite=None, extension=None, **kwds)

Saves the signal in the specified format.

The function gets the format from the specified extension (see Supported formats in the User Guide for more information):

'hspy' for HyperSpy’s HDF5 specification
'rpl' for Ripple (useful to export to Digital Micrograph)
'msa' for EMSA/MSA single spectrum saving.
'unf' for SEMPER unf binary format.
'blo' for Blockfile diffraction stack saving.
Many image formats such as 'png', 'tiff', 'jpeg'…

If no extension is provided the default file format as defined in the preferences is used. Please note that not all the formats supports saving datasets of arbitrary dimensions, e.g. 'msa' only supports 1D data, and blockfiles only supports image stacks with a navigation_dimension < 2.

Each format accepts a different set of parameters. For details see the specific format documentation.

Parameters:

filename (str or None) – If None (default) and tmp_parameters.filename and tmp_parameters.folder are defined, the filename and path will be taken from there. A valid extension can be provided e.g. 'my_file.rpl' (see extension parameter).
overwrite (None or bool) – If None, if the file exists it will query the user. If True(False) it does(not) overwrite the file if it exists.
extension (None or str) –
The extension of the file that defines the file format. Allowable string values are: {'hspy', 'hdf5', 'rpl', 'msa', 'unf', 'blo', 'emd', and common image extensions e.g. 'tiff', 'png', etc.} 'hspy' and 'hdf5' are equivalent. Use 'hdf5' if compatibility with HyperSpy versions older than 1.2 is required. If None, the extension is determined from the following list in this order:
1. the filename
2. Signal.tmp_parameters.extension
3. 'hspy' (the default extension)
chunks (tuple or True or None (default)) – HyperSpy, Nexus and EMD NCEM format only. Define chunks used when saving. The chunk shape should follow the order of the array (s.data.shape), not the shape of the axes_manager. If None and lazy signal, the dask array chunking is used. If None and non-lazy signal, the chunks are estimated automatically to have at least one chunk per signal space. If True, the chunking is determined by the the h5py guess_chunk function.
save_original_metadata (bool , default : False) – Nexus file only. Option to save hyperspy.original_metadata with the signal. A loaded Nexus file may have a large amount of data when loaded which you may wish to omit on saving
use_default (bool , default : False) – Nexus file only. Define the default dataset in the file. If set to True the signal or first signal in the list of signals will be defined as the default (following Nexus v3 data rules).
write_dataset (bool, optional) – Only for hspy files. If True, write the dataset, otherwise, don’t write it. Useful to save attributes without having to write the whole dataset. Default is True.
close_file (bool, optional) – Only for hdf5-based files and some zarr store. Close the file after writing. Default is True.

set_noise_variance(variance)

Set the noise variance of the signal.

Equivalent to s.metadata.set_item("Signal.Noise_properties.variance", variance).

Parameters:: variance (None or float or BaseSignal (or subclasses)) – Value or values of the noise variance. A value of None is equivalent to clearing the variance.
Return type:: None

set_signal_origin(origin)

Set the signal_origin metadata value.

The signal_origin attribute specifies if the data was obtained through experiment or simulation.

Parameters:: origin (str) – Typically 'experiment' or 'simulation'

set_signal_type(signal_type='')

Set the signal type and convert the current signal accordingly.

The signal_type attribute specifies the type of data that the signal contains e.g. electron energy-loss spectroscopy data, photoemission spectroscopy data, etc.

When setting signal_type to a “known” type, HyperSpy converts the current signal to the most appropriate hyperspy.signal.BaseSignal subclass. Known signal types are signal types that have a specialized hyperspy.signal.BaseSignal subclass associated, usually providing specific features for the analysis of that type of signal.

HyperSpy ships with a minimal set of known signal types. External packages can register extra signal types. To print a list of registered signal types in the current installation, call hyperspy.utils.print_known_signal_types(), and see the developer guide for details on how to add new signal_types. A non-exhaustive list of HyperSpy extensions is also maintained here: https://github.com/hyperspy/hyperspy-extensions-list.

Parameters:: signal_type (str, optional) – If no arguments are passed, the signal_type is set to undefined and the current signal converted to a generic signal subclass. Otherwise, set the signal_type to the given signal type or to the signal type corresponding to the given signal type alias. Setting the signal_type to a known signal type (if exists) is highly advisable. If none exists, it is good practice to set signal_type to a value that best describes the data signal type.

See also

hyperspy.utils.print_known_signal_types()

Examples

Let’s first print all known signal types:

>>> s = hs.signals.Signal1D([0, 1, 2, 3])
>>> s
<Signal1D, title: , dimensions: (|4)>
>>> hs.print_known_signal_types()
+--------------------+---------------------+--------------------+----------+
|    signal_type     |       aliases       |     class name     | package  |
+--------------------+---------------------+--------------------+----------+
| DielectricFunction | dielectric function | DielectricFunction | hyperspy |
|      EDS_SEM       |                     |   EDSSEMSpectrum   | hyperspy |
|      EDS_TEM       |                     |   EDSTEMSpectrum   | hyperspy |
|        EELS        |       TEM EELS      |    EELSSpectrum    | hyperspy |
|      hologram      |                     |   HologramImage    | hyperspy |
|      MySignal      |                     |      MySignal      | hspy_ext |
+--------------------+---------------------+--------------------+----------+

We can set the signal_type using the signal_type:

>>> s.set_signal_type("EELS")
>>> s
<EELSSpectrum, title: , dimensions: (|4)>
>>> s.set_signal_type("EDS_SEM")
>>> s
<EDSSEMSpectrum, title: , dimensions: (|4)>

or any of its aliases:

>>> s.set_signal_type("TEM EELS")
>>> s
<EELSSpectrum, title: , dimensions: (|4)>

To set the signal_type to undefined, simply call the method without arguments:

>>> s.set_signal_type()
>>> s
<Signal1D, title: , dimensions: (|4)>

split(axis='auto', number_of_parts='auto', step_sizes='auto')

Splits the data into several signals.

The split can be defined by giving the number_of_parts, a homogeneous step size, or a list of customized step sizes. By default ('auto'), the function is the reverse of stack().

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name. If 'auto' and if the object has been created with stack() (and stack_metadata=True), this method will return the former list of signals (information stored in metadata._HyperSpy.Stacking_history). If it was not created with stack(), the last navigation axis will be used.
number_of_parts (str or int) – Number of parts in which the spectrum image will be split. The splitting is homogeneous. When the axis size is not divisible by the number_of_parts the remainder data is lost without warning. If number_of_parts and step_sizes is 'auto', number_of_parts equals the length of the axis, step_sizes equals one, and the axis is suppressed from each sub-spectrum.
step_sizes (str, list (of ints), or int) – Size of the split parts. If 'auto', the step_sizes equals one. If an int is given, the splitting is homogeneous.

Examples

>>> s = hs.signals.Signal1D(random.random([4,3,2]))
>>> s
    <Signal1D, title: , dimensions: (3, 4|2)>
>>> s.split()
    [<Signal1D, title: , dimensions: (3 |2)>,
    <Signal1D, title: , dimensions: (3 |2)>,
    <Signal1D, title: , dimensions: (3 |2)>,
    <Signal1D, title: , dimensions: (3 |2)>]
>>> s.split(step_sizes=2)
    [<Signal1D, title: , dimensions: (3, 2|2)>,
    <Signal1D, title: , dimensions: (3, 2|2)>]
>>> s.split(step_sizes=[1,2])
    [<Signal1D, title: , dimensions: (3, 1|2)>,
    <Signal1D, title: , dimensions: (3, 2|2)>]

Raises:: NotImplementedError – If trying to split along a non-uniform axis.
Returns:: splitted – A list of the split signals
Return type:: list

squeeze()

Remove single-dimensional entries from the shape of an array and the axes. See numpy.squeeze() for more details.

Returns:: s – A new signal object with single-entry dimensions removed
Return type:: signal

Examples

>>> s = hs.signals.Signal2D(np.random.random((2,1,1,6,8,8)))
<Signal2D, title: , dimensions: (6, 1, 1, 2|8, 8)>
>>> s = s.squeeze()
>>> s
<Signal2D, title: , dimensions: (6, 2|8, 8)>

std(axis=None, out=None, rechunk=True)

Returns a signal with the standard deviation of the signal along at least one axis.

Parameters:

axis (int, str, DataAxis, tuple (of DataAxis) or None) – Either one on its own, or many axes in a tuple can be passed. In both cases the axes can be passed directly, or specified using the index in axes_manager or the name of the axis. Any duplicates are removed. If None, the operation is performed over all navigation axes (default).
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the standard deviation of the provided Signal over the specified axes

Return type:

BaseSignal (or subclasses)

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.std(-1).data.shape
(64,64)

sum(axis=None, out=None, rechunk=True)

Sum the data over the given axes.

Parameters:

axis (int, str, DataAxis, tuple (of DataAxis) or None) – Either one on its own, or many axes in a tuple can be passed. In both cases the axes can be passed directly, or specified using the index in axes_manager or the name of the axis. Any duplicates are removed. If None, the operation is performed over all navigation axes (default).
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the sum of the provided Signal along the specified axes.

Return type:

BaseSignal (or subclasses)

Note

If you intend to calculate the numerical integral of an unbinned signal, please use the integrate1D() function instead. To avoid erroneous misuse of the sum function as integral, it raises a warning when working with an unbinned, non-uniform axis.

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.sum(-1).data.shape
(64,64)

swap_axes(axis1, axis2, optimize=False)

Swap two axes in the signal.

Parameters:

axis1 (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
axis2 (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
optimize (bool) – If True, the location of the data in memory is optimised for the fastest iteration over the navigation axes. This operation can cause a peak of memory usage and requires considerable processing times for large datasets and/or low specification hardware. See the Transposing (changing signal spaces) section of the HyperSpy user guide for more information. When operating on lazy signals, if True, the chunks are optimised for the new axes configuration.

Returns:

s – A copy of the object with the axes swapped.

Return type:

BaseSignal (or subclass)

See also

rollaxis

to_device()

Transfer data array from host to GPU device memory using cupy.asarray. Lazy signals are not supported by this method, see user guide for information on how to process data lazily using the GPU.

Raises:

BaseException – Raise expection if cupy is not installed.
BaseException – Raise expection if signal is lazy.

Return type:

None.

to_host()

Transfer data array from GPU device to host memory.

Raises:: BaseException – Raise expection if signal is lazy.
Return type:: None.

transpose(signal_axes=None, navigation_axes=None, optimize=False)

Transposes the signal to have the required signal and navigation axes.

Parameters:

signal_axes (None, int, or iterable type) – The number (or indices) of axes to convert to signal axes
navigation_axes (None, int, or iterable type) – The number (or indices) of axes to convert to navigation axes
optimize (bool) – If True, the location of the data in memory is optimised for the fastest iteration over the navigation axes. This operation can cause a peak of memory usage and requires considerable processing times for large datasets and/or low specification hardware. See the Transposing (changing signal spaces) section of the HyperSpy user guide for more information. When operating on lazy signals, if True, the chunks are optimised for the new axes configuration.

Note

With the exception of both axes parameters (signal_axes and navigation_axes getting iterables, generally one has to be None (i.e. “floating”). The other one specifies either the required number or explicitly the indices of axes to move to the corresponding space. If both are iterables, full control is given as long as all axes are assigned to one space only.

Examples

>>> # just create a signal with many distinct dimensions
>>> s = hs.signals.BaseSignal(np.random.rand(1,2,3,4,5,6,7,8,9))
>>> s
<BaseSignal, title: , dimensions: (|9, 8, 7, 6, 5, 4, 3, 2, 1)>

>>> s.transpose() # swap signal and navigation spaces
<BaseSignal, title: , dimensions: (9, 8, 7, 6, 5, 4, 3, 2, 1|)>

>>> s.T # a shortcut for no arguments
<BaseSignal, title: , dimensions: (9, 8, 7, 6, 5, 4, 3, 2, 1|)>

>>> # roll to leave 5 axes in navigation space
>>> s.transpose(signal_axes=5)
<BaseSignal, title: , dimensions: (4, 3, 2, 1|9, 8, 7, 6, 5)>

>>> # roll leave 3 axes in navigation space
>>> s.transpose(navigation_axes=3)
<BaseSignal, title: , dimensions: (3, 2, 1|9, 8, 7, 6, 5, 4)>

>>> # 3 explicitly defined axes in signal space
>>> s.transpose(signal_axes=[0, 2, 6])
<BaseSignal, title: , dimensions: (8, 6, 5, 4, 2, 1|9, 7, 3)>

>>> # A mix of two lists, but specifying all axes explicitly
>>> # The order of axes is preserved in both lists
>>> s.transpose(navigation_axes=[1, 2, 3, 4, 5, 8], signal_axes=[0, 6, 7])
<BaseSignal, title: , dimensions: (8, 7, 6, 5, 4, 1|9, 3, 2)>

unfold(unfold_navigation=True, unfold_signal=True)

Modifies the shape of the data by unfolding the signal and navigation dimensions separately

Parameters:

unfold_navigation (bool) – Whether or not to unfold the navigation dimension(s) (default: True)
unfold_signal (bool) – Whether or not to unfold the signal dimension(s) (default: True)

Returns:

needed_unfolding – Whether or not one of the axes needed unfolding (and that unfolding was performed)

Return type:

bool

Note

It doesn’t make sense to perform an unfolding when the total number of dimensions is < 2.

unfold_navigation_space()

Modify the shape of the data to obtain a navigation space of dimension 1

Returns:: needed_unfolding – Whether or not the navigation space needed unfolding (and whether it was performed)
Return type:: bool

unfold_signal_space()

Modify the shape of the data to obtain a signal space of dimension 1

Returns:: needed_unfolding – Whether or not the signal space needed unfolding (and whether it was performed)
Return type:: bool

unfolded(unfold_navigation=True, unfold_signal=True)

Use this function together with a with statement to have the signal be unfolded for the scope of the with block, before automatically refolding when passing out of scope.

See also

unfold, fold

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> with s.unfolded():
        # Do whatever needs doing while unfolded here
        pass

update_plot(): If this Signal has been plotted, update the signal and navigator plots, as appropriate.

valuemax(axis, out=None, rechunk=True)

Returns a signal with the value of coordinates of the maximum along an axis.

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the calibrated coordinate values of the maximum along the specified axis.

Return type:

BaseSignal (or subclasses)

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.valuemax(-1).data.shape
(64,64)

valuemin(axis, out=None, rechunk=True)

Returns a signal with the value of coordinates of the minimum along an axis.

Parameters:

axis (int, str, or DataAxis) – The axis can be passed directly, or specified using the index of the axis in the Signal’s axes_manager or the axis name.
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the calibrated coordinate values of the minimum along the specified axis.

Return type:

BaseSignal (or subclasses)

var(axis=None, out=None, rechunk=True)

Returns a signal with the variances of the signal along at least one axis.

Parameters:

axis (int, str, DataAxis, tuple (of DataAxis) or None) – Either one on its own, or many axes in a tuple can be passed. In both cases the axes can be passed directly, or specified using the index in axes_manager or the name of the axis. Any duplicates are removed. If None, the operation is performed over all navigation axes (default).
out (BaseSignal (or subclasses) or None) – If None, a new Signal is created with the result of the operation and returned (default). If a Signal is passed, it is used to receive the output of the operation, and nothing is returned.
rechunk (bool) – Only has effect when operating on lazy signal. If True (default), the data may be automatically rechunked before performing this operation.

Returns:

s – A new Signal containing the variance of the provided Signal over the specified axes

Return type:

BaseSignal (or subclasses)

Examples

>>> import numpy as np
>>> s = BaseSignal(np.random.random((64,64,1024)))
>>> s.data.shape
(64,64,1024)
>>> s.var(-1).data.shape
(64,64)

class hyperspy.signal.ModelManager(signal, dictionary=None)

Bases: object

Container for models

pop(name)

Returns the restored model and removes it from storage

Parameters:: name (str) – The name of the model to restore and remove

See also

restore, store, remove

remove(name)

Removes the given model

Parameters:: name (str) – The name of the model to remove

See also

restore, store, pop

restore(name)

Returns the restored model

Parameters:: name (str) – The name of the model to restore

See also

remove, store, pop

store(model, name=None)

If the given model was created from this signal, stores it

Parameters:

model (BaseModel (or subclass)) – The model to store in the signal
name (str or None) – The name for the model to be stored with

See also

remove, restore, pop