Signal2D
#
- class hyperspy.api.signals.Signal2D(*args, **kwargs)#
Bases:
BaseSignal
,CommonSignal2D
General 2D signal class.
Create a signal instance.
- 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.- raggedbool or
None
, optional Define whether the signal is ragged or not. Overwrite the
ragged
value in theattributes
dictionary. If None, it does nothing. Default is None.
- data
- add_ramp(ramp_x, ramp_y, offset=0)#
Add a linear ramp to the signal.
- Parameters:
- ramp_x: float
Slope of the ramp in x-direction.
- ramp_y: float
Slope of the ramp in y-direction.
- offset: float, optional
Offset of the ramp at the signal fulcrum.
Notes
The fulcrum of the linear ramp is at the origin and the slopes are given in units of the axis with the according scale taken into account. Both are available via the axes_manager of the signal.
- align2D(crop=True, fill_value=nan, shifts=None, expand=False, interpolation_order=1, show_progressbar=None, num_workers=None, **kwargs)#
Align the images in-place using
scipy.ndimage.shift()
.The images can be aligned using either user-provided shifts or by first estimating the shifts.
See
estimate_shift2D()
for more details on estimating image shifts.- Parameters:
- cropbool
If True, the data will be cropped not to include regions with missing data
- fill_value
int
,float
,numpy.nan
The areas with missing data are filled with the given value. Default is np.nan.
- shifts
None
ornumpy.ndarray
The array of shifts must be in pixel units. The shape must be the navigation shape using numpy order convention. If
None
the shifts are estimated usingestimate_shift2D()
.- expandbool
If True, the data will be expanded to fit all data after alignment. Overrides
crop
.- interpolation_order: int
The order of the spline interpolation. Default is 1, linear interpolation.
- show_progressbar
None
or bool If
True
, display a progress bar. IfNone
, the default from the preferences settings is used.- num_workers
None
orint
Number of worker used by dask. If None, default to dask default value.
- **kwargs
dict
Keyword arguments passed to
estimate_shift2D()
.
- Returns:
numpy.ndarray
The estimated shifts are returned only if
shifts
is None
- Raises:
NotImplementedError
If one of the signal axes is a non-uniform axis.
See also
- calibrate(x0=None, y0=None, x1=None, y1=None, new_length=None, units=None, interactive=True, display=True, toolkit=None)#
Calibrate the x and y signal dimensions.
Can be used either interactively, or by passing values as parameters.
- Parameters:
- x0, y0, x1, y1
float
,int
, optional If interactive is False, these must be set. If given in floats the input will be in scaled axis values. If given in integers, the input will be in non-scaled pixel values. Similar to how integer and float input works when slicing using isig and inav.
- new_lengthscalar, optional
If interactive is False, this must be set.
- units
str
, optional If interactive is False, this is used to set the axes units.
- interactivebool, default
True
If True, will use a plot with an interactive line for calibration. If False, x0, y0, x1, y1 and new_length must be set.
- displaybool, default
True
- toolkit
str
, optional
- x0, y0, x1, y1
Examples
>>> s = hs.signals.Signal2D(np.random.random((100, 100))) >>> s.calibrate()
Running non-interactively
>>> s = hs.signals.Signal2D(np.random.random((100, 100))) >>> s.calibrate(x0=10, y0=10, x1=60, y1=10, new_length=100, ... interactive=False, units="nm")
- create_model(dictionary=None)#
Create a model for the current signal
- Parameters:
- dictionary
None
ordict
, optional A dictionary to be used to recreate a model. Usually generated using
hyperspy.model.BaseModel.as_dictionary()
- dictionary
- Returns:
- crop_signal(top=None, bottom=None, left=None, right=None, convert_units=False)#
Crops in signal space and in place.
- Parameters:
See also
- estimate_shift2D(reference='current', correlation_threshold=None, chunk_size=30, roi=None, normalize_corr=False, sobel=True, medfilter=True, hanning=True, plot=False, dtype='float', show_progressbar=None, sub_pixel_factor=1)#
Estimate the shifts in an image using phase correlation.
This method can only estimate the shift by comparing bi-dimensional features that should not change position between frames. To decrease the memory usage, the time of computation and the accuracy of the results it is convenient to select a region of interest by setting the
roi
argument.- Parameters:
- reference{‘current’, ‘cascade’ ,’stat’}
If ‘current’ (default) the image at the current coordinates is taken as reference. If ‘cascade’ each image is aligned with the previous one. If ‘stat’ the translation of every image with all the rest is estimated and by performing statistical analysis on the result the translation is estimated.
- correlation_threshold
None
,str
orfloat
This parameter is only relevant when reference=’stat’. If float, the shift estimations with a maximum correlation value lower than the given value are not used to compute the estimated shifts. If ‘auto’ the threshold is calculated automatically as the minimum maximum correlation value of the automatically selected reference image.
- chunk_size
None
orint
If int and reference=’stat’ the number of images used as reference are limited to the given value.
- roi
tuple
ofint
orfloat
Define the region of interest (left, right, top, bottom). If int (float), the position is given by axis index (value). Note that ROIs can be used in place of a tuple.
- normalize_corrbool, default
False
If True, use phase correlation to align the images, otherwise use cross correlation.
- sobelbool, default
True
Apply a Sobel filter for edge enhancement
- medfilterbool, default
True
Apply a median filter for noise reduction
- hanningbool, default
True
Apply a 2D hanning filter
- plotbool or
str
If True plots the images after applying the filters and the phase correlation. If ‘reuse’, it will also plot the images, but it will only use one figure, and continuously update the images in that figure as it progresses through the stack.
- dtype
str
ornumpy.dtype
Typecode or data-type in which the calculations must be performed.
- show_progressbar
None
or bool If
True
, display a progress bar. IfNone
, the default from the preferences settings is used.- sub_pixel_factor
float
Estimate shifts with a sub-pixel accuracy of 1/sub_pixel_factor parts of a pixel. Default is 1, i.e. no sub-pixel accuracy.
- Returns:
numpy.ndarray
Estimated shifts in pixels.
See also
Notes
The statistical analysis approach to the translation estimation when using
reference='stat'
roughly follows [*]. If you use it please cite their article.References
- find_peaks(method='local_max', interactive=True, current_index=False, show_progressbar=None, num_workers=None, display=True, toolkit=None, get_intensity=False, **kwargs)#
Find peaks in a 2D signal.
Function to locate the positive peaks in an image using various, user specified, methods. Returns a structured array containing the peak positions.
- Parameters:
- method
str
Select peak finding algorithm to implement. Available methods are:
‘local_max’ - simple local maximum search using the
skimage.feature.peak_local_max()
function‘max’ - simple local maximum search using the
find_peaks_max()
.‘minmax’ - finds peaks by comparing maximum filter results with minimum filter, calculates centers of mass. See the
find_peaks_minmax()
function.‘zaefferer’ - based on gradient thresholding and refinement by local region of interest optimisation. See the
find_peaks_zaefferer()
function.‘stat’ - based on statistical refinement and difference with respect to mean intensity. See the
find_peaks_stat()
function.‘laplacian_of_gaussian’ - a blob finder using the laplacian of Gaussian matrices approach. See the
find_peaks_log()
function.‘difference_of_gaussian’ - a blob finder using the difference of Gaussian matrices approach. See the
find_peaks_dog()
function.‘template_matching’ - A cross correlation peakfinder. This method requires providing a template with the
template
parameter, which is used as reference pattern to perform the template matching to the signal. It uses theskimage.feature.match_template()
function and the peaks position are obtained by using minmax method on the template matching result.
- interactivebool
If True, the method parameter can be adjusted interactively. If False, the results will be returned.
- current_indexbool
If True, the computation will be performed for the current index.
- get_intensitybool
If True, the intensity of the peak will be returned as an additional column, the last one.
- show_progressbar
None
or bool If
True
, display a progress bar. IfNone
, the default from the preferences settings is used.- num_workers
None
orint
Number of worker used by dask. If None, default to dask default value.
- displaybool
If True, display the user interface widgets. If False, return the widgets container in a dictionary, usually for customisation or testing.
- toolkit
str
, iterable ofstr
orNone
If None (default), all available widgets are displayed or returned. If string, only the widgets of the selected toolkit are displayed if available. If an interable of toolkit strings, the widgets of all listed toolkits are displayed or returned.
- **kwargs
dict
Keywords parameters associated with above methods, see the documentation of each method for more details.
- method
- Returns:
- peaks
BaseSignal
ornumpy.ndarray
numpy.ndarray if current_index=True. Ragged signal with shape (npeaks, 2) that contains the x, y pixel coordinates of peaks found in each image sorted first along y and then along x.
- peaks
Notes
As a convenience, the ‘local_max’ method accepts the ‘distance’ and ‘threshold’ argument, which will be map to the ‘min_distance’ and ‘threshold_abs’ of the
skimage.feature.peak_local_max()
function.
- plot(navigator='auto', plot_markers=True, autoscale='v', norm='auto', vmin=None, vmax=None, gamma=1.0, linthresh=0.01, linscale=0.1, scalebar=True, scalebar_color='white', axes_ticks=None, axes_off=False, axes_manager=None, no_nans=False, colorbar=True, centre_colormap='auto', min_aspect=0.1, navigator_kwds={}, **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
, orBaseSignal
(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 thenavigation_shape
+signal_shape
must be equal to thenavigator_shape
of the current object (for a dynamic navigator). If the signaldtype
is RGB or RGBA this parameter has no effect and the value is always set to'slider'
.- axes_manager
None
orAxesManager
If None, the signal’s
axes_manager
attribute is used.- plot_markersbool, 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()
.- colorbarbool, optional
If true, a colorbar is plotted for non-RGB images.
- autoscale
str
, optional The string must contain any combination of the
'x'
,'y'
and'v'
characters. If'x'
or'y'
are in the string, the corresponding axis limits are set to cover the full range of the data at a given position. If'v'
(for values) is in the string, the contrast of the image will be set automatically according tovmin` and ``vmax
when the data or navigation indices change. Default is'v'
.- norm
str
{"auto"` | ``"linear"
|"power"
|"log"
|"symlog"
} ormatplotlib.colors.Normalize
Set the norm of the image to display. If
"auto"
, a linear scale is used except if whenpower_spectrum=True
in case of complex data type."symlog"
can be used to display negative value on a negative scale - readmatplotlib.colors.SymLogNorm
and thelinthresh
andlinscale
parameter for more details.- vmin, vmax{scalar,
str
}, optional vmin
andvmax
are used to normalise the displayed data. It can be a float or a string. If string, it should be formatted as'xth'
, where'x'
must be an float in the [0, 100] range.'x'
is used to compute the x-th percentile of the data. Seenumpy.percentile()
for more information.- gamma
float
, optional Parameter used in the power-law normalisation when the parameter
norm="power"
. Readmatplotlib.colors.PowerNorm
for more details. Default value is 1.0.- linthresh
float
, optional When used with
norm="symlog"
, define the range within which the plot is linear (to avoid having the plot go to infinity around zero). Default value is 0.01.- linscale
float
, optional This allows the linear range (-linthresh to linthresh) to be stretched relative to the logarithmic range. Its value is the number of powers of base to use for each half of the linear range. See
matplotlib.colors.SymLogNorm
for more details. Defaulf value is 0.1.- scalebarbool, optional
If True and the units and scale of the x and y axes are the same a scale bar is plotted.
- scalebar_color
str
, optional A valid MPL color string; will be used as the scalebar color.
- axes_ticks{
None
, bool}, optional If True, plot the axes ticks. If None axes_ticks are only plotted when the scale bar is not plotted. If False the axes ticks are never plotted.
- axes_offbool, default
False
- no_nansbool, optional
If True, set nans to zero for plotting.
- centre_colormapbool or
"auto"
If True the centre of the color scheme is set to zero. This is specially useful when using diverging color schemes. If “auto” (default), diverging color schemes are automatically centred.
- min_aspect
float
, optional Set the minimum aspect ratio of the image and the figure. To keep the image in the aspect limit the pixels are made rectangular.
- **kwargs
dict
Only when plotting an image: additional (optional) keyword arguments for
matplotlib.pyplot.imshow()
.
- navigator