hyperspy.drawing package¶

Subpackages¶

Submodules¶

hyperspy.drawing.figure module¶

class hyperspy.drawing.figure.BlittedFigure¶

Bases: object

title¶

hyperspy.drawing.image module¶

class hyperspy.drawing.image.ImagePlot¶

Bases: hyperspy.drawing.figure.BlittedFigure

Class to plot an image with the necessary machinery to update the image when the coordinates of an AxesManager change.

data_fuction¶

function or method – A function that returns a 2D array when called without any arguments.

pixel_units¶

{None, string} – The pixel units for the scale bar. Normally

scalebar, plot_ticks, colorbar, plot_indices

bool

title¶

str – The title is printed at the top of the image.

vmin, vmax

float – Limit the range of the color map scale to the given values.

min_aspect¶

float – Set the minimum aspect ratio of the image and the figure. To keep the image in the aspect limit the pixels are made rectangular.

saturated_pixels¶

scalar – The percentage of pixels that are left out of the bounds. For example, the low and high bounds of a value of 1 are the 0.5% and 99.5% percentiles. It must be in the [0, 100] range.

centre_colormap¶

{“auto”, True, False} – 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.

add_marker(marker)¶

adjust_contrast()¶

axes_ticks¶

close()¶

configure()¶

connect()¶

create_axis()¶

create_figure(max_size=8, min_size=2)¶

disconnect()¶

on_key_press(event)¶

optimize_colorbar(number_of_ticks=5, tolerance=5, step_prec_max=1)¶

optimize_contrast(data)¶

plot(**kwargs)¶

scalebar¶

set_contrast(vmin, vmax)¶

update(**kwargs)¶

vmax¶

vmin¶

hyperspy.drawing.marker module¶

class hyperspy.drawing.marker.MarkerBase¶

Bases: object

Marker that can be added to the signal figure

marker_properties¶

dictionary – Accepts a dictionary of valid (i.e. recognized by mpl.plot) containing valid line properties. In addition it understands the keyword type that can take the following values: {‘line’, ‘text’}

add_data(**kwargs)¶

Add data to the structured array. Each field of data should have the same dimensions than the nagivation axes. The other fields are not changed.

close()¶

get_data_position(ind)¶

marker_properties

set_data(x1=None, y1=None, x2=None, y2=None, text=None, size=None)¶

Set data to the structured array. Each field of data should have the same dimensions than the nagivation axes. The other fields are overwritten.

set_marker_properties(**kwargs)¶

Set the line_properties attribute using keyword arguments.

hyperspy.drawing.mpl_he module¶

class hyperspy.drawing.mpl_he.MPL_HyperExplorer¶

Bases: object

assign_pointer()¶

close()¶

close_navigator_plot()¶

is_active()¶

plot(**kwargs)¶

plot_navigator()¶

plot_signal()¶

hyperspy.drawing.mpl_hie module¶

class hyperspy.drawing.mpl_hie.MPL_HyperImage_Explorer¶

Bases: hyperspy.drawing.mpl_he.MPL_HyperExplorer

plot_signal(colorbar=True, scalebar=True, scalebar_color='white', axes_ticks=None, saturated_pixels=0, vmin=None, vmax=None, no_nans=False, centre_colormap='auto', **kwargs)¶

Plot image.

Parameters:

colorbar (bool, optional) – If true, a colorbar is plotted for non-RGB images. scalebar (bool, 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. saturated_pixels (scalar) – The percentage of pixels that are left out of the bounds. For example, the low and high bounds of a value of 1 are the 0.5% and 99.5% percentiles. It must be in the [0, 100] range. vmax (vmin,) – vmin and vmax are used to normalize luminance data. no_nans (bool, optional) – If True, set nans to zero for plotting. optional (**kwargs,) – Additional key word arguments passed to matplotlib.imshow()

hyperspy.drawing.mpl_hse module¶

class hyperspy.drawing.mpl_hse.MPL_HyperSignal1D_Explorer¶

Bases: hyperspy.drawing.mpl_he.MPL_HyperExplorer

Plots the current spectrum to the screen and a map with a cursor to explore the SI.

add_right_pointer()¶

auto_update_plot¶

key2switch_right_pointer(event)¶

plot_signal()¶

remove_right_pointer()¶

right_pointer_on¶

I’m the ‘x’ property.

hyperspy.drawing.signal module¶

hyperspy.drawing.signal1d module¶

class hyperspy.drawing.signal1d.Signal1DFigure(title='')¶

Bases: hyperspy.drawing.figure.BlittedFigure

add_line(line, ax='left')¶

add_marker(marker)¶

close()¶

create_axis()¶

create_figure()¶

create_right_axis()¶

plot()¶

update()¶

class hyperspy.drawing.signal1d.Signal1DLine¶

Bases: object

Line that can be added to Signal1DFigure.

type¶

{‘scatter’, ‘step’, ‘line’} – Select the line drawing style.

line_properties¶

dictionary – Accepts a dictionary of valid (i.e. recognized by mpl.plot) containing valid line properties. In addition it understands the keyword type that can take the following values: {‘scatter’, ‘step’, ‘line’}

set_line_properties()¶

Enables setting the line_properties attribute using keyword arguments.

Raises:	ValueError – If an invalid keyword value is passed to line_properties.

close()¶

color¶

line_properties

plot(data=1)¶

set_line_properties(**kwargs)

type

update(force_replot=False)¶

Update the current spectrum figure

hyperspy.drawing.utils module¶

class hyperspy.drawing.utils.ColorCycle¶

Bases: object

hyperspy.drawing.utils.animate_legend(figure='last')¶

Animate the legend of a figure.

A spectrum can be toggle on and off by clicking on the legended line.

Parameters:	figure ('last' | matplotlib.figure) – If ‘last’ pick the last figure

Note

Code inspired from legend_picking.py in the matplotlib gallery

hyperspy.drawing.utils.centre_colormap_values(vmin, vmax)¶

Calculate vmin and vmax to set the colormap midpoint to zero.

Parameters:	vmax (vmin,) – The range of data to display.
Returns:	cvmin, cvmax – The values to obtain a centre colormap.
Return type:	scalar

hyperspy.drawing.utils.contrast_stretching(data, saturated_pixels)¶

Calculate bounds that leaves out a given percentage of the data.

Parameters:	data (numpy array) – saturated_pixels (scalar) – The percentage of pixels that are left out of the bounds. For example, the low and high bounds of a value of 1 are the 0.5% and 99.5% percentiles. It must be in the [0, 100] range.
Returns:	vmin, vmax – The low and high bounds
Return type:	scalar
Raises:	ValueError if the value of saturated_pixels is out of the valid range.

hyperspy.drawing.utils.create_figure(window_title=None, _on_figure_window_close=None, **kwargs)¶

Create a matplotlib figure.

This function adds the possibility to execute another function when the figure is closed and to easily set the window title. Any keyword argument is passed to the plt.figure function

Parameters:	window_title (string) – _on_figure_window_close (function) –
Returns:	fig
Return type:	plt.figure

hyperspy.drawing.utils.on_figure_window_close(figure, function)¶

Connects a close figure signal to a given function.

Parameters:	figure (mpl figure instance) – function (function) –

hyperspy.drawing.utils.plot_RGB_map(im_list, normalization='single', dont_plot=False)¶

Plot 2 or 3 maps in RGB.

Parameters:	im_list (list of Signal2D instances) – normalization ({'single', 'global'}) – dont_plot (bool) –
Returns:	array
Return type:	RGB matrix

hyperspy.drawing.utils.plot_histograms(signal_list, bins='freedman', range_bins=None, color=None, line_style=None, legend='auto', fig=None, **kwargs)¶

Plot the histogram of every signal in the list in the same figure.

This function creates a histogram for each signal and plot the list with the utils.plot.plot_spectra function.

Parameters:

signal_list (iterable) – Ordered spectra list to plot. If style is “cascade” or “mosaic” the spectra can have different size and axes. bins (int or list or str, optional) – If bins is a string, then it must be one of: ‘knuth’ : use Knuth’s rule to determine bins ‘scotts’ : use Scott’s rule to determine bins ‘freedman’ : use the Freedman-diaconis rule to determine bins ‘blocks’ : use bayesian blocks for dynamic bin widths range_bins (tuple or None, optional.) – the minimum and maximum range for the histogram. If not specified, it will be (x.min(), x.max()) color (valid matplotlib color or a list of them or None, optional.) – Sets the color of the lines of the plots. If a list, if its length is less than the number of spectra to plot, the colors will be cycled. If If None, use default matplotlib color cycle. line_style (valid matplotlib line style or a list of them or None,) – optional. – The main line style are ‘-‘,’–’,’steps’,’-.’,’:’. If a list, if its length is less than the number of spectra to plot, line_style will be cycled. If If None, use continuous lines, eg: (‘-‘,’–’,’steps’,’-.’,’:’) legend (None or list of str or 'auto', optional.) – Display a legend. If ‘auto’, the title of each spectra (metadata.General.title) is used. legend_picking (bool, optional.) – If true, a spectrum can be toggle on and off by clicking on the legended line. fig (matplotlib figure or None, optional.) – If None, a default figure will be created. **kwargs – other keyword arguments (weight and density) are described in np.histogram().

Example

Histograms of two random chi-square distributions >>> img = hs.signals.Signal2D(np.random.chisquare(1,[10,10,100])) >>> img2 = hs.signals.Signal2D(np.random.chisquare(2,[10,10,100])) >>> hs.plot.plot_histograms([img,img2],legend=[‘hist1’,’hist2’])

Returns:	ax – An array is returned when style is “mosaic”.
Return type:	matplotlib axes or list of matplotlib axes

hyperspy.drawing.utils.plot_images(images, cmap=None, no_nans=False, per_row=3, label='auto', labelwrap=30, suptitle=None, suptitle_fontsize=18, colorbar='multi', centre_colormap='auto', saturated_pixels=0, scalebar=None, scalebar_color='white', axes_decor='all', padding=None, tight_layout=False, aspect='auto', min_asp=0.1, namefrac_thresh=0.4, fig=None, *args, **kwargs)¶

Plot multiple images as sub-images in one figure.

Parameters:	images (list) – images should be a list of Signals (Images) to plot If any signal is not an image, a ValueError will be raised multi-dimensional images will have each plane plotted as a separate image cmap (matplotlib colormap, optional) – The colormap used for the images, by default read from pyplot no_nans (bool, optional) – If True, set nans to zero for plotting. per_row (int, optional) – The number of plots in each row label (None, str, or list of str, optional) – Control the title labeling of the plotted images. If None, no titles will be shown. If ‘auto’ (default), function will try to determine suitable titles using Image titles, falling back to the ‘titles’ option if no good short titles are detected. Works best if all images to be plotted have the same beginning to their titles. If ‘titles’, the title from each image’s metadata.General.title will be used. If any other single str, images will be labeled in sequence using that str as a prefix. If a list of str, the list elements will be used to determine the labels (repeated, if necessary). labelwrap (int, optional) – integer specifying the number of characters that will be used on one line If the function returns an unexpected blank figure, lower this value to reduce overlap of the labels between each figure suptitle (str, optional) – Title to use at the top of the figure. If called with label=’auto’, this parameter will override the automatically determined title. suptitle_fontsize (int, optional) – Font size to use for super title at top of figure colorbar ({'multi', None, 'single'}) – Controls the type of colorbars that are plotted. If None, no colorbar is plotted. If ‘multi’ (default), individual colorbars are plotted for each (non-RGB) image If ‘single’, all (non-RGB) images are plotted on the same scale, and one colorbar is shown for all centre_colormap ({"auto", True, False}) – 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. saturated_pixels (scalar) – The percentage of pixels that are left out of the bounds. For example, the low and high bounds of a value of 1 are the 0.5% and 99.5% percentiles. It must be in the [0, 100] range. scalebar ({None, 'all', list of ints}, optional) – If None (or False), no scalebars will be added to the images. If ‘all’, scalebars will be added to all images. If list of ints, scalebars will be added to each image specified. scalebar_color (str, optional) – A valid MPL color string; will be used as the scalebar color axes_decor ({'all', 'ticks', 'off', None}, optional) – Controls how the axes are displayed on each image; default is ‘all’ If ‘all’, both ticks and axis labels will be shown If ‘ticks’, no axis labels will be shown, but ticks/labels will If ‘off’, all decorations and frame will be disabled If None, no axis decorations will be shown, but ticks/frame will padding (None or dict, optional) – This parameter controls the spacing between images. If None, default options will be used Otherwise, supply a dictionary with the spacing options as keywords and desired values as values Values should be supplied as used in pyplot.subplots_adjust(), and can be: ‘left’, ‘bottom’, ‘right’, ‘top’, ‘wspace’ (width), and ‘hspace’ (height) tight_layout (bool, optional) – If true, hyperspy will attempt to improve image placement in figure using matplotlib’s tight_layout If false, repositioning images inside the figure will be left as an exercise for the user. aspect (str or numeric, optional) – If ‘auto’, aspect ratio is auto determined, subject to min_asp. If ‘square’, image will be forced onto square display. If ‘equal’, aspect ratio of 1 will be enforced. If float (or int/long), given value will be used. min_asp (float, optional) – Minimum aspect ratio to be used when plotting images namefrac_thresh (float, optional) – Threshold to use for auto-labeling. This parameter controls how much of the titles must be the same for the auto-shortening of labels to activate. Can vary from 0 to 1. Smaller values encourage shortening of titles by auto-labeling, while larger values will require more overlap in titles before activing the auto-label code. fig (mpl figure, optional) – If set, the images will be plotted to an existing MPL figure **kwargs, optional (*args,) – Additional arguments passed to matplotlib.imshow()
Returns:	axes_list – a list of subplot axes that hold the images
Return type:	list

See also

plot_spectra()

Plotting of multiple spectra

plot_signals()

Plotting of multiple signals

plot_histograms()

Compare signal histograms

Notes

interpolation is a useful parameter to provide as a keyword argument to control how the space between pixels is interpolated. A value of 'nearest' will cause no interpolation between pixels.

tight_layout is known to be quite brittle, so an option is provided to disable it. Turn this option off if output is not as expected, or try adjusting label, labelwrap, or per_row

hyperspy.drawing.utils.plot_signals(signal_list, sync=True, navigator='auto', navigator_list=None)¶

Plot several signals at the same time.

Parameters:

signal_list (list of Signal instances) – If sync is set to True, the signals must have the same navigation shape, but not necessarily the same signal shape. sync (True or False, default "True") – If True: the signals will share navigation, all the signals must have the same navigation shape for this to work, but not necessarily the same signal shape. navigator ({"auto", None, "spectrum", "slider", Signal}, default "auto") – See signal.plot docstring for full description navigator_list ({List of navigator arguments, None}, default None) – Set different navigator options for the signals. Must use valid navigator arguments: “auto”, None, “spectrum”, “slider”, or a hyperspy Signal. The list must have the same size as signal_list. If None, the argument specified in navigator will be used.

Example

>>> s_cl = hs.load("coreloss.dm3")
>>> s_ll = hs.load("lowloss.dm3")
>>> hs.plot.plot_signals([s_cl, s_ll])

Specifying the navigator:

>>> s_cl = hs.load("coreloss.dm3")
>>> s_ll = hs.load("lowloss.dm3")
>>> hs.plot.plot_signals([s_cl, s_ll], navigator="slider")

Specifying the navigator for each signal:

>>> s_cl = hs.load("coreloss.dm3")
>>> s_ll = hs.load("lowloss.dm3")
>>> s_edx = hs.load("edx.dm3")
>>> s_adf = hs.load("adf.dm3")
>>> hs.plot.plot_signals(
        [s_cl, s_ll, s_edx], navigator_list=["slider",None,s_adf])

hyperspy.drawing.utils.plot_spectra(spectra, style='default', color=None, line_style=None, padding=1.0, legend=None, legend_picking=True, legend_loc='upper right', fig=None, ax=None, **kwargs)¶

Plot several spectra in the same figure.

Extra keyword arguments are passed to matplotlib.figure.

Parameters:

spectra (iterable object) – Ordered spectra list to plot. If style is “cascade” or “mosaic” the spectra can have different size and axes. style ({'default', 'overlap','cascade', 'mosaic', 'heatmap'}) – The style of the plot. The default is “overlap” and can be customized in preferences. color (matplotlib color or a list of them or None) – Sets the color of the lines of the plots (no action on ‘heatmap’). If a list, if its length is less than the number of spectra to plot, the colors will be cycled. If None, use default matplotlib color cycle. line_style (matplotlib line style or a list of them or None) – Sets the line style of the plots (no action on ‘heatmap’). The main line style are ‘-‘,’–’,’steps’,’-.’,’:’. If a list, if its length is less than the number of spectra to plot, line_style will be cycled. If If None, use continuous lines, eg: (‘-‘,’–’,’steps’,’-.’,’:’) padding (float, optional, default 0.1) – Option for “cascade”. 1 guarantees that there is not overlapping. However, in many cases a value between 0 and 1 can produce a tighter plot without overlapping. Negative values have the same effect but reverse the order of the spectra without reversing the order of the colors. legend (None or list of str or 'auto') – If list of string, legend for “cascade” or title for “mosaic” is displayed. If ‘auto’, the title of each spectra (metadata.General.title) is used. legend_picking (bool) – If true, a spectrum can be toggle on and off by clicking on the legended line. legend_loc (str or int) – This parameter controls where the legend is placed on the figure; see the pyplot.legend docstring for valid values fig (matplotlib figure or None) – If None, a default figure will be created. Specifying fig will not work for the ‘heatmap’ style. ax (matplotlib ax (subplot) or None) – If None, a default ax will be created. Will not work for ‘mosaic’ or ‘heatmap’ style. **kwargs – remaining keyword arguments are passed to matplotlib.figure() or matplotlib.subplots(). Has no effect on ‘heatmap’ style.

Example

>>> s = hs.load("some_spectra")
>>> hs.plot.plot_spectra(s, style='cascade', color='red', padding=0.5)

To save the plot as a png-file

>>> hs.plot.plot_spectra(s).figure.savefig("test.png")

Returns:	ax – An array is returned when style is “mosaic”.
Return type:	matplotlib axes or list of matplotlib axes

hyperspy.drawing.utils.subplot_parameters(fig)¶

Returns a list of the subplot parameters of a mpl figure.

Parameters:	fig (mpl figure) –
Returns:	tuple
Return type:	(left, bottom, right, top, wspace, hspace)

hyperspy.drawing.widgets module¶

class hyperspy.drawing.widgets.DraggableHorizontalLine(axes_manager)¶

Bases: hyperspy.drawing.widgets.DraggablePatch

class hyperspy.drawing.widgets.DraggableLabel(axes_manager)¶

Bases: hyperspy.drawing.widgets.DraggablePatch

class hyperspy.drawing.widgets.DraggablePatch(axes_manager=None)¶

Bases: object

button_release(event)¶

whenever a mouse button is released

close(window=None)¶

connect(ax)¶

disconnect(ax)¶

draw_patch(*args)¶

is_on()¶

onpick(event)¶

set_mpl_ax(ax)¶

set_on(value)¶

class hyperspy.drawing.widgets.DraggableSquare(axes_manager)¶

Bases: hyperspy.drawing.widgets.ResizebleDraggablePatch

calculate_position()¶

calculate_size()¶

class hyperspy.drawing.widgets.DraggableVerticalLine(axes_manager)¶

Bases: hyperspy.drawing.widgets.DraggablePatch

class hyperspy.drawing.widgets.ModifiableSpanSelector(ax, left_limit=None, right_limit=None, **kwargs)¶

Bases: matplotlib.widgets.SpanSelector

dummy(*args, **kwargs)¶

mm_on_press(event)¶

mm_on_release(event)¶

move_left(event)¶

move_rect(event)¶

move_right(event)¶

release(event)¶

When the button is realeased, the span stays in the screen and the iteractivity machinery passes to modify mode

turn_off()¶

update_range()¶

class hyperspy.drawing.widgets.ResizebleDraggablePatch(axes_manager)¶

Bases: hyperspy.drawing.widgets.DraggablePatch

connect(ax)¶

decrease_size()¶

increase_size()¶

on_key_press(event)¶

set_size(size)¶

class hyperspy.drawing.widgets.Scale_Bar(ax, units, pixel_size=None, color='white', position=None, max_size_ratio=0.25, lw=2, length=None, animated=False)¶

Bases: object

calculate_line_position(pad=0.05)¶

calculate_size(max_size_ratio=0.25)¶

calculate_text_position(pad=0.01)¶

get_units_string()¶

plot_scale(line_width=1)¶

remove()¶

set_color(c)¶

set_length(length)¶

set_position(x, y)¶

set_tex_bold()¶

hyperspy.drawing.widgets.in_interval(number, interval)¶

Module contents¶