hyperspy.docstrings.plot module

Common docstring snippets for plot.

hyperspy.docstrings.plot.BASE_PLOT_DOCSTRING = 'Plot the signal at the current coordinates.\n\n For multidimensional datasets an optional figure,\n the "navigator", with a cursor to navigate that data is\n raised. In any case it is possible to navigate the data using\n the sliders. Currently only signals with signal_dimension equal to\n 0, 1 and 2 can be plotted.\n\n Parameters\n ----------\n navigator : {"auto", None, "slider", "spectrum", Signal}\n If "auto", if navigation_dimension > 0, a navigator is\n provided to explore the data.\n If navigation_dimension is 1 and the signal is an image\n the navigator is a spectrum obtained by integrating\n over the signal axes (the image).\n If navigation_dimension is 1 and the signal is a spectrum\n the navigator is an image obtained by stacking horizontally\n all the spectra in the dataset.\n If navigation_dimension is > 1, the navigator is an image\n obtained by integrating the data over the signal axes.\n Additionaly, if navigation_dimension > 2 a window\n with one slider per axis is raised to navigate the data.\n For example,\n if the dataset consists of 3 navigation axes X, Y, Z and one\n signal axis, E, the default navigator will be an image\n obtained by integrating the data over E at the current Z\n index and a window with sliders for the X, Y and Z axes\n will be raised. Notice that changing the Z-axis index\n changes the navigator in this case.\n If "slider" and the navigation dimension > 0 a window\n with one slider per axis is raised to navigate the data.\n If "spectrum" and navigation_dimension > 0 the navigator\n is always a spectrum obtained by integrating the data\n over all other axes.\n If None, no navigator will be provided.\n Alternatively a Signal instance can be provided. The signal\n dimension must be 1 (for a spectrum navigator) or 2 (for a\n image navigator) and navigation_shape must be 0 (for a static\n navigator) or navigation_shape + signal_shape must be equal\n to the navigator_shape of the current object (for a dynamic\n navigator).\n If the signal dtype is RGB or RGBA this parameters has no\n effect and is always "slider".\n axes_manager : {None, axes_manager}\n If None `axes_manager` is used.\n plot_markers : bool, default True\n Plot markers added using s.add_marker(marker, permanent=True).\n Note, a large number of markers might lead to very slow plotting.\n norm : {\'auto\', \'linear\', \'log\', mpl `Normalize` instance or subclass},\n default is \'auto\'.\n Plot the intensity scale on a linear or logarithmic scale. If\n \'auto\', plot the intensity on a linear scale except when\n `power_spectrum` is True, which can be used only for compatible\n signal. For Signal2D, a matplotlib `Normalize` subclass or\n instance can be provided.\n '

hyperspy.docstrings.plot.BASE_PLOT_DOCSTRING_PARAMETERS = 'navigator : {"auto", None, "slider", "spectrum", Signal}\n If "auto", if navigation_dimension > 0, a navigator is\n provided to explore the data.\n If navigation_dimension is 1 and the signal is an image\n the navigator is a spectrum obtained by integrating\n over the signal axes (the image).\n If navigation_dimension is 1 and the signal is a spectrum\n the navigator is an image obtained by stacking horizontally\n all the spectra in the dataset.\n If navigation_dimension is > 1, the navigator is an image\n obtained by integrating the data over the signal axes.\n Additionaly, if navigation_dimension > 2 a window\n with one slider per axis is raised to navigate the data.\n For example,\n if the dataset consists of 3 navigation axes X, Y, Z and one\n signal axis, E, the default navigator will be an image\n obtained by integrating the data over E at the current Z\n index and a window with sliders for the X, Y and Z axes\n will be raised. Notice that changing the Z-axis index\n changes the navigator in this case.\n If "slider" and the navigation dimension > 0 a window\n with one slider per axis is raised to navigate the data.\n If "spectrum" and navigation_dimension > 0 the navigator\n is always a spectrum obtained by integrating the data\n over all other axes.\n If None, no navigator will be provided.\n Alternatively a Signal instance can be provided. The signal\n dimension must be 1 (for a spectrum navigator) or 2 (for a\n image navigator) and navigation_shape must be 0 (for a static\n navigator) or navigation_shape + signal_shape must be equal\n to the navigator_shape of the current object (for a dynamic\n navigator).\n If the signal dtype is RGB or RGBA this parameters has no\n effect and is always "slider".\n axes_manager : {None, axes_manager}\n If None `axes_manager` is used.\n plot_markers : bool, default True\n Plot markers added using s.add_marker(marker, permanent=True).\n Note, a large number of markers might lead to very slow plotting.\n norm : {\'auto\', \'linear\', \'log\', mpl `Normalize` instance or subclass},\n default is \'auto\'.\n Plot the intensity scale on a linear or logarithmic scale. If\n \'auto\', plot the intensity on a linear scale except when\n `power_spectrum` is True, which can be used only for compatible\n signal. For Signal2D, a matplotlib `Normalize` subclass or\n instance can be provided.\n '

hyperspy.docstrings.plot.COMPLEX_DOCSTRING = "power_spectrum : bool, default is False.\n If True, plot the power spectrum instead of the actual signal, if\n False, plot the real and imaginary parts of the complex signal.\n representation : {'cartesian' or 'polar'}\n Determines if the real and imaginary part of the complex data is plotted ('cartesian',\n default), or if the amplitude and phase should be used ('polar').\n same_axes : bool, default True\n If True (default) plot the real and\n imaginary parts (or amplitude and phase) in the same figure if\n the signal is one-dimensional.\n fft_shift : bool, default False\n If True, shift the zero-frequency component.\n See `numpy.fft.fftshift` for more details.\n "

hyperspy.docstrings.plot.KWARGS_DOCSTRING = '**kwargs, optional\n Only for 2D signals: additional key word arguments for \n `matplotlib.pyplot.imshow`.'

hyperspy.docstrings.plot.PLOT2D_DOCSTRING = 'colorbar : bool, optional\n If true, a colorbar is plotted for non-RGB images.\n scalebar : bool, optional\n If True and the units and scale of the x and y axes are the same a\n scale bar is plotted.\n scalebar_color : str, optional\n A valid MPL color string; will be used as the scalebar color.\n axes_ticks : {None, bool}, optional\n If True, plot the axes ticks. If None axes_ticks are only\n plotted when the scale bar is not plotted. If False the axes ticks\n are never plotted.\n saturated_pixels: scalar\n The percentage of pixels that are left out of the bounds.\n For example, the low and high bounds of a value of 1 are the 0.5%\n and 99.5% percentiles. It must be in the [0, 100] range.\n vmin, vmax : scalar, optional\n `vmin` and `vmax` are used to normalize luminance data.\n no_nans : bool, optional\n If True, set nans to zero for plotting.\n centre_colormap : {"auto", True, False}\n If True the centre of the color scheme is set to zero. This is\n specially useful when using diverging color schemes. If "auto"\n (default), diverging color schemes are automatically centred.\n min_aspect : float\n Set the minimum aspect ratio of the image and the figure. To\n keep the image in the aspect limit the pixels are made\n rectangular.'