topostats.plottingfuncs#

Plotting data.

Attributes#

Classes#

Colormap

Class for setting the Colormap.

Images

Plots image arrays.

Functions#

add_pixel_to_nm_to_plotting_config(→ dict)

Add the pixel to nanometre scaling factor to plotting configs.

dilate_binary_image(→ numpy.typing.NDArray)

Dilate a supplied binary image a given number of times.

load_mplstyle(→ None)

Load the Matplotlibrc parameter file.

add_bounding_boxes_to_plot(→ tuple)

Add the bounding boxes to a plot.

set_n_ticks(→ None)

Set the number of ticks along the y and x axes and lets matplotlib assign the values.

Module Contents#

topostats.plottingfuncs.LOGGER_NAME = 'topostats'#
class topostats.plottingfuncs.Colormap(name: str = 'nanoscope')[source]#

Class for setting the Colormap.

Parameters:

name (str) – Name of colormap to use.

__str__() str[source]#

Return string representation of object.

Returns:

String detailing the colormap.

Return type:

str

set_cmap(name: str) None[source]#

Set the ColorMap.

Parameters:

name (str) – Name of the colormap to return.

get_cmap() matplotlib.cm[source]#

Return the matplotlib.cm colormap object.

Returns:

Matplotlib Color map object.

Return type:

matplotlib.cm

static nanoscope() matplotlib.colors.LinearSegmentedColormap[source]#

Matplotlib compatible colormap that replicates the Bruker Nanoscope colorscale.

The colormap is implemented in Gwyddion’s GwyGradient via ‘Nanoscope.txt’.

Returns:

MatplotLib LinearSegmentedColourmap that replicates Bruker Nanoscope colorscale.

Return type:

LinearSegmentedColormap

static gwyddion() matplotlib.colors.LinearSegmentedColormap[source]#

Set RGBA colour map for the Gwyddion.net colour gradient.

Returns:

The ‘gwyddion’ colormap.

Return type:

LinearSegmentedColormap

static blu() matplotlib.colors.ListedColormap[source]#

Set RGBA colour map of just the colour blue.

Returns:

The ‘blu’ colormap.

Return type:

ListedColormap

topostats.plottingfuncs.LOGGER#
topostats.plottingfuncs.add_pixel_to_nm_to_plotting_config(plotting_config: dict, pixel_to_nm_scaling: float) dict[source]#

Add the pixel to nanometre scaling factor to plotting configs.

Ensures plots are in nanometres and not pixels.

Parameters:
  • plotting_config (dict) – TopoStats plotting configuration dictionary.

  • pixel_to_nm_scaling (float) – Pixel to nanometre scaling factor for the image.

Returns:

Updated plotting config with the pixel to nanometre scaling factor applied to all the image configurations.

Return type:

dict

topostats.plottingfuncs.dilate_binary_image(binary_image: numpy.typing.NDArray, dilation_iterations: int) numpy.typing.NDArray[source]#

Dilate a supplied binary image a given number of times.

Parameters:
  • binary_image (npt.NDArray) – Binary image to be dilated.

  • dilation_iterations (int) – Number of dilation iterations to be performed.

Returns:

Dilated binary image.

Return type:

npt.NDArray

topostats.plottingfuncs.load_mplstyle(style: str | pathlib.Path) None[source]#

Load the Matplotlibrc parameter file.

Parameters:

style (str | Path) – Path to a Matplotlib Style file.

class topostats.plottingfuncs.Images(data: numpy.array, output_dir: str | pathlib.Path, filename: str, style: str | pathlib.Path = None, pixel_to_nm_scaling: float = 1.0, masked_array: numpy.array = None, title: str = None, image_type: str = 'non-binary', image_set: str = 'core', core_set: bool = False, pixel_interpolation: str | None = None, cmap: str | None = None, mask_cmap: str = 'jet_r', region_properties: dict = None, zrange: list = None, colorbar: bool = True, axes: bool = True, num_ticks: tuple[int | None] = (None, None), save: bool = True, savefig_format: str | None = None, histogram_log_axis: bool = True, histogram_bins: int | None = None, savefig_dpi: str | float | None = None)[source]#

Plots image arrays.

Parameters:
  • data (np.array) – Numpy array to plot.

  • output_dir (Union[str, Path]) – Output directory to save the file to.

  • filename (Union[str, Path]) – Filename to save image as.

  • style (dict) – Filename of matploglibrc Params.

  • pixel_to_nm_scaling (float) – The scaling factor showing the real length of 1 pixel, in nm.

  • masked_array (npt.NDArray) – Optional mask array to overlay onto an image.

  • title (str) – Title for plot.

  • image_type (str) – The image data type - binary or non-binary.

  • image_set (str) – The set of images to process - core or all.

  • core_set (bool) – Flag to identify image as part of the core image set or not.

  • pixel_interpolation (str | None) – Interpolation to use (default: None).

  • cmap (str) – Colour map to use (default ‘nanoscope’, ‘afmhot’ also available).

  • mask_cmap (str) – Colour map to use for the secondary (masked) data (default ‘jet_r’, ‘blu’ proivides more contrast).

  • region_properties (dict) – Dictionary of region properties, adds bounding boxes if specified.

  • zrange (list) – Lower and upper bound to clip core images to.

  • colorbar (bool) – Optionally add a colorbar to plots, default is False.

  • axes (bool) – Optionally add/remove axes from the image.

  • num_ticks (tuple[int | None]) – The number of x and y ticks to display on the image.

  • save (bool) – Whether to save the image.

  • savefig_format (str) – Format to save the image as.

  • histogram_log_axis (bool) – Optionally use a logarithmic y axis for the histogram plots.

  • histogram_bins (int) – Number of bins for histograms to use.

  • savefig_dpi (str | float | None) – The resolution of the saved plot (default ‘figure’).

plot_histogram_and_save() tuple | None[source]#

Plot and save a histogram of the height map.

Returns:

Matplotlib.pyplot figure object and Matplotlib.pyplot axes object.

Return type:

tuple | None

plot_and_save()[source]#

Plot and save the image.

Returns:

Matplotlib.pyplot figure object and Matplotlib.pyplot axes object.

Return type:

tuple

save_figure()[source]#

Save figures as plt.savefig objects.

Returns:

Matplotlib.pyplot figure object and Matplotlib.pyplot axes object.

Return type:

tuple

topostats.plottingfuncs.add_bounding_boxes_to_plot(fig, ax, shape: tuple, region_properties: list, pixel_to_nm_scaling: float) tuple[source]#

Add the bounding boxes to a plot.

Parameters:
  • fig (plt.figure.Figure) – Matplotlib.pyplot figure object.

  • ax (plt.axes._subplots.AxesSubplot) – Matplotlib.pyplot axes object.

  • shape (tuple) – Tuple of the image-to-be-plot’s shape.

  • region_properties (list) – Region properties to add bounding boxes from.

  • pixel_to_nm_scaling (float) – The scaling factor from px to nm.

Returns:

Matplotlib.pyplot figure object and Matplotlib.pyplot axes object.

Return type:

tuple

topostats.plottingfuncs.set_n_ticks(ax: matplotlib.pyplot.Axes.axes, n_xy: list[int | None, int | None]) None[source]#

Set the number of ticks along the y and x axes and lets matplotlib assign the values.

Parameters:
  • ax (plt.Axes.axes) – The axes to add ticks to.

  • n_xy (list[int, int]) – The number of ticks.

Returns:

The axes with the new ticks.

Return type:

plt.Axes.axes