pytom3d

Submodules

pytom3d.core module

class pytom3d.core.Topography(name: str = 'unnamed')[source]

Bases: object

add_points(fxy: callable, std_noise=None) → None[source]

Adds a function-generated z-coordinate to the grid points.

Parameters

fxycallable: A function that takes x and y coordinates and returns z.
std_noisefloat or None, optional: Standard deviation of Gaussian noise to be added to z (default is None).

Returns

None

cardinality()[source]

Update the cardinality attribute based on the number of data points.

This method calculates and updates the ‘N’ attribute, representing the cardinality (number of data points) of the object.

Parameters

None

Returns

None

centroid() → None[source]: Update the centroid of the data.

Returns

None

cut(ax: str = None, lo: float = -inf, up: float = inf, tol=1e-08, out=False) → None[source]

Cut data points along a specified axis within a given range.

Parameters

axstr, optional: The axis to cut along (choose from ‘x’, ‘y’, ‘z’), by default None.
lofloat, optional: The lower bound for cutting, by default -np.inf.
upfloat, optional: The upper bound for cutting, by default np.inf.
tolfloat, optional: The tolerance for considering values close to bounds, by default 1e-8.
outbool, optional: If True, keep the points outside the specified range, by default False.

Returns

List[Tuple]: A list containing information about the cut event.

Raises

KeyError: If the specified axis is not valid.
ValueError: If the resulting cloud has no points after cutting.

edges() → None[source]: Update the minimum and maximum values along each dimension.

Returns

None

export(path_to_file: str, filename: str, extension: str = '.csv', delimiter: str = ',') → None[source]

Export the grid points to a file in CSV format.

Parameters

path_to_filestr: The path to the directory where the file will be saved.
filenamestr: The name of the file (without extension).
extensionstr, optional: The file extension (default is “.csv”).
delimiterstr, optional: The delimiter used in the CSV file (default is “,”).

Returns

None

filter_points(indices: ndarray) → List[Tuple][source]

Filter points by index.

Parameters

indicesnp.ndarray: The vector of indices of the points that are kept in the cloud.

Returns

List[Tuple]: A list containing information about the filtering event.

fit(regressor, **args: Dict) → None[source]

Fit a regressor to the data.

Parameters

regressorsklearn regressor class or callable: The regressor to be used for fitting. If a sklearn regressor class is provided, it is instantiated and fitted to the data. If a callable (e.g., a spline function) is provided, it is used directly for fitting.
**argsadditional keyword arguments: Additional arguments to be passed to the regressor constructor if using a sklearn regressor class.

Notes

This method first tries to fit the provided regressor to the data using the sklearn API. If an AttributeError occurs (indicating that the regressor doesn’t have a ‘fit’ method), it assumes that a callable (e.g., a spline function) is provided and uses it directly for fitting.

Returns

None

Examples

# Example 1: Fit a linear regression model model.fit(LinearRegression())

# Example 2: Fit a custom spline function model.fit(scipy.bisplrep, **args)

flip(v: ndarray = array([1., 1., 1.])) → List[Tuple][source]

Flip the data points along each axis.

Parameters

vnp.ndarray, optional: The scaling factors along each axis, by default np.array([1., 1., 1.]).

Returns

List[Tuple]: A list containing information about the flip event.

get_topography(x, y, z, unc: ndarray = None)[source]

Set the topography data for the object.

This method sets the topography data for the object by updating the ‘P’ attribute with the provided x, y, and z coordinates. It also updates the uncertainty information if provided. Additionally, it calculates and updates the cardinality, edges, and centroid attributes.

Parameters

xnp.ndarray: X-coordinates of the data points.
ynp.ndarray: Y-coordinates of the data points.
znp.ndarray: Z-coordinates of the data points.
uncnp.ndarray, optional: Uncertainty information associated with the data points, by default None.

Returns

None

history()[source]

Print the event history.

Prints each recorded event in the history list, displaying key-value pairs.

Returns

None

Notes

Each event is separated by a line of dashes for better readability.

make_grid(x_bounds: List[float], y_bounds: List[float], x_res: int = 10, y_res: int = 10) → None[source]

Initializes the grid within specified x and y bounds with given resolution.

Parameters

x_boundslist of float: The bounds for the x-axis [x_min, x_max].
y_boundslist of float: The bounds for the y-axis [y_min, y_max].
x_resint, optional: The resolution of the grid along the x-axis (default is 10).
y_resint, optional: The resolution of the grid along the y-axis (default is 10).

Notes

z-value is initialli set to zero.

Returns

None

pick_points(axis: int, loc: float, tol: float = 0.001) → Tuple[ndarray][source]

Pick points based on proximity to a specified location along a given axis.

Parameters

axisint: The axis along which to pick points. 0 for x-axis, 1 for y-axis.
locfloat: The location along the specified axis to which points are compared.
tolfloat, optional: The tolerance value for proximity. Default is 1e-3.

Returns

picked_locsarray_like: Array of locations of the picked points.
picked_coordsarray_like: Array of coordinates (along the other axis) of the picked points.
picked_valuesarray_like: Array of values of the picked points.

pick_scans(axis: int, loc: float, centre: float = None, lower: float = None, upper: float = None, tol: float = 0.001) → Tuple[source]

Pick scans based on proximity to specified locations and bounds.

Parameters

axisint: The axis along which to pick scans. 0 for x-axis, 1 for y-axis.
locfloat: The location along the specified axis to which points are compared.
centrefloat, optional: The central location for the bounding box. If provided, lower and upper must also be provided. Default is None.
lowerfloat, optional: The lower bound for the bounding box. Used in combination with centre and upper. Default is None.
upperfloat, optional: The upper bound for the bounding box. Used in combination with centre and lower. Default is None.
tolfloat, optional: The tolerance value for proximity. Default is 1e-3.

Returns

scanslist of Scan objects: List of Scan objects picked based on the specified conditions.

pred(X: ndarray) → ndarray[source]

Predict target values for input data.

Parameters

Xnp.ndarray: Input data for prediction.

Returns

np.ndarray: If using a sklearn regressor, returns a tuple containing the predicted values and their standard deviations. If using a callable (e.g., a spline function), returns an array of predicted values.

Notes

If an exception occurs during prediction, it assumes that a callable regressor is provided, and uses it directly for prediction.

read(file_path: str, reader: callable, **reader_args)[source]

Read data from file.

Parameters

file_pathstr: The path to the file.
readercallable: A callable pandas reader function to read data from the file.
**reader_args: Additional arguments to pass to the reader.

Returns

None

rotate(t_deg: ndarray = array([0., 0., 0.]), rot_mat: ndarray = None) → List[Tuple][source]

Rotate the data points about the origin.

Parameters

t_degnp.ndarray, optional: The rotation angles in degrees around x, y, and z axes, by default np.array([0., 0., 0.]).
rot_matnp.ndarray, optional: Custom rotation matrix. If provided, t_deg will be ignored, by default None.

Returns

List[Tuple]: A list containing information about the rotation event.

rotate_about_centre(c: ndarray = array([0., 0., 0.]), t_deg: ndarray = array([0., 0., 0.]), rot_mat: ndarray = None) → List[Tuple][source]

Rotate the data points about a specified center. Wraps translate and rotate.

Parameters

cnp.ndarray, optional: The center of rotation, by default np.array([0., 0., 0.]).
t_degnp.ndarray, optional: The rotation angles in degrees around x, y, and z axes, by default np.array([0., 0., 0.]).
rot_matnp.ndarray, optional: Custom rotation matrix. If provided, t_deg will be ignored, by default None.

Returns

List[Tuple]: A list containing information about the rotation event.

rotate_by_svd() → None[source]

Rotate the data points using Singular Value Decomposition (SVD).

Wraps translate and rotate via rotate_about_centre.

This method applies rotation to the data points based on the results obtained from Singular Value Decomposition (SVD). It uses the rotate_about_centre method to perform the rotation.

The rotation is achieved by specifying the rotation matrix as Vt obtained from the SVD, and the center of rotation (G) is set as the negative of the centroid of the data points.

Parameters

None

Returns

None

svd() → List[Tuple][source]

Perform Singular Value Decomposition (SVD) on the data points.

Returns

List[Tuple]: A list containing information about the SVD event.

Notes

The SVD decomposes the data matrix into three matrices U, S, and V, such that P = U * S * V^T.

translate(v: ndarray = array([0, 0, 0]), aux: bool = False) → List[Tuple][source]

Translate the data points by the given vector.

Parameters

vnp.ndarray, optional: The translation vector, by default np.array([0, 0, 0]).
auxbool, optional: If True, indicates an auxiliary translation, by default False.

Returns

List[Tuple]: A list containing information about the translation event.

pytom3d.util module

pytom3d.util.contour_data_wrapper(path: str, match: str, pop_first=True, take_first=False) → Tuple[ndarray][source]

Wrapper function for generating contour data.

Parameters

pathstr: Path to the directory containing data files.
matchstr: A string used to match the desired data files.
pop_firstbool, optional: If True, remove and discard the first element of the resulting list. Default is False.
take_firstbool, optional: If True, return only the first matching file. Overrides pop_first. Default is False.

Returns

Tuple[np.ndarray]: A tuple containing the x-coordinates, y-coordinates, mean value, and standard deviation.

pytom3d.util.export_regressor(regressor, folder: str = './', filename: str = 'my_regressor', extension: str = '.rg', excluded_keys: List[str] = [], forced_values: List[Any] = []) → None[source]

Export the attributes of a regressor to a dictionary for storage or inspection.

Parameters

regressorobject: The regressor object to export.
path_to_filestr, optional: The path to the file for exporting the regressor attributes. Default is “./”.
excluded_keyslist of str, optional: A list of attribute keys to be excluded from the exported dictionary.
forced_valueslist of any, optional: A list of values to be used for attributes specified in excluded_keys.

Returns

The dictionary containing the attributes of the regressor.

Raises

AssertionError: If the length of excluded_keys is not equal to the length of forced_values.

Notes

This function is provided for convenience to handle backward compatibility of scikit-learn.

pytom3d.util.gather_data(match: str, inp: List[int], out: int, path: str, *list_path: List[str]) → None[source]

Load data from multiple files, extract specified columns, and save to a csv file.

Parameters

matchstr: A regular expression pattern to match against the file paths.
inpList[int]: List of column indices to extract as input features.
outint: Index of the column to extract as the output feature.
pathstr: Path to the output Excel file.
*list_pathList[str]: Variable number of file paths containing the data.

Returns

None

pytom3d.util.get_coordinates(inp: List[int], *list_path: List[str]) → ndarray[source]

Load and return the specified columns as coordinates from the first file path provided.

Parameters

inpList[int]: List of column indices to extract as coordinates.
list_pathList[str]: Variable number of file paths containing the data. Only the first file path is used.

Returns

np.ndarray: An array containing the coordinates extracted from the specified columns of the first file.

pytom3d.util.import_regressor(folder: str = './', filename: str = 'my_regressor.rg', init_regressor=None) → dict[source]

Import a regressor’s attributes from a saved file and update an initialized regressor object.

Parameters

folderstr, optional: The path to the folder containing the saved regressor attributes file. Default is “./”.
filenamestr, optional: The name of the file containing the saved regressor attributes. Default is “my_regressor.rg”.
init_regressorobject: The initialized regressor object to be updated with the imported attributes.

Returns

dict: A dictionary containing the imported regressor attributes.

Notes

This function is provided for convenience to handle backward compatibility of scikit-learn.

pytom3d.util.list_files(folder: str = './', extension: str = '.gpr') → List[str][source]

List files in a folder.

Parameters

folderstr, optional: Path to the folder to search for files. Default is “./”.
extensionstr, optional: File extension to filter files. Default is “.gpr”.

Returns

List[str]: A list of file paths with the specified extension in the folder.

pytom3d.util.lite_dict(gpr_obj: Any)[source]

Load lite version of the regressor.

Parameters

gpr_objAny: Object of the Gaussian Process Regressor.

Returns

Dict[str, Any]: Dictionary containing selected parameters and training data.

Notes

This function loads a lite version of the Gaussian Process Regressor by extracting specific parameters and training data for testing purposes.

pytom3d.util.load(path: str = './')[source]

Load an object from a binary file using pickle.

Parameters

path: str, optional
The path of the file. Default is “./”.

Returns

Any: The loaded object.

pytom3d.util.predict_at_node(xx, yy, regressor)[source]

Predict the value at a specific node in a regression model.

Parameters

xxfloat: The x-coordinate of the node.
yyfloat: The y-coordinate of the node.
regressornumpy.ndarray: The regression model containing node information.

Returns

float: The predicted value at the specified node.

Raises

Exception: If there is not exactly one node matching the specified coordinates.

pytom3d.util.prediction_wrapper(regressor, x, y) → Tuple[ndarray][source]

Predict the target variable and its uncertainty for given x and y coordinates using a regressor.

Parameters

regressorRegressor: The trained regressor model.
xfloat: The x-coordinate for prediction.
yfloat: The y-coordinate for prediction.

Returns

tuple: A tuple containing the predicted value and its associated standard deviation (uncertainty).

pytom3d.util.printer(func: callable)[source]

A decorator for class methods that saves a figure if ‘save’ is True.

Borrowed from https://github.com/aletgn/b-fade/blob/master/src/bfade/util.py

This decorator wraps a method that generates a figure and a title, and it saves the figure to the specified location if ‘save’ is True.

Parameters

funccallable: The function to be decorated, which generates a figure and a title.

Returns

callable: The decorated function.

pytom3d.util.recursive_search(path: str, extension: str = '.dat', match: str = None, pop_first: bool = False, take_first: bool = False) → List[str][source]

Recursively search for files with a specified extension in a directory and its subdirectories, optionally filtering by a match string and returning a modified list based on flags.

Parameters

pathstr: The path to the directory to search in.
extensionstr, optional: The file extension to search for. Default is “.dat”.
matchstr, optional: A substring to match in the file names. Default is None.
pop_firstbool, optional: If True, remove and discard the first element of the resulting list. Default is False.
take_firstbool, optional: If True, return only the first matching file. Overrides pop_first. Default is False.

Returns

List[str]: A list of file paths matching the specified criteria.

pytom3d.util.save(obj, folder: str = './', filename: str = 'my_file', extension: str = '.bin') → None[source]

Save the given object to a binary file using pickle.

Parameters

obj: Any
The object to be saved.
folder: str, optional
The directory path where the file will be saved. Default is “./”.
filename: str, optional
The name of the file to be saved. Default is “my_file”.
extension: str, optional
The file extension. Default is “.bin”.

Returns

None

pytom3d.util.scan_data_wrapper(path: str, match: str, pop_first=True, take_first=False) → Tuple[ndarray][source]

Wrapper function for generating scan data.

Parameters

pathstr: Path to the directory containing data files.
matchstr: A string used to match the desired data files.
pop_firstbool, optional: If True, remove and discard the first element of the resulting list. Default is False.
take_firstbool, optional: If True, return only the first matching file. Overrides pop_first. Default is False.

Returns

Tuple[np.ndarray]: A tuple containing the x-coordinates, mean value, and standard deviation.

pytom3d.util.trials(regressor, mesh, n: int = 1, folder: str = './') → None[source]

Generate and save trial data using a Gaussian Process Regression model.

Parameters

regressor :: The regressor of the topography.
meshTopography: The topogrphy object containing mesh data points for prediction.
nint, optional: Number of trials to generate (default is 1).
folderstr, optional: The folder path to save the trial data files (default is “./”).

Returns

None

pytom3d.util.update(method: callable)[source]

Decorator to update edges, centroid, cardinality, and record history after executing a method.

Parameters

methodcallable: The method to be decorated.

Returns

callable: Decorated method.

Notes

This decorator assumes that the decorated method returns a list of tuples, where each tuple contains key-value pairs to be recorded in the event history.

pytom3d.viewer module

class pytom3d.viewer.PostViewer(**kwargs)[source]

Bases: object

basic_contour(top_cnt, bot_cnt, cbarlabeltop: str = 'Expected Value') → Tuple[source]

config(save: bool = False, folder: str = './', fmt: str = 'png', dpi: int = 300) → None[source]

Configure settings for saving plots.

Borrowed from https://github.com/aletgn/b-fade/

Parameters

savebool, optional: Flag indicating whether to save plots. The default is False.
folderstr, optional: Folder path where plots will be saved. The default is “./”.
fmtstr, optional: Format for saving plots. The default is “png”.
dpiint, optional: Dots per inch for saving plots. The default is 300.

Returns

None

config_canvas(cmap: str = 'RdYlBu_r', levels: int = 8, x_lim: List[float] = [-100, 100], y_lim_top: List[float] = [10, 20], y_lim_bot: List[float] = [-10, -20], y_lim_scan: str = [-140, 140], cbar_lim: List[float] = [-140, 140], loc: str = 'best', bbox_to_anchor: List[float] = None) → None[source]

Configure canvas for plotting.

Parameters

cmapstr, optional: Colormap to use for plotting. Default is “RdYlBu_r”.
levelsint, optional: Number of levels for the colorbar. Default is 8.
x_limList[float], optional: Limits for the x-axis. Default is [-100, 100].
y_lim_topList[float], optional: Limits for the top part of the y-axis. Default is [10, 20].
y_lim_botList[float], optional: Limits for the bottom part of the y-axis. Default is [-10, -20].
y_lim_scanList[float], optional: Limits for the y-axis for scan data. Default is [-140, 140].
cbar_limList[float], optional: Limits for the color bar. Default is [-140, 140].
locstr, optional: Location of the legend. Default is “best”.
bbox_to_anchorList[float], optional: Anchor point for the legend bounding box. Default is None.

Returns

None: This function does not return anything. It only sets instance attributes.

config_colourbar(top=None, bot=None) → None[source]

Configure color bar limits based on top and bottom bounds.

Parameters

topTuple: Tuple containing data for the top bounds. It should contain the mean and standard deviation.
botTuple: Tuple containing data for the bottom bounds. It should contain the mean and standard deviation.

Returns

None: This function does not return anything. It only sets the instance attributes for color bar limits.

config_scan_view(xlabel='$x$ [mm]', ylabel='$y$ [mm]', x_lim=[-20, 20], y_lim=[-70, 70], legend_config=None)[source]

contour(top_cnt, bot_cnt, cbarlabeltop: str = 'Expected Value', cbarlabelbot: str = 'Uncertainty') → Tuple[source]

Create contour plots for the provided data.

Parameters

top_cntTopography object: Data for the top contour plot.
bot_cntTopography object: Data for the bottom contour plot.
cbarlabeltopstr: Label of the top colour bar
cbarlabelbotstr: Label of the bot colour bar

Returns

figFigure: The matplotlib figure object.
namestr: The name associated with the plot.

contour_and_scan_2(top_cnt, bot_cnt, top_scan=None, bot_scan=None, top_err=None, bot_err=None) → None[source]

Plot contour and scan data.

Parameters

top_cntobject: Object containing data for the top contours.
bot_cntobject: Object containing data for the bottom contours.
top_scanobject, optional: Object containing data for the top scan. Default is None.
bot_scanobject, optional: Object containing data for the bottom scan. Default is None.
top_errobject, optional: Object containing data for the top scan. Default is None.
bot_errobject, optional: Object containing data for the bottom scan. Default is None.

Returns

None: This function does not return anything. It only displays the plot.

scan_compare(fills: List, bars: List, regular: List = None, strip: Dict = None) → Tuple[source]

Compare scans by plotting filled regions and error bars.

Parameters

fillslist of scans: List of data for the filled regions.
barslist of scans: List of data for the error bars.
regularlist of scans: List of data for without fills and bars. The default is None

Returns

figFigure: The matplotlib figure object.
namestr: The name associated with the plot.
stripDict: Dictionary containing the information to draw a strip over the plot.

Example

strip = {“xs”: [-10, 10], “label”: “FILL”, “color”: “grey”, “alpha”: 0.2,
“labelleft”: “LEFT”, “labelright”: “RIGHT”, “epsh”: 5, “epsv”: 10}

scan_view(swap: bool = False, *scan: List) → None[source]

Plot scan data.

Parameters

swapbool, optional: If True, swap x and y axes in the plot. Default is False.
*scanList: List of scan data to plot. Each scan data should be provided as a list-like object.

Returns

None

scan_view_and_bar(swap: bool = False, strip: Dict = None, *scan: List) → None[source]

Plot scan data with error bars.

Parameters

swapbool, optional: If True, swap x and y axes in the plot. Default is False.
scanList: List of scan data to plot. Each scan data should be provided as a list-like object.
stripDict: Dictionary containing the information to draw a strip over the plot.

Example

strip = {“xs”: [-10, 10], “label”: “FILL”, “color”: “grey”, “alpha”: 0.2,
“labelleft”: “LEFT”, “labelright”: “RIGHT”, “epsh”: 5, “epsv”: 10}

Returns

None

scan_view_and_fill(swap: bool = False, *scan: List) → None[source]

Plot scan data with filled error regions.

Parameters

swapbool, optional: If True, swap x and y axes in the plot. Default is False.
*scanList: List of scan data to plot. Each scan data should be provided as a list-like object.

Returns

None

class pytom3d.viewer.Viewer(name: str = 'unnamed')[source]

Bases: object

config(save: bool = False, folder: str = './', fmt: str = 'png', dpi: int = 300) → None[source]

Configure settings for saving plots.

Borrowed from https://github.com/aletgn/b-fade/

Parameters

savebool, optional: Flag indicating whether to save plots. The default is False.
folderstr, optional: Folder path where plots will be saved. The default is “./”.
fmtstr, optional: Format for saving plots. The default is “png”.
dpiint, optional: Dots per inch for saving plots. The default is 300.

Returns

None

config_3d(point_size: float = 0.3, cmap: str = 'RdYlBu_r', xlabel: str = '$x_g$ [mm]', ylabel: str = '$y_g$ [mm]', zlabel: str = '$u(x_g, y_g)$ [mm]', cbarlabel: str = 'Aux', x_lim: List = [-110, 110], y_lim: List = [-38, 38], z_lim: List = None, zticks: int = 10, zoom: float = 1) → None[source]

Configure the 3D plot parameters.

Parameters

point_sizefloat, optional: Size of the points in the plot (default is 0.3).
cmapstr, optional: Colormap for the plot (default is “RdYlBu_r”).
xlabelstr, optional: Label for the x-axis (default is r’$x_g$ [mm]’).
ylabelstr, optional: Label for the y-axis (default is r’$y_g$ [mm]’).
zlabelstr, optional: Label for the z-axis (default is r’$u(x_g, y_g)$ [mm]’).
cbarlabelstr, optional: Label for the colourbar (default is ‘Aux’).
x_limlist of int, optional: Limits for the x-axis (default is [-110, 110]).
y_limlist of int, optional: Limits for the y-axis (default is [-38, 38]).
z_limlist of int or None, optional: Limits for the z-axis (default is None).
zticksint, optional: Number of ticks on the z-axis (default is 10).
zoomfloat, optional: Zoom level for the plot (default is 1).

Returns

None

contour(topography)[source]

scatter3D(*data: List[Topography]) → None[source]

Generate a 3D scatter plot for the given Topography data.

Parameters

dataList[Topography]: A list of Topography objects for which a 3D scatter plot will be generated.
x_limList[float], optional: Limits for the x-axis. Default is None.
y_limList[float], optional: Limits for the y-axis. Default is None.
z_limList[float], optional: Limits for the z-axis. Default is None.

Returns

None

scatter3DRegression(regression: Topography, reference: Topography = None) → None[source]

scatter3DRegressorUnc(*data: List)[source]

Create a 3D scatter plot of the regressor uncertainties.

Parameters

dataList: Set of Topographies.

Returns

matplotlib.figure.Figure: The created figure.
str: The name of the plot.

set_limits(x: List[float] = None, y: List[float] = None, z: List[float] = None) → None[source]

Set the limits for the x, y, and z axes.

Parameters

xList[float], optional: The limits for the x-axis.
yList[float], optional: The limits for the y-axis.
zList[float], optional: The limits for the z-axis.

Returns

None

views2D(*data: List[Topography]) → None[source]

Generate 2D scatter plots for the XY, XZ, and YZ planes of multiple Topography objects.

Parameters

dataList[Topography]: A list of Topography objects for which 2D scatter plots will be generated.

Returns

None

pytom3d.viewer.cbar_bounds(v: ndarray, w: ndarray) → Tuple[float][source]

Calculate the lower and upper bounds for color bar.

Parameters

vnp.ndarray: First array for comparison.
wnp.ndarray: Second array for comparison.

Returns

Tuple[float]: A tuple containing the lower and upper bounds for the color bar.

pytom3d.viewer.cfg_matplotlib(font_size: int = 12, font_family: str = 'sans-serif', use_latex: bool = False, interactive: bool = False) → None[source]

Set Matplotlib RC parameters for font size, font family, and LaTeX usage.

Borrowed from https://github.com/aletgn/b-fade/blob/master/src/bfade/util.py

Parameters

font_sizeint, optional: Font size. The default is 12.
font_familystr, optional: Font family. The default is ‘sans-serif’.
use_latexbool, optional: Enable LaTeX text rendering. The default is False.
interactive: bool, optional: Whether to keep matplotlib windows open.

Returns

None

pytom3d.viewer.discrete_colorbar(cmap: str, lower_bound: float, upper_bound: float, levels: int) → Tuple[_ScalarMappable, BoundaryNorm][source]

Create a discrete colorbar with custom colormap and bounds.

Parameters

cmapstr: Name of the colormap to use.
lower_boundfloat: Lower bound for the colorbar.
upper_boundfloat: Upper bound for the colorbar.
levelsint: Number of discrete levels for the colorbar.

Returns

Tuple[plt.ScalarMappable, BoundaryNorm]: A tuple containing the ScalarMappable object for mapping scalar data to colors, and the BoundaryNorm object for normalizing scalar data to the colormap’s range.