mtpy.imaging.mtplot_tools package

Submodules

mtpy.imaging.mtplot_tools.arrows module

Created on Sun Sep 25 15:16:31 2022

@author: jpeacock

class mtpy.imaging.mtplot_tools.arrows.MTArrows(**kwargs)[source]

Bases: object

Helper class to configure arrow properties for plotting induction vectors.

This class manages arrow styling parameters for real and imaginary components of induction vectors in magnetotelluric plots.

Parameters:

**kwargs (dict, optional) – Keyword arguments to set arrow properties. Valid keys correspond to the class attributes listed below.

arrow_size

Multiplier to scale the arrow size, by default 2.5

Type:

float

arrow_head_length

Length of the arrow head, by default 0.15 * arrow_size

Type:

float

arrow_head_width

Width of the arrow head, by default 0.1 * arrow_size

Type:

float

arrow_lw

Line width of the arrow, by default 0.5 * arrow_size

Type:

float

arrow_threshold

Threshold value above which arrows will not be plotted. This helps filter out poor quality data. Applied before scaling by arrow_size, by default 2

Type:

float

arrow_color_imag

Color for imaginary component arrows, by default ‘c’ (cyan)

Type:

str

arrow_color_real

Color for real component arrows, by default ‘k’ (black)

Type:

str

arrow_direction

Arrow direction convention: - 0: arrows point away from conductor - 1: arrows point toward conductor (Parkinson convention) By default 1

Type:

int

Notes

The data inherently points away from conductors, so arrow_direction=1 flips the arrows to follow the Parkinson convention.

mtpy.imaging.mtplot_tools.base module

Base classes for plotting classes

author:

jpeacock

class mtpy.imaging.mtplot_tools.base.PlotBase(**kwargs)[source]

Bases: PlotSettings

Base class for plotting objects.

Provides core plotting functionality including figure management, saving, updating, and redrawing plots.

Parameters:

**kwargs (dict) – Keyword arguments passed to PlotSettings parent class

logger

Logger instance for the class

Type:

loguru.Logger

plot() → None[source]

Create the plot.

This method should be overridden by subclasses to implement specific plotting functionality.

redraw_plot() → None[source]

Recreate the plot after updating attributes.

Closes the current figure and calls plot() to create a new one with updated attributes.

Examples

>>> # Change the color and marker of the xy components
>>> p1.xy_color = (0.5, 0.5, 0.9)
>>> p1.xy_marker = '*'
>>> p1.redraw_plot()
save_plot(save_fn: str | Path, file_format: str = 'pdf', orientation: str = 'portrait', fig_dpi: int | None = None, close_plot: bool = True) → None[source]

Save the figure to a file.

Parameters:

  • save_fn (str | Path) –

    Full path to save figure to. Can be: - Directory path: file will be saved as save_fn/basename.file_format - Full path: file will be saved to the given path, format inferred

    from extension

  • file_format (str, optional) – File format for saved figure (pdf, eps, jpg, png, svg), by default ‘pdf’

  • orientation (str, optional) – Page orientation (‘landscape’ or ‘portrait’), by default ‘portrait’

  • fig_dpi (int | None, optional) – Resolution in dots-per-inch. If None, uses the figure’s dpi, by default None

  • close_plot (bool, optional) – Whether to close the plot after saving, by default True

Examples

>>> # Save plot as jpg
>>> p1.save_plot(r'/home/MT/figures', file_format='jpg')
update_plot() → None[source]

Update the plot after changing figure or axes properties.

Uses matplotlib’s canvas draw method to refresh the display after modifying figure or axes attributes.

Examples

>>> [ax.grid(True, which='major') for ax in [p1.axr, p1.axp]]
>>> p1.update_plot()
class mtpy.imaging.mtplot_tools.base.PlotBaseMaps(**kwargs)[source]

Bases: PlotBase

Base object for plot classes that use map views.

Includes methods for interpolation of data onto map grids.

Parameters:

**kwargs (dict) – Keyword arguments passed to PlotBase parent class and for setting interpolation parameters

cell_size

Size of grid cells for interpolation, by default 0.002

Type:

float

n_padding_cells

Number of padding cells around data extent, by default 10

Type:

int

interpolation_method

Interpolation method (‘delaunay’, ‘linear’, ‘nearest’, ‘cubic’), by default ‘delaunay’

Type:

str

interpolation_power

Power parameter for inverse distance weighting, by default 5

Type:

int

nearest_neighbors

Number of nearest neighbors to use in interpolation, by default 7

Type:

int

add_raster(ax, raster_fn: str | Path, add_colorbar: bool = True, **kwargs)[source]

Add a raster image to a matplotlib axis.

Parameters:

  • ax (matplotlib.axes.Axes) – Matplotlib axis to add raster to

  • raster_fn (str | Path) – Path to raster file (readable by rasterio)

  • add_colorbar (bool, optional) – Whether to add a colorbar, by default True

  • **kwargs (dict) – Additional keyword arguments passed to rasterio plotting

Returns:

The raster plot object

Return type:

matplotlib image or collection

static get_interp1d_functions_t(tf, interp_type: str = 'slinear') → dict | None[source]

Create 1D interpolation functions for tipper components.

Parameters:

  • tf (MT or Transfer Function object) – Transfer function object containing tipper data

  • interp_type (str, optional) – Type of interpolation (‘linear’, ‘slinear’, ‘cubic’), by default ‘slinear’

Returns:

Dictionary containing interpolation functions for tipper components (tzx, tzy) with ‘real’, ‘imag’, ‘err’, and ‘model_err’ sub-keys. Returns None if no Tipper data available.

Return type:

dict | None

static get_interp1d_functions_z(tf, interp_type: str = 'slinear') → dict | None[source]

Create 1D interpolation functions for impedance tensor components.

Parameters:

  • tf (MT or Transfer Function object) – Transfer function object containing impedance data

  • interp_type (str, optional) – Type of interpolation (‘linear’, ‘slinear’, ‘cubic’), by default ‘slinear’

Returns:

Dictionary containing interpolation functions for each impedance component (zxx, zxy, zyx, zyy) with ‘real’, ‘imag’, ‘err’, and ‘model_err’ sub-keys. Returns None if no Z data available.

Return type:

dict | None

interpolate_to_map(plot_array, component: str)[source]

Interpolate data points onto a 2D map grid.

Parameters:

  • plot_array (np.ndarray) – Array containing data to interpolate

  • component (str) – Name of the component being interpolated

Returns:

Interpolated grid data and coordinates

Return type:

tuple

class mtpy.imaging.mtplot_tools.base.PlotBaseProfile(tf_list, **kwargs)[source]

Bases: PlotBase

Base object for profile plots like pseudo sections.

Provides functionality for creating profile views of MT data along a linear transect.

Parameters:

  • tf_list (list or MTCollection) – List of transfer function objects or MTCollection

  • **kwargs (dict) – Additional keyword arguments for PlotBase parent class and profile settings

mt_data

MT data to plot

Type:

list or MTCollection

profile_vector

Profile direction vector

Type:

array-like | None

profile_angle

Profile angle in degrees

Type:

float | None

profile_line

Profile line parameters (slope, intercept)

Type:

tuple | None

profile_reverse

Whether to reverse profile direction, by default False

Type:

bool

x_stretch

Horizontal stretching factor for profile, by default 5000

Type:

float

y_stretch

Vertical stretching factor for profile, by default 1000

Type:

float

y_scale

Y-axis scale type (‘period’ or ‘frequency’), by default ‘period’

Type:

str

property rotation_angle: float

Get rotation angle for data.

Returns:

Rotation angle in degrees

Return type:

float

mtpy.imaging.mtplot_tools.ellipses module

Created on Sun Sep 25 15:19:16 2022

@author: jpeacock

class mtpy.imaging.mtplot_tools.ellipses.MTEllipse(**kwargs)[source]

Bases: object

Helper class for managing phase tensor ellipse properties.

Configures ellipse visualization parameters including size, color mapping, and color ranges for plotting phase tensor ellipses.

Parameters:

**kwargs (dict, optional) – Keyword arguments to set ellipse properties. Valid keys correspond to the class attributes listed below.

ellipse_size

Size of ellipse in points, by default 2

Type:

float

ellipse_colorby

Property to color ellipses by: - ‘phimin’ or ‘phiminang’: minimum phase (default) - ‘phimax’ or ‘phimaxang’: maximum phase - ‘skew’: skew angle (beta) - ‘skew_seg’: skew in discrete segments - ‘normalized_skew’: normalized skew (Booker, 2014) - ‘normalized_skew_seg’: normalized skew in discrete segments - ‘phidet’: determinant of phase tensor - ‘ellipticity’: ellipticity of phase tensor - ‘strike’ or ‘azimuth’: strike angle

Type:

str

ellipse_range

Range for color mapping as (min, max, step), by default (0, 90, 10)

Type:

tuple[float, float, float]

ellipse_cmap

Colormap name: - ‘mt_yl2rd’: yellow to red - ‘mt_bl2yl2rd’: blue to yellow to red - ‘mt_wh2bl’: white to blue - ‘mt_rd2bl’: red to blue - ‘mt_bl2wh2rd’: blue to white to red - ‘mt_bl2gr2rd’: blue to green to red (default) - ‘mt_rd2gr2bl’: red to green to blue - ‘mt_seg_bl2wh2rd’: discrete blue to white to red

Type:

str

ellipse_spacing

Spacing between ellipses, by default 1

Type:

float

property ellipse_cmap_bounds: ndarray | None

Get colormap boundaries for discrete coloring.

Returns:

Array of boundary values for colormap bins, or None if ellipse_range is not properly defined

Return type:

np.ndarray | None

property ellipse_cmap_n_segments: float

Calculate number of segments for colormap.

Returns:

Number of colormap segments based on ellipse_range

Return type:

float

property ellipse_properties: dict[str, float | tuple | str]

Get dictionary of ellipse properties.

Returns:

Dictionary containing ellipse visualization parameters: - size: ellipse size - range: color range tuple - cmap: colormap name - colorby: property to color by - spacing: ellipse spacing

Return type:

dict[str, float | tuple | str]

get_color_map() → None[source]

Set appropriate colormap based on colorby property.

Automatically sets colormap to ‘mt_seg_bl2wh2rd’ for segmented skew coloring modes.

get_pt_color_array(pt_object) → ndarray[source]

Extract appropriate array from phase tensor for coloring ellipses.

Parameters:

pt_object (PhaseTensor) – Phase tensor object containing phase tensor properties

Returns:

Array of values corresponding to the selected colorby property

Return type:

np.ndarray

Raises:

NameError – If ellipse_colorby is not a supported option

get_range() → None[source]

Set appropriate color range based on colorby property.

Automatically determines color range if not explicitly set: - Skew-based: (-9, 9, 3) - Ellipticity: (0, 1, 0.1) - Phase-based: (0, 90, 5)

mtpy.imaging.mtplot_tools.map_interpolation_tools module

Created on Sun Sep 25 15:06:58 2022

@author: jpeacock

mtpy.imaging.mtplot_tools.map_interpolation_tools.get_plot_xy(plot_array: ndarray, cell_size: float, n_padding_cells: int) → tuple[ndarray, ndarray][source]

Generate uniform x and y coordinates for interpolation grid.

Creates regular grid coordinates with padding around the data extent.

Parameters:

  • plot_array (np.ndarray) – Structured array with ‘longitude’ and ‘latitude’ fields

  • cell_size (float) – Size of grid cells in decimal degrees

  • n_padding_cells (int) – Number of padding cells to add around data extent

Returns:

plot_x : 1D array of x (longitude) coordinates plot_y : 1D array of y (latitude) coordinates

Return type:

tuple[np.ndarray, np.ndarray]

mtpy.imaging.mtplot_tools.map_interpolation_tools.griddata_interpolate(x: ndarray, y: ndarray, values: ndarray, new_x: ndarray, new_y: ndarray, interpolation_method: str = 'cubic') → tuple[ndarray, ndarray, ndarray][source]

Interpolate scattered data to regular grid using scipy.interpolate.griddata.

Parameters:

  • x (np.ndarray) – X coordinates of data points

  • y (np.ndarray) – Y coordinates of data points

  • values (np.ndarray) – Values at data points

  • new_x (np.ndarray) – Target x coordinates for interpolation

  • new_y (np.ndarray) – Target y coordinates for interpolation

  • interpolation_method (str, optional) – Interpolation method: ‘nearest’, ‘linear’, or ‘cubic’, by default ‘cubic’

Returns:

grid_x : 2D array of x coordinates grid_y : 2D array of y coordinates image : 2D array of interpolated values

Return type:

tuple[np.ndarray, np.ndarray, np.ndarray]

mtpy.imaging.mtplot_tools.map_interpolation_tools.in_hull(p: ndarray, hull: ndarray | Delaunay) → ndarray[source]

Test if points are within a convex hull.

Uses Delaunay triangulation for efficient point-in-hull testing. Falls back to linear programming for collinear point configurations.

Parameters:

  • p (np.ndarray) – Points to test, shape (n_points, n_dimensions)

  • hull (np.ndarray | Delaunay) – Either array of hull vertices or pre-computed Delaunay object

Returns:

Boolean array indicating which points are inside the hull

Return type:

np.ndarray

mtpy.imaging.mtplot_tools.map_interpolation_tools.interpolate_to_map(plot_array: ndarray, component: str, cell_size: float = 0.002, n_padding_cells: int = 10, interpolation_method: str = 'delaunay', interpolation_power: float = 5, nearest_neighbors: int = 7) → tuple[ndarray | Triangulation, ndarray, ndarray | None][source]

Interpolate MT data to regular map grid.

Dispatcher function that selects appropriate interpolation method. Supports both griddata-based (nearest, linear, cubic) and triangulation-based (delaunay, fancy) methods.

Parameters:

  • plot_array (np.ndarray) – Structured array with ‘longitude’, ‘latitude’, and component fields

  • component (str) – Name of component to interpolate (e.g., ‘res_xy’, ‘phase_xy’)

  • cell_size (float, optional) – Size of grid cells in decimal degrees, by default 0.002

  • n_padding_cells (int, optional) – Number of padding cells around data extent, by default 10

  • interpolation_method (str, optional) – Interpolation method: - ‘nearest’, ‘linear’, ‘cubic’: scipy.interpolate.griddata methods - ‘delaunay’, ‘fancy’, ‘triangulate’: triangulation with IDW by default ‘delaunay’

  • interpolation_power (float, optional) – Power parameter for IDW in triangulation methods, by default 5

  • nearest_neighbors (int, optional) – Number of nearest neighbors for IDW, by default 7

Returns:

grid_x or triangulation : Grid x coordinates (2D) or triangulation object grid_y or image : Grid y coordinates (2D) or interpolated values image or inside_indices : Interpolated values or boolean hull mask

Return type:

tuple[np.ndarray | tri.Triangulation, np.ndarray, np.ndarray | None]

Notes

Return types differ by method: - griddata methods: (grid_x, grid_y, image) - triangulation methods: (triangulation, image, inside_indices)

mtpy.imaging.mtplot_tools.map_interpolation_tools.interpolate_to_map_griddata(plot_array: ndarray, component: str, cell_size: float = 0.002, n_padding_cells: int = 10, interpolation_method: str = 'cubic') → tuple[ndarray, ndarray, ndarray][source]

Interpolate MT data to regular map grid using griddata method.

Parameters:

  • plot_array (np.ndarray) – Structured array with ‘longitude’, ‘latitude’, and component fields

  • component (str) – Name of component to interpolate (e.g., ‘res_xy’, ‘phase_xy’)

  • cell_size (float, optional) – Size of grid cells in decimal degrees, by default 0.002

  • n_padding_cells (int, optional) – Number of padding cells around data extent, by default 10

  • interpolation_method (str, optional) – Interpolation method: ‘nearest’, ‘linear’, or ‘cubic’, by default ‘cubic’

Returns:

grid_x : 2D array of x (longitude) coordinates grid_y : 2D array of y (latitude) coordinates image : 2D array of interpolated values (log10 for resistivity)

Return type:

tuple[np.ndarray, np.ndarray, np.ndarray]

mtpy.imaging.mtplot_tools.map_interpolation_tools.interpolate_to_map_triangulate(plot_array: ndarray, component: str, cell_size: float = 0.002, n_padding_cells: int = 10, nearest_neighbors: int = 7, interp_pow: float = 4) → tuple[Triangulation, ndarray, ndarray][source]

Interpolate MT data to map using Delaunay triangulation method.

Uses triangulation with inverse distance weighting (IDW) for interpolation. Automatically applies log10 transform to resistivity components.

Parameters:

  • plot_array (np.ndarray) – Structured array with required fields: - ‘latitude’: latitude in decimal degrees - ‘longitude’: longitude in decimal degrees - component field (e.g., ‘res_xy’, ‘phase_xy’)

  • component (str) – Name of component to interpolate (must be field in plot_array)

  • cell_size (float, optional) – Size of grid cells in decimal degrees, by default 0.002

  • n_padding_cells (int, optional) – Number of padding cells around data extent, by default 10

  • nearest_neighbors (int, optional) – Number of nearest neighbors for IDW interpolation, by default 7

  • interp_pow (float, optional) – Power parameter for IDW interpolation, by default 4

Returns:

triangulation : Matplotlib triangulation object with mask image : Flattened array of interpolated values (log10 for resistivity) inside_indices : Boolean array indicating triangles inside hull

Return type:

tuple[tri.Triangulation, np.ndarray, np.ndarray]

mtpy.imaging.mtplot_tools.map_interpolation_tools.triangulate_interpolation(x: ndarray, y: ndarray, values: ndarray, padded_x: ndarray, padded_y: ndarray, new_x: ndarray, new_y: ndarray, nearest_neighbors: int = 7, interp_pow: float = 4) → tuple[Triangulation, ndarray, ndarray][source]

Interpolate using Delaunay triangulation and inverse distance weighting.

Creates triangulation on target grid and uses IDW interpolation with k-nearest neighbors from source data points. Masks triangles outside the convex hull.

Parameters:

  • x (np.ndarray) – X coordinates of source data points

  • y (np.ndarray) – Y coordinates of source data points

  • values (np.ndarray) – Values at source data points

  • padded_x (np.ndarray) – Padded x coordinates defining convex hull boundary

  • padded_y (np.ndarray) – Padded y coordinates defining convex hull boundary

  • new_x (np.ndarray) – Target x coordinates for interpolation

  • new_y (np.ndarray) – Target y coordinates for interpolation

  • nearest_neighbors (int, optional) – Number of nearest neighbors for IDW interpolation, by default 7

  • interp_pow (float, optional) – Power parameter for IDW (higher = more weight to closer points), by default 4

Returns:

triangulation : Matplotlib triangulation object with mask image : Flattened array of interpolated values inside_indices : Boolean array indicating triangles inside hull

Return type:

tuple[tri.Triangulation, np.ndarray, np.ndarray]

mtpy.imaging.mtplot_tools.plot_settings module

Created on Sun Sep 25 15:20:43 2022

@author: jpeacock

class mtpy.imaging.mtplot_tools.plot_settings.PlotSettings(**kwargs)[source]

Bases: MTArrows, MTEllipse

Comprehensive plot settings for MT data visualization.

Combines arrow and ellipse settings with figure, marker, line, and colorbar properties for creating publication-quality MT plots.

Parameters:

**kwargs (dict, optional) – Keyword arguments to set any class attributes. Valid keys correspond to the extensive list of attributes below.

fig_num

Figure number, by default 1

Type:

int

fig_dpi

Figure DPI resolution, by default 150

Type:

int

fig_size

Figure size (width, height) in inches, by default None

Type:

tuple | None

show_plot

Whether to display plot, by default True

Type:

bool

font_size

Base font size for plot text, by default 7

Type:

int

font_weight

Font weight (‘normal’, ‘bold’, etc.), by default ‘bold’

Type:

str

marker_size

Size of plot markers, by default 2.5

Type:

float

marker_lw

Line width of marker edges, by default 0.75

Type:

float

marker_color

Default marker color, by default ‘b’

Type:

str

marker

Default marker style, by default ‘v’

Type:

str

lw

Default line width, by default 1

Type:

float

plot_title

Plot title text, by default None

Type:

str | None

xy_ls, yx_ls, det_ls, skew_ls, strike_ls

Line styles for different components, by default ‘:’

Type:

str

xy_marker, yx_marker, det_marker, skew_marker

Marker styles for different components

Type:

str

strike_inv_marker, strike_pt_marker, strike_tip_marker

Marker styles for strike indicators

Type:

str

xy_color, yx_color, det_color, skew_color

RGB colors for different components

Type:

tuple

strike_inv_color, strike_pt_color, strike_tip_color

RGB colors for strike indicators

Type:

tuple

x_limits, y_limits, res_limits, phase_limits

Axis limits for various plot types

Type:

tuple | None

tipper_limits, strike_limits, skew_limits, pt_limits

Additional axis limits

Type:

tuple | None

plot_z

Whether to plot impedance, by default True

Type:

bool

plot_tipper

Tipper plotting mode (‘n’, ‘y’, ‘yri’), by default ‘n’

Type:

str

plot_pt

Whether to plot phase tensor, by default False

Type:

bool

plot_strike

Whether to plot strike, by default False

Type:

bool

plot_skew

Whether to plot skew, by default False

Type:

bool

text_size

Text annotation size, by default 7

Type:

int

text_weight

Text annotation weight, by default ‘normal’

Type:

str

text_color

Text annotation color, by default ‘k’

Type:

str

text_ha

Text horizontal alignment, by default ‘center’

Type:

str

text_va

Text vertical alignment, by default ‘baseline’

Type:

str

text_angle

Text rotation angle, by default 0

Type:

float

text_x_pad, text_y_pad

Text padding in x/y directions, by default 0

Type:

float

text_rotation

Additional text rotation, by default 0

Type:

float

subplot_left, subplot_right, subplot_bottom, subplot_top

Subplot spacing parameters

Type:

float

subplot_wspace, subplot_hspace

Width/height spacing between subplots

Type:

float | None

cb_orientation

Colorbar orientation (‘vertical’ or ‘horizontal’), by default ‘vertical’

Type:

str

cb_position

Colorbar position [left, bottom, width, height], by default None

Type:

tuple | None

property arrow_imag_properties: dict[str, str | float | bool]

Get matplotlib arrow properties for imaginary tipper component.

Returns:

Dictionary of arrow kwargs including line width, colors, head dimensions, and length settings

Return type:

dict[str, str | float | bool]

property arrow_real_properties: dict[str, str | float | bool]

Get matplotlib arrow properties for real tipper component.

Returns:

Dictionary of arrow kwargs including line width, colors, head dimensions, and length settings

Return type:

dict[str, str | float | bool]

property det_error_bar_properties: dict[str, str | float]

Get matplotlib errorbar properties for determinant component.

Returns:

Dictionary of errorbar kwargs including marker style, size, colors, line styles, and cap properties

Return type:

dict[str, str | float]

property font_dict: dict[str, int | str]

Get font properties dictionary for matplotlib.

Adjusts key names based on matplotlib version for compatibility.

Returns:

Font properties with ‘size’/’fontsize’ and ‘weight’/’fontweight’ depending on matplotlib version

Return type:

dict[str, int | str]

make_pt_cb(ax: Axes) → mcb.Colorbar[source]

Create phase tensor colorbar.

Generates colorbar for phase tensor plots with appropriate color mapping, normalization, and labeling based on ellipse settings.

Parameters:

ax (Axes) – Matplotlib axes to attach colorbar to

Returns:

Configured matplotlib colorbar object

Return type:

mcb.Colorbar

property period_label_dict: dict[int, str]

Get LaTeX-formatted period labels for log10 scale.

Returns:

Dictionary mapping integer exponents to LaTeX strings in format $10^{exponent}$ for range -20 to 20

Return type:

dict[int, str]

set_period_limits(period: ndarray) → tuple[float, float][source]

Calculate period axis limits as powers of 10.

Parameters:

period (np.ndarray) – Array of period values

Returns:

Minimum and maximum limits as powers of 10 (floor and ceil)

Return type:

tuple[float, float]

set_phase_limits(phase: ndarray, mode: str = 'od') → tuple[float, float] | list[float][source]

Calculate appropriate phase axis limits.

Parameters:

  • phase (np.ndarray) – Phase array with shape (n_periods, 2, 2) for full tensor or (n_periods,) for determinant

  • mode (str, optional) – Plotting mode: - ‘od’: off-diagonal components (0-90 range with adjustments) - ‘d’: diagonal components (-180 to 180) - ‘det’ or ‘det_only’: determinant phase by default ‘od’

Returns:

[min_limit, max_limit] in degrees Defaults to [0, 90] for ‘od’ or [-180, 180] for others if calculation fails

Return type:

tuple[float, float] | list[float]

set_resistivity_limits(resistivity: ndarray, mode: str = 'od', scale: str = 'log') → list[float][source]

Calculate appropriate resistivity axis limits.

Parameters:

  • resistivity (np.ndarray) – Resistivity array with shape (n_periods, 2, 2) for full tensor or (n_periods,) for determinant

  • mode (str, optional) – Plotting mode: - ‘od’: off-diagonal components (xy, yx) - ‘d’: diagonal components (xx, yy) - ‘det’ or ‘det_only’: determinant only - ‘all’: all components by default ‘od’

  • scale (str, optional) – Scale type (‘log’ or ‘linear’), by default ‘log’

Returns:

[min_limit, max_limit] as powers of 10 Defaults to [0.1, 10000] if calculation fails

Return type:

list[float]

property text_dict: dict[str, int | str | float]

Get text annotation properties dictionary.

Returns:

Dictionary of text properties including size, weight, rotation angle, and color

Return type:

dict[str, int | str | float]

property xy_error_bar_properties: dict[str, str | float]

Get matplotlib errorbar properties for xy component.

Returns:

Dictionary of errorbar kwargs including marker style, size, colors, line styles, and cap properties

Return type:

dict[str, str | float]

property yx_error_bar_properties: dict[str, str | float]

Get matplotlib errorbar properties for yx component.

Returns:

Dictionary of errorbar kwargs including marker style, size, colors, line styles, and cap properties

Return type:

dict[str, str | float]

mtpy.imaging.mtplot_tools.plotters module

Simple plotters elements that can be assembled in various plotting classes

Created on Sun Sep 25 15:27:28 2022

author:

jpeacock

mtpy.imaging.mtplot_tools.plotters.add_raster(ax: Axes, raster_fn: str, add_colorbar: bool = True, **kwargs) → tuple[Axes, mcb.Colorbar | None][source]

Add a raster image to axes using rasterio.

Overlays a georeferenced raster (e.g., GeoTIFF) on matplotlib axes with optional colorbar.

Parameters:

  • ax (Axes) – Matplotlib axes to add raster to

  • raster_fn (str) – Path to raster file (must be readable by rasterio)

  • add_colorbar (bool, optional) – Whether to add colorbar, by default True

  • **kwargs (dict, optional) – Additional keyword arguments passed to rasterio.plot.show

Returns:

Updated axes and colorbar (None if add_colorbar=False)

Return type:

tuple[Axes, mcb.Colorbar | None]

mtpy.imaging.mtplot_tools.plotters.plot_errorbar(ax: Axes, x_array: np.ndarray, y_array: np.ndarray, y_error: np.ndarray | None = None, x_error: np.ndarray | None = None, **kwargs) → ErrorbarContainer[source]

Create error bar plot with customizable properties.

Convenience function to generate matplotlib errorbar plots with sensible defaults that can be overridden via kwargs.

Parameters:

  • ax (Axes) – Matplotlib axes to plot on

  • x_array (np.ndarray) – Array of x values

  • y_array (np.ndarray) – Array of y values

  • y_error (np.ndarray | None, optional) – Array of y-direction error values, by default None

  • x_error (np.ndarray | None, optional) – Array of x-direction error values, by default None

  • **kwargs (dict, optional) – Additional errorbar properties: - color : marker, line, and error bar color - marker : marker style - mew : marker edge width - mec : marker edge color - ms : marker size - ls : line style - lw : line width - capsize : error bar cap size - capthick : error bar cap thickness - ecolor : error bar color - elinewidth : error bar line width - picker : pick radius in points

Returns:

Matplotlib errorbar container with line data and error bars

Return type:

ErrorbarContainer

mtpy.imaging.mtplot_tools.plotters.plot_phase(ax: Axes, period: np.ndarray, phase: np.ndarray | None, error: np.ndarray | None, yx: bool = False, **properties) → list[ErrorbarContainer | None][source]

Plot phase with error bars.

Plots only non-zero phase values. Optionally adds 180 degrees to yx component for proper quadrant representation.

Parameters:

  • ax (Axes) – Matplotlib axes to plot on

  • period (np.ndarray) – Array of period values

  • phase (np.ndarray | None) – Array of phase values (degrees)

  • error (np.ndarray | None) – Array of phase error values

  • yx (bool, optional) – If True, adds 180 degrees to phase for yx component, by default False

  • **properties (dict, optional) – Additional errorbar properties passed to plot_errorbar

Returns:

List containing errorbar container or [None] if no data

Return type:

list[ErrorbarContainer | None]

mtpy.imaging.mtplot_tools.plotters.plot_pt_lateral(ax: Axes, pt_obj, color_array: np.ndarray, ellipse_properties: dict, y_shift: float = 0, fig: Figure | None = None, edge_color: str | tuple | None = None, n_index: int = 0) → tuple[Axes | None, mcb.Colorbar | None][source]

Plot phase tensor ellipses on lateral (period) axis.

Creates phase tensor ellipse plot with ellipses scaled by phimin/phimax and oriented by azimuth. Includes optional colorbar.

Parameters:

  • ax (Axes) – Matplotlib axes to plot on

  • pt_obj (PhaseTensor) – Phase tensor object with frequency, phimin, phimax, and azimuth

  • color_array (np.ndarray) – Array of values for coloring ellipses

  • ellipse_properties (dict) – Dictionary with keys: - ‘size’: ellipse size scaling factor - ‘spacing’: spacing between ellipses on period axis - ‘colorby’: property to color by - ‘cmap’: colormap name - ‘range’: [min, max, step] for color mapping

  • y_shift (float, optional) – Vertical offset for ellipse centers, by default 0

  • fig (Figure | None, optional) – Figure for adding colorbar, by default None

  • edge_color (str | tuple | None, optional) – Color for ellipse edges, by default None

  • n_index (int, optional) – Index for controlling colorbar creation (only at 0), by default 0

Returns:

Colorbar axes and colorbar object (both None if n_index != 0)

Return type:

tuple[Axes | None, mcb.Colorbar | None]

mtpy.imaging.mtplot_tools.plotters.plot_resistivity(ax: Axes, period: np.ndarray, resistivity: np.ndarray | None, error: np.ndarray | None, **properties) → list[ErrorbarContainer | None][source]

Plot apparent resistivity with error bars.

Plots only non-zero resistivity values on the given axes.

Parameters:

  • ax (Axes) – Matplotlib axes to plot on

  • period (np.ndarray) – Array of period values

  • resistivity (np.ndarray | None) – Array of apparent resistivity values (ohm-m)

  • error (np.ndarray | None) – Array of resistivity error values

  • **properties (dict, optional) – Additional errorbar properties passed to plot_errorbar

Returns:

List containing errorbar container or [None] if no data

Return type:

list[ErrorbarContainer | None]

mtpy.imaging.mtplot_tools.plotters.plot_tipper_lateral(axt: Axes | None, t_obj, plot_tipper: str | bool, real_properties: dict, imag_properties: dict, font_size: int = 6, legend: bool = True, zero_reference: bool = False, arrow_direction: int = 1) → tuple[Axes | None, list[Line2D] | None, list[str] | None][source]

Plot tipper arrows on lateral (period) axis.

Creates tipper arrow plot showing real and/or imaginary components as arrows with magnitude and direction.

Parameters:

  • axt (Axes | None) – Matplotlib axes to plot on (returns None if None)

  • t_obj (Tipper) – Tipper object with frequency, mag_real, angle_real, mag_imag, and angle_imag attributes

  • plot_tipper (str | bool) – Tipper plotting mode: - ‘yri’: plot both real and imaginary - ‘yr’: plot real only - ‘yi’: plot imaginary only - ‘y’: plot both - False/None: no plot

  • real_properties (dict) – Arrow properties for real component (matplotlib arrow kwargs)

  • imag_properties (dict) – Arrow properties for imaginary component (matplotlib arrow kwargs)

  • font_size (int, optional) – Font size for axis labels and legend, by default 6

  • legend (bool, optional) – Whether to show legend, by default True

  • zero_reference (bool, optional) – Whether to plot zero reference line, by default False

  • arrow_direction (int, optional) – Arrow direction multiplier (1 or -1), by default 1

Returns:

Updated axes, legend handles list, and legend labels list All None if axt is None or t_obj is None

Return type:

tuple[Axes | None, list[Line2D] | None, list[str] | None]

mtpy.imaging.mtplot_tools.utils module

Utility functions for plotting

Created on Sun Sep 25 15:49:01 2022

author:

jpeacock

mtpy.imaging.mtplot_tools.utils.add_colorbar_axis(ax: Axes, fig: Figure) → Axes[source]

Add colorbar axes positioned to the left of given axes.

Creates a new axes for colorbar placement with dimensions calculated relative to the input axes position.

Parameters:

  • ax (Axes) – Reference axes for positioning colorbar

  • fig (Figure) – Figure to add colorbar axes to

Returns:

New axes object for colorbar

Return type:

Axes

mtpy.imaging.mtplot_tools.utils.get_log_tick_labels(ax: Axes, spacing: float = 1) → tuple[list[str], list[float]][source]

Get LaTeX-formatted tick labels for logarithmic period axis.

Generates tick labels in format $10^{exponent}$ for valid tick positions.

Parameters:

  • ax (Axes) – Axes to extract tick positions from

  • spacing (float, optional) – Spacing factor for tick positions, by default 1

Returns:

Tick labels (LaTeX strings) and corresponding tick positions

Return type:

tuple[list[str], list[float]]

mtpy.imaging.mtplot_tools.utils.get_period_limits(period: ndarray) → tuple[float, float][source]

Calculate period axis limits as powers of 10.

Parameters:

period (np.ndarray) – Array of period values

Returns:

Minimum and maximum limits as powers of 10 (floor and ceil)

Return type:

tuple[float, float]

mtpy.imaging.mtplot_tools.utils.make_color_list(cbax: Axes, nseg: float, ckmin: float, ckmax: float, ckstep: float) → mcb.ColorbarBase[source]

Create segmented blue-white-red colorbar.

Generates a colorbar with blue-to-white-to-red color transition using discrete segments with specified bounds.

Parameters:

  • cbax (Axes) – Axes to place colorbar in

  • nseg (float) – Number of color segments

  • ckmin (float) – Minimum value for colorbar range

  • ckmax (float) – Maximum value for colorbar range

  • ckstep (float) – Step size between color boundaries

Returns:

Colorbar object with segmented colormap

Return type:

mcb.ColorbarBase

mtpy.imaging.mtplot_tools.utils.make_value_str(value: float, value_list: str | list[str] | None = None, spacing: str = '{0:^8}', value_format: str = '{0: .2f}', append: bool = False, add: bool = False) → list[str] | str[source]

Format value as string for file output.

Helper function for writing values to file. Converts value to formatted string and either appends to list, concatenates to string, or returns standalone string.

Parameters:

  • value (float) – Numeric value to format

  • value_list (list[str] | str | None, optional) – Existing list or string to modify, by default None

  • spacing (str, optional) – Format string for spacing (e.g., ‘{0:^8}’), by default ‘{0:^8}’

  • value_format (str, optional) – Format string for value (e.g., ‘{0: .2f}’), by default ‘{0: .2f}’

  • append (bool, optional) – If True, append to value_list (must be list), by default False

  • add (bool, optional) – If True, concatenate to value_list (must be str), by default False

Returns:

Modified list/string or standalone formatted string

Return type:

list[str] | str

Notes

If both append and add are False, returns formatted string. If append is True, appends to list and returns list. If add is True, concatenates to string and returns string.

mtpy.imaging.mtplot_tools.utils.round_to_step(num: float, base: float = 5) → float[source]

Round number to nearest multiple of base.

Parameters:

  • num (float) – Number to round

  • base (float, optional) – Base value to round to multiples of, by default 5

Returns:

Rounded value

Return type:

float

Module contents

class mtpy.imaging.mtplot_tools.MTArrows(**kwargs)[source]

Bases: object

Helper class to configure arrow properties for plotting induction vectors.

This class manages arrow styling parameters for real and imaginary components of induction vectors in magnetotelluric plots.

Parameters:

**kwargs (dict, optional) – Keyword arguments to set arrow properties. Valid keys correspond to the class attributes listed below.

arrow_size

Multiplier to scale the arrow size, by default 2.5

Type:

float

arrow_head_length

Length of the arrow head, by default 0.15 * arrow_size

Type:

float

arrow_head_width

Width of the arrow head, by default 0.1 * arrow_size

Type:

float

arrow_lw

Line width of the arrow, by default 0.5 * arrow_size

Type:

float

arrow_threshold

Threshold value above which arrows will not be plotted. This helps filter out poor quality data. Applied before scaling by arrow_size, by default 2

Type:

float

arrow_color_imag

Color for imaginary component arrows, by default ‘c’ (cyan)

Type:

str

arrow_color_real

Color for real component arrows, by default ‘k’ (black)

Type:

str

arrow_direction

Arrow direction convention: - 0: arrows point away from conductor - 1: arrows point toward conductor (Parkinson convention) By default 1

Type:

int

Notes

The data inherently points away from conductors, so arrow_direction=1 flips the arrows to follow the Parkinson convention.

class mtpy.imaging.mtplot_tools.MTEllipse(**kwargs)[source]

Bases: object

Helper class for managing phase tensor ellipse properties.

Configures ellipse visualization parameters including size, color mapping, and color ranges for plotting phase tensor ellipses.

Parameters:

**kwargs (dict, optional) – Keyword arguments to set ellipse properties. Valid keys correspond to the class attributes listed below.

ellipse_size

Size of ellipse in points, by default 2

Type:

float

ellipse_colorby

Property to color ellipses by: - ‘phimin’ or ‘phiminang’: minimum phase (default) - ‘phimax’ or ‘phimaxang’: maximum phase - ‘skew’: skew angle (beta) - ‘skew_seg’: skew in discrete segments - ‘normalized_skew’: normalized skew (Booker, 2014) - ‘normalized_skew_seg’: normalized skew in discrete segments - ‘phidet’: determinant of phase tensor - ‘ellipticity’: ellipticity of phase tensor - ‘strike’ or ‘azimuth’: strike angle

Type:

str

ellipse_range

Range for color mapping as (min, max, step), by default (0, 90, 10)

Type:

tuple[float, float, float]

ellipse_cmap

Colormap name: - ‘mt_yl2rd’: yellow to red - ‘mt_bl2yl2rd’: blue to yellow to red - ‘mt_wh2bl’: white to blue - ‘mt_rd2bl’: red to blue - ‘mt_bl2wh2rd’: blue to white to red - ‘mt_bl2gr2rd’: blue to green to red (default) - ‘mt_rd2gr2bl’: red to green to blue - ‘mt_seg_bl2wh2rd’: discrete blue to white to red

Type:

str

ellipse_spacing

Spacing between ellipses, by default 1

Type:

float

property ellipse_cmap_bounds: ndarray | None

Get colormap boundaries for discrete coloring.

Returns:

Array of boundary values for colormap bins, or None if ellipse_range is not properly defined

Return type:

np.ndarray | None

property ellipse_cmap_n_segments: float

Calculate number of segments for colormap.

Returns:

Number of colormap segments based on ellipse_range

Return type:

float

property ellipse_properties: dict[str, float | tuple | str]

Get dictionary of ellipse properties.

Returns:

Dictionary containing ellipse visualization parameters: - size: ellipse size - range: color range tuple - cmap: colormap name - colorby: property to color by - spacing: ellipse spacing

Return type:

dict[str, float | tuple | str]

get_color_map() → None[source]

Set appropriate colormap based on colorby property.

Automatically sets colormap to ‘mt_seg_bl2wh2rd’ for segmented skew coloring modes.

get_pt_color_array(pt_object) → ndarray[source]

Extract appropriate array from phase tensor for coloring ellipses.

Parameters:

pt_object (PhaseTensor) – Phase tensor object containing phase tensor properties

Returns:

Array of values corresponding to the selected colorby property

Return type:

np.ndarray

Raises:

NameError – If ellipse_colorby is not a supported option

get_range() → None[source]

Set appropriate color range based on colorby property.

Automatically determines color range if not explicitly set: - Skew-based: (-9, 9, 3) - Ellipticity: (0, 1, 0.1) - Phase-based: (0, 90, 5)

class mtpy.imaging.mtplot_tools.PlotBase(**kwargs)[source]

Bases: PlotSettings

Base class for plotting objects.

Provides core plotting functionality including figure management, saving, updating, and redrawing plots.

Parameters:

**kwargs (dict) – Keyword arguments passed to PlotSettings parent class

logger

Logger instance for the class

Type:

loguru.Logger

plot() → None[source]

Create the plot.

This method should be overridden by subclasses to implement specific plotting functionality.

redraw_plot() → None[source]

Recreate the plot after updating attributes.

Closes the current figure and calls plot() to create a new one with updated attributes.

Examples

>>> # Change the color and marker of the xy components
>>> p1.xy_color = (0.5, 0.5, 0.9)
>>> p1.xy_marker = '*'
>>> p1.redraw_plot()
save_plot(save_fn: str | Path, file_format: str = 'pdf', orientation: str = 'portrait', fig_dpi: int | None = None, close_plot: bool = True) → None[source]

Save the figure to a file.

Parameters:

  • save_fn (str | Path) –

    Full path to save figure to. Can be: - Directory path: file will be saved as save_fn/basename.file_format - Full path: file will be saved to the given path, format inferred

    from extension

  • file_format (str, optional) – File format for saved figure (pdf, eps, jpg, png, svg), by default ‘pdf’

  • orientation (str, optional) – Page orientation (‘landscape’ or ‘portrait’), by default ‘portrait’

  • fig_dpi (int | None, optional) – Resolution in dots-per-inch. If None, uses the figure’s dpi, by default None

  • close_plot (bool, optional) – Whether to close the plot after saving, by default True

Examples

>>> # Save plot as jpg
>>> p1.save_plot(r'/home/MT/figures', file_format='jpg')
update_plot() → None[source]

Update the plot after changing figure or axes properties.

Uses matplotlib’s canvas draw method to refresh the display after modifying figure or axes attributes.

Examples

>>> [ax.grid(True, which='major') for ax in [p1.axr, p1.axp]]
>>> p1.update_plot()
class mtpy.imaging.mtplot_tools.PlotBaseMaps(**kwargs)[source]

Bases: PlotBase

Base object for plot classes that use map views.

Includes methods for interpolation of data onto map grids.

Parameters:

**kwargs (dict) – Keyword arguments passed to PlotBase parent class and for setting interpolation parameters

cell_size

Size of grid cells for interpolation, by default 0.002

Type:

float

n_padding_cells

Number of padding cells around data extent, by default 10

Type:

int

interpolation_method

Interpolation method (‘delaunay’, ‘linear’, ‘nearest’, ‘cubic’), by default ‘delaunay’

Type:

str

interpolation_power

Power parameter for inverse distance weighting, by default 5

Type:

int

nearest_neighbors

Number of nearest neighbors to use in interpolation, by default 7

Type:

int

add_raster(ax, raster_fn: str | Path, add_colorbar: bool = True, **kwargs)[source]

Add a raster image to a matplotlib axis.

Parameters:

  • ax (matplotlib.axes.Axes) – Matplotlib axis to add raster to

  • raster_fn (str | Path) – Path to raster file (readable by rasterio)

  • add_colorbar (bool, optional) – Whether to add a colorbar, by default True

  • **kwargs (dict) – Additional keyword arguments passed to rasterio plotting

Returns:

The raster plot object

Return type:

matplotlib image or collection

static get_interp1d_functions_t(tf, interp_type: str = 'slinear') → dict | None[source]

Create 1D interpolation functions for tipper components.

Parameters:

  • tf (MT or Transfer Function object) – Transfer function object containing tipper data

  • interp_type (str, optional) – Type of interpolation (‘linear’, ‘slinear’, ‘cubic’), by default ‘slinear’

Returns:

Dictionary containing interpolation functions for tipper components (tzx, tzy) with ‘real’, ‘imag’, ‘err’, and ‘model_err’ sub-keys. Returns None if no Tipper data available.

Return type:

dict | None

static get_interp1d_functions_z(tf, interp_type: str = 'slinear') → dict | None[source]

Create 1D interpolation functions for impedance tensor components.

Parameters:

  • tf (MT or Transfer Function object) – Transfer function object containing impedance data

  • interp_type (str, optional) – Type of interpolation (‘linear’, ‘slinear’, ‘cubic’), by default ‘slinear’

Returns:

Dictionary containing interpolation functions for each impedance component (zxx, zxy, zyx, zyy) with ‘real’, ‘imag’, ‘err’, and ‘model_err’ sub-keys. Returns None if no Z data available.

Return type:

dict | None

interpolate_to_map(plot_array, component: str)[source]

Interpolate data points onto a 2D map grid.

Parameters:

  • plot_array (np.ndarray) – Array containing data to interpolate

  • component (str) – Name of the component being interpolated

Returns:

Interpolated grid data and coordinates

Return type:

tuple

class mtpy.imaging.mtplot_tools.PlotBaseProfile(tf_list, **kwargs)[source]

Bases: PlotBase

Base object for profile plots like pseudo sections.

Provides functionality for creating profile views of MT data along a linear transect.

Parameters:

  • tf_list (list or MTCollection) – List of transfer function objects or MTCollection

  • **kwargs (dict) – Additional keyword arguments for PlotBase parent class and profile settings

mt_data

MT data to plot

Type:

list or MTCollection

profile_vector

Profile direction vector

Type:

array-like | None

profile_angle

Profile angle in degrees

Type:

float | None

profile_line

Profile line parameters (slope, intercept)

Type:

tuple | None

profile_reverse

Whether to reverse profile direction, by default False

Type:

bool

x_stretch

Horizontal stretching factor for profile, by default 5000

Type:

float

y_stretch

Vertical stretching factor for profile, by default 1000

Type:

float

y_scale

Y-axis scale type (‘period’ or ‘frequency’), by default ‘period’

Type:

str

property rotation_angle: float

Get rotation angle for data.

Returns:

Rotation angle in degrees

Return type:

float

class mtpy.imaging.mtplot_tools.PlotSettings(**kwargs)[source]

Bases: MTArrows, MTEllipse

Comprehensive plot settings for MT data visualization.

Combines arrow and ellipse settings with figure, marker, line, and colorbar properties for creating publication-quality MT plots.

Parameters:

**kwargs (dict, optional) – Keyword arguments to set any class attributes. Valid keys correspond to the extensive list of attributes below.

fig_num

Figure number, by default 1

Type:

int

fig_dpi

Figure DPI resolution, by default 150

Type:

int

fig_size

Figure size (width, height) in inches, by default None

Type:

tuple | None

show_plot

Whether to display plot, by default True

Type:

bool

font_size

Base font size for plot text, by default 7

Type:

int

font_weight

Font weight (‘normal’, ‘bold’, etc.), by default ‘bold’

Type:

str

marker_size

Size of plot markers, by default 2.5

Type:

float

marker_lw

Line width of marker edges, by default 0.75

Type:

float

marker_color

Default marker color, by default ‘b’

Type:

str

marker

Default marker style, by default ‘v’

Type:

str

lw

Default line width, by default 1

Type:

float

plot_title

Plot title text, by default None

Type:

str | None

xy_ls, yx_ls, det_ls, skew_ls, strike_ls

Line styles for different components, by default ‘:’

Type:

str

xy_marker, yx_marker, det_marker, skew_marker

Marker styles for different components

Type:

str

strike_inv_marker, strike_pt_marker, strike_tip_marker

Marker styles for strike indicators

Type:

str

xy_color, yx_color, det_color, skew_color

RGB colors for different components

Type:

tuple

strike_inv_color, strike_pt_color, strike_tip_color

RGB colors for strike indicators

Type:

tuple

x_limits, y_limits, res_limits, phase_limits

Axis limits for various plot types

Type:

tuple | None

tipper_limits, strike_limits, skew_limits, pt_limits

Additional axis limits

Type:

tuple | None

plot_z

Whether to plot impedance, by default True

Type:

bool

plot_tipper

Tipper plotting mode (‘n’, ‘y’, ‘yri’), by default ‘n’

Type:

str

plot_pt

Whether to plot phase tensor, by default False

Type:

bool

plot_strike

Whether to plot strike, by default False

Type:

bool

plot_skew

Whether to plot skew, by default False

Type:

bool

text_size

Text annotation size, by default 7

Type:

int

text_weight

Text annotation weight, by default ‘normal’

Type:

str

text_color

Text annotation color, by default ‘k’

Type:

str

text_ha

Text horizontal alignment, by default ‘center’

Type:

str

text_va

Text vertical alignment, by default ‘baseline’

Type:

str

text_angle

Text rotation angle, by default 0

Type:

float

text_x_pad, text_y_pad

Text padding in x/y directions, by default 0

Type:

float

text_rotation

Additional text rotation, by default 0

Type:

float

subplot_left, subplot_right, subplot_bottom, subplot_top

Subplot spacing parameters

Type:

float

subplot_wspace, subplot_hspace

Width/height spacing between subplots

Type:

float | None

cb_orientation

Colorbar orientation (‘vertical’ or ‘horizontal’), by default ‘vertical’

Type:

str

cb_position

Colorbar position [left, bottom, width, height], by default None

Type:

tuple | None

property arrow_imag_properties: dict[str, str | float | bool]

Get matplotlib arrow properties for imaginary tipper component.

Returns:

Dictionary of arrow kwargs including line width, colors, head dimensions, and length settings

Return type:

dict[str, str | float | bool]

property arrow_real_properties: dict[str, str | float | bool]

Get matplotlib arrow properties for real tipper component.

Returns:

Dictionary of arrow kwargs including line width, colors, head dimensions, and length settings

Return type:

dict[str, str | float | bool]

property det_error_bar_properties: dict[str, str | float]

Get matplotlib errorbar properties for determinant component.

Returns:

Dictionary of errorbar kwargs including marker style, size, colors, line styles, and cap properties

Return type:

dict[str, str | float]

property font_dict: dict[str, int | str]

Get font properties dictionary for matplotlib.

Adjusts key names based on matplotlib version for compatibility.

Returns:

Font properties with ‘size’/’fontsize’ and ‘weight’/’fontweight’ depending on matplotlib version

Return type:

dict[str, int | str]

make_pt_cb(ax: Axes) → mcb.Colorbar[source]

Create phase tensor colorbar.

Generates colorbar for phase tensor plots with appropriate color mapping, normalization, and labeling based on ellipse settings.

Parameters:

ax (Axes) – Matplotlib axes to attach colorbar to

Returns:

Configured matplotlib colorbar object

Return type:

mcb.Colorbar

property period_label_dict: dict[int, str]

Get LaTeX-formatted period labels for log10 scale.

Returns:

Dictionary mapping integer exponents to LaTeX strings in format $10^{exponent}$ for range -20 to 20

Return type:

dict[int, str]

set_period_limits(period: ndarray) → tuple[float, float][source]

Calculate period axis limits as powers of 10.

Parameters:

period (np.ndarray) – Array of period values

Returns:

Minimum and maximum limits as powers of 10 (floor and ceil)

Return type:

tuple[float, float]

set_phase_limits(phase: ndarray, mode: str = 'od') → tuple[float, float] | list[float][source]

Calculate appropriate phase axis limits.

Parameters:

  • phase (np.ndarray) – Phase array with shape (n_periods, 2, 2) for full tensor or (n_periods,) for determinant

  • mode (str, optional) – Plotting mode: - ‘od’: off-diagonal components (0-90 range with adjustments) - ‘d’: diagonal components (-180 to 180) - ‘det’ or ‘det_only’: determinant phase by default ‘od’

Returns:

[min_limit, max_limit] in degrees Defaults to [0, 90] for ‘od’ or [-180, 180] for others if calculation fails

Return type:

tuple[float, float] | list[float]

set_resistivity_limits(resistivity: ndarray, mode: str = 'od', scale: str = 'log') → list[float][source]

Calculate appropriate resistivity axis limits.

Parameters:

  • resistivity (np.ndarray) – Resistivity array with shape (n_periods, 2, 2) for full tensor or (n_periods,) for determinant

  • mode (str, optional) – Plotting mode: - ‘od’: off-diagonal components (xy, yx) - ‘d’: diagonal components (xx, yy) - ‘det’ or ‘det_only’: determinant only - ‘all’: all components by default ‘od’

  • scale (str, optional) – Scale type (‘log’ or ‘linear’), by default ‘log’

Returns:

[min_limit, max_limit] as powers of 10 Defaults to [0.1, 10000] if calculation fails

Return type:

list[float]

property text_dict: dict[str, int | str | float]

Get text annotation properties dictionary.

Returns:

Dictionary of text properties including size, weight, rotation angle, and color

Return type:

dict[str, int | str | float]

property xy_error_bar_properties: dict[str, str | float]

Get matplotlib errorbar properties for xy component.

Returns:

Dictionary of errorbar kwargs including marker style, size, colors, line styles, and cap properties

Return type:

dict[str, str | float]

property yx_error_bar_properties: dict[str, str | float]

Get matplotlib errorbar properties for yx component.

Returns:

Dictionary of errorbar kwargs including marker style, size, colors, line styles, and cap properties

Return type:

dict[str, str | float]

mtpy.imaging.mtplot_tools.add_raster(ax: Axes, raster_fn: str, add_colorbar: bool = True, **kwargs) → tuple[Axes, mcb.Colorbar | None][source]

Add a raster image to axes using rasterio.

Overlays a georeferenced raster (e.g., GeoTIFF) on matplotlib axes with optional colorbar.

Parameters:

  • ax (Axes) – Matplotlib axes to add raster to

  • raster_fn (str) – Path to raster file (must be readable by rasterio)

  • add_colorbar (bool, optional) – Whether to add colorbar, by default True

  • **kwargs (dict, optional) – Additional keyword arguments passed to rasterio.plot.show

Returns:

Updated axes and colorbar (None if add_colorbar=False)

Return type:

tuple[Axes, mcb.Colorbar | None]

mtpy.imaging.mtplot_tools.get_log_tick_labels(ax: Axes, spacing: float = 1) → tuple[list[str], list[float]][source]

Get LaTeX-formatted tick labels for logarithmic period axis.

Generates tick labels in format $10^{exponent}$ for valid tick positions.

Parameters:

  • ax (Axes) – Axes to extract tick positions from

  • spacing (float, optional) – Spacing factor for tick positions, by default 1

Returns:

Tick labels (LaTeX strings) and corresponding tick positions

Return type:

tuple[list[str], list[float]]

mtpy.imaging.mtplot_tools.griddata_interpolate(x: ndarray, y: ndarray, values: ndarray, new_x: ndarray, new_y: ndarray, interpolation_method: str = 'cubic') → tuple[ndarray, ndarray, ndarray][source]

Interpolate scattered data to regular grid using scipy.interpolate.griddata.

Parameters:

  • x (np.ndarray) – X coordinates of data points

  • y (np.ndarray) – Y coordinates of data points

  • values (np.ndarray) – Values at data points

  • new_x (np.ndarray) – Target x coordinates for interpolation

  • new_y (np.ndarray) – Target y coordinates for interpolation

  • interpolation_method (str, optional) – Interpolation method: ‘nearest’, ‘linear’, or ‘cubic’, by default ‘cubic’

Returns:

grid_x : 2D array of x coordinates grid_y : 2D array of y coordinates image : 2D array of interpolated values

Return type:

tuple[np.ndarray, np.ndarray, np.ndarray]

mtpy.imaging.mtplot_tools.interpolate_to_map(plot_array: ndarray, component: str, cell_size: float = 0.002, n_padding_cells: int = 10, interpolation_method: str = 'delaunay', interpolation_power: float = 5, nearest_neighbors: int = 7) → tuple[ndarray | Triangulation, ndarray, ndarray | None][source]

Interpolate MT data to regular map grid.

Dispatcher function that selects appropriate interpolation method. Supports both griddata-based (nearest, linear, cubic) and triangulation-based (delaunay, fancy) methods.

Parameters:

  • plot_array (np.ndarray) – Structured array with ‘longitude’, ‘latitude’, and component fields

  • component (str) – Name of component to interpolate (e.g., ‘res_xy’, ‘phase_xy’)

  • cell_size (float, optional) – Size of grid cells in decimal degrees, by default 0.002

  • n_padding_cells (int, optional) – Number of padding cells around data extent, by default 10

  • interpolation_method (str, optional) – Interpolation method: - ‘nearest’, ‘linear’, ‘cubic’: scipy.interpolate.griddata methods - ‘delaunay’, ‘fancy’, ‘triangulate’: triangulation with IDW by default ‘delaunay’

  • interpolation_power (float, optional) – Power parameter for IDW in triangulation methods, by default 5

  • nearest_neighbors (int, optional) – Number of nearest neighbors for IDW, by default 7

Returns:

grid_x or triangulation : Grid x coordinates (2D) or triangulation object grid_y or image : Grid y coordinates (2D) or interpolated values image or inside_indices : Interpolated values or boolean hull mask

Return type:

tuple[np.ndarray | tri.Triangulation, np.ndarray, np.ndarray | None]

Notes

Return types differ by method: - griddata methods: (grid_x, grid_y, image) - triangulation methods: (triangulation, image, inside_indices)

mtpy.imaging.mtplot_tools.plot_errorbar(ax: Axes, x_array: np.ndarray, y_array: np.ndarray, y_error: np.ndarray | None = None, x_error: np.ndarray | None = None, **kwargs) → ErrorbarContainer[source]

Create error bar plot with customizable properties.

Convenience function to generate matplotlib errorbar plots with sensible defaults that can be overridden via kwargs.

Parameters:

  • ax (Axes) – Matplotlib axes to plot on

  • x_array (np.ndarray) – Array of x values

  • y_array (np.ndarray) – Array of y values

  • y_error (np.ndarray | None, optional) – Array of y-direction error values, by default None

  • x_error (np.ndarray | None, optional) – Array of x-direction error values, by default None

  • **kwargs (dict, optional) – Additional errorbar properties: - color : marker, line, and error bar color - marker : marker style - mew : marker edge width - mec : marker edge color - ms : marker size - ls : line style - lw : line width - capsize : error bar cap size - capthick : error bar cap thickness - ecolor : error bar color - elinewidth : error bar line width - picker : pick radius in points

Returns:

Matplotlib errorbar container with line data and error bars

Return type:

ErrorbarContainer

mtpy.imaging.mtplot_tools.plot_phase(ax: Axes, period: np.ndarray, phase: np.ndarray | None, error: np.ndarray | None, yx: bool = False, **properties) → list[ErrorbarContainer | None][source]

Plot phase with error bars.

Plots only non-zero phase values. Optionally adds 180 degrees to yx component for proper quadrant representation.

Parameters:

  • ax (Axes) – Matplotlib axes to plot on

  • period (np.ndarray) – Array of period values

  • phase (np.ndarray | None) – Array of phase values (degrees)

  • error (np.ndarray | None) – Array of phase error values

  • yx (bool, optional) – If True, adds 180 degrees to phase for yx component, by default False

  • **properties (dict, optional) – Additional errorbar properties passed to plot_errorbar

Returns:

List containing errorbar container or [None] if no data

Return type:

list[ErrorbarContainer | None]

mtpy.imaging.mtplot_tools.plot_pt_lateral(ax: Axes, pt_obj, color_array: np.ndarray, ellipse_properties: dict, y_shift: float = 0, fig: Figure | None = None, edge_color: str | tuple | None = None, n_index: int = 0) → tuple[Axes | None, mcb.Colorbar | None][source]

Plot phase tensor ellipses on lateral (period) axis.

Creates phase tensor ellipse plot with ellipses scaled by phimin/phimax and oriented by azimuth. Includes optional colorbar.

Parameters:

  • ax (Axes) – Matplotlib axes to plot on

  • pt_obj (PhaseTensor) – Phase tensor object with frequency, phimin, phimax, and azimuth

  • color_array (np.ndarray) – Array of values for coloring ellipses

  • ellipse_properties (dict) – Dictionary with keys: - ‘size’: ellipse size scaling factor - ‘spacing’: spacing between ellipses on period axis - ‘colorby’: property to color by - ‘cmap’: colormap name - ‘range’: [min, max, step] for color mapping

  • y_shift (float, optional) – Vertical offset for ellipse centers, by default 0

  • fig (Figure | None, optional) – Figure for adding colorbar, by default None

  • edge_color (str | tuple | None, optional) – Color for ellipse edges, by default None

  • n_index (int, optional) – Index for controlling colorbar creation (only at 0), by default 0

Returns:

Colorbar axes and colorbar object (both None if n_index != 0)

Return type:

tuple[Axes | None, mcb.Colorbar | None]

mtpy.imaging.mtplot_tools.plot_resistivity(ax: Axes, period: np.ndarray, resistivity: np.ndarray | None, error: np.ndarray | None, **properties) → list[ErrorbarContainer | None][source]

Plot apparent resistivity with error bars.

Plots only non-zero resistivity values on the given axes.

Parameters:

  • ax (Axes) – Matplotlib axes to plot on

  • period (np.ndarray) – Array of period values

  • resistivity (np.ndarray | None) – Array of apparent resistivity values (ohm-m)

  • error (np.ndarray | None) – Array of resistivity error values

  • **properties (dict, optional) – Additional errorbar properties passed to plot_errorbar

Returns:

List containing errorbar container or [None] if no data

Return type:

list[ErrorbarContainer | None]

mtpy.imaging.mtplot_tools.plot_tipper_lateral(axt: Axes | None, t_obj, plot_tipper: str | bool, real_properties: dict, imag_properties: dict, font_size: int = 6, legend: bool = True, zero_reference: bool = False, arrow_direction: int = 1) → tuple[Axes | None, list[Line2D] | None, list[str] | None][source]

Plot tipper arrows on lateral (period) axis.

Creates tipper arrow plot showing real and/or imaginary components as arrows with magnitude and direction.

Parameters:

  • axt (Axes | None) – Matplotlib axes to plot on (returns None if None)

  • t_obj (Tipper) – Tipper object with frequency, mag_real, angle_real, mag_imag, and angle_imag attributes

  • plot_tipper (str | bool) – Tipper plotting mode: - ‘yri’: plot both real and imaginary - ‘yr’: plot real only - ‘yi’: plot imaginary only - ‘y’: plot both - False/None: no plot

  • real_properties (dict) – Arrow properties for real component (matplotlib arrow kwargs)

  • imag_properties (dict) – Arrow properties for imaginary component (matplotlib arrow kwargs)

  • font_size (int, optional) – Font size for axis labels and legend, by default 6

  • legend (bool, optional) – Whether to show legend, by default True

  • zero_reference (bool, optional) – Whether to plot zero reference line, by default False

  • arrow_direction (int, optional) – Arrow direction multiplier (1 or -1), by default 1

Returns:

Updated axes, legend handles list, and legend labels list All None if axt is None or t_obj is None

Return type:

tuple[Axes | None, list[Line2D] | None, list[str] | None]

mtpy.imaging.mtplot_tools.triangulate_interpolation(x: ndarray, y: ndarray, values: ndarray, padded_x: ndarray, padded_y: ndarray, new_x: ndarray, new_y: ndarray, nearest_neighbors: int = 7, interp_pow: float = 4) → tuple[Triangulation, ndarray, ndarray][source]

Interpolate using Delaunay triangulation and inverse distance weighting.

Creates triangulation on target grid and uses IDW interpolation with k-nearest neighbors from source data points. Masks triangles outside the convex hull.

Parameters:

  • x (np.ndarray) – X coordinates of source data points

  • y (np.ndarray) – Y coordinates of source data points

  • values (np.ndarray) – Values at source data points

  • padded_x (np.ndarray) – Padded x coordinates defining convex hull boundary

  • padded_y (np.ndarray) – Padded y coordinates defining convex hull boundary

  • new_x (np.ndarray) – Target x coordinates for interpolation

  • new_y (np.ndarray) – Target y coordinates for interpolation

  • nearest_neighbors (int, optional) – Number of nearest neighbors for IDW interpolation, by default 7

  • interp_pow (float, optional) – Power parameter for IDW (higher = more weight to closer points), by default 4

Returns:

triangulation : Matplotlib triangulation object with mask image : Flattened array of interpolated values inside_indices : Boolean array indicating triangles inside hull

Return type:

tuple[tri.Triangulation, np.ndarray, np.ndarray]