Plotting utilities and functions

The package uses a few utility classes and functions to make consistent plotting easy thoughout the package. This reference page documents the lower-level utilities used to make this happen.

Note

The built-in routines to plot Section and Plan objects are not documented here, look for documentation on those high-level methods in their respective module documentation.

The functions are defined in deltametrics.plot.

Default styling

By default, each variable receives a set of styling definitions. The default parameters of each styling variable are defined below:

(png, hires.png)

Plotting utility objects

These objects are mostly used internally to help make plots appear consistent across the library. You may want to examine these to change the style of plotting across the package.

VariableInfo(name, **kwargs)	Variable styling and information.
VariableSet([override_dict])	A default set of properties for variables.

Plotting convenience functions

These functions may be helpful in making figures and exploring during analyses. Mostly, these functions provide a component of a plot.

deltametrics.plot.aerial_view(elevation_data, datum=0, ax=None, ticks=False, colorbar_kw={}, **kwargs)

Show an aerial plot of an elevation dataset.

See also: implementation wrapper for a cube.

Parameters

• elevation_data – 2D array of elevations.

• datum – Sea level reference, default value is 0.

• ax (Axes object, optional) – A matplotlib Axes object to plot the section. Optional; if not provided, a call is made to plt.gca() to get the current (or create a new) Axes object.

• ticks – Whether to show ticks. Default is false, no tick labels.

• colorbar_kw – Dictionary of keyword args passed to append_colorbar().

• **kwargs – Optionally, arguments accepted by cartographic_colormap(), or imshow.

Returns

Colorbar instance appended to the axes.

Return type

colorbar

Examples

golfcube = dm.sample_data.golf()
elevation_data = golfcube['eta'][-1, :, :]

fig, ax = plt.subplots()
dm.plot.aerial_view( elevation_data, ax=ax)
plt.show()

(png, hires.png)

deltametrics.plot.overlay_sparse_array(sparse_array, ax=None, cmap='Reds', alpha_clip=(None, 90))

Convenient plotting method to overlay a sparse 2D array on an image.

Should only be used with data arrays that are sparse: i.e., where many elements take the value 0 (or very near 0).

Original implementation was borrowed from the dorado project’s implementation of show_nourishment_area.

Parameters

• sparse_array – 2d array to plot.

• ax (Axes object, optional) – A matplotlib Axes object to plot the section. Optional; if not provided, a call is made to plt.gca() to get the current (or create a new) Axes object.

• cmap – Maplotlib colormap to use. String or colormap object.

• alpha_clip – Two element tuple, specifying the percentiles of the data in sparse_array to clip for display. First element specifies the lower bound to clip, second element is the upper bound. Either or both elements can be None to indicate no clipping. Default is (None, 90).

Returns

image object returned from imshow.

Return type

image

Examples

Here, we use the normalized discharge field from the model as an example of sparse data.

golfcube = dm.sample_data.golf()
elevation_data = golfcube['eta'][-1, :, :]
sparse_data = (golfcube['discharge'][-1, ...] /
              (golfcube.meta['h0'].data *
               golfcube.meta['u0'][-1].data))

fig, ax = plt.subplots(1, 3, figsize=(8, 3))
for axi in ax.ravel():
    dm.plot.aerial_view( elevation_data, ax=axi)

dm.plot.overlay_sparse_array(
    sparse_data, ax=ax[0])  # default clip is (None, 90)
dm.plot.overlay_sparse_array(
    sparse_data, alpha_clip=(None, None), ax=ax[1])
dm.plot.overlay_sparse_array(
    sparse_data, alpha_clip=(70, 90), ax=ax[2])

plt.tight_layout()
plt.show()

(png, hires.png)

DeltaMetrics plot routines

These functions are similar to the convenience functions above, but mostly produce their own plots entirely, rather than adding a component of a plot.

show_one_dimensional_trajectory_to_strata(e)	1d elevation to stratigraphy.
show_histograms(*args[, sets, ax])	Show multiple histograms, including as sets.

DeltaMetrics colormaps

deltametrics.plot.cartographic_colormap(H_SL=0.0, h=4.5, n=1.0)

Colormap for an elevation map style.

Parameters

• H_SL (float, optional) – Sea level for the colormap. This is the break-point between blues and greens. Default value is 0.0.

• h (float, optional) – Channel depth for the colormap. This is some characteristic below sea-level relief for the colormap to extend to through the range of blues. Default value is 4.5.

• n (float, optional) – Surface topography relief for the colormap. This is some characteristic above sea-level relief for the colormap to extend to through the range of greens. Default value is 1.0.

Returns

• delta (matplotib.colors.ListedColormap) – The colormap object, which can then be used by other matplotlib plotting routines (see examples below).

• norm (matplotib.colors.BoundaryNorm) – The color normalization object, which can then be used by other matplotlib plotting routines (see examples below).

Examples

To display with default depth and relief parameters (left) and with adjust parameters to highlight depth variability (right):

rcm8cube = dm.sample_data.cube.rcm8()

cmap0, norm0 = dm.plot.cartographic_colormap(H_SL=0)
cmap1, norm1 = dm.plot.cartographic_colormap(H_SL=0, h=5, n=0.5)

fig, ax = plt.subplots(1, 2, figsize=(10, 4))
im0 = ax[0].imshow(rcm8cube['eta'][-1, ...], origin='lower',
               cmap=cmap0, norm=norm0)
cb0 = dm.plot.append_colorbar(im0, ax[0])
im1 = ax[1].imshow(rcm8cube['eta'][-1, ...], origin='lower',
               cmap=cmap1, norm=norm1)
cb1 = dm.plot.append_colorbar(im1, ax[1])
plt.show()

(png, hires.png)

deltametrics.plot.aerial_colormap()

Colormap for a pesudorealistic looking aerial shot.

Warning

Not implemented.

Plotting utility functions

These functions are mostly used internally.

deltametrics.plot.append_colorbar(ci, ax, size=2, labelsize=9, **kwargs)

Append a colorbar, consistently placed.

Adjusts some parameters of the parent axes as well.

Parameters

• ci (matplotlib.pyplot.pcolormesh, matplotlib.pyplot.ImageAxes) – The colored object generated via matplotlib that the colormap should be stolen from.

• ax (matplotlib.Axes) – The instance of axes to place the colorbar next to.

• adjust (bool) – Whether to adjust some minor attributes of the parent axis, for presentation.

• **kwargs – Passed to matplotlib.pyplot.colorbar.

Returns

cb – The colorbar instance created.

Return type

matplotlib.colorbar instance.

deltametrics.plot.get_display_arrays(VarInst, data=None)

Get arrays for display of Variables.

Function takes as argument a VariableInstance from a Section or Planform and an optional data argument, which specifies the data type to return, and gives back arrays of 1) data, 2) display x-coordinates and 3) display y-coordinates.

Parameters

• VarInst (BaseSectionVariable subclass) – The Variable instance to visualize. May be any subclass of BaseSectionVariable or BasePlanformVariable.

• data (str, optional) – The type of data to visualize. Supported options are ‘spacetime’, ‘preserved’, and ‘stratigraphy’. Default is to display full spacetime plot for variable generated from a DataCube, and stratigraphy for a StratigraphyCube variable. Note that variables sourced from a StratigraphyCube do not support the spacetime or preserved options, and a variable from DataCube will only support stratigraphy if the Cube has computed “quick” stratigraphy.

Returns

data, X, Y – Three matching-size ndarray representing the 1) data, 2) display x-coordinates and 3) display y-coordinates.

Return type

ndarray

deltametrics.plot.get_display_lines(VarInst, data=None)

Get lines for display of Variables.

Function takes as argument a VariableInstance from a Section or Planform and an optional data argument, which specifies the data type to return, and gives back data and line segments for display.

Parameters

• VarInst (BaseSectionVariable subclass) – The Variable instance to visualize. May be any subclass of BaseSectionVariable or BasePlanformVariable.

• data (str, optional) – The type of data to visualize. Supported options are ‘spacetime’, ‘preserved’, and ‘stratigraphy’. Default is to display full spacetime plot for variable generated from a DataCube. and stratigraphy for a StratigraphyCube variable. Variables from DataCube will only support stratigraphy if the Cube has computed “quick” stratigraphy.

• note:: (.) – Not currently implemented for variables from the StratigraphyCube.

Returns

vals, segments – An off-by-one sized array of data values (vals) to color the line segments (segments). The segments are organized along the 0th dimension of the array.

Return type

ndarray

deltametrics.plot.get_display_limits(VarInst, data=None)

Get limits to resize the display of Variables.

Function takes as argument a VariableInstance from a Section or Planform and an optional data argument, which specifies how to determine the limits to return.

Parameters

• VarInst (BaseSectionVariable subclass) – The Variable instance to visualize. May be any subclass of BaseSectionVariable or BasePlanformVariable.

• data (str, optional) – The type of data to compute limits for. Typically this will be the same value used with either get_display_arrays or get_display_lines. Supported options are ‘spacetime’, ‘preserved’, and ‘stratigraphy’.

Returns

xmin, xmax, ymin, ymax – Values to use as limits on a plot. Use with, for example, ax.set_xlim((xmin, xmax)).

Return type

float

deltametrics.plot._fill_steps(where, x=1, y=1, y0=0, **kwargs)

Fill rectangles where the boolean indicates True.

Creates an x by y matplotlib.patches.Rectangle at each index where the index i in boolean where indicates, with the lower left of the patch at (i, y0).

This utility function is utilized internally. Most often, it is used to highlight where time has been preserved (or eliminated) in a timeseries of data. For example, see show_one_dimensional_trajectory_to_strata.

Parameters

• where (ndarray) – Boolean numpy ndarray indicating which locations to create a patch at.

• x (float) – The x-direction width of the Rectangles.

• y (float) – The y-direction height of the Rectangles.

• y0 (float) – The y-direction origin (i.e., lower-left y-value) of the Rectangles.

• **kwargs – Additional matplotlib keyword arguments passed to the Rectangle instantiation. This is where you can set most matplotlib **kwargs (e.g., color, edgecolor).

Returns

pc – Collection of Rectangle Patch objects.

Return type

matplotlib.patches.PatchCollection

deltametrics.plot._scale_lightness(rgb, scale_l)

Utility to scale the lightness of some color.

Make a color perceptually lighter or darker. Adapted from https://stackoverflow.com/a/60562502/4038393.

Parameters

• rgb (tuple) – A three element tuple of the RGB values for the color.

• scale_l (float) – The scale factor, relative to a value of 1. A value of 1 performs no scaling, and values less than 1 darken the color, whereas values greater than 1 brighten the color.

Returns

scaled – Scaled color RGB tuple.

Return type

tuple

Example

(png, hires.png)