# Plot Modifications: Overplotting Contours, Velocities, Particles, and More¶

After a plot is generated using the standard tools (e.g. SlicePlot, ProjectionPlot, etc.), it can be annotated with any number of callbacks before being saved to disk. These callbacks can modify the plots by adding lines, text, markers, streamlines, velocity vectors, contours, and more.

Callbacks can be applied to plots created with SlicePlot, ProjectionPlot, OffAxisSlicePlot, or OffAxisProjectionPlot by calling one of the annotate_ methods that hang off of the plot object. The annotate_ methods are dynamically generated based on the list of available callbacks. For example:

slc = SlicePlot(ds,0,'density')
slc.annotate_title('This is a Density plot')


would add the TitleCallback() to the plot object. All of the callbacks listed below are available via similar annotate_ functions.

To clear one or more annotations from an existing plot, see the annotate_clear() function.

For a brief demonstration of a few of these callbacks in action together, see the cookbook recipe: Annotating Plots to Include Lines, Text, Shapes, etc..

## Coordinate Systems in Callbacks¶

Many of the callbacks (e.g. TextLabelCallback) are specified to occur at user-defined coordinate locations (like where to place a marker or text on the plot). There are several different coordinate systems used to identify these locations. These coordinate systems can be specified with the coord_system keyword in the relevant callback, which is by default set to data. The valid coordinate systems are:

data – the 3D dataset coordinates

plot – the 2D coordinates defined by the actual plot limits

axis – the MPL axis coordinates: (0,0) is lower left; (1,1) is upper right

figure – the MPL figure coordinates: (0,0) is lower left, (1,1) is upper right

Here we will demonstrate these different coordinate systems for an projection of the x-plane (i.e. with axes in the y and z directions):

import yt

s = yt.SlicePlot(ds, 'x', 'density')
s.set_axes_unit('kpc')

# Plot marker and text in data coords
s.annotate_marker((0.2, 0.5, 0.9), coord_system='data')
s.annotate_text((0.2, 0.5, 0.9), 'data: (0.2, 0.5, 0.9)', coord_system='data')

# Plot marker and text in plot coords
s.annotate_marker((200, -300), coord_system='plot')
s.annotate_text((200, -300), 'plot: (200, -300)', coord_system='plot')

# Plot marker and text in axis coords
s.annotate_marker((0.1, 0.2), coord_system='axis')
s.annotate_text((0.1, 0.2), 'axis: (0.1, 0.2)', coord_system='axis')

# Plot marker and text in figure coords
# N.B. marker will not render outside of axis bounds
s.annotate_marker((0.1, 0.2), coord_system='figure',
plot_args={'color':'black'})
s.annotate_text((0.1, 0.2), 'figure: (0.1, 0.2)', coord_system='figure',
text_args={'color':'black'})
s.save()


Note that for non-cartesian geometries and coord_system="data", the coordinates are still interpreted in the corresponding cartesian system. For instance using a polar dataset from AMRVAC :

import yt

s = yt.plot2d(ds, 'density')
s.set_background_color("density", "black")

# Plot marker and text in data coords
s.annotate_marker((0.2, 0.5, 0.9), coord_system='data')
s.annotate_text((0.2, 0.5, 0.9), 'data: (0.2, 0.5, 0.9)', coord_system='data')

# Plot marker and text in plot coords
s.annotate_marker((0.4, -0.5), coord_system='plot')
s.annotate_text((0.4, -0.5), 'plot: (0.4, -0.5)', coord_system='plot')

# Plot marker and text in axis coords
s.annotate_marker((0.1, 0.2), coord_system='axis')
s.annotate_text((0.1, 0.2), 'axis: (0.1, 0.2)', coord_system='axis')

# Plot marker and text in figure coords
# N.B. marker will not render outside of axis bounds
s.annotate_marker((0.6, 0.2), coord_system='figure')
s.annotate_text((0.6, 0.2), 'figure: (0.6, 0.2)', coord_system='figure')
s.save()


## Available Callbacks¶

The underlying functions are more thoroughly documented in Callback List.

### Clear Callbacks (Some or All)¶

annotate_clear(index=None)

This function will clear previous annotations (callbacks) in the plot. If no index is provided, it will clear all annotations to the plot. If an index is provided, it will clear only the Nth annotation to the plot. Note that the index goes from 0..N, and you can specify the index of the last added annotation as -1.

import yt
p = yt.SlicePlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
p.annotate_scale()
p.annotate_timestamp()

# Oops, I didn't want any of that.
p.annotate_clear()
p.save()


### Overplot Arrow¶

annotate_arrow(self, pos, length=0.03, coord_system='data', plot_args=None)

(This is a proxy for ArrowCallback.)

Overplot an arrow pointing at a position for highlighting a specific feature. Arrow points from lower left to the designated position with arrow length “length”.

import yt
slc = yt.SlicePlot(ds, 'z', 'density', width=(10,'kpc'), center='c')
slc.annotate_arrow((0.5, 0.5, 0.5), length=0.06, plot_args={'color':'blue'})
slc.save()


### Clump Finder Callback¶

annotate_clumps(self, clumps, plot_args=None)

(This is a proxy for ClumpContourCallback.)

Take a list of clumps and plot them as a set of contours.

import yt
import numpy as np
from yt.data_objects.level_sets.api import \
Clump, find_clumps

data_source = ds.disk([0.5, 0.5, 0.5], [0., 0., 1.],
(8., 'kpc'), (1., 'kpc'))

c_min = 10**np.floor(np.log10(data_source['density']).min()  )
c_max = 10**np.floor(np.log10(data_source['density']).max()+1)

master_clump = Clump(data_source, 'density')

find_clumps(master_clump, c_min, c_max, 2.0)
leaf_clumps = master_clump.leaves

prj = yt.ProjectionPlot(ds, 2, 'density', center='c', width=(20,'kpc'))
prj.annotate_clumps(leaf_clumps)
prj.save('clumps')


### Overplot Contours¶

annotate_contour(self, field, ncont=5, factor=4, take_log=False, clim=None, plot_args=None, label=False, text_args=None, data_source=None)

(This is a proxy for ContourCallback.)

Add contours in field to the plot. ncont governs the number of contours generated, factor governs the number of points used in the interpolation, take_log governs how it is contoured and clim gives the (upper, lower) limits for contouring.

import yt
s = yt.SlicePlot(ds, "x", "density", center="max")
s.annotate_contour("temperature")
s.save()


### Overplot Quivers¶

#### Axis-Aligned Data Sources¶

annotate_quiver(self, field_x, field_y, factor=16, scale=None, scale_units=None, normalize=False, plot_args=None)

(This is a proxy for QuiverCallback.)

Adds a ‘quiver’ plot to any plot, using the field_x and field_y from the associated data, skipping every factor datapoints in the discretization. scale is the data units per arrow length unit using scale_units. If normalize is True, the fields will be scaled by their local (in-plane) length, allowing morphological features to be more clearly seen for fields with substantial variation in field strength. Additional arguments can be passed to the plot_args dictionary, see matplotlib.axes.Axes.quiver for more info.

import yt
p = yt.ProjectionPlot(ds, 'z', 'density', center=[0.5, 0.5, 0.5],
weight_field='density', width=(20, 'kpc'))
p.annotate_quiver('velocity_x', 'velocity_y', factor=16,
plot_args={"color": "purple"})
p.save()


#### Off-Axis Data Sources¶

annotate_cquiver(self, field_x, field_y, factor=16, scale=None, scale_units=None, normalize=False, plot_args=None)

(This is a proxy for CuttingQuiverCallback.)

Get a quiver plot on top of a cutting plane, using the field_x and field_y from the associated data, skipping every factor datapoints in the discretization. scale is the data units per arrow length unit using scale_units. If normalize is True, the fields will be scaled by their local (in-plane) length, allowing morphological features to be more clearly seen for fields with substantial variation in field strength. Additional arguments can be passed to the plot_args dictionary, see matplotlib.axes.Axes.quiver for more info.

import yt
s = yt.OffAxisSlicePlot(ds, [1,1,0], ["density"], center="c")
s.annotate_cquiver('cutting_plane_velocity_x', 'cutting_plane_velocity_y',
factor=10, plot_args={'color':'orange'})
s.zoom(1.5)
s.save()


### Overplot Grids¶

annotate_grids(self, alpha=0.7, min_pix=1, min_pix_ids=20, draw_ids=False, id_loc='lower left', periodic=True, min_level=None, max_level=None, cmap='B-W Linear_r', edgecolors=None, linewidth=1.0)

(This is a proxy for GridBoundaryCallback.)

Adds grid boundaries to a plot, optionally with alpha-blending via the alpha keyword. Cuttoff for display is at min_pix wide. draw_ids puts the grid id in the id_loc corner of the grid. (id_loc can be upper/lower left/right. draw_ids is not so great in projections…)

import yt
slc = yt.SlicePlot(ds, 'z', 'density', width=(10,'kpc'), center='max')
slc.annotate_grids()
slc.save()


### Overplot Cell Edges¶

annotate_cell_edges(line_width=0.002, alpha=1.0, color='black')

(This is a proxy for CellEdgesCallback.)

Annotate the edges of cells, where the line_width relative to size of the longest plot axis is specified. The alpha of the overlaid image and the color of the lines are also specifiable. Note that because the lines are drawn from both sides of a cell, the image sometimes has the effect of doubling the line width. Color here is a matplotlib color name or a 3-tuple of RGB float values.

import yt
slc = yt.SlicePlot(ds, 'z', 'density', width=(10,'kpc'), center='max')
slc.annotate_cell_edges()
slc.save()


### Overplot Halo Annotations¶

annotate_halos(self, halo_catalog, circle_args=None, width=None, annotate_field=None, radius_field='virial_radius', center_field_prefix='particle_position', text_args=None, factor=1.0)

(This is a proxy for HaloCatalogCallback.)

Accepts a HaloCatalog and plots a circle at the location of each halo with the radius of the circle corresponding to the virial radius of the halo. Also accepts a loaded halo catalog dataset or a data container from a halo catalog dataset. If width is set to None (default) all halos are plotted, otherwise it accepts a tuple in the form (1.0, ‘Mpc’) to only display halos that fall within a slab with width width centered on the center of the plot data. The appearance of the circles can be changed with the circle_kwargs dictionary, which is supplied to the Matplotlib patch Circle. One can label each of the halos with the annotate_field, which accepts a field contained in the halo catalog to add text to the plot near the halo (example: annotate_field= 'particle_mass' will write the halo mass next to each halo, whereas 'particle_identifier' shows the halo number). The size of the circles is found from the field radius_field which is 'virial_radius' by default. If another radius has been found as part of your halo analysis workflow, you can save that field and use it as the radius_field to change the size of the halos. The position of each halo is determined using center_field_prefix in the following way. If 'particle_position' is the value of center_field_prefix as is the default, the x value of the halo position is stored in the field 'particle_position_x', y is 'particle_position_y', and z is 'particle_position_z'. If you have stored another set of coordinates for each halo as part of your halo analysis as fields such as 'halo_position_x', you can use these fields to determine halo position by passing 'halo_position' to center_field_prefix. font_kwargs contains the arguments controlling the text appearance of the annotated field. Factor is the number the virial radius is multiplied by for plotting the circles. Ex: factor=2.0 will plot circles with twice the radius of each halo virial radius.

import yt

prj = yt.ProjectionPlot(data_ds, 'z', 'density')
prj.annotate_halos(halos_ds, annotate_field='particle_identifier')
prj.save()


### Overplot a Straight Line¶

annotate_line(self, p1, p2, coord_system='data', plot_args=None)

(This is a proxy for LinePlotCallback.)

Overplot a line with endpoints at p1 and p2. p1 and p2 should be 2D or 3D coordinates consistent with the coordinate system denoted in the “coord_system” keyword.

import yt
p = yt.ProjectionPlot(ds, 'z', 'density', center='m', width=(10, 'kpc'))
p.annotate_line((0.3, 0.4), (0.8, 0.9), coord_system='axis')
p.save()


### Overplot Magnetic Field Quivers¶

annotate_magnetic_field(self, factor=16, scale=None, scale_units=None, normalize=False, plot_args=None)

(This is a proxy for MagFieldCallback.)

Adds a ‘quiver’ plot of magnetic field to the plot, skipping every factor datapoints in the discretization. scale is the data units per arrow length unit using scale_units. If normalize is True, the magnetic fields will be scaled by their local (in-plane) length, allowing morphological features to be more clearly seen for fields with substantial variation in field strength. Additional arguments can be passed to the plot_args dictionary, see matplotlib.axes.Axes.quiver for more info.

import yt
parameters={"time_unit":(1, 'Myr'), "length_unit":(1, 'Mpc'),
"mass_unit":(1e17, 'Msun')})
p = yt.ProjectionPlot(ds, 'z', 'density', center='c', width=(300, 'kpc'))
p.save()


### Annotate a Point With a Marker¶

annotate_marker(self, pos, marker='x', coord_system='data', plot_args=None)

(This is a proxy for MarkerAnnotateCallback.)

Overplot a marker on a position for highlighting specific features.

import yt
s = yt.SlicePlot(ds, 'z', 'density', center='c', width=(10, 'kpc'))
s.annotate_marker((-2,-2), coord_system='plot',
plot_args={'color':'blue','s':500})
s.save()


### Overplotting Particle Positions¶

annotate_particles(self, width, p_size=1.0, col='k', marker='o', stride=1, ptype='all', alpha=1.0, data_source=None)

(This is a proxy for ParticleCallback.)

Adds particle positions, based on a thick slab along axis with a width along the line of sight. p_size controls the number of pixels per particle, and col governs the color. ptype will restrict plotted particles to only those that are of a given type. data_source will only plot particles contained within the data_source object.

WARNING: if data_source is a yt.data_objects.selection_data_containers.YTCutRegion then the width parameter is ignored.

import yt
p = yt.ProjectionPlot(ds, "x", "density", center='m', width=(10, 'Mpc'))
p.annotate_particles((10, 'Mpc'))
p.save()


To plot only the central particles

import yt
p = yt.ProjectionPlot(ds, "x", "density", center='m', width=(10, 'Mpc'))
sp = ds.sphere([0.5,0.5,0.5],ds.quan(1,'Mpc'))
p.annotate_particles((10, 'Mpc'),data_source=sp)
p.save()


### Overplot a Circle on a Plot¶

annotate_sphere(self, center, radius, circle_args=None, coord_system='data', text=None, text_args=None)

(This is a proxy for SphereCallback.)

Overplot a circle with designated center and radius with optional text.

import yt
p = yt.ProjectionPlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
circle_args={'color':'black'})
p.save()


### Overplot Streamlines¶

annotate_streamlines(self, field_x, field_y, factor=16, density=1, display_threshold=None, plot_args=None)

(This is a proxy for StreamlineCallback.)

Add streamlines to any plot, using the field_x and field_y from the associated data, using nx and ny starting points that are bounded by xstart and ystart. To begin streamlines from the left edge of the plot, set start_at_xedge to True; for the bottom edge, use start_at_yedge. A line with the qmean vector magnitude will cover 1.0/factor of the image.

import yt
s = yt.SlicePlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
s.annotate_streamlines('velocity_x', 'velocity_y')
s.save()


### Overplot Line Integral Convolution¶

annotate_line_integral_convolution(self, field_x, field_y, texture=None, kernellen=50.0, lim=0.5, 0.6, cmap='binary', alpha=0.8, const_alpha=False)

(This is a proxy for LineIntegralConvolutionCallback.)

Add line integral convolution to any plot, using the field_x and field_y from the associated data. A white noise background will be used for texture as default. Adjust the bounds of lim in the range of [0, 1] which applies upper and lower bounds to the values of line integral convolution and enhance the visibility of plots. When const_alpha=False, alpha will be weighted spatially by the values of line integral convolution; otherwise a constant value of the given alpha is used.

import yt
s = yt.SlicePlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
s.annotate_line_integral_convolution('velocity_x', 'velocity_y', lim=(0.5,0.65))
s.save()


### Overplot Text¶

annotate_text(self, pos, text, coord_system='data', text_args=None, inset_box_args=None)

(This is a proxy for TextLabelCallback.)

Overplot text on the plot at a specified position. If you desire an inset box around your text, set one with the inset_box_args dictionary keyword.

import yt
s = yt.SlicePlot(ds, 'z', 'density', center='max', width=(10, 'kpc'))
s.annotate_text((2, 2), 'Galaxy!', coord_system='plot')
s.save()


annotate_title(self, title='Plot')

(This is a proxy for TitleCallback.)

Accepts a title and adds it to the plot.

import yt
p = yt.ProjectionPlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
p.annotate_title('Density Plot')
p.save()


### Overplot Quivers for the Velocity Field¶

annotate_velocity(self, factor=16, scale=None, scale_units=None, normalize=False, plot_args=None)

(This is a proxy for VelocityCallback.)

Adds a ‘quiver’ plot of velocity to the plot, skipping every factor datapoints in the discretization. scale is the data units per arrow length unit using scale_units. If normalize is True, the velocity fields will be scaled by their local (in-plane) length, allowing morphological features to be more clearly seen for fields with substantial variation in field strength. Additional arguments can be passed to the plot_args dictionary, see matplotlib.axes.Axes.quiver for more info.

import yt
p = yt.SlicePlot(ds, 'z', 'density', center='m', width=(10, 'kpc'))
p.save()


### Add the Current Time and/or Redshift¶

annotate_timestamp(x_pos=None, y_pos=None, corner='lower_left', time=True, redshift=False, time_format='t = {time:.1f} {units}', time_unit=None, time_offset=None, redshift_format='z = {redshift:.2f}', draw_inset_box=False, coord_system='axis', text_args=None, inset_box_args=None)

(This is a proxy for TimestampCallback.)

Annotates the timestamp and/or redshift of the data output at a specified location in the image (either in a present corner, or by specifying (x,y) image coordinates with the x_pos, y_pos arguments. If no time_units are specified, it will automatically choose appropriate units. It allows for custom formatting of the time and redshift information, the specification of an inset box around the text, and changing the value of the timestamp via a constant offset.

import yt
p = yt.SlicePlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
p.annotate_timestamp()
p.save()


### Add a Physical Scale Bar¶

annotate_scale(corner='lower_right', coeff=None, unit=None, pos=None,
scale_text_format="{scale} {units}", max_frac=0.16, min_frac=0.015, coord_system='axis', text_args=None, size_bar_args=None, draw_inset_box=False, inset_box_args=None)

(This is a proxy for ScaleCallback.)

Annotates the scale of the plot at a specified location in the image (either in a preset corner, or by specifying (x,y) image coordinates with the pos argument. Coeff and units (e.g. 1 Mpc or 100 kpc) refer to the distance scale you desire to show on the plot. If no coeff and units are specified, an appropriate pair will be determined such that your scale bar is never smaller than min_frac or greater than max_frac of your plottable axis length. Additional customization of the scale bar is possible by adjusting the text_args and size_bar_args dictionaries. The text_args dictionary accepts matplotlib’s font_properties arguments to override the default font_properties for the current plot. The size_bar_args dictionary accepts keyword arguments for the AnchoredSizeBar class in matplotlib’s axes_grid toolkit. Finally, the format of the scale bar text can be adjusted using the scale_text_format keyword argument.

import yt
p = yt.SlicePlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
p.annotate_scale()
p.save()


### Annotate Triangle Facets Callback¶

annotate_triangle_facets(triangle_vertices, plot_args=None)

(This is a proxy for TriangleFacetsCallback.)

This add a line collection of a SlicePlot’s plane-intersection with the triangles to the plot. This callback is ideal for a dataset representing a geometric model of triangular facets.

import h5py
import os
import yt

# Create the desired slice plot
s = yt.SlicePlot(pf, 'z', ('moab','TALLY_TAG'))

#get triangle vertices from file (in this case hdf5)

#setup file path for yt test directory
filename = os.path.join(yt.config.ytcfg.get("yt", "test_data_dir"),
"MoabTest/mcnp_n_impr_fluka.h5m")
f = h5py.File(filename, "r")
coords = f["/tstt/nodes/coordinates"][:]
conn = f["/tstt/elements/Tri3/connectivity"][:]
points = coords[conn-1]

# Annotate slice-triangle intersection contours to the plot
s.annotate_triangle_facets(points, plot_args={"colors": 'black'})
s.save()


### Annotate Mesh Lines Callback¶

annotate_mesh_lines(plot_args=None)

(This is a proxy for MeshLinesCallback.)

This draws the mesh line boundaries over a plot using a Matplotlib line collection. This callback is only useful for unstructured or semi-structured mesh datasets.

import yt
sl = yt.SlicePlot(ds, 2, ('connect1', 'nodal_aux'))
sl.annotate_mesh_lines(plot_args={'color':'black'})
sl.save()


### Overplot the Path of a Ray¶

annotate_ray(ray, plot_args=None)

(This is a proxy for RayCallback.)

Adds a line representing the projected path of a ray across the plot. The ray can be either a YTOrthoRay, YTRay, or a LightRay object. annotate_ray() will properly account for periodic rays across the volume.

import yt