Zoomed inset in matplotlib without re-plotting data

Question

I\'m working on some matplotlib plots and need to have a zoomed inset. This is possible with the zoomed_inset_axes from the axes_grid1 toolkit. See

Answer 1

I recently worked on a solution to this problem in a piece of software I am writing, and decided to share it here in case anyone is still dealing with this issue. This solution requires no replotting, simply the use of a custom zoom axes class instead of the default one. It works using a custom Renderer, which acts as a middle-man between the matplotlib Artists and the actual Renderer. Artists are then simply drawn using the custom Renderer instead of the original Renderer provided. Below is the implementation:

from matplotlib.path import Path
from matplotlib.axes import Axes
from matplotlib.axes._axes import _make_inset_locator
from matplotlib.transforms import Bbox, Transform, IdentityTransform, Affine2D
from matplotlib.backend_bases import RendererBase
import matplotlib._image as _image
import numpy as np


class TransformRenderer(RendererBase):
    """
    A matplotlib renderer which performs transforms to change the final location of plotted
    elements, and then defers drawing work to the original renderer.
    """
    def __init__(self, base_renderer: RendererBase, mock_transform: Transform, transform: Transform,
                 bounding_axes: Axes):
        """
        Constructs a new TransformRender.

        :param base_renderer: The renderer to use for finally drawing objects.
        :param mock_transform: The transform or coordinate space which all passed paths/triangles/images will be
                               converted to before being placed back into display coordinates by the main transform.
                               For example if the parent axes transData is passed, all objects will be converted to
                               the parent axes data coordinate space before being transformed via the main transform
                               back into coordinate space.
        :param transform: The main transform to be used for plotting all objects once converted into the mock_transform
                          coordinate space. Typically this is the child axes data coordinate space (transData).
        :param bounding_axes: The axes to plot everything within. Everything outside of this axes will be clipped.
        """
        super().__init__()
        self.__renderer = base_renderer
        self.__mock_trans = mock_transform
        self.__core_trans = transform
        self.__bounding_axes = bounding_axes

    def _get_axes_display_box(self) -> Bbox:
        """
        Private method, get the bounding box of the child axes in display coordinates.
        """
        return self.__bounding_axes.patch.get_bbox().transformed(self.__bounding_axes.transAxes)

    def _get_transfer_transform(self, orig_transform):
        """
        Private method, returns the transform which translates and scales coordinates as if they were originally
        plotted on the child axes instead of the parent axes.

        :param orig_transform: The transform that was going to be originally used by the object/path/text/image.

        :return: A matplotlib transform which goes from original point data -> display coordinates if the data was
                 originally plotted on the child axes instead of the parent axes.
        """
        # We apply the original transform to go to display coordinates, then apply the parent data transform inverted
        # to go to the parent axes coordinate space (data space), then apply the child axes data transform to
        # go back into display space, but as if we originally plotted the artist on the child axes....
        return orig_transform + self.__mock_trans.inverted() + self.__core_trans

    # We copy all of the properties of the renderer we are mocking, so that artists plot themselves as if they were
    # placed on the original renderer.
    @property
    def height(self):
        return self.__renderer.get_canvas_width_height()[1]

    @property
    def width(self):
        return self.__renderer.get_canvas_width_height()[0]

    def get_text_width_height_descent(self, s, prop, ismath):
        return self.__renderer.get_text_width_height_descent(s, prop, ismath)

    def get_canvas_width_height(self):
        return self.__renderer.get_canvas_width_height()

    def get_texmanager(self):
        return self.__renderer.get_texmanager()

    def get_image_magnification(self):
        return self.__renderer.get_image_magnification()

    def _get_text_path_transform(self, x, y, s, prop, angle, ismath):
        return self.__renderer._get_text_path_transform(x, y, s, prop, angle, ismath)

    def option_scale_image(self):
        return False

    def points_to_pixels(self, points):
        return self.__renderer.points_to_pixels(points)

    def flipy(self):
        return self.__renderer.flipy()

    # Actual drawing methods below:

    def draw_path(self, gc, path: Path, transform: Transform, rgbFace=None):
        # Convert the path to display coordinates, but if it was originally drawn on the child axes.
        path = path.deepcopy()
        path.vertices = self._get_transfer_transform(transform).transform(path.vertices)
        bbox = self._get_axes_display_box()

        # We check if the path intersects the axes box at all, if not don't waste time drawing it.
        if(not path.intersects_bbox(bbox, True)):
            return

        # Change the clip to the sub-axes box
        gc.set_clip_rectangle(bbox)

        self.__renderer.draw_path(gc, path, IdentityTransform(), rgbFace)

    def _draw_text_as_path(self, gc, x, y, s: str, prop, angle, ismath):
        # If the text field is empty, don't even try rendering it...
        if((s is None) or (s.strip() == "")):
            return
        # Call the super class instance, which works for all cases except one checked above... (Above case causes error)
        super()._draw_text_as_path(gc, x, y, s, prop, angle, ismath)

    def draw_gouraud_triangle(self, gc, points, colors, transform):
        # Pretty much identical to draw_path, transform the points and adjust clip to the child axes bounding box.
        points = self._get_transfer_transform(transform).transform(points)
        path = Path(points, closed=True)
        bbox = self._get_axes_display_box()

        if(not path.intersects_bbox(bbox, True)):
            return

        gc.set_clip_rectangle(bbox)

        self.__renderer.draw_gouraud_triangle(gc, path.vertices, colors, IdentityTransform())

    # Images prove to be especially messy to deal with...
    def draw_image(self, gc, x, y, im, transform=None):
        mag = self.get_image_magnification()
        shift_data_transform = self._get_transfer_transform(IdentityTransform())
        axes_bbox = self._get_axes_display_box()
        # Compute the image bounding box in display coordinates.... Image arrives pre-magnified.
        img_bbox_disp = Bbox.from_bounds(x, y, im.shape[1], im.shape[0])
        # Now compute the output location, clipping it with the final axes patch.
        out_box = img_bbox_disp.transformed(shift_data_transform)
        clipped_out_box = Bbox.intersection(out_box, axes_bbox)

        if(clipped_out_box is None):
            return

        # We compute what the dimensions of the final output image within the sub-axes are going to be.
        x, y, out_w, out_h = clipped_out_box.bounds
        out_w, out_h = int(np.ceil(out_w * mag)), int(np.ceil(out_h * mag))

        if((out_w <= 0) or (out_h <= 0)):
            return

        # We can now construct the transform which converts between the original image (a 2D numpy array which starts
        # at the origin) to the final zoomed image (also a 2D numpy array which starts at the origin).
        img_trans = (
            Affine2D().scale(1/mag, 1/mag).translate(img_bbox_disp.x0, img_bbox_disp.y0)
            + shift_data_transform
            + Affine2D().translate(-clipped_out_box.x0, -clipped_out_box.y0).scale(mag, mag)
        )

        # We resize and zoom the original image onto the out_arr.
        out_arr = np.zeros((out_h, out_w, im.shape[2]), dtype=im.dtype)
        _image.resample(im, out_arr, img_trans, _image.NEAREST, alpha=1)
        _image.resample(im[:, :, 3], out_arr[:, :, 3], img_trans, _image.NEAREST, alpha=1)

        gc.set_clip_rectangle(clipped_out_box)

        x, y = clipped_out_box.x0, clipped_out_box.y0

        if(self.option_scale_image()):
            self.__renderer.draw_image(gc, x, y, out_arr, None)
        else:
            self.__renderer.draw_image(gc, x, y, out_arr)

class ZoomViewAxes(Axes):
    """
    A zoom axes which automatically displays all of the elements it is currently zoomed in on. Does not require
    Artists to be plotted twice.
    """
    def __init__(self, axes_of_zoom: Axes, rect: Bbox, transform = None, zorder = 5, **kwargs):
        """
        Construct a new zoom axes.

        :param axes_of_zoom: The axes to zoom in on which this axes will be nested inside.
        :param rect: The bounding box to place this axes in, within the parent axes.
        :param transform: The transform to use when placing this axes in the parent axes. Defaults to
                          'axes_of_zoom.transData'.
        :param zorder: An integer, the z-order of the axes. Defaults to 5, which means it is drawn on top of most
                       object in the plot.
        :param kwargs: Any other keyword arguments which the Axes class accepts.
        """
        if(transform is None):
            transform = axes_of_zoom.transData

        inset_loc = _make_inset_locator(rect.bounds, transform, axes_of_zoom)
        bb = inset_loc(None, None)

        super().__init__(axes_of_zoom.figure, bb.bounds, zorder=zorder, **kwargs)

        self.__zoom_axes = axes_of_zoom
        self.set_axes_locator(inset_loc)

        axes_of_zoom.add_child_axes(self)

    def draw(self, renderer=None, inframe=False):
        super().draw(renderer, inframe)

        if(not self.get_visible()):
            return

        axes_children = [
            *self.__zoom_axes.collections,
            *self.__zoom_axes.patches,
            *self.__zoom_axes.lines,
            *self.__zoom_axes.texts,
            *self.__zoom_axes.artists,
            *self.__zoom_axes.images
        ]

        img_boxes = []
        # We need to temporarily disable the clip boxes of all of the images, in order to allow us to continue
        # rendering them it even if it is outside of the parent axes (they might still be visible in this zoom axes).
        for img in self.__zoom_axes.images:
            img_boxes.append(img.get_clip_box())
            img.set_clip_box(img.get_window_extent(renderer))

        # Sort all rendered item by their z-order so the render in layers correctly...
        axes_children.sort(key=lambda obj: obj.get_zorder())

        # Construct mock renderer and draw all artists to it.
        mock_renderer = TransformRenderer(renderer, self.__zoom_axes.transData, self.transData, self)

        for artist in axes_children:
            if(artist is not self):
                artist.draw(mock_renderer)

        # Reset all of the image clip boxes...
        for img, box in zip(self.__zoom_axes.images, img_boxes):
            img.set_clip_box(box)

        # We need to redraw the splines if enabled, as we have finally drawn everything... This avoids other objects
        # being drawn over the splines
        if(self.axison and self._frameon):
            for spine in self.spines.values():
                spine.draw(renderer)

The example done using the custom zoom axes:

import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1.inset_locator import mark_inset
from zoomaxes import ZoomViewAxes
from matplotlib.transforms import Bbox

import numpy as np


def get_demo_image():
    from matplotlib.cbook import get_sample_data
    import numpy as np
    f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
    z = np.load(f)
    # z is a numpy array of 15x15
    return z, (-3, 4, -4, 3)


fig, ax = plt.subplots(figsize=[5, 4])

# prepare the demo image
Z, extent = get_demo_image()
Z2 = np.zeros([150, 150], dtype="d")
ny, nx = Z.shape
Z2[30:30 + ny, 30:30 + nx] = Z

# extent = [-3, 4, -4, 3]
ax.imshow(Z2, extent=extent, interpolation="nearest",
          origin="lower")

axins = ZoomViewAxes(ax, Bbox.from_bounds(0.6, 0.6, 0.35, 0.35), ax.transAxes)  # Use the new zoom axes...

# sub region of the original image
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)

plt.xticks(visible=False)
plt.yticks(visible=False)

# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")

plt.draw()
plt.show()

Result

Nearly identical image of plot shown in question:

Advantages:

• Fully automatic, and will redraw when changes are made to the plot.
• Works on pretty much all artists. (I have personally tested lines, boxes, arrows, text, and images)
• Avoids using blitting techniques, meaning zoom quality/depth is infinitely high. (Exception would be zooming on images obviously)

Disadvantages:

• Not as fast as bliting techniques. All artists of the parent axes have to be looped over and drawn for every ZoomViewAxes instance.

Answer 2

I think the following does what you want. Note that you use the returned handle to the first imshow and add it to the axis for the insert. You need to make a copy so you have a separate handle for each figure,

import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset

import numpy as np
import copy

def get_demo_image():
    from matplotlib.cbook import get_sample_data
    import numpy as np
    f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
    z = np.load(f)
    # z is a numpy array of 15x15
    return z, (-3,4,-4,3)

fig, ax = plt.subplots(figsize=[5,4])

# prepare the demo image
Z, extent = get_demo_image()
Z2 = np.zeros([150, 150], dtype="d")
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z

# extent = [-3, 4, -4, 3]
im = ax.imshow(Z2, extent=extent, interpolation="nearest",
          origin="lower")

#Without copy, image is shown in insert only
imcopy = copy.copy(im)
axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6
axins.add_artist(imcopy)

# sub region of the original image
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)

plt.xticks(visible=False)
plt.yticks(visible=False)

# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")

plt.draw()
plt.show()

For your wrapper function, this would be something like,

def plot_with_zoom(*args, **kwargs):
    im = ax.imshow(*args, **kwargs)
    imcopy = copy.copy(im)
    axins.add_artist(imcopy)

However, as imshow just displays the data stored in array Z as an image, I would think this solution would actually be slower than two separate calls to imshow. For plots which take more time, e.g. a contour plot or pcolormesh, this approach may be sensible...

EDIT:

Beyond a single imshow, and for multiple plots of different types. Plotting functions all return different handles (e.g. plot returns a list of lines, imshow returns a matplotlib.image.AxesImage, etc). You could keep adding these handles to a list (or dict) as you plot (or use a collection if they are similar enough). Then you could write a general function which adds them to an axis using add_artist or add_patch methods from the zoomed axis, probably with if type checking to deal with the various types used in the plot. A simpler method may be to loop over ax.get_children() and reuse anything which isn't an element of the axis itself.

Another option may be to look into blitting techniques, rasterization or other techniques used to speed up animation, for example using fig.canvas.copy_from_bbox or fig.canvas.tostring_rgb to copy the entire figure as an image (see why is plotting with Matplotlib so slow?‌​low). You could also draw the figure, save it to a non-vector graphic (with savefig or to a StringIO buffer), read back in and plot a zoomed in version.