我们可以用迭代的方式来面对这个问题：
1.从最大可能的缩放开始（使插图占据图的整个高度）。因此，插图的一部分将与图重叠。
1.检查重叠开始点之前存在多少垂直间隙。
1.根据插图的“当前”高度，将其缩小以避免重叠。
1.在重新缩放之后，插图的宽度也会减小，因此我们可以使用留下的自由垂直间隙再次将其放大。
1.返回到2.，直到达到收敛/最大迭代次数。
在实践中，收敛是快速的，并且对于给定的数据在少于10次迭代中达到。
目视检查：

左上角 * 位置处的插图的代码**。

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset
def get_inset_max_zoom(x, y, xlim_inset, ylim_inset, max_iters=10):
    """ Zoom that maximizes inset size without overlapping the artists """
    # width and height of the inset in non-scaled coordinates.
    inset_w = xlim_inset[1] - xlim_inset[0]
    inset_h = ylim_inset[1] - ylim_inset[0]
    # max y-coordinate of the whole plot.
    y_max_plot = y.max()
    # start with maximum zoom.
    y_gap = y_max_plot - y.min()
    zoom = y_gap / inset_h
    for i in range(max_iters):
        y_affected_max = y[x < zoom * inset_w].max()
        # recalculate zoom by adjusting the gap.
        y_gap = y_max_plot - y_affected_max
        zoom = y_gap / inset_h
    return zoom
if __name__ == "__main__":
    # Change the seed to show produce different values.
    rng = np.random.RandomState(seed=0)
    # main plot.
    fig, ax = plt.subplots()
    xin = np.linspace(0, rng.uniform(.5, 4), 1000)
    x_samples = rng.uniform(
        0.9, 1.1, (1, 1000)) * np.sqrt(xin[:, np.newaxis])
    ax.fill_between(xin, x_samples.min(1), x_samples.max(1))
    # get xy pairs.
    y = x_samples.ravel()
    x = np.repeat(xin, x_samples.shape[1])
    # define the limits and location of the zoom inset.
    xlim_inset = (.05, .1)
    idx = np.logical_and(xin > xlim_inset[0], xin < xlim_inset[1])
    ylim_inset = (x_samples.min(1)[idx].min(), x_samples.max(1)[idx].max())
    loc = 'upper left'
    # get max zoom.
    zoom = get_inset_max_zoom(x, y, xlim_inset, ylim_inset, max_iters=5)
    # create the inset.
    axins = zoomed_inset_axes(ax, zoom=zoom, loc=loc, borderpad=0.5)
    axins.set(
        xlim=xlim_inset,
        ylim=ylim_inset,
        xticks=[], yticks=[])
    # connect the bboxes.
    mark_inset(ax, axins, loc1=4, loc2=3, fc="none", ec="0.5")
    # plot within the inset.
    axins.fill_between(xin, x_samples.min(1), x_samples.max(1))

泛化到4个角位置{* 左上角 、 右上角 、 右下角 、 左下角 *}。例如，对于loc = 'lower right'：

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset
def get_inset_max_zoom_given_loc(
        x, y, xlim_inset, ylim_inset, loc='upper left', max_iters=10):
    """ Zoom that maximizes inset size without overlapping the artists """
    # width and height of the inset in non-scaled coordinates.
    inset_w = xlim_inset[1] - xlim_inset[0]
    inset_h = ylim_inset[1] - ylim_inset[0]
    # handy variables.
    is_left = 'left' in loc
    is_upper = 'upper' in loc
    y_min_plot, y_max_plot = y.min(), y.max()
    y_xtm_plot = y_max_plot if is_upper else y_min_plot
    x_max_plot = x.max()
    # start with maximum zoom.
    y_gap = y_max_plot - y_min_plot
    zoom = y_gap / inset_h
    for i in range(max_iters):
        # get affected x-coordinate range.
        if is_left:
            x_affected = x < zoom * inset_w
        else:
            x_affected = x > x_max_plot - zoom * inset_w
        # get affected y-coordinate extremum.
        y_affected = y[x_affected]
        y_affected_xtm = y_affected.max() if is_upper else y_affected.min()
        # recalculate zoom by adjusting the gap.
        y_gap = abs(y_xtm_plot - y_affected_xtm)
        zoom = y_gap / inset_h
    return zoom
if __name__ == "__main__":
    # Change the seed to show produce different values.
    rng = np.random.RandomState(seed=0)
    # main plot.
    fig, ax = plt.subplots()
    xin = np.linspace(0, rng.uniform(.5, 4), 1000)
    x_samples = rng.uniform(
        0.9, 1.1, (1, 1000)) * np.sqrt(xin[:, np.newaxis])
    ax.fill_between(xin, x_samples.min(1), x_samples.max(1))
    # get xy pairs.
    y = x_samples.ravel()
    x = np.repeat(xin, x_samples.shape[1])
    # define the limits and location of the zoom inset.
    xlim_inset = (.05, .1)
    idx = np.logical_and(xin > xlim_inset[0], xin < xlim_inset[1])
    ylim_inset = (x_samples.min(1)[idx].min(), x_samples.max(1)[idx].max())
    loc = 'lower right'
    # get max zoom.
    zoom = get_inset_max_zoom_given_loc(
        x, y, xlim_inset, ylim_inset, loc=loc, max_iters=10)
    # create the inset.
    axins = zoomed_inset_axes(ax, zoom=zoom, loc=loc, borderpad=0.5)
    axins.set(
        xlim=xlim_inset,
        ylim=ylim_inset,
        xticks=[], yticks=[])
    # connect the bboxes.
    mark_inset(ax, axins, loc1=4, loc2=3, fc="none", ec="0.5")
    # plot within the inset.
    axins.fill_between(xin, x_samples.min(1), x_samples.max(1))