轴缩放效果

轴缩放效果示例

  1. from matplotlib.transforms import (
  2.     Bbox, TransformedBbox, blended_transform_factory)
  4. from mpl_toolkits.axes_grid1.inset_locator import (
  5.     BboxPatch, BboxConnector, BboxConnectorPatch)
  8. def connect_bbox(bbox1, bbox2,
  9.                  loc1a, loc2a, loc1b, loc2b,
  10.                  prop_lines, prop_patches=None):
  11.     if prop_patches is None:
  12.         prop_patches = {
  13.             **prop_lines,
  14.             "alpha": prop_lines.get("alpha", 1) * 0.2,
  15.         }
  17.     c1 = BboxConnector(bbox1, bbox2, loc1=loc1a, loc2=loc2a, **prop_lines)
  18.     c1.set_clip_on(False)
  19.     c2 = BboxConnector(bbox1, bbox2, loc1=loc1b, loc2=loc2b, **prop_lines)
  20.     c2.set_clip_on(False)
  22.     bbox_patch1 = BboxPatch(bbox1, **prop_patches)
  23.     bbox_patch2 = BboxPatch(bbox2, **prop_patches)
  25.     p = BboxConnectorPatch(bbox1, bbox2,
  26.                            # loc1a=3, loc2a=2, loc1b=4, loc2b=1,
  27.                            loc1a=loc1a, loc2a=loc2a, loc1b=loc1b, loc2b=loc2b,
  28.                            **prop_patches)
  29.     p.set_clip_on(False)
  31.     return c1, c2, bbox_patch1, bbox_patch2, p
  34. def zoom_effect01(ax1, ax2, xmin, xmax, **kwargs):
  35.     """
  36.     ax1 : the main axes
  37.     ax1 : the zoomed axes
  38.     (xmin,xmax) : the limits of the colored area in both plot axes.
  40.     connect ax1 & ax2. The x-range of (xmin, xmax) in both axes will
  41.     be marked.  The keywords parameters will be used ti create
  42.     patches.
  44.     """
  46.     trans1 = blended_transform_factory(ax1.transData, ax1.transAxes)
  47.     trans2 = blended_transform_factory(ax2.transData, ax2.transAxes)
  49.     bbox = Bbox.from_extents(xmin, 0, xmax, 1)
  51.     mybbox1 = TransformedBbox(bbox, trans1)
  52.     mybbox2 = TransformedBbox(bbox, trans2)
  54.     prop_patches = {**kwargs, "ec": "none", "alpha": 0.2}
  56.     c1, c2, bbox_patch1, bbox_patch2, p = connect_bbox(
  57.         mybbox1, mybbox2,
  58.         loc1a=3, loc2a=2, loc1b=4, loc2b=1,
  59.         prop_lines=kwargs, prop_patches=prop_patches)
  61.     ax1.add_patch(bbox_patch1)
  62.     ax2.add_patch(bbox_patch2)
  63.     ax2.add_patch(c1)
  64.     ax2.add_patch(c2)
  65.     ax2.add_patch(p)
  67.     return c1, c2, bbox_patch1, bbox_patch2, p
  70. def zoom_effect02(ax1, ax2, **kwargs):
  71.     """
  72.     ax1 : the main axes
  73.     ax1 : the zoomed axes
  75.     Similar to zoom_effect01.  The xmin & xmax will be taken from the
  76.     ax1.viewLim.
  77.     """
  79.     tt = ax1.transScale + (ax1.transLimits + ax2.transAxes)
  80.     trans = blended_transform_factory(ax2.transData, tt)
  82.     mybbox1 = ax1.bbox
  83.     mybbox2 = TransformedBbox(ax1.viewLim, trans)
  85.     prop_patches = {**kwargs, "ec": "none", "alpha": 0.2}
  87.     c1, c2, bbox_patch1, bbox_patch2, p = connect_bbox(
  88.         mybbox1, mybbox2,
  89.         loc1a=3, loc2a=2, loc1b=4, loc2b=1,
  90.         prop_lines=kwargs, prop_patches=prop_patches)
  92.     ax1.add_patch(bbox_patch1)
  93.     ax2.add_patch(bbox_patch2)
  94.     ax2.add_patch(c1)
  95.     ax2.add_patch(c2)
  96.     ax2.add_patch(p)
  98.     return c1, c2, bbox_patch1, bbox_patch2, p
  101. import matplotlib.pyplot as plt
  103. plt.figure(1, figsize=(5, 5))
  104. ax1 = plt.subplot(221)
  105. ax2 = plt.subplot(212)
  106. ax2.set_xlim(0, 1)
  107. ax2.set_xlim(0, 5)
  108. zoom_effect01(ax1, ax2, 0.2, 0.8)
  111. ax1 = plt.subplot(222)
  112. ax1.set_xlim(2, 3)
  113. ax2.set_xlim(0, 5)
  114. zoom_effect02(ax1, ax2)
  116. plt.show()

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