Python Bbox.get_points方法代码示例

# 需要导入模块: from matplotlib.transforms import Bbox [as 别名]
# 或者: from matplotlib.transforms.Bbox import get_points [as 别名]

#.........这里部分代码省略.........
        txt_size = np.array(estimate_text_size_px(label, fig=ax.figure,
                                                  size=font_size_pts))
        txt_size = txt_size.reshape([1, 2])

        # this initial offset is needed to shift the center of the label
        # to the data point
        offset0 = -txt_size / 2

        # now we can shift the label away from the data point by an amount
        # equal to half the text width/height + the padding
        offset1 = padding_px + txt_size / 2

        for key, abs_px_arr in cand_map.items():
            ioff = np.array(key, dtype='i').reshape(1, 2) - 1
            total_offset = offset0 + ioff * offset1
            # approx lower left corner of the text box in absolute pixels
            abs_px_arr[:, :, 0] = root_px + total_offset
            # approx upper right corner of the text box in absolute pixels
            abs_px_arr[:, :, 1] = abs_px_arr[:, :, 0] + txt_size

        # candidates_abs_px[i] has root @ root_px[i % n_candidates]
        candidates_abs_px = np.concatenate([cand_map[c] for c in choices],
                                           axis=0)

        # find how many other things each candidate overlaps
        n_overlaps = np.zeros_like(candidates_abs_px[:, 0, 0])

        for k, candidate in enumerate(candidates_abs_px):
            cand_bbox = Bbox(candidate.T)

            # penalty for each time a box overlaps a path that's already
            # on the plot
            for ipth, path in enumerate(paths_px):
                if path.intersects_bbox(cand_bbox, filled=is_filled[ipth]):
                    n_overlaps[k] += 1

            # slightly larger penalty if we intersect a text box that we
            # just added to the plot
            for ipth, path in enumerate(bbox_paths_px):
                if path.intersects_bbox(cand_bbox, filled=is_filled[ipth]):
                    n_overlaps[k] += 5

            # big penalty if the candidate is out of the current view
            if not (ax.bbox.contains(*cand_bbox.min) and
                    ax.bbox.contains(*cand_bbox.max)):
                n_overlaps[k] += 100

        # sort candidates by distance between center of text box and magnet
        magnet_dist = np.linalg.norm(np.mean(candidates_abs_px, axis=-1)
                                     - magnet_px, axis=1)
        isorted = np.argsort(magnet_dist)
        magnet_dist = np.array(magnet_dist[isorted])
        candidates_abs_px = np.array(candidates_abs_px[isorted, :, :])
        n_overlaps = np.array(n_overlaps[isorted])
        root_dat = np.array(root_dat[isorted % n_candidates, :])
        root_px = np.array(root_px[isorted % n_candidates, :])

        # sort candidates so the ones with the fewest overlaps are first
        # but do it with a stable algorithm so among the best candidates,
        # choose the one closest to the magnet
        sargs = np.argsort(n_overlaps, kind='mergesort')

        # >>> debug >>>
        if dax is not None:
            for _candidate, n_overlap in zip(candidates_abs_px, n_overlaps):
                _cand_bbox = Bbox(_candidate.T)
                _x0 = _cand_bbox.get_points()[0]
                _bbox_center = np.mean(_candidate, axis=-1)
                _ray_x = [_bbox_center[0], magnet_px[0]]
                _ray_y = [_bbox_center[1], magnet_px[1]]
                dax.plot(_ray_x, _ray_y, '-', alpha=0.3, color='grey')
                _rect = mpatches.Rectangle(_x0, _cand_bbox.width,
                                           _cand_bbox.height, fill=False)
                dax.add_patch(_rect)
                plt.text(_x0[0], _x0[1], label, color='gray')
                plt.text(_x0[0], _x0[1], '{0}'.format(n_overlap))
        # <<< debug <<<

        # pick winning candidate and add its bounding box to this list of
        # paths to avoid
        winner_abs_px = candidates_abs_px[sargs[0], :, :]
        xy_root_px = root_px[sargs[0], :]
        xy_root_dat = np.array(root_dat[sargs[0], :])
        xy_txt_offset = np.array(winner_abs_px[:, 0] - xy_root_px)

        corners = Bbox(winner_abs_px.T).corners()[(0, 1, 3, 2), :]
        bbox_paths_px += [Path(corners)]

        # a = plt.annotate(label, xy=xy_root_dat, xycoords='data',
        #                  xytext=xy_txt_offset, textcoords="offset pixels",
        #                  color=color, **kwargs)
        a = ax.annotate(label, xy=xy_root_dat, xycoords='data',
                        xytext=xy_txt_offset, textcoords="offset pixels",
                        color=color, **kwargs)
        annotations.append(a)

    if rand_state is not None:
        np.random.set_state(rand_state)

    return annotations