text: from __future__ import absolute_import, division, print_function
  
  import numpy as np
  
  from glue.utils import unbroadcast, broadcast_to
  
  __all__ = ['points_inside_poly', 'polygon_line_intersections', 'floodfill']
  
  
  def points_inside_poly(x, y, vx, vy):
  
      if x.dtype.kind == 'M' and vx.dtype.kind == 'M':
          vx = vx.astype(x.dtype).astype(float)
          x = x.astype(float)
  
      if y.dtype.kind == 'M' and vy.dtype.kind == 'M':
          vy = vy.astype(y.dtype).astype(float)
          y = y.astype(float)
  
      original_shape = x.shape
  
      x = unbroadcast(x)
      y = unbroadcast(y)
  
      x = x.astype(float)
      y = y.astype(float)
  
      x, y = np.broadcast_arrays(x, y)
  
      reduced_shape = x.shape
  
      x = x.flat
      y = y.flat
  
      from matplotlib.path import Path
      p = Path(np.column_stack((vx, vy)))
  
      keep = ((x >= np.min(vx)) &
              (x <= np.max(vx)) &
              (y >= np.min(vy)) &
              (y <= np.max(vy)))
  
      inside = np.zeros(len(x), bool)
  
      x = x[keep]
      y = y[keep]
  
      coords = np.column_stack((x, y))
  
      inside[keep] = p.contains_points(coords).astype(bool)
  
      good = np.isfinite(x) & np.isfinite(y)
      inside[keep][~good] = False
  
      inside = inside.reshape(reduced_shape)
      inside = broadcast_to(inside, original_shape)
  
      return inside
  
  
  def polygon_line_intersections(px, py, xval=None, yval=None):
      """
      Find all the segments of intersection between a polygon and an infinite
      horizontal/vertical line.
  
      The polygon is assumed to be closed. Due to numerical precision, the
      behavior at the edges of polygons is not always predictable, i.e. a point
      on the edge of a polygon may be considered inside or outside the polygon.
  
      Parameters
      ----------
      px, py : `~numpy.ndarray`
          The vertices of the polygon
      xval : float, optional
          The x coordinate of the line (for vertical lines). This should only be
          specified if yval is not specified.
      yval : float, optional
          The y coordinate of the line (for horizontal lines). This should only be
          specified if xval is not specified.
  
      Returns
      -------
      segments : list
          A list of segments given as tuples of coordinates along the line.
      """
  
      if xval is not None and yval is not None:
          raise ValueError("Only one of xval or yval should be specified")
      elif xval is None and yval is None:
          raise ValueError("xval or yval should be specified")
      if yval is not None:
          return polygon_line_intersections(py, px, xval=yval)
  
      px = np.asarray(px, dtype=float)
      py = np.asarray(py, dtype=float)
  
      # Make sure that the polygon is closed
      if px[0] != px[-1] or py[0] != py[-1]:
          px = np.hstack([px, px[0]])
          py = np.hstack([py, py[0]])
  
      # For convenience
      x1, x2 = px[:-1], px[1:]
      y1, y2 = py[:-1], py[1:]
  
      # Vertices that intersect
      keep1 = (px == xval)
      points1 = py[keep1]
  
      # Segments (excluding vertices) that intersect
      keep2 = ((x1 < xval) & (x2 > xval)) | ((x2 < xval) & (x1 > xval))
      points2 = (y1 + (y2 - y1) * (xval - x1) / (x2 - x1))[keep2]
  
      # Make unique and sort
      points = np.array(np.sort(np.unique(np.hstack([points1, points2]))))
  
      # Because of various corner cases, we don't actually know which pairs of
      # points are inside the polygon, so we check this using the mid-points
      ymid = 0.5 * (points[:-1] + points[1:])
      xmid = np.repeat(xval, len(ymid))
      keep = points_inside_poly(xmid, ymid, px, py)
  
      segments = list(zip(points[:-1][keep], points[1:][keep]))
  
      return segments
  
  
  def floodfill(data, start_coords, threshold):
  
      from scipy.ndimage.measurements import label
  
      # Determine value at the starting coordinates
      value = data[start_coords]
  
      # Determine all pixels that match
      mask = (data > value * (2 - threshold)) & (data < value * threshold)
  
      # Determine all individual chunks
      labels, num_features = label(mask)
  
      mask = labels == labels[start_coords]
  
      return mask
  