:py:mod:`uavfpy.planner.coverage.polygon`
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.. py:module:: uavfpy.planner.coverage.polygon

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Module Contents
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Classes
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.. autoapisummary::

   uavfpy.planner.coverage.polygon.RandomPolygon



Functions
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.. autoapisummary::

   uavfpy.planner.coverage.polygon.beta_clusters
   uavfpy.planner.coverage.polygon.remove_close_points
   uavfpy.planner.coverage.polygon.stupid_spiky_polygon



.. py:function:: beta_clusters(clusters: int = 3, ppc: int = 20, alpha: float = 4.0, beta: float = 4.0) -> numpy.ndarray

   
















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.. py:function:: remove_close_points(points: numpy.ndarray, eps: float = 0.07) -> numpy.ndarray

   
















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.. py:class:: RandomPolygon(n, points=None, holes=2)

   Bases: :py:obj:`object`

   
















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   .. py:method:: removable_interiors(self, dt: scipy.spatial.Delaunay) -> tuple

      
      find indices of interior simplices that are safe to remove in dt
















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   .. py:method:: removable_exteriors(self, dt: scipy.spatial.Delaunay, points: numpy.ndarray, ax: matplotlib.pyplot.Axes = None) -> tuple

      
      find indices to safe
















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   .. py:method:: del_tri(self, dt: scipy.spatial.Delaunay, rm: int) -> scipy.spatial.Delaunay

      
      Alters dt in place to remove tri at `rm`
















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   .. py:method:: _dotself(self, x: numpy.ndarray) -> numpy.ndarray

      
















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   .. py:method:: ar(self, M: numpy.ndarray) -> numpy.float64

      
      aspect ratio
















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   .. py:method:: polygon(self, points: numpy.ndarray, holes: int = 0, removals: int = 30) -> nx.DiGraph()

      
      Create a non-convex polygon from points. This algorithm works by first creating a convex
      Delaunay triangulation over the points, and then removing triangles to form a non-convex
      polygon. Thus, the general shape of the initial point distribution will infrom the subsequent
      non-convex polygon.

      This algorithm is slow for large numbers of points.

      :Parameters:

          **points** : np.ndarray, holes, optional
              The number of interior holes in the polygon, default 0

      :Returns:

          nx.Digraph
              Graph containing the polygon.













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   .. py:method:: addcw(self, H: networkx.DiGraph, e1: int, e2: int) -> float

      
      determine which way the edge is pointing

      e.g.

      Q2  │  Q1
      ─────┼─────
      Q3  │  Q4

      A "positive" cw value goes from Q2 to Q4. A "negative"
      cw value means the edge goes from Q4 to Q2. It's relative
      so we only look at directions not absolute positions.















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   .. py:method:: plot(self, G: networkx.DiGraph, ax: matplotlib.pyplot.Axes, posattr: str = 'points', arrows: bool = False, nodecolor: str = 'k', ecolor: str = None, ecolorattr: str = 'weight', ecmap='tab10', node_text: bool = True, style: str = '-', m: str = '.', draw_nodes=True) -> matplotlib.pyplot.Axes

      
      Draw a DiGraph `G` with points stored in `posattr` onto `ax`
















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.. py:function:: stupid_spiky_polygon(R_inner: int, R_outer: int, n=10)

   
















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