ó
    Eñi›  ã                   ó>   • S SK r/ SQrS rS rSS jrSS jrSS jrg)	é    N)Údelaunay_plot_2dÚconvex_hull_plot_2dÚvoronoi_plot_2dc                  óJ   • SS K Jn   U R                  5       R                  5       $ )Nr   )Úmatplotlib.pyplotÚpyplotÚfigureÚgca)Úplts    ÚU/home/mande/repo/quber/.venv/lib/python3.13/site-packages/scipy/spatial/_plotutils.pyÚ	_get_axesr      s   € Ý#à�:‰:‹<×ÑÓÐó    c                 óÜ   • S[         R                  " USS9-  nUR                  SS9U-
  nUR                  SS9U-   nU R	                  US   US   5        U R                  US   US   5        g )Ngš™™™™™¹?r   ©Úaxisé   )ÚnpÚptpÚminÚmaxÚset_xlimÚset_ylim)ÚaxÚpointsÚmarginÚxy_minÚxy_maxs        r   Ú_adjust_boundsr      sm   € Ø”2—6’6˜& qÑ)Ñ)€FØ�Z‰Z˜QˆZÐ &Ñ(€FØ�Z‰Z˜QˆZÐ &Ñ(€FØ‡K�K��q‘	˜6 !™9Ô%Ø‡K�K��q‘	˜6 !™9Õ%r   c                 ób  • U R                   R                  S   S:w  a  [        S5      eU R                   R                  u  p#U=(       d
    [	        5       nUR                  X#S5        UR                  X#U R                  R                  5       5        [        XR                   5        UR                  $ )aÆ  
Plot the given Delaunay triangulation in 2-D

Parameters
----------
tri : scipy.spatial.Delaunay instance
    Triangulation to plot
ax : matplotlib.axes.Axes instance, optional
    Axes to plot on

Returns
-------
fig : matplotlib.figure.Figure instance
    Figure for the plot

See Also
--------
Delaunay
matplotlib.pyplot.triplot

Notes
-----
Requires Matplotlib.

Examples
--------

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.spatial import Delaunay, delaunay_plot_2d

The Delaunay triangulation of a set of random points:

>>> rng = np.random.default_rng()
>>> points = rng.random((30, 2))
>>> tri = Delaunay(points)

Plot it:

>>> _ = delaunay_plot_2d(tri)
>>> plt.show()

r   é   z!Delaunay triangulation is not 2-DÚo)r   ÚshapeÚ
ValueErrorÚTr   ÚplotÚtriplotÚ	simplicesÚcopyr   r	   )Útrir   ÚxÚys       r   r   r      s‚   € ðX ‡z�z×Ñ˜Ñ˜aÓÜÐ<Ó=Ð=à�:‰:�<‰<�D€Aà	×	Œy‹{€BØ‡G�GˆA�#ÔØ‡J�Jˆq�S—]‘]×'Ñ'Ó)Ô*ä�2—z‘zÔ"à�9‰9Ðr   c                 ó¸  • SSK Jn  U R                  R                  S   S:w  a  [	        S5      eU=(       d
    [        5       nUR                  U R                  SS2S4   U R                  SS2S4   S5        U R                   Vs/ s H  o0R                  U   PM     nnUR                  U" USS	S
95        [        XR                  5        UR                  $ s  snf )a®  
Plot the given convex hull diagram in 2-D

Parameters
----------
hull : scipy.spatial.ConvexHull instance
    Convex hull to plot
ax : matplotlib.axes.Axes instance, optional
    Axes to plot on

Returns
-------
fig : matplotlib.figure.Figure instance
    Figure for the plot

See Also
--------
ConvexHull

Notes
-----
Requires Matplotlib.


Examples
--------

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.spatial import ConvexHull, convex_hull_plot_2d

The convex hull of a random set of points:

>>> rng = np.random.default_rng()
>>> points = rng.random((30, 2))
>>> hull = ConvexHull(points)

Plot it:

>>> _ = convex_hull_plot_2d(hull)
>>> plt.show()

r   ©ÚLineCollectionr   r    zConvex hull is not 2-DNr!   ÚkÚsolid)ÚcolorsÚ	linestyle)Úmatplotlib.collectionsr.   r   r"   r#   r   r%   r'   Úadd_collectionr   r	   )Úhullr   r.   ÚsimplexÚline_segmentss        r   r   r   N   s»   € õX 6à‡{�{×Ñ˜Ñ˜qÓ ÜÐ1Ó2Ð2à	×	Œy‹{€BØ‡G�GˆD�K‰Kš˜1˜Ñ˜tŸ{™{ª1¨a¨4Ñ0°#Ô6Ø9=¿ºÓHº¨g—[‘[ Ô)¹€MÐHØ×Ñ‘n ]Ø,/Ø/6ñ8ô 9ô �2—{‘{Ô#à�9‰9Ðùò Is   ÂCc           
      óà  • SSK Jn  U R                  R                  S   S:w  a  [	        S5      eU=(       d
    [        5       nUR                  SS5      (       aF  UR                  SS	5      nUR                  U R                  S	S	2S4   U R                  S	S	2S4   S
US9  UR                  SS5      (       a5  UR                  U R                  S	S	2S4   U R                  S	S	2S4   S5        UR                  SS5      nUR                  SS5      nUR                  SS5      nU R                  R                  SS9n[        R                  " U R                  SS9n	/ n
/ n[        U R                  U R                  5       GH›  u  pÍ[        R                  " U5      n[        R                   " US:¬  5      (       a   U
R#                  U R                  U   5        MZ  XÝS:¬     S   nU R                  US      U R                  US      -
  nU[        R$                  R'                  U5      -  n[        R(                  " US   * US   /5      nU R                  U   R                  SS9n[        R*                  " [        R,                  " UU-
  U5      5      U-  nU R.                  (       a  U* n[1        U	R3                  5       U	R5                  5       -  5      nU R                  U   UU	R3                  5       -  U-  -   nUR#                  U R                  U   U/5        GMž     UR7                  U" U
UUUSS95        UR7                  U" UUUUSS95        [9        XR                  5        UR:                  $ )a™  
Plot the given Voronoi diagram in 2-D

Parameters
----------
vor : scipy.spatial.Voronoi instance
    Diagram to plot
ax : matplotlib.axes.Axes instance, optional
    Axes to plot on
show_points : bool, optional
    Add the Voronoi points to the plot.
show_vertices : bool, optional
    Add the Voronoi vertices to the plot.
line_colors : string, optional
    Specifies the line color for polygon boundaries
line_width : float, optional
    Specifies the line width for polygon boundaries
line_alpha : float, optional
    Specifies the line alpha for polygon boundaries
point_size : float, optional
    Specifies the size of points

Returns
-------
fig : matplotlib.figure.Figure instance
    Figure for the plot

See Also
--------
Voronoi

Notes
-----
Requires Matplotlib. For degenerate input, including collinearity and
other violations of general position, it may be preferable to
calculate the Voronoi diagram with Qhull options ``QJ`` for random
joggling, or ``Qt`` to enforce triangulated output. Otherwise, some
Voronoi regions may not be visible.

Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.spatial import Voronoi, voronoi_plot_2d

Create a set of points for the example:

>>> rng = np.random.default_rng()
>>> points = rng.random((10,2))

Generate the Voronoi diagram for the points:

>>> vor = Voronoi(points)

Use `voronoi_plot_2d` to plot the diagram:

>>> fig = voronoi_plot_2d(vor)

Use `voronoi_plot_2d` to plot the diagram again, with some settings
customized:

>>> fig = voronoi_plot_2d(vor, show_vertices=False, line_colors='orange',
...                       line_width=2, line_alpha=0.6, point_size=2)
>>> plt.show()

r   r-   r   r    zVoronoi diagram is not 2-DÚshow_pointsTÚ
point_sizeNÚ.)Ú
markersizeÚshow_verticesr!   Úline_colorsr/   Ú
line_widthg      ð?Ú
line_alphar   r0   )r1   ÚlwÚalphar2   Údashed)r3   r.   r   r"   r#   r   Úgetr%   ÚverticesÚmeanr   r   ÚzipÚridge_pointsÚridge_verticesÚasarrayÚallÚappendÚlinalgÚnormÚarrayÚsignÚdotÚfurthest_siteÚabsr   r   r4   r   r	   )Úvorr   Úkwr.   r:   r>   r?   r@   ÚcenterÚ	ptp_boundÚfinite_segmentsÚinfinite_segmentsÚpointidxr6   ÚiÚtÚnÚmidpointÚ	directionÚaspect_factorÚ	far_points                        r   r   r   Š   sï  € õF 6à
‡z�z×Ñ˜Ñ˜aÓÜÐ5Ó6Ð6à	×	Œy‹{€Bà	‡v�vˆm˜T×"Ñ"Ø—V‘V˜L¨$Ó/ˆ
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š1˜a˜4Ñ  #§*¡*ªQ°¨TÑ"2°CÀJˆÑOØ	‡v�vˆo˜t×$Ñ$Ø
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˜8 A™;Ñ'¨#¯*©*°X¸a±[Ñ*AÑAˆAØ”—‘—‘ Ó"Ñ"ˆAÜ—’˜1˜Q™4˜%  1¡˜Ó'ˆAà—z‘z (Ñ+×0Ñ0°aÐ0Ð8ˆHÜŸš¤§¢ x°&Ñ'8¸!Ó <Ó=ÀÑAˆIØ×!×!Ø&˜J�	Ü 	§¡£°)·-±-³/Ñ AÓBˆMØŸ™ Q™¨)°i·m±m³oÑ*EÈÑ*UÑUˆIà×$Ñ$ c§l¡l°1¡o°yÐ%A×Bñ% Gð( ×Ñ‘n _Ø,7Ø(2Ø+5Ø/6ñ	8ô 9ð
 ×Ñ‘nÐ%6Ø,7Ø(2Ø+5Ø/7ñ	9ô :ô �2—z‘zÔ"à�9‰9Ðr   )N)Únumpyr   Ú__all__r   r   r   r   r   © r   r   Ú<module>re      s)   ðÛ â
H€òò&ô7ôt9õxzr   