
    wgH                     &    d dl mZ  G d de      Zy)   )xrangec                   @    e Zd Zd Zd
dZd Zd Zd ZddZd Z	d Z
y	)MatrixCalculusMethodsc                      fd}d}d}	  ||       j                   k  rn|dz  }||z  }t        t        d j                   j	                  |d                        }|} j
                  } xj                  |dz   z  c_        	 |d|z  z  }|j                  } j                  |      }	 j                  |      }
 j                  |      } j                  d      }t        d|dz         D ]E  }| j                  ||z
  dz         d|z  |z
  dz   |z  z  z  }||z  }||z  }|
|z  }
|	d|z  |z  z  }	G  j                  |	|
      }t        |      D ]  }||z  }	 	 | _        |dz  S # | _        w xY w)	a,  
        Exponential of a matrix using Pade approximants.

        See G. H. Golub, C. F. van Loan 'Matrix Computations',
        third Ed., page 572

        TODO:
         - find a good estimate for q
         - reduce the number of matrix multiplications to improve
           performance
        c                     j                  d      dd| z  z
  z  j                  |       dz  z  j                  d| z        dz  d| z  dz   z  z  S )Nr         )mpf	factorial)pctxs    ]/home/mcse/projects/flask/flask-venv/lib/python3.12/site-packages/mpmath/matrices/calculus.pyeps_padez1MatrixCalculusMethods._exp_pade.<locals>.eps_pade   s_    771:!A#&a !#$%(]]1Q3%7%:acAg%FH H          r	   infr   r   )epsintmaxmagmnormprecdpsrowseyer
   rangelu_solve_mat)r   ar   qextraqjextrar   nadennumxckcxfs   `               r   	_exp_padezMatrixCalculusMethods._exp_pade   s   	H {SWW$FA  	
VAswwsyy51234xx519	!Q$AB''"+C''"+CA
A1ac] $SWWQUQY'!A#'A+):;;aCqSr	Qw|#$   c*A1X aC CHs
 CHs   CE+ +	E4c                    |dk(  rZ| j                   }	 | j                  |      }| xj                   d|j                  z  z  c_         | j                  |      }|| _         |S | j                  |      }| j                   }t	        t        d| j                  | j                  |d                        }|t	        d|dz  z        z  }	 | xj                   dd|z  z   z  c_         | j                  }|d|z  z  }|}|dz  |z   }d}		 ||d| j                  |	      z  z  z  }| j                  |d      |k  rn||z  }|	dz  }	;t        |      D ]  }	||z  }	 	 || _         |dz  }|S # || _         w xY w# || _         w xY w)a  
        Computes the matrix exponential of a square matrix `A`, which is defined
        by the power series

        .. math ::

            \exp(A) = I + A + \frac{A^2}{2!} + \frac{A^3}{3!} + \ldots

        With method='taylor', the matrix exponential is computed
        using the Taylor series. With method='pade', Pade approximants
        are used instead.

        **Examples**

        Basic examples::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = True
            >>> expm(zeros(3))
            [1.0  0.0  0.0]
            [0.0  1.0  0.0]
            [0.0  0.0  1.0]
            >>> expm(eye(3))
            [2.71828182845905               0.0               0.0]
            [             0.0  2.71828182845905               0.0]
            [             0.0               0.0  2.71828182845905]
            >>> expm([[1,1,0],[1,0,1],[0,1,0]])
            [ 3.86814500615414  2.26812870852145  0.841130841230196]
            [ 2.26812870852145  2.44114713886289   1.42699786729125]
            [0.841130841230196  1.42699786729125    1.6000162976327]
            >>> expm([[1,1,0],[1,0,1],[0,1,0]], method='pade')
            [ 3.86814500615414  2.26812870852145  0.841130841230196]
            [ 2.26812870852145  2.44114713886289   1.42699786729125]
            [0.841130841230196  1.42699786729125    1.6000162976327]
            >>> expm([[1+j, 0], [1+j,1]])
            [(1.46869393991589 + 2.28735528717884j)                        0.0]
            [  (1.03776739863568 + 3.536943175722j)  (2.71828182845905 + 0.0j)]

        Matrices with large entries are allowed::

            >>> expm(matrix([[1,2],[2,3]])**25)
            [5.65024064048415e+2050488462815550  9.14228140091932e+2050488462815550]
            [9.14228140091932e+2050488462815550  1.47925220414035e+2050488462815551]

        The identity `\exp(A+B) = \exp(A) \exp(B)` does not hold for
        noncommuting matrices::

            >>> A = hilbert(3)
            >>> B = A + eye(3)
            >>> chop(mnorm(A*B - B*A))
            0.0
            >>> chop(mnorm(expm(A+B) - expm(A)*expm(B)))
            0.0
            >>> B = A + ones(3)
            >>> mnorm(A*B - B*A)
            1.8
            >>> mnorm(expm(A+B) - expm(A)*expm(B))
            42.0927851137247

        pader   r	   r         ?
       )r   matrixr   r-   r   r   r   r   r   r
   r   )
r   Amethodr   resr#   tolTYr*   s
             r   expmzMatrixCalculusMethods.expm5   s   z V88D JJqMAaffH$mmA&JJJqMxxAswwsyy51234	ST3Y	HHQqS H77(C!Q$AA1qAAQ!CGGAJ,''99Q&,QQ  AY aC CH	Q1  , CHs   AE" BE. "	E+.	E7c                 ,   d| j                  || j                  z        | j                  || j                   z        z   z  }t        |j                  | j                        j                  t
                    s|j                  | j                        }|S )a  
        Gives the cosine of a square matrix `A`, defined in analogy
        with the matrix exponential.

        Examples::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = True
            >>> X = eye(3)
            >>> cosm(X)
            [0.54030230586814               0.0               0.0]
            [             0.0  0.54030230586814               0.0]
            [             0.0               0.0  0.54030230586814]
            >>> X = hilbert(3)
            >>> cosm(X)
            [ 0.424403834569555  -0.316643413047167  -0.221474945949293]
            [-0.316643413047167   0.820646708837824  -0.127183694770039]
            [-0.221474945949293  -0.127183694770039   0.909236687217541]
            >>> X = matrix([[1+j,-2],[0,-j]])
            >>> cosm(X)
            [(0.833730025131149 - 0.988897705762865j)  (1.07485840848393 - 0.17192140544213j)]
            [                                     0.0               (1.54308063481524 + 0.0j)]
        r0   r:   r#   sumapplyimabsrer   r4   Bs      r   cosmzMatrixCalculusMethods.cosm   sl    0 388AceeG$sxxCEE6
';;<177366?((-.Ar   c                 ,   d| j                  || j                  z        | j                  || j                   z        z
  z  }t        |j                  | j                        j                  t
                    s|j                  | j                        }|S )a  
        Gives the sine of a square matrix `A`, defined in analogy
        with the matrix exponential.

        Examples::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = True
            >>> X = eye(3)
            >>> sinm(X)
            [0.841470984807897                0.0                0.0]
            [              0.0  0.841470984807897                0.0]
            [              0.0                0.0  0.841470984807897]
            >>> X = hilbert(3)
            >>> sinm(X)
            [0.711608512150994  0.339783913247439  0.220742837314741]
            [0.339783913247439  0.244113865695532  0.187231271174372]
            [0.220742837314741  0.187231271174372  0.155816730769635]
            >>> X = matrix([[1+j,-2],[0,-j]])
            >>> sinm(X)
            [(1.29845758141598 + 0.634963914784736j)  (-1.96751511930922 + 0.314700021761367j)]
            [                                    0.0                  (0.0 - 1.1752011936438j)]
        y             r<   rB   s      r   sinmzMatrixCalculusMethods.sinm   sl    0 sxx#%%(388AvJ+??@177366?((-.Ar   c                 n    | j                   dz  }| j                  ||z  |      | j                  |      z  S )Ng333333?)r#   sqrtmsqrt)r   r4   _may_rotateus       r   
_sqrtm_rotz MatrixCalculusMethods._sqrtm_rot   s3     EE3Jyy1k*SXXa[88r   c                 Z   | j                  |      }|dz  |k(  r|S | j                  }|rd| j                  |      }t        | j	                  |            d| j
                  z  k  r)| j                  |      dk  r| j                  ||dz
        S 	 | xj                  dz  c_        | j
                  dz  }|}|dz  x}}d}		 |}
	 d|| j                  |      z   z  d|| j                  |      z   z  }}| j                  ||
z
  d      }| j                  |d      }|||z  k  rnL|r)|	dkD  r$||d	z  k  s| j                  ||dz
        || _        S |	dz  }	|	| j                  kD  r| j                  || _        |dz  }|S # t        $ r |r| j                  ||dz
        }Y 0 w xY w# || _        w xY w)
a  
        Computes a square root of the square matrix `A`, i.e. returns
        a matrix `B = A^{1/2}` such that `B^2 = A`. The square root
        of a matrix, if it exists, is not unique.

        **Examples**

        Square roots of some simple matrices::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = True
            >>> sqrtm([[1,0], [0,1]])
            [1.0  0.0]
            [0.0  1.0]
            >>> sqrtm([[0,0], [0,0]])
            [0.0  0.0]
            [0.0  0.0]
            >>> sqrtm([[2,0],[0,1]])
            [1.4142135623731  0.0]
            [            0.0  1.0]
            >>> sqrtm([[1,1],[1,0]])
            [ (0.920442065259926 - 0.21728689675164j)  (0.568864481005783 + 0.351577584254143j)]
            [(0.568864481005783 + 0.351577584254143j)  (0.351577584254143 - 0.568864481005783j)]
            >>> sqrtm([[1,0],[0,1]])
            [1.0  0.0]
            [0.0  1.0]
            >>> sqrtm([[-1,0],[0,1]])
            [(0.0 - 1.0j)           0.0]
            [         0.0  (1.0 + 0.0j)]
            >>> sqrtm([[j,0],[0,j]])
            [(0.707106781186547 + 0.707106781186547j)                                       0.0]
            [                                     0.0  (0.707106781186547 + 0.707106781186547j)]

        A square root of a rotation matrix, giving the corresponding
        half-angle rotation matrix::

            >>> t1 = 0.75
            >>> t2 = t1 * 0.5
            >>> A1 = matrix([[cos(t1), -sin(t1)], [sin(t1), cos(t1)]])
            >>> A2 = matrix([[cos(t2), -sin(t2)], [sin(t2), cos(t2)]])
            >>> sqrtm(A1)
            [0.930507621912314  -0.366272529086048]
            [0.366272529086048   0.930507621912314]
            >>> A2
            [0.930507621912314  -0.366272529086048]
            [0.366272529086048   0.930507621912314]

        The identity `(A^2)^{1/2} = A` does not necessarily hold::

            >>> A = matrix([[4,1,4],[7,8,9],[10,2,11]])
            >>> sqrtm(A**2)
            [ 4.0  1.0   4.0]
            [ 7.0  8.0   9.0]
            [10.0  2.0  11.0]
            >>> sqrtm(A)**2
            [ 4.0  1.0   4.0]
            [ 7.0  8.0   9.0]
            [10.0  2.0  11.0]
            >>> A = matrix([[-4,1,4],[7,-8,9],[10,2,11]])
            >>> sqrtm(A**2)
            [  7.43715112194995  -0.324127569985474   1.8481718827526]
            [-0.251549715716942    9.32699765900402  2.48221180985147]
            [  4.11609388833616   0.775751877098258   13.017955697342]
            >>> chop(sqrtm(A)**2)
            [-4.0   1.0   4.0]
            [ 7.0  -8.0   9.0]
            [10.0   2.0  11.0]

        For some matrices, a square root does not exist::

            >>> sqrtm([[0,1], [0,0]])
            Traceback (most recent call last):
              ...
            ZeroDivisionError: matrix is numerically singular

        Two examples from the documentation for Matlab's ``sqrtm``::

            >>> mp.dps = 15; mp.pretty = True
            >>> sqrtm([[7,10],[15,22]])
            [1.56669890360128  1.74077655955698]
            [2.61116483933547  4.17786374293675]
            >>>
            >>> X = matrix(\
            ...   [[5,-4,1,0,0],
            ...   [-4,6,-4,1,0],
            ...   [1,-4,6,-4,1],
            ...   [0,1,-4,6,-4],
            ...   [0,0,1,-4,5]])
            >>> Y = matrix(\
            ...   [[2,-1,-0,-0,-0],
            ...   [-1,2,-1,0,-0],
            ...   [0,-1,2,-1,0],
            ...   [-0,0,-1,2,-1],
            ...   [-0,-0,-0,-1,2]])
            >>> mnorm(sqrtm(X) - Y)
            4.53155328326114e-19

        r2      r	   r1      r0   r      gMbP?)r3   r   detr@   r?   r   rA   rL   inverseZeroDivisionErrorr   NoConvergence)r   r4   rJ   r   dr7   r9   ZIr*   Yprevmag1mag2s                r   rH   zMatrixCalculusMethods.sqrtm   s   F JJqMQ3!8Hxx
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                  R	 || _        |	d|z  z  }	|	S # || _        w xY w)a  
        Computes a logarithm of the square matrix `A`, i.e. returns
        a matrix `B = \log(A)` such that `\exp(B) = A`. The logarithm
        of a matrix, if it exists, is not unique.

        **Examples**

        Logarithms of some simple matrices::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = True
            >>> X = eye(3)
            >>> logm(X)
            [0.0  0.0  0.0]
            [0.0  0.0  0.0]
            [0.0  0.0  0.0]
            >>> logm(2*X)
            [0.693147180559945                0.0                0.0]
            [              0.0  0.693147180559945                0.0]
            [              0.0                0.0  0.693147180559945]
            >>> logm(expm(X))
            [1.0  0.0  0.0]
            [0.0  1.0  0.0]
            [0.0  0.0  1.0]

        A logarithm of a complex matrix::

            >>> X = matrix([[2+j, 1, 3], [1-j, 1-2*j, 1], [-4, -5, j]])
            >>> B = logm(X)
            >>> nprint(B)
            [ (0.808757 + 0.107759j)    (2.20752 + 0.202762j)   (1.07376 - 0.773874j)]
            [ (0.905709 - 0.107795j)  (0.0287395 - 0.824993j)  (0.111619 + 0.514272j)]
            [(-0.930151 + 0.399512j)   (-2.06266 - 0.674397j)  (0.791552 + 0.519839j)]
            >>> chop(expm(B))
            [(2.0 + 1.0j)           1.0           3.0]
            [(1.0 - 1.0j)  (1.0 - 2.0j)           1.0]
            [        -4.0          -5.0  (0.0 + 1.0j)]

        A matrix `X` close to the identity matrix, for which
        `\log(\exp(X)) = \exp(\log(X)) = X` holds::

            >>> X = eye(3) + hilbert(3)/4
            >>> X
            [              1.25             0.125  0.0833333333333333]
            [             0.125  1.08333333333333              0.0625]
            [0.0833333333333333            0.0625                1.05]
            >>> logm(expm(X))
            [              1.25             0.125  0.0833333333333333]
            [             0.125  1.08333333333333              0.0625]
            [0.0833333333333333            0.0625                1.05]
            >>> expm(logm(X))
            [              1.25             0.125  0.0833333333333333]
            [             0.125  1.08333333333333              0.0625]
            [0.0833333333333333            0.0625                1.05]

        A logarithm of a rotation matrix, giving back the angle of
        the rotation::

            >>> t = 3.7
            >>> A = matrix([[cos(t),sin(t)],[-sin(t),cos(t)]])
            >>> chop(logm(A))
            [             0.0  -2.58318530717959]
            [2.58318530717959                0.0]
            >>> (2*pi-t)
            2.58318530717959

        For some matrices, a logarithm does not exist::

            >>> logm([[1,0], [0,0]])
            Traceback (most recent call last):
              ...
            ZeroDivisionError: matrix is numerically singular

        Logarithm of a matrix with large entries::

            >>> logm(hilbert(3) * 10**20).apply(re)
            [ 45.5597513593433  1.27721006042799  0.317662687717978]
            [ 1.27721006042799  42.5222778973542   2.24003708791604]
            [0.317662687717978  2.24003708791604    42.395212822267]

        r1   rO   r2   r	   r   g      ?r   )r3   r   r   rH   r   rT   )r   r4   r   r7   rW   rC   nr8   XLr*   s              r   logmzMatrixCalculusMethods.logm^  s5   d JJqMxx	HHNH''C-C1AAAIIaLQ99QqS%(50	 
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 CH	QT	 CHs   B?C0 0	C9c                    | j                  |      }| j                  |      }| j                  }	 | xj                  dz  c_        | j                  |      r|t	        |      z  }nZ| j                  |dz        r#t	        |dz        }| j                  |      |z  }n#| j                  || j                  |      z        }|| _        |dz  }|S # || _        w xY w)aF  
        Computes `A^r = \exp(A \log r)` for a matrix `A` and complex
        number `r`.

        **Examples**

        Powers and inverse powers of a matrix::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = True
            >>> A = matrix([[4,1,4],[7,8,9],[10,2,11]])
            >>> powm(A, 2)
            [ 63.0  20.0   69.0]
            [174.0  89.0  199.0]
            [164.0  48.0  179.0]
            >>> chop(powm(powm(A, 4), 1/4.))
            [ 4.0  1.0   4.0]
            [ 7.0  8.0   9.0]
            [10.0  2.0  11.0]
            >>> powm(extraprec(20)(powm)(A, -4), -1/4.)
            [ 4.0  1.0   4.0]
            [ 7.0  8.0   9.0]
            [10.0  2.0  11.0]
            >>> chop(powm(powm(A, 1+0.5j), 1/(1+0.5j)))
            [ 4.0  1.0   4.0]
            [ 7.0  8.0   9.0]
            [10.0  2.0  11.0]
            >>> powm(extraprec(5)(powm)(A, -1.5), -1/(1.5))
            [ 4.0  1.0   4.0]
            [ 7.0  8.0   9.0]
            [10.0  2.0  11.0]

        A Fibonacci-generating matrix::

            >>> powm([[1,1],[1,0]], 10)
            [89.0  55.0]
            [55.0  34.0]
            >>> fib(10)
            55.0
            >>> powm([[1,1],[1,0]], 6.5)
            [(16.5166626964253 - 0.0121089837381789j)  (10.2078589271083 + 0.0195927472575932j)]
            [(10.2078589271083 + 0.0195927472575932j)  (6.30880376931698 - 0.0317017309957721j)]
            >>> (phi**6.5 - (1-phi)**6.5)/sqrt(5)
            (10.2078589271083 - 0.0195927472575932j)
            >>> powm([[1,1],[1,0]], 6.2)
            [ (14.3076953002666 - 0.008222855781077j)  (8.81733464837593 + 0.0133048601383712j)]
            [(8.81733464837593 + 0.0133048601383712j)  (5.49036065189071 - 0.0215277159194482j)]
            >>> (phi**6.2 - (1-phi)**6.2)/sqrt(5)
            (8.81733464837593 - 0.0133048601383712j)

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	HHNHyy|QK1Q3!HIIaLA%HHQsxx{]+CH	Q CHs   BC 	CN)taylor)r   )__name__
__module____qualname__r-   r:   rD   rF   rL   rH   r_   rf    r   r   r   r      s2    ,\\|::9IVpdCr   r   N)libmp.backendr   objectr   rk   r   r   <module>rn      s    "NF Nr   