class_docstr: Help lazily evaluate a matrix exponential.
  
  The idea is to not do more work than we need for high expm precision,
  so we lazily compute matrix powers and store or precompute
  other properties of the matrix.
class_docstr_tok: ["Help","lazily","evaluate","a","matrix","exponential",".","The","idea","is","to","not","do","more","work","than","we","need","for","high","expm","precision",",","so","we","lazily","compute","matrix","powers","and","store","or","precompute","other","properties","of","the","matrix","."]
docstr: Initialize the object.
  
  Parameters
  ----------
  A : a dense or sparse square numpy matrix or ndarray
      The matrix to be exponentiated.
  structure : str, optional
      A string describing the structure of matrix `A`.
      Only `upper_triangular` is currently supported.
  use_exact_onenorm : bool, optional
      If True then only the exact one-norm of matrix powers and products
      will be used. Otherwise, the one-norm of powers and products
      may initially be estimated.
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code: def __init__(self, A, structure=None, use_exact_onenorm=False):
      """
      Initialize the object.
  
      Parameters
      ----------
      A : a dense or sparse square numpy matrix or ndarray
          The matrix to be exponentiated.
      structure : str, optional
          A string describing the structure of matrix `A`.
          Only `upper_triangular` is currently supported.
      use_exact_onenorm : bool, optional
          If True then only the exact one-norm of matrix powers and products
          will be used. Otherwise, the one-norm of powers and products
          may initially be estimated.
      """
      self.A = A
      self._A2 = None
      self._A4 = None
      self._A6 = None
      self._A8 = None
      self._A10 = None
      self._d4_exact = None
      self._d6_exact = None
      self._d8_exact = None
      self._d10_exact = None
      self._d4_approx = None
      self._d6_approx = None
      self._d8_approx = None
      self._d10_approx = None
      self.ident = _ident_like(A)
      self.structure = structure
      self.use_exact_onenorm = use_exact_onenorm
  
imports: import numpy
  from scipy.linalg._basic import solve, solve_triangular
  from scipy.sparse._base import issparse
  from scipy.sparse.linalg import spsolve
  from scipy.sparse._sputils import is_pydata_spmatrix, isintlike
  import scipy.sparse
  import scipy.sparse.linalg
  from scipy.sparse.linalg._interface import LinearOperator
  from scipy.sparse._construct import eye
  from ._expm_multiply import _ident_like, _exact_1_norm
label_desc: Use image node_id 1 to create a new _ExpmPadeHelper object  with example: obj = _ExpmPadeHelper(A, structure, use_exact_onenorm)