sympy.Pow: lambda x, y: x**y,
sympy.exp: lambda x: core.exp(x),
sympy.log: lambda x: core.log(x),
sympy.sin: lambda x: core.sin(x),
sympy.asin: lambda x: core.asin(x),
sympy.sinh: lambda x: core.sinh(x),
sympy.asinh: lambda x: core.asinh(x),
sympy.cos: lambda x: core.cos(x),
sympy.acos: lambda x: core.acos(x),
sympy.cosh: lambda x: core.cosh(x),
sympy.acosh: lambda x: core.acosh(x),
sympy.tan: lambda x: core.tan(x),
sympy.atan: lambda x: core.atan(x),
sympy.tanh: lambda x: core.tanh(x),
sympy.atanh: lambda x: core.atanh(x),
sympy.ceiling: lambda x: core.ceil(x),
sympy.floor: lambda x: core.floor(x),
sympy.Derivative: _nondifferentiable,
}
except ImportError: #pragma:nocover
_sympy_available = False
# A "public" attribute indicating that differentiate() can be called
# ... this provides a bit of future-proofing for alternative approaches
# to symbolic differentiation.
differentiate_available = _sympy_available
class NondifferentiableError(ValueError):
"""A Pyomo-specific ValueError raised for non-differentiable expressions"""
pass
sympy.Mul: _prod,
sympy.Pow: lambda x,y: x**y,
sympy.log: lambda x: core.log(x),
sympy.sin: lambda x: core.sin(x),
sympy.asin: lambda x: core.asin(x),
sympy.sinh: lambda x: core.sinh(x),
sympy.asinh: lambda x: core.asinh(x),
sympy.cos: lambda x: core.cos(x),
sympy.acos: lambda x: core.acos(x),
sympy.cosh: lambda x: core.cosh(x),
sympy.acosh: lambda x: core.acosh(x),
sympy.tan: lambda x: core.tan(x),
sympy.atan: lambda x: core.atan(x),
sympy.tanh: lambda x: core.tanh(x),
sympy.atanh: lambda x: core.atanh(x),
sympy.ceiling: lambda x: core.ceil(x),
sympy.floor: lambda x: core.floor(x),
sympy.Derivative: _nondifferentiable,
}
except ImportError: #pragma:nocover
_sympy_available = False
class NondifferentiableError(ValueError):
"""A Pyomo-specific ValueError raised for non-differentiable expressions"""
pass
def differentiate(expr, wrt=None, wrt_list=None):
if not _sympy_available:
raise RuntimeError(