Mpmath Python

mpmath Python

Produce arbitrary-precision numerical code with explicit input precision, working precision, conditioning, convergence evidence, and output accuracy.

Core model

Arbitrary precision does not repair an inexact Python float. mp.mpf(0.1) captures the already-rounded binary value; mp.mpf("0.1") represents the decimal input at current precision. Precision controls arithmetic performed after construction, not hidden source accuracy. Read numbers and precision.

Ordered workflow

1. Define the mathematical quantity, real/complex domain, target error (absolute, relative, or digits), and credible input accuracy.
2. Construct constants without premature binary-float rounding.
3. Estimate conditioning/singularities and choose a stable formulation and algorithm before increasing precision.
4. Compute inside scoped guard digits; never change global mp.dps without restoring it.
5. Repeat at higher working precision or with another method and compare a residual, enclosure, or known identity.
6. Round/serialize only at the output boundary and report achieved evidence, not merely configured digits.
7. Test near singularities, cancellation, complex branches, poor initial guesses, non-convergence, and inputs whose original accuracy is lower than mp.dps.

Intent map

• Use direct special functions before reimplementing series or converting through machine floats.
• Use quad/specialized quadrature after splitting discontinuities or difficult intervals and defining endpoint behavior.
• Use findroot with an initial guess/interval suited to the solver, then check the residual independently. verify=False removes one check; it does not make a root valid.
• Use diff, numerical sums/products, ODE, transform, or matrix routines only after checking their documented convergence and domain contract.
• Use workdps(total_digits) for a local absolute precision target; use extraprec/extradps when guard precision is relative to the caller.
• Use interval arithmetic when a guaranteed enclosure is required and the interval algorithm actually provides it. Do not assume iv.findroot encloses a root; the official documentation explicitly warns otherwise.

Read algorithms and validation.

Canonical anchor

from mpmath import mp


def _exp_ratio_at(x_text: str, work_digits: int) -> mp.mpf:
    with mp.workdps(work_digits):
        # Construct inside the context: mpf rounds input at current precision.
        x = mp.mpf(x_text)
        return mp.expm1(x) / x if x else mp.one


def stable_cancellation(x_text: str, digits: int = 50) -> mp.mpf:
    if digits < 2:
        raise ValueError("digits must be at least 2")
    lower = _exp_ratio_at(x_text, digits + 10)
    higher = _exp_ratio_at(x_text, digits + 20)
    with mp.workdps(digits):
        lower_rounded = +lower
        higher_rounded = +higher
        if lower_rounded != higher_rounded:
            raise ArithmeticError("result did not stabilize at requested precision")
        return higher_rounded

The stable formulation matters more than simply raising mp.dps. Constructing the input inside each working context prevents caller precision from truncating the decimal first. Unary plus inside workdps(digits) explicitly rounds the returned value to the requested precision; merely leaving a context does not.

High-risk rules

• Never seed a high-precision computation with an accidental Python float when the original decimal or exact ratio is available.
• Do not promise dps correct digits. Guard digits and precision-doubling tests provide evidence; ill-conditioning can consume them.
• Do not compare high-precision outputs to machine-float expected values as the oracle. Use exact identities, strings, independent methods, or higher precision.
• Root-finding success requires a small residual and the intended root/domain. Multiple roots and steep/flat functions can defeat naive convergence checks.
• Quadrature requires singularity, oscillation, infinite interval, and branch analysis. Split intervals or select specialized methods under a documented rule.
• Global context mutations create order-dependent tests and concurrency hazards. Scope them with context managers or clone a context where isolation is needed.
• Mixing mpmath and NumPy often creates object arrays or downcasts to float. Make conversion, vectorization, and precision loss explicit.
• Formatting many digits is not evidence they are correct.

Version grounding and completion

Official current documentation identifies mpmath 1.3.0. It is not installed in this foundry. Inspect version, context behavior, and exact callable signatures before relying on them. Read verification.

Completion requires exact input construction, a scoped precision policy, a stable algorithm, residual/enclosure or precision-doubling evidence, domain and failure handling, and serialization that does not silently reduce accuracy.

References

• Numbers and precision
• Algorithms and validation
• Verification and grounding