5.1. Numdifftools summary

5.1.1. numdifftools.core module

Derivative(fun[, step, method, order, n])	Calculate n-th derivative with finite difference approximation
Gradient(fun[, step, method, order, n])	Calculate Gradient with finite difference approximation
Jacobian(fun[, step, method, order, n])	Calculate Jacobian with finite difference approximation
Hessdiag(f[, step, method, order])	Calculate Hessian diagonal with finite difference approximation
Hessian(f[, step, method, order])	Calculate Hessian with finite difference approximation
directionaldiff(f, x0, vec, **options)	Return directional derivative of a function of n variables

5.1.2. Step generators

BasicMaxStepGenerator(base_step, step_ratio, ...)	Generates a sequence of steps of decreasing magnitude
BasicMinStepGenerator(base_step, step_ratio, ...)	Generates a sequence of steps of decreasing magnitude
MinStepGenerator([base_step, step_ratio, ...])	Generates a sequence of steps
MaxStepGenerator([base_step, step_ratio, ...])	Generates a sequence of steps

5.1.3. numdifftools.extrapolation module

convolve(sequence, rule, **kwds)	Wrapper around scipy.ndimage.convolve1d that allows complex input.
Dea([limexp])	Extrapolate a slowly convergent sequence using repeated Shanks transformations.
dea3(v_0, v_1, v_2[, symmetric])	Extrapolate a slowly convergent sequence using Shanks transformations.
Richardson([step_ratio, step, order, num_terms])	Extrapolates a sequence with Richardsons method

5.1.4. numdifftools.limits module

CStepGenerator([base_step, step_ratio, ...])	Generates a sequence of steps
Limit(fun[, step, method, order, full_output])	Compute limit of a function at a given point
Residue(f[, step, method, order, ...])	Compute residue of a function at a given point

5.1.5. numdifftools.multicomplex module

Bicomplex(z1, z2)

Creates an instance of a Bicomplex object.

5.1.6. numdifftools.nd_algopy module

Derivative(fun[, n, method, full_output])	Calculate n-th derivative with Algorithmic Differentiation method
Gradient(fun[, n, method, full_output])	Calculate Gradient with Algorithmic Differentiation method
Jacobian(fun[, n, method, full_output])	Calculate Jacobian with Algorithmic Differentiation method
Hessdiag(f[, method, full_output])	Calculate Hessian diagonal with Algorithmic Differentiation method
Hessian(f[, method, full_output])	Calculate Hessian with Algorithmic Differentiation method
directionaldiff(f, x0, vec, **options)	Return directional derivative of a function of n variables

5.1.7. numdifftools.nd_scipy module

Gradient(fun[, step, method, order, bounds, ...])	Calculate Gradient with finite difference approximation
Jacobian(fun[, step, method, order, bounds, ...])	Calculate Jacobian with finite difference approximation

5.1.8. numdifftools.nd_statsmodels module

Hessian(fun[, step, method, order])	Calculate Hessian with finite difference approximation
Jacobian(fun[, step, method, order])	Calculate Jacobian with finite difference approximation