Numerical Differentiation

Methods for approximating derivatives using finite differences.

English API (Aliases)

numpyy.forward_diff(f, x, h=None, pedagogique=None)

Calcule la derivee premiere par differences finies avant.

Parameters:

  • f (callable) – Fonction a deriver.

  • x (float) – Point d’evaluation.

  • h (float, optional) – Pas de derivation.

  • pedagogique (bool, optional) – Affiche les etapes si True.

Returns:

Approximation de la derivee.

Return type:

float

numpyy.backward_diff(f, x, h=None, pedagogique=None)
numpyy.centered_diff(f, x, h=None, pedagogique=None)

Calcule la derivee premiere par differences finies centrees.

Parameters:

  • f (callable) – Fonction a deriver.

  • x (float) – Point d’evaluation.

  • h (float, optional) – Pas de derivation.

  • pedagogique (bool, optional) – Affiche les etapes si True.

Returns:

Approximation de la derivee.

Return type:

float

numpyy.second_derivative(f, x, h=None, pedagogique=None)
numpyy.third_derivative(f, x, h=None, pedagogique=None)
numpyy.nth_derivative(f, x, n, h=None)
numpyy.optimal_h(f, x, methode='centree')

Backend French API

numpyy.derivation.diff_avant(f, x, h=None, pedagogique=None)[source]

Calcule la derivee premiere par differences finies avant.

Parameters:

  • f (callable) – Fonction a deriver.

  • x (float) – Point d’evaluation.

  • h (float, optional) – Pas de derivation.

  • pedagogique (bool, optional) – Affiche les etapes si True.

Returns:

Approximation de la derivee.

Return type:

float

numpyy.derivation.diff_centree(f, x, h=None, pedagogique=None)[source]

Calcule la derivee premiere par differences finies centrees.

Parameters:

  • f (callable) – Fonction a deriver.

  • x (float) – Point d’evaluation.

  • h (float, optional) – Pas de derivation.

  • pedagogique (bool, optional) – Affiche les etapes si True.

Returns:

Approximation de la derivee.

Return type:

float

Example: Centered Difference

import numpyy as ny
f = lambda x: x**2
print(ny.centered_diff(f, 1.0, h=0.01))