Easy Algebraic Numerical Differentiation for Python
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eand
.gitignore
LICENSE
README
setup.py

README

##############
# 0. License #
##############

eand package (Easy Algebraic Numerical Differentiation)
Copyright (C) 2013 Tu-Hoa Pham

This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.

##################
# 1. What is it? #
##################

eand (Easy Algebraic Numerical Differentiation) is a Python
module implementing numerical differentiation algorithms based on the
algebraic framework initiated in Fliess and Sira-Ramirez 2003 [1].

###############
# 2. Contents #
###############

The current version contains the following sub-packages:

  a. kmand: (Kappa,Mu)-Algebraic Numerical Differentiation
     A one-dimensional derivative estimator working on regularly-spaced
     samples based on Mboup et al. 2009 [2].
     
  b. mddig: MultiDimensional Differentiation on Irregular Grids
     A multidimensional derivative estimator handling irregular sampling
     grids based on Riachy et al. 2011 [3].

###################
# 3. Installation #
###################

Dependencies: numpy, scipy, matplotlib.

##########################
# 4. Future developments #
##########################

Future work will more or less deal with the following issues:
  a. Multi-dimensional numerical differentiation
  b. Fine-tuning of differentiation parameters

#################
# 5. References #
#################

[1] FLIESS, M., AND SIRA-RAMIREZ, H. 2003.
An algebraic framework for linear identification.
ESAIM: Control, Optimisation and Calculus of Variations 9 (7), 151–168.

[2] MBOUP, M., JOIN, C., AND FLIESS, M. 2009.
Numerical differentiation with annihilators in noisy environment.
Numerical Algorithms 50, 4, 439–467.

[3] RIACHY, S., MBOUP, M., AND RICHARD, J.-P. 2011.
Numerical differentiation on irregular grids.
In WIFAC.