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Easy Algebraic Numerical Differentiation for Python
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############## # 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.
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