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توضیحات

A library for computing simulations of stochastic differential equations in symbolic form

ویژگی	مقدار
سیستم عامل	-
نام فایل	coindy-0.0.1
نام	coindy
نسخه کتابخانه	0.0.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	-
ایمیل نویسنده	Paul Mucchielli <paulmucc@gmail.com>
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/coindy/
مجوز	GPL-3.0-only

# CoinDy - Stochastic Differential Equations CoinDy is a package allowing the symbolic computation of Stochastic Differential Equations and their simulation for mechanical as well as Itō systems. This package can help in deriving the complex derivative terms needed to apply explicit algorithms such as the ones described by Kloeden and Platen. It can also be used as a teaching tool, to confirm hand-derived formulas or to obtain quick results. Build Status: Passed ## Installation CoinDy can be installed by using: pip install coindy It is for now supported on Python 3.9 and later. ## Theory Itō SDE: $$\mathbf{g} = \mathbf{a}dt + \mathbf{B}\mathrm{d}\mathbf{w}_t$$ Itō's Lemma: $$\mathrm{d}\mathbf{g} = \mathcal{L}^0(\mathbf{g})\mathrm{d}t + \mathcal{L}^1(\mathbf{g}) \mathrm{d}\mathbf{w}$$ First Kolmogorov operator: $$\mathcal{L}^0(\cdot) = \left(\frac{\partial (\cdot)}{\partial t} + \sum_{i=1}^{p} a_i \frac{\partial (\cdot)}{\partial y_{i}} + \frac{1}{2} \sum_{i=1}^{p} \sum_{j=1}^p \sum_{k=1}^n B_{ij} B_{kj} \frac{\partial^2 (\cdot)}{\partial y_i \partial y_j}\right)$$ Second Kolmogorov operator: $$\mathcal{L}^1(\cdot) = \left[\mathcal{L}^1_1(\cdot), \ldots, \mathcal{L}^1_n(\cdot)\right] \qquad \mathcal{L}^1_j(\cdot) = \sum_{i=1}^p B_{ij} \frac{\partial (\cdot)}{\partial y_i}$$ ## Classes - `SDEModel(n_dof, n_rvs, algorithm='all')` - Main class, which performs the computation of derivative terms need for explicit integration schemes and their simulation. It makes use of the utilities listed below to provide an easy to manipulate object. For a more detailed investigation of symbolic terms, the utilities can be used. A use case for using `SDEModel` is demonstrated in `coindy.sde_demo.py`. ## Utility functions In `coindy.ito_utils`: - `a, B = ito_sde_form(M, C, K, f, x, B_init)` - Translates a system from a mechanical formulation to an Itō SDE formulation. The terms `a` and `B` represent the drift vector and diffusion matrix respectively where $d\mathbf{x} = \mathbf{a}*dt + \mathbf{B} * d\mathbf{w}_t$. These results would be similar to the ones used by querying `SDEModel.sde_terms['a']` and `SDEModel.sde_terms['B']`. - `Lo = L0(X, a, B, x, t)` - Compute the result of applying the first Kolmogorov operator to vector `X` with `a` and `B` resulting from `ito_sde_form`. - `Lj = LJ(X, B, x, j)` - Compute the result of applying the second Kolmogorov operator to `X` where `X` can be a vector or a matrix at index `j`. This computes $$\mathcal{L}^1_j(\cdot) = \sum_{i=1}^p B_{ij} \frac{\partial (\cdot)}{\partial y_i} \mathrm{d}w_j$$ where $\mathcal{L}^1_j(\cdot)$ is the j<sup>th</sup> vector of $\mathcal{L}^1(\cdot)$. - `Lj = LJ_total(X, B, x)` - Compute all the vectors of $\mathcal{L}^1(\cdot)$. Equivalent of running `LJ(X, B, x, j)` for all `j`. - `l1l1 = L1L1_total(X, x)` - Compute the double application of the second Kolmogorov operator to matrix `X`. In `coindy.simulation_utils`: - `dW = generate_wiener_increment(time_step, time_stop, n_dof)` - Utility that generates an n_dof * (time_stop/time_step) matrix of Wiener increments - `term = constant_substitution(term, x, constant_map)` - Utility that substitutes the symbols in `term` by the constants in `constant_map` and excluding the symbols in `x`. ## Scripts - `sde_demo.py` - Script demonstrating the use of SDEModel for derivation and simulation of Itо̄ SDEs. ## Example: Integrate a one-dimensional mechanical oscillator with mass 1kg, damping 2.5 N s/m, stiffness 5 N/m and stochastic force amplitude 0.01 N. The initial conditions are ``x0 = 0.01, x1 = 0``. ```python from coindy import SDEModel, display_matrix n_dof = 1 n_rvs = 1 equations = {'M': [['m']], 'C': [['c']], 'K': [['k']], 'f': [['0', '0', 's']]} constant_map = {'m': 1, 'c': 2.5, 'k': 5, 's': 0.01} initial_values = {'x0': 0.01, 'x1': 0} SDEModel.show_progress = True sde_model = SDEModel(n_dof, n_rvs) sde_model.equations = equations sde_model.compute_ito_sde_terms() # Print drift term display_matrix(sde_model.sde_terms['a'], 'a') sde_model.simulate([0.01, 10], constant_map, initial_values) ``` ## References - Kloeden, P. E., Platen, E. and Schurz, H. (2003) [*Numerical Solution of SDE Through Computer Experiments*](https://doi.org/10.1007/978-3-642-57913-4). Berlin, Heidelberg: Springer Berlin Heidelberg (Universitext). - Kloeden, P. E. and Platen, E. (1992) [*Numerical Solution of Stochastic Differential Equations*](https://doi.org/10.1007/978-3-662-12616-5), Springer. Berlin, Heidelberg: Springer Berlin Heidelberg. - Roy, D. and Visweswara Rao, G. (2017) [*Stochastic dynamics, filtering and optimization, Stochastic Dynamics, Filtering and Optimization*](https://doi.org/10.1017/9781316863107). - Cyganowski, S., Kloeden, P. and Ombach, J. (2002) [*From Elementary Probability to Stochastic Differential Equations with MAPLE®*](https://doi.org/10.1007/978-3-642-56144-3). Berlin, Heidelberg: Springer Berlin Heidelberg (Universitext).

نیازمندی

مقدار	نام
>=3.4.2	matplotlib
>=1.20.2	numpy
>=12.4.4	rich
>=1.8	sympy
-	tomli
-	pytest

زبان مورد نیاز

مقدار	نام
>=3.9	Python

نحوه نصب

نصب پکیج whl coindy-0.0.1:

pip install coindy-0.0.1.whl

نصب پکیج tar.gz coindy-0.0.1:

pip install coindy-0.0.1.tar.gz