# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2019 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
import os.path
import configparser
import numpy as np
from pymor.algorithms.timestepping import ImplicitEulerTimeStepper
from pymor.models.basic import StationaryModel
from pymor.models.basic import InstationaryModel
from pymor.operators.constructions import LincombOperator
from pymor.operators.numpy import NumpyMatrixOperator
from pymor.parameters.spaces import CubicParameterSpace
from pymor.parameters.functionals import ExpressionParameterFunctional
[docs]def discretize_stationary_from_disk(parameter_file):
"""Load a linear affinely decomposed |StationaryModel| from file.
The model is defined via an `.ini`-style file as follows ::
[system-matrices]
L_1.mat: l_1(μ_1,...,μ_n)
L_2.mat: l_2(μ_1,...,μ_n)
...
[rhs-vectors]
F_1.mat: f_1(μ_1,...,μ_n)
F_2.mat: f_2(μ_1,...,μ_n)
...
[parameter]
μ_1: a_1,b_1
...
μ_n: a_n,b_n
[products]
Prod1: P_1.mat
Prod2: P_2.mat
...
Here, `L_1.mat`, `L_2.mat`, ..., `F_1.mat`, `F_2.mat`, ... are files
containing matrices `L_1`, `L_2`, ... and vectors `F_1.mat`, `F_2.mat`, ...
which correspond to the affine components of the operator and right-hand
side. The respective coefficient functionals, are given via the
string expressions `l_1(...)`, `l_2(...)`, ..., `f_1(...)` in the
(scalar-valued) |Parameter| components `w_1`, ..., `w_n`. The allowed lower
and upper bounds `a_i, b_i` for the component `μ_i` are specified in the
`[parameters]` section. The resulting operator and right-hand side are
then of the form ::
L(μ) = l_1(μ)*L_1 + l_2(μ)*L_2+ ...
F(μ) = f_1(μ)*F_1 + f_2(μ)*L_2+ ...
In the `[products]` section, an optional list of inner products `Prod1`, `Prod2`, ..
with corresponding matrices `P_1.mat`, `P_2.mat` can be specified.
Example::
[system-matrices]
matrix1.mat: 1.
matrix2.mat: 1. - theta**2
[rhs-vectors]
rhs.mat: 1.
[parameter]
theta: 0, 0.5
[products]
h1: h1.mat
l2: mass.mat
Parameters
----------
parameter_file
Path to the parameter file.
Returns
-------
m
The |StationaryModel| that has been generated.
"""
assert ".ini" == parameter_file[-4:], f'Given file is not an .ini file: {parameter_file}'
assert os.path.isfile(parameter_file)
base_path = os.path.dirname(parameter_file)
# Get input from parameter file
config = configparser.ConfigParser()
config.optionxform = str
config.read(parameter_file)
# Assert that all needed entries given
assert 'system-matrices' in config.sections()
assert 'rhs-vectors' in config.sections()
assert 'parameter' in config.sections()
system_mat = config.items('system-matrices')
rhs_vec = config.items('rhs-vectors')
parameter = config.items('parameter')
# Dict of parameters types and ranges
parameter_type = {}
parameter_range = {}
# get parameters
for i in range(len(parameter)):
parameter_name = parameter[i][0]
parameter_list = tuple(float(j) for j in parameter[i][1].replace(" ", "").split(','))
parameter_range[parameter_name] = parameter_list
# Assume scalar parameter dependence
parameter_type[parameter_name] = 0
# Create parameter space
parameter_space = CubicParameterSpace(parameter_type=parameter_type, ranges=parameter_range)
# Assemble operators
system_operators, system_functionals = [], []
# get parameter functionals and system matrices
for i in range(len(system_mat)):
path = os.path.join(base_path, system_mat[i][0])
expr = system_mat[i][1]
parameter_functional = ExpressionParameterFunctional(expr, parameter_type=parameter_type)
system_operators.append(NumpyMatrixOperator.from_file(path, source_id='STATE', range_id='STATE'))
system_functionals.append(parameter_functional)
system_lincombOperator = LincombOperator(system_operators, coefficients=system_functionals)
# get rhs vectors
rhs_operators, rhs_functionals = [], []
for i in range(len(rhs_vec)):
path = os.path.join(base_path, rhs_vec[i][0])
expr = rhs_vec[i][1]
parameter_functional = ExpressionParameterFunctional(expr, parameter_type=parameter_type)
op = NumpyMatrixOperator.from_file(path, range_id='STATE')
assert isinstance(op.matrix, np.ndarray)
op = op.with_(matrix=op.matrix.reshape((-1, 1)))
rhs_operators.append(op)
rhs_functionals.append(parameter_functional)
rhs_lincombOperator = LincombOperator(rhs_operators, coefficients=rhs_functionals)
# get products if given
if 'products' in config.sections():
product = config.items('products')
products = {}
for i in range(len(product)):
product_name = product[i][0]
product_path = os.path.join(base_path, product[i][1])
products[product_name] = NumpyMatrixOperator.from_file(product_path, source_id='STATE', range_id='STATE')
else:
products = None
# Create and return stationary model
return StationaryModel(operator=system_lincombOperator, rhs=rhs_lincombOperator,
parameter_space=parameter_space, products=products)
[docs]def discretize_instationary_from_disk(parameter_file, T=None, steps=None, u0=None, time_stepper=None):
"""Load a linear affinely decomposed |InstationaryModel| from file.
Similarly to :func:`discretize_stationary_from_disk`, the model is
specified via an `ini.`-file of the following form ::
[system-matrices]
L_1.mat: l_1(μ_1,...,μ_n)
L_2.mat: l_2(μ_1,...,μ_n)
...
[rhs-vectors]
F_1.mat: f_1(μ_1,...,μ_n)
F_2.mat: f_2(μ_1,...,μ_n)
...
[mass-matrix]
D.mat
[initial-solution]
u0: u0.mat
[parameter]
μ_1: a_1,b_1
...
μ_n: a_n,b_n
[products]
Prod1: P_1.mat
Prod2: P_2.mat
...
[time]
T: final time
steps: number of time steps
Parameters
----------
parameter_file
Path to the '.ini' parameter file.
T
End-time of desired solution. If `None`, the value specified in the
parameter file is used.
steps
Number of time steps to. If `None`, the value specified in the
parameter file is used.
u0
Initial solution. If `None` the initial solution is obtained
from parameter file.
time_stepper
The desired :class:`time stepper <pymor.algorithms.timestepping.TimeStepperInterface>`
to use. If `None`, implicit Euler time stepping is used.
Returns
-------
m
The |InstationaryModel| that has been generated.
"""
assert ".ini" == parameter_file[-4:], "Given file is not an .ini file"
assert os.path.isfile(parameter_file)
base_path = os.path.dirname(parameter_file)
# Get input from parameter file
config = configparser.ConfigParser()
config.optionxform = str
config.read(parameter_file)
# Assert that all needed entries given
assert 'system-matrices' in config.sections()
assert 'mass-matrix' in config.sections()
assert 'rhs-vectors' in config.sections()
assert 'parameter' in config.sections()
system_mat = config.items('system-matrices')
mass_mat = config.items('mass-matrix')
rhs_vec = config.items('rhs-vectors')
parameter = config.items('parameter')
# Dict of parameters types and ranges
parameter_type = {}
parameter_range = {}
# get parameters
for i in range(len(parameter)):
parameter_name = parameter[i][0]
parameter_list = tuple(float(j) for j in parameter[i][1].replace(" ", "").split(','))
parameter_range[parameter_name] = parameter_list
# Assume scalar parameter dependence
parameter_type[parameter_name] = 0
# Create parameter space
parameter_space = CubicParameterSpace(parameter_type=parameter_type, ranges=parameter_range)
# Assemble operators
system_operators, system_functionals = [], []
# get parameter functionals and system matrices
for i in range(len(system_mat)):
path = os.path.join(base_path, system_mat[i][0])
expr = system_mat[i][1]
parameter_functional = ExpressionParameterFunctional(expr, parameter_type=parameter_type)
system_operators.append(NumpyMatrixOperator.from_file(path, source_id='STATE', range_id='STATE'))
system_functionals.append(parameter_functional)
system_lincombOperator = LincombOperator(system_operators, coefficients=system_functionals)
# get rhs vectors
rhs_operators, rhs_functionals = [], []
for i in range(len(rhs_vec)):
path = os.path.join(base_path, rhs_vec[i][0])
expr = rhs_vec[i][1]
parameter_functional = ExpressionParameterFunctional(expr, parameter_type=parameter_type)
op = NumpyMatrixOperator.from_file(path, range_id='STATE')
assert isinstance(op.matrix, np.ndarray)
op = op.with_(matrix=op.matrix.reshape((-1, 1)))
rhs_operators.append(op)
rhs_functionals.append(parameter_functional)
rhs_lincombOperator = LincombOperator(rhs_operators, coefficients=rhs_functionals)
# get mass matrix
path = os.path.join(base_path, mass_mat[0][1])
mass_operator = NumpyMatrixOperator.from_file(path, source_id='STATE', range_id='STATE')
# Obtain initial solution if not given
if u0 is None:
u_0 = config.items('initial-solution')
path = os.path.join(base_path, u_0[0][1])
op = NumpyMatrixOperator.from_file(path, range_id='STATE')
assert isinstance(op.matrix, np.ndarray)
u0 = op.with_(matrix=op.matrix.reshape((-1, 1)))
# get products if given
if 'products' in config.sections():
product = config.items('products')
products = {}
for i in range(len(product)):
product_name = product[i][0]
product_path = os.path.join(base_path, product[i][1])
products[product_name] = NumpyMatrixOperator.from_file(product_path, source_id='STATE', range_id='STATE')
else:
products = None
# Further specifications
if 'time' in config.sections():
if T is None:
assert 'T' in config.options('time')
T = float(config.get('time', 'T'))
if steps is None:
assert 'steps' in config.options('time')
steps = int(config.get('time', 'steps'))
# Use implicit euler time stepper if no time-stepper given
if time_stepper is None:
time_stepper = ImplicitEulerTimeStepper(steps)
else:
time_stepper = time_stepper(steps)
# Create and return instationary model
return InstationaryModel(operator=system_lincombOperator, rhs=rhs_lincombOperator,
parameter_space=parameter_space, initial_data=u0, T=T,
time_stepper=time_stepper, mass=mass_operator, products=products)