diff --git a/doc/sphinx/source/tutorialCVodeDisc.py b/doc/sphinx/source/tutorialCVodeDisc.py index e3dd0332..b7bf59c4 100644 --- a/doc/sphinx/source/tutorialCVodeDisc.py +++ b/doc/sphinx/source/tutorialCVodeDisc.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- """ -Tutorial example showing how to use the explicit solver CVode for a discontinious problem. +Tutorial example showing how to use the explicit solver CVode for a discontinuous problem. To run the example simply type, run tutorialCVodeDisc.py (in IPython) diff --git a/examples/cvode_basic.py b/examples/cvode_basic.py index c17cf024..20c11264 100644 --- a/examples/cvode_basic.py +++ b/examples/cvode_basic.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -37,7 +37,7 @@ def run_example(with_plots=True): #Define the rhs def f(t,y): ydot = -y[0] - return N.array([ydot]) + return np.array([ydot]) #Define an Assimulo problem exp_mod = Explicit_Problem(f, y0=4, name = r'CVode Test Example: $\dot y = - y$') @@ -57,13 +57,13 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.plot(t1, y1, color="b") - P.plot(t2, y2, color="r") - P.title(exp_mod.name) - P.ylabel('y') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t1, y1, color="b") + pl.plot(t2, y2, color="r") + pl.title(exp_mod.name) + pl.ylabel('y') + pl.xlabel('Time') + pl.show() #Basic test nose.tools.assert_almost_equal(y2[-1][0], 0.00347746, 5) diff --git a/examples/cvode_basic_backward.py b/examples/cvode_basic_backward.py index c299a19c..c5454800 100644 --- a/examples/cvode_basic_backward.py +++ b/examples/cvode_basic_backward.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -34,7 +34,7 @@ def run_example(with_plots=True): #Define the rhs def f(t,y): ydot = -y[0] - return N.array([ydot]) + return np.array([ydot]) #Define an Assimulo problem exp_mod = Explicit_Problem(f,t0=5, y0=0.02695, name = r'CVode Test Example (reverse time): $\dot y = - y$ ') @@ -54,12 +54,12 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.plot(t, y, color="b") - P.title(exp_mod.name) - P.ylabel('y') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t, y, color="b") + pl.title(exp_mod.name) + pl.ylabel('y') + pl.xlabel('Time') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0], 4.00000000, 3) diff --git a/examples/cvode_gyro.py b/examples/cvode_gyro.py index 605a152f..eb9590a1 100644 --- a/examples/cvode_gyro.py +++ b/examples/cvode_gyro.py @@ -75,12 +75,12 @@ def f(t, u): # Plot if with_plots: - import pylab as P - P.plot(t, y/10000.) - P.xlabel('Time') - P.ylabel('States, scaled by $10^4$') - P.title(exp_mod.name) - P.show() + import pylab as pl + pl.plot(t, y/10000.) + pl.xlabel('Time') + pl.ylabel('States, scaled by $10^4$') + pl.title(exp_mod.name) + pl.show() #Basic tests nose.tools.assert_almost_equal(y[-1][0], 692.800241862) diff --git a/examples/cvode_stability.py b/examples/cvode_stability.py index 2a8b3bd2..4a7a7eaf 100644 --- a/examples/cvode_stability.py +++ b/examples/cvode_stability.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -54,9 +54,9 @@ def handle_result(self, solver, t, y): def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - yd_2 = N.sin(t*y[1]) + yd_2 = np.sin(t*y[1]) - return N.array([yd_0,yd_1, yd_2]) + return np.array([yd_0,yd_1, yd_2]) y0 = [2.0,-0.6, 0.1] #Initial conditions @@ -76,20 +76,20 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.subplot(211) - P.plot(t,y[:,2]) - P.ylabel("State: $y_1$") - P.subplot(212) - P.plot(t,exp_mod.order) - P.ylabel("Order") - P.suptitle(exp_mod.name) - P.xlabel("Time") - P.show() + import pylab as pl + pl.subplot(211) + pl.plot(t,y[:,2]) + pl.ylabel("State: $y_1$") + pl.subplot(212) + pl.plot(t,exp_mod.order) + pl.ylabel("Order") + pl.suptitle(exp_mod.name) + pl.xlabel("Time") + pl.show() #Basic test x1 = y[:,0] - nose.tools.assert_less(N.abs(float(x1[-1]) - 1.8601438), 1e-1) + nose.tools.assert_less(np.abs(float(x1[-1]) - 1.8601438), 1e-1) return exp_mod, exp_sim diff --git a/examples/cvode_with_disc.py b/examples/cvode_with_disc.py index 26f409ca..1752b2cb 100644 --- a/examples/cvode_with_disc.py +++ b/examples/cvode_with_disc.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -50,7 +50,7 @@ def rhs(self,t,y,sw): yd_1 = 0.0 yd_2 = 0.0 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #Sets a name to our function name = 'ODE with discontinuities and a function with consistency problem' @@ -65,7 +65,7 @@ def state_events(self,t,y,sw): event_1 = -y[2] + 1.0 event_2 = -t + 1.0 - return N.array([event_0,event_1,event_2]) + return np.array([event_0,event_1,event_2]) #Responsible for handling the events. @@ -148,12 +148,12 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title(exp_mod.name) - P.ylabel('States') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title(exp_mod.name) + pl.ylabel('States') + pl.xlabel('Time') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0],8.0) diff --git a/examples/cvode_with_initial_sensitivity.py b/examples/cvode_with_initial_sensitivity.py index 84c21858..848eeb16 100644 --- a/examples/cvode_with_initial_sensitivity.py +++ b/examples/cvode_with_initial_sensitivity.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -57,12 +57,12 @@ def f(t, y, p): yd_1 = k21*y1-(k02+k12)*y2 yd_2 = k31*y1-k13*y3 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #The initial conditions y0 = [0.0,0.0,0.0] #Initial conditions for y p0 = [0.0, 0.0, 0.0] #Initial conditions for parameters - yS0 = N.array([[1,0,0],[0,1,0],[0,0,1.]]) + yS0 = np.array([[1,0,0],[0,1,0],[0,0,1.]]) #Create an Assimulo explicit problem exp_mod = Explicit_Problem(f, y0, p0=p0,name='Example: Computing Sensitivities') @@ -88,39 +88,39 @@ def f(t, y, p): #Plot if with_plots: - import pylab as P + import pylab as pl title_text=r"Sensitivity w.r.t. ${}$" legend_text=r"$\mathrm{{d}}{}/\mathrm{{d}}{}$" - P.figure(1) - P.subplot(221) - P.plot(t, N.array(exp_sim.p_sol[0])[:,0], - t, N.array(exp_sim.p_sol[0])[:,1], - t, N.array(exp_sim.p_sol[0])[:,2]) - P.title(title_text.format('p_1')) - P.legend((legend_text.format('y_1','p_1'), + pl.figure(1) + pl.subplot(221) + pl.plot(t, np.array(exp_sim.p_sol[0])[:,0], + t, np.array(exp_sim.p_sol[0])[:,1], + t, np.array(exp_sim.p_sol[0])[:,2]) + pl.title(title_text.format('p_1')) + pl.legend((legend_text.format('y_1','p_1'), legend_text.format('y_1','p_2'), legend_text.format('y_1','p_3'))) - P.subplot(222) - P.plot(t, N.array(exp_sim.p_sol[1])[:,0], - t, N.array(exp_sim.p_sol[1])[:,1], - t, N.array(exp_sim.p_sol[1])[:,2]) - P.title(title_text.format('p_2')) - P.legend((legend_text.format('y_2','p_1'), + pl.subplot(222) + pl.plot(t, np.array(exp_sim.p_sol[1])[:,0], + t, np.array(exp_sim.p_sol[1])[:,1], + t, np.array(exp_sim.p_sol[1])[:,2]) + pl.title(title_text.format('p_2')) + pl.legend((legend_text.format('y_2','p_1'), legend_text.format('y_2','p_2'), legend_text.format('y_2','p_3'))) - P.subplot(223) - P.plot(t, N.array(exp_sim.p_sol[2])[:,0], - t, N.array(exp_sim.p_sol[2])[:,1], - t, N.array(exp_sim.p_sol[2])[:,2]) - P.title(title_text.format('p_3')) - P.legend((legend_text.format('y_3','p_1'), + pl.subplot(223) + pl.plot(t, np.array(exp_sim.p_sol[2])[:,0], + t, np.array(exp_sim.p_sol[2])[:,1], + t, np.array(exp_sim.p_sol[2])[:,2]) + pl.title(title_text.format('p_3')) + pl.legend((legend_text.format('y_3','p_1'), legend_text.format('y_3','p_2'), legend_text.format('y_3','p_3'))) - P.subplot(224) - P.title('ODE Solution') - P.plot(t, y) - P.suptitle(exp_mod.name) - P.show() + pl.subplot(224) + pl.title('ODE Solution') + pl.plot(t, y) + pl.suptitle(exp_mod.name) + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0], 1577.6552477, 5) diff --git a/examples/cvode_with_jac.py b/examples/cvode_with_jac.py index 8ca3c19e..7bb4d1fe 100644 --- a/examples/cvode_with_jac.py +++ b/examples/cvode_with_jac.py @@ -15,12 +15,11 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem - def run_example(with_plots=True): r""" Example for demonstrating the use of a user supplied Jacobian @@ -45,11 +44,11 @@ def run_example(with_plots=True): def f(t,y): yd_0 = y[1] yd_1 = -9.82 - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) #Defines the Jacobian def jac(t,y): - j = N.array([[0,1.],[0,0]]) + j = np.array([[0,1.],[0,0]]) return j #Defines an Assimulo explicit problem @@ -71,12 +70,12 @@ def jac(t,y): #Plot if with_plots: - import pylab as P - P.plot(t,y,linestyle="dashed",marker="o") #Plot the solution - P.xlabel('Time') - P.ylabel('State') - P.title(exp_mod.name) - P.show() + import pylab as pl + pl.plot(t,y,linestyle="dashed",marker="o") #Plot the solution + pl.xlabel('Time') + pl.ylabel('State') + pl.title(exp_mod.name) + pl.show() #Basic tests nose.tools.assert_almost_equal(y[-1][0],-121.75000000,4) diff --git a/examples/cvode_with_jac_sparse.py b/examples/cvode_with_jac_sparse.py index e4dd330c..ca0ccb6b 100644 --- a/examples/cvode_with_jac_sparse.py +++ b/examples/cvode_with_jac_sparse.py @@ -15,8 +15,8 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -import scipy.sparse as SP +import numpy as np +import scipy.sparse as sps import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -50,7 +50,7 @@ def f(t,y): yd_0 = -0.04*y[0] + 1e4*y[1]*y[2] yd_2 = 3e7*y[1]*y[1] yd_1 = -yd_0 - yd_2 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #Defines the Jacobian def jac(t,y): @@ -59,7 +59,7 @@ def jac(t,y): rowvals = [0, 1, 0, 1, 2, 0, 1] data = [-0.04, 0.04, 1e4*y[2], -1e4*y[2]-6e7*y[1], 6e7*y[1], 1e4*y[1], -1e4*y[1]] - J = SP.csc_matrix((data, rowvals, colptrs)) + J = sps.csc_matrix((data, rowvals, colptrs)) return J #Defines an Assimulo explicit problem @@ -85,12 +85,12 @@ def jac(t,y): #Plot if with_plots: - import pylab as P - P.plot(t,y[:,1],linestyle="dashed",marker="o") #Plot the solution - P.xlabel('Time') - P.ylabel('State') - P.title(exp_mod.name) - P.show() + import pylab as pl + pl.plot(t,y[:,1],linestyle="dashed",marker="o") #Plot the solution + pl.xlabel('Time') + pl.ylabel('State') + pl.title(exp_mod.name) + pl.show() #Basic tests nose.tools.assert_almost_equal(y[-1][0],0.9851,3) diff --git a/examples/cvode_with_jac_spgmr.py b/examples/cvode_with_jac_spgmr.py index 0ddcb8ec..3914b901 100644 --- a/examples/cvode_with_jac_spgmr.py +++ b/examples/cvode_with_jac_spgmr.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -48,12 +48,12 @@ def f(t,y): yd_0 = y[1] yd_1 = -9.82 - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) #Defines the Jacobian*vector product def jacv(t,y,fy,v): - j = N.array([[0,1.],[0,0]]) - return N.dot(j,v) + j = np.array([[0,1.],[0,0]]) + return np.dot(j,v) y0 = [1.0,0.0] #Initial conditions @@ -77,12 +77,12 @@ def jacv(t,y,fy,v): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.xlabel('Time') - P.ylabel('State') - P.title(exp_mod.name) - P.show() + import pylab as pl + pl.plot(t,y) + pl.xlabel('Time') + pl.ylabel('State') + pl.title(exp_mod.name) + pl.show() #Basic tests nose.tools.assert_almost_equal(y[-1][0],-121.75000000,4) diff --git a/examples/cvode_with_parameters.py b/examples/cvode_with_parameters.py index 9388ce3e..9746e1ad 100644 --- a/examples/cvode_with_parameters.py +++ b/examples/cvode_with_parameters.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -50,7 +50,7 @@ def f(t, y, p): yd_1 = p[0]*y[0]-p[1]*y[1]*y[2]-p[2]*y[1]**2 yd_2 = p[2]*y[1]**2 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #The initial conditions y0 = [1.0,0.0,0.0] #Initial conditions for y @@ -65,11 +65,11 @@ def f(t, y, p): #Create an Assimulo explicit solver (CVode) exp_sim = CVode(exp_mod) - #Sets the solver paramters + #Sets the solver parameters exp_sim.iter = 'Newton' exp_sim.discr = 'BDF' exp_sim.rtol = 1.e-4 - exp_sim.atol = N.array([1.0e-8, 1.0e-14, 1.0e-6]) + exp_sim.atol = np.array([1.0e-8, 1.0e-14, 1.0e-6]) exp_sim.sensmethod = 'SIMULTANEOUS' #Defines the sensitivity method used exp_sim.suppress_sens = False #Dont suppress the sensitivity variables in the error test. exp_sim.report_continuously = True @@ -79,12 +79,12 @@ def f(t, y, p): #Plot if with_plots: - import pylab as P - P.plot(t, y) - P.title(exp_mod.name) - P.xlabel('Time') - P.ylabel('State') - P.show() + import pylab as pl + pl.plot(t, y) + pl.title(exp_mod.name) + pl.xlabel('Time') + pl.ylabel('State') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0], 9.05518032e-01, 4) diff --git a/examples/cvode_with_parameters_fcn.py b/examples/cvode_with_parameters_fcn.py index 8e1f95b5..ee6156bd 100644 --- a/examples/cvode_with_parameters_fcn.py +++ b/examples/cvode_with_parameters_fcn.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -50,19 +50,19 @@ def f(t, y, p): yd_1 = p[0]*y[0]-p[1]*y[1]*y[2]-p[2]*y[1]**2 yd_2 = p[2]*y[1]**2 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) def jac(t,y, p): - J = N.array([[-p[0], p[1]*y[2], p[1]*y[1]], + J = np.array([[-p[0], p[1]*y[2], p[1]*y[1]], [p[0], -p[1]*y[2]-2*p[2]*y[1], -p[1]*y[1]], [0.0, 2*p[2]*y[1],0.0]]) return J def fsens(t, y, s, p): - J = N.array([[-p[0], p[1]*y[2], p[1]*y[1]], + J = np.array([[-p[0], p[1]*y[2], p[1]*y[1]], [p[0], -p[1]*y[2]-2*p[2]*y[1], -p[1]*y[1]], [0.0, 2*p[2]*y[1],0.0]]) - P = N.array([[-y[0],y[1]*y[2],0], + P = np.array([[-y[0],y[1]*y[2],0], [y[0], -y[1]*y[2], -y[1]**2], [0,0,y[1]**2]]) return J.dot(s)+P @@ -82,11 +82,11 @@ def fsens(t, y, s, p): #Create an Assimulo explicit solver (CVode) exp_sim = CVode(exp_mod) - #Sets the solver paramters + #Sets the solver parameters exp_sim.iter = 'Newton' exp_sim.discr = 'BDF' exp_sim.rtol = 1.e-4 - exp_sim.atol = N.array([1.0e-8, 1.0e-14, 1.0e-6]) + exp_sim.atol = np.array([1.0e-8, 1.0e-14, 1.0e-6]) exp_sim.sensmethod = 'SIMULTANEOUS' #Defines the sensitivity method used exp_sim.suppress_sens = False #Dont suppress the sensitivity variables in the error test. exp_sim.report_continuously = True @@ -96,12 +96,12 @@ def fsens(t, y, s, p): #Plot if with_plots: - import pylab as P - P.plot(t, y) - P.title(exp_mod.name) - P.xlabel('Time') - P.ylabel('State') - P.show() + import pylab as pl + pl.plot(t, y) + pl.title(exp_mod.name) + pl.xlabel('Time') + pl.ylabel('State') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0], 9.05518032e-01, 4) diff --git a/examples/cvode_with_parameters_modified.py b/examples/cvode_with_parameters_modified.py index 2510c1ee..396b934c 100644 --- a/examples/cvode_with_parameters_modified.py +++ b/examples/cvode_with_parameters_modified.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import CVode from assimulo.problem import Explicit_Problem @@ -48,7 +48,7 @@ def f(t, y, p): yd_1 = p[0]*y[0]-p[1]*y[1]*y[2]-p3*y[1]**2 yd_2 = p3*y[1]**2 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #The initial conditions y0 = [1.0,0.0,0.0] #Initial conditions for y @@ -63,11 +63,11 @@ def f(t, y, p): #Create an Assimulo explicit solver (CVode) exp_sim = CVode(exp_mod) - #Sets the paramters + #Sets the parameters exp_sim.iter = 'Newton' exp_sim.discr = 'BDF' exp_sim.rtol = 1.e-4 - exp_sim.atol = N.array([1.0e-8, 1.0e-14, 1.0e-6]) + exp_sim.atol = np.array([1.0e-8, 1.0e-14, 1.0e-6]) exp_sim.sensmethod = 'SIMULTANEOUS' #Defines the sensitivity method used exp_sim.suppress_sens = False #Dont suppress the sensitivity variables in the error test. exp_sim.report_continuously = True @@ -77,12 +77,12 @@ def f(t, y, p): #Plot if with_plots: - import pylab as P - P.plot(t, y) - P.xlabel('Time') - P.ylabel('State') - P.title(exp_mod.name) - P.show() + import pylab as pl + pl.plot(t, y) + pl.xlabel('Time') + pl.ylabel('State') + pl.title(exp_mod.name) + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0], 9.05518032e-01, 4) diff --git a/examples/dasp3_basic.py b/examples/dasp3_basic.py index 9561479b..eaa83eed 100644 --- a/examples/dasp3_basic.py +++ b/examples/dasp3_basic.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose try: @@ -56,7 +56,7 @@ def run_example(with_plots=True): #Define the slow rhs def dydt(t,y,z): eps=(1./3)*1.e-3 - yp = N.array([-(.6*z[0]+.8*y[2])*y[0]+10.*y[1], + yp = np.array([-(.6*z[0]+.8*y[2])*y[0]+10.*y[1], -10.*y[1]+ 1.6*z[0] *y[2], -1.33*eps**2*y[2]*(y[0]+2.*z[0])]) return yp @@ -64,14 +64,14 @@ def dydt(t,y,z): #Define the fast rhs def dzdt(t,y,z): eps=(1./3)*1.e-3 - zp = N.array([1.6*z[0]*y[2]-.6*z[0]*y[0] + zp = np.array([1.6*z[0]*y[2]-.6*z[0]*y[0] -45.*(eps*z[0])**2+.8*y[2]*y[0]]) return zp #The initial values y0 = [ 3.0, 0.216, 1.0] z0 = [1.35] - eps = N.array([.33333333e-3]) + eps = np.array([.33333333e-3]) #Define an Assimulo problem exp_mod = SingPerturbed_Problem(dydt, dzdt, @@ -91,13 +91,13 @@ def dzdt(t,y,z): #Plot if with_plots: - import pylab as P - P.semilogy(t, y, color="b") - P.grid() - P.title(exp_mod.name+r' $\varepsilon = \frac{1}{3} 10^{-3}$') - P.xlabel('Time') - P.ylabel('y') - P.show() + import pylab as pl + pl.semilogy(t, y, color="b") + pl.grid() + pl.title(exp_mod.name+r' $\varepsilon = \frac{1}{3} 10^{-3}$') + pl.xlabel('Time') + pl.ylabel('y') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1,0], 10.860063849896818, 3) diff --git a/examples/dopri5_basic.py b/examples/dopri5_basic.py index 35d50b7b..413d6a48 100644 --- a/examples/dopri5_basic.py +++ b/examples/dopri5_basic.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import Dopri5 from assimulo.problem import Explicit_Problem @@ -35,7 +35,7 @@ def run_example(with_plots=True): #Defines the rhs def f(t,y): ydot = -y[0] - return N.array([ydot]) + return np.array([ydot]) #Define an Assimulo problem exp_mod = Explicit_Problem(f, 4.0, @@ -48,12 +48,12 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title(exp_mod.name) - P.xlabel('Time') - P.ylabel('State') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title(exp_mod.name) + pl.xlabel('Time') + pl.ylabel('State') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0],0.02695199,5) diff --git a/examples/dopri5_with_disc.py b/examples/dopri5_with_disc.py index 3d50ed45..5cf21136 100644 --- a/examples/dopri5_with_disc.py +++ b/examples/dopri5_with_disc.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import Dopri5 from assimulo.problem import Explicit_Problem @@ -34,7 +34,7 @@ #Extend Assimulos problem definition class Extended_Problem(Explicit_Problem): - #Sets the initial conditons directly into the problem + #Sets the initial conditions directly into the problem y0 = [0.0, -1.0, 0.0] sw0 = [False,True,True] @@ -50,7 +50,7 @@ def rhs(self,t,y,sw): yd_1 = 0.0 yd_2 = 0.0 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #Sets a name to our function name = 'ODE with discontinuities and a function with consistency problem' @@ -65,7 +65,7 @@ def state_events(self,t,y,sw): event_1 = -y[2] + 1.0 event_2 = -t + 1.0 - return N.array([event_0,event_1,event_2]) + return np.array([event_0,event_1,event_2]) #Responsible for handling the events. @@ -147,12 +147,12 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title(exp_mod.name) - P.ylabel('States') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title(exp_mod.name) + pl.ylabel('States') + pl.xlabel('Time') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0],8.0) diff --git a/examples/euler_basic.py b/examples/euler_basic.py index bb2b73aa..40de392d 100644 --- a/examples/euler_basic.py +++ b/examples/euler_basic.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import ExplicitEuler from assimulo.problem import Explicit_Problem @@ -36,7 +36,7 @@ def run_example(with_plots=True): #Defines the rhs def f(t,y): ydot = -y[0] - return N.array([ydot]) + return np.array([ydot]) #Define an Assimulo problem exp_mod = Explicit_Problem(f, 4.0, @@ -52,13 +52,13 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.plot(t1, y1, color="b") - P.plot(t2, y2, color="r") - P.title(exp_mod.name) - P.ylabel('y') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t1, y1, color="b") + pl.plot(t2, y2, color="r") + pl.title(exp_mod.name) + pl.ylabel('y') + pl.xlabel('Time') + pl.show() #Basic test nose.tools.assert_almost_equal(y2[-1][0], 0.02628193) diff --git a/examples/euler_vanderpol.py b/examples/euler_vanderpol.py index a24c764b..d9212f5a 100644 --- a/examples/euler_vanderpol.py +++ b/examples/euler_vanderpol.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import ImplicitEuler from assimulo.problem import Explicit_Problem @@ -47,7 +47,7 @@ def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) #Define the Jacobian def jac(t,y): @@ -56,7 +56,7 @@ def jac(t,y): jd_10 = -1.0*my-2*y[0]*y[1]*my jd_11 = my*(1.-y[0]**2) - return N.array([[jd_00,jd_01],[jd_10,jd_11]]) + return np.array([[jd_00,jd_01],[jd_10,jd_11]]) y0 = [2.0,-0.6] #Initial conditions @@ -77,16 +77,16 @@ def jac(t,y): #Plot if with_plots: - import pylab as P - P.plot(t,y[:,0], marker='o') - P.title(exp_mod.name) - P.ylabel("State: $y_1$") - P.xlabel("Time") - P.show() + import pylab as pl + pl.plot(t,y[:,0], marker='o') + pl.title(exp_mod.name) + pl.ylabel("State: $y_1$") + pl.xlabel("Time") + pl.show() #Basic test x1 = y[:,0] - nose.tools.assert_less(N.abs(float(x1[-1]) - 1.8601438), 1e-1) + nose.tools.assert_less(np.abs(float(x1[-1]) - 1.8601438), 1e-1) return exp_mod, exp_sim diff --git a/examples/euler_with_disc.py b/examples/euler_with_disc.py index 9d64948e..8707ecbc 100644 --- a/examples/euler_with_disc.py +++ b/examples/euler_with_disc.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import ExplicitEuler from assimulo.problem import Explicit_Problem @@ -34,7 +34,7 @@ #Extend Assimulos problem definition class Extended_Problem(Explicit_Problem): - #Sets the initial conditons directly into the problem + #Sets the initial conditions directly into the problem y0 = [0.0, -1.0, 0.0] sw0 = [False,True,True] @@ -50,7 +50,7 @@ def rhs(self,t,y,sw): yd_1 = 0.0 yd_2 = 0.0 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #Sets a name to our function name = 'ODE with discontinuities and a function with consistency problem' @@ -65,7 +65,7 @@ def state_events(self,t,y,sw): event_1 = -y[2] + 1.0 event_2 = -t + 1.0 - return N.array([event_0,event_1,event_2]) + return np.array([event_0,event_1,event_2]) #Responsible for handling the events. @@ -144,12 +144,12 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title("Solution of a differential equation with discontinuities") - P.ylabel('States') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title("Solution of a differential equation with discontinuities") + pl.ylabel('States') + pl.xlabel('Time') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0],8.0) diff --git a/examples/glimda_vanderpol.py b/examples/glimda_vanderpol.py index 3bf01976..834d5181 100644 --- a/examples/glimda_vanderpol.py +++ b/examples/glimda_vanderpol.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import GLIMDA from assimulo.problem import Implicit_Problem @@ -50,7 +50,7 @@ def f(t,y,yd): res_0 = yd[0]-yd_0 res_1 = yd[1]-yd_1 - return N.array([res_0,res_1]) + return np.array([res_0,res_1]) y0 = [2.0,-0.6] #Initial conditions yd0 = [-.6,-200000.] @@ -72,17 +72,17 @@ def f(t,y,yd): #Plot if with_plots: - import pylab as P - P.subplot(211) - P.plot(t,y[:,0])#, marker='o') - P.xlabel('Time') - P.ylabel('State') - P.subplot(212) - P.plot(t,yd[:,0]*1.e-5)#, marker='o') - P.xlabel('Time') - P.ylabel('State derivatives scaled with $10^{-5}$') - P.suptitle(imp_mod.name) - P.show() + import pylab as pl + pl.subplot(211) + pl.plot(t,y[:,0])#, marker='o') + pl.xlabel('Time') + pl.ylabel('State') + pl.subplot(212) + pl.plot(t,yd[:,0]*1.e-5)#, marker='o') + pl.xlabel('Time') + pl.ylabel('State derivatives scaled with $10^{-5}$') + pl.suptitle(imp_mod.name) + pl.show() #Basic test x1 = y[:,0] diff --git a/examples/ida_basic_backward.py b/examples/ida_basic_backward.py index 7a34bb15..e7698879 100644 --- a/examples/ida_basic_backward.py +++ b/examples/ida_basic_backward.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import IDA from assimulo.problem import Implicit_Problem @@ -35,7 +35,7 @@ def run_example(with_plots=True): #Define the rhs def f(t,y,yd): res = yd[0] + y[0] - return N.array([res]) + return np.array([res]) #Define an Assimulo problem imp_mod = Implicit_Problem(f,t0=5, y0=0.02695, yd0=-0.02695, @@ -54,12 +54,12 @@ def f(t,y,yd): #Plot if with_plots: - import pylab as P - P.plot(t, y, color="b") - P.xlabel('Time') - P.ylabel('State') - P.title(imp_mod.name) - P.show() + import pylab as pl + pl.plot(t, y, color="b") + pl.xlabel('Time') + pl.ylabel('State') + pl.title(imp_mod.name) + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0], 4.00000000, 3) diff --git a/examples/ida_with_disc.py b/examples/ida_with_disc.py index 152763a3..1905b66f 100644 --- a/examples/ida_with_disc.py +++ b/examples/ida_with_disc.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import IDA from assimulo.problem import Implicit_Problem @@ -34,7 +34,7 @@ #Extend Assimulos problem definition class Extended_Problem(Implicit_Problem): - #Sets the initial conditons directly into the problem + #Sets the initial conditions directly into the problem y0 = [0.0, -1.0, 0.0] yd0 = [-1.0, 0.0, 0.0] sw0 = [False,True,True] @@ -53,7 +53,7 @@ def res(self,t,y,yd,sw): res_1 = -y[1] + (-1.0 if sw[1] else 3.0) res_2 = -y[2] + (0.0 if sw[2] else 2.0) - return N.array([res_0,res_1,res_2]) + return np.array([res_0,res_1,res_2]) #Sets a name to our function name = 'ODE with discontinuities and a function with consistency problem' @@ -68,7 +68,7 @@ def state_events(self,t,y,yd,sw): event_1 = -y[2] + 1.0 event_2 = -t + 1.0 - return N.array([event_0,event_1,event_2]) + return np.array([event_0,event_1,event_2]) #Responsible for handling the events. @@ -150,12 +150,12 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title(imp_mod.name) - P.ylabel('States') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title(imp_mod.name) + pl.ylabel('States') + pl.xlabel('Time') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0],8.0) diff --git a/examples/ida_with_initial_sensitivity.py b/examples/ida_with_initial_sensitivity.py index bf3b3813..3081eb45 100644 --- a/examples/ida_with_initial_sensitivity.py +++ b/examples/ida_with_initial_sensitivity.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import IDA from assimulo.problem import Implicit_Problem @@ -57,13 +57,13 @@ def f(t, y, yd,p): res_1 = -yd2 + k21*y1-(k02+k12)*y2 res_2 = -yd3 + k31*y1-k13*y3 - return N.array([res_0,res_1,res_2]) + return np.array([res_0,res_1,res_2]) #The initial conditions y0 = [0.0,0.0,0.0] #Initial conditions for y yd0 = [49.3,0.,0.] p0 = [0.0, 0.0, 0.0] #Initial conditions for parameters - yS0 = N.array([[1,0,0],[0,1,0],[0,0,1.]]) + yS0 = np.array([[1,0,0],[0,1,0],[0,0,1.]]) #Create an Assimulo implicit problem imp_mod = Implicit_Problem(f,y0,yd0,p0=p0,name='Example: Computing Sensitivities') @@ -74,7 +74,7 @@ def f(t, y, yd,p): #Create an Assimulo explicit solver (IDA) imp_sim = IDA(imp_mod) - #Sets the paramters + #Sets the parameters imp_sim.rtol = 1e-7 imp_sim.atol = 1e-6 imp_sim.pbar = [1,1,1] #pbar is used to estimate the tolerances for the parameters @@ -87,31 +87,31 @@ def f(t, y, yd,p): #Plot if with_plots: - import pylab as P - P.figure(1) - P.subplot(221) - P.plot(t, N.array(imp_sim.p_sol[0])[:,0], - t, N.array(imp_sim.p_sol[0])[:,1], - t, N.array(imp_sim.p_sol[0])[:,2]) - P.title("Parameter p1") - P.legend(("p1/dy1","p1/dy2","p1/dy3")) - P.subplot(222) - P.plot(t, N.array(imp_sim.p_sol[1])[:,0], - t, N.array(imp_sim.p_sol[1])[:,1], - t, N.array(imp_sim.p_sol[1])[:,2]) - P.title("Parameter p2") - P.legend(("p2/dy1","p2/dy2","p2/dy3")) - P.subplot(223) - P.plot(t, N.array(imp_sim.p_sol[2])[:,0], - t, N.array(imp_sim.p_sol[2])[:,1], - t, N.array(imp_sim.p_sol[2])[:,2]) - P.title("Parameter p3") - P.legend(("p3/dy1","p3/dy2","p3/dy3")) - P.subplot(224) - P.title('ODE Solution') - P.plot(t, y) - P.suptitle(imp_mod.name) - P.show() + import pylab as pl + pl.figure(1) + pl.subplot(221) + pl.plot(t, np.array(imp_sim.p_sol[0])[:,0], + t, np.array(imp_sim.p_sol[0])[:,1], + t, np.array(imp_sim.p_sol[0])[:,2]) + pl.title("Parameter p1") + pl.legend(("p1/dy1","p1/dy2","p1/dy3")) + pl.subplot(222) + pl.plot(t, np.array(imp_sim.p_sol[1])[:,0], + t, np.array(imp_sim.p_sol[1])[:,1], + t, np.array(imp_sim.p_sol[1])[:,2]) + pl.title("Parameter p2") + pl.legend(("p2/dy1","p2/dy2","p2/dy3")) + pl.subplot(223) + pl.plot(t, np.array(imp_sim.p_sol[2])[:,0], + t, np.array(imp_sim.p_sol[2])[:,1], + t, np.array(imp_sim.p_sol[2])[:,2]) + pl.title("Parameter p3") + pl.legend(("p3/dy1","p3/dy2","p3/dy3")) + pl.subplot(224) + pl.title('ODE Solution') + pl.plot(t, y) + pl.suptitle(imp_mod.name) + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0], 1577.6552477,3) diff --git a/examples/ida_with_jac.py b/examples/ida_with_jac.py index 577e54b9..9cfa3027 100644 --- a/examples/ida_with_jac.py +++ b/examples/ida_with_jac.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np from assimulo.solvers import IDA from assimulo.problem import Implicit_Problem import nose @@ -51,11 +51,11 @@ def f(t,y,yd): res_3 = yd[3]+y[4]*y[1]+9.82 res_4 = y[2]**2+y[3]**2-y[4]*(y[0]**2+y[1]**2)-y[1]*9.82 - return N.array([res_0,res_1,res_2,res_3,res_4]) + return np.array([res_0,res_1,res_2,res_3,res_4]) #Defines the Jacobian def jac(c,t,y,yd): - jacobian = N.zeros([len(y),len(y)]) + jacobian = np.zeros([len(y),len(y)]) #Derivative jacobian[0,0] = 1*c @@ -80,7 +80,7 @@ def jac(c,t,y,yd): return jacobian - #The initial conditons + #The initial conditions y0 = [1.0,0.0,0.0,0.0,5] #Initial conditions yd0 = [0.0,0.0,0.0,-9.82,0.0] #Initial conditions @@ -95,7 +95,7 @@ def jac(c,t,y,yd): #Create an Assimulo implicit solver (IDA) imp_sim = IDA(imp_mod) #Create a IDA solver - #Sets the paramters + #Sets the parameters imp_sim.atol = 1e-6 #Default 1e-6 imp_sim.rtol = 1e-6 #Default 1e-6 imp_sim.suppress_alg = True #Suppres the algebraic variables on the error test @@ -114,12 +114,12 @@ def jac(c,t,y,yd): #Plot if with_plots: - import pylab as P - P.plot(t,y,linestyle="dashed",marker="o") #Plot the solution - P.xlabel('Time') - P.ylabel('State') - P.title(imp_mod.name) - P.show() + import pylab as pl + pl.plot(t,y,linestyle="dashed",marker="o") #Plot the solution + pl.xlabel('Time') + pl.ylabel('State') + pl.title(imp_mod.name) + pl.show() return imp_mod, imp_sim diff --git a/examples/ida_with_jac_spgmr.py b/examples/ida_with_jac_spgmr.py index 1db858de..4650a5ae 100644 --- a/examples/ida_with_jac_spgmr.py +++ b/examples/ida_with_jac_spgmr.py @@ -15,12 +15,11 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import IDA from assimulo.problem import Implicit_Problem - def run_example(with_plots=True): r""" An example for IDA with scaled preconditioned GMRES method @@ -48,14 +47,14 @@ def res(t,y,yd): res_0 = yd[0] - y[1] res_1 = yd[1] + 9.82 - return N.array([res_0,res_1]) + return np.array([res_0,res_1]) #Defines the Jacobian*vector product def jacv(t,y,yd,res,v,c): - jy = N.array([[0,-1.],[0,0]]) - jyd = N.array([[1,0.],[0,1]]) + jy = np.array([[0,-1.],[0,0]]) + jyd = np.array([[1,0.],[0,1]]) j = jy+c*jyd - return N.dot(j,v) + return np.dot(j,v) #Initial conditions y0 = [1.0,0.0] @@ -83,12 +82,12 @@ def jacv(t,y,yd,res,v,c): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.xlabel('Time') - P.ylabel('State') - P.title(imp_mod.name) - P.show() + import pylab as pl + pl.plot(t,y) + pl.xlabel('Time') + pl.ylabel('State') + pl.title(imp_mod.name) + pl.show() return imp_mod,imp_sim if __name__=='__main__': diff --git a/examples/ida_with_parameters.py b/examples/ida_with_parameters.py index 4a7198be..6c5cc9f5 100644 --- a/examples/ida_with_parameters.py +++ b/examples/ida_with_parameters.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import IDA from assimulo.problem import Implicit_Problem @@ -50,9 +50,9 @@ def f(t, y, yd, p): res2 = p[0]*y[0]-p[1]*y[1]*y[2]-p[2]*y[1]**2-yd[1] res3 = y[0]+y[1]+y[2]-1 - return N.array([res1,res2,res3]) + return np.array([res1,res2,res3]) - #The initial conditons + #The initial conditions y0 = [1.0, 0.0, 0.0] #Initial conditions for y yd0 = [0.1, 0.0, 0.0] #Initial conditions for dy/dt p0 = [0.040, 1.0e4, 3.0e7] #Initial conditions for parameters @@ -63,8 +63,8 @@ def f(t, y, yd, p): #Create an Assimulo implicit solver (IDA) imp_sim = IDA(imp_mod) #Create a IDA solver - #Sets the paramters - imp_sim.atol = N.array([1.0e-8, 1.0e-14, 1.0e-6]) + #Sets the parameters + imp_sim.atol = np.array([1.0e-8, 1.0e-14, 1.0e-6]) imp_sim.algvar = [1.0,1.0,0.0] imp_sim.suppress_alg = False #Suppres the algebraic variables on the error test imp_sim.report_continuously = True #Store data continuous during the simulation @@ -87,12 +87,12 @@ def f(t, y, yd, p): #Plot if with_plots: - import pylab as P - P.plot(t, y) - P.title(imp_mod.name) - P.xlabel('Time') - P.ylabel('State') - P.show() + import pylab as pl + pl.plot(t, y) + pl.title(imp_mod.name) + pl.xlabel('Time') + pl.ylabel('State') + pl.show() return imp_mod, imp_sim diff --git a/examples/ida_with_user_defined_handle_result.py b/examples/ida_with_user_defined_handle_result.py index 09e76ad8..365305ec 100644 --- a/examples/ida_with_user_defined_handle_result.py +++ b/examples/ida_with_user_defined_handle_result.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np from assimulo.solvers import IDA from assimulo.problem import Implicit_Problem import nose @@ -53,7 +53,7 @@ def f(t,y,yd): res_3 = yd[3]+y[4]*y[1]+9.82 res_4 = y[2]**2+y[3]**2-y[4]*(y[0]**2+y[1]**2)-y[1]*9.82 - return N.array([res_0,res_1,res_2,res_3,res_4]) + return np.array([res_0,res_1,res_2,res_3,res_4]) def handle_result(solver, t ,y, yd): global order @@ -64,7 +64,7 @@ def handle_result(solver, t ,y, yd): solver.yd_sol.extend([yd]) - #The initial conditons + #The initial conditions y0 = [1.0,0.0,0.0,0.0,5] #Initial conditions yd0 = [0.0,0.0,0.0,-9.82,0.0] #Initial conditions @@ -78,7 +78,7 @@ def handle_result(solver, t ,y, yd): #Create an Assimulo implicit solver (IDA) imp_sim = IDA(imp_mod) #Create a IDA solver - #Sets the paramters + #Sets the parameters imp_sim.atol = 1e-6 #Default 1e-6 imp_sim.rtol = 1e-6 #Default 1e-6 imp_sim.suppress_alg = True #Suppres the algebraic variables on the error test @@ -92,19 +92,19 @@ def handle_result(solver, t ,y, yd): #Plot if with_plots: - import pylab as P - P.figure(1) - P.plot(t,y,linestyle="dashed",marker="o") #Plot the solution - P.xlabel('Time') - P.ylabel('State') - P.title(imp_mod.name) + import pylab as pl + pl.figure(1) + pl.plot(t,y,linestyle="dashed",marker="o") #Plot the solution + pl.xlabel('Time') + pl.ylabel('State') + pl.title(imp_mod.name) - P.figure(2) - P.plot([0] + N.add.accumulate(N.diff(t)).tolist(), order) - P.title("Used order during the integration") - P.xlabel("Time") - P.ylabel("Order") - P.show() + pl.figure(2) + pl.plot([0] + np.add.accumulate(np.diff(t)).tolist(), order) + pl.title("Used order during the integration") + pl.xlabel("Time") + pl.ylabel("Order") + pl.show() #Basic tests nose.tools.assert_almost_equal(y[-1][0],0.9401995, places=4) diff --git a/examples/kinsol_ors.py b/examples/kinsol_ors.py index e1744cee..29f263ab 100644 --- a/examples/kinsol_ors.py +++ b/examples/kinsol_ors.py @@ -15,19 +15,16 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -import scipy.linalg as LIN -import scipy.io as IO -import scipy.sparse as SPARSE -import scipy.sparse.linalg as LINSP -import nose import os +import nose +import numpy as np +import scipy as sp +import scipy.sparse as sps from assimulo.solvers import KINSOL from assimulo.problem import Algebraic_Problem import warnings -import scipy.sparse -warnings.simplefilter("ignore", scipy.sparse.SparseEfficiencyWarning) +warnings.simplefilter("ignore", sps.SparseEfficiencyWarning) file_path = os.path.dirname(os.path.realpath(__file__)) @@ -39,25 +36,25 @@ def run_example(with_plots=True): Iterative Methods for Sparse Linear Systems. """ #Read the original matrix - A_original = IO.mmread(os.path.join(file_path,"kinsol_ors_matrix.mtx")) + A_original = sp.io.mmread(os.path.join(file_path,"kinsol_ors_matrix.mtx")) #Scale the original matrix - A = SPARSE.spdiags(1.0/A_original.diagonal(), 0, len(A_original.diagonal()), len(A_original.diagonal())) * A_original + A = sps.spdiags(1.0/A_original.diagonal(), 0, len(A_original.diagonal()), len(A_original.diagonal())) * A_original #Preconditioning by Symmetric Gauss Seidel if True: - D = SPARSE.spdiags(A.diagonal(), 0, len(A_original.diagonal()), len(A_original.diagonal())) - Dinv = SPARSE.spdiags(1.0/A.diagonal(), 0, len(A_original.diagonal()), len(A_original.diagonal())) - E = -SPARSE.tril(A,k=-1) - F = -SPARSE.triu(A,k=1) + D = sps.spdiags(A.diagonal(), 0, len(A_original.diagonal()), len(A_original.diagonal())) + Dinv = sps.spdiags(1.0/A.diagonal(), 0, len(A_original.diagonal()), len(A_original.diagonal())) + E = -sps.tril(A,k=-1) + F = -sps.triu(A,k=1) L = (D-E).dot(Dinv) U = D-F Prec = L.dot(U) - solvePrec = LINSP.factorized(Prec) + solvePrec = sps.linalg.factorized(Prec) #Create the RHS - b = A.dot(N.ones(A.shape[0])) + b = A.dot(np.ones(A.shape[0])) #Define the res def res(x): @@ -77,7 +74,7 @@ def prec_setup(u,f, uscale, fscale): def prec_solve(r): return solvePrec(r) - y0 = N.random.rand(A.shape[0]) + y0 = np.random.rand(A.shape[0]) #Define an Assimulo problem alg_mod = Algebraic_Problem(res, y0=y0, jac=jac, jacv=jacv, name = 'ORS Example') @@ -91,7 +88,7 @@ def prec_solve(r): def setup_param(solver): solver.linear_solver = "spgmr" solver.max_dim_krylov_subspace = 10 - solver.ftol = LIN.norm(res(solver.y0))*1e-9 + solver.ftol = np.linalg.norm(res(solver.y0))*1e-9 solver.max_iter = 300 solver.verbosity = 10 solver.globalization_strategy = "none" @@ -105,27 +102,27 @@ def setup_param(solver): #Solve Preconditioned system y_prec = alg_solver_prec.solve() - print("Error , in y: ", LIN.norm(y-N.ones(len(y)))) - print("Error (preconditioned), in y: ", LIN.norm(y_prec-N.ones(len(y_prec)))) + print("Error , in y: ", np.linalg.norm(y-np.ones(len(y)))) + print("Error (preconditioned), in y: ", np.linalg.norm(y_prec-np.ones(len(y_prec)))) if with_plots: - import pylab as P - P.figure(4) - P.semilogy(alg_solver.get_residual_norm_nonlinear_iterations(), label="Original") - P.semilogy(alg_solver_prec.get_residual_norm_nonlinear_iterations(), label='Preconditioned') - P.xlabel("Number of Iterations") - P.ylabel("Residual Norm") - P.title("Solution Progress") - P.legend() - P.grid() + import pylab as pl + pl.figure(4) + pl.semilogy(alg_solver.get_residual_norm_nonlinear_iterations(), label="Original") + pl.semilogy(alg_solver_prec.get_residual_norm_nonlinear_iterations(), label='Preconditioned') + pl.xlabel("Number of Iterations") + pl.ylabel("Residual Norm") + pl.title("Solution Progress") + pl.legend() + pl.grid() - P.figure(5) - P.plot(y, label="Original") - P.plot(y_prec, label="Preconditioned") - P.legend() - P.grid() + pl.figure(5) + pl.plot(y, label="Original") + pl.plot(y_prec, label="Preconditioned") + pl.legend() + pl.grid() - P.show() + pl.show() #Basic test for j in range(len(y)): diff --git a/examples/kinsol_with_jac.py b/examples/kinsol_with_jac.py index 8171d447..6fcf0f38 100644 --- a/examples/kinsol_with_jac.py +++ b/examples/kinsol_with_jac.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import KINSOL from assimulo.problem import Algebraic_Problem @@ -36,10 +36,10 @@ def run_example(with_plots=True): def res(y): r1 = 2*y[0]+3*y[1]-6 r2 = 4*y[0]+9*y[1]-15 - return N.array([r1,r2]) + return np.array([r1,r2]) def jac(y): - return N.array([[2.,3.],[4.,9.]]) + return np.array([[2.,3.],[4.,9.]]) #Define an Assimulo problem alg_mod = Algebraic_Problem(res, y0=[0,0], jac=jac, name='KINSOL example with Jac') diff --git a/examples/lsodar_bouncing_ball.py b/examples/lsodar_bouncing_ball.py index d7d8a2e9..775c7603 100644 --- a/examples/lsodar_bouncing_ball.py +++ b/examples/lsodar_bouncing_ball.py @@ -15,8 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import nose -import numpy as N +import numpy as np from assimulo.solvers import LSODAR from assimulo.problem import Explicit_Problem @@ -28,7 +27,7 @@ #Extend Assimulos problem definition class Extended_Problem(Explicit_Problem): - #Sets the initial conditons directly into the problem + #Sets the initial conditions directly into the problem y0 = [2.0, 0] # position and (downward) velocity sw0=[True,False] @@ -42,7 +41,7 @@ def rhs(self,t,y,sw): """ yd_0 = y[1] yd_1 = self.g - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) #Sets a name to our function name = 'Bouncing Ball Problem' @@ -56,7 +55,7 @@ def state_events(self,t,y,sw): event_0 = y[0] if sw[0] else 5 # hits the ground event_1 = y[1] if sw[1] else 5 # velocity changes sign at topmost point - return N.array([event_0,event_1]) + return np.array([event_0,event_1]) #Responsible for handling the events. @@ -132,23 +131,23 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.subplot(221) - P.plot(t,y) - P.title('LSODAR Bouncing ball (one step mode)') - P.ylabel('States: $y$ and $\dot y$') - P.subplot(223) - P.plot(exp_sim.t_sol,exp_sim.h_sol) - P.title('LSODAR step size plot') - P.xlabel('Time') - P.ylabel('Sepsize') - P.subplot(224) - P.plot(exp_sim.t_sol,exp_sim.nq_sol) - P.title('LSODAR order plot') - P.xlabel('Time') - P.ylabel('Order') - P.suptitle(exp_mod.name) - P.show() + import pylab as pl + pl.subplot(221) + pl.plot(t,y) + pl.title('LSODAR Bouncing ball (one step mode)') + pl.ylabel('States: $y$ and $\dot y$') + pl.subplot(223) + pl.plot(exp_sim.t_sol,exp_sim.h_sol) + pl.title('LSODAR step size plot') + pl.xlabel('Time') + pl.ylabel('Sepsize') + pl.subplot(224) + pl.plot(exp_sim.t_sol,exp_sim.nq_sol) + pl.title('LSODAR order plot') + pl.xlabel('Time') + pl.ylabel('Order') + pl.suptitle(exp_mod.name) + pl.show() return exp_mod, exp_sim if __name__=="__main__": diff --git a/examples/lsodar_vanderpol.py b/examples/lsodar_vanderpol.py index 1b35386d..ee86735d 100644 --- a/examples/lsodar_vanderpol.py +++ b/examples/lsodar_vanderpol.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import LSODAR from assimulo.problem import Explicit_Problem @@ -47,7 +47,7 @@ def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) y0 = [2.0,-0.6] #Initial conditions @@ -66,16 +66,16 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.plot(t,y[:,0], marker='o') - P.title(exp_mod.name) - P.ylabel("State: $y_1$") - P.xlabel("Time") - P.show() + import pylab as pl + pl.plot(t,y[:,0], marker='o') + pl.title(exp_mod.name) + pl.ylabel("State: $y_1$") + pl.xlabel("Time") + pl.show() #Basic test x1 = y[:,0] - nose.tools.assert_less(N.abs(x1[-1] - 1.706168035), 1e-3) + nose.tools.assert_less(np.abs(x1[-1] - 1.706168035), 1e-3) return exp_mod, exp_sim diff --git a/examples/lsodar_with_disc.py b/examples/lsodar_with_disc.py index 3a286af7..a2952485 100644 --- a/examples/lsodar_with_disc.py +++ b/examples/lsodar_with_disc.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import LSODAR from assimulo.problem import Explicit_Problem @@ -34,7 +34,7 @@ #Extend Assimulos problem definition class Extended_Problem(Explicit_Problem): - #Sets the initial conditons directly into the problem + #Sets the initial conditions directly into the problem y0 = [0.0, -1.0, 0.0] sw0 = [False,True,True] @@ -50,7 +50,7 @@ def rhs(self,t,y,sw): yd_1 = 0.0 yd_2 = 0.0 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #Sets a name to our function name = 'ODE with discontinuities and a function with consistency problem' @@ -65,7 +65,7 @@ def state_events(self,t,y,sw): event_1 = -y[2] + 1.0 event_2 = -t + 1.0 - return N.array([event_0,event_1,event_2]) + return np.array([event_0,event_1,event_2]) #Responsible for handling the events. def handle_event(self, solver, event_info): @@ -144,12 +144,12 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title("Solution of a differential equation with discontinuities") - P.ylabel('States') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title("Solution of a differential equation with discontinuities") + pl.ylabel('States') + pl.xlabel('Time') + pl.show() #Basic test nose.tools.assert_almost_equal(y[-1][0],8.0) diff --git a/examples/mech_system_pendulum.py b/examples/mech_system_pendulum.py index 3a632f70..852b2e11 100644 --- a/examples/mech_system_pendulum.py +++ b/examples/mech_system_pendulum.py @@ -15,37 +15,35 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import assimulo.special_systems as ass -import numpy as N import nose -import scipy.linalg as sl +import numpy as np +from assimulo.special_systems import Mechanical_System from assimulo.solvers import IDA, ODASSL - def pendulum(): g=13.7503671 n_p=2 n_la=1 def forces(t, p, v): - return N.array([0.,-g]) + return np.array([0.,-g]) def GT(p): - return N.array([p[0],p[1]]).reshape((2,1)) + return np.array([p[0],p[1]]).reshape((2,1)) def constr3(t,y): p=y[0:2] - return N.array([p[0]**2+p[1]**2-1.]) + return np.array([p[0]**2+p[1]**2-1.]) def constr2(t,y): p,v=y[0:2],y[2:4] - return N.array([p[0]*v[0]+p[1]*v[1]]) + return np.array([p[0]*v[0]+p[1]*v[1]]) def constr1(t,y): p,v,la=y[0:2],y[2:4],y[4:5] - return N.array([v[0]**2+v[1]**2 - la[0] * (p[0]**2 + p[1]**2) - p[1] * g]) - return ass.Mechanical_System(n_p, forces, n_la, - [1.,0.], [0.,0.], - [0], - [0.,0.], [0.,-g], GT=GT, - constr3 = constr3, - constr2 = constr2, - constr1 = constr1) + return np.array([v[0]**2+v[1]**2 - la[0] * (p[0]**2 + p[1]**2) - p[1] * g]) + return Mechanical_System(n_p, forces, n_la, + [1.,0.], [0.,0.], + [0], + [0.,0.], [0.,-g], GT=GT, + constr3 = constr3, + constr2 = constr2, + constr1 = constr1) def run_example(index="ind1", with_plots=True, with_test=False): my_pend_sys=pendulum() @@ -59,10 +57,10 @@ def run_example(index="ind1", with_plots=True, with_test=False): final_residual=my_pend.res(0.,dae_pend.y,dae_pend.yd) print(my_pend.name+" Residuals after the integration run\n") - print(final_residual, 'Norm: ', sl.norm(final_residual)) + print(final_residual, 'Norm: ', np.linalg.norm(final_residual)) if with_test: - nose.tools.assert_less(sl.norm(final_residual), 1.5e-1) + nose.tools.assert_less(np.linalg.norm(final_residual), 1.5e-1) if with_plots: dae_pend.plot(mask=[1,1]+(len(my_pend.y0)-2)*[0]) return my_pend, dae_pend diff --git a/examples/radau5dae_time_events.py b/examples/radau5dae_time_events.py index c61ee2b9..b4b69dd2 100644 --- a/examples/radau5dae_time_events.py +++ b/examples/radau5dae_time_events.py @@ -15,12 +15,11 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import Radau5DAE from assimulo.problem import Implicit_Problem - class VanDerPolProblem(Implicit_Problem): def __init__(self, **kargs): Implicit_Problem.__init__(self, **kargs) @@ -34,7 +33,7 @@ def res(self, t,y,yd): res_0 = yd[0]-yd_0 res_1 = yd[1]-yd_1 - return N.array([res_0,res_1]) + return np.array([res_0,res_1]) def time_events(self, t,y,yd,sw): events = [1.0, 2.0, 2.5, 3.0] @@ -65,16 +64,16 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y[:,0], marker='o') - P.xlabel('Time') - P.ylabel('State') - P.title(imp_mod.name) - P.show() + import pylab as pl + pl.plot(t,y[:,0], marker='o') + pl.xlabel('Time') + pl.ylabel('State') + pl.title(imp_mod.name) + pl.show() #Basic test x1 = y[:,0] - nose.tools.assert_less(N.abs(float(x1[-1]) - 1.14330840983), 1e-3) + nose.tools.assert_less(np.abs(float(x1[-1]) - 1.14330840983), 1e-3) return imp_mod, imp_sim if __name__=='__main__': diff --git a/examples/radau5dae_vanderpol.py b/examples/radau5dae_vanderpol.py index e84358f3..ea5612ce 100644 --- a/examples/radau5dae_vanderpol.py +++ b/examples/radau5dae_vanderpol.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import Radau5DAE from assimulo.problem import Implicit_Problem @@ -50,7 +50,7 @@ def f(t,y,yd): res_0 = yd[0]-yd_0 res_1 = yd[1]-yd_1 - return N.array([res_0,res_1]) + return np.array([res_0,res_1]) y0 = [2.0,-0.6] #Initial conditions yd0 = [-.6,-200000.] @@ -72,21 +72,21 @@ def f(t,y,yd): #Plot if with_plots: - import pylab as P - P.subplot(211) - P.plot(t,y[:,0])#, marker='o') - P.xlabel('Time') - P.ylabel('State') - P.subplot(212) - P.plot(t,yd[:,0]*1.e-5)#, marker='o') - P.xlabel('Time') - P.ylabel('State derivatives scaled with $10^{-5}$') - P.suptitle(imp_mod.name) - P.show() + import pylab as pl + pl.subplot(211) + pl.plot(t,y[:,0])#, marker='o') + pl.xlabel('Time') + pl.ylabel('State') + pl.subplot(212) + pl.plot(t,yd[:,0]*1.e-5)#, marker='o') + pl.xlabel('Time') + pl.ylabel('State derivatives scaled with $10^{-5}$') + pl.suptitle(imp_mod.name) + pl.show() #Basic test x1 = y[:,0] - nose.tools.assert_less(N.abs(float(x1[-1]) - 1.706168035), 1e-3) + nose.tools.assert_less(np.abs(float(x1[-1]) - 1.706168035), 1e-3) return imp_mod, imp_sim diff --git a/examples/radau5ode_vanderpol.py b/examples/radau5ode_vanderpol.py index 6a046c09..bbc852f9 100644 --- a/examples/radau5ode_vanderpol.py +++ b/examples/radau5ode_vanderpol.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import Radau5ODE from assimulo.problem import Explicit_Problem @@ -46,7 +46,7 @@ def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) y0 = [2.0,-0.6] #Initial conditions @@ -67,16 +67,16 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.plot(t,y[:,0])#, marker='o') - P.xlabel('Time') - P.ylabel('State') - P.title(exp_mod.name) - P.show() + import pylab as pl + pl.plot(t,y[:,0])#, marker='o') + pl.xlabel('Time') + pl.ylabel('State') + pl.title(exp_mod.name) + pl.show() #Basic test x1 = y[:,0] - nose.tools.assert_less(N.abs(float(x1[-1]) - 1.706168035), 1e-3) + nose.tools.assert_less(np.abs(float(x1[-1]) - 1.706168035), 1e-3) return exp_mod, exp_sim diff --git a/examples/radau5ode_with_disc.py b/examples/radau5ode_with_disc.py index af14b49e..544c34f7 100644 --- a/examples/radau5ode_with_disc.py +++ b/examples/radau5ode_with_disc.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import Radau5ODE from assimulo.problem import Explicit_Problem @@ -50,7 +50,7 @@ def rhs(self,t,y,sw): yd_1 = 0.0 yd_2 = 0.0 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #Sets a name to our function name = 'ODE with discontinuities and a function with consistency problem' @@ -65,8 +65,7 @@ def state_events(self,t,y,sw): event_1 = -y[2] + 1.0 event_2 = -t + 1.0 - return N.array([event_0,event_1,event_2]) - + return np.array([event_0,event_1,event_2]) #Responsible for handling the events. def handle_event(self, solver, event_info): @@ -141,12 +140,12 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title("Solution of a differential equation with discontinuities") - P.ylabel('States') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title("Solution of a differential equation with discontinuities") + pl.ylabel('States') + pl.xlabel('Time') + pl.show() return exp_mod, exp_sim diff --git a/examples/radau5ode_with_disc_sparse.py b/examples/radau5ode_with_disc_sparse.py index 3a994040..9ef1d6f3 100644 --- a/examples/radau5ode_with_disc_sparse.py +++ b/examples/radau5ode_with_disc_sparse.py @@ -15,8 +15,8 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -import scipy.sparse as sp +import numpy as np +import scipy.sparse as sps import nose from assimulo.solvers import Radau5ODE from assimulo.problem import Explicit_Problem @@ -51,10 +51,10 @@ def rhs(self,t,y,sw): yd_1 = 0.0 yd_2 = 0.0 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) def jac(self, t, y): - return sp.csc_matrix((len(y), len(y)), dtype = 'float') + return sps.csc_matrix((len(y), len(y)), dtype = 'float') jac_nnz = 0 @@ -71,7 +71,7 @@ def state_events(self,t,y,sw): event_1 = -y[2] + 1.0 event_2 = -t + 1.0 - return N.array([event_0,event_1,event_2]) + return np.array([event_0,event_1,event_2]) #Responsible for handling the events. @@ -148,12 +148,12 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title("Solution of a differential equation with discontinuities") - P.ylabel('States') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title("Solution of a differential equation with discontinuities") + pl.ylabel('States') + pl.xlabel('Time') + pl.show() return exp_mod, exp_sim diff --git a/examples/radau5ode_with_jac_sparse.py b/examples/radau5ode_with_jac_sparse.py index 3fece4d3..633f0535 100644 --- a/examples/radau5ode_with_jac_sparse.py +++ b/examples/radau5ode_with_jac_sparse.py @@ -15,9 +15,9 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -import scipy.sparse as SP import nose +import numpy as np +import scipy.sparse as sps from assimulo.solvers import Radau5ODE from assimulo.problem import Explicit_Problem @@ -48,7 +48,7 @@ def f(t,y): yd_0 = -0.04*y[0] + 1e4*y[1]*y[2] yd_2 = 3e7*y[1]*y[1] yd_1 = -yd_0 - yd_2 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #Defines the Jacobian def jac(t,y): @@ -57,7 +57,7 @@ def jac(t,y): rowvals = [0, 1, 0, 1, 2, 0, 1] data = [-0.04, 0.04, 1e4*y[2], -1e4*y[2]-6e7*y[1], 6e7*y[1], 1e4*y[1], -1e4*y[1]] - J = SP.csc_matrix((data, rowvals, colptrs)) + J = sps.csc_matrix((data, rowvals, colptrs)) return J #Defines an Assimulo explicit problem @@ -81,12 +81,12 @@ def jac(t,y): #Plot if with_plots: - import pylab as P - P.plot(t,y[:,1],linestyle="dashed",marker="o") #Plot the solution - P.xlabel('Time') - P.ylabel('State') - P.title(exp_mod.name) - P.show() + import pylab as pl + pl.plot(t,y[:,1],linestyle="dashed",marker="o") #Plot the solution + pl.xlabel('Time') + pl.ylabel('State') + pl.title(exp_mod.name) + pl.show() #Basic tests nose.tools.assert_almost_equal(y[-1][0], 0.9851, 3) diff --git a/examples/rodasode_vanderpol.py b/examples/rodasode_vanderpol.py index ca0e37d5..61da426b 100644 --- a/examples/rodasode_vanderpol.py +++ b/examples/rodasode_vanderpol.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import RodasODE from assimulo.problem import Explicit_Problem @@ -47,13 +47,13 @@ def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) #Define the jacobian def jac(t,y): eps = 1.e-6 my = 1./eps - j = N.array([[0.0,1.0],[my*((-2.0*y[0])*y[1]-1.0), my*(1.0-y[0]**2)]]) + j = np.array([[0.0,1.0],[my*((-2.0*y[0])*y[1]-1.0), my*(1.0-y[0]**2)]]) return j y0 = [2.0,-0.6] #Initial conditions @@ -76,17 +76,17 @@ def jac(t,y): #Plot if with_plots: - import pylab as P - P.plot(t,y[:,0], marker='o') - P.title(exp_mod.name) - P.ylabel("State: $y_1$") - P.xlabel("Time") - P.show() + import pylab as pl + pl.plot(t,y[:,0], marker='o') + pl.title(exp_mod.name) + pl.ylabel("State: $y_1$") + pl.xlabel("Time") + pl.show() #Basic test x1 = y[:,0] - nose.tools.assert_less(N.abs(float(x1[-1]) - 1.706168035), 1e-3) + nose.tools.assert_less(np.abs(float(x1[-1]) - 1.706168035), 1e-3) return exp_mod, exp_sim diff --git a/examples/rungekutta34_basic.py b/examples/rungekutta34_basic.py index 24167e63..fea7749f 100644 --- a/examples/rungekutta34_basic.py +++ b/examples/rungekutta34_basic.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import RungeKutta34 from assimulo.problem import Explicit_Problem @@ -36,7 +36,7 @@ def run_example(with_plots=True): #Defines the rhs def f(t,y): ydot = -y[0] - return N.array([ydot]) + return np.array([ydot]) #Define an Assimulo problem exp_mod = Explicit_Problem(f, 4.0, @@ -53,12 +53,12 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.plot(t, y) - P.title(exp_mod.name) - P.ylabel('y') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t, y) + pl.title(exp_mod.name) + pl.ylabel('y') + pl.xlabel('Time') + pl.show() return exp_mod, exp_sim diff --git a/examples/rungekutta34_with_disc.py b/examples/rungekutta34_with_disc.py index 21bae04f..97e222b7 100644 --- a/examples/rungekutta34_with_disc.py +++ b/examples/rungekutta34_with_disc.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import RungeKutta34 from assimulo.problem import Explicit_Problem @@ -34,7 +34,7 @@ #Extend Assimulos problem definition class Extended_Problem(Explicit_Problem): - #Sets the initial conditons directly into the problem + #Sets the initial conditions directly into the problem y0 = [0.0, -1.0, 0.0] sw0 = [False,True,True] @@ -50,7 +50,7 @@ def rhs(self,t,y,sw): yd_1 = 0.0 yd_2 = 0.0 - return N.array([yd_0,yd_1,yd_2]) + return np.array([yd_0,yd_1,yd_2]) #Sets a name to our function name = 'ODE with discontinuities and a function with consistency problem' @@ -65,7 +65,7 @@ def state_events(self,t,y,sw): event_1 = -y[2] + 1.0 event_2 = -t + 1.0 - return N.array([event_0,event_1,event_2]) + return np.array([event_0,event_1,event_2]) #Responsible for handling the events. @@ -142,12 +142,12 @@ def run_example(with_plots=True): #Plot if with_plots: - import pylab as P - P.plot(t,y) - P.title(exp_mod.name) - P.ylabel('States') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t,y) + pl.title(exp_mod.name) + pl.ylabel('States') + pl.xlabel('Time') + pl.show() return exp_mod, exp_sim diff --git a/examples/rungekutta4_basic.py b/examples/rungekutta4_basic.py index 4a3a7473..cb009129 100644 --- a/examples/rungekutta4_basic.py +++ b/examples/rungekutta4_basic.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import nose from assimulo.solvers import RungeKutta4 from assimulo.problem import Explicit_Problem @@ -37,7 +37,7 @@ def run_example(with_plots=True): #Defines the rhs def f(t,y): ydot = -y[0] - return N.array([ydot]) + return np.array([ydot]) #Define an Assimulo problem exp_mod = Explicit_Problem(f, 4.0, @@ -53,12 +53,12 @@ def f(t,y): #Plot if with_plots: - import pylab as P - P.plot(t, y) - P.title(exp_mod.name) - P.ylabel('y') - P.xlabel('Time') - P.show() + import pylab as pl + pl.plot(t, y) + pl.title(exp_mod.name) + pl.ylabel('y') + pl.xlabel('Time') + pl.show() return exp_mod, exp_sim diff --git a/src/algebraic.pxd b/src/algebraic.pxd index 285b46df..aa9d535b 100644 --- a/src/algebraic.pxd +++ b/src/algebraic.pxd @@ -15,16 +15,16 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np cdef class Algebraic: cdef public object problem cdef public dict options, solver_options, problem_info cdef public dict statistics - cdef public N.ndarray y - cdef public N.ndarray y0 + cdef public np.ndarray y + cdef public np.ndarray y0 cdef _reset_solution_variables(self) diff --git a/src/algebraic.pyx b/src/algebraic.pyx index f71452bd..c1053006 100644 --- a/src/algebraic.pyx +++ b/src/algebraic.pyx @@ -17,8 +17,8 @@ # distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from timeit import default_timer as timer from assimulo.exception import Algebraic_Exception, AssimuloException @@ -44,8 +44,8 @@ cdef class Algebraic: self.problem = problem if hasattr(problem, 'y0'): - self.y0 = N.array(problem.y0,dtype=realtype) if len(N.array(problem.y0,dtype=realtype).shape)>0 else N.array([problem.y0],dtype=realtype) - self.y = N.array(problem.y0,dtype=realtype) if len(N.array(problem.y0,dtype=realtype).shape)>0 else N.array([problem.y0],dtype=realtype) + self.y0 = np.array(problem.y0,dtype=realtype) if len(np.array(problem.y0,dtype=realtype).shape)>0 else np.array([problem.y0],dtype=realtype) + self.y = np.array(problem.y0,dtype=realtype) if len(np.array(problem.y0,dtype=realtype).shape)>0 else np.array([problem.y0],dtype=realtype) self.problem_info["dim"] = len(self.y0) else: raise Algebraic_Exception('y0 must be specified in the problem.') diff --git a/src/explicit_ode.pxd b/src/explicit_ode.pxd index c83ff540..17082230 100644 --- a/src/explicit_ode.pxd +++ b/src/explicit_ode.pxd @@ -15,16 +15,16 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from assimulo.ode cimport ODE cdef class Explicit_ODE(ODE): - cpdef _simulate(self, double t0, double tfinal,N.ndarray output_list,int COMPLETE_STEP, int INTERPOLATE_OUTPUT,int TIME_EVENT) - cpdef report_solution(self, double t, N.ndarray y, opts) - cpdef event_locator(self, double t_low, double t_high, N.ndarray y_high) + cpdef _simulate(self, double t0, double tfinal,np.ndarray output_list,int COMPLETE_STEP, int INTERPOLATE_OUTPUT,int TIME_EVENT) + cpdef report_solution(self, double t, np.ndarray y, opts) + cpdef event_locator(self, double t_low, double t_high, np.ndarray y_high) cdef extern from "string.h": void *memcpy(void *s1, void *s2, int n) diff --git a/src/explicit_ode.pyx b/src/explicit_ode.pyx index f91b8074..cb274518 100644 --- a/src/explicit_ode.pyx +++ b/src/explicit_ode.pyx @@ -21,8 +21,8 @@ cimport cython import itertools import sys -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from timeit import default_timer as timer from assimulo.ode cimport ODE @@ -39,20 +39,20 @@ realtype = float @cython.wraparound(False) cdef void py2c_d(double* dest, object source, int dim): """Copy 1D numpy (double) array to (double *) C vector.""" - if not (isinstance(source, N.ndarray) and source.flags.contiguous and source.dtype == N.float64): - source = N.ascontiguousarray(source, dtype=N.float64) + if not (isinstance(source, np.ndarray) and source.flags.contiguous and source.dtype == np.float64): + source = np.ascontiguousarray(source, dtype=np.float64) assert source.size >= dim, "The dimension of the vector is {} and not equal to the problem dimension {}. Please verify the output vectors from the min/max/nominal/evalute methods in the Problem class.".format(source.size, dim) - memcpy(dest, N.PyArray_DATA(source), dim*sizeof(double)) + memcpy(dest, np.PyArray_DATA(source), dim*sizeof(double)) @cython.boundscheck(False) @cython.wraparound(False) -cdef void c2py_d(N.ndarray[double, ndim=1, mode='c'] dest, double* source, int dim): +cdef void c2py_d(np.ndarray[double, ndim=1, mode='c'] dest, double* source, int dim): """Copy (double *) C vector to 1D numpy array.""" - memcpy(N.PyArray_DATA(dest), source, dim*sizeof(double)) + memcpy(np.PyArray_DATA(dest), source, dim*sizeof(double)) cdef int callback_event(int n_y, int n_g, double t, double* y_in, double* g_out, void* f_event_EXT) noexcept: """Event indicator callback function to event_locator.c""" - cdef N.ndarray[double, ndim=1, mode="c"]y_py = N.empty(n_y, dtype = N.double) + cdef np.ndarray[double, ndim=1, mode="c"]y_py = np.empty(n_y, dtype = np.double) c2py_d(y_py, y_in, n_y) ret, g_high = (f_event_EXT)(t, y_py) if ret < 0: ## immediate return, no not try to copy g_high @@ -119,7 +119,7 @@ cdef class Explicit_ODE(ODE): See information in the __init__ method. """ - y0 = N.array(y0) if len(N.array(y0).shape)>0 else N.array([y0]) + y0 = np.array(y0) if len(np.array(y0).shape)>0 else np.array([y0]) if len(self.y) != len(y0): raise Explicit_ODE_Exception('y0 must be of the same length as the original problem.') @@ -129,12 +129,12 @@ cdef class Explicit_ODE(ODE): self.y = y0 if sw0 is not None: - self.sw = (N.array(sw0,dtype=bool) if len(N.array(sw0,dtype=bool).shape)>0 else N.array([sw0],dtype=bool)).tolist() + self.sw = (np.array(sw0,dtype=bool) if len(np.array(sw0,dtype=bool).shape)>0 else np.array([sw0],dtype=bool)).tolist() #Clear logs self.clear_logs() - cpdef _simulate(self, double t0, double tfinal, N.ndarray output_list, int REPORT_CONTINUOUSLY, int INTERPOLATE_OUTPUT, + cpdef _simulate(self, double t0, double tfinal, np.ndarray output_list, int REPORT_CONTINUOUSLY, int INTERPOLATE_OUTPUT, int TIME_EVENT): """ INTERNAL FUNCTION, FOR SIMULATION USE METHOD SIMULATE. @@ -181,7 +181,7 @@ cdef class Explicit_ODE(ODE): cdef double tevent cdef int flag, output_index cdef dict opts - cdef double eps = N.finfo(float).eps*100 #Machine Epsilon + cdef double eps = np.finfo(float).eps*100 #Machine Epsilon cdef backward = 1 if self.backward else 0 y0 = self.y @@ -274,7 +274,7 @@ cdef class Explicit_ODE(ODE): if self.t == tfinal: #Finished simulation (might occur due to event at the final time) break - cpdef report_solution(self, double t, N.ndarray y, opts): + cpdef report_solution(self, double t, np.ndarray y, opts): '''Is called after each successful step in case the complete step option is active. Here possible interpolation is done and the result handeled. Furthermore possible step events are checked. @@ -330,17 +330,17 @@ cdef class Explicit_ODE(ODE): return flag_initialize - cpdef event_locator(self, double t_low, double t_high, N.ndarray y_high): + cpdef event_locator(self, double t_low, double t_high, np.ndarray y_high): '''Checks if an event occurs in [t_low, t_high], if that is the case event localization is started. Event localization finds the earliest small interval that contains a change in domain. The right endpoint of this interval is then returned as the time to restart the integration at. ''' cdef int n_g = self.problem_info["dimRoot"] - cdef N.ndarray[double, mode="c", ndim=1] g_low_c = N.array(self.g_old) - cdef N.ndarray[double, mode="c", ndim=1] g_mid_c = N.empty(n_g, dtype = N.double) - cdef N.ndarray[double, mode="c", ndim=1] g_high_c = N.empty(n_g, dtype = N.double) - cdef N.ndarray[double, mode="c", ndim=1] y_high_c = N.array(y_high) + cdef np.ndarray[double, mode="c", ndim=1] g_low_c = np.array(self.g_old) + cdef np.ndarray[double, mode="c", ndim=1] g_mid_c = np.empty(n_g, dtype = np.double) + cdef np.ndarray[double, mode="c", ndim=1] g_high_c = np.empty(n_g, dtype = np.double) + cdef np.ndarray[double, mode="c", ndim=1] y_high_c = np.array(y_high) cdef int nstatefcns = 0 cdef int ret = f_event_locator(len(y_high), n_g, 1.e-13, t_low, &t_high, &y_high_c[0], &g_low_c[0], &g_mid_c[0], &g_high_c[0], @@ -350,7 +350,7 @@ cdef class Explicit_ODE(ODE): self.statistics["nstatefcns"] += nstatefcns if ret == ID_PY_EVENT: - event_info = N.zeros(n_g, dtype = int) + event_info = np.zeros(n_g, dtype = int) for i in range(n_g): if (g_low_c[i] > 0) != (g_high_c[i] > 0): event_info[i] = 1 if g_high_c[i] > 0 else -1 @@ -380,15 +380,15 @@ cdef class Explicit_ODE(ODE): - See http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot for information about the available options for **kwargs. """ - import pylab as P + import pylab as pl if len(self.t_sol) > 0: - P.xlabel('time') - P.ylabel('state') - P.title(self.problem.name) + pl.xlabel('time') + pl.ylabel('state') + pl.title(self.problem.name) if not mask: - P.plot(self.t_sol, self.y_sol, **kwargs) + pl.plot(self.t_sol, self.y_sol, **kwargs) else: if not isinstance(mask, list): raise Explicit_ODE_Exception('Mask must be a list of integers') @@ -397,9 +397,8 @@ cdef class Explicit_ODE(ODE): 'the number of variables.') for i in range(len(mask)): if mask[i]: - P.plot(self.t_sol, N.array(self.y_sol)[:,i],**kwargs) + pl.plot(self.t_sol, np.array(self.y_sol)[:,i],**kwargs) - - P.show() + pl.show() else: self.log_message("No result for plotting found.",NORMAL) diff --git a/src/implicit_ode.pxd b/src/implicit_ode.pxd index ccea18ad..4d0c3c96 100644 --- a/src/implicit_ode.pxd +++ b/src/implicit_ode.pxd @@ -15,10 +15,10 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from assimulo.ode cimport ODE cdef class Implicit_ODE(ODE): - cpdef _simulate(self, double t0, double tfinal,N.ndarray output_list,int COMPLETE_STEP, int INTERPOLATE_OUTPUT,int TIME_EVENT) + cpdef _simulate(self, double t0, double tfinal,np.ndarray output_list,int COMPLETE_STEP, int INTERPOLATE_OUTPUT,int TIME_EVENT) diff --git a/src/implicit_ode.pyx b/src/implicit_ode.pyx index 2ee634ce..a2103f11 100644 --- a/src/implicit_ode.pyx +++ b/src/implicit_ode.pyx @@ -20,8 +20,8 @@ import itertools import sys import warnings -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from timeit import default_timer as timer from assimulo.ode cimport ODE @@ -60,7 +60,7 @@ cdef class Implicit_ODE(ODE): if hasattr(problem, 'yd0'): - self.yd0 = N.array(problem.yd0,dtype=realtype) if len(N.array(problem.yd0,dtype=realtype).shape)>0 else N.array([problem.yd0],dtype=realtype) + self.yd0 = np.array(problem.yd0,dtype=realtype) if len(np.array(problem.yd0,dtype=realtype).shape)>0 else np.array([problem.yd0],dtype=realtype) else: if isinstance(self.problem, cExplicit_Problem): #The problem is an explicit, get the yd0 values from the right-hand-side self.problem_info["type"] = 0 #Change to explicit problem @@ -109,8 +109,8 @@ cdef class Implicit_ODE(ODE): See information in the __init__ method. """ - y0 = N.array(y0) if len(N.array(y0).shape)>0 else N.array([y0]) - yd0 = N.array(yd0) if len(N.array(yd0).shape)>0 else N.array([yd0]) + y0 = np.array(y0) if len(np.array(y0).shape)>0 else np.array([y0]) + yd0 = np.array(yd0) if len(np.array(yd0).shape)>0 else np.array([yd0]) if len(self.y) != len(y0) or len(self.yd) != len(yd0): raise Implicit_ODE_Exception('y0/yd0 must be of the same length as the original problem.') @@ -121,12 +121,12 @@ cdef class Implicit_ODE(ODE): self.yd = yd0 if sw0 is not None: - self.sw = (N.array(sw0,dtype=bool) if len(N.array(sw0,dtype=bool).shape)>0 else N.array([sw0],dtype=bool)).tolist() + self.sw = (np.array(sw0,dtype=bool) if len(np.array(sw0,dtype=bool).shape)>0 else np.array([sw0],dtype=bool)).tolist() #Clear logs self.clear_logs() - cpdef _simulate(self, double t0, double tfinal,N.ndarray output_list,int REPORT_CONTINUOUSLY, int INTERPOLATE_OUTPUT, + cpdef _simulate(self, double t0, double tfinal,np.ndarray output_list,int REPORT_CONTINUOUSLY, int INTERPOLATE_OUTPUT, int TIME_EVENT): """ INTERNAL FUNCTION, FOR SIMULATION USE METHOD SIMULATE. @@ -158,7 +158,7 @@ cdef class Implicit_ODE(ODE): cdef int flag, output_index cdef dict opts cdef int type = self.problem_info["type"] - cdef double eps = N.finfo(float).eps*100 #Machine Epsilon + cdef double eps = np.finfo(float).eps*100 #Machine Epsilon cdef backward = 1 if self.backward else 0 y0 = self.y @@ -408,7 +408,7 @@ cdef class Implicit_ODE(ODE): (t_low, g_low) = (t_mid, g_mid[0:n_g]) side = 2 - event_info = N.array([0] * n_g) + event_info = np.array([0] * n_g) for i in xrange(n_g): if (g_low[i] > 0) != (g_high[i] > 0): event_info[i] = 1 if g_high[i] > 0 else -1 @@ -445,12 +445,12 @@ cdef class Implicit_ODE(ODE): - See http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot for information about the available options for **kwargs. """ - import pylab as P + import pylab as pl if len(self.t_sol) > 0: - P.figure(1) + pl.figure(1) if not mask: - P.plot(self.t_sol, self.y_sol, **kwargs) + pl.plot(self.t_sol, self.y_sol, **kwargs) else: if not isinstance(mask, list): raise Implicit_ODE_Exception('Mask must be a list of integers') @@ -459,21 +459,21 @@ cdef class Implicit_ODE(ODE): 'the number of variables.') for i in range(len(mask)): if mask[i]: - P.plot(self.t_sol, N.array(self.y_sol)[:,i], **kwargs) + pl.plot(self.t_sol, np.array(self.y_sol)[:,i], **kwargs) - P.xlabel('time') - P.ylabel('state') - P.title(self.problem.name) + pl.xlabel('time') + pl.ylabel('state') + pl.title(self.problem.name) if der and not mask: - P.figure(2) - P.plot(self.t_sol, self.yd_sol, **kwargs) - P.xlabel('time') - P.ylabel('state derivatives') - P.title(self.problem.name) + pl.figure(2) + pl.plot(self.t_sol, self.yd_sol, **kwargs) + pl.xlabel('time') + pl.ylabel('state derivatives') + pl.title(self.problem.name) elif mask and der: - P.figure(2) + pl.figure(2) if not isinstance(mask, list): raise Implicit_ODE_Exception('Mask must be a list of integers') if not len(mask)==len(self.yd_sol[-1]): @@ -481,13 +481,13 @@ cdef class Implicit_ODE(ODE): 'the number of variables.') for i in range(len(mask)): if mask[i]: - P.plot(self.t_sol, N.array(self.yd_sol)[:,i], **kwargs) + pl.plot(self.t_sol, np.array(self.yd_sol)[:,i], **kwargs) - P.xlabel('time') - P.ylabel('state derivatives') - P.title(self.problem.name) + pl.xlabel('time') + pl.ylabel('state derivatives') + pl.title(self.problem.name) - P.show() + pl.show() else: self.log_message("No result for plotting found.",NORMAL) @@ -496,6 +496,3 @@ cdef class OverdeterminedDAE(Implicit_ODE): def check_instance(self): if not isinstance(self.problem, (Overdetermined_Problem, Implicit_Problem)): raise Implicit_ODE_Exception('The problem needs to be a subclass of Overdetermined_Problem or of Implicit_Problem.') - - - diff --git a/src/lib/radau_core.py b/src/lib/radau_core.py index 36d2fc4a..e207ed2d 100644 --- a/src/lib/radau_core.py +++ b/src/lib/radau_core.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np from assimulo.ode import NORMAL class Radau_Exception(Exception): @@ -58,13 +58,13 @@ def plot_stepsize(self): """ Plots the step-size. """ - import pylab as P + import pylab as pl - P.semilogy(N.diff(self.t),drawstyle='steps-post') - P.title(self.problem.name) - P.ylabel('Step length') - P.xlabel('Number of steps') - P.show() + pl.semilogy(np.diff(self.t),drawstyle='steps-post') + pl.title(self.problem.name) + pl.ylabel('Step length') + pl.xlabel('Number of steps') + pl.show() def _set_newt(self, newt): """ @@ -357,10 +357,10 @@ def _get_usejac(self): def _set_atol(self,atol): - self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self._leny) + self.options["atol"] = self.options["atol"]*np.ones(self._leny) elif len(self.options["atol"]) != self._leny: raise Radau_Exception("atol must be of length one or same as the dimension of the problem.") diff --git a/src/lib/sundials_callbacks.pxi b/src/lib/sundials_callbacks.pxi index 2d0753f3..1ea78894 100644 --- a/src/lib/sundials_callbacks.pxi +++ b/src/lib/sundials_callbacks.pxi @@ -33,9 +33,9 @@ cdef N_Vector N_VNewEmpty_Euclidean(long int n) noexcept: return v cdef inline N_Vector arr2nv(x) noexcept: - x=N.array(x) + x=np.array(x) cdef long int n = len(x) - cdef N.ndarray[realtype, ndim=1,mode='c'] ndx=x + cdef np.ndarray[realtype, ndim=1,mode='c'] ndx=x cdef void* data_ptr=PyArray_DATA(ndx) IF SUNDIALS_VERSION >= (6,0,0): cdef SUNDIALS.SUNContext ctx = NULL @@ -48,34 +48,34 @@ cdef inline N_Vector arr2nv(x) noexcept: return v cdef inline N_Vector arr2nv_euclidean(x) noexcept: - x=N.array(x) + x=np.array(x) cdef long int n = len(x) - cdef N.ndarray[realtype, ndim=1,mode='c'] ndx=x + cdef np.ndarray[realtype, ndim=1,mode='c'] ndx=x cdef void* data_ptr=PyArray_DATA(ndx) cdef N_Vector v=N_VNewEmpty_Euclidean(n) memcpy((v.content).data, data_ptr, n*sizeof(realtype)) return v cdef inline void arr2nv_inplace(x, N_Vector out) noexcept: - x=N.array(x) + x=np.array(x) cdef long int n = len(x) - cdef N.ndarray[realtype, ndim=1,mode='c'] ndx=x + cdef np.ndarray[realtype, ndim=1,mode='c'] ndx=x cdef void* data_ptr=PyArray_DATA(ndx) memcpy((out.content).data, data_ptr, n*sizeof(realtype)) -cdef inline N.ndarray nv2arr(N_Vector v) noexcept: +cdef inline np.ndarray nv2arr(N_Vector v) noexcept: cdef long int n = (v.content).length cdef realtype* v_data = (v.content).data - cdef N.ndarray[realtype, ndim=1, mode='c'] x=N.empty(n) + cdef np.ndarray[realtype, ndim=1, mode='c'] x=np.empty(n) memcpy(PyArray_DATA(x), v_data, n*sizeof(realtype)) return x -cdef inline void nv2arr_inplace(N_Vector v, N.ndarray o) noexcept: +cdef inline void nv2arr_inplace(N_Vector v, np.ndarray o) noexcept: cdef long int n = (v.content).length cdef realtype* v_data = (v.content).data memcpy(PyArray_DATA(o), v_data, n*sizeof(realtype)) -cdef inline void nv2mat_inplace(int Ns, N_Vector *v, N.ndarray o) noexcept: +cdef inline void nv2mat_inplace(int Ns, N_Vector *v, np.ndarray o) noexcept: cdef long int i,j, Nf for i in range(Ns): Nf = (v[i].content).length @@ -84,6 +84,6 @@ cdef inline void nv2mat_inplace(int Ns, N_Vector *v, N.ndarray o) noexcept: cdef inline realtype2arr(realtype *data, int n) noexcept: """Create new numpy array from realtype*""" - cdef N.ndarray[realtype, ndim=1, mode='c'] x=N.empty(n) + cdef np.ndarray[realtype, ndim=1, mode='c'] x=np.empty(n) memcpy(PyArray_DATA(x), data, n*sizeof(realtype)) return x diff --git a/src/lib/sundials_callbacks_ida_cvode.pxi b/src/lib/sundials_callbacks_ida_cvode.pxi index eaa776cb..5f84ea4e 100644 --- a/src/lib/sundials_callbacks_ida_cvode.pxi +++ b/src/lib/sundials_callbacks_ida_cvode.pxi @@ -17,6 +17,7 @@ import cython import traceback +import scipy.sparse as sps from assimulo.exception import AssimuloRecoverableError cdef int cv_rhs(realtype t, N_Vector yv, N_Vector yvdot, void* problem_data) noexcept: @@ -25,7 +26,7 @@ cdef int cv_rhs(realtype t, N_Vector yv, N_Vector yvdot, void* problem_data) noe right-hand-side function. """ cdef ProblemData pData = problem_data - cdef N.ndarray y = pData.work_y + cdef np.ndarray y = pData.work_y cdef realtype* resptr=(yvdot.content).data cdef int i @@ -60,8 +61,8 @@ cdef int cv_sens_rhs_all(int Ns, realtype t, N_Vector yv, N_Vector yvdot, N_Vector tmp1, N_Vector tmp2) noexcept: cdef ProblemData pData = problem_data - cdef N.ndarray y = pData.work_y - cdef N.ndarray s = pData.work_ys + cdef np.ndarray y = pData.work_y + cdef np.ndarray s = pData.work_ys cdef realtype* resptr cdef int i, j @@ -81,7 +82,7 @@ cdef int cv_sens_rhs_all(int Ns, realtype t, N_Vector yv, N_Vector yvdot, resptr[j] = sens_rhs[j,i] return CV_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return CV_REC_ERR except Exception: traceback.print_exc() @@ -99,7 +100,7 @@ IF SUNDIALS_VERSION >= (3,0,0): """ cdef ProblemData pData = problem_data cdef SUNMatrixContent_Sparse Jacobian = Jac.content - cdef N.ndarray y = pData.work_y + cdef np.ndarray y = pData.work_y cdef int i cdef sunindextype nnz = Jacobian.NNZ cdef int ret_nnz @@ -123,8 +124,8 @@ IF SUNDIALS_VERSION >= (3,0,0): else: jac=(pData.JAC)(t,y) - if not isinstance(jac, sparse.csc_matrix): - jac = sparse.csc_matrix(jac) + if not isinstance(jac, sps.csc_matrix): + jac = sps.csc_matrix(jac) raise AssimuloException("The Jacobian must be stored on Scipy's CSC format.") ret_nnz = jac.nnz if ret_nnz > nnz: @@ -137,7 +138,7 @@ IF SUNDIALS_VERSION >= (3,0,0): colptrs[i] = jac.indptr[i] return CVDLS_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description) except Exception: traceback.print_exc() @@ -152,15 +153,15 @@ ELSE: Sparse Jacobian function. """ cdef ProblemData pData = problem_data - cdef N.ndarray y = pData.work_y + cdef np.ndarray y = pData.work_y cdef int i cdef int nnz = Jacobian.NNZ cdef int ret_nnz cdef int dim = Jacobian.N cdef realtype* data = Jacobian.data - cdef N.ndarray[realtype, ndim=1, mode='c'] jdata - cdef N.ndarray[int, ndim=1, mode='c'] jindices - cdef N.ndarray[int, ndim=1, mode='c'] jindptr + cdef np.ndarray[realtype, ndim=1, mode='c'] jdata + cdef np.ndarray[int, ndim=1, mode='c'] jindices + cdef np.ndarray[int, ndim=1, mode='c'] jindptr IF SUNDIALS_VERSION >= (2,6,3): cdef int* rowvals = Jacobian.rowvals[0] @@ -188,8 +189,8 @@ ELSE: else: jac=(pData.JAC)(t,y) - if not isinstance(jac, sparse.csc_matrix): - jac = sparse.csc_matrix(jac) + if not isinstance(jac, sps.csc_matrix): + jac = sps.csc_matrix(jac) raise AssimuloException("The Jacobian must be stored on Scipy's CSC format.") ret_nnz = jac.nnz if ret_nnz > nnz: @@ -204,7 +205,7 @@ ELSE: memcpy(colptrs, jindptr.data, (dim+1)*sizeof(int)) return CVDLS_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description) except Exception: traceback.print_exc() @@ -221,7 +222,7 @@ IF SUNDIALS_VERSION >= (3,0,0): cdef SUNMatrixContent_Dense Jacobian = Jac.content cdef ProblemData pData = problem_data cdef realtype* col_i=Jacobian.cols[0] - cdef N.ndarray y = pData.work_y + cdef np.ndarray y = pData.work_y cdef int i,j, Neq = pData.dim nv2arr_inplace(yv, y) @@ -233,7 +234,7 @@ IF SUNDIALS_VERSION >= (3,0,0): jac=(pData.JAC)(t,y,sw=pData.sw,p=p) else: jac=(pData.JAC)(t,y,p) - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description) except Exception: traceback.print_exc() @@ -244,13 +245,13 @@ IF SUNDIALS_VERSION >= (3,0,0): jac=(pData.JAC)(t,y,sw=pData.sw) else: jac=(pData.JAC)(t,y) - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description) except Exception: traceback.print_exc() return CVDLS_JACFUNC_UNRECVR - if isinstance(jac, sparse.csc_matrix): + if isinstance(jac, sps.csc_matrix): for j in range(Neq): col_i = Jacobian.cols[j] for i in range(jac.indptr[j], jac.indptr[j+1]): @@ -271,7 +272,7 @@ ELSE: """ cdef ProblemData pData = problem_data cdef realtype* col_i=DENSE_COL(Jacobian,0) - cdef N.ndarray y = pData.work_y + cdef np.ndarray y = pData.work_y cdef int i,j nv2arr_inplace(yv, y) @@ -283,7 +284,7 @@ ELSE: jac=(pData.JAC)(t,y,sw=pData.sw,p=p) else: jac=(pData.JAC)(t,y,p) - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description) except Exception: traceback.print_exc() @@ -294,13 +295,13 @@ ELSE: jac=(pData.JAC)(t,y,sw=pData.sw) else: jac=(pData.JAC)(t,y) - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description) except Exception: traceback.print_exc() return CVDLS_JACFUNC_UNRECVR - if isinstance(jac, sparse.csc_matrix): + if isinstance(jac, sps.csc_matrix): for j in range(Neq): col_i = DENSE_COL(Jacobian, j) for i in range(jac.indptr[j], jac.indptr[j+1]): @@ -321,9 +322,9 @@ cdef int cv_jacv(N_Vector vv, N_Vector Jv, realtype t, N_Vector yv, N_Vector fyv Jacobian times vector function. """ cdef ProblemData pData = problem_data - cdef N.ndarray y = nv2arr(yv) - cdef N.ndarray v = nv2arr(vv) - cdef N.ndarray fy = nv2arr(fyv) + cdef np.ndarray y = nv2arr(yv) + cdef np.ndarray v = nv2arr(vv) + cdef np.ndarray fy = nv2arr(fyv) cdef int i cdef realtype* jacvptr=(Jv.content).data @@ -340,7 +341,7 @@ cdef int cv_jacv(N_Vector vv, N_Vector Jv, realtype t, N_Vector yv, N_Vector fyv jacvptr[i] = jacv[i] return SPGMR_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return SPGMR_ATIMES_FAIL_REC except Exception: traceback.print_exc() @@ -356,7 +357,7 @@ cdef int cv_jacv(N_Vector vv, N_Vector Jv, realtype t, N_Vector yv, N_Vector fyv jacvptr[i] = jacv[i] return SPGMR_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return SPGMR_ATIMES_FAIL_REC except Exception: traceback.print_exc() @@ -370,8 +371,8 @@ IF SUNDIALS_VERSION >= (3,0,0): For information see CVODES documentation 4.6.9 """ cdef ProblemData pData = problem_data - cdef N.ndarray y = nv2arr(yy) - cdef N.ndarray fy = nv2arr(fyy) + cdef np.ndarray y = nv2arr(yy) + cdef np.ndarray fy = nv2arr(fyy) cdef object ret try: @@ -392,9 +393,9 @@ IF SUNDIALS_VERSION >= (3,0,0): For information see CVODES documentation 4.6.8 """ cdef ProblemData pData = problem_data - cdef N.ndarray y = nv2arr(yy) - cdef N.ndarray r = nv2arr(rr) - cdef N.ndarray fy = nv2arr(fyy) + cdef np.ndarray y = nv2arr(yy) + cdef np.ndarray r = nv2arr(rr) + cdef np.ndarray fy = nv2arr(fyy) cdef realtype* zptr=(z.content).data cdef int i @@ -417,8 +418,8 @@ ELSE: For information see CVODES documentation 4.6.9 """ cdef ProblemData pData = problem_data - cdef N.ndarray y = nv2arr(yy) - cdef N.ndarray fy = nv2arr(fyy) + cdef np.ndarray y = nv2arr(yy) + cdef np.ndarray fy = nv2arr(fyy) cdef object ret try: @@ -439,9 +440,9 @@ ELSE: For information see CVODES documentation 4.6.8 """ cdef ProblemData pData = problem_data - cdef N.ndarray y = nv2arr(yy) - cdef N.ndarray r = nv2arr(rr) - cdef N.ndarray fy = nv2arr(fyy) + cdef np.ndarray y = nv2arr(yy) + cdef np.ndarray r = nv2arr(rr) + cdef np.ndarray fy = nv2arr(fyy) cdef realtype* zptr=(z.content).data cdef int i @@ -461,9 +462,9 @@ cdef int cv_prec(realtype t, N Vector yv, N Vector fyv, void *problem_data, N Vector tmp): cdef ProblemData pData = problem_data - cdef N.ndarray y = nv2arr(yv) - cdef N.ndarray fy = nv2arr(fyv) - cdef N.ndarray r = nv2arr(rv) + cdef np.ndarray y = nv2arr(yv) + cdef np.ndarray fy = nv2arr(fyv) + cdef np.ndarray r = nv2arr(rv) cdef int i cdef realtype* zptr=(z.content).data @@ -486,7 +487,7 @@ cdef int cv_root(realtype t, N_Vector yv, realtype *gout, void* problem_data) no Root-finding function. """ cdef ProblemData pData = problem_data - cdef N.ndarray y = pData.work_y + cdef np.ndarray y = pData.work_y cdef int i nv2arr_inplace(yv, y) @@ -511,9 +512,9 @@ cdef int ida_res(realtype t, N_Vector yv, N_Vector yvdot, N_Vector residual, voi residual function. """ cdef ProblemData pData = problem_data - cdef N.ndarray[realtype, ndim=1, mode='c'] res #Used for return from the user function - cdef N.ndarray y = pData.work_y - cdef N.ndarray yd = pData.work_yd + cdef np.ndarray[realtype, ndim=1, mode='c'] res #Used for return from the user function + cdef np.ndarray y = pData.work_y + cdef np.ndarray yd = pData.work_yd cdef realtype* resptr=(residual.content).data cdef int i @@ -533,7 +534,7 @@ cdef int ida_res(realtype t, N_Vector yv, N_Vector yvdot, N_Vector residual, voi resptr[i] = res[i] return IDA_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return IDA_REC_ERR # recoverable error (see Sundials description) except Exception: traceback.print_exc() @@ -551,7 +552,7 @@ cdef int ida_res(realtype t, N_Vector yv, N_Vector yvdot, N_Vector residual, voi resptr[i] = res[i] return IDA_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return IDA_REC_ERR # recoverable error (see Sundials description) except Exception: traceback.print_exc() @@ -566,10 +567,10 @@ IF SUNDIALS_VERSION >= (3,0,0): """ cdef SUNMatrixContent_Dense Jacobian = Jac.content cdef ProblemData pData = problem_data - cdef N.ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function + cdef np.ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function cdef realtype* col_i=Jacobian.cols[0] - cdef N.ndarray y = pData.work_y - cdef N.ndarray yd = pData.work_yd + cdef np.ndarray y = pData.work_y + cdef np.ndarray yd = pData.work_yd cdef int i,j, Neq = pData.dim nv2arr_inplace(yv, y) @@ -588,7 +589,7 @@ IF SUNDIALS_VERSION >= (3,0,0): for j in range(Neq): col_i[j] = jac[j,i] return IDADLS_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return IDADLS_JACFUNC_RECVR #Recoverable Error except Exception: traceback.print_exc() @@ -605,7 +606,7 @@ IF SUNDIALS_VERSION >= (3,0,0): for j in range(Neq): col_i[j] = jac[j,i] return IDADLS_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return IDADLS_JACFUNC_RECVR #Recoverable Error except Exception: traceback.print_exc() @@ -618,10 +619,10 @@ ELSE: Jacobian function. """ cdef ProblemData pData = problem_data - cdef N.ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function + cdef np.ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function cdef realtype* col_i=DENSE_COL(Jacobian,0) - cdef N.ndarray y = pData.work_y - cdef N.ndarray yd = pData.work_yd + cdef np.ndarray y = pData.work_y + cdef np.ndarray yd = pData.work_yd cdef int i,j nv2arr_inplace(yv, y) @@ -640,7 +641,7 @@ ELSE: for j in range(Neq): col_i[j] = jac[j,i] return IDADLS_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return IDADLS_JACFUNC_RECVR #Recoverable Error except Exception: traceback.print_exc() @@ -657,7 +658,7 @@ ELSE: for j in range(Neq): col_i[j] = jac[j,i] return IDADLS_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return IDADLS_JACFUNC_RECVR #Recoverable Error except Exception: traceback.print_exc() @@ -670,9 +671,9 @@ cdef int ida_root(realtype t, N_Vector yv, N_Vector yvdot, realtype *gout, void* root function. """ cdef ProblemData pData = problem_data - cdef N.ndarray[realtype, ndim=1, mode='c'] root #Used for return from the user function - cdef N.ndarray y = pData.work_y - cdef N.ndarray yd = pData.work_yd + cdef np.ndarray[realtype, ndim=1, mode='c'] root #Used for return from the user function + cdef np.ndarray y = pData.work_y + cdef np.ndarray yd = pData.work_yd cdef int i nv2arr_inplace(yv, y) @@ -699,10 +700,10 @@ cdef int ida_jacv(realtype t, N_Vector yy, N_Vector yp, N_Vector rr, N_Vector vv Jacobian times vector function. """ cdef ProblemData pData = problem_data - cdef N.ndarray y = nv2arr(yy) - cdef N.ndarray yd = nv2arr(yp) - cdef N.ndarray v = nv2arr(vv) - cdef N.ndarray res = nv2arr(rr) + cdef np.ndarray y = nv2arr(yy) + cdef np.ndarray yd = nv2arr(yp) + cdef np.ndarray v = nv2arr(vv) + cdef np.ndarray res = nv2arr(rr) cdef int i cdef realtype* jacvptr=(Jv.content).data @@ -719,7 +720,7 @@ cdef int ida_jacv(realtype t, N_Vector yy, N_Vector yp, N_Vector rr, N_Vector vv jacvptr[i] = jacv[i] return SPGMR_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return SPGMR_ATIMES_FAIL_REC except Exception: traceback.print_exc() @@ -735,7 +736,7 @@ cdef int ida_jacv(realtype t, N_Vector yy, N_Vector yp, N_Vector rr, N_Vector vv jacvptr[i] = jacv[i] return SPGMR_SUCCESS - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return SPGMR_ATIMES_FAIL_REC except Exception: traceback.print_exc() @@ -794,11 +795,11 @@ cdef class ProblemData: int memSizeJac #dim*dim*sizeof(realtype) used when copying memory int verbose #Defines the verbosity object PREC_DATA #Arbitrary data from the preconditioner - N.ndarray work_y - N.ndarray work_yd - N.ndarray work_ys + np.ndarray work_y + np.ndarray work_yd + np.ndarray work_ys cdef create_work_arrays(self): - self.work_y = N.empty(self.dim) - self.work_yd = N.empty(self.dim) - self.work_ys = N.empty((self.dim, self.dimSens)) + self.work_y = np.empty(self.dim) + self.work_yd = np.empty(self.dim) + self.work_ys = np.empty((self.dim, self.dimSens)) diff --git a/src/lib/sundials_callbacks_kinsol.pxi b/src/lib/sundials_callbacks_kinsol.pxi index 213e0536..4cadca78 100644 --- a/src/lib/sundials_callbacks_kinsol.pxi +++ b/src/lib/sundials_callbacks_kinsol.pxi @@ -29,7 +29,7 @@ IF SUNDIALS_VERSION >= (3,0,0): cdef SUNMatrixContent_Dense Jacobian = Jac.content cdef ProblemDataEquationSolver pData = problem_data cdef realtype* col_i=Jacobian.cols[0] - cdef N.ndarray x = nv2arr(xv) + cdef np.ndarray x = nv2arr(xv) cdef int i,j, Neq = pData.dim try: @@ -52,7 +52,7 @@ ELSE: """ cdef ProblemDataEquationSolver pData = problem_data cdef realtype* col_i=DENSE_COL(Jacobian,0) - cdef N.ndarray x = nv2arr(xv) + cdef np.ndarray x = nv2arr(xv) cdef int i,j try: @@ -70,8 +70,8 @@ ELSE: cdef int kin_jacv(N_Vector vv, N_Vector Jv, N_Vector vx, int* new_u, void *problem_data) noexcept: cdef ProblemDataEquationSolver pData = problem_data - cdef N.ndarray x = nv2arr(vx) - cdef N.ndarray v = nv2arr(vv) + cdef np.ndarray x = nv2arr(vx) + cdef np.ndarray v = nv2arr(vv) cdef int i cdef realtype* jacvptr=(Jv.content).data @@ -83,7 +83,7 @@ cdef int kin_jacv(N_Vector vv, N_Vector Jv, N_Vector vx, int* new_u, jacvptr[i] = jacv[i] return SPGMR_SUCCESS - except (N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except (np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return SPGMR_ATIMES_FAIL_REC except Exception: traceback.print_exc() @@ -94,7 +94,7 @@ cdef int kin_res(N_Vector xv, N_Vector fval, void *problem_data) noexcept: Residual fct called by KINSOL """ cdef ProblemDataEquationSolver pData = problem_data - cdef N.ndarray x = nv2arr(xv) + cdef np.ndarray x = nv2arr(xv) cdef realtype* resptr = (fval.content).data cdef int i @@ -104,7 +104,7 @@ cdef int kin_res(N_Vector xv, N_Vector fval, void *problem_data) noexcept: for i in range(pData.dim): resptr[i] = res[i] return KIN_SUCCESS - except (N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except (np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return KIN_REC_ERR except Exception: traceback.print_exc() @@ -123,14 +123,14 @@ IF SUNDIALS_VERSION >= (3,0,0): """ cdef ProblemDataEquationSolver pData = problem_data - cdef N.ndarray fscale = nv2arr(fscaleN) - cdef N.ndarray uscale = nv2arr(uscaleN) - cdef N.ndarray r = nv2arr(v) + cdef np.ndarray fscale = nv2arr(fscaleN) + cdef np.ndarray uscale = nv2arr(uscaleN) + cdef np.ndarray r = nv2arr(v) cdef realtype* zptr=(v.content).data try: zres = (pData.PREC_SOLVE)(r) - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return KIN_REC_ERR except Exception: traceback.print_exc() @@ -148,14 +148,14 @@ IF SUNDIALS_VERSION >= (3,0,0): """ cdef ProblemDataEquationSolver pData = problem_data - cdef N.ndarray fscale = nv2arr(fscaleN) - cdef N.ndarray uscale = nv2arr(uscaleN) - cdef N.ndarray u = nv2arr(uN) - cdef N.ndarray fval = nv2arr(fvalN) + cdef np.ndarray fscale = nv2arr(fscaleN) + cdef np.ndarray uscale = nv2arr(uscaleN) + cdef np.ndarray u = nv2arr(uN) + cdef np.ndarray fval = nv2arr(fvalN) try: (pData.PREC_SETUP)(u, fval, uscale, fscale) - except (N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except (np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return KIN_REC_ERR except Exception: traceback.print_exc() @@ -176,14 +176,14 @@ ELSE: """ cdef ProblemDataEquationSolver pData = problem_data - cdef N.ndarray fscale = nv2arr(fscaleN) - cdef N.ndarray uscale = nv2arr(uscaleN) - cdef N.ndarray r = nv2arr(v) + cdef np.ndarray fscale = nv2arr(fscaleN) + cdef np.ndarray uscale = nv2arr(uscaleN) + cdef np.ndarray r = nv2arr(v) cdef realtype* zptr=(v.content).data try: zres = (pData.PREC_SOLVE)(r) - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return KIN_REC_ERR except Exception: traceback.print_exc() @@ -201,14 +201,14 @@ ELSE: """ cdef ProblemDataEquationSolver pData = problem_data - cdef N.ndarray fscale = nv2arr(fscaleN) - cdef N.ndarray uscale = nv2arr(uscaleN) - cdef N.ndarray u = nv2arr(uN) - cdef N.ndarray fval = nv2arr(fvalN) + cdef np.ndarray fscale = nv2arr(fscaleN) + cdef np.ndarray uscale = nv2arr(uscaleN) + cdef np.ndarray u = nv2arr(uN) + cdef np.ndarray fval = nv2arr(fvalN) try: (pData.PREC_SETUP)(u, fval, uscale, fscale) - except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): + except(np.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError): return KIN_REC_ERR except Exception: traceback.print_exc() diff --git a/src/ode.pxd b/src/ode.pxd index 9d3d43c0..9d819306 100644 --- a/src/ode.pxd +++ b/src/ode.pxd @@ -15,8 +15,8 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from assimulo.support cimport Statistics @@ -34,8 +34,8 @@ cdef class ODE: cdef public int display_counter cdef public int chattering_clear_counter cdef public int chattering_ok_print - cdef public N.ndarray y,yd, p - cdef public N.ndarray y0, yd0, p0, sw0 + cdef public np.ndarray y,yd, p + cdef public np.ndarray y0, yd0, p0, sw0 cdef double elapsed_step_time, time_integration_start cdef int time_limit_activated, display_progress_activated cdef double clock_start diff --git a/src/ode.pyx b/src/ode.pyx index 1a34403a..9707e217 100644 --- a/src/ode.pyx +++ b/src/ode.pyx @@ -17,8 +17,8 @@ # distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np import itertools import multiprocessing from timeit import default_timer as timer @@ -67,7 +67,7 @@ cdef class ODE: #Data object for storing the event data self.event_data = [] - self._event_info = N.array([]) + self._event_info = np.array([]) if problem is None: raise ODE_Exception('The problem needs to be a subclass of a Problem.') @@ -92,7 +92,7 @@ cdef class ODE: self.problem_info["step_events"] = True if hasattr(problem, 'y0'): - self.y0 = N.array(problem.y0,dtype=realtype) if len(N.array(problem.y0,dtype=realtype).shape)>0 else N.array([problem.y0],dtype=realtype) + self.y0 = np.array(problem.y0,dtype=realtype) if len(np.array(problem.y0,dtype=realtype).shape)>0 else np.array([problem.y0],dtype=realtype) self.problem_info["dim"] = len(self.y0) else: raise ODE_Exception('y0 must be specified in the problem.') @@ -103,12 +103,12 @@ cdef class ODE: self.problem_info["neq"] = self.problem_info["dim"] if hasattr(problem, "p0"): - self.p0 = N.array(problem.p0,dtype=realtype) if len(N.array(problem.p0,dtype=realtype).shape)>0 else N.array([problem.p0],dtype=realtype) + self.p0 = np.array(problem.p0,dtype=realtype) if len(np.array(problem.p0,dtype=realtype).shape)>0 else np.array([problem.p0],dtype=realtype) self.problem_info["dimSens"] = len(self.p0) self.p = self.p0.copy() if hasattr(problem, "sw0"): - self.sw0 = N.array(problem.sw0,dtype=bool) if len(N.array(problem.sw0,dtype=bool).shape)>0 else N.array([problem.sw0],dtype=bool) + self.sw0 = np.array(problem.sw0,dtype=bool) if len(np.array(problem.sw0,dtype=bool).shape)>0 else np.array([problem.sw0],dtype=bool) self.problem_info["switches"] = True self.sw = self.sw0.tolist() @@ -272,18 +272,18 @@ cdef class ODE: #Determine the output list if ncp != 0: - output_list = N.linspace(t0,tfinal,ncp+1)[1:] + output_list = np.linspace(t0,tfinal,ncp+1)[1:] output_index = 0 elif ncp_list is not None: if self.options["backward"]: - output_list = N.array(ncp_list, dtype=realtype, ndmin=1)[N.logical_and(N.array(ncp_list, dtype=realtype, ndmin=1)=tfinal)] - output_list = -N.sort(-output_list) + output_list = np.array(ncp_list, dtype=realtype, ndmin=1)[np.logical_and(np.array(ncp_list, dtype=realtype, ndmin=1)=tfinal)] + output_list = -np.sort(-output_list) if output_list[-1] > tfinal: #Add the last point if necessary! - output_list = N.append(output_list, tfinal) + output_list = np.append(output_list, tfinal) else: - output_list = N.array(ncp_list, dtype=realtype, ndmin=1)[N.logical_and(N.array(ncp_list, dtype=realtype, ndmin=1)>t0,N.array(ncp_list, dtype=realtype, ndmin=1)<=tfinal)] + output_list = np.array(ncp_list, dtype=realtype, ndmin=1)[np.logical_and(np.array(ncp_list, dtype=realtype, ndmin=1)>t0,np.array(ncp_list, dtype=realtype, ndmin=1)<=tfinal)] if output_list[-1] < tfinal: #Add the last point if necessary! - output_list = N.append(output_list, tfinal) + output_list = np.append(output_list, tfinal) output_index = 0 else: output_list = None @@ -330,9 +330,9 @@ cdef class ODE: #Return the results if isinstance(self.problem, (Explicit_Problem, Delay_Explicit_Problem, SingPerturbed_Problem)): - return self.t_sol, N.array(self.y_sol) + return self.t_sol, np.array(self.y_sol) else: - return self.t_sol, N.array(self.y_sol), N.array(self.yd_sol) + return self.t_sol, np.array(self.y_sol), np.array(self.yd_sol) def _simulate(self,t0, tfinal, output_list, REPORT_CONTINUOUSLY, INTERPOLATE_OUTPUT, TIME_EVENT): pass @@ -586,7 +586,7 @@ cdef class ODE: Reduces a tol vector to a scalar if it is an ndarray and all entries are the same. Used for printing solver options in a more compact way """ - if isinstance(tol_vec, N.ndarray) and (tol_vec == tol_vec[0]).all(): + if isinstance(tol_vec, np.ndarray) and (tol_vec == tol_vec[0]).all(): return tol_vec[0] else: return tol_vec @@ -595,11 +595,11 @@ cdef class ODE: self.chattering_clear_counter = 0 if event_info[0] is not None and len(event_info[0]) > 0: if self.chattering_check is None: - self.chattering_check = abs(N.array(event_info[0])) + self.chattering_check = abs(np.array(event_info[0])) else: - self.chattering_check += abs(N.array(event_info[0])) + self.chattering_check += abs(np.array(event_info[0])) if max(self.chattering_check) > 5 and self.chattering_ok_print: self.chattering_ok_print = 0 self.log_message("Warning: Possible chattering detected at t = %e in state event(s): "%self.t + - str(N.where(self.chattering_check == max(self.chattering_check))[0]), NORMAL) + str(np.where(self.chattering_check == max(self.chattering_check))[0]), NORMAL) diff --git a/src/problem.pxd b/src/problem.pxd index ef88a0b2..a0ac016e 100644 --- a/src/problem.pxd +++ b/src/problem.pxd @@ -15,8 +15,8 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np cdef class cProblem: cdef public int _sensitivity_result @@ -27,14 +27,14 @@ cdef class cProblem: cpdef finalize(self,object solver) cdef class cImplicit_Problem(cProblem): - cpdef res_internal(self, N.ndarray[double, ndim=1] res, double t, N.ndarray[double, ndim=1] y, N.ndarray[double, ndim=1] yd) + cpdef res_internal(self, np.ndarray[double, ndim=1] res, double t, np.ndarray[double, ndim=1] y, np.ndarray[double, ndim=1] yd) cdef class cOverdetermined_Problem(cProblem): - cpdef res_internal(self, N.ndarray[double, ndim=1] res, double t, N.ndarray[double, ndim=1] y, N.ndarray[double, ndim=1] yd) + cpdef res_internal(self, np.ndarray[double, ndim=1] res, double t, np.ndarray[double, ndim=1] y, np.ndarray[double, ndim=1] yd) cdef class cExplicit_Problem(cProblem): - cpdef int rhs_internal(self, N.ndarray[double, ndim=1] yd, double t, N.ndarray[double, ndim=1] y) - cpdef N.ndarray res(self, t, y, yd, sw=*) + cpdef int rhs_internal(self, np.ndarray[double, ndim=1] yd, double t, np.ndarray[double, ndim=1] y) + cpdef np.ndarray res(self, t, y, yd, sw=*) cdef class cDelay_Explicit_Problem(cExplicit_Problem): pass diff --git a/src/problem.pyx b/src/problem.pyx index a65ac01f..66c80538 100644 --- a/src/problem.pyx +++ b/src/problem.pyx @@ -17,8 +17,8 @@ # distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from assimulo.support import set_type_shape_array @@ -86,7 +86,7 @@ cdef class cImplicit_Problem(cProblem): self.yd0 = set_type_shape_array(yd0) - def handle_result(self, solver, double t, N.ndarray[double, ndim=1] y, N.ndarray[double, ndim=1] yd): + def handle_result(self, solver, double t, np.ndarray[double, ndim=1] y, np.ndarray[double, ndim=1] yd): """ Method for specifying how the result is handled. By default the data is stored in three vectors: solver.(t/y/yd). @@ -102,7 +102,7 @@ cdef class cImplicit_Problem(cProblem): for i in range(solver.problem_info["dimSens"]): solver.p_sol[i] += [solver.interpolate_sensitivity(t, i=i)] - cpdef res_internal(self, N.ndarray[double, ndim=1] res, double t, N.ndarray[double, ndim=1] y, N.ndarray[double, ndim=1] yd): + cpdef res_internal(self, np.ndarray[double, ndim=1] res, double t, np.ndarray[double, ndim=1] y, np.ndarray[double, ndim=1] yd): try: res[:] = self.res(t,y,yd) except Exception: @@ -120,7 +120,7 @@ cdef class cOverdetermined_Problem(cProblem): self.yd0 = set_type_shape_array(yd0) - def handle_result(self, solver, double t, N.ndarray[double, ndim=1] y, N.ndarray[double, ndim=1] yd): + def handle_result(self, solver, double t, np.ndarray[double, ndim=1] y, np.ndarray[double, ndim=1] yd): """ Method for specifying how the result is to be handled. As default the data is stored in three vectors: solver.(t/y/yd). @@ -131,7 +131,7 @@ cdef class cOverdetermined_Problem(cProblem): solver.y_sol.extend([y]) solver.yd_sol.extend([yd]) - cpdef res_internal(self, N.ndarray[double, ndim=1] res, double t, N.ndarray[double, ndim=1] y, N.ndarray[double, ndim=1] yd): + cpdef res_internal(self, np.ndarray[double, ndim=1] res, double t, np.ndarray[double, ndim=1] y, np.ndarray[double, ndim=1] yd): try: res[:] = self.res(t,y,yd) except Exception: @@ -145,7 +145,7 @@ cdef class cExplicit_Problem(cProblem): cProblem.__init__(self, y0, t0, p0, sw0, name) if rhs is not None: self.rhs = rhs - def handle_result(self, solver, double t, N.ndarray[double, ndim=1] y): + def handle_result(self, solver, double t, np.ndarray[double, ndim=1] y): """ Method for specifying how the result is to be handled. As default the data is stored in two vectors: solver.(t/y). @@ -160,14 +160,14 @@ cdef class cExplicit_Problem(cProblem): for i in range(solver.problem_info["dimSens"]): solver.p_sol[i] += [solver.interpolate_sensitivity(t, i=i)] - cpdef int rhs_internal(self, N.ndarray[double, ndim=1] yd, double t, N.ndarray[double, ndim=1] y): + cpdef int rhs_internal(self, np.ndarray[double, ndim=1] yd, double t, np.ndarray[double, ndim=1] y): try: yd[:] = self.rhs(t,y) except Exception: return ID_FAIL return ID_OK - cpdef N.ndarray res(self, t, y, yd, sw=None): + cpdef np.ndarray res(self, t, y, yd, sw=None): if sw == None: return yd-self.rhs(t,y) else: @@ -206,7 +206,7 @@ cdef class cSingPerturbed_Problem(cExplicit_Problem): # classical explicit problem without exposing the structure # of a singularly perturbed problem if yy0 is not None and zz0 is not None: - y0 = N.hstack((self.yy0,self.zz0)) + y0 = np.hstack((self.yy0,self.zz0)) elif yy0 is not None: y0 = self.yy0 elif zz0 is not None: @@ -224,7 +224,7 @@ cdef class cSingPerturbed_Problem(cExplicit_Problem): # a diagonal matrix if self.eps != None: zzdot /= self.eps - return N.hstack((yydot,zzdot)) + return np.hstack((yydot,zzdot)) class Delay_Explicit_Problem(cDelay_Explicit_Problem): pass diff --git a/src/solvers/dasp3.py b/src/solvers/dasp3.py index 650315af..3318aebf 100644 --- a/src/solvers/dasp3.py +++ b/src/solvers/dasp3.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . -import numpy as N +import numpy as np from assimulo.problem import SingPerturbed_Problem from assimulo.exception import Explicit_ODE_Exception, DASP3_Exception @@ -69,13 +69,13 @@ def __init__(self, problem): self.m=self.problem.m # Set initial values - self.wsy=N.empty((10*self.n,)) + self.wsy=np.empty((10*self.n,)) self.wsy[:self.n]=self.problem.yy0 - self.wsz=N.empty((max(9*self.m,1),)) # array must be at least 1 element long + self.wsz=np.empty((max(9*self.m,1),)) # array must be at least 1 element long self.wsz[:self.m]=self.problem.zz0 # - Default values - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance self.statistics.add_key("nyder", "Number of slow function evaluations (Y)") @@ -93,15 +93,15 @@ def _solout(self, t, wsy, wsz, n, m, jstop): This method is called after every successful step taken by DASP3 """ self._tlist.append(t) - self._ylist.append(N.hstack((wsy[:n],wsz[:m]))) + self._ylist.append(np.hstack((wsy[:n],wsz[:m]))) if self._opts["report_continuously"]: - initialize_flag = self.report_solution(t, N.hstack((wsy[:n],wsz[:m])), self._opts) + initialize_flag = self.report_solution(t, np.hstack((wsy[:n],wsz[:m])), self._opts) if initialize_flag: jstop = -1 else: self._tlist.append(t) - self._ylist.append(N.hstack((wsy[:n],wsz[:m]))) + self._ylist.append(np.hstack((wsy[:n],wsz[:m]))) return jstop @@ -113,13 +113,13 @@ def integrate(self, t, y, tf, opts): m = self.problem.m n = self.problem.n - a = N.empty((m,m)) - w = N.empty((m,m)) - slu= N.empty((2*m,)) - ips= N.empty((m,),'int32') - ind = N.empty((2*m,),'int32') - eq= N.empty((m,),'bool') - wght=N.ones((m+n,)) + a = np.empty((m,m)) + w = np.empty((m,m)) + slu= np.empty((2*m,)) + ips= np.empty((m,),'int32') + ind = np.empty((2*m,),'int32') + eq= np.empty((m,),'bool') + wght=np.ones((m+n,)) #Store the opts self._opts = opts @@ -156,10 +156,10 @@ def print_statistics(self, verbose=NORMAL): def _set_atol(self,atol): - self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self.problem_info["dim"]) + self.options["atol"] = self.options["atol"]*np.ones(self.problem_info["dim"]) elif len(self.options["atol"]) != self.problem_info["dim"]: raise DASP3_Exception("atol must be of length one or same as the dimension of the problem.") diff --git a/src/solvers/euler.pyx b/src/solvers/euler.pyx index 7f696b69..aa64c9f9 100644 --- a/src/solvers/euler.pyx +++ b/src/solvers/euler.pyx @@ -17,10 +17,9 @@ # distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -cimport numpy as N -import numpy as N -import numpy.linalg as LIN -import scipy.sparse as sp +cimport numpy as np +import numpy as np +import scipy.sparse as sps from assimulo.explicit_ode cimport Explicit_ODE from assimulo.exception import AssimuloException @@ -49,8 +48,8 @@ cdef class ImplicitEuler(Explicit_ODE): with :math:`h` being the step-size and :math:`y_n` the previous solution to the equation. """ - cdef N.ndarray yd1 - cdef N.ndarray _old_jac + cdef np.ndarray yd1 + cdef np.ndarray _old_jac cdef object f cdef public object event_func cdef int _leny @@ -58,10 +57,10 @@ cdef class ImplicitEuler(Explicit_ODE): cdef int _needjac cdef int _curjac cdef int _steps_since_last_jac - cdef N.ndarray _yold - cdef N.ndarray _ynew - #cdef N.ndarray _event_info - cdef public N.ndarray g_old + cdef np.ndarray _yold + cdef np.ndarray _ynew + #cdef np.ndarray _event_info + cdef public np.ndarray g_old cdef double _told cdef double _h cdef double _inith @@ -73,13 +72,13 @@ cdef class ImplicitEuler(Explicit_ODE): self.options["h"] = 0.01 self.options["usejac"] = True if (self.problem_info["jac_fcn"]) else False self.options["newt"] = 7 #Maximum number of newton iterations - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance #Internal temporary result vector - self.yd1 = N.array([0.0]*len(self.y0)) - self._yold = N.array([0.0]*len(self.y0)) - self._ynew = N.array([0.0]*len(self.y0)) + self.yd1 = np.array([0.0]*len(self.y0)) + self._yold = np.array([0.0]*len(self.y0)) + self._ynew = np.array([0.0]*len(self.y0)) #Solver support self.supports["report_continuously"] = True @@ -88,7 +87,7 @@ cdef class ImplicitEuler(Explicit_ODE): self._leny = len(self.y) #Dimension of the problem - self._eps = N.finfo('double').eps + self._eps = np.finfo('double').eps self._needjac = True #Do we need a new jacobian? self._curjac = False #Is the current jacobian up to date? self._steps_since_last_jac = 0 #Keep track on how long ago we updated the jacobian @@ -107,7 +106,7 @@ cdef class ImplicitEuler(Explicit_ODE): return self.problem.rhs(t, y, self.sw) self.f = f self.event_func = event_func - self._event_info = N.array([0] * self.problem_info["dimRoot"]) + self._event_info = np.array([0] * self.problem_info["dimRoot"]) ret, self.g_old = self.event_func(self.t, self.y) if ret < 0: raise self._py_err @@ -141,7 +140,7 @@ cdef class ImplicitEuler(Explicit_ODE): usejac = property(_get_usejac,_set_usejac) - cpdef step(self,double t,N.ndarray y,double tf,dict opts): + cpdef step(self,double t,np.ndarray y,double tf,dict opts): cdef double h h = self.options["h"] @@ -153,7 +152,7 @@ cdef class ImplicitEuler(Explicit_ODE): t, y = self._step(t,y,h) return ID_COMPLETE, t, y - cpdef integrate(self, double t,N.ndarray y,double tf, dict opts): + cpdef integrate(self, double t,np.ndarray y,double tf, dict opts): cdef double h cdef list tr,yr @@ -261,10 +260,10 @@ cdef class ImplicitEuler(Explicit_ODE): def _set_atol(self,atol): - self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self._leny) + self.options["atol"] = self.options["atol"]*np.ones(self._leny) elif len(self.options["atol"]) != self._leny: raise AssimuloException("atol must be of length one or same as the dimension of the problem.") @@ -332,13 +331,13 @@ cdef class ImplicitEuler(Explicit_ODE): if self.usejac: #Retrieve the user-defined jacobian jac = self.problem.jac(t,y) - if isinstance(jac, sp.csc_matrix): + if isinstance(jac, sps.csc_matrix): jac = jac.toarray() else: #Calculate a numeric jacobian - delt = N.array([(self._eps*max(abs(yi),1.e-5))**0.5 for yi in y])*N.identity(self._leny) #Calculate a disturbance - Fdelt = N.array([self.f(t,y+e) for e in delt]) #Add the disturbance (row by row) + delt = np.array([(self._eps*max(abs(yi),1.e-5))**0.5 for yi in y])*np.identity(self._leny) #Calculate a disturbance + Fdelt = np.array([self.f(t,y+e) for e in delt]) #Add the disturbance (row by row) grad = ((Fdelt-self.f(t,y)).T/delt.diagonal()).T - jac = N.array(grad).T + jac = np.array(grad).T self.statistics["nfcnjacs"] += 1+self._leny #Add the number of function evaluations @@ -347,7 +346,7 @@ cdef class ImplicitEuler(Explicit_ODE): - cdef double WRMS(self, N.ndarray x, N.ndarray w): + cdef double WRMS(self, np.ndarray x, np.ndarray w): """ Calculates the Weighted Root-mean-square. """ @@ -361,17 +360,17 @@ cdef class ImplicitEuler(Explicit_ODE): return (sum/N)**0.5 - cdef tuple _step(self,double t,N.ndarray y,double h): + cdef tuple _step(self,double t,np.ndarray y,double h): """ This calculates the next step in the integration. """ cdef double new_norm = 0 cdef double old_norm = 0 cdef double tn1 = t+h - cdef N.ndarray yn = y.copy() #Old y - #cdef N.ndarray yn1 = y.copy() #First newton guess - cdef N.ndarray yn1 = y+h*self.f(t,y) #First newton guess - cdef N.ndarray I = N.eye(self._leny) + cdef np.ndarray yn = y.copy() #Old y + #cdef np.ndarray yn1 = y.copy() #First newton guess + cdef np.ndarray yn1 = y+h*self.f(t,y) #First newton guess + cdef np.ndarray I = np.eye(self._leny) self.statistics["nfcns"] += 1 FLAG_CONV = False @@ -389,12 +388,12 @@ cdef class ImplicitEuler(Explicit_ODE): #jac = self._jacobian(tn1, yn1) - #ynew = yn1 - N.dot(LIN.inv(h*jac-I),(yn-yn1+h*self.problem.rhs(tn1,yn1))) - ynew = yn1 - LIN.solve(h*jac-I, yn-yn1+h*self.f(tn1,yn1) ) + #ynew = yn1 - np.dot(np.linalg.inv(h*jac-I),(yn-yn1+h*self.problem.rhs(tn1,yn1))) + ynew = yn1 - np.linalg.solve(h*jac-I, yn-yn1+h*self.f(tn1,yn1) ) self.statistics["nfcns"] += 1 - #print tn1, self.WRMS(ynew-yn1, 1.0/(self.rtol*N.abs(yn1)+self.atol)) - new_norm = self.WRMS(ynew-yn1, 1.0/(self.rtol*N.abs(yn1)+self.atol)) + #print tn1, self.WRMS(ynew-yn1, 1.0/(self.rtol*np.abs(yn1)+self.atol)) + new_norm = self.WRMS(ynew-yn1, 1.0/(self.rtol*np.abs(yn1)+self.atol)) if new_norm < 0.1: #Newton converged FLAG_CONV = True @@ -505,13 +504,13 @@ cdef class ExplicitEuler(Explicit_ODE): with :math:`h` being the step-size and :math:`y_n` the previous solution to the equation. """ - cdef N.ndarray yd1 + cdef np.ndarray yd1 cdef object f cdef public object event_func - cdef N.ndarray _yold - cdef N.ndarray _ynew - #cdef N.ndarray _event_info - cdef public N.ndarray g_old + cdef np.ndarray _yold + cdef np.ndarray _ynew + #cdef np.ndarray _event_info + cdef public np.ndarray g_old cdef double _told cdef double _h cdef double _inith @@ -525,9 +524,9 @@ cdef class ExplicitEuler(Explicit_ODE): #Internal temporary result vector - self.yd1 = N.array([0.0]*len(self.y0)) - self._yold = N.array([0.0]*len(self.y0)) - self._ynew = N.array([0.0]*len(self.y0)) + self.yd1 = np.array([0.0]*len(self.y0)) + self._yold = np.array([0.0]*len(self.y0)) + self._ynew = np.array([0.0]*len(self.y0)) self._inith = 0 #Used for taking an initial step of correct length after an event. #Solver support @@ -548,7 +547,7 @@ cdef class ExplicitEuler(Explicit_ODE): return self.problem.rhs(t, y, self.sw) self.f = f self.event_func = event_func - self._event_info = N.array([0] * self.problem_info["dimRoot"]) + self._event_info = np.array([0] * self.problem_info["dimRoot"]) ret, self.g_old = self.event_func(self.t, self.y) if ret < 0: raise self._py_err @@ -556,7 +555,7 @@ cdef class ExplicitEuler(Explicit_ODE): else: self.f = self.problem.rhs - cpdef step(self,double t,N.ndarray y,double tf,dict opts): + cpdef step(self,double t,np.ndarray y,double tf,dict opts): cdef double h h = self.options["h"] @@ -568,7 +567,7 @@ cdef class ExplicitEuler(Explicit_ODE): t, y = self._step(t,y,h) return ID_COMPLETE, t, y - cpdef integrate(self, double t,N.ndarray y,double tf, dict opts): + cpdef integrate(self, double t,np.ndarray y,double tf, dict opts): cdef double h cdef list tr,yr @@ -634,7 +633,7 @@ cdef class ExplicitEuler(Explicit_ODE): return flag, tr, yr - cdef tuple _step(self,double t,N.ndarray y,double h): + cdef tuple _step(self,double t,np.ndarray y,double h): """ This calculates the next step in the integration. """ diff --git a/src/solvers/glimda.py b/src/solvers/glimda.py index 31c72427..a2571608 100644 --- a/src/solvers/glimda.py +++ b/src/solvers/glimda.py @@ -15,8 +15,8 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N import sys +import numpy as np from assimulo.exception import GLIMDA_Exception from assimulo.ode import ID_PY_COMPLETE, NORMAL @@ -58,10 +58,10 @@ def __init__(self, problem): self.options["minord"] = 1 #Minimum order used self.options["order"] = 0 #Variable order (0) or fixed order (1-3) self.options["maxsteps"] = 100000 #Maximum number of steps - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance - self.options["maxh"] = N.inf #Maximum step-size. - self.options["minh"] = N.finfo(N.double).eps #Minimum step-size + self.options["maxh"] = np.inf #Maximum step-size. + self.options["minh"] = np.finfo(np.double).eps #Minimum step-size self.options["maxretry"] = 15 #Maximum number of consecutive retries #Solver support @@ -129,8 +129,8 @@ def integrate(self, t, y, yd, tf, opts): IADCONST = 1 #The leading term (A,D) is constant #Options vectors - IOPT = N.array([0]*9) #Integer options - ROPT = N.array([0.0]*11) #Real options + IOPT = np.array([0]*9) #Integer options + ROPT = np.array([0.0]*11) #Real options #Setting work options IOPT[0] = self._get_print_level() #Print level (default 2) @@ -168,7 +168,7 @@ def integrate(self, t, y, yd, tf, opts): yret, ydret, istats, flag = glimda(res_dummy, qeval_dummy, dfdy_dummy, dfdx_dummy,dqdx_dummy,t,tf,y.copy(),yd.copy(),self.inith, - self.atol, self.rtol*N.ones(self.problem_info["dim"]),ITOL,INUMA,INUMD,INUMB, + self.atol, self.rtol*np.ones(self.problem_info["dim"]),ITOL,INUMA,INUMD,INUMB, IODE, IADCONST, IOPT, ROPT, self._solout) #print self._tlist, self._ylist #Checking return @@ -340,10 +340,10 @@ def _get_min_h(self): def _set_atol(self,atol): - self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self._leny) + self.options["atol"] = self.options["atol"]*np.ones(self._leny) elif len(self.options["atol"]) != self._leny: raise GLIMDA_Exception("atol must be of length one or same as the dimension of the problem.") diff --git a/src/solvers/kinsol.pyx b/src/solvers/kinsol.pyx index 709795e6..d37102dd 100644 --- a/src/solvers/kinsol.pyx +++ b/src/solvers/kinsol.pyx @@ -17,8 +17,8 @@ # distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from assimulo.algebraic cimport Algebraic @@ -66,7 +66,7 @@ cdef class KINSOL(Algebraic): self.pData = ProblemDataEquationSolver() - self._eps = N.finfo('double').eps + self._eps = np.finfo('double').eps self._added_linear_solver = False #Populate the ProblemData @@ -76,11 +76,11 @@ cdef class KINSOL(Algebraic): self.options["ftol"] = self._eps**(1.0/3.0) self.options["stol"] = self._eps**(2.0/3.0) self.options["strategy"] = KIN_LINESEARCH - self.options["y_scale"] = N.array([1.0]*self.problem_info["dim"]) - self.options["f_scale"] = N.array([1.0]*self.problem_info["dim"]) - #self.options["y_nominal"] = N.array([1.0]*self.problem_info["dim"]) - #self.options["y_min"] = N.array([MIN_VALUE]*self.problem_info["dim"]) - #self.options["y_max"] = N.array([MAX_VALUE]*self.problem_info["dim"]) + self.options["y_scale"] = np.array([1.0]*self.problem_info["dim"]) + self.options["f_scale"] = np.array([1.0]*self.problem_info["dim"]) + #self.options["y_nominal"] = np.array([1.0]*self.problem_info["dim"]) + #self.options["y_min"] = np.array([MIN_VALUE]*self.problem_info["dim"]) + #self.options["y_max"] = np.array([MAX_VALUE]*self.problem_info["dim"]) self.options["linear_solver"] = "DENSE" self.options["max_iter"] = 200 #Maximum number of nonlinear iterations self.options["no_initial_setup"] = False #Specifies wheter or not a call to the setup function should be made @@ -138,7 +138,7 @@ cdef class KINSOL(Algebraic): elif self.options["y_min"]: self.options["y_scale"][i] = max(1.0, abs(self.options["y_min"][i])) else: - self.options["y_scale"] = N.array([value]) if isinstance(value, (float, int)) else N.array(value) + self.options["y_scale"] = np.array([value]) if isinstance(value, (float, int)) else np.array(value) arr2nv_inplace(self.options["y_scale"], self.y_scale) @@ -149,7 +149,7 @@ cdef class KINSOL(Algebraic): if isinstance(value, str) and value.upper() == "AUTOMATIC": pass else: - self.options["f_scale"] = N.array([value]) if isinstance(value, (float, int)) else N.array(value) + self.options["f_scale"] = np.array([value]) if isinstance(value, (float, int)) else np.array(value) arr2nv_inplace(self.options["f_scale"], self.f_scale) diff --git a/src/solvers/odepack.py b/src/solvers/odepack.py index 44aad118..e3943c32 100644 --- a/src/solvers/odepack.py +++ b/src/solvers/odepack.py @@ -17,9 +17,8 @@ import sys import logging -import numpy as N -import scipy.linalg as Sc -import scipy.sparse as sp +import numpy as np +import scipy.sparse as sps from assimulo.exception import ODEPACK_Exception, RKStarter_Exception from assimulo.ode import ID_PY_COMPLETE, ID_PY_EVENT, NORMAL @@ -40,7 +39,7 @@ def g_dummy(t,y): return y def jac_dummy(t,y): - return N.zeros((len(y),len(y))) + return np.zeros((len(y),len(y))) class LSODAR(Explicit_ODE): """ @@ -70,7 +69,7 @@ def __init__(self, problem): Explicit_ODE.__init__(self, problem) #Calls the base class #Default values - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance self.options["usejac"] = False self.options["maxsteps"] = 100000 @@ -91,10 +90,10 @@ def __init__(self, problem): self.supports["report_continuously"] = True self.supports["interpolated_output"] = True - self._RWORK = N.array([0.0]*(22 + self.problem_info["dim"] * + self._RWORK = np.array([0.0]*(22 + self.problem_info["dim"] * max(16,self.problem_info["dim"]+9) + 3*self.problem_info["dimRoot"])) - self._IWORK = N.array([0]*(20 + self.problem_info["dim"])) + self._IWORK = np.array([0]*(20 + self.problem_info["dim"])) def initialize(self): @@ -151,14 +150,14 @@ def autostart(self,t,y,sw0=[]): t0=t tf=RWORK[0] T=abs(tf-t0) - direction=N.sign(tf-t0) + direction=np.sign(tf-t0) #Perturb initial condition and compute rough Lipschitz constant - cent=Sc.norm(y)/normscale/100. - v0=y+cent*N.random.rand(len(y),1) + cent=np.linalg.norm(y)/normscale/100. + v0=y+cent*np.random.rand(len(y),1) u0prime=f(t,y,sw0) v0prime=f(t,v0,sw0) - Lip=Sc.norm(u0prime-v0prime)/Sc.norm(y-v0) + Lip=np.linalg.norm(u0prime-v0prime)/np.linalg.norm(y-v0) h=direction*min(1e-3*T,max(1e-8*T,0.05/Lip)) #step 1: fwd Euler step u1=y+h*u0prime @@ -168,14 +167,14 @@ def autostart(self,t,y,sw0=[]): u0comp=u1-h*u1prime #step 3: estimate of local error du=u0comp-y - dunorm=Sc.norm(du) + dunorm=np.linalg.norm(du) errnorm=dunorm/normscale #step 4: new estimate of Lipschitz constant u0comprime=f(t0,u0comp,sw0) - L=Sc.norm(u0comprime-u0prime)/dunorm - M=N.dot(du,u0comprime-u0prime)/dunorm**2 + L=np.linalg.norm(u0comprime-u0prime)/dunorm + M=np.dot(du,u0comprime-u0prime)/dunorm**2 #step 5: construct a refined starting stepsize - theta1=tolscale/N.sqrt(errnorm) + theta1=tolscale/np.sqrt(errnorm) theta2=tolscale/abs(h*(L+M/2)) h=h*(theta1+theta2)/2 h=direction*min(3e-3*T,abs(h)) @@ -203,7 +202,7 @@ def integrate_start(self, t, y): # a) get previous stepsize if any hu, nqu ,nq ,nyh, nqnyh = get_lsod_common() #H = hu if hu != 0. else 1.e-4 # this needs some reflections - #H =(abs(RWORK[0]-t)*((self.options["rtol"])**(1/(self.rkstarter+1))))/(100*Sc.norm(self.problem.rhs(t,y,self.sw))+10)#if hu != 0. else 1.e-4 + #H =(abs(RWORK[0]-t)*((self.options["rtol"])**(1/(self.rkstarter+1))))/(100*np.linalg.norm(self.problem.rhs(t,y,self.sw))+10)#if hu != 0. else 1.e-4 H=1e-2 #H=self.autostart(t,y) #H=3*H @@ -248,7 +247,7 @@ def integrate_start(self, t, y): #RWORK[6]=dls001.hmin #RWORK[5]=dls001.hmxi - number_of_fevals=N.array([1,2,4,7,11]) + number_of_fevals=np.array([1,2,4,7,11]) # d) Reset statistics IWORK[9:13]=[0]*4 dls001.nst=1 @@ -271,7 +270,7 @@ def _jacobian(self, t, y): """ jac = self.problem.jac(t,y) - if isinstance(jac, sp.csc_matrix): + if isinstance(jac, sps.csc_matrix): jac = jac.toarray() return jac @@ -285,7 +284,7 @@ def integrate(self, t, y, tf, opts): ISTATE, RWORK, IWORK = self.integrate_start( t, y) JT = 1 if self.usejac else 2#Jacobian type indicator - JROOT = N.array([0]*self.problem_info["dimRoot"]) + JROOT = np.array([0]*self.problem_info["dimRoot"]) #Setting work options RWORK[0] = tf #Do not integrate past tf @@ -311,7 +310,7 @@ def state_events(t,y): else: g_fcn = g_dummy - #jac_dummy = (lambda t,y:N.zeros((len(y),len(y)))) if not self.usejac else self.problem.jac + #jac_dummy = (lambda t,y:np.zeros((len(y),len(y)))) if not self.usejac else self.problem.jac jac_fcn = jac_dummy if not self.usejac else self._jacobian #Extra args to rhs and state_events @@ -332,7 +331,7 @@ def state_events(t,y): #Tolerances: atol = self.atol - rtol = self.rtol*N.ones(self.problem_info["dim"]) + rtol = self.rtol*np.ones(self.problem_info["dim"]) rhs = self.problem.rhs #if normal_mode == 0: @@ -486,10 +485,10 @@ def _get_usejac(self): def _set_atol(self,atol): - self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self._leny) + self.options["atol"] = self.options["atol"]*np.ones(self._leny) elif len(self.options["atol"]) != self._leny: raise ODEPACK_Exception("atol must be of length one or same as the dimension of the problem.") @@ -673,17 +672,17 @@ class RKStarterNordsieck(object): See: Mohammadi (2013): https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=4196026&fileOId=4196027 """ # Gamma matrix of Gear's RK starter which produce Nordsieck vector at t0 - Gamma_0=[N.array([[1.,0.], # 1st order + Gamma_0=[np.array([[1.,0.], # 1st order [0.,1.]]), - N.array([[1.,0.,0.], # 2nd order + np.array([[1.,0.,0.], # 2nd order [0.,1.,-1.], [0.,0.,1.]]), - N.array([[1.,0.,0.,0.], # 3rd order + np.array([[1.,0.,0.,0.], # 3rd order [0.,1.,-5./3.,1.], [0.,0.,3.,-2.], [0.,0.,0.,1.], [0.,0.,-4./3.,0.]]), - N.array([[1.,0.,0.,0.,0.], # 4th order + np.array([[1.,0.,0.,0.,0.], # 4th order [0.,1.,-5./6.,4./9.,-1./9.], [0.,0.,0.,0.,0.], [0.,0.,1./2.,-4./9.,1./9.], @@ -693,19 +692,19 @@ class RKStarterNordsieck(object): # A matrices of RK starter with equidistanced states - A_s=[ N.array([1]), # 1st order - N.array([[0.,0],[1,0]]), # 2nd order - N.array([[0.,0.,0.,0.,0.],[1./2,0.,0.,0.,0.], # 3rd order + A_s=[ np.array([1]), # 1st order + np.array([[0.,0],[1,0]]), # 2nd order + np.array([[0.,0.,0.,0.,0.],[1./2,0.,0.,0.,0.], # 3rd order [0.,3./4,0.,0.,0.],[2./9,1./3,4./9,0.,0.], [17./72,1./6,2./9,-1./8,0.]]), - N.array([[0.,0.,0.,0.,0.,0.,0.], # 4th order + np.array([[0.,0.,0.,0.,0.,0.,0.], # 4th order [1./6.,0.,0.,0.,0.,0.,0.], [0.,1./6.,0.,0.,0.,0.,0.], [0.,0.,1./3.,0.,0.,0.,0.], [1./18.,1./9.,1./9.,1./18.,0.,0.,0.], [2.5,-3.,-3.,2.25,2.25,0.,0.], [10./45.,-8./45.,-8./45.,-4./45.,13./15.,1./45.,0.]]), - N.array([[0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.], # 5th order + np.array([[0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.], # 5th order [1./20,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.], [3./160,9./160,0.,0.,0.,0.,0., 0.,0.,0.,0.,0.,0.,0.], [3./40,-9./40,6./20,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.], @@ -724,54 +723,54 @@ class RKStarterNordsieck(object): 38892959/120069679,11804845./141497517,0.]], dtype=object)] co_ord_s=[[],[],[4],[4,6],[6,9,11]] - b_s=[ N.array([1]), - N.array([1./2,1./2]), - N.array([1./6,0.,0.,1./6,2./3]), - N.array([29./1062.,83./531.,83./531.,83./1062.,2./531.,56./531.,251./531.]), # b-vectors of of 1st to 5th order RK starter method - N.array([0.03877310906055409,0.,3021245/89251943,5956469/58978530,851373/32201684,11559106/149527791,11325471/112382620,0.,-12983/235976962,17692261/82454251,0., + b_s=[ np.array([1]), + np.array([1./2,1./2]), + np.array([1./6,0.,0.,1./6,2./3]), + np.array([29./1062.,83./531.,83./531.,83./1062.,2./531.,56./531.,251./531.]), # b-vectors of of 1st to 5th order RK starter method + np.array([0.03877310906055409,0.,3021245/89251943,5956469/58978530,851373/32201684,11559106/149527791,11325471/112382620,0.,-12983/235976962,17692261/82454251,0., 38892959/120069679,11804845./141497517,0.])] - C_s=[ N.array([0.]), - N.array([0.]), - N.array([0.,1./2,3./4,1.,1./2,1.]), - N.array([0.,1./6,1./6,1./3,1./3,1.,1.,2./3,1.]), - N.array([0.,1./20,3./40,3./20,1./4,7./28,1./4,2./4,1.,2./4,3./4,3./4,1.,1.])] # C-values in Butcher tableau of 8-stages Runge-Kutta + C_s=[ np.array([0.]), + np.array([0.]), + np.array([0.,1./2,3./4,1.,1./2,1.]), + np.array([0.,1./6,1./6,1./3,1./3,1.,1.,2./3,1.]), + np.array([0.,1./20,3./40,3./20,1./4,7./28,1./4,2./4,1.,2./4,3./4,3./4,1.,1.])] # C-values in Butcher tableau of 8-stages Runge-Kutta # A matrices of RK starter with non-equidistanced stages - A_n=[ N.array([1]), - N.array([[0.,0.],[0.,0.]]), - N.array([[0.,0.,0.,0.],[1./2,0.,0.,0.],[0.,1./2,0.,0.],[0.,0.,1.,0.]]), # 3rd order - N.array([[0.,0.,0.,0.,0.,0.],[2./5,0.,0.,0.,0.,0.],[-3./20,3./4,0.,0.,0.,0.], # 4th order + A_n=[ np.array([1]), + np.array([[0.,0.],[0.,0.]]), + np.array([[0.,0.,0.,0.],[1./2,0.,0.,0.],[0.,1./2,0.,0.],[0.,0.,1.,0.]]), # 3rd order + np.array([[0.,0.,0.,0.,0.,0.],[2./5,0.,0.,0.,0.,0.],[-3./20,3./4,0.,0.,0.,0.], # 4th order [19./44,-15./44,40./44,0.,0.,0.],[-31./64,185./192,5./64,-11./192,0.,0.],[11./72,25./72,25./72,11./72,0.,0.]])] - b_n=[ N.array([0.]), - N.array([1./2.,1./2.]), - N.array([[5./24,1./6,1./6,-1/24],[1./6,1./3,1./3,1./6]]), - N.array([[802./5625,68./225,-67./225,-143./5625,144./625,6./125],[699./5000,81./200,-39./200,99./5000,144./625,0.],[11./72,25./72,25./72,11./72,0.,0.]])] + b_n=[ np.array([0.]), + np.array([1./2.,1./2.]), + np.array([[5./24,1./6,1./6,-1/24],[1./6,1./3,1./3,1./6]]), + np.array([[802./5625,68./225,-67./225,-143./5625,144./625,6./125],[699./5000,81./200,-39./200,99./5000,144./625,0.],[11./72,25./72,25./72,11./72,0.,0.]])] - C_n=[ N.array([0.]), - N.array([0.]), - N.array([0.,1./2,1./2,1.]), - N.array([0.,2./5,3./5,1.,1./2,1.])] + C_n=[ np.array([0.]), + np.array([0.]), + np.array([0.,1./2,1./2,1.]), + np.array([0.,2./5,3./5,1.,1./2,1.])] #co_ord_n=[[],[],[1./2,1.],[2./5,3./5,1.]] - #A=N.array([[1.,0.,0.,0.], + #A=np.array([[1.,0.,0.,0.], # [1.,1./9.,1./27,1./81.], # [1.,4./9.,8./27,16./81.], # [1.,1.,1.,1.]]) - A=[N.array([0.]), # Convert the state values to Nordsieck vector - N.array([0.]), - N.array([1.]), - N.array([[1./4,1./8],[1.,1.]]), - N.array([[1./9,1./27,1./81.], + A=[np.array([0.]), # Convert the state values to Nordsieck vector + np.array([0.]), + np.array([1.]), + np.array([[1./4,1./8],[1.,1.]]), + np.array([[1./9,1./27,1./81.], [4./9.,8./27,16./81], [1.,1.,1.]]), - N.array([[1./16,1./64,1./256,1./1024], + np.array([[1./16,1./64,1./256,1./1024], [1./4,1./8,1./16,1./32], [9./16,27./64,81./256,243./1024], [1.,1.,1.,1.]])] - scale=N.array([1, 1, 1/2., 1./6., 1./24., 1./120.]).reshape(-1,1) + scale=np.array([1, 1, 1/2., 1./6., 1./24., 1./120.]).reshape(-1,1) def __init__(self, rhs, H, method='RKs_f', eval_at=0., number_of_steps=4): @@ -818,16 +817,16 @@ def RKs_f(self,t0,y0,sw0): b_s=b_s[s-1] co_ord_s=co_ord_s[s-1] H=(s-1)*self.H - K=N.zeros((N.size(A_s,0),len(y0))) - for i in range(N.size(A_s,0)): - K[i,:]=self.f(t0+C_s[i]*H,y0+H*N.dot(A_s[i,:],K),sw0) - y=N.zeros((s,len(y0))) + K=np.zeros((np.size(A_s,0),len(y0))) + for i in range(np.size(A_s,0)): + K[i,:]=self.f(t0+C_s[i]*H,y0+H*np.dot(A_s[i,:],K),sw0) + y=np.zeros((s,len(y0))) y[0,:]=y0 for i in range(1,s): if i==s-1: - y[i,:]=y0+H*N.dot(b_s,K) + y[i,:]=y0+H*np.dot(b_s,K) else: - y[i,:]=y0+H*N.dot(A_s[co_ord_s[i-1],:],K) + y[i,:]=y0+H*np.dot(A_s[co_ord_s[i-1],:],K) return y def RKn_f(self,t0,y0,sw0): s=self.number_of_steps @@ -837,13 +836,13 @@ def RKn_f(self,t0,y0,sw0): C_n=C_n[s-1] b_n=b_n[s-1] - K=N.zeros((N.size(A_n,0),len(y0))) - for i in range(N.size(A_n,0)): - K[i,:]=self.f(t0+C_n[i]*H,y0+H*N.dot(A_n[i,:],K),sw0) - y=N.zeros((s,len(y0))) + K=np.zeros((np.size(A_n,0),len(y0))) + for i in range(np.size(A_n,0)): + K[i,:]=self.f(t0+C_n[i]*H,y0+H*np.dot(A_n[i,:],K),sw0) + y=np.zeros((s,len(y0))) y[0,:]=y0 for i in range(1,s): - y[i,:]=y0+H*N.dot(b_n[i-1],K) + y[i,:]=y0+H*np.dot(b_n[i-1],K) return y @@ -861,7 +860,7 @@ def rk_like4(self, t0, y0, sw0): k4 = h*f(y0 + 3./4. * k1 + 9./4. * k3) k5 = h*f(y0 + k1/2. + k2 + k3/2. + 2. * k4) k6 = h*f(y0+k1/12.+2. * k2 + k3/4. + 2./3. * k4 + 2. * k5) - return N.array([y0,k1,k2,k3,k4,k5,k6]) + return np.array([y0,k1,k2,k3,k4,k5,k6]) def rk_like3(self, t0, y0, sw0): """ rk_like computes Runge-Kutta stages @@ -875,7 +874,7 @@ def rk_like3(self, t0, y0, sw0): k2 = h*f(y0 + k1) k3 = h*f(y0 + k1+ k2) k4 = h*f(y0 + 3./2. * k1) - return N.array([y0,k1,k2,k3,k4]) + return np.array([y0,k1,k2,k3,k4]) def rk_like2(self, t0, y0, sw0): """ rk_like2 computes Runge-Kutta 2nd-stages @@ -886,7 +885,7 @@ def rk_like2(self, t0, y0, sw0): h=self.H/2. k1=h*f(y0) k2=h*f(y0+k1) - return N.array([y0,k1,k2]) + return np.array([y0,k1,k2]) def rk_like13(self, t0, y0, sw0): """ rk_like6 computes Runge-Kutta 8th-stages @@ -894,21 +893,21 @@ def rk_like13(self, t0, y0, sw0): h = self.H self.Gamma_2=self.Gamma_0[3] f=lambda y: self.f(t0 , y , sw0) - K=N.zeros((6,len(y0))) - sol=N.zeros((3,len(y0))) - b=N.zeros((2,len(y0))) #remove the fifth stage value that is for error estimation - nord = N.zeros((4,len(y0))) #Nordsieck vector + K=np.zeros((6,len(y0))) + sol=np.zeros((3,len(y0))) + b=np.zeros((2,len(y0))) #remove the fifth stage value that is for error estimation + nord = np.zeros((4,len(y0))) #Nordsieck vector for i in range(5): - K[i,:]= f(y0+h*N.dot(self.Gamma_2[i,:],K)) + K[i,:]= f(y0+h*np.dot(self.Gamma_2[i,:],K)) c=0 for i in range(3): - sol[i,:]=y0+h*N.dot(self.Gamma_2[i+3,:],K) + sol[i,:]=y0+h*np.dot(self.Gamma_2[i+3,:],K) if i!=0: b[c,:]=sol[i,:]-y0-(c+1)*h/2*K[0,:] c+=1 nord[0,:] = y0 nord[1,:] = h*K[0,:] - nord[2:,:] = Sc.solve(self.A[self.number_of_steps],b) + nord[2:,:] = np.linalg.solve(self.A[self.number_of_steps],b) return nord def rk_like14(self, t0, y0, sw0): """ @@ -917,21 +916,21 @@ def rk_like14(self, t0, y0, sw0): h = self.H Gamma_2=self.Gamma_0[4] f=lambda y: self.f(t0 , y , sw0) - K=N.zeros((8,len(y0))) - sol=N.zeros((4,len(y0))) - b=N.zeros((3,len(y0))) #remove the fifth stage value that is for error estimation - nord = N.zeros((5,len(y0))) #Nordsieck vector + K=np.zeros((8,len(y0))) + sol=np.zeros((4,len(y0))) + b=np.zeros((3,len(y0))) #remove the fifth stage value that is for error estimation + nord = np.zeros((5,len(y0))) #Nordsieck vector for i in range(7): - K[i,:]= f(y0+h*N.dot(Gamma_2[i,:],K)) + K[i,:]= f(y0+h*np.dot(Gamma_2[i,:],K)) c=0 for i in range(4): - sol[i,:]=y0+h*N.dot(Gamma_2[i+4,:],K) + sol[i,:]=y0+h*np.dot(Gamma_2[i+4,:],K) if i!=1: b[c,:]=sol[i,:]-y0-(c+1)*h/3*K[0,:] c+=1 nord[0,:] = y0 nord[1,:] = h*K[0,:] - nord[2:,:] = Sc.solve(self.A[self.number_of_steps],b) + nord[2:,:] = np.linalg.solve(self.A[self.number_of_steps],b) return nord def rk_like15(self, t0, y0, sw0): """ @@ -940,62 +939,62 @@ def rk_like15(self, t0, y0, sw0): h = self.H Gamma_2=self.Gamma_0[5] f=lambda y: self.f(t0 , y , sw0) - K=N.zeros((14,len(y0))) - sol=N.zeros((8,len(y0))) - b=N.zeros((4,len(y0))) #remove the fifth stage value that is for error estimation - nord = N.zeros((6,len(y0))) #Nordsieck vector + K=np.zeros((14,len(y0))) + sol=np.zeros((8,len(y0))) + b=np.zeros((4,len(y0))) #remove the fifth stage value that is for error estimation + nord = np.zeros((6,len(y0))) #Nordsieck vector for i in range(13): - K[i,:]= f(y0+h*N.dot(Gamma_2[i,:],K)) + K[i,:]= f(y0+h*np.dot(Gamma_2[i,:],K)) c=0 for i in range(8): - sol[i,:]=y0+h*N.dot(Gamma_2[i+6,:],K) + sol[i,:]=y0+h*np.dot(Gamma_2[i+6,:],K) if (i!=1) and (i!=2) and (i!=4) and (i!=6): b[c,:]=sol[i,:]-y0-(c+1)*h/4*K[0,:] c+=1 nord[0,:] = y0 nord[1,:] = h*K[0,:] - nord[2:,:] = Sc.solve(self.A[self.number_of_steps],b) + nord[2:,:] = np.linalg.solve(self.A[self.number_of_steps],b) return nord def nordsieck(self,k): """ Nordsieck array computed at initial point """ - nord=self.scale[:self.number_of_steps+1]*N.dot(self.Gamma_0[self.number_of_steps-1].T,k) + nord=self.scale[:self.number_of_steps+1]*np.dot(self.Gamma_0[self.number_of_steps-1].T,k) return nord def Nordsieck_RKn(self,t0,y,sw0): s=self.number_of_steps H=(s-1)*self.H - co_nord=[N.array([1./2,1.]),N.array([2./5,3./5,1.])] - l=N.size(y,0) + co_nord=[np.array([1./2,1.]),np.array([2./5,3./5,1.])] + l=np.size(y,0) y0=y[0,:] yf=self.f(t0,y0,sw0) if l==3: - co=N.array([co_nord[0]]) - nord_n=N.vander(co_nord[0],self.number_of_steps+1) + co=np.array([co_nord[0]]) + nord_n=np.vander(co_nord[0],self.number_of_steps+1) b=y[1:]-y0-co.T*yf - nord=Sc.solve(nord_n[0:2,0:2],b) + nord=np.linalg.solve(nord_n[0:2,0:2],b) elif l==4: - co=N.array([co_nord[1]]) - nord_n=N.vander(co_nord[1],self.number_of_steps+1) + co=np.array([co_nord[1]]) + nord_n=np.vander(co_nord[1],self.number_of_steps+1) b=y[1:]-y0-H*co.T*yf - nord=Sc.solve(nord_n[0:3,0:3],b) - nord=N.vstack((y0,H*yf,nord[::-1])) + nord=np.linalg.solve(nord_n[0:3,0:3],b) + nord=np.vstack((y0,H*yf,nord[::-1])) return nord def Nordsieck_RKs(self,t0,y,sw0): s=self.number_of_steps H=(s-1)*self.H - co_nord=[N.array([1]),N.array([1./2,1]),N.array([1./3,2./3,1]), - N.array([1./4,2./4,3./4,1.])] + co_nord=[np.array([1]),np.array([1./2,1]),np.array([1./3,2./3,1]), + np.array([1./4,2./4,3./4,1.])] A=self.A y0=y[0,:] yf=self.f(t0,y0,sw0) co=co_nord[s-2] - co=N.array([co]) + co=np.array([co]) b=y[1:]-y0-H*co.T*yf - nord=Sc.solve(A[s],b) - nord=N.vstack((y0,H*yf,nord)) + nord=np.linalg.solve(A[s],b) + nord=np.vstack((y0,H*yf,nord)) return nord diff --git a/src/solvers/radar5.py b/src/solvers/radar5.py index 6ded7715..54c06880 100644 --- a/src/solvers/radar5.py +++ b/src/solvers/radar5.py @@ -16,8 +16,8 @@ # along with this program. If not, see . import sys -import numpy as N -import pylab as P +import numpy as np +import pylab as pl from assimulo.ode import NORMAL, ID_PY_COMPLETE, ID_PY_EVENT from assimulo.explicit_ode import Explicit_ODE @@ -51,15 +51,15 @@ def __init__(self, problem): self.options["inith"] = 0.01 self.options["newt"] = 7 #Maximum number of newton iterations self.options["thet"] = 1.e-3 #Boundary for re-calculation of jac -# self.options["fnewt"] = 0.0 #Stopping critera for Newtons Method - self.options["fnewt"] = 0.03 #Stopping critera for Newtons Method +# self.options["fnewt"] = 0.0 #Stopping criteria for Newtons Method + self.options["fnewt"] = 0.03 #Stopping criteria for Newtons Method self.options["quot1"] = 1.0 #Parameters for changing step-size (lower bound) self.options["quot2"] = 1.2 #Parameters for changing step-size (upper bound) self.options["fac1"] = 0.2 #Parameters for step-size selection (lower bound) self.options["fac2"] = 8.0 #Parameters for step-size selection (upper bound) - self.options["maxh"] = N.inf #Maximum step-size. + self.options["maxh"] = np.inf #Maximum step-size. self.options["safe"] = 0.9 #Safety factor - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance self.options["usejac"] = True if self.problem_info["jac_fcn"] else False self.options["maxsteps"] = 10000 @@ -70,7 +70,7 @@ def __init__(self, problem): self.options["mxst"] = 100 # The maximum number of stored dense output points self.options["usejaclag"] = True if self.problem_info["jaclag_fcn"] else False - SQ6 = N.sqrt(6.0) + SQ6 = np.sqrt(6.0) C1 = (4.0-SQ6)/10.0 C2 = (4.0+SQ6)/10.0 self.C1M1 = C1-1.0 @@ -96,9 +96,9 @@ def __init__(self, problem): flat_lagcompmap = [] for comp in self.problem.lagcompmap: flat_lagcompmap.extend(comp) - self._nrdens = len(N.unique(flat_lagcompmap)) - self._ipast = N.unique(flat_lagcompmap).tolist()+[0] - self._grid = N.array([]) + self._nrdens = len(np.unique(flat_lagcompmap)) + self._ipast = np.unique(flat_lagcompmap).tolist()+[0] + self._grid = np.array([]) # if hasattr(problem, 'pbar'): @@ -127,7 +127,7 @@ def _solout(self,nr, told, t, hold, y, cont,irtrn): while output_list[output_index] <= t: self._tlist.append(output_list[output_index]) - yval = N.empty(self._leny) + yval = np.empty(self._leny) for i in range(self._leny): # yval[i] = radar5.contr5(i+1,self.problem_info["dim"],output_list[output_index],t,hold) yval[i] = radar5.contr5(i+1,self.problem_info["dim"],output_list[output_index],cont,t,hold) @@ -144,7 +144,7 @@ def _solout(self,nr, told, t, hold, y, cont,irtrn): #def coutput(self,t): #Nx = self.problem_info["dim"] - #y = N.zeros(Nx) + #y = np.zeros(Nx) #theta, pos = radar5.lagr5(10, t, None, self.arglag, self.past, self.problem.phi, self.problem.ipast) #for i in range(1,Nx+1): @@ -159,11 +159,11 @@ def coutput(self, t, i = -1): i: solution component (default -1 gives the whole vector) """ Nx = self.problem_info["dim"] - y = N.zeros(Nx) + y = np.zeros(Nx) # t belongs to the interval (tk[ik], tk[ik+1]) - ik = N.searchsorted(self.tk, t) - 1 + ik = np.searchsorted(self.tk, t) - 1 I = self.idif*ik @@ -175,7 +175,7 @@ def coutput(self, t, i = -1): # The line below this comment is what's effectively happening, # but it is unfortunately extremely slow compared to the # vectorized version below that doesn't use the cpoly function: - #return N.array([self.cpoly(i, I, theta) for i in range(self.problem_info["dim"])]) + #return np.array([self.cpoly(i, I, theta) for i in range(self.problem_info["dim"])]) nrds = self._nrdens I = I + 1 I2 = I + self.problem_info["dim"] @@ -233,11 +233,11 @@ def integrate(self, t, y, tf, opts): MLMAS = self.problem_info["dim"] #The mass matrix is full MUMAS = self.problem_info["dim"] #See MLMAS IOUT = 1 #solout is called after every step - WORK = N.array([0.0]*30) #Work (double) vector - IWORK = N.array([0]*30) #Work (integer) vector + WORK = np.array([0.0]*30) #Work (double) vector + IWORK = np.array([0]*30) #Work (integer) vector #Setting work options - WORK[0] = N.finfo(N.double).eps # Rounding unit + WORK[0] = np.finfo(np.double).eps # Rounding unit WORK[1] = self.safe WORK[2] = self.thet WORK[3] = self.fnewt @@ -261,9 +261,9 @@ def integrate(self, t, y, tf, opts): self.idif = 4*self._nrdens + 2 lrpast = self.mxst*self.idif - past = N.zeros(lrpast) + past = np.zeros(lrpast) - #past = N.zeros(self.mxst*(4*self.problem.nrdens+2)) + #past = np.zeros(self.mxst*(4*self.problem.nrdens+2)) # print WORK # print IWORK @@ -284,7 +284,7 @@ def integrate(self, t, y, tf, opts): y.copy(), \ tf, \ self.inith, \ - self.rtol*N.ones(self.problem_info["dim"]), \ + self.rtol*np.ones(self.problem_info["dim"]), \ self.atol, \ ITOL, \ jac_dummy, \ @@ -315,8 +315,8 @@ def integrate(self, t, y, tf, opts): t, y, h, iwork, flag, past = a[0] #self.past = copy.deepcopy(past) self.past = past - self.tk = N.trim_zeros(self.past[::self.idif], 'b') - self.hk = N.trim_zeros(self.past[self.idif-1:-1:self.idif], 'b') + self.tk = np.trim_zeros(self.past[::self.idif], 'b') + self.hk = np.trim_zeros(self.past[self.idif-1:-1:self.idif], 'b') #Checking return if flag == 1: @@ -374,11 +374,11 @@ def plot_stepsize(self): """ Plots the step-size. """ - P.semilogy(N.diff(self.t),drawstyle='steps-post') - P.title(self.problem.name) - P.ylabel('Step length') - P.xlabel('Number of steps') - P.show() + pl.semilogy(np.diff(self.t),drawstyle='steps-post') + pl.title(self.problem.name) + pl.ylabel('Step length') + pl.xlabel('Number of steps') + pl.show() def _set_newt(self, newt): """ @@ -677,10 +677,10 @@ def _get_usejaclag(self): def _set_atol(self,atol): - self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self._leny) + self.options["atol"] = self.options["atol"]*np.ones(self._leny) elif len(self.options["atol"]) != self._leny: raise Radar_Exception("atol must be of length one or same as the dimension of the problem.") diff --git a/src/solvers/radau5.py b/src/solvers/radau5.py index 4a105604..16efa4ce 100644 --- a/src/solvers/radau5.py +++ b/src/solvers/radau5.py @@ -15,9 +15,9 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -import scipy as S -import scipy.sparse as sp +import numpy as np +import scipy as sp +import scipy.sparse as sps from assimulo.exception import ( AssimuloException, @@ -99,14 +99,14 @@ def __init__(self, problem): self.options["inith"] = 0.01 self.options["newt"] = 7 #Maximum number of newton iterations self.options["thet"] = 1.e-3 #Boundary for re-calculation of jac - self.options["fnewt"] = None #Stopping critera for Newtons Method + self.options["fnewt"] = None #Stopping criteria for Newtons Method self.options["quot1"] = 1.0 #Parameters for changing step-size (lower bound) self.options["quot2"] = 1.2 #Parameters for changing step-size (upper bound) self.options["fac1"] = 0.2 #Parameters for step-size selection (lower bound) self.options["fac2"] = 8.0 #Parameters for step-size selection (upper bound) self.options["maxh"] = None #Maximum step-size. self.options["safe"] = 0.9 #Safety factor - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance self.options["usejac"] = True if self.problem_info["jac_fcn"] else False self.options["maxsteps"] = 100000 @@ -120,7 +120,7 @@ def __init__(self, problem): self._leny = len(self.y) #Dimension of the problem self._type = '(explicit)' self._event_info = None - self._werr = N.zeros(self._leny) + self._werr = np.zeros(self._leny) def _get_linear_solver(self): return self.options["linear_solver"] @@ -245,7 +245,7 @@ def f(t, y): return rhs, [ret] except BaseException as E: rhs = y.copy() - if isinstance(E, (N.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable + if isinstance(E, (np.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable ret = 1 #Recoverable error else: self._py_err = E @@ -255,7 +255,7 @@ def f(t, y): self.event_func = event_func self._event_info = [0] * self.problem_info["dimRoot"] ret, self.g_old = self.event_func(self.t, self.y) - self.g_old = N.array(self.g_old) + self.g_old = np.array(self.g_old) if ret < 0: raise self._py_err self.statistics["nstatefcns"] += 1 @@ -266,7 +266,7 @@ def f(t, y): rhs = self.problem.rhs(t, y) except BaseException as E: rhs = y.copy() - if isinstance(E, (N.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable + if isinstance(E, (np.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable ret = 1 #Recoverable error else: self._py_err = E @@ -275,7 +275,7 @@ def f(t, y): self.f = f def interpolate(self, time): - y = N.empty(self._leny) + y = np.empty(self._leny) self.rad_memory.interpolate(time, y) return y @@ -283,7 +283,7 @@ def get_weighted_local_errors(self): """ Returns the vector of weighted estimated local errors at the current step. """ - return N.abs(self._werr) + return np.abs(self._werr) def _solout(self, nrsol, told, t, y, werr): """ @@ -339,11 +339,11 @@ def _jacobian(self, t, y): ret = 0 try: jac = self.problem.jac(t,y) - if isinstance(jac, sp.csc_matrix) and (self.options["linear_solver"] == "DENSE"): + if isinstance(jac, sps.csc_matrix) and (self.options["linear_solver"] == "DENSE"): jac = jac.toarray() except BaseException as E: - jac = N.eye(len(y)) - if isinstance(E, (N.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable + jac = np.eye(len(y)) + if isinstance(E, (np.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable ret = 1 #Recoverable error else: self._py_err = E @@ -369,7 +369,7 @@ def integrate(self, t, y, tf, opts): self._py_err = None ## reset self._opts = opts self.rad_memory.reinit() - t, y, flag = self.radau5.radau5_py_solve(self.f, t, y.copy(), tf, self.inith, self.rtol*N.ones(self.problem_info["dim"]), self.atol, + t, y, flag = self.radau5.radau5_py_solve(self.f, t, y.copy(), tf, self.inith, self.rtol*np.ones(self.problem_info["dim"]), self.atol, jac_dummy, IJAC, self._solout, IOUT, self.rad_memory) #Retrieving statistics @@ -454,12 +454,12 @@ def __init__(self, problem): self.options["inith"] = 0.01 self.options["newt"] = 7 #Maximum number of newton iterations self.options["thet"] = 1.e-3 #Boundary for re-calculation of jac - self.options["fnewt"] = 0 #Stopping critera for Newtons Method + self.options["fnewt"] = 0 #Stopping criteria for Newtons Method self.options["quot1"] = 1.0 #Parameters for changing step-size (lower bound) self.options["quot2"] = 1.2 #Parameters for changing step-size (upper bound) self.options["fac1"] = 0.2 #Parameters for step-size selection (lower bound) self.options["fac2"] = 8.0 #Parameters for step-size selection (upper bound) - self.options["maxh"] = N.inf #Maximum step-size. + self.options["maxh"] = np.inf #Maximum step-size. self.options["safe"] = 0.9 #Safety factor self.options["atol"] = 1.0e-6 #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance @@ -476,8 +476,8 @@ def __init__(self, problem): self._leny = len(self.y) #Dimension of the problem self._oldh = 0.0 #Old stepsize self._olderr = 1.0 #Old error - self._eps = N.finfo('double').eps - self._col_poly = N.zeros(self._leny*3) + self._eps = np.finfo('double').eps + self._col_poly = np.zeros(self._leny*3) self._type = '(explicit)' self._curiter = 0 #Number of current iterations @@ -485,10 +485,10 @@ def __init__(self, problem): self.f = problem.rhs_internal #Internal temporary result vector - self.Y1 = N.array([0.0]*len(self.y0)) - self.Y2 = N.array([0.0]*len(self.y0)) - self.Y3 = N.array([0.0]*len(self.y0)) - self._f0 = N.array([0.0]*len(self.y0)) + self.Y1 = np.array([0.0]*len(self.y0)) + self.Y2 = np.array([0.0]*len(self.y0)) + self.Y3 = np.array([0.0]*len(self.y0)) + self._f0 = np.array([0.0]*len(self.y0)) #Solver support self.supports["one_step_mode"] = True @@ -523,8 +523,8 @@ def step_generator(self, t, y, tf, opts): self._tc = t self._yc = y - if self.h > N.abs(tf-t): - self.h = N.abs(tf-t) + if self.h > np.abs(tf-t): + self.h = np.abs(tf-t) if t < tf: yield ID_PY_OK, t, y @@ -576,7 +576,7 @@ def _step(self, t, y): """ This calculates the next step in the integration. """ - self._scaling = N.array(abs(y)*self.rtol + self.atol) #The scaling used. + self._scaling = np.array(abs(y)*self.rtol + self.atol) #The scaling used. while True: #Loop for integrating one step. @@ -663,15 +663,15 @@ def _radau_F(self, Z, t, y): self.statistics["nfcns"] += 3 - return N.hstack((N.hstack((self.Y1,self.Y2)),self.Y3)) + return np.hstack((np.hstack((self.Y1,self.Y2)),self.Y3)) def calc_start_values(self): """ Calculate newton starting values. """ if self._first: - Z = N.zeros(self._leny*3) - W = N.zeros(self._leny*3) + Z = np.zeros(self._leny*3) + W = np.zeros(self._leny*3) else: Z = self._Z cq = self.C*self.h/self._oldh#self._oldoldh#self._oldh @@ -682,7 +682,7 @@ def calc_start_values(self): Z[leny:2*leny] = cq[1,0]*(newtval[:leny]+(cq[1,0]-self.C[1,0]+1.)*(newtval[leny:2*leny]+(cq[1,0]-self.C[0,0]+1.)*newtval[2*leny:3*leny])) Z[2*leny:3*leny]= cq[2,0]*(newtval[:leny]+(cq[2,0]-self.C[1,0]+1.)*(newtval[leny:2*leny]+(cq[2,0]-self.C[0,0]+1.)*newtval[2*leny:3*leny])) - W = N.dot(self.T2,Z) + W = np.dot(self.T2,Z) return Z, W @@ -694,8 +694,8 @@ def newton(self,t,y): for k in range(20): self._curiter = 0 #Reset the iteration - self._fac_con = max(self._fac_con, self._eps)**0.8; - self._theta = abs(self.thet); + self._fac_con = max(self._fac_con, self._eps)**0.8 + self._theta = abs(self.thet) if self._needjac: self._jac = self.jacobian(t,y) @@ -707,13 +707,13 @@ def newton(self,t,y): self._g = self._gamma/self.h self._B = self._g*self.I - self._jac - self._P1,self._L1,self._U1 = S.linalg.lu(self._B) #LU decomposition - self._P2,self._L2,self._U2 = S.linalg.lu(self._a*self.I-self._jac) - self._P3,self._L3,self._U3 = S.linalg.lu(self._b*self.I-self._jac) + self._P1,self._L1,self._U1 = sp.linalg.lu(self._B) #LU decomposition + self._P2,self._L2,self._U2 = sp.linalg.lu(self._a*self.I-self._jac) + self._P3,self._L3,self._U3 = sp.linalg.lu(self._b*self.I-self._jac) self._needLU = False - if min(abs(N.diag(self._U1))) 0: thq = newnrm/oldnrm if i == 1: self._theta = thq else: - self._theta = N.sqrt(thq*thqold) + self._theta = np.sqrt(thq*thqold) thqold = thq if self._theta < 0.99: #Convergence @@ -760,10 +760,10 @@ def newton(self,t,y): oldnrm = max(newnrm,self._eps) #Store oldnorm W = W+Z #Perform the iteration - Z = N.dot(self.T3,W) #Calculate the new Z values + Z = np.dot(self.T3,W) #Calculate the new Z values if self._fac_con*newnrm <= self.fnewt: #Convergence? - self._itfail = False; + self._itfail = False break else: #Iteration failed @@ -828,17 +828,17 @@ def estimate_error(self): temp = 1./self.h*(self.E[0]*self._Z[:self._leny]+self.E[1]*self._Z[self._leny:2*self._leny]+self.E[2]*self._Z[2*self._leny:3*self._leny]) scal = self._scaling#/self.h - err_v = N.linalg.solve(self._U1,N.linalg.solve(self._L1,N.linalg.solve(self._P1,self._f0+temp))) - err = N.linalg.norm(err_v/scal) - err = max(err/N.sqrt(self._leny),1.e-10) + err_v = np.linalg.solve(self._U1,np.linalg.solve(self._L1,np.linalg.solve(self._P1,self._f0+temp))) + err = np.linalg.norm(err_v/scal) + err = max(err/np.sqrt(self._leny),1.e-10) if (self._rejected or self._first) and err >= 1.: #If the step was rejected, use the more expensive error estimation self.statistics["nfcns"] += 1 - err_new = N.array([0.0]*self._leny) + err_new = np.array([0.0]*self._leny) self.f(err_new,self._tc,self._yc+err_v) - err_v = N.linalg.solve(self._U1,N.linalg.solve(self._L1,N.linalg.solve(self._P1,err_new+temp))) - err = N.linalg.norm(err_v/scal) - err = max(err/N.sqrt(self._leny),1.e-10) + err_v = np.linalg.solve(self._U1,np.linalg.solve(self._L1,np.linalg.solve(self._P1,err_new+temp))) + err = np.linalg.norm(err_v/scal) + err = max(err/np.sqrt(self._leny),1.e-10) return err @@ -854,10 +854,10 @@ def jacobian(self, t, y): if self.usejac: #Retrieve the user-defined jacobian cjac = self.problem.jac(t,y) else: #Calculate a numeric jacobian - delt = N.array([(self._eps*max(abs(yi),1.e-5))**0.5 for yi in y])*N.identity(self._leny) #Calculate a disturbance - Fdelt = N.array([self.problem.rhs(t,y+e) for e in delt]) #Add the disturbance (row by row) + delt = np.array([(self._eps*max(abs(yi),1.e-5))**0.5 for yi in y])*np.identity(self._leny) #Calculate a disturbance + Fdelt = np.array([self.problem.rhs(t,y+e) for e in delt]) #Add the disturbance (row by row) grad = ((Fdelt-self.problem.rhs(t,y)).T/delt.diagonal()).T - cjac = N.array(grad).T + cjac = np.array(grad).T self.statistics["nfcnjacs"] += 1+self._leny #Add the number of function evaluations @@ -878,37 +878,37 @@ def interpolate(self, t, k=0): def _load_parameters(self): #Parameters - A = N.zeros([3,3]) - A[0,0] = (88.-7.*N.sqrt(6.))/360.0 - A[0,1] = (296.-169.*N.sqrt(6.))/1800.0 - A[0,2] = (-2.0+3.0*N.sqrt(6.))/225.0 - A[1,0] = (296.0+169.0*N.sqrt(6.))/1800.0 - A[1,1] = (88.+7.*N.sqrt(6.))/360.0 - A[1,2] = (-2.-3.*N.sqrt(6.))/225.0 - A[2,0] = (16.0-N.sqrt(6.))/36.0 - A[2,1] = (16.0+N.sqrt(6.))/36.0 + A = np.zeros([3,3]) + A[0,0] = (88.-7.*np.sqrt(6.))/360.0 + A[0,1] = (296.-169.*np.sqrt(6.))/1800.0 + A[0,2] = (-2.0+3.0*np.sqrt(6.))/225.0 + A[1,0] = (296.0+169.0*np.sqrt(6.))/1800.0 + A[1,1] = (88.+7.*np.sqrt(6.))/360.0 + A[1,2] = (-2.-3.*np.sqrt(6.))/225.0 + A[2,0] = (16.0-np.sqrt(6.))/36.0 + A[2,1] = (16.0+np.sqrt(6.))/36.0 A[2,2] = (1.0/9.0) - C = N.zeros([3,1]) - C[0,0]=(4.0-N.sqrt(6.0))/10.0 - C[1,0]=(4.0+N.sqrt(6.0))/10.0 + C = np.zeros([3,1]) + C[0,0]=(4.0-np.sqrt(6.0))/10.0 + C[1,0]=(4.0+np.sqrt(6.0))/10.0 C[2,0]=1.0 - B = N.zeros([1,3]) - B[0,0]=(16.0-N.sqrt(6.0))/36.0 - B[0,1]=(16.0+N.sqrt(6.0))/36.0 + B = np.zeros([1,3]) + B[0,0]=(16.0-np.sqrt(6.0))/36.0 + B[0,1]=(16.0+np.sqrt(6.0))/36.0 B[0,2]=1.0/9.0 - E = N.zeros(3) - E[0] = -13.0-7.*N.sqrt(6.) - E[1] = -13.0+7.0*N.sqrt(6.) + E = np.zeros(3) + E[0] = -13.0-7.*np.sqrt(6.) + E[1] = -13.0+7.0*np.sqrt(6.) E[2] = -1.0 E = 1.0/3.0*E - Ainv = N.linalg.inv(A) - [eig, T] = N.linalg.eig(Ainv) - eig = N.array([eig[2],eig[0],eig[1]]) - J = N.diag(eig) + Ainv = np.linalg.inv(A) + [eig, T] = np.linalg.eig(Ainv) + eig = np.array([eig[2],eig[0],eig[1]]) + J = np.diag(eig) self._alpha = eig[1] self._beta = eig[2] @@ -920,13 +920,13 @@ def _load_parameters(self): T[:,0] = temp2 T[:,1] = temp0 T[:,2] = temp1 - Tinv = N.linalg.inv(T) + Tinv = np.linalg.inv(T) - I = N.eye(self._leny) - I3 = N.eye(3) - T1 = N.kron(J,I) - T2 = N.kron(Tinv,I) - T3 = N.kron(T,I) + I = np.eye(self._leny) + I3 = np.eye(3) + T1 = np.kron(J,I) + T2 = np.kron(Tinv,I) + T3 = np.kron(T,I) self.A = A self.B = B @@ -972,14 +972,14 @@ def __init__(self, problem): self.options["inith"] = 0.01 self.options["newt"] = 7 #Maximum number of newton iterations self.options["thet"] = 1.e-3 #Boundary for re-calculation of jac - self.options["fnewt"] = 0.0 #Stopping critera for Newtons Method + self.options["fnewt"] = 0.0 #Stopping criteria for Newtons Method self.options["quot1"] = 1.0 #Parameters for changing step-size (lower bound) self.options["quot2"] = 1.2 #Parameters for changing step-size (upper bound) self.options["fac1"] = 0.2 #Parameters for step-size selection (lower bound) self.options["fac2"] = 8.0 #Parameters for step-size selection (upper bound) - self.options["maxh"] = N.inf #Maximum step-size. + self.options["maxh"] = np.inf #Maximum step-size. self.options["safe"] = 0.9 #Safety factor - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance self.options["usejac"] = True if self.problem_info["jac_fcn"] else False self.options["maxsteps"] = 100000 @@ -1035,11 +1035,11 @@ def f(t, y): res = self.problem.res(t, y[:leny], y[leny:2*leny], self.sw) except BaseException as E: res = y[:leny].copy() - if isinstance(E, (N.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable + if isinstance(E, (np.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable ret = -1 #Recoverable error else: ret = -2 #Non-recoverable - return N.append(y[leny:2*leny],res), [ret] + return np.append(y[leny:2*leny],res), [ret] self._f = f self.event_func = event_func self._event_info = [0] * self.problem_info["dimRoot"] @@ -1053,15 +1053,15 @@ def f(t, y): res = self.problem.res(t, y[:leny], y[leny:2*leny]) except BaseException as E: res = y[:leny].copy() - if isinstance(E, (N.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable + if isinstance(E, (np.linalg.LinAlgError, ZeroDivisionError, AssimuloRecoverableError)): ## recoverable ret = -1 #Recoverable error else: ret = -2 #Non-recoverable - return N.append(y[leny:2*leny],res), [ret] + return np.append(y[leny:2*leny],res), [ret] self._f = f def interpolate(self, time, k=0): - y = N.empty(self._leny*2) + y = np.empty(self._leny*2) for i in range(self._leny*2): # Note: index shift to Fortan based indices y[i] = self.radau5.contr5(i+1, time, self.cont) @@ -1113,7 +1113,7 @@ def _solout(self, nrsol, told, t, y, cont, werr, lrc, irtrn): return irtrn def _mas_f(self, am): - #return N.array([[1]*self._leny+[0]*self._leny]) + #return np.array([[1]*self._leny+[0]*self._leny]) return self._mass_matrix def integrate(self, t, y, yd, tf, opts): @@ -1129,8 +1129,8 @@ def integrate(self, t, y, yd, tf, opts): MLMAS = 0 #The mass matrix is only defined on the diagonal MUMAS = 0 #The mass matrix is only defined on the diagonal IOUT = 1 #solout is called after every step - WORK = N.array([0.0]*(5*((self.problem_info["dim"]*2)**2+12)+20)) #Work (double) vector - IWORK = N.array([0]*(3*(self.problem_info["dim"]*2)+20),dtype=N.intc) #Work (integer) vector + WORK = np.array([0.0]*(5*((self.problem_info["dim"]*2)**2+12)+20)) #Work (double) vector + IWORK = np.array([0]*(3*(self.problem_info["dim"]*2)+20),dtype=np.intc) #Work (integer) vector #Setting work options WORK[1] = self.safe @@ -1165,14 +1165,14 @@ def integrate(self, t, y, yd, tf, opts): self._opts = opts #Create y = [y, yd] - y = N.append(y,yd) + y = np.append(y,yd) #Create mass matrix - #self._mass_matrix = N.array([[1]*self._leny+[0]*self._leny]) - self._mass_matrix = N.array([[0]*self._leny]) + #self._mass_matrix = np.array([[1]*self._leny+[0]*self._leny]) + self._mass_matrix = np.array([[0]*self._leny]) - atol = N.append(self.atol, self.atol) + atol = np.append(self.atol, self.atol) - t, y, h, iwork, flag = self.radau5.radau5(self._f, t, y.copy(), tf, self.inith, self.rtol*N.ones(self.problem_info["dim"]*2), atol, + t, y, h, iwork, flag = self.radau5.radau5(self._f, t, y.copy(), tf, self.inith, self.rtol*np.ones(self.problem_info["dim"]*2), atol, ITOL, jac_dummy, IJAC, MLJAC, MUJAC, self._mas_f, IMAS, MLMAS, MUMAS, self._solout, IOUT, WORK, IWORK) @@ -1250,16 +1250,16 @@ def __init__(self, problem): self.options["inith"] = 0.01 self.options["newt"] = 7 #Maximum number of newton iterations self.options["thet"] = 1.e-3 #Boundary for re-calculation of jac - self.options["fnewt"] = 0 #Stopping critera for Newtons Method + self.options["fnewt"] = 0 #Stopping criteria for Newtons Method self.options["quot1"] = 1.0 #Parameters for changing step-size (lower bound) self.options["quot2"] = 1.2 #Parameters for changing step-size (upper bound) self.options["fac1"] = 0.2 #Parameters for step-size selection (lower bound) self.options["fac2"] = 8.0 #Parameters for step-size selection (upper bound) - self.options["maxh"] = N.inf #Maximum step-size. + self.options["maxh"] = np.inf #Maximum step-size. self.options["safe"] = 0.9 #Safety factor - self.options["atol"] = N.array([1.0e-6]*self._leny) #Absolute tolerance + self.options["atol"] = np.array([1.0e-6]*self._leny) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance - self.options["index"] = N.array([1]*self._leny+[2]*self._leny) + self.options["index"] = np.array([1]*self._leny+[2]*self._leny) self.options["usejac"] = True if self.problem_info["jac_fcn"] else False self.options["maxsteps"] = 10000 @@ -1272,20 +1272,20 @@ def __init__(self, problem): self._rejected = True #Is the last step rejected? self._oldh = 0.0 #Old stepsize self._olderr = 1.0 #Old error - self._eps = N.finfo('double').eps - self._col_poly = N.zeros(self._2leny*3) + self._eps = np.finfo('double').eps + self._col_poly = np.zeros(self._2leny*3) self._type = '(implicit)' self._curiter = 0 #Number of current iterations #RES-Function self.f = problem.res_internal - self.RES = N.array([0.0]*len(self.y0)) + self.RES = np.array([0.0]*len(self.y0)) #Internal temporary result vector - self.Y1 = N.array([0.0]*len(self.y0)) - self.Y2 = N.array([0.0]*len(self.y0)) - self.Y3 = N.array([0.0]*len(self.y0)) - self._f0 = N.array([0.0]*len(self.y0)) + self.Y1 = np.array([0.0]*len(self.y0)) + self.Y2 = np.array([0.0]*len(self.y0)) + self.Y3 = np.array([0.0]*len(self.y0)) + self._f0 = np.array([0.0]*len(self.y0)) #Solver support @@ -1310,9 +1310,9 @@ def _set_index(self, index): """ if len(index) == self._2leny: - ind = N.array(index) + ind = np.array(index) elif len(index) == self._leny: - ind = N.array(index+(N.array(index)+1).tolist()) + ind = np.array(index+(np.array(index)+1).tolist()) else: raise Implicit_ODE_Exception('Wrong number of variables in the index vector.') self.options["index"] = ind @@ -1349,7 +1349,7 @@ def step_generator(self, t, y, yd, tf, opts): if self.fnewt == 0: self.fnewt = max(10.*self._eps/self.rtol,min(0.03,self.rtol**0.5)) - self._f0 = self._ode_f(t,N.append(y,yd)) + self._f0 = self._ode_f(t,np.append(y,yd)) self.statistics["nfcns"] +=1 self._tc = t self._yc = y @@ -1363,8 +1363,8 @@ def step_generator(self, t, y, yd, tf, opts): self._yc = y self._ydc = yd - if self.h > N.abs(tf-t): - self.h = N.abs(tf-t) + if self.h > np.abs(tf-t): + self.h = np.abs(tf-t) if t < tf: yield ID_PY_OK, t,y,yd @@ -1414,10 +1414,10 @@ def integrate(self, t, y, yd, tf, opts): def _ode_f(self, t, y): #self.res_fcn(t,y[:self._leny],y[self._leny:]) - #return N.hstack((y[self._leny:],self.res_fcn(t,y[:self._leny],y[self._leny:]))) + #return np.hstack((y[self._leny:],self.res_fcn(t,y[:self._leny],y[self._leny:]))) self.f(self.RES,t,y[:self._leny],y[self._leny:]) - return N.hstack((y[self._leny:],self.RES)) + return np.hstack((y[self._leny:],self.RES)) def _radau_F(self, Z, t, y, yd): @@ -1425,7 +1425,7 @@ def _radau_F(self, Z, t, y, yd): Z2 = Z[self._2leny:2*self._2leny] Z3 = Z[2*self._2leny:3*self._2leny] - q = N.append(y,yd) + q = np.append(y,yd) sol1 = self._ode_f(t+self.C[0]*self.h, q+Z1) sol2 = self._ode_f(t+self.C[1]*self.h, q+Z2) @@ -1433,13 +1433,13 @@ def _radau_F(self, Z, t, y, yd): self.statistics["nfcns"] += 3 - return N.hstack((N.hstack((sol1,sol2)),sol3)) + return np.hstack((np.hstack((sol1,sol2)),sol3)) def _step(self, t, y, yd): """ This calculates the next step in the integration. """ - self._scaling = N.array(abs(N.append(y,yd))*self.rtol + self.atol.tolist()*2) #The scaling used. + self._scaling = np.array(abs(np.append(y,yd))*self.rtol + self.atol.tolist()*2) #The scaling used. while True: #Loop for integrating one step. @@ -1469,7 +1469,7 @@ def _step(self, t, y, yd): tn = t+self.h #Preform the step yn = y+self._Z[2*self._2leny:3*self._2leny][:self._leny] ydn = yd+self._Z[2*self._2leny:3*self._2leny][self._leny:] - self._f0 = self._ode_f(t,N.append(yn,ydn)) + self._f0 = self._ode_f(t,np.append(yn,ydn)) self.statistics["nfcns"] += 1 self._oldoldh = self._oldh #Store the old(old) step-size for use in the test below. @@ -1513,8 +1513,8 @@ def newton(self,t,y,yd): for k in range(20): self._curiter = 0 #Reset the iteration - self._fac_con = max(self._fac_con, self._eps)**0.8; - self._theta = abs(self.thet); + self._fac_con = max(self._fac_con, self._eps)**0.8 + self._theta = abs(self.thet) if self._needjac: self._jac = self.jacobian(t,y,yd) @@ -1526,13 +1526,13 @@ def newton(self,t,y,yd): self._g = self._gamma/self.h self._B = self._g*self.M - self._jac - self._P1,self._L1,self._U1 = S.linalg.lu(self._B) #LU decomposition - self._P2,self._L2,self._U2 = S.linalg.lu(self._a*self.M-self._jac) - self._P3,self._L3,self._U3 = S.linalg.lu(self._b*self.M-self._jac) + self._P1,self._L1,self._U1 = sp.linalg.lu(self._B) #LU decomposition + self._P2,self._L2,self._U2 = sp.linalg.lu(self._a*self.M-self._jac) + self._P3,self._L3,self._U3 = sp.linalg.lu(self._b*self.M-self._jac) self._needLU = False - if min(abs(N.diag(self._U1))) 0: thq = newnrm/oldnrm if i == 1: self._theta = thq else: - self._theta = N.sqrt(thq*thqold) + self._theta = np.sqrt(thq*thqold) thqold = thq if self._theta < 0.99: #Convergence @@ -1583,7 +1583,7 @@ def newton(self,t,y,yd): oldnrm = max(newnrm,self._eps) #Store oldnorm W = W+Z #Perform the iteration - Z = N.dot(self.T3,W) #Calculate the new Z values + Z = np.dot(self.T3,W) #Calculate the new Z values if self._fac_con*newnrm <= self.fnewt: #Convergence? self._itfail = False @@ -1615,21 +1615,21 @@ def newton(self,t,y,yd): def estimate_error(self): temp = 1./self.h*(self.E[0]*self._Z[:self._2leny]+self.E[1]*self._Z[self._2leny:2*self._2leny]+self.E[2]*self._Z[2*self._2leny:3*self._2leny]) - temp = N.dot(self.M,temp) + temp = np.dot(self.M,temp) self._scaling = self._scaling/self.h**(self.index-1)#hfac scal = self._scaling#/self.h - err_v = N.linalg.solve(self._U1,N.linalg.solve(self._L1,N.linalg.solve(self._P1,self._f0+temp))) - err = N.linalg.norm(err_v/scal) - err = max(err/N.sqrt(self._2leny),1.e-10) + err_v = np.linalg.solve(self._U1,np.linalg.solve(self._L1,np.linalg.solve(self._P1,self._f0+temp))) + err = np.linalg.norm(err_v/scal) + err = max(err/np.sqrt(self._2leny),1.e-10) if (self._rejected or self._first) and err >= 1.: #If the step was rejected, use the more expensive error estimation self.statistics["nfcns"] += 1 - err_v = self._ode_f(self._tc,N.append(self._yc,self._ydc)+err_v) - err_v = N.linalg.solve(self._U1,N.linalg.solve(self._L1,N.linalg.solve(self._P1,err_v+temp))) - err = N.linalg.norm(err_v/scal) - err = max(err/N.sqrt(self._2leny),1.e-10) + err_v = self._ode_f(self._tc,np.append(self._yc,self._ydc)+err_v) + err_v = np.linalg.solve(self._U1,np.linalg.solve(self._L1,np.linalg.solve(self._P1,err_v+temp))) + err = np.linalg.norm(err_v/scal) + err = max(err/np.sqrt(self._2leny),1.e-10) return err @@ -1662,15 +1662,15 @@ def jacobian(self, t, y, yd): self._needLU = True #A new LU-decomposition is needed self._needjac = False #A new jacobian is not needed - q = N.append(y,yd) + q = np.append(y,yd) if self.usejac: #Retrieve the user-defined jacobian cjac = self.problem.jac(t,y,yd) else: #Calculate a numeric jacobian - delt = N.array([(self._eps*max(abs(yi),1.e-5))**0.5 for yi in q])*N.identity(self._2leny) #Calculate a disturbance - Fdelt = N.array([self._ode_f(t,q+e) for e in delt]) #Add the disturbance (row by row) + delt = np.array([(self._eps*max(abs(yi),1.e-5))**0.5 for yi in q])*np.identity(self._2leny) #Calculate a disturbance + Fdelt = np.array([self._ode_f(t,q+e) for e in delt]) #Add the disturbance (row by row) grad = ((Fdelt-self._ode_f(t,q)).T/delt.diagonal()).T - cjac = N.array(grad).T + cjac = np.array(grad).T self.statistics["nfcnjacs"] += 1+self._2leny #Add the number of function evaluations self.statistics["njacs"] += 1 #add the number of jacobian evaluation @@ -1729,8 +1729,8 @@ def calc_start_values(self): Calculate newton starting values. """ if self._first: - Z = N.zeros(self._2leny*3) - W = N.zeros(self._2leny*3) + Z = np.zeros(self._2leny*3) + W = np.zeros(self._2leny*3) else: Z = self._Z cq = self.C*self.h/self._oldh#self._oldoldh#self._oldh @@ -1741,46 +1741,46 @@ def calc_start_values(self): Z[leny:2*leny] = cq[1,0]*(newtval[:leny]+(cq[1,0]-self.C[1,0]+1.)*(newtval[leny:2*leny]+(cq[1,0]-self.C[0,0]+1.)*newtval[2*leny:3*leny])) Z[2*leny:3*leny]= cq[2,0]*(newtval[:leny]+(cq[2,0]-self.C[1,0]+1.)*(newtval[leny:2*leny]+(cq[2,0]-self.C[0,0]+1.)*newtval[2*leny:3*leny])) - W = N.dot(self.T2,Z) + W = np.dot(self.T2,Z) return Z, W def _load_parameters(self): #Parameters - A = N.zeros([3,3]) - A[0,0] = (88.-7.*N.sqrt(6.))/360.0 - A[0,1] = (296.-169.*N.sqrt(6.))/1800.0 - A[0,2] = (-2.0+3.0*N.sqrt(6.))/225.0 - A[1,0] = (296.0+169.0*N.sqrt(6.))/1800.0 - A[1,1] = (88.+7.*N.sqrt(6.))/360.0 - A[1,2] = (-2.-3.*N.sqrt(6.))/225.0 - A[2,0] = (16.0-N.sqrt(6.))/36.0 - A[2,1] = (16.0+N.sqrt(6.))/36.0 + A = np.zeros([3,3]) + A[0,0] = (88.-7.*np.sqrt(6.))/360.0 + A[0,1] = (296.-169.*np.sqrt(6.))/1800.0 + A[0,2] = (-2.0+3.0*np.sqrt(6.))/225.0 + A[1,0] = (296.0+169.0*np.sqrt(6.))/1800.0 + A[1,1] = (88.+7.*np.sqrt(6.))/360.0 + A[1,2] = (-2.-3.*np.sqrt(6.))/225.0 + A[2,0] = (16.0-np.sqrt(6.))/36.0 + A[2,1] = (16.0+np.sqrt(6.))/36.0 A[2,2] = (1.0/9.0) - C = N.zeros([3,1]) - C[0,0]=(4.0-N.sqrt(6.0))/10.0 - C[1,0]=(4.0+N.sqrt(6.0))/10.0 + C = np.zeros([3,1]) + C[0,0]=(4.0-np.sqrt(6.0))/10.0 + C[1,0]=(4.0+np.sqrt(6.0))/10.0 C[2,0]=1.0 - B = N.zeros([1,3]) - B[0,0]=(16.0-N.sqrt(6.0))/36.0 - B[0,1]=(16.0+N.sqrt(6.0))/36.0 + B = np.zeros([1,3]) + B[0,0]=(16.0-np.sqrt(6.0))/36.0 + B[0,1]=(16.0+np.sqrt(6.0))/36.0 B[0,2]=1.0/9.0 - E = N.zeros(3) - E[0] = -13.0-7.*N.sqrt(6.) - E[1] = -13.0+7.0*N.sqrt(6.) + E = np.zeros(3) + E[0] = -13.0-7.*np.sqrt(6.) + E[1] = -13.0+7.0*np.sqrt(6.) E[2] = -1.0 E = 1.0/3.0*E - M = N.array([[1.,0.],[0.,0.]]) + M = np.array([[1.,0.],[0.,0.]]) - Ainv = N.linalg.inv(A) - [eig, T] = N.linalg.eig(Ainv) - eig = N.array([eig[2],eig[0],eig[1]]) - J = N.diag(eig) + Ainv = np.linalg.inv(A) + [eig, T] = np.linalg.eig(Ainv) + eig = np.array([eig[2],eig[0],eig[1]]) + J = np.diag(eig) self._alpha = eig[1] self._beta = eig[2] @@ -1792,14 +1792,14 @@ def _load_parameters(self): T[:,0] = temp2 T[:,1] = temp0 T[:,2] = temp1 - Tinv = N.linalg.inv(T) - - I = N.eye(self._2leny) - M = N.kron(M,N.eye(self._leny)) - I3 = N.eye(3) - T1 = N.kron(J,M) - T2 = N.kron(Tinv,I) - T3 = N.kron(T,I) + Tinv = np.linalg.inv(T) + + I = np.eye(self._2leny) + M = np.kron(M,np.eye(self._leny)) + I3 = np.eye(3) + T1 = np.kron(J,M) + T2 = np.kron(Tinv,I) + T3 = np.kron(T,I) self.A = A self.B = B diff --git a/src/solvers/rosenbrock.py b/src/solvers/rosenbrock.py index e32a54cc..6e689b0c 100644 --- a/src/solvers/rosenbrock.py +++ b/src/solvers/rosenbrock.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np import scipy.sparse as sp from assimulo.ode import NORMAL, ID_PY_EVENT, ID_PY_COMPLETE @@ -32,7 +32,7 @@ def _set_atol(self,atol): self.options["atol"] = set_type_shape_array(atol) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self._leny) + self.options["atol"] = self.options["atol"]*np.ones(self._leny) elif len(self.options["atol"]) != self._leny: raise Rodas_Exception("atol must be of length one or same as the dimension of the problem.") @@ -290,9 +290,9 @@ def __init__(self, problem): self.options["inith"] = 0.01 self.options["fac1"] = 0.2 #Parameters for step-size selection (lower bound) self.options["fac2"] = 6.0 #Parameters for step-size selection (upper bound) - self.options["maxh"] = N.inf #Maximum step-size. + self.options["maxh"] = np.inf #Maximum step-size. self.options["safe"] = 0.9 #Safety factor - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance self.options["usejac"] = True if self.problem_info["jac_fcn"] else False self.options["maxsteps"] = 10000 @@ -331,7 +331,7 @@ def f(t, y): self.f = self.problem.rhs def interpolate(self, time): - y = N.empty(self._leny) + y = np.empty(self._leny) for i in range(self._leny): y[i] = rodas.contro(i+1, time, self.cont) @@ -397,8 +397,8 @@ def integrate(self, t, y, tf, opts): MLMAS = self.problem_info["dim"] #The mass matrix is full MUMAS = self.problem_info["dim"] #The mass matrix is full IOUT = 1 #Solout is called after every accepted step - WORK = N.array([0.0]*(2*self.problem_info["dim"]**2+14*self.problem_info["dim"]+20)) - IWORK = N.array([0]*(self.problem_info["dim"]+20)) + WORK = np.array([0.0]*(2*self.problem_info["dim"]**2+14*self.problem_info["dim"]+20)) + IWORK = np.array([0]*(self.problem_info["dim"]+20)) #Setting work options WORK[1] = self.maxh @@ -423,7 +423,7 @@ def integrate(self, t, y, tf, opts): #Store the opts self._opts = opts - t, y, h, iwork, flag = rodas.rodas(self.f, IFCN, t, y.copy(), tf, self.inith, self.rtol*N.ones(self.problem_info["dim"]), self.atol, + t, y, h, iwork, flag = rodas.rodas(self.f, IFCN, t, y.copy(), tf, self.inith, self.rtol*np.ones(self.problem_info["dim"]), self.atol, ITOL, jac_dummy, IJAC, MLJAC, MUJAC, dfx_dummy, IDFX, mas_dummy, IMAS, MLMAS, MUMAS, self._solout, IOUT, WORK, IWORK) #Checking return diff --git a/src/solvers/runge_kutta.py b/src/solvers/runge_kutta.py index 32b2eff2..913f8c15 100644 --- a/src/solvers/runge_kutta.py +++ b/src/solvers/runge_kutta.py @@ -15,7 +15,7 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N +import numpy as np from assimulo.ode import ID_PY_EVENT, ID_PY_COMPLETE, NORMAL, ID_PY_OK from assimulo.explicit_ode import Explicit_ODE @@ -57,9 +57,9 @@ def __init__(self, problem): self.options["fac1"] = 0.2 #Parameters for step-size selection (lower bound) self.options["fac2"] = 10.0 #Parameters for step-size selection (upper bound) self.options["beta"] = 0.04 - self.options["maxh"] = N.inf #Maximum step-size. + self.options["maxh"] = np.inf #Maximum step-size. self.options["inith"] = 0.0 - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance self.options["maxsteps"] = 100000 @@ -97,7 +97,7 @@ def f(t, y): self.f = self.problem.rhs def interpolate(self, time): - y = N.empty(self._leny) + y = np.empty(self._leny) for i in range(self._leny): y[i] = dopri5.contd5(i+1, time, self.cont, self.lrc) @@ -145,8 +145,8 @@ def _solout(self, nrsol, told, t, y, cont, lrc, irtrn): def integrate(self, t, y, tf, opts): ITOL = 1 #Both atol and rtol are vectors IOUT = 2 #Dense out in solout - WORK = N.array([0.0]*(8*self.problem_info["dim"]+5*self.problem_info["dim"]+21)) - IWORK = N.array([0]*(self.problem_info["dim"]+21)) + WORK = np.array([0.0]*(8*self.problem_info["dim"]+5*self.problem_info["dim"]+21)) + IWORK = np.array([0]*(self.problem_info["dim"]+21)) #Setting work options WORK[1] = self.safe @@ -169,7 +169,7 @@ def integrate(self, t, y, tf, opts): #Store the opts self._opts = opts - t, y, iwork, flag = dopri5.dopri5(self.f, t, y.copy(), tf, self.rtol*N.ones(self.problem_info["dim"]), self.atol, ITOL, self._solout, IOUT, WORK, IWORK) + t, y, iwork, flag = dopri5.dopri5(self.f, t, y.copy(), tf, self.rtol*np.ones(self.problem_info["dim"]), self.atol, ITOL, self._solout, IOUT, WORK, IWORK) #Checking return if flag == 1: @@ -208,10 +208,10 @@ def print_statistics(self, verbose=NORMAL): def _set_atol(self,atol): - self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self._leny) + self.options["atol"] = self.options["atol"]*np.ones(self._leny) elif len(self.options["atol"]) != self._leny: raise Dopri5_Exception("atol must be of length one or same as the dimension of the problem.") @@ -452,11 +452,11 @@ def __init__(self, problem): self.options["maxsteps"] = 10000 #Internal temporary result vector - self.Y1 = N.array([0.0]*len(self.y0)) - self.Y2 = N.array([0.0]*len(self.y0)) - self.Y3 = N.array([0.0]*len(self.y0)) - self.Y4 = N.array([0.0]*len(self.y0)) - self.Z3 = N.array([0.0]*len(self.y0)) + self.Y1 = np.array([0.0]*len(self.y0)) + self.Y2 = np.array([0.0]*len(self.y0)) + self.Y3 = np.array([0.0]*len(self.y0)) + self.Y4 = np.array([0.0]*len(self.y0)) + self.Z3 = np.array([0.0]*len(self.y0)) #Solver support self.supports["report_continuously"] = True @@ -521,7 +521,7 @@ def _get_initial_step(self): def _set_atol(self,atol): try: - atol_arr = N.array(atol, dtype=float) + atol_arr = np.array(atol, dtype=float) if (atol_arr <= 0.0).any(): raise Explicit_ODE_Exception('Absolute tolerance must be a positive float or a float vector.') except (ValueError,TypeError): @@ -620,7 +620,7 @@ def _iter(self,t,y,tf,opts): self.set_problem_data() maxsteps = self.options["maxsteps"] h = self.options["inith"] - h = min(h, N.abs(tf-t)) + h = min(h, np.abs(tf-t)) self.f(self.Y1, t, y) flag = ID_PY_OK @@ -648,7 +648,7 @@ def _iter(self,t,y,tf,opts): pass opts["output_index"] = output_index h=self.adjust_stepsize(h,error) - h=min(h, N.abs(tf-t)) + h=min(h, np.abs(tf-t)) else: break else: @@ -697,7 +697,7 @@ def _step(self, t, y, h): """ self.statistics["nfcns"] += 5 - scaling = N.array(abs(y)*self.rtol + self.atol) # to normalize the error + scaling = np.array(abs(y)*self.rtol + self.atol) # to normalize the error f = self.f f(self.Y2, t + h/2., y + h*self.Y1/2.) @@ -705,7 +705,7 @@ def _step(self, t, y, h): f(self.Z3, t + h, y - h*self.Y1 + 2.0*h*self.Y2) f(self.Y4, t + h, y + h*self.Y3) - error = N.linalg.norm(h/6.0*(2.0*self.Y2 + self.Z3 - 2.0*self.Y3 - self.Y4)/scaling) #normalized + error = np.linalg.norm(h/6.0*(2.0*self.Y2 + self.Z3 - 2.0*self.Y3 - self.Y4)/scaling) #normalized t_next = t + h y_next = y + h/6.0*(self.Y1 + 2.0*self.Y2 + 2.0*self.Y3 + self.Y4) @@ -784,10 +784,10 @@ def __init__(self, problem): self.options["h"] = 0.01 #Internal temporary result vector - self.Y1 = N.array([0.0]*len(self.y0)) - self.Y2 = N.array([0.0]*len(self.y0)) - self.Y3 = N.array([0.0]*len(self.y0)) - self.Y4 = N.array([0.0]*len(self.y0)) + self.Y1 = np.array([0.0]*len(self.y0)) + self.Y2 = np.array([0.0]*len(self.y0)) + self.Y3 = np.array([0.0]*len(self.y0)) + self.Y4 = np.array([0.0]*len(self.y0)) #RHS-Function self.f = problem.rhs_internal @@ -838,12 +838,12 @@ def _get_h(self): def _iter(self,t,y,tf): h = self.options["h"] - h = min(h, N.abs(tf-t)) + h = min(h, np.abs(tf-t)) while t+h < tf: t, y = self._step(t, y, h) yield ID_PY_OK, t,y - h=min(h, N.abs(tf-t)) + h=min(h, np.abs(tf-t)) else: t, y = self._step(t, y, h) yield ID_PY_COMPLETE, t, y diff --git a/src/solvers/sdirk_dae.pyx b/src/solvers/sdirk_dae.pyx index f7f1b36d..f5668503 100644 --- a/src/solvers/sdirk_dae.pyx +++ b/src/solvers/sdirk_dae.pyx @@ -16,7 +16,7 @@ # along with this program. If not, see . import sys -import numpy as N +import numpy as np from assimulo.exception import ODEPACK_Exception, RKStarter_Exception from assimulo.ode import ID_PY_COMPLETE, ID_PY_EVENT, NORMAL @@ -56,7 +56,7 @@ class SDIRK_DAE(Implicit_ODE): Implicit_ODE.__init__(self, problem) #Calls the base class #Default values - self.options["atol"] = 1.0e-6*N.ones(self.problem_info["dim"]) #Absolute tolerance + self.options["atol"] = 1.0e-6*np.ones(self.problem_info["dim"]) #Absolute tolerance self.options["rtol"] = 1.0e-6 #Relative tolerance self.options["usejac"] = False self.options["hmax"] = 0. @@ -90,11 +90,11 @@ class SDIRK_DAE(Implicit_ODE): # first call or classical restart after a discontinuity ISTATE=1 - RWORK = N.array([0.0]*(22 + self.problem_info["dim"] * + RWORK = np.array([0.0]*(22 + self.problem_info["dim"] * max(16,self.problem_info["dim"]+9) + 3*self.problem_info["dimRoot"])) # Integer work array - IWORK = N.array([0]*(20 + self.problem_info["dim"])) + IWORK = np.array([0]*(20 + self.problem_info["dim"])) return ISTATE, RWORK, IWORK @@ -109,7 +109,7 @@ class SDIRK_DAE(Implicit_ODE): ISTATE, RWORK, IWORK = self.integrate_start( t, y) JT = 1 if self.usejac else 2#Jacobian type indicator - JROOT = N.array([0]*self.problem_info["dimRoot"]) + JROOT = np.array([0]*self.problem_info["dimRoot"]) #Setting work options RWORK[0] = tf #Do not integrate past tf @@ -125,7 +125,7 @@ class SDIRK_DAE(Implicit_ODE): #Dummy methods g_dummy = (lambda t:x) if not self.problem_info["state_events"] else self.problem.state_events - jac_dummy = (lambda t,y:N.zeros((len(y),len(y)))) if not self.usejac else self.problem.jac + jac_dummy = (lambda t,y:np.zeros((len(y),len(y)))) if not self.usejac else self.problem.jac #Extra args to rhs and state_events rhs_extra_args = (self.sw,) if self.problem_info["switches"] else () @@ -148,7 +148,7 @@ class SDIRK_DAE(Implicit_ODE): while (ISTATE == 2 or ISTATE == 1) and t < tf: y, t, ISTATE, RWORK, IWORK, roots = dlsodar(self.problem.rhs, y.copy(), t, tf, ITOL, - self.rtol*N.ones(self.problem_info["dim"]), self.atol, + self.rtol*np.ones(self.problem_info["dim"]), self.atol, ITASK, ISTATE, IOPT, RWORK, IWORK, jac_dummy, JT, g_dummy, JROOT, f_extra_args = rhs_extra_args, g_extra_args = g_extra_args) @@ -189,7 +189,7 @@ class SDIRK_DAE(Implicit_ODE): output_index += 1 y, t, ISTATE, RWORK, IWORK, roots = dlsodar(self.problem.rhs, y.copy(), t, tout, ITOL, - self.rtol*N.ones(self.problem_info["dim"]), self.atol, + self.rtol*np.ones(self.problem_info["dim"]), self.atol, ITASK, ISTATE, IOPT, RWORK, IWORK, jac_dummy, JT, g_dummy, JROOT, f_extra_args = rhs_extra_args, g_extra_args = g_extra_args) @@ -297,10 +297,10 @@ class SDIRK_DAE(Implicit_ODE): def _set_atol(self,atol): - self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self._leny) + self.options["atol"] = self.options["atol"]*np.ones(self._leny) elif len(self.options["atol"]) != self._leny: raise ODEPACK_Exception("atol must be of length one or same as the dimension of the problem.") @@ -463,17 +463,17 @@ class RKStarterNordsieck(object): See: Mohammadi (2013): https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=4196026&fileOId=4196027 """ - Gamma_0=[N.array([[1.,0.], # 1st order + Gamma_0=[np.array([[1.,0.], # 1st order [0.,1.]]), - N.array([[1.,0.,0.], # 2nd order + np.array([[1.,0.,0.], # 2nd order [0.,1.,-1.], [0.,0.,1.]]), - N.array([[1.,0.,0.,0.], # 3rd order + np.array([[1.,0.,0.,0.], # 3rd order [0.,1.,-5./3.,1.], [0.,0.,3.,-2.], [0.,0.,0.,1.], [0.,0.,-4./3.,0.]]), - N.array([[1.,0.,0.,0.,0.], # 4th order + np.array([[1.,0.,0.,0.,0.], # 4th order [0.,1.,-5./6.,4./9.,-1./9.], [0.,0.,0.,0.,0.], [0.,0.,1./2.,-4./9.,1./9.], @@ -481,7 +481,7 @@ class RKStarterNordsieck(object): [0.,0.,-3.,10./3.,-4./3.], [0.,0.,1.,-11./9.,5./9.]])] - scale=N.array([1, 1, 1/2., 1./6., 1./24., 1./120.]).reshape(-1,1) + scale=np.array([1, 1, 1/2., 1./6., 1./24., 1./120.]).reshape(-1,1) def __init__(self, rhs, H, eval_at=0.,number_of_steps=4): """ @@ -530,7 +530,7 @@ class RKStarterNordsieck(object): k4 = h*f(y0 + 3./4. * k1 + 9./4. * k3) k5 = h*f(y0 + k1/2. + k2 + k3/2. + 2. * k4) k6 = h*f(y0+k1/12.+2. * k2 + k3/4. + 2./3. * k4 + 2. * k5) - return N.array([y0,k1,k2,k3,k4,k5,k6]) + return np.array([y0,k1,k2,k3,k4,k5,k6]) def rk_like3(self, t0, y0, sw0): """ rk_like computes Runge-Kutta stages @@ -544,7 +544,7 @@ class RKStarterNordsieck(object): k2 = h*f(y0 + k1) k3 = h*f(y0 + k1+ k2) k4 = h*f(y0 + 3./2. * k1) - return N.array([y0,k1,k2,k3,k4]) + return np.array([y0,k1,k2,k3,k4]) def rk_like2(self, t0, y0, sw0): """ rk_like2 computes Runge-Kutta 2nd-stages @@ -555,12 +555,12 @@ class RKStarterNordsieck(object): h=self.H/2 k1=h*f(y0) k2=h*f(y0+k1) - return N.array([y0,k1,k2]) + return np.array([y0,k1,k2]) def nordsieck(self,k): """ Nordsieck array computed at initial point """ - nord=self.scale[:self.number_of_steps+1]*N.dot(self.Gamma_0[self.number_of_steps-1].T,k) + nord=self.scale[:self.number_of_steps+1]*np.dot(self.Gamma_0[self.number_of_steps-1].T,k) return nord def __call__(self, t0 , y0, sw0=[]): """ diff --git a/src/solvers/sundials.pyx b/src/solvers/sundials.pyx index da7ac200..761197b4 100644 --- a/src/solvers/sundials.pyx +++ b/src/solvers/sundials.pyx @@ -17,11 +17,9 @@ # distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np -import scipy.sparse as sparse - from assimulo.exception import AssimuloException from assimulo.explicit_ode cimport Explicit_ODE @@ -71,9 +69,9 @@ cdef class IDA(Implicit_ODE): cdef public object event_func #cdef public dict statistics cdef object pt_root, pt_fcn, pt_jac, pt_jacv, pt_sens - cdef public N.ndarray yS0 - #cdef N.ndarray _event_info - cdef public N.ndarray g_old + cdef public np.ndarray yS0 + #cdef np.ndarray _event_info + cdef public np.ndarray g_old cdef SUNDIALS.SUNMatrix sun_matrix cdef SUNDIALS.SUNLinearSolver sun_linearsolver @@ -86,7 +84,7 @@ cdef class IDA(Implicit_ODE): self.set_problem_data() #Solver options - self.options["atol"] = N.array([1.0e-6]*self.problem_info["dim"]) #The absolute tolerance + self.options["atol"] = np.array([1.0e-6]*self.problem_info["dim"]) #The absolute tolerance self.options["rtol"] = 1.0e-6 #The relative tolerance self.options["lsoff"] = False #Turn on or off the linesearch algorithm self.options["tout1"] = 0.0001 #Direction of initial calculation @@ -100,7 +98,7 @@ cdef class IDA(Implicit_ODE): self.options["usejac"] = True if (self.problem_info["jac_fcn"] or self.problem_info["jacv_fcn"]) else False self.options["usesens"] = True if self.problem_info["dimSens"] > 0 else False self.options["inith"] = 0.0 #Initial step-size - self.options["algvar"] = N.array([1.0]*self.problem_info["dim"]) + self.options["algvar"] = np.array([1.0]*self.problem_info["dim"]) self.options["sensmethod"] = 'STAGGERED' self.options["dqtype"] = "CENTERED" self.options["dqrhomax"] = 0.0 @@ -117,7 +115,7 @@ cdef class IDA(Implicit_ODE): if hasattr(problem, 'pbar'): self.pbar = problem.pbar elif hasattr(problem, 'p0'): - self.pbar = N.array([N.abs(x) if N.abs(x) > 0 else 1.0 for x in self.p0]) + self.pbar = np.array([np.abs(x) if np.abs(x) > 0 else 1.0 for x in self.p0]) if hasattr(problem, 'algvar'): self.algvar = problem.algvar if hasattr(problem, 'yS0'): @@ -132,8 +130,8 @@ cdef class IDA(Implicit_ODE): self.pData.memSize = self.pData.dim*sizeof(realtype) #Set the ndarray to the problem struct - #self.yTemp = N.zeros(self.pData.dim, dtype=float, order='c') - #self.ydTemp = N.zeros(self.pData.dim, dtype=float, order='c') + #self.yTemp = np.zeros(self.pData.dim, dtype=float, order='c') + #self.ydTemp = np.zeros(self.pData.dim, dtype=float, order='c') #self.pData.y = self.yTemp #self.pData.yd = self.ydTemp @@ -423,7 +421,7 @@ cdef class IDA(Implicit_ODE): self.event_func = event_func self.g_old = self.event_func(self.t, self.y, self.yd) self.statistics["nstatefcns"] += 1 - self._event_info = N.array([0] * self.problem_info["dimRoot"]) + self._event_info = np.array([0] * self.problem_info["dimRoot"]) cdef initialize_sensitivity_options(self): """ @@ -517,12 +515,12 @@ cdef class IDA(Implicit_ODE): if self.pData.dimSens > 0: self.initialize_sensitivity_options() - cpdef integrate(self,double t,N.ndarray[ndim=1, dtype=realtype] y,N.ndarray[ndim=1, dtype=realtype] yd,double tf,dict opts): + cpdef integrate(self,double t,np.ndarray[ndim=1, dtype=realtype] y,np.ndarray[ndim=1, dtype=realtype] yd,double tf,dict opts): cdef int flag, output_index, normal_mode cdef N_Vector yout, ydout cdef double tret = 0.0, tout cdef list tr = [], yr = [], ydr = [] - cdef N.ndarray output_list + cdef np.ndarray output_list yout = arr2nv(y) ydout = arr2nv(yd) @@ -619,12 +617,12 @@ cdef class IDA(Implicit_ODE): return flag, tr, yr, ydr - cpdef step(self,double t,N.ndarray y, N.ndarray yd, double tf,dict opts): + cpdef step(self,double t,np.ndarray y, np.ndarray yd, double tf,dict opts): cdef int flag cdef N_Vector yout, ydout cdef double tret = t cdef double tr - cdef N.ndarray yr, ydr + cdef np.ndarray yr, ydr yout = arr2nv(y) ydout = arr2nv(yd) @@ -735,7 +733,7 @@ cdef class IDA(Implicit_ODE): cpdef get_last_estimated_errors(self): cdef flag - cdef N.ndarray err, pyweight, pyele + cdef np.ndarray err, pyweight, pyele IF SUNDIALS_VERSION >= (6,0,0): cdef SUNDIALS.SUNContext ctx = NULL cdef void* comm = NULL @@ -763,14 +761,14 @@ cdef class IDA(Implicit_ODE): return err - cpdef N.ndarray interpolate(self,double t,int k = 0): + cpdef np.ndarray interpolate(self,double t,int k = 0): """ Calls the internal IDAGetDky for the interpolated values at time t. t must be within the last internal step. k is the derivative of y which can be from zero to the current order. """ cdef flag - cdef N.ndarray res + cdef np.ndarray res IF SUNDIALS_VERSION >= (6,0,0): cdef SUNDIALS.SUNContext ctx = NULL cdef void* comm = NULL @@ -820,7 +818,7 @@ cdef class IDA(Implicit_ODE): ELSE: cdef N_Vector dkyS=N_VNew_Serial(self.pData.dim) cdef flag - cdef N.ndarray res + cdef np.ndarray res if i==-1: @@ -836,7 +834,7 @@ cdef class IDA(Implicit_ODE): N_VDestroy(dkyS) - return N.array(matrix) + return np.array(matrix) else: flag = SUNDIALS.IDAGetSensDky1(self.ida_mem, t, k, i, dkyS) @@ -1003,11 +1001,11 @@ cdef class IDA(Implicit_ODE): See SUNDIALS IDA documentation 4.5.2 for more details. """ - #self.options["atol"] = N.array(atol,dtype=float) if len(N.array(atol,dtype=float).shape)>0 else N.array([atol],dtype=float) + #self.options["atol"] = np.array(atol,dtype=float) if len(np.array(atol,dtype=float).shape)>0 else np.array([atol],dtype=float) self.options["atol"] = set_type_shape_array(atol) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self.pData.dim) + self.options["atol"] = self.options["atol"]*np.ones(self.pData.dim) elif len(self.options["atol"]) != self.pData.dim: raise AssimuloException("atol must be of length one or same as the dimension of the problem.") if (self.options["atol"]<=0.0).any(): @@ -1181,7 +1179,7 @@ cdef class IDA(Implicit_ODE): linear_solver = property(_get_linear_solver, _set_linear_solver) def _set_algvar(self,algvar): - self.options["algvar"] = N.array(algvar,dtype=float) if len(N.array(algvar,dtype=float).shape)>0 else N.array([algvar],dtype=float) + self.options["algvar"] = np.array(algvar,dtype=float) if len(np.array(algvar,dtype=float).shape)>0 else np.array([algvar],dtype=float) if len(self.options["algvar"]) != self.pData.dim: raise AssimuloException('When setting the algebraic variables, the' \ @@ -1519,9 +1517,9 @@ cdef class CVode(Explicit_ODE): cdef public object event_func #cdef public dict statistics cdef object pt_root, pt_fcn, pt_jac, pt_jacv, pt_sens,pt_prec_solve,pt_prec_setup - cdef public N.ndarray yS0 - #cdef N.ndarray _event_info - cdef public N.ndarray g_old + cdef public np.ndarray yS0 + #cdef np.ndarray _event_info + cdef public np.ndarray g_old cdef SUNDIALS.SUNMatrix sun_matrix cdef SUNDIALS.SUNLinearSolver sun_linearsolver cdef SUNDIALS.SUNNonlinearSolver sun_nonlinearsolver @@ -1538,7 +1536,7 @@ cdef class CVode(Explicit_ODE): self.set_problem_data() #Solver options - self.options["atol"] = N.array([1.0e-6]*self.problem_info["dim"]) #The absolute tolerance + self.options["atol"] = np.array([1.0e-6]*self.problem_info["dim"]) #The absolute tolerance self.options["rtol"] = 1.0e-6 #The relative tolerance self.options["maxh"] = 0.0 #Maximum step-size self.options["minh"] = 0.0 #Minimal step-size @@ -1579,7 +1577,7 @@ cdef class CVode(Explicit_ODE): if hasattr(problem, 'pbar'): self.pbar = problem.pbar elif hasattr(problem, 'p0'): - self.pbar = N.array([N.abs(x) if N.abs(x) > 0 else 1.0 for x in self.problem.p0]) + self.pbar = np.array([np.abs(x) if np.abs(x) > 0 else 1.0 for x in self.problem.p0]) if hasattr(problem, 'yS0'): self.yS0 = problem.yS0 @@ -1634,7 +1632,7 @@ cdef class CVode(Explicit_ODE): """ Returns the vector of weighted estimated local errors at the current step. """ - return N.abs(self.get_local_errors()*self.get_error_weights()) + return np.abs(self.get_local_errors()*self.get_error_weights()) cpdef get_last_order(self): """ @@ -1721,8 +1719,8 @@ cdef class CVode(Explicit_ODE): self.pData.memSize = self.pData.dim*sizeof(realtype) #Set the ndarray to the problem struct - #self.yTemp = N.zeros(self.pData.dim, dtype=float, order='c') - #self.ydTemp = N.zeros(self.pData.dim, dtype=float, order='c') + #self.yTemp = np.zeros(self.pData.dim, dtype=float, order='c') + #self.ydTemp = np.zeros(self.pData.dim, dtype=float, order='c') #self.pData.y = self.yTemp #self.pData.yd = self.ydTemp @@ -1913,17 +1911,17 @@ cdef class CVode(Explicit_ODE): self.event_func = event_func self.g_old = self.event_func(self.t, self.y) self.statistics["nstatefcns"] += 1 - self._event_info = N.array([0] * self.problem_info["dimRoot"]) + self._event_info = np.array([0] * self.problem_info["dimRoot"]) - cpdef N.ndarray interpolate(self,double t,int k = 0): + cpdef np.ndarray interpolate(self,double t,int k = 0): """ Calls the internal CVodeGetDky for the interpolated values at time t. t must be within the last internal step. k is the derivative of y which can be from zero to the current order. """ cdef flag - cdef N.ndarray res + cdef np.ndarray res IF SUNDIALS_VERSION >= (6,0,0): cdef SUNDIALS.SUNContext ctx = NULL cdef void* comm = NULL @@ -1944,7 +1942,7 @@ cdef class CVode(Explicit_ODE): return res - cpdef N.ndarray interpolate_sensitivity(self, realtype t, int k = 0, int i=-1): + cpdef np.ndarray interpolate_sensitivity(self, realtype t, int k = 0, int i=-1): """ This method calls the internal method CVodeGetSensDky which computes the k-th derivatives of the interpolating polynomials for the sensitivity variables at time t. @@ -1974,7 +1972,7 @@ cdef class CVode(Explicit_ODE): ELSE: cdef N_Vector dkyS=N_VNew_Serial(self.pData.dim) cdef int flag - cdef N.ndarray res + cdef np.ndarray res if i==-1: @@ -1989,7 +1987,7 @@ cdef class CVode(Explicit_ODE): N_VDestroy(dkyS) - return N.array(matrix) + return np.array(matrix) else: flag = SUNDIALS.CVodeGetSensDky1(self.cvode_mem, t, k, i, dkyS) if flag <0: @@ -2012,12 +2010,12 @@ cdef class CVode(Explicit_ODE): self.initialize_cvode() - cpdef step(self,double t,N.ndarray y,double tf,dict opts): + cpdef step(self,double t,np.ndarray y,double tf,dict opts): cdef int flag cdef N_Vector yout cdef double tret = t cdef double tr - cdef N.ndarray yr + cdef np.ndarray yr yout = arr2nv(y) @@ -2057,12 +2055,12 @@ cdef class CVode(Explicit_ODE): return flag, tr, yr - cpdef integrate(self,double t,N.ndarray[ndim=1, dtype=realtype] y,double tf,dict opts): + cpdef integrate(self,double t,np.ndarray[ndim=1, dtype=realtype] y,double tf,dict opts): cdef int flag, output_index, normal_mode cdef N_Vector yout cdef double tret = self.t, tout cdef list tr = [], yr = [] - cdef N.ndarray output_list + cdef np.ndarray output_list if self.options["norm"] == "EUCLIDEAN": yout = arr2nv_euclidean(y) @@ -2447,7 +2445,7 @@ cdef class CVode(Explicit_ODE): #Tolerances self.nv_atol = arr2nv(self.options["atol"]) - if SUNDIALS_CVODE_RTOL_VEC and isinstance(self.options["rtol"], N.ndarray): + if SUNDIALS_CVODE_RTOL_VEC and isinstance(self.options["rtol"], np.ndarray): self.nv_rtol = arr2nv(self.options["rtol"]) flag = SUNDIALS.CVodeVVtolerances(self.cvode_mem, self.nv_rtol, self.nv_atol) else: @@ -2592,7 +2590,7 @@ cdef class CVode(Explicit_ODE): self.options["atol"] = set_type_shape_array(atol) if len(self.options["atol"]) == 1: - self.options["atol"] = self.options["atol"]*N.ones(self.pData.dim) + self.options["atol"] = self.options["atol"]*np.ones(self.pData.dim) elif len(self.options["atol"]) != self.pData.dim: raise AssimuloException("atol must be of length one or same as the dimension of the problem.") if (self.options["atol"]<=0.0).any(): @@ -2622,7 +2620,7 @@ cdef class CVode(Explicit_ODE): """ rtol = set_type_shape_array(rtol) ## convert to appropriate numpy array - rtol = N.array([rtol[0]]) if N.all(N.isclose(rtol, rtol[0])) else rtol ## reduce if possible + rtol = np.array([rtol[0]]) if np.all(np.isclose(rtol, rtol[0])) else rtol ## reduce if possible if (rtol<0.0).any(): raise AssimuloException('Relative tolerance(s) must be a non-negative.') if rtol.size == 1: diff --git a/src/special_systems.pyx b/src/special_systems.pyx index 8ed7dd2d..97af77ff 100644 --- a/src/special_systems.pyx +++ b/src/special_systems.pyx @@ -17,8 +17,8 @@ # distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from assimulo.problem import Implicit_Problem, Overdetermined_Problem from assimulo.support import set_type_shape_array @@ -107,10 +107,10 @@ cdef class cMechanical_System: elif index=='ind3': constraint=self.constr3 elif index in ('ovstab2', 'ggl2'): - constraint=lambda t,y: N.hstack((self.constr2(t,y), + constraint=lambda t,y: np.hstack((self.constr2(t,y), self.constr3(t,y))) elif index=='ovstab1': - constraint=lambda t,y: N.hstack((self.constr1(t,y), + constraint=lambda t,y: np.hstack((self.constr1(t,y), self.constr3(t,y), self.constr2(t,y))) else: @@ -121,18 +121,18 @@ cdef class cMechanical_System: def with_constraint(t,y,yd): p,v,la=y[0:n_p], y[n_p:n_v], y[n_v:] residual=func(t,y,yd) - return N.hstack(( + return np.hstack(( residual[0:n_p], - residual[n_p:n_v]+N.dot(self.GT(p),la.reshape(-1,)), + residual[n_p:n_v]+np.dot(self.GT(p),la.reshape(-1,)), constraint(t,y) )) else: def with_constraint(t,y,yd): p,v,la,mue=y[0:n_p], y[n_p:n_v], y[n_v:n_v+n_la],y[n_v+n_la:] residual=func(t,y,yd) - return N.hstack(( - residual[0:n_p]+N.dot(self.GT(p),mue.reshape(-1,)), - residual[n_p:n_v]+N.dot(self.GT(p),la.reshape(-1,)), + return np.hstack(( + residual[0:n_p]+np.dot(self.GT(p),mue.reshape(-1,)), + residual[n_p:n_v]+np.dot(self.GT(p),la.reshape(-1,)), constraint(t,y) )) @@ -141,9 +141,9 @@ cdef class cMechanical_System: def res(t,y,yd): p,pd=y[0:n_p], yd[0:n_p] v,vd=y[n_p:n_v], yd[n_p:n_v] - Mvd = N.dot(M,vd) if M is not None else vd + Mvd = np.dot(M,vd) if M is not None else vd - return N.hstack((pd - v, Mvd - self.forces(t,p,v))) + return np.hstack((pd - v, Mvd - self.forces(t,p,v))) if n_la==0: return res @@ -158,13 +158,13 @@ cdef class cMechanical_System: raise ValueError(index_error) # 1. Treatment of initial conditions depending on the index - y0=N.hstack((self.pos0,self.vel0)) - yd0=N.hstack((self.posd0,self.veld0)) + y0=np.hstack((self.pos0,self.vel0)) + yd0=np.hstack((self.posd0,self.veld0)) if self.constrained: if index is None or index=='ind0': raise ValueError(index_error) - y0=N.hstack((y0,self.lam0)) - yd0=N.hstack((yd0,N.zeros(self.lam0.shape))) + y0=np.hstack((y0,self.lam0)) + yd0=np.hstack((yd0,np.zeros(self.lam0.shape))) # 2. Indicating algebraic variables if index == 'ind1': algvar = (self.pos0.size + self.vel0.size) * [1]\ @@ -184,16 +184,16 @@ cdef class cMechanical_System: + self.lam0.size*[0] neq=len(algvar)+self.lam0.size elif index == 'ggl1': - mue = N.zeros(self.lam0.shape) - gamma = N.zeros(self.lam0.shape) - y0 = N.hstack((y0,mue,gamma)) - yd0 = N.hstack((yd0,mue,gamma)) + mue = np.zeros(self.lam0.shape) + gamma = np.zeros(self.lam0.shape) + y0 = np.hstack((y0,mue,gamma)) + yd0 = np.hstack((yd0,mue,gamma)) algvar = (self.pos0.size + self.vel0.size) * [1] \ + 3*self.lam0.size*[0] elif index == 'ggl2': - mue = N.zeros(self.lam0.shape) - y0 = N.hstack((y0,mue)) - yd0 = N.hstack((yd0,N.zeros(mue.shape))) + mue = np.zeros(self.lam0.shape) + y0 = np.hstack((y0,mue)) + yd0 = np.hstack((yd0,np.zeros(mue.shape))) algvar = (self.pos0.size + self.vel0.size) * [1] \ + 2*self.lam0.size*[0] elif index is None: diff --git a/src/support.pxd b/src/support.pxd index 3648edc6..93e04c47 100644 --- a/src/support.pxd +++ b/src/support.pxd @@ -15,9 +15,6 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import numpy as N -cimport numpy as N - cdef class Statistics: cdef public object statistics_msg cdef public object statistics diff --git a/src/support.pyx b/src/support.pyx index 214179bb..9f0ca002 100644 --- a/src/support.pyx +++ b/src/support.pyx @@ -17,8 +17,8 @@ # distutils: define_macros=NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -import numpy as N -cimport numpy as N +import numpy as np +cimport numpy as np from collections import OrderedDict @@ -28,7 +28,7 @@ def set_type_shape_array(var, datatype=realtype): """ Helper function to convert a scalar or list to a 1D-array """ - return N.array(var, dtype = datatype).reshape(-1,) + return np.array(var, dtype = datatype).reshape(-1,) cdef class Statistics: def __init__(self): diff --git a/tests/solvers/test_euler.py b/tests/solvers/test_euler.py index f7c0547f..501f2e8a 100644 --- a/tests/solvers/test_euler.py +++ b/tests/solvers/test_euler.py @@ -20,8 +20,8 @@ from assimulo.solvers.euler import ExplicitEuler, ImplicitEuler from assimulo.problem import Explicit_Problem from assimulo.exception import AssimuloException, TimeLimitExceeded -import numpy as N -import scipy.sparse as sp +import numpy as np +import scipy.sparse as sps float_regex = "[\s]*[\d]*.[\d]*((e|E)(\+|\-)\d\d|)" @@ -30,8 +30,8 @@ class Extended_Problem(Explicit_Problem): #Sets the initial conditions directly into the problem y0 = [0.0, -1.0, 0.0] sw0 = [False,True,True] - event_array = N.array([0.0,0.0,0.0]) - rhs_array = N.array([0.0,0.0,0.0]) + event_array = np.array([0.0,0.0,0.0]) + rhs_array = np.array([0.0,0.0,0.0]) #The right-hand-side function (rhs) def rhs(self,t,y,sw): @@ -155,7 +155,7 @@ def test_h(self): @testattr(stddist = True) def test_time_event(self): - f = lambda t,y: N.array(1.0) + f = lambda t,y: np.array(1.0) global tnext global nevent tnext = 0.0 @@ -230,8 +230,8 @@ def test_switches(self): """ This tests that the switches are actually turned when override. """ - f = lambda t,x,sw: N.array([1.0]) - state_events = lambda t,x,sw: N.array([x[0]-1.]) + f = lambda t,x,sw: np.array([1.0]) + state_events = lambda t,x,sw: np.array([x[0]-1.]) def handle_event(solver, event_info): solver.sw = [False] #Override the switches to point to another instance @@ -292,8 +292,8 @@ def test_usejac_csc_matrix(self): """ This tests the functionality of the property usejac. """ - f = lambda t,x: N.array([x[1], -9.82]) #Defines the rhs - jac = lambda t,x: sp.csc_matrix(N.array([[0.,1.],[0.,0.]])) #Defines the jacobian + f = lambda t,x: np.array([x[1], -9.82]) #Defines the rhs + jac = lambda t,x: sps.csc_matrix(np.array([[0.,1.],[0.,0.]])) #Defines the jacobian exp_mod = Explicit_Problem(f, [1.0,0.0]) exp_mod.jac = jac @@ -321,7 +321,7 @@ def test_h(self): @testattr(stddist = True) def test_time_event(self): - f = lambda t,y: N.array(1.0) + f = lambda t,y: np.array(1.0) global tnext global nevent tnext = 0.0 @@ -388,18 +388,18 @@ def test_stiff_problem(self): t,y = simulator.simulate(1) - y_correct = lambda t: N.exp(-15*t) + y_correct = lambda t: np.exp(-15*t) - abs_err = N.abs(y[:,0]-y_correct(N.array(t))) - nose.tools.assert_less(N.max(abs_err), 0.1) + abs_err = np.abs(y[:,0]-y_correct(np.array(t))) + nose.tools.assert_less(np.max(abs_err), 0.1) @testattr(stddist = True) def test_switches(self): """ This tests that the switches are actually turned when override. """ - f = lambda t,x,sw: N.array([1.0]) - state_events = lambda t,x,sw: N.array([x[0]-1.]) + f = lambda t,x,sw: np.array([1.0]) + state_events = lambda t,x,sw: np.array([x[0]-1.]) def handle_event(solver, event_info): solver.sw = [False] #Override the switches to point to another instance diff --git a/tests/solvers/test_glimda.py b/tests/solvers/test_glimda.py index 6489e85c..ba7d06be 100644 --- a/tests/solvers/test_glimda.py +++ b/tests/solvers/test_glimda.py @@ -21,7 +21,7 @@ from assimulo.problem import Implicit_Problem, Explicit_Problem from assimulo.exception import GLIMDA_Exception -import numpy as N +import numpy as np class Test_GLIMDA: """ @@ -41,7 +41,7 @@ def f(t,y,yd): res_0 = yd[0]-yd_0 res_1 = yd[1]-yd_1 - return N.array([res_0,res_1]) + return np.array([res_0,res_1]) y0 = [2.0,-0.6] #Initial conditions yd0 = [-.6,-200000.] @@ -64,7 +64,7 @@ def test_simulate_explicit(self): """ Test a simulation of an explicit problem using GLIMDA. """ - f = lambda t,y:N.array(-y) + f = lambda t,y:np.array(-y) y0 = [1.0] problem = Explicit_Problem(f,y0) @@ -74,7 +74,7 @@ def test_simulate_explicit(self): t,y = simulator.simulate(1.0) - nose.tools.assert_almost_equal(float(y[-1]), float(N.exp(-1.0)),4) + nose.tools.assert_almost_equal(float(y[-1]), float(np.exp(-1.0)),4) @testattr(stddist = True) def test_maxord(self): @@ -143,8 +143,8 @@ def test_minh(self): """ Tests the minimum stepsize of GLIMDA. """ - nose.tools.assert_equal(self.sim.minh, N.finfo(N.double).eps) - nose.tools.assert_equal(self.sim.options["minh"], N.finfo(N.double).eps) + nose.tools.assert_equal(self.sim.minh, np.finfo(np.double).eps) + nose.tools.assert_equal(self.sim.options["minh"], np.finfo(np.double).eps) self.sim.minh = 1e-5 @@ -171,8 +171,8 @@ def test_maxh(self): """ Tests the maximum stepsize of GLIMDA. """ - nose.tools.assert_equal(self.sim.maxh, N.inf) - nose.tools.assert_equal(self.sim.options["maxh"], N.inf) + nose.tools.assert_equal(self.sim.maxh, np.inf) + nose.tools.assert_equal(self.sim.options["maxh"], np.inf) self.sim.maxh = 1e5 diff --git a/tests/solvers/test_odepack.py b/tests/solvers/test_odepack.py index 50e8718f..b5037e67 100644 --- a/tests/solvers/test_odepack.py +++ b/tests/solvers/test_odepack.py @@ -22,18 +22,18 @@ from assimulo.problem import Explicit_Problem from assimulo.exception import TimeLimitExceeded -import numpy as N -import scipy.sparse as sp +import numpy as np +import scipy.sparse as sps float_regex = "[\s]*[\d]*.[\d]*((e|E)(\+|\-)\d\d|)" class Extended_Problem(Explicit_Problem): - #Sets the initial conditons directly into the problem + #Sets the initial conditions directly into the problem y0 = [0.0, -1.0, 0.0] sw0 = [False,True,True] - event_array = N.array([0.0,0.0,0.0]) - rhs_array = N.array([0.0,0.0,0.0]) + event_array = np.array([0.0,0.0,0.0]) + rhs_array = np.array([0.0,0.0,0.0]) #The right-hand-side function (rhs) def rhs(self,t,y,sw): @@ -132,12 +132,12 @@ def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) def jac(t,y): eps = 1.e-6 my = 1./eps - J = N.zeros([2,2]) + J = np.zeros([2,2]) J[0,0]=0. J[0,1]=1. @@ -149,14 +149,14 @@ def jac(t,y): def jac_sparse(t,y): eps = 1.e-6 my = 1./eps - J = N.zeros([2,2]) + J = np.zeros([2,2]) J[0,0]=0. J[0,1]=1. J[1,0]=my*(-2.*y[0]*y[1]-1.) J[1,1]=my*(1.-y[0]**2) - return sp.csc_matrix(J) + return sps.csc_matrix(J) #Define an Assimulo problem y0 = [2.0,-0.6] #Initial conditions @@ -216,8 +216,8 @@ def test_readcommon(self): This tests the LSODAR's subroutine dsrcar (read functionality). """ self.sim.simulate(1.) #Simulate 2 seconds - r=N.ones((245,),'d') - i=N.ones((55,),'i') + r=np.ones((245,),'d') + i=np.ones((55,),'i') dsrcar(r,i,1) nose.tools.assert_almost_equal(r[217], 2.22044605e-16, 20) nose.tools.assert_equal(i[36], 3) @@ -227,8 +227,8 @@ def test_writereadcommon(self): """ This tests the LSODAR's subroutine dsrcar (write and read functionality). """ - r=N.ones((245,),'d') - i=N.ones((55,),'i') + r=np.ones((245,),'d') + i=np.ones((55,),'i') dsrcar(r,i,2) r[0]=100. i[0]=10 @@ -243,24 +243,24 @@ def test_rkstarter(self): """ pass """ - A=N.array([[0.,1.],[-4.,0.]]) + A=np.array([[0.,1.],[-4.,0.]]) def f(t,x,sw0): - return N.dot(A,N.array(x)) + return np.dot(A,np.array(x)) H = 1.e-8 # Compute the exact solution at h=0,H/4,H/2,3H/4,H - T=N.array([0.,H/4.,H/2.,3./4.*H,H]) - y0=N.array([1.,0.]) + T=np.array([0.,H/4.,H/2.,3./4.*H,H]) + y0=np.array([1.,0.]) from scipy.linalg import expm - exact=N.array([N.dot(expm(A*t),y0) for t in T]) + exact=np.array([np.dot(expm(A*t),y0) for t in T]) #polynomial interpolation from scipy import polyfit coeff = polyfit(T,exact,4) - d1coeff=N.array([4,3,2,1]).reshape(-1,1)*coeff[:-1,:] - d2coeff=N.array([3,2,1]).reshape(-1,1)*d1coeff[:-1,:] - d3coeff=N.array([2,1]).reshape(-1,1)*d2coeff[:-1,:] - d4coeff=N.array([1]).reshape(-1,1)*d3coeff[:-1,:] + d1coeff=np.array([4,3,2,1]).reshape(-1,1)*coeff[:-1,:] + d2coeff=np.array([3,2,1]).reshape(-1,1)*d1coeff[:-1,:] + d3coeff=np.array([2,1]).reshape(-1,1)*d2coeff[:-1,:] + d4coeff=np.array([1]).reshape(-1,1)*d3coeff[:-1,:] h=H/4. - nordsieck_at_0=N.array([coeff[-1,:],h*d1coeff[-1,:],h**2/2.*d2coeff[-1,:], + nordsieck_at_0=np.array([coeff[-1,:],h*d1coeff[-1,:],h**2/2.*d2coeff[-1,:], h**3/6.*d3coeff[-1,:],h**4/24.*d4coeff[-1,:]]) rkNordsieck=odepack.RKStarterNordsieck(f,H) computed=rkNordsieck(0,y0) @@ -274,7 +274,7 @@ def test_interpol(self): t_sol,y_sol=self.sim.simulate(1.,ncp_list=[0.5]) self.sim.reset() t_sol1,y_sol1=self.sim.simulate(0.5) - ind05=N.nonzero(N.array(t_sol)==0.5)[0][0] + ind05=np.nonzero(np.array(t_sol)==0.5)[0][0] nose.tools.assert_almost_equal(y_sol[ind05,0],y_sol1[-1,0],6) @testattr(stddist = True) diff --git a/tests/solvers/test_radau5.py b/tests/solvers/test_radau5.py index a681eb57..5731d623 100644 --- a/tests/solvers/test_radau5.py +++ b/tests/solvers/test_radau5.py @@ -24,8 +24,8 @@ from assimulo.problem import Implicit_Problem from assimulo.lib.radau_core import Radau_Exception from assimulo.exception import TimeLimitExceeded -import scipy.sparse as sp -import numpy as N +import scipy.sparse as sps +import numpy as np import re float_regex = "[\s]*[\d]*.[\d]*((e|E)(\+|\-)\d\d|)" @@ -63,14 +63,14 @@ def jac(self, t, y): if self.jac_raise: raise KeyboardInterrupt('jac') else: - return -N.eye(self.dim) + return -np.eye(self.dim) def state_events(self,t,y,sw): if self.event_raise: self.n_e += 1 if self.n_e % self.event_n == 0: raise KeyboardInterrupt('event') - return N.ones(len(sw)) + return np.ones(len(sw)) def handle_event(self, solver, event_info): pass @@ -80,8 +80,8 @@ class Extended_Problem(Explicit_Problem): #Sets the initial conditions directly into the problem y0 = [0.0, -1.0, 0.0] sw0 = [False,True,True] - event_array = N.array([0.0,0.0,0.0]) - rhs_array = N.array([0.0,0.0,0.0]) + event_array = np.array([0.0,0.0,0.0]) + rhs_array = np.array([0.0,0.0,0.0]) #The right-hand-side function (rhs) def rhs(self,t,y,sw): @@ -179,12 +179,12 @@ def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) def jac(t,y): eps = 1.e-6 my = 1./eps - J = N.zeros([2,2]) + J = np.zeros([2,2]) J[0,0]=0. J[0,1]=1. @@ -360,7 +360,7 @@ def test_maxh(self): """ self.sim.maxh = 0.01 self.sim.simulate(0.5) - nose.tools.assert_less_equal(max(N.diff(self.sim.t_sol))-N.finfo('double').eps, 0.01) + nose.tools.assert_less_equal(max(np.diff(self.sim.t_sol))-np.finfo('double').eps, 0.01) @testattr(stddist = True) def test_newt(self): @@ -438,12 +438,12 @@ def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) def jac(t,y): eps = 1.e-6 my = 1./eps - J = N.zeros([2,2]) + J = np.zeros([2,2]) J[0,0]=0. J[0,1]=1. @@ -455,14 +455,14 @@ def jac(t,y): def jac_sparse(t,y): eps = 1.e-6 my = 1./eps - J = N.zeros([2,2]) + J = np.zeros([2,2]) J[0,0]=0. J[0,1]=1. J[1,0]=my*(-2.*y[0]*y[1]-1.) J[1,1]=my*(1.-y[0]**2) - return sp.csc_matrix(J) + return sps.csc_matrix(J) #Define an Assimulo problem y0 = [2.0,-0.6] #Initial conditions @@ -659,7 +659,7 @@ def test_maxh(self): """ self.sim.maxh = 0.01 self.sim.simulate(0.5) - nose.tools.assert_less_equal(max(N.diff(self.sim.t_sol))-N.finfo('double').eps, 0.01) + nose.tools.assert_less_equal(max(np.diff(self.sim.t_sol))-np.finfo('double').eps, 0.01) @testattr(stddist = True) def test_newt(self): @@ -744,8 +744,8 @@ def test_switches(self): """ This tests that the switches are actually turned when override. """ - f = lambda t,x,sw: N.array([1.0]) - state_events = lambda t,x,sw: N.array([x[0]-1.]) + f = lambda t,x,sw: np.array([1.0]) + state_events = lambda t,x,sw: np.array([x[0]-1.]) def handle_event(solver, event_info): solver.sw = [False] #Override the switches to point to another instance @@ -797,8 +797,8 @@ def test_repeated_unexpected_step_rejections(self): This tests the error for repeated unexpected step rejections """ def f(t, y): - raise N.linalg.LinAlgError() - y0 = N.array([1.]) + raise np.linalg.LinAlgError() + y0 = np.array([1.]) prob = Explicit_Problem(f, y0) sim = Radau5ODE(prob) @@ -812,7 +812,7 @@ def test_sparse_solver_jac_disabled(self): This tests the trying to simulate using the sparse linear solver, with no analytical jacobian provided. """ f = lambda t, y: [y] - y0 = N.array([1.]) + y0 = np.array([1.]) prob = Explicit_Problem(f, y0) sim = Radau5ODE(prob) @@ -828,7 +828,7 @@ def test_solver_no_jac(self): This tests the error when trying to simulate using an analytical jacobian, with none provided """ f = lambda t, y: [y] - y0 = N.array([1.]) + y0 = np.array([1.]) prob = Explicit_Problem(f, y0) sim = Radau5ODE(prob) @@ -844,8 +844,8 @@ def test_solver_sparse_jac_wrong_format(self): This tests the error when using a sparse jacobian of the wrong format """ f = lambda t, y: [y] - jac = lambda t, y: sp.spdiags([1], 0, 1, 1, format = 'csr') - y0 = N.array([1.]) + jac = lambda t, y: sps.spdiags([1], 0, 1, 1, format = 'csr') + y0 = np.array([1.]) prob = Explicit_Problem(f, y0) prob.jac = jac prob.jac_nnz = 1 @@ -865,8 +865,8 @@ def test_solver_sparse_jac_nnz_too_small(self): """ n = 5 f = lambda t, y: y - jac = lambda t, y: sp.eye(n, n, dtype = N.double, format = 'csc') - y0 = N.array([1.]*n) + jac = lambda t, y: sps.eye(n, n, dtype = np.double, format = 'csc') + y0 = np.array([1.]*n) prob = Explicit_Problem(f, y0) prob.jac = jac prob.jac_nnz = 1 @@ -886,8 +886,8 @@ def test_solver_sparse_jac_nnz_zero(self): """ n = 5 f = lambda t, y: [0.]*n - jac = lambda t, y: sp.csc_matrix((n, n), dtype = N.double) - y0 = N.array([1.]*n) + jac = lambda t, y: sps.csc_matrix((n, n), dtype = np.double) + y0 = np.array([1.]*n) prob = Explicit_Problem(f, y0) prob.jac = jac prob.jac_nnz = 0 @@ -904,8 +904,8 @@ def test_sparse_solver_no_nnz(self): This tests the error when trying to simulate using the sparse linear solver, without specifying the number of non-zero elements """ f = lambda t, y: [y] - jac = lambda t, y: sp.spdiags([1], 0, 1, 1, format = 'csc') - y0 = N.array([1.]) + jac = lambda t, y: sps.spdiags([1], 0, 1, 1, format = 'csc') + y0 = np.array([1.]) prob = Explicit_Problem(f, y0) prob.jac = jac @@ -923,8 +923,8 @@ def test_sparse_solver_invalid_nnz_type(self): This tests the error when trying to simulate using the sparse linear solver with invalid inputs for nnz; wrong type. """ f = lambda t, y: [y] - jac = lambda t, y: sp.spdiags([1], 0, 1, 1, format = 'csc') - y0 = N.array([1.]) + jac = lambda t, y: sps.spdiags([1], 0, 1, 1, format = 'csc') + y0 = np.array([1.]) for nnz in [None, "test"]: prob = Explicit_Problem(f, y0) @@ -945,8 +945,8 @@ def test_sparse_solver_invalid_nnz_negative(self): This tests the error when trying to simulate using the sparse linear solver with invalid inputs for nnz; negative. """ f = lambda t, y: [y] - jac = lambda t, y: sp.spdiags([1], 0, 1, 1, format = 'csc') - y0 = N.array([1.]) + jac = lambda t, y: sps.spdiags([1], 0, 1, 1, format = 'csc') + y0 = np.array([1.]) for nnz in [-2, -10]: prob = Explicit_Problem(f, y0) @@ -967,8 +967,8 @@ def test_sparse_solver_invalid_nnz_too_large(self): This tests the error when trying to simulate using the sparse linear solver with invalid inputs for nnz; too_large. """ f = lambda t, y: [y] - jac = lambda t, y: sp.spdiags([1], 0, 1, 1, format = 'csc') - y0 = N.array([1.]) + jac = lambda t, y: sps.spdiags([1], 0, 1, 1, format = 'csc') + y0 = np.array([1.]) for nnz in [5, 100]: prob = Explicit_Problem(f, y0) @@ -988,19 +988,19 @@ def test_sparse_solver_jacobian(self): ## Take trivial problem with somewhat arbitrary jacobians ## Test that functions for internal processing of jacobian do not produces segfaults jacobians = [ - (lambda t, y: sp.csc_matrix(N.array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]])), 9), - (lambda t, y: sp.csc_matrix(N.array([[0., 1., 1.], [1., 0., 1.], [1., 1., 0.]])), 6), - (lambda t, y: sp.csc_matrix(N.array([[0., 1., 1.], [1., 1., 1.], [1., 1., 1.]])), 8), - (lambda t, y: sp.csc_matrix(N.array([[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]])), 1), - (lambda t, y: sp.csc_matrix(N.array([[0., 0., 0.], [1., 0., 0.], [0., 0., 0.]])), 1), - (lambda t, y: sp.csc_matrix(N.array([[0., 0., 0.], [0., 0., 0.], [0., 1., 0.]])), 1), - (lambda t, y: sp.csc_matrix(N.array([[0., 0., 1.], [0., 0., 0.], [0., 0., 0.]])), 1), - (lambda t, y: sp.csc_matrix(N.array([[1., 0., 0.], [0., 0., 0.], [0., 0., 0.]])), 1), + (lambda t, y: sps.csc_matrix(np.array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]])), 9), + (lambda t, y: sps.csc_matrix(np.array([[0., 1., 1.], [1., 0., 1.], [1., 1., 0.]])), 6), + (lambda t, y: sps.csc_matrix(np.array([[0., 1., 1.], [1., 1., 1.], [1., 1., 1.]])), 8), + (lambda t, y: sps.csc_matrix(np.array([[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]])), 1), + (lambda t, y: sps.csc_matrix(np.array([[0., 0., 0.], [1., 0., 0.], [0., 0., 0.]])), 1), + (lambda t, y: sps.csc_matrix(np.array([[0., 0., 0.], [0., 0., 0.], [0., 1., 0.]])), 1), + (lambda t, y: sps.csc_matrix(np.array([[0., 0., 1.], [0., 0., 0.], [0., 0., 0.]])), 1), + (lambda t, y: sps.csc_matrix(np.array([[1., 0., 0.], [0., 0., 0.], [0., 0., 0.]])), 1), ] for i, (jac, nnz) in enumerate(jacobians): f = lambda t, y: y - y0 = 1.*N.ones(3) + y0 = 1.*np.ones(3) prob = Explicit_Problem(f, y0) prob.jac = jac prob.jac_nnz = nnz @@ -1039,7 +1039,7 @@ def test_linear_solver(self): def test_keyboard_interrupt_fcn(self): """Test that KeyboardInterrupts in right-hand side terminate the simulation. Radau5 + C + explicit problem.""" - y0 = N.array([1., 1.]) + y0 = np.array([1., 1.]) aux = KeyboardInterruptAux(dim = len(y0), fcn = True) prob = Explicit_Problem(aux.f, y0) sim = Radau5ODE(prob) @@ -1053,7 +1053,7 @@ def test_keyboard_interrupt_fcn(self): def test_keyboard_interrupt_jac(self): """Test that KeyboardInterrupts in jacobian terminate the simulation. Radau5 + C + explicit problem.""" - y0 = N.array([1., 1.]) + y0 = np.array([1., 1.]) aux = KeyboardInterruptAux(dim = len(y0), jac = True) prob = Explicit_Problem(aux.f, y0) prob.jac = aux.jac @@ -1070,7 +1070,7 @@ def test_keyboard_interrupt_jac(self): def test_keyboard_interrupt_jac_sparse(self): """Test that KeyboardInterrupts in jacobian terminate the simulation. Radau5 + C + explicit problem + sparse jac.""" - y0 = N.array([1., 1.]) + y0 = np.array([1., 1.]) aux = KeyboardInterruptAux(dim = len(y0), jac = True) prob = Explicit_Problem(aux.f, y0) prob.jac = aux.jac @@ -1089,9 +1089,9 @@ def test_keyboard_interrupt_jac_sparse(self): def test_keyboard_interrupt_event_indicator(self): """Test that KeyboardInterrupts in event indicator function resp. solout callback correctly terminate solution.""" - y0 = N.array([1.]) + y0 = np.array([1.]) aux = KeyboardInterruptAux(dim = len(y0), event = True, event_n = 3) - prob = Explicit_Problem(aux.f, y0, sw0 = N.array([1.])) + prob = Explicit_Problem(aux.f, y0, sw0 = np.array([1.])) prob.state_events = aux.state_events prob.handle_event = aux.handle_event sim = Radau5ODE(prob) @@ -1168,7 +1168,7 @@ def f(t,y,yd): res_0 = yd[0]-yd_0 res_1 = yd[1]-yd_1 - return N.array([res_0,res_1]) + return np.array([res_0,res_1]) y0 = [2.0,-0.6] #Initial conditions yd0 = [-.6,-200000.] @@ -1232,7 +1232,7 @@ def test_simulate_explicit(self): """ Test a simulation of an explicit problem using Radau5DAE. """ - f = lambda t,y:N.array(-y) + f = lambda t,y:np.array(-y) y0 = [1.0] problem = Explicit_Problem(f,y0) @@ -1242,7 +1242,7 @@ def test_simulate_explicit(self): t,y = simulator.simulate(1.0) - nose.tools.assert_almost_equal(float(y[-1]), float(N.exp(-1.0)),4) + nose.tools.assert_almost_equal(float(y[-1]), float(np.exp(-1.0)),4) @testattr(stddist = True) def test_time_event(self): @@ -1347,15 +1347,15 @@ def test_maxh(self): """ self.sim.maxh = 0.01 self.sim.simulate(0.5) - nose.tools.assert_less_equal(max(N.diff(self.sim.t_sol))-N.finfo('double').eps, 0.01) + nose.tools.assert_less_equal(max(np.diff(self.sim.t_sol))-np.finfo('double').eps, 0.01) @testattr(stddist = True) def test_switches(self): """ This tests that the switches are actually turned when override. """ - res = lambda t,x,xd,sw: N.array([1.0 - xd]) - state_events = lambda t,x,xd,sw: N.array([x[0]-1.]) + res = lambda t,x,xd,sw: np.array([1.0 - xd]) + state_events = lambda t,x,xd,sw: np.array([x[0]-1.]) def handle_event(solver, event_info): solver.sw = [False] #Override the switches to point to another instance @@ -1390,8 +1390,8 @@ def test_step_size_too_small(self): This tests the error for too small step-sizes """ f = lambda t, y, yd: -y - y0 = N.array([1.]) - yd0 = N.array([0.]) + y0 = np.array([1.]) + yd0 = np.array([0.]) prob = Implicit_Problem(f, y0, yd0) @@ -1413,8 +1413,8 @@ def test_repeated_unexpected_step_rejections(self): This tests the error for repeated unexpected step rejections in Radau5DAE. """ def f(t, y, yd): - raise N.linalg.LinAlgError() - prob = Implicit_Problem(f, N.array([1.]), N.array([1.])) + raise np.linalg.LinAlgError() + prob = Implicit_Problem(f, np.array([1.]), np.array([1.])) sim = Radau5DAE(prob) err_msg = f'Repeated unexpected step rejections.' @@ -1440,7 +1440,7 @@ def f(t,y,yd): res_0 = yd[0]-yd_0 res_1 = yd[1]-yd_1 - return N.array([res_0,res_1]) + return np.array([res_0,res_1]) y0 = [2.0,-0.6] #Initial conditions yd0 = [-.6,-200000.] @@ -1561,14 +1561,14 @@ def test_maxh(self): """ self.sim.maxh = 0.01 self.sim.simulate(0.5) - nose.tools.assert_less_equal(max(N.diff(self.sim.t_sol))-N.finfo('double').eps, 0.01) + nose.tools.assert_less_equal(max(np.diff(self.sim.t_sol))-np.finfo('double').eps, 0.01) @testattr(stddist = True) def test_keyboard_interrupt_fcn(self): """Test that KeyboardInterrupts in right-hand side terminate the simulation. Radau5 + C + implicit problem.""" - y0 = N.array([1., 1.]) - yd = N.array([0., 0.]) + y0 = np.array([1., 1.]) + yd = np.array([0., 0.]) aux = KeyboardInterruptAux(dim = len(y0), fcn = True) prob = Implicit_Problem(aux.f_impl, y0, yd) sim = Radau5DAE(prob) diff --git a/tests/solvers/test_rosenbrock.py b/tests/solvers/test_rosenbrock.py index 90ab6ee7..f5a157ab 100644 --- a/tests/solvers/test_rosenbrock.py +++ b/tests/solvers/test_rosenbrock.py @@ -20,8 +20,8 @@ from assimulo.solvers.rosenbrock import RodasODE from assimulo.problem import Explicit_Problem from assimulo.exception import TimeLimitExceeded -import numpy as N -import scipy.sparse as sp +import numpy as np +import scipy.sparse as sps float_regex = "[\s]*[\d]*.[\d]*((e|E)(\+|\-)\d\d|)" @@ -34,26 +34,26 @@ def f(t,y): yd_0 = y[1] yd_1 = my*((1.-y[0]**2)*y[1]-y[0]) - return N.array([yd_0,yd_1]) + return np.array([yd_0,yd_1]) #Define the jacobian def jac(t,y): eps = 1.e-6 my = 1./eps - j = N.array([[0.0,1.0],[my*((-2.0*y[0])*y[1]-1.0), my*(1.0-y[0]**2)]]) + j = np.array([[0.0,1.0],[my*((-2.0*y[0])*y[1]-1.0), my*(1.0-y[0]**2)]]) return j def jac_sparse(t,y): eps = 1.e-6 my = 1./eps - J = N.zeros([2,2]) + J = np.zeros([2,2]) J[0,0]=0. J[0,1]=1. J[1,0]=my*(-2.*y[0]*y[1]-1.) J[1,1]=my*(1.-y[0]**2) - return sp.csc_matrix(J) + return sps.csc_matrix(J) y0 = [2.0,-0.6] #Initial conditions diff --git a/tests/solvers/test_rungekutta.py b/tests/solvers/test_rungekutta.py index 4882f4ca..0634242e 100644 --- a/tests/solvers/test_rungekutta.py +++ b/tests/solvers/test_rungekutta.py @@ -20,7 +20,7 @@ from assimulo.solvers.runge_kutta import Dopri5, RungeKutta34, RungeKutta4 from assimulo.problem import Explicit_Problem from assimulo.exception import Explicit_ODE_Exception, TimeLimitExceeded -import numpy as N +import numpy as np float_regex = "[\s]*[\d]*.[\d]*((e|E)(\+|\-)\d\d|)" @@ -86,8 +86,8 @@ def test_switches(self): """ This tests that the switches are actually turned when override. """ - f = lambda t,x,sw: N.array([1.0]) - state_events = lambda t,x,sw: N.array([x[0]-1.]) + f = lambda t,x,sw: np.array([1.0]) + state_events = lambda t,x,sw: np.array([x[0]-1.]) def handle_event(solver, event_info): solver.sw = [False] #Override the switches to point to another instance @@ -217,8 +217,8 @@ def test_switches(self): """ This tests that the switches are actually turned when override. """ - f = lambda t,x,sw: N.array([1.0]) - state_events = lambda t,x,sw: N.array([x[0]-1.]) + f = lambda t,x,sw: np.array([1.0]) + state_events = lambda t,x,sw: np.array([x[0]-1.]) def handle_event(solver, event_info): solver.sw = [False] #Override the switches to point to another instance diff --git a/tests/solvers/test_sundials.py b/tests/solvers/test_sundials.py index fe7d29cc..3f477dfa 100644 --- a/tests/solvers/test_sundials.py +++ b/tests/solvers/test_sundials.py @@ -22,7 +22,7 @@ from assimulo.problem import Implicit_Problem from assimulo.exception import AssimuloException, TimeLimitExceeded, TerminateSimulation import numpy as np -import scipy.sparse as sp +import scipy.sparse as sps class Extended_Problem(Explicit_Problem): @@ -496,7 +496,7 @@ def test_usejac_csc_matrix(self): This tests the functionality of the property usejac. """ f = lambda t,x: np.array([x[1], -9.82]) #Defines the rhs - jac = lambda t,x: sp.csc_matrix(np.array([[0.,1.],[0.,0.]])) #Defines the jacobian + jac = lambda t,x: sps.csc_matrix(np.array([[0.,1.],[0.,0.]])) #Defines the jacobian exp_mod = Explicit_Problem(f, [1.0,0.0]) exp_mod.jac = jac diff --git a/tests/test_examples.py b/tests/test_examples.py index 18cd51cd..2e667481 100644 --- a/tests/test_examples.py +++ b/tests/test_examples.py @@ -15,7 +15,6 @@ # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . -import nose from assimulo import testattr from assimulo.exception import AssimuloException from assimulo.examples import * diff --git a/tests/test_solvers.py b/tests/test_solvers.py index 156e083b..58543c82 100644 --- a/tests/test_solvers.py +++ b/tests/test_solvers.py @@ -19,7 +19,7 @@ import numpy as np from assimulo import testattr from assimulo.problem import Explicit_Problem, Implicit_Problem -from assimulo.solvers import Radau5DAE, Radau5ODE, Dopri5, RodasODE +from assimulo.solvers import Radau5DAE, Dopri5, RodasODE def res(t,y,yd,sw): return np.array([yd+y])