def comparison_plot(f, u, Omega, filename='tmp.pdf',
plot_title='', ymin=None, ymax=None,
u_legend='approximation'):
"""Compare f(x) and u(x) for x in Omega in a plot."""
x = sm.Symbol('x')
print 'f:', f
f = sm.lambdify([x], f, modules="numpy")
u = sm.lambdify([x], u, modules="numpy")
if len(Omega) != 2:
raise ValueError('Omega=%s must be an interval (2-list)' % str(Omega))
# When doing symbolics, Omega can easily contain symbolic expressions,
# assume .evalf() will work in that case to obtain numerical
# expressions, which then must be converted to float before calling
# linspace below
if not isinstance(Omega[0], (int,float)):
Omega[0] = float(Omega[0].evalf())
if not isinstance(Omega[1], (int,float)):
Omega[1] = float(Omega[1].evalf())
resolution = 401 # no of points in plot
xcoor = linspace(Omega[0], Omega[1], resolution)
# Vectorized functions expressions does not work with
# lambdify'ed functions without the modules="numpy"
exact = f(xcoor)
approx = u(xcoor)
plot(xcoor, approx, '-')
hold('on')
plot(xcoor, exact, '-')
legend([u_legend, 'exact'])
title(plot_title)
xlabel('x')
if ymin is not None and ymax is not None:
axis([xcoor[0], xcoor[-1], ymin, ymax])
savefig(filename)
def comparison_plot(u, Omega, u_e=None, filename='tmp.eps',
plot_title='', ymin=None, ymax=None):
x = sp.Symbol('x')
u = sp.lambdify([x], u, modules="numpy")
if len(Omega) != 2:
raise ValueError('Omega=%s must be an interval (2-list)' % str(Omega))
# When doing symbolics, Omega can easily contain symbolic expressions,
# assume .evalf() will work in that case to obtain numerical
# expressions, which then must be converted to float before calling
# linspace below
if not isinstance(Omega[0], (int,float)):
Omega[0] = float(Omega[0].evalf())
if not isinstance(Omega[1], (int,float)):
Omega[1] = float(Omega[1].evalf())
resolution = 401 # no of points in plot
xcoor = linspace(Omega[0], Omega[1], resolution)
# Vectorized functions expressions does not work with
# lambdify'ed functions without the modules="numpy"
approx = u(xcoor)
plot(xcoor, approx)
legends = ['approximation']
if u_e is not None:
exact = u_e(xcoor)
hold('on')
plot(xcoor, exact)
legends = ['exact']
legend(legends)
title(plot_title)
xlabel('x')
if ymin is not None and ymax is not None:
axis([xcoor[0], xcoor[-1], ymin, ymax])
savefig(filename)
def demo1(f=f1, nx=4, ny=3, viewx=58, viewy=345, plain_gnuplot=True):
vertices, cells = mesh(nx, ny, x=[0, 1], y=[0, 1], diagonal='right')
if f == 'basis':
# basis function
zvalues = np.zeros(vertices.shape[0])
zvalues[int(round(len(zvalues) / 2.)) + int(round(nx / 2.))] = 1
else:
zvalues = fill(f1, vertices)
if plain_gnuplot:
plt = Gnuplotter()
else:
import scitools.std as plt
"""
if plt.backend == 'gnuplot':
gpl = plt.get_backend()
gpl('unset border; unset xtics; unset ytics; unset ztics')
#gpl('replot')
"""
draw_mesh(vertices, cells, plt)
draw_surface(zvalues, vertices, cells, plt)
plt.axis([0, 1, 0, 1, 0, zvalues.max()])
plt.view(viewx, viewy)
if plain_gnuplot:
plt.savefig('tmp')
else:
plt.savefig('tmp.pdf')
plt.savefig('tmp.eps')
plt.savefig('tmp.png')
plt.show()
def u_P1():
"""
Plot P1 basis functions and a resulting u to
illustrate what it means to use finite elements.
"""
import matplotlib.pyplot as plt
x = [0, 1.5, 2.5, 3.5, 4]
phi = [np.zeros(len(x)) for i in range(len(x)-2)]
for i in range(len(phi)):
phi[i][i+1] = 1
#u = 5*x*np.exp(-0.25*x**2)*(4-x)
u = [0, 8, 5, 4, 0]
for i in range(len(phi)):
plt.plot(x, phi[i], 'r-') #, label=r'$\varphi_%d$' % i)
plt.text(x[i+1], 1.2, r'$\varphi_%d$' % i)
plt.plot(x, u, 'b-', label='$u$')
plt.legend(loc='upper left')
plt.axis([0, x[-1], 0, 9])
plt.savefig('tmp_u_P1.png')
plt.savefig('tmp_u_P1.pdf')
# Mark elements
for xi in x[1:-1]:
plt.plot([xi, xi], [0, 9], 'm--')
# Mark nodes
#plt.plot(x, np.zeros(len(x)), 'ro', markersize=4)
plt.savefig('tmp_u_P1_welms.png')
plt.savefig('tmp_u_P1_welms.pdf')
plt.show()
def u_P1():
"""
Plot P1 basis functions and a resulting u to
illustrate what it means to use finite elements.
"""
import matplotlib.pyplot as plt
x = [0, 1.5, 2.5, 3.5, 4]
phi = [np.zeros(len(x)) for i in range(len(x)-2)]
for i in range(len(phi)):
phi[i][i+1] = 1
#u = 5*x*np.exp(-0.25*x**2)*(4-x)
u = [0, 8, 5, 4, 0]
for i in range(len(phi)):
plt.plot(x, phi[i], 'r-') #, label=r'$\varphi_%d$' % i)
plt.text(x[i+1], 1.2, r'$\varphi_%d$' % i)
plt.plot(x, u, 'b-', label='$u$')
plt.legend(loc='upper left')
plt.axis([0, x[-1], 0, 9])
plt.savefig('u_example_P1.png')
plt.savefig('u_example_P1.pdf')
# Mark elements
for xi in x[1:-1]:
plt.plot([xi, xi], [0, 9], 'm--')
# Mark nodes
#plt.plot(x, np.zeros(len(x)), 'ro', markersize=4)
plt.savefig('u_example_P1_welms.png')
plt.savefig('u_example_P1_welms.pdf')
plt.show()
def demo1(f=f1, nx=4, ny=3, viewx=58, viewy=345, plain_gnuplot=True):
vertices, cells = mesh(nx, ny, x=[0,1], y=[0,1], diagonal='right')
if f == 'basis':
# basis function
zvalues = np.zeros(vertices.shape[0])
zvalues[int(round(len(zvalues)/2.)) + int(round(nx/2.))] = 1
else:
zvalues = fill(f1, vertices)
if plain_gnuplot:
plt = Gnuplotter()
else:
import scitools.std as plt
"""
if plt.backend == 'gnuplot':
gpl = plt.get_backend()
gpl('unset border; unset xtics; unset ytics; unset ztics')
#gpl('replot')
"""
draw_mesh(vertices, cells, plt)
draw_surface(zvalues, vertices, cells, plt)
plt.axis([0, 1, 0, 1, 0, zvalues.max()])
plt.view(viewx, viewy)
if plain_gnuplot:
plt.savefig('tmp')
else:
plt.savefig('tmp.pdf')
plt.savefig('tmp.eps')
plt.savefig('tmp.png')
plt.show()
def visualize(u, t, title='', filename='tmp'):
plt.plot(t, u, 'b-')
plt.xlabel('t')
plt.ylabel('u')
dt = t[1] - t[0]
plt.title('dt=%g' % dt)
umin = 1.2*u.min(); umax = 1.2*u.max()
plt.axis([t[0], t[-1], umin, umax])
plt.title(title)
plt.savefig(filename + '.png')
plt.savefig(filename + '.pdf')
plt.show()
def visualize(u, t, title='', filename='tmp'):
plt.plot(t, u, 'b-')
plt.xlabel('t')
plt.ylabel('u')
dt = t[1] - t[0]
plt.title('dt=%g' % dt)
umin = 1.2*u.min(); umax = 1.2*u.max()
plt.axis([t[0], t[-1], umin, umax])
plt.title(title)
plt.savefig(filename + '.png')
plt.savefig(filename + '.pdf')
plt.show()
def fe_basis_function_figure(d,
target_elm=[1],
N_e=3,
derivative=0,
filename='tmp.pdf',
labels=False):
"""
Draw all basis functions (or their derivative), of degree d,
associated with element target_elm (may be list of elements).
Add a mesh with N_e elements.
"""
nodes, elements = mesh_uniform(N_e, d)
"""
x = 1.1
print locate_element_vectorized(x, elements, nodes)
print locate_element_scalar(x, elements, nodes)
x = 0.1, 0.4, 0.8
print locate_element_vectorized(x, elements, nodes)
"""
if isinstance(target_elm, int):
target_elm = [target_elm] # wrap in list
# Draw the basis functions for element 1
phi_drawn = [] # list of already drawn phi functions
ymin = ymax = 0
for e in target_elm:
for i in elements[e]:
if not i in phi_drawn:
x, y = phi_glob(i, elements, nodes, derivative=derivative)
if x is None and y is None:
return # abort
ymax = max(ymax, max(y))
ymin = min(ymin, min(y))
plt.plot(x, y, '-')
plt.hold('on')
if labels:
if plt.backend == 'gnuplot':
if derivative == 0:
plt.legend(r'basis func. %d' % i)
else:
plt.legend(r'derivative of basis func. %d' % i)
elif plt.backend == 'matplotlib':
if derivative == 0:
plt.legend(r'\varphi_%d' % i)
else:
plt.legend(r"\varphi_%d'(x)" % i)
phi_drawn.append(i)
plt.axis([nodes[0], nodes[-1], ymin - 0.1, ymax + 0.1])
plot_fe_mesh(nodes, elements, element_marker=[ymin - 0.1, ymax + 0.1])
plt.hold('off')
plt.savefig(filename)
def visualize(u, t, title="", filename="tmp"):
plt.plot(t, u, "b-")
plt.xlabel("t")
plt.ylabel("u")
dt = t[1] - t[0]
plt.title("dt=%g" % dt)
umin = 1.2 * u.min()
umax = 1.2 * u.max()
plt.axis([t[0], t[-1], umin, umax])
plt.title(title)
plt.savefig(filename + ".png")
plt.savefig(filename + ".pdf")
plt.show()
def fe_basis_function_figure(d, target_elm=[1], n_e=3,
derivative=0, filename='tmp.pdf',
labels=False):
"""
Draw all basis functions (or their derivative), of degree d,
associated with element target_elm (may be list of elements).
Add a mesh with n_e elements.
"""
nodes, elements = mesh_uniform(n_e, d)
"""
x = 1.1
print locate_element_vectorized(x, elements, nodes)
print locate_element_scalar(x, elements, nodes)
x = 0.1, 0.4, 0.8
print locate_element_vectorized(x, elements, nodes)
"""
if isinstance(target_elm, int):
target_elm = [target_elm] # wrap in list
# Draw the basis functions for element 1
phi_drawn = [] # list of already drawn phi functions
ymin = ymax = 0
for e in target_elm:
for i in elements[e]:
if not i in phi_drawn:
x, y = phi_glob(i, elements, nodes,
derivative=derivative)
if x is None and y is None:
return # abort
ymax = max(ymax, max(y))
ymin = min(ymin, min(y))
plt.plot(x, y, '-')
plt.hold('on')
if labels:
if plt.backend == 'gnuplot':
if derivative == 0:
plt.legend(r'basis function no. %d' % i)
else:
plt.legend(r'derivative of basis function no. %d' % i)
elif plt.backend == 'matplotlib':
if derivative == 0:
plt.legend(r'\varphi_%d' % i)
else:
plt.legend(r"\varphi_%d'(x)" % i)
phi_drawn.append(i)
plt.axis([nodes[0], nodes[-1], ymin-0.1, ymax+0.1])
plot_fe_mesh(nodes, elements, element_marker=[ymin-0.1, ymax+0.1])
plt.hold('off')
plt.savefig(filename)
def plot_empirical_freq_and_amplitude(u, t):
minima, maxima = minmax(t, u)
p = periods(maxima)
a = amplitudes(minima, maxima)
plt.figure()
from math import pi
w = 2 * pi / p
plt.plot(range(len(p)), w, 'r-')
plt.hold('on')
plt.plot(range(len(a)), a, 'b-')
ymax = 1.1 * max(w.max(), a.max())
ymin = 0.9 * min(w.min(), a.min())
plt.axis([0, max(len(p), len(a)), ymin, ymax])
plt.legend(['estimated frequency', 'estimated amplitude'],
loc='upper right')
return len(maxima)
def plot_empirical_freq_and_amplitude(u, t):
minima, maxima = minmax(t, u)
p = periods(maxima)
a = amplitudes(minima, maxima)
plt.figure()
from math import pi
w = 2*pi/p
plt.plot(range(len(p)), w, 'r-')
plt.hold('on')
plt.plot(range(len(a)), a, 'b-')
ymax = 1.1*max(w.max(), a.max())
ymin = 0.9*min(w.min(), a.min())
plt.axis([0, max(len(p), len(a)), ymin, ymax])
plt.legend(['estimated frequency', 'estimated amplitude'],
loc='upper right')
return len(maxima)
def draw_sparsity_pattern(elements, num_nodes):
"""Illustrate the matrix sparsity pattern."""
import matplotlib.pyplot as plt
sparsity_pattern = {}
for e in elements:
for i in range(len(e)):
for j in range(len(e)):
sparsity_pattern[(e[i],e[j])] = 1
y = [i for i, j in sorted(sparsity_pattern)]
x = [j for i, j in sorted(sparsity_pattern)]
y.reverse()
plt.plot(x, y, 'bo')
ax = plt.gca()
ax.set_aspect('equal')
plt.axis('off')
plt.plot([-1, num_nodes, num_nodes, -1, -1], [-1, -1, num_nodes, num_nodes, -1], 'k-')
plt.savefig('tmp_sparsity_pattern.pdf')
plt.savefig('tmp_sparsity_pattern.png')
plt.show()
def draw_sparsity_pattern(elements, num_nodes):
"""Illustrate the matrix sparsity pattern."""
import matplotlib.pyplot as plt
sparsity_pattern = {}
for e in elements:
for i in range(len(e)):
for j in range(len(e)):
sparsity_pattern[(e[i],e[j])] = 1
y = [i for i, j in sorted(sparsity_pattern)]
x = [j for i, j in sorted(sparsity_pattern)]
y.reverse()
plt.plot(x, y, 'bo')
ax = plt.gca()
ax.set_aspect('equal')
plt.axis('off')
plt.plot([-1, num_nodes, num_nodes, -1, -1], [-1, -1, num_nodes, num_nodes, -1], 'k-')
plt.savefig('tmp_sparsity_pattern.pdf')
plt.savefig('tmp_sparsity_pattern.png')
plt.show()
def plot_boundaries(outer_boundary, inner_boundaries=[], marked_points=None):
if not isinstance(inner_boundaries, (tuple, list)):
inner_boundaries = [inner_boundaries]
boundaries = [outer_boundary]
boundaries.extend(inner_boundaries)
# Find max/min of plotting area
plot_area = [
min([b.x.min() for b in boundaries]),
max([b.x.max() for b in boundaries]),
min([b.y.min() for b in boundaries]),
max([b.y.max() for b in boundaries]),
]
aspect = (plot_area[3] - plot_area[2]) / (plot_area[1] - plot_area[0])
for b in boundaries:
plot(b.x, b.y, daspect=[aspect, 1, 1], daspectratio="manual")
hold("on")
axis(plot_area)
title("Specification of domain with %d boundaries" % len(boundaries))
if marked_points:
for pt, name in marked_points:
text(pt[0], pt[1], name)
def plot_boundaries(outer_boundary, inner_boundaries=[], marked_points=None):
if not isinstance(inner_boundaries, (tuple, list)):
inner_boundaries = [inner_boundaries]
boundaries = [outer_boundary]
boundaries.extend(inner_boundaries)
# Find max/min of plotting area
plot_area = [
min([b.x.min() for b in boundaries]),
max([b.x.max() for b in boundaries]),
min([b.y.min() for b in boundaries]),
max([b.y.max() for b in boundaries])
]
aspect = (plot_area[3] - plot_area[2]) / (plot_area[1] - plot_area[0])
for b in boundaries:
plot(b.x, b.y, daspect=[aspect, 1, 1], daspectratio='manual')
hold('on')
axis(plot_area)
title('Specification of domain with %d boundaries' % len(boundaries))
if marked_points:
for pt, name in marked_points:
text(pt[0], pt[1], name)
def amplification_factor(names):
# Use SciTools since it adds markers to colored lines
from scitools.std import (
plot, title, xlabel, ylabel, hold, savefig,
axis, legend, grid, show, figure)
figure()
curves = {}
p = linspace(0, 3, 99)
curves['exact'] = A_exact(p)
plot(p, curves['exact'])
hold('on')
name2theta = dict(FE=0, BE=1, CN=0.5)
for name in names:
curves[name] = A(p, name2theta[name])
plot(p, curves[name])
axis([p[0], p[-1], -20, 20])
#semilogy(p, curves[name])
plot([p[0], p[-1]], [0, 0], '--') # A=0 line
title('Amplification factors')
grid('on')
legend(['exact'] + names, loc='lower left', fancybox=True)
xlabel(r'$p=-a\cdot dt$')
ylabel('Amplification factor')
savefig('A_growth.png'); savefig('A_growth.pdf')
def amplification_factor(names):
# Use SciTools since it adds markers to colored lines
from scitools.std import (plot, title, xlabel, ylabel, hold, savefig,
axis, legend, grid, show, figure)
figure()
curves = {}
p = linspace(0, 3, 99)
curves['exact'] = A_exact(p)
plot(p, curves['exact'])
hold('on')
name2theta = dict(FE=0, BE=1, CN=0.5)
for name in names:
curves[name] = A(p, name2theta[name])
plot(p, curves[name])
axis([p[0], p[-1], -20, 20])
#semilogy(p, curves[name])
plot([p[0], p[-1]], [0, 0], '--') # A=0 line
title('Amplification factors')
grid('on')
legend(['exact'] + names, loc='lower left', fancybox=True)
xlabel(r'$p=-a\cdot dt$')
ylabel('Amplification factor')
savefig('A_growth.png')
savefig('A_growth.pdf')
def estimate(truncation_error, T, N_0, m, makeplot=True):
"""
Compute the truncation error in a problem with one independent
variable, using m meshes, and estimate the convergence
rate of the truncation error.
The user-supplied function truncation_error(dt, N) computes
the truncation error on a uniform mesh with N intervals of
length dt::
R, t, R_a = truncation_error(dt, N)
where R holds the truncation error at points in the array t,
and R_a are the corresponding theoretical truncation error
values (None if not available).
The truncation_error function is run on a series of meshes
with 2**i*N_0 intervals, i=0,1,...,m-1.
The values of R and R_a are restricted to the coarsest mesh.
and based on these data, the convergence rate of R (pointwise)
and time-integrated R can be estimated empirically.
"""
N = [2**i*N_0 for i in range(m)]
R_I = np.zeros(m) # time-integrated R values on various meshes
R = [None]*m # time series of R restricted to coarsest mesh
R_a = [None]*m # time series of R_a restricted to coarsest mesh
dt = np.zeros(m)
legends_R = []; legends_R_a = [] # all legends of curves
for i in range(m):
dt[i] = T/float(N[i])
R[i], t, R_a[i] = truncation_error(dt[i], N[i])
R_I[i] = np.sqrt(dt[i]*np.sum(R[i]**2))
if i == 0:
t_coarse = t # the coarsest mesh
stride = N[i]/N_0
R[i] = R[i][::stride] # restrict to coarsest mesh
R_a[i] = R_a[i][::stride]
if makeplot:
plt.figure(1)
plt.plot(t_coarse, R[i], log='y')
legends_R.append('N=%d' % N[i])
plt.hold('on')
plt.figure(2)
plt.plot(t_coarse, R_a[i] - R[i], log='y')
plt.hold('on')
legends_R_a.append('N=%d' % N[i])
if makeplot:
plt.figure(1)
plt.xlabel('time')
plt.ylabel('pointwise truncation error')
plt.legend(legends_R)
plt.savefig('R_series.png')
plt.savefig('R_series.pdf')
plt.figure(2)
plt.xlabel('time')
plt.ylabel('pointwise error in estimated truncation error')
plt.legend(legends_R_a)
plt.savefig('R_error.png')
plt.savefig('R_error.pdf')
# Convergence rates
r_R_I = convergence_rates(dt, R_I)
print 'R integrated in time; r:',
print ' '.join(['%.1f' % r for r in r_R_I])
R = np.array(R) # two-dim. numpy array
r_R = [convergence_rates(dt, R[:,n])[-1]
for n in range(len(t_coarse))]
# Plot convergence rates
if makeplot:
plt.figure()
plt.plot(t_coarse, r_R)
plt.xlabel('time')
plt.ylabel('r')
plt.axis([t_coarse[0], t_coarse[-1], 0, 2.5])
plt.title('Pointwise rate $r$ in truncation error $\sim\Delta t^r$')
plt.savefig('R_rate_series.png')
plt.savefig('R_rate_series.pdf')
