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Fipy error:’’The Factor is exactly singular’’, when applying Neumann boundary conditions

We’re trying to solve a one-dimensional Coupled Continuity-Poisson problem in Fipy. When applying
Dirichlet’s conditions, it gives the correct results, but when we change the boundaries conditions to Neumann’s which is closer to our problem, it gives “The Factor is exactly singular’’ error.
Any help is highly appreciated. The code is as follows (0<x<2.5):
from fipy import *
from fipy import Grid1D, CellVariable, TransientTerm, DiffusionTerm, Viewer
import numpy as np
import math
import matplotlib.pyplot as plt
from matplotlib import cm
from cachetools import cached, TTLCache #caching to increase the speed of python
cache = TTLCache(maxsize=100, ttl=86400) #creating the cache object: the
#first argument= the number of objects we store in the cache.
#____________________________________________________
nx=50
dx=0.05
L=nx*dx
e=math.e
m = Grid1D(nx=nx, dx=dx)
print(np.log(e))
#____________________________________________________
phi = CellVariable(mesh=m, hasOld=True, value=0.)
ne = CellVariable(mesh=m, hasOld=True, value=0.)
phi_face = phi.faceValue
ne_face = ne.faceValue
x = m.cellCenters[0]
t0 = Variable()
phi.setValue((x-1)**3)
ne.setValue(-6*(x-1))
#____________________________________________________
#cached(cache)
def S(x,t):
f=6*(x-1)*e**(-t)+54*((x-1)**2)*e**(-2.*t)
return f
#____________________________________________________
#Boundary Condition:
valueleft_phi=3*e**(-t0)
valueright_phi=6.75*e**(-t0)
valueleft_ne=-6*e**(-t0)
valueright_ne=-6*e**(-t0)
phi.faceGrad.constrain([valueleft_phi], m.facesLeft)
phi.faceGrad.constrain([valueright_phi], m.facesRight)
ne.faceGrad.constrain([valueleft_ne], m.facesLeft)
ne.faceGrad.constrain([valueright_ne], m.facesRight)
#____________________________________________________
eqn0 = DiffusionTerm(1.,var=phi)==ImplicitSourceTerm(-1.,var=ne)
eqn1 = TransientTerm(1.,var=ne) ==
VanLeerConvectionTerm(phi.faceGrad,var=ne)+S(x,t0)
eqn = eqn0 & eqn1
#____________________________________________________
steps = 1.e4
dt=1.e-4
T=dt*steps
F=dt/(dx**2)
print('F=',F)
#____________________________________________________
vi = Viewer(phi)
with open('out2.txt', 'w') as output:
while t0()<T:
print(t0)
phi.updateOld()
ne.updateOld()
res=1.e30
#for sweep in range(steps):
while res > 1.e-4:
res = eqn.sweep(dt=dt)
t0.setValue(t0()+dt)
for m in range(nx):
output.write(str(phi[m])+' ') #+ os.linesep
output.write('\n')
if __name__ == '__main__':
vi.plot()
#____________________________________________________
data = np.loadtxt('out2.txt')
X, T = np.meshgrid(np.linspace(0, L, len(data[0,:])), np.linspace(0, T,
len(data[:,0])))
fig = plt.figure(3)
ax = fig.add_subplot(111,projection='3d')
ax.plot_surface(X, T, Z=data)
plt.show(block=True)

The issue with these equations, particularly eqn0, is that they admit an infinite number of solutions when Neumann boundary conditions are applied on both boundaries. You can fix this by pinning a value somewhere with an internal fixed value. E.g., based on the analytical solution given in the comments, phi = (x-1)**3 * exp(-t), we can pin phi = 0 at x = 1 with
mask = (m.x > 1-dx/2) & (m.x < 1+dx/2)
largeValue = 1e6
value = 0.
#____________________________________________________
eqn0 = (DiffusionTerm(1.,var=phi)==ImplicitSourceTerm(-1.,var=ne)
+ ImplicitSourceTerm(mask * largeValue, var=phi) - mask * largeValue * value)
At this point, the solutions still do not agree with the expected solutions. This is because, while you have called ne.faceGrad.constrain() for the left and right boundaries, does not appear in the discretized equations. You can see this if you plot ne; the gradient is zero at both boundaries despite the constraint because FiPy never "sees" the constraint.
What does appear is the flux . By applying fixed flux boundary conditions, I obtain the expected solutions:
ne_left = 6 * numerix.exp(-t0)
ne_right = -9 * numerix.exp(-t0)
eqn1 = (TransientTerm(1.,var=ne)
== VanLeerConvectionTerm(phi.faceGrad * m.interiorFaces,var=ne)
+ S(x,t0)
+ (m.facesLeft * ne_left * phi.faceGrad).divergence
+ (m.facesRight * ne_right * phi.faceGrad).divergence)
You can probably get better convergence properties with
eqn1 = (TransientTerm(1.,var=ne)
== DiffusionTerm(coeff=ne.faceValue * m.interiorFaces, var=phi)
+ S(x,t0)
+ (m.facesLeft * ne_left * phi.faceGrad).divergence
+ (m.facesRight * ne_right * phi.faceGrad).divergence)
but either formulation seems to work.
Note: phi.faceGrad.constrain() is fine, because the flux does appear in DiffusionTerm(coeff=1., var=phi).
Separately, it appears (based on "The Factor is exactly singular") that you are solving with the SciPy LinearLUSolver. The PETSc LinearLUSolver does better, but the baseline value of the solution wanders all over the place. Calling
res = eqn.sweep(dt=dt, solver=LinearGMRESSolver())
also seems to produce stable results (without pinning an internal value). This behavior probably shouldn't be relied on; pinning a value is the right thing to do.

Mathematical operations with ConvectionTerm() or DiffusionTerm() in FiPy

I'm currently learning how to use FiPy and eventually want to use it to solve some biological problems. I have been trying to implement the following PDE system which describes hyphal growth of fungi:
PDE system
I can implement the system without problems, as long as I ignore that the change of si over time depends on the absolute change of p over space abs(dp/dx) in the fourth equation dsi/dt. Here is my code without abs():
from fipy import *
##### produce mesh
nx= 100.
ny= nx
dx = 1/100
dy = dx
L = nx*dx
mesh = Grid2D(nx=nx,ny=ny,dx=dx,dy=dy)
x,y = mesh.cellCenters
##### parameters
b=1e+7 # branching rate (no. branches * cm^-1 * hyphae * day^-1 * (mol glucose)^-1)
f = 10. # fusion rate (no. fusions in cm * day^-1)
v = 1e+5 # cm * mol^-1 * day^-1
d = 0.5 # day^-1
r = 0. # degradation of hyphae
c1 = 9e+2 # cm * mol^-1 * day^-1
c2 = 1e-7 # mol * cm^-1
c3 = 1e+3 # cm * mol^-1 * day^-1, c1/c3 = 90% efficiency
c4 = 1e-8 # cm^-1
De = 1e-3 # diffusion of external substrate (cm² * s^-1)
Di = 1e-2 # diffusion of internal substrate (cm³ * day^-1)
Da = 0.
##### define state variables:
m = CellVariable(name="m",mesh=mesh,hasOld=True,value=0.)
mi = CellVariable(name="mi",mesh=mesh,hasOld=True,value=0.)
p = CellVariable(name="p",mesh=mesh,hasOld=True,value=0.)
si = CellVariable(name="si",mesh=mesh,hasOld=True,value=0.)
se = CellVariable(name="se",mesh=mesh,hasOld=True,value=0.)
##### differential equations
eqm = (TransientTerm(var=m)== si*v*p - ImplicitSourceTerm(var=m,coeff=d))
eqmi = (TransientTerm(var=mi)==m*d - ImplicitSourceTerm(var=mi,coeff=r))
eqp = (TransientTerm(var=p)== - ConvectionTerm(var=p,coeff=[[v]]*si) + b*si*m - ImplicitSourceTerm(var=p,coeff=f*m))
eqsi = (TransientTerm(var=si)== DiffusionTerm(var=si,coeff=Di*m)- DiffusionTerm(var=p,coeff=Da*m*si) + ImplicitSourceTerm(var=si,coeff=c1*m*se) - ImplicitSourceTerm(var=si,coeff=c2*v*p) - ConvectionTerm(var=p,coeff=[[c4*Da]]*(m*si)))
eqse = (TransientTerm(var=se) == DiffusionTerm(var=se,coeff=De) - ImplicitSourceTerm(var=se,coeff=c3*m*si))
# initial values
m0 = 100.
mi0 = 0.
p0 = 500.
si0 = 1e-5
se0 = 3e-5
r = 3. # radius of initial plug
m.setValue(m0,where=((x>(L/2-(r*dx)))&(x<(L/2+(r*dx)))&(y>(L/2-(r*dy)))&(y<(L/2+(r*dy)))))
mi.setValue(mi0,where=((x>(L/2-(r*dx)))&(x<(L/2+(r*dx)))&(y>(L/2-(r*dy)))&(y<(L/2+(r*dy)))))
p.setValue(p0,where=((x>(L/2-(r*dx)))&(x<(L/2+(r*dx)))&(y>(L/2-(r*dy)))&(y<(L/2+(r*dy)))))
si.setValue(si0,where=((x>(L/2-(r*dx)))&(x<(L/2+(r*dx)))&(y>(L/2-(r*dy)))&(y<(L/2+(r*dy)))))
se.setValue(se0)
# boundary conditions
#----------------
# simulation
eq = eqm & eqmi & eqp & eqsi & eqse
vi = Viewer((m))
from builtins import range
for t in range(100):
m.updateOld()
mi.updateOld()
p.updateOld()
si.updateOld()
se.updateOld()
eq.solve(dt=0.1)
print(t)
vi.plot()
Now, I tried to simply write
eqsi = (TransientTerm(var=si)== DiffusionTerm(var=si,coeff=Di*m)- DiffusionTerm(var=p,coeff=Da*m*si) + ImplicitSourceTerm(var=si,coeff=c1*m*se) - ImplicitSourceTerm(var=si,coeff=c2*v*p) - abs(ConvectionTerm(var=p,coeff=[[c4*Da]]*(m*si))))
which (expectedly) gives out an error: "bad operand type for abs(): 'PowerLawConvectionTerm'"
I tried to work my way around it by adding another CellVariable dpdx:
dpdx= CellVariable(name="dpdx",mesh=mesh,hasOld=True,value=0.)
eqdpdx= (dpdx == ConvectionTerm(var=p,coeff=[[1]]))
eqsi = (TransientTerm(var=si)== DiffusionTerm(var=si,coeff=Di*m)- DiffusionTerm(var=p,coeff=Da*m*si) + ImplicitSourceTerm(var=si,coeff=c1*m*se) - ImplicitSourceTerm(var=si,coeff=c2*v*p) - abs(dpdx)*c4*Da*m*si)
which then gives the error "ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", which is probably caused by the fact that eqdpdx is not a derivative function?
My question is: Is it possible at all to perform mathematical operations with a ConvectionTerm? Can I somehow express the absolute change of p?
And also, how can I express non derivative function that change over space like eqdpdx (or any dynamic parameters)?
I looked through the FiPy manual and could not find a solution - I am sorry if my question is trivial or has been answered elsewhere.

It's important to understand what FiPy Terms are. They are human-readable expressions of parts of PDEs that can be discretized to linear algebra. If you apply some potentially non-linear function to that linear algebra, then it's not linear algebra anymore. This use is not supported and I'm not sure how it even could be.
Fortunately, you don't need it. dp/dx is not a ConvectionTerm, it's a gradient. I would write this term as
- ImplicitSourceTerm(coeff=c4*Da*m*p.grad.mag, var=si)
As a side note, 1D equations are the Devil's work. They will lead you astray every single time. You can use nabla notation like we do in the manual or you can use Einstein notation, but always keep clear in your head whether your expression is scalar or vector (or tensor or ...).

How to write equation terms with independent variable in FiPy?

I am looking to solve a diffusion equation using FiPy and have read some of their documentation, but can't seem to find anything that relates to writing a diffusion term that includes additional terms that are functions of the independent variable (i.e. space). The closest thing that I found was on the FAQ, where they suggest rewriting additional terms as a ConvectionTerm. However, I believe this only applies to the case where the additional terms are functions of the solution variable rather than the independent variable. For example, I am trying to solve a 1D diffusion equation with the following diffusion term (where derivatives are w.r.t. to the independent variable x, and y is the solution variable):
D * sin(x) * Div_x {sin(x) * Grad_x {y}}
I feel that this is a pretty simple expression, but I can't find how to express it in FiPy notation. Any help would be hugely appreciated!
Exact Code:
from fipy import Variable,FaceVariable,CellVariable,Grid1D,ImplicitSourceTerm,TransientTerm,DiffusionTerm,Viewer,ConvectionTerm
from fipy.tools import numerix
D = 1
c0 = 1
ka = 1
r0 = 1
nx = 100
dx = 2*math.pi/100
mesh = Grid1D(nx=nx, dx=dx)
conc = CellVariable(name="concentration", mesh=mesh, value=0.) # This is the "phi" in the docs
valueLeft = c0
valueRight = 0
conc.constrain(valueRight, mesh.facesRight)
conc.constrain(valueLeft, mesh.facesLeft)
timeStepDuration = 0.9 * dx**2 / (2 * D)
steps = 100
show_per_steps = 50
A = 1 / (r0**2 * numerix.sin(mesh.x)[0])
dA = -(numerix.cos(mesh.x)[0])/(r0**2 * numerix.sin(mesh.x)[0]**2)
dsindA = (numerix.cos(mesh.x)[0])**3/(numerix.sin(mesh.x)[0])**2
eqX = TransientTerm() + ImplicitSourceTerm(ka) == DiffusionTerm(D*A*numerix.sin(mesh.x)[0]) - ConvectionTerm(D*dA*numerix.cos(mesh.x)[0])+ D*conc*dsindA
from builtins import range
for step in range(steps):
eqX.solve(var=conc, dt=timeStepDuration)
if __name__ == '__main__' and step % show_per_steps == 0:
viewer = Viewer(vars=(conc), datamin=0., datamax=c0)
viewer.plot()

FiPy allows the terms' coefficients to be functions of space. For example, the following works in FiPy,
from fipy import Grid1D, CellVariable, Viewer
from fipy import TransientTerm, numerix, DiffusionTerm
from fipy import LinearLUSolver
m = Grid1D(nx=100, Lx=numerix.pi / 4.)
v = CellVariable(mesh=m)
v[:] = m.x**2
eqn = TransientTerm() == DiffusionTerm(numerix.sin(m.x))
vi = Viewer(v, colorbar=None)
vi.plot()
solver = LinearLUSolver()
for i in range(10):
eqn.solve(v, dt=0.1, solver=solver)
vi.plot()
print('step', i)
input('stopped')
In the above, the diffusion coefficient is a function of space. The m.x is a CellVariable that holds the cell center positions. numerix is used which enables operations on FiPy variables in the same way as Numpy does for Numpy arrays.
Now, in the above question there is a sin(x) outside of the derivative which is not allowed in FiPy. Everything needs to fit inside of the derivative to work with FiPy. So, we need to rewrite the term so that all the coefficients are inside of the derivative. For any general case, we can write
which allows us to use a diffusion, convection and source to represent the term in FiPy. If f=sin(x) and g=sin(x) then the FiPy code would be
s = numerix.sin(m.cellCenters)
c = numerix.cos(m.cellCenters)
eqn = ... + DiffusionTerm(D * s[0] * s[0]) - ConvectionTerm(D * s * c) + D * y * (c[0] * c[0] - s[0] * s[0])
The ... are included as I don't know the full equation.

How to distinguish if Python or Matlab is wrong/faulty?

I am trying to use SVD and an Eigendecomposition for some data analysis using Dynamic Mode Decomposition. I am running into a simple problem of getting different results from Matlab and Python. I'm confused and don't know why Python is giving me wrong results/matrix values but everything looks (I think IS) correct.
So instead of using real data this time and looking at large data sets, I generated data. I will try to look at an eigenvalue plot after the eigendecomposition. I also use a delay embedding for the data because I will work with a data vector which is only (2x100), so I will perform a type of Hankel matrix to enrich the data with 10 delays.
clear all; close all; clc;
data = linspace(1,100);
data2 = linspace(2,101);
data = [data;data2];
numDelays = 10;
relTol= 10^-6;
%% Create first and second snap shot matrices for DMD. Any columns with missing
% data are not used.
disp('Constructing Data Matricies:')
X = zeros((numDelays+1)*size(data,1),size(data,2)-(numDelays+1));
Y = zeros(size(X));
for i = 1:numDelays+1
X(1 + (i-1)*size(data,1):i*size(data,1),:) = ...
data(:,(i):size(data,2)-(numDelays+1) + (i-1));
Y(1 + (i-1)*size(data,1):i*size(data,1),:) = ...
data(:,(i+1):size(data,2)-(numDelays+1) + (i));
end
[U,S,V] = svd(X);
r = find(diag(S)>S(1,1)*relTol,1,'last');
disp(['DMD subspace dimension:',num2str(r)])
U = U(:,1:r);
S = S(1:r,1:r);
V = V(:,1:r);
Atil = (U'*Y)*V*(S^-1);
[what,lambda] = eig(Atil);
Phi = (Y*V)*(S^-1)*what;
Keigs = diag(lambda);
tt = linspace(0,2*pi,101);
figure;
plot(real(Keigs),imag(Keigs),'ro')
hold on
plot(cos(tt),sin(tt),'--')
import scipy.io as sc
import math as m
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
from numpy import dot, multiply, diag, power, pi, exp, sin, cos, cosh, tanh, real, imag
from scipy.linalg import expm, sinm, cosm, fractional_matrix_power, svd, eig, inv
def dmd(X, Y, relTol):
U2,Sig2,Vh2 = svd(X, False) # SVD of input matrix
S = np.zeros((Sig2.shape[0], Sig2.shape[0])) # Create S matrix with zeros based on Diag of S
np.fill_diagonal(S, Sig2) # Fill diagonal of S matrix with the nonzero values
r = np.count_nonzero(np.diag(S) > S[0,0] * relTol) # rank truncation
U = U2[:,:r]
Sig = diag(Sig2)[:r,:r] #GOOD =)
V = Vh2.conj().T[:,:r]
Atil = dot(dot(dot(U.conj().T, Y), V), inv(Sig)) # build A tilde
print(Atil)
mu,W = eig(Atil)
Phi = dot(dot(dot(Y, V), inv(Sig)), W) # build DMD modes
return mu, Phi
data = np.array([(np.linspace(1,100,100)),(np.linspace(2,101,100))])
Data = np.array(data)
######### Choose number of Delays ###########
# observable (coordinates of feature points). Setting to zero means only
# experimental observables will be used.
numDelays = 10
relTol = 10**-6
########## Create Data Matrices for DMD ###############
# Create first and second snap shot matrices for DMD. Any columns with missing
# data are not used.
X = np.zeros(((numDelays + 1) * data.shape[0], data.shape[1] - (numDelays + 1)))
Y = np.zeros(X.shape)
for i in range(1, numDelays + 2):
X[0 + (i - 1) * Data.shape[0]:i * Data.shape[0], :] = Data[:, (i):Data.shape[1] - (numDelays + 1) + (i - 0)]
Y[0 + (i - 1) * Data.shape[0]:i * Data.shape[0], :] = Data[:, (i + 0):Data.shape[1] - (numDelays + 1) + (i)]
Keigs, Phi = dmd(X, Y, relTol)
tt = np.linspace(0,2*np.pi,101)
plt.figure()
plt.plot(np.cos(tt),np.sin(tt),'--')
plt.plot(Keigs.real,Keigs.imag,'ro')
plt.title('DMD Eigenvalues')
plt.xlabel(r'Real $\ lambda$')
plt.ylabel(r'Imaginary $\ lambda$')
# plt.axes().set_aspect('equal')
plt.show()
So in matlab and python, I get my eigenvalues to all sit on the unit circle (as expect) and I get precisely one, sitting at 1.
So the problem comes when I look at the matrices from SVD, they appear to have different values. The only matrix that is the same is the 'S or Sig' matrix. The rest will differ a number or +/- sign. The biggest thing that peaked my interest is the Atil matrix.
In matlab, it looks like,
[1.0157, -0.3116; 7.91229e-4, 0.9843]
And python it looks like,
[1.0, -4.508e-15; -4.439e-18, 1.0]
Now this may look slightly off due to numerical error possibly but when I look at real data and these differ, it messes up my analysis.

SVD of a non-square matrix is not unique in U and V. Even if you have a square matrix with non-zero, non-degenerate singular values, singular vectors in U and V are only unique up to a sign factor.
https://math.stackexchange.com/questions/644327/how-unique-on-non-unique-are-u-and-v-in-singular-value-decomposition-svd
Moreover, Matlab (LAPACK + BLAS) and scipy.linalg.svd may use different algorithms for SVD.
This can lead to the differences you have experienced.

Artefacts from Riemann sum in scipy.signal.convolve

Short summary: How do I quickly calculate the finite convolution of two arrays?
Problem description
I am trying to obtain the finite convolution of two functions f(x), g(x) defined by
To achieve this, I have taken discrete samples of the functions and turned them into arrays of length steps:
xarray = [x * i / steps for i in range(steps)]
farray = [f(x) for x in xarray]
garray = [g(x) for x in xarray]
I then tried to calculate the convolution using the scipy.signal.convolve function. This function gives the same results as the algorithm conv suggested here. However, the results differ considerably from analytical solutions. Modifying the algorithm conv to use the trapezoidal rule gives the desired results.
To illustrate this, I let
f(x) = exp(-x)
g(x) = 2 * exp(-2 * x)
the results are:
Here Riemann represents a simple Riemann sum, trapezoidal is a modified version of the Riemann algorithm to use the trapezoidal rule, scipy.signal.convolve is the scipy function and analytical is the analytical convolution.
Now let g(x) = x^2 * exp(-x) and the results become:
Here 'ratio' is the ratio of the values obtained from scipy to the analytical values. The above demonstrates that the problem cannot be solved by renormalising the integral.
The question
Is it possible to use the speed of scipy but retain the better results of a trapezoidal rule or do I have to write a C extension to achieve the desired results?
An example
Just copy and paste the code below to see the problem I am encountering. The two results can be brought to closer agreement by increasing the steps variable. I believe that the problem is due to artefacts from right hand Riemann sums because the integral is overestimated when it is increasing and approaches the analytical solution again as it is decreasing.
EDIT: I have now included the original algorithm 2 as a comparison which gives the same results as the scipy.signal.convolve function.
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
import math
def convolveoriginal(x, y):
'''
The original algorithm from http://www.physics.rutgers.edu/~masud/computing/WPark_recipes_in_python.html.
'''
P, Q, N = len(x), len(y), len(x) + len(y) - 1
z = []
for k in range(N):
t, lower, upper = 0, max(0, k - (Q - 1)), min(P - 1, k)
for i in range(lower, upper + 1):
t = t + x[i] * y[k - i]
z.append(t)
return np.array(z) #Modified to include conversion to numpy array
def convolve(y1, y2, dx = None):
'''
Compute the finite convolution of two signals of equal length.
#param y1: First signal.
#param y2: Second signal.
#param dx: [optional] Integration step width.
#note: Based on the algorithm at http://www.physics.rutgers.edu/~masud/computing/WPark_recipes_in_python.html.
'''
P = len(y1) #Determine the length of the signal
z = [] #Create a list of convolution values
for k in range(P):
t = 0
lower = max(0, k - (P - 1))
upper = min(P - 1, k)
for i in range(lower, upper):
t += (y1[i] * y2[k - i] + y1[i + 1] * y2[k - (i + 1)]) / 2
z.append(t)
z = np.array(z) #Convert to a numpy array
if dx != None: #Is a step width specified?
z *= dx
return z
steps = 50 #Number of integration steps
maxtime = 5 #Maximum time
dt = float(maxtime) / steps #Obtain the width of a time step
time = [dt * i for i in range (steps)] #Create an array of times
exp1 = [math.exp(-t) for t in time] #Create an array of function values
exp2 = [2 * math.exp(-2 * t) for t in time]
#Calculate the analytical expression
analytical = [2 * math.exp(-2 * t) * (-1 + math.exp(t)) for t in time]
#Calculate the trapezoidal convolution
trapezoidal = convolve(exp1, exp2, dt)
#Calculate the scipy convolution
sci = signal.convolve(exp1, exp2, mode = 'full')
#Slice the first half to obtain the causal convolution and multiply by dt
#to account for the step width
sci = sci[0:steps] * dt
#Calculate the convolution using the original Riemann sum algorithm
riemann = convolveoriginal(exp1, exp2)
riemann = riemann[0:steps] * dt
#Plot
plt.plot(time, analytical, label = 'analytical')
plt.plot(time, trapezoidal, 'o', label = 'trapezoidal')
plt.plot(time, riemann, 'o', label = 'Riemann')
plt.plot(time, sci, '.', label = 'scipy.signal.convolve')
plt.legend()
plt.show()
Thank you for your time!

or, for those who prefer numpy to C. It will be slower than the C implementation, but it's just a few lines.
>>> t = np.linspace(0, maxtime-dt, 50)
>>> fx = np.exp(-np.array(t))
>>> gx = 2*np.exp(-2*np.array(t))
>>> analytical = 2 * np.exp(-2 * t) * (-1 + np.exp(t))
this looks like trapezoidal in this case (but I didn't check the math)
>>> s2a = signal.convolve(fx[1:], gx, 'full')*dt
>>> s2b = signal.convolve(fx, gx[1:], 'full')*dt
>>> s = (s2a+s2b)/2
>>> s[:10]
array([ 0.17235682, 0.29706872, 0.38433313, 0.44235042, 0.47770012,
0.49564748, 0.50039326, 0.49527721, 0.48294359, 0.46547582])
>>> analytical[:10]
array([ 0. , 0.17221333, 0.29682141, 0.38401317, 0.44198216,
0.47730244, 0.49523485, 0.49997668, 0.49486489, 0.48254154])
largest absolute error:
>>> np.max(np.abs(s[:len(analytical)-1] - analytical[1:]))
0.00041657780840698155
>>> np.argmax(np.abs(s[:len(analytical)-1] - analytical[1:]))
6

Short answer: Write it in C!
Long answer
Using the cookbook about numpy arrays I rewrote the trapezoidal convolution method in C. In order to use the C code one requires three files (https://gist.github.com/1626919)
The C code (performancemodule.c).
The setup file to build the code and make it callable from python (performancemodulesetup.py).
The python file that makes use of the C extension (performancetest.py)
The code should run upon downloading by doing the following
Adjust the include path in performancemodule.c.
Run the following
python performancemodulesetup.py build
python performancetest.py
You may have to copy the library file performancemodule.so or performancemodule.dll into the same directory as performancetest.py.
Results and performance
The results agree neatly with one another as shown below:
The performance of the C method is even better than scipy's convolve method. Running 10k convolutions with array length 50 requires
convolve (seconds, microseconds) 81 349969
scipy.signal.convolve (seconds, microseconds) 1 962599
convolve in C (seconds, microseconds) 0 87024
Thus, the C implementation is about 1000 times faster than the python implementation and a bit more than 20 times as fast as the scipy implementation (admittedly, the scipy implementation is more versatile).
EDIT: This does not solve the original question exactly but is sufficient for my purposes.