Rewrite Matlab ODE into Python - IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed

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Ramón Wilhelm
Ramón Wilhelm 2021-4-13
回答： Jan 2021-4-13
How can I rewrite the following ODE calculation code into Python code?
countries = 150;
verbose = false;
export_raw_data = true;
if verbose
countries=1;
end
if export_raw_data
raw_features = zeros(4*countries, 501);
raw_features(1,:) = 1:501;
end
features = zeros(countries,8);
rng('default') % For reproducibility
betas = normrnd(0.0005,0.0001,[1,countries]);
for i=1:countries
res = simVirusSpreading(betas(i), false);
times = res(:,1);
% Add randomness to the observations
rand_res = zeros(size(res,1),5);
rand_res(:,1) = times;
for j=2:5
rand_res(:,j) = arrayfun(@(x) normrnd(0,0.025*x)+x, res(:,j));
end
if verbose
fprintf("Beta = %.3f%%\n",betas(1)*100);
plot(times,[res(:,2) rand_res(:,2), res(:,3) rand_res(:,3),res(:,4) rand_res(:,4),res(:,5) rand_res(:,5)]);
end
if export_raw_data
days = [0:500];
for var = 2:5
F = griddedInterpolant(rand_res(:,1),rand_res(:,var));
raw_features((i-1)*4+var,:)=F(days);
end
end
ti = times(times<=50);
n_50 = rand_res(length(ti),3);
d_50 = rand_res(length(ti),3) - rand_res(length(ti)-1,3);
ti = times(times<=150);
n_150 = rand_res(length(ti),3);
d_150 = rand_res(length(ti),3) - rand_res(length(ti)-1,3);
ti = times(times<=300);
n_300 = rand_res(length(ti),3);
d_300 = rand_res(length(ti),3) - rand_res(length(ti)-1,3);
[M,ii] = max(rand_res(:,3));
t_peak = times(ii);
if verbose
fprintf("n_50=%.4f, n_150=%.4f, n_300=%.4f\nd_50=%.4f, d_150=%.4f, d_300=%.4f\nt_peak=%.4f\nx_500=%.4f\n",n_50, n_150, n_300, d_50, d_150, d_300, t_peak, rand_res(end,5) );
end
features(i,:)=[n_50, n_150, n_300, d_50, d_150, d_300, t_peak, rand_res(end,5)];
end
writematrix(features,'../2020 DL data/epidemic_process.csv');
if export_raw_data
writematrix(raw_features,'../2020 DL data/epidemic_process_raw_data.csv');
end
My approach to write the Matlab code above into Python
from scipy.interpolate import RegularGridInterpolator
countries = 150
verbose = False
export_raw_data = True
if verbose:
countries = 1
if export_raw_data:
raw_features = np.zeros([4 * countries, 501])
raw_features[0,:] = np.arange(1, 502)
features = np.zeros([countries, 8])
betas = np.random.default_rng().normal(0.0005, 0.0001, [1, countries])
for i in range(0, countries):
res = simVirusSpreading(betas[0][i], False)
times = res[:,0]
rand_res = np.zeros([res.shape[0], 5])
print(rand_res.shape)
rand_res[0] = times[:,0]
for j in range(1, 5):
rand_res[:,j] = (lambda x: normrnd(0,0.025*x)+x, res[:,j]);
#[tSol, ySol] = ode45(@(t,y) virusSpreading(t, y, beta, mu, nu), t_span, y0);
#sol = solve_ivp(lambda t, y: virusSpreading(t, y, beta, mu, nu), t_span, y0);
##rand_res(:,j) = arrayfun(@(x) normrnd(0,0.025*x)+x, res(:,j));
if verbose:
##fprintf("Beta = %.3f%%\n",betas(1)*100);
plt.plot(times,[res[:,1], rand_res[:,1], res[:,2], rand_res[:,2],res[:,3], rand_res[:,3],res[:,4], rand_res[:,4]]);
if export_raw_data:
days = np.arange(0, 501)
for var in range(1,5):
F = RegularGridInterpolator(rand_res[:,0], rand_res[:,var])
raw_features[(i-1)*4+var,:] = F[days]
ti = times[times<=50]
n_50 = rand_res[(ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1]), 2]
# d_50 = rand_res(length(ti),3) - rand_res(length(ti)-1,3);
d_50 = rand_res[(ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1]), 2] - rand_res[ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1] -1, 3]
ti = times[times<=150]
n_150 = rand_res[(ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1]), 2]
d_150 = rand_res[(ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1]), 2] - rand_res[(ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1])-1, 3]
ti = times[times<=300]
n_300 = rand_res[(ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1]), 2]
d_300 = rand_res[ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1], 2] - rand_res[(ti.shape[0] if ti.shape[0] >= ti.shape[1] else ti.shape[1]) -1, 3]
M, ii = rand_res[:, 2].max(axis=0)
t_peak = times[ii]
if verbose:
##fprintf("n_50=%.4f, n_150=%.4f, n_300=%.4f\nd_50=%.4f, d_150=%.4f, d_300=%.4f\nt_peak=%.4f\nx_500=%.4f\n",n_50, n_150, n_300, d_50, d_150, d_300, t_peak, rand_res(end,5) );
continue
features[i,:] = [n_50, n_150, n_300, d_50, d_150, d_300, t_peak, rand_res[end,4]]
np.savetxt("../epidemic_process.csv", features, delimiter=",")
if export_raw_data:
np.savetxt("../epidemic_process.csv", raw_features, delimiter=",")
I always receive in Python the following code line:
IndexError Traceback (most recent call last)
<ipython-input-145-fced60228e63> in <module>
73 for i in range(0, countries):
74 res = simVirusSpreading(betas[0][i], False)
---> 75 times = res[:,0]
76 rand_res = np.zeros([res.shape[0], 5])
77 print(rand_res.shape)
IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
If you need further information about the "simVirusSpreading" function, please let me know and I will provide the code for it as well.

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回答（1 个）

Jan
Jan 2021-4-13
The error message tells, that res replied by simVirusSpreading() is a vector, but you try to access it as a matrix with 2 indices. I have too few experiences in Python and this is a Matlab forum, but with pure guessing I'd try this:
times = res[:]

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