def success(listOfObjects):
l = listOfObjects
res = l.strip('][').split(', ')
res = [re.sub(r'[\']', '', line) for line in res]
#print(url_for('static', filename='css/style.css'))
x_cal0 = np.array([79975.5,0.5064,146.785,5.53])
x_cal = scaler.transform(x_cal0.reshape(1, -1))[0]
df_state2 = state_simil(df_state, x_cal)
df_state2['p_def'] =(np.random.rand(df_state2.shape[0])*0.1)+0.3
fig_div = create_plot(df_state2)
p = 0.3238543
fig_div2 = plot2(p)
with open("templates/plot1.html", 'w') as f:
f.write(html_string.format(fig_div,fig_div2))
return render_template('plot1.html')
X = np.linspace(0, Xmax, nX) # Discretisation of distance
eps = (E - E0) * n * FRT # adimensional potential waveform
delta = np.sqrt(D * t[-1]) # cm, diffusion layer thickness
K0 = ks * delta / D # Normalised standard rate constant
#%% Simulation
for k in range(1, nT):
# Boundary condition, Butler-Volmer:
C[k, 0] = (C[k - 1, 1] +
dX * K0 * np.exp(-alpha * eps[k])) / (1 + dX * K0 * (np.exp(
(1 - alpha) * eps[k]) + np.exp(-alpha * eps[k])))
# Solving finite differences:
for j in range(1, nX - 1):
C[k, j] = C[k - 1, j] + lamb * (C[k - 1, j + 1] - 2 * C[k - 1, j] +
C[k - 1, j - 1])
# Denormalising:
i = n * F * Ageo * D * cB * (-C[:, 2] + 4 * C[:, 1] - 3 * C[:, 0]) / (2 * dX *
delta)
cR = C * cB
cO = cB - cR
x = X * delta
end = time.time()
print(end - start)
#%% Plot
p.plot(E, i, "$E$ / V", "$i$ / A")
p.plot2(x, cR[-1, :] * 1e6, x, cO[-1, :] * 1e6, "[R]", "[O]", "x / cm",
"c($t_{end}$,$x$=0) / mM")
cluster_mean = np.mean(cluster_data, axis=0)
sse = np.sum(np.square(cluster_data - cluster_mean))
t_sse.append(sse)
return np.sum(t_sse)
if __name__ == '__main__':
# Generate Data
X, y = make_blobs(n_samples=300, centers=3, n_features=2,
random_state=0, cluster_std=0.6)
# Plot Data
#plot1(X)
# K-Means Clustering
kmeans = KMeans(k=3, max_iters=100)
labels = kmeans.fit_predict(X)
print(kmeans.inertia_)
#plot2(X, labels)
kmeans_sk = KMeansSK(n_clusters=3, max_iter=100, init='random')
labels = kmeans_sk.fit_predict(X)
plot2(X, labels)
print(kmeans_sk.inertia_)
# min Ck |Xi - Ck|