def work():
''' This code for debugging stuff will change often
'''
import matplotlib.pyplot as plt
from eos import Nominal, Spline_eos
from fit import Opt
vt = data()
nom = Spline_eos(Nominal(),precondition=True)
stick = Stick(nom)
opt = Opt(
nom,
{'stick':stick},
{'stick':vt})
cs,costs = opt.fit(max_iter=10)
D, ep, Sigma_inv = stick.compare(vt, cs[-1])
info = np.dot(D.T, np.dot(Sigma_inv, D))
_vals,_vecs = np.linalg.eigh(info)
_vals = np.maximum(_vals, 0)
i = np.argsort(_vals)[-1::-1]
vals = _vals[i]
vecs = _vecs[i]
n_vals = len(np.where(vals > vals[0]*1e-20)[0])
n_vecs = len(np.where(vals > vals[0]*1e-2)[0])
fig = plt.figure()
ax = fig.add_subplot(2,2,1)
costs = np.array(costs)
x = range(len(costs))
c_min = costs.min()
c_max = costs.max()
if c_min > 0:
y = costs
ylabel = r'$\log_{e}(p(y|c_i))$'
else:
offset = (c_max-c_min)*1e-5 -c_min
y = costs + offset
ylabel = r'$\log_{e}(p(y|\hat c_i)) + {0:.3e}$'.format(offset)
ax.semilogy(x,y)
ax.semilogy(x,y, 'kx')
ax.set_ylabel(ylabel)
ax.set_xlabel(r'$i$')
ax.set_xticks(x)
v = np.linspace(.2,50,500)
ax = fig.add_subplot(2,2,2)
ax.loglog(v,nom(v))
ax = fig.add_subplot(2,2,3)
ax.semilogy(range(n_vals), vals[:n_vals])
ax = fig.add_subplot(2,2,4)
for vec in vecs[:n_vecs]:
f = nom.new_c(vec)
ax.semilogx(v,f(v))
plt.show()
vecs = _vecs[i]
# for j,v in enumerate(vals):
# print('v[{0:2d}] = {1:.3e}'.format(j,v))
return 0
def work():
''' This code for debugging stuff will change often
'''
import matplotlib.pyplot as plt
from eos import Nominal, Spline_eos
from fit import Opt
vt = data()
nom = Spline_eos(Nominal(), precondition=True)
stick = Stick(nom)
opt = Opt(nom, {'stick': stick}, {'stick': vt})
cs, costs = opt.fit(max_iter=10)
D, ep, Sigma_inv = stick.compare(vt, cs[-1])
info = np.dot(D.T, np.dot(Sigma_inv, D))
_vals, _vecs = np.linalg.eigh(info)
_vals = np.maximum(_vals, 0)
i = np.argsort(_vals)[-1::-1]
vals = _vals[i]
vecs = _vecs[i]
n_vals = len(np.where(vals > vals[0] * 1e-20)[0])
n_vecs = len(np.where(vals > vals[0] * 1e-2)[0])
fig = plt.figure()
ax = fig.add_subplot(2, 2, 1)
costs = np.array(costs)
x = range(len(costs))
c_min = costs.min()
c_max = costs.max()
if c_min > 0:
y = costs
ylabel = r'$\log_{e}(p(y|c_i))$'
else:
offset = (c_max - c_min) * 1e-5 - c_min
y = costs + offset
ylabel = r'$\log_{e}(p(y|\hat c_i)) + {0:.3e}$'.format(offset)
ax.semilogy(x, y)
ax.semilogy(x, y, 'kx')
ax.set_ylabel(ylabel)
ax.set_xlabel(r'$i$')
ax.set_xticks(x)
v = np.linspace(.2, 50, 500)
ax = fig.add_subplot(2, 2, 2)
ax.loglog(v, nom(v))
ax = fig.add_subplot(2, 2, 3)
ax.semilogy(range(n_vals), vals[:n_vals])
ax = fig.add_subplot(2, 2, 4)
for vec in vecs[:n_vecs]:
f = nom.new_c(vec)
ax.semilogx(v, f(v))
plt.show()
vecs = _vecs[i]
# for j,v in enumerate(vals):
# print('v[{0:2d}] = {1:.3e}'.format(j,v))
return 0
