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
def test_log_like():
from eos import Nominal, Spline_eos
vt = (data())
gun = Gun(Spline_eos(Nominal()))
ll = gun.log_like(*gun.compare(vt))
if close(-ll, 970963830.012):
return 0
else:
return 1
def work():
''' This code for debugging stuff will change often
'''
import matplotlib.pyplot as plt
from eos import Nominal, Spline_eos
vt = (data())
gun = Gun(Spline_eos(Nominal(), precondition=False))
gun.debug_plot(vt, 'test')
gun.debug_plot(None, None, show=True)
return 0
def test_C():
from eos import Experiment, Spline_eos
n_t = 15
N = 10
eos = Spline_eos(Experiment(), N=N, v_min=.38, v_max=4.2)
C = Gun(eos).fit_C(n_t=n_t)
assert C.shape == (n_t, N)
assert close(C[1, 0], 2.05695528e-07)
assert close(C[-1, -1], 6.45823399e-07)
return 0
def test_Pq():
'''
'''
from eos import Nominal, Spline_eos
vt = (data())
eos = Spline_eos(Nominal(), precondition=False)
c = eos.get_c()
gun = Gun(eos)
P, q = eos.Pq_like(*gun.compare(vt, c))
assert P.shape == (50, 50)
assert q.shape == (50, )
assert P[23, 23] == P.max()
value = 3.286529435e-10
assert close(P.max(),
value), 'P.max={0:.9e} != {1:.9e}'.format(P.max(), value)
i = 22
assert np.argmin(q) == i, 'argmin={0} != 11'.format(np.argmin(q), i)
value = 5.680863201e-01
assert close(-q[i],
value), '-q[{2}]={0:.9e} != {1:.9e}'.format(-q[i], value, i)
return 0
def test_Pq():
'''
'''
from eos import Nominal, Spline_eos
vt = (data())
eos = Spline_eos(Nominal(),precondition=False)
c = eos.get_c()
gun = Gun(eos)
P,q = eos.Pq_like(*gun.compare(vt, c))
assert P.shape == (50,50)
assert q.shape == (50,)
assert P[23,23] == P.max()
value = 3.286529435e-10
assert close(P.max(), value), 'P.max={0:.9e} != {1:.9e}'.format(
P.max(), value)
i = 22
assert np.argmin(q) == i, 'argmin={0} != 11'.format(
np.argmin(q), i)
value = 5.680863201e-01
assert close(-q[i], value), '-q[{2}]={0:.9e} != {1:.9e}'.format(
-q[i], value, i)
return 0
def test_B_ep():
''' B[i,j] = dv(t[i])/dc_velocity[j]
ep[i] = v_simulation(t[i]) - v_experiment(t[i])
'''
from eos import Nominal, Spline_eos
vt = (data())
eos = Spline_eos(Nominal())
B, ep = Gun(eos).fit_B_ep(vt)
assert B.shape == (1000, 500)
assert ep.shape == (1000, )
assert np.argmax(ep) == 345
assert close(ep[345], 1461.8632379214614)
assert np.argmax(B[300, :]) == 165
assert close(B[300, 165], 0.66576300914271824)
return 0
def make_stick():
from eos import Nominal, Spline_eos
return Stick(Spline_eos(Nominal()))
