def response_func(self):
iters = self.iteration_num
ndims = len(self.params)
# calculate the optimal flat grid based on the hierarchal grid
vecs = []
for p in self.params:
x = []
for iteration in range(0, iters):
x = np.concatenate((x, p.values[iteration]))
last = None
mindist = 1e309
for v in sorted(x):
if v != last:
if last != None:
mindist = min(mindist, v-last)
last = v
debug("%s: %s %s grids" % (p.name, mindist,
(p.pdf.range[1] - p.pdf.range[0])/mindist))
vecs.append(np.arange(p.pdf.range[0], p.pdf.range[1] + mindist, mindist))
xx = meshgridn(*vecs)
pts = np.vstack(map(np.ndarray.flatten, xx)).T
# add column for results
pts = np.append(pts, np.zeros((len(pts),1)), axis=1)
# interpolate function requires array in contiguous memory
if pts.flags['C_CONTIGUOUS'] == False:
pts = np.ascontiguousarray(pts)
self._uqsolver.interpolate(pts)
return SampledFunc(pts, params=self.params)
"""
def test_meshgrid_example():
# official example for meshgrid() from numpy docs
X, Y = meshgridn([1, 2, 3], [4, 5, 6, 7])
assert (X == np.array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3],
[1, 2, 3]])).all()
assert (Y == np.array([[4, 4, 4],
[5, 5, 5],
[6, 6, 6],
[7, 7, 7]])).all()
def minmax(self):
import heapq
from scipy.optimize import fmin_l_bfgs_b
dims = len(self.vars)
if dims < 3:
steps = 20
elif dims < 5:
steps = 10
else:
steps = 3
d = []
for v in self.vars:
d.append(np.linspace(*v[1], num=steps))
xx = meshgridn(*d)
pts = np.vstack(map(np.ndarray.flatten, xx)).T
res = self.evala(pts)
if type(res) is not np.ndarray:
return res, res
h = []
for i, p in enumerate(pts):
if isinstance(p, np.ndarray):
p = p.tolist()
heapq.heappush(h, (res[i], p))
bounds = [x[1] for x in self.vars]
minval = float('inf')
for p in heapq.nsmallest(5, h):
pt, v, d = fmin_l_bfgs_b(self.evala,
p[1],
approx_grad=True,
bounds=bounds)
if d['warnflag'] == 0 and v < minval:
minval = v
maxval = float('inf')
for p in heapq.nlargest(5, h):
pt, v, d = fmin_l_bfgs_b(lambda x: -self.evala(x),
p[1],
approx_grad=True,
bounds=bounds)
if d['warnflag'] == 0 and v < maxval:
maxval = v
maxval = -maxval
return minval, maxval
def _plot2(self, steps=40, legend=True, title=True, labels=True, **kwargs):
# 2 dimensions plus result on Z-axis = 3D plot
x = np.linspace(*self.vars[0][1], num=steps + 1)
y = np.linspace(*self.vars[1][1], num=steps + 1)
xx = meshgridn(x, y)
pts = np.vstack(map(np.ndarray.flatten, xx)).T
pts = np.array(self.evala(pts))
fig = kwargs.get('fig')
if fig is None:
fig = plt.figure()
ax = kwargs.get('ax')
if ax is None:
ax = Axes3D(fig, azim=30, elev=30)
X, Y = xx
Z = pts.reshape(X.shape)
surf = ax.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
cmap=cm.jet,
alpha=0.7,
linewidth=0)
# legend
if legend:
fig.colorbar(surf, shrink=0.5)
# titles
xlab = self.vars[0][0]
ylab = self.vars[1][0]
if self.params:
if xlab != self.params[0].description:
xlab = "%s (%s)" % (xlab, self.params[0].description)
if ylab != self.params[1].description:
ylab = "%s (%s)" % (ylab, self.params[1].description)
if labels:
plt.xlabel(xlab)
plt.ylabel(ylab)
if title:
plt.title('Response Plot')
return ax
def minmax(self):
import heapq
from scipy.optimize import fmin_l_bfgs_b
dims = len(self.vars)
if dims < 3:
steps = 20
elif dims < 5:
steps = 10
else:
steps = 3
d = []
for v in self.vars:
d.append(np.linspace(*v[1], num=steps))
xx = meshgridn(*d)
pts = np.vstack(list(map(np.ndarray.flatten, xx))).T
res = self.evala(pts)
if type(res) is not np.ndarray:
return res, res
h = []
for i, p in enumerate(pts):
if isinstance(p, np.ndarray):
p = p.tolist()
heapq.heappush(h, (res[i], p))
bounds = [x[1] for x in self.vars]
minval = float('inf')
for p in heapq.nsmallest(5, h):
pt, v, d = fmin_l_bfgs_b(self.evala, p[1], approx_grad=True, bounds=bounds)
if d['warnflag'] == 0 and v < minval:
minval = v
maxval = float('inf')
for p in heapq.nlargest(5, h):
pt, v, d = fmin_l_bfgs_b(lambda x: -self.evala(x), p[1], approx_grad=True, bounds=bounds)
if d['warnflag'] == 0 and v < maxval:
maxval = v
maxval = -maxval
return minval, maxval
def test_meshgrid_4d():
xx = meshgridn([1, 2], [3, 4], [5, 6], [7, 8])
assert len(xx) == 4
assert (np.vstack(map(np.ndarray.flatten, xx)).T ==
np.array([[1, 3, 5, 7],
[2, 3, 5, 7],
[1, 4, 5, 7],
[2, 4, 5, 7],
[1, 3, 6, 7],
[2, 3, 6, 7],
[1, 4, 6, 7],
[2, 4, 6, 7],
[1, 3, 5, 8],
[2, 3, 5, 8],
[1, 4, 5, 8],
[2, 4, 5, 8],
[1, 3, 6, 8],
[2, 3, 6, 8],
[1, 4, 6, 8],
[2, 4, 6, 8]])).all()
def test_meshgrid_4d():
xx = meshgridn([1, 2], [3, 4], [5, 6], [7, 8])
assert len(xx) == 4
assert (np.vstack(map(np.ndarray.flatten, xx)).T == np.array([[1, 3, 5, 7],
[2, 3, 5, 7],
[1, 4, 5, 7],
[2, 4, 5, 7],
[1, 3, 6, 7],
[2, 3, 6, 7],
[1, 4, 6, 7],
[2, 4, 6, 7],
[1, 3, 5, 8],
[2, 3, 5, 8],
[1, 4, 5, 8],
[2, 4, 5, 8],
[1, 3, 6, 8],
[2, 3, 6, 8],
[1, 4, 6, 8],
[2, 4, 6,
8]])).all()
def _plot2(self, steps=40, legend=True, title=True, labels=True, **kwargs):
# 2 dimensions plus result on Z-axis = 3D plot
x = np.linspace(*self.vars[0][1], num=steps+1)
y = np.linspace(*self.vars[1][1], num=steps+1)
xx = meshgridn(x, y)
pts = np.vstack(map(np.ndarray.flatten, xx)).T
pts = np.array(self.evala(pts))
fig = kwargs.get('fig')
if fig is None:
fig = plt.figure()
ax = kwargs.get('ax')
if ax is None:
ax = Axes3D(fig, azim=30, elev=30)
X, Y = xx
Z = pts.reshape(X.shape)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, alpha=0.7, linewidth=0)
# legend
if legend:
fig.colorbar(surf, shrink=0.5)
# titles
xlab = self.vars[0][0]
ylab = self.vars[1][0]
if self.params:
if xlab != self.params[0].description:
xlab = "%s (%s)" % (xlab, self.params[0].description)
if ylab != self.params[1].description:
ylab = "%s (%s)" % (ylab, self.params[1].description)
if labels:
plt.xlabel(xlab)
plt.ylabel(ylab)
if title:
plt.title('Response Plot')
return ax
def test_meshgrid_1d():
X = meshgridn([1, 2])
assert len(X) == 1
assert (X == np.array([1, 2])).all()
def test_meshgrid_empty():
X = meshgridn([])
assert len(X) == 1
assert len(X[0]) == 0
def test_meshgrid_example():
# official example for meshgrid() from numpy docs
X, Y = meshgridn([1, 2, 3], [4, 5, 6, 7])
assert (X == np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])).all()
assert (Y == np.array([[4, 4, 4], [5, 5, 5], [6, 6, 6], [7, 7, 7]])).all()
def test_meshgrid_1d():
X = meshgridn([1, 2])
assert len(X) == 1
assert (X == np.array([1, 2])).all()
def test_meshgrid_empty():
X = meshgridn([])
assert len(X) == 1
assert len(X[0]) == 0