def plot_block(block):
# block specifies the parameter grid points
# fblock defines a parameter grid that is finer spaced than the gridpoints
# Query for the weights at the grid points.
ww = np.empty((len(block), my_pca.m))
for i,param in enumerate(block):
weights = my_pca.get_weights(param)
ww[i, :] = weights
# Determine the active dimension by finding the one that has unique > 1
uni = np.array([len(np.unique(block[:, i])) for i in range(len(Starfish.parname))])
active_dim = np.where(uni > 1)[0][0]
ublock = block.copy()
ablock = ublock[:,active_dim]
ublock = np.delete(ublock, active_dim, axis=1)
nactive = len(ablock)
fblock = []
for par in ublock[0, :]:
# Create a space of the parameter the same length as the active
fblock.append(par * np.ones((npoints,)))
# find min and max of active dim. Create a linspace of `npoints` spanning from
# min to max
active = np.linspace(ablock[0], ablock[-1], npoints)
fblock.insert(active_dim, active)
fgrid = np.vstack(fblock).T
# Draw multiple times at the location.
weight_draws = []
for i in range(ndraw):
weight_draws.append(emulator.draw_many_weights(fgrid))
# Now make all of the plots
for eig_i in range(my_pca.m):
fig, ax = plt.subplots(nrows=1, figsize=(6,6))
x0 = block[:, active_dim] # x-axis
# Weight values at grid points
y0 = ww[:, eig_i]
ax.plot(x0, y0, "bo")
x1 = fgrid[:, active_dim]
for i in range(ndraw):
y1 = weight_draws[i][:, eig_i]
ax.plot(x1, y1)
ax.set_ylabel(r"$w_{:}$".format(eig_i))
ax.set_xlabel(Starfish.parname[active_dim])
fstring = "w{:}".format(eig_i) + Starfish.parname[active_dim] + "".join(["{:.1f}".format(ub) for ub in ublock[0, :]])
fig.savefig(Starfish.config["plotdir"] + fstring + ".png")
plt.close('all')
def plot_block(block):
# block specifies the parameter grid points
# fblock defines a parameter grid that is finer spaced than the gridpoints
# Query for the weights at the grid points.
ww = np.empty((len(block), my_pca.m))
for i,param in enumerate(block):
weights = my_pca.get_weights(param)
ww[i, :] = weights
# Determine the active dimension by finding the one that has unique > 1
uni = np.array([len(np.unique(block[:, i])) for i in range(len(Starfish.parname))])
active_dim = np.where(uni > 1)[0][0]
ublock = block.copy()
ablock = ublock[:,active_dim]
ublock = np.delete(ublock, active_dim, axis=1)
nactive = len(ablock)
fblock = []
for par in ublock[0, :]:
# Create a space of the parameter the same length as the active
fblock.append(par * np.ones((npoints,)))
# find min and max of active dim. Create a linspace of `npoints` spanning from
# min to max
active = np.linspace(ablock[0], ablock[-1], npoints)
fblock.insert(active_dim, active)
fgrid = np.vstack(fblock).T
# Draw multiple times at the location.
weight_draws = []
for i in range(ndraw):
weight_draws.append(emulator.draw_many_weights(fgrid))
# Now make all of the plots
for eig_i in range(my_pca.m):
fig, ax = plt.subplots(nrows=1, figsize=(6,6))
x0 = block[:, active_dim] # x-axis
# Weight values at grid points
y0 = ww[:, eig_i]
ax.plot(x0, y0, "bo")
x1 = fgrid[:, active_dim]
for i in range(ndraw):
y1 = weight_draws[i][:, eig_i]
ax.plot(x1, y1)
ax.set_ylabel(r"$w_{:}$".format(eig_i))
ax.set_xlabel(Starfish.parname[active_dim])
fstring = "w{:}".format(eig_i) + Starfish.parname[active_dim] + "".join(["{:.1f}".format(ub) for ub in ublock[0, :]])
fig.savefig(Starfish.config["plotdir"] + fstring + ".png")
plt.close('all')