def test_visualize():
#This fails if GSL is not installed
try:
design = Design()
except ImportError:
return
#Add the dimensions
design.add_parameter("Omega_m", min=0.1, max=0.9, label=r"$\Omega_m$")
design.add_parameter("w", min=-2.0, max=-0.1, label=r"$w$")
design.add_parameter("sigma8", min=0.01, max=1.6, label=r"$\sigma_8$")
#Lay down 50 points
design.put_points(50)
print(design)
#The cost function should be the diagonal one
np.testing.assert_approx_equal(design.diagonalCost(Lambda=1.0),
design.cost(p=2.0, Lambda=1.0))
#Visualize the 3d diagonal configuration
design.visualize(color="blue")
design.set_title("Cost={0:.2f}".format(design.diagonalCost(Lambda=1.0)))
design.savefig("design_diagonal_3d.png")
#Visualize the 2d (Om,si8) projection
fig, ax = plt.subplots()
design.visualize(fig=fig,
ax=ax,
parameters=["Omega_m", "sigma8"],
color="red",
marker=".")
design.savefig("design_diagonal_2d.png")
#Now perform the sampling of the parameter space
deltaPerc = design.sample(Lambda=1.0, p=2.0, seed=1, maxIterations=100000)
#Visualize the 3d configuration
design.visualize(color="blue")
design.set_title("Cost={0:.2f}".format(design.cost(Lambda=1.0, p=2.0)))
design.savefig("design_3d.png")
#Visualize the 2d (Om,si8) projection
fig, ax = plt.subplots()
design.visualize(fig=fig,
ax=ax,
parameters=["Omega_m", "sigma8"],
color="red",
marker=".")
design.savefig("design_2d.png")
#Visualize the changes in the cost function
fig, ax = plt.subplots()
ax.plot(design.cost_values)
ax.set_xlabel(r"$N$")
ax.set_ylabel("cost")
ax.set_title("Last change={0:.1e}%".format(deltaPerc * 100))
ax.set_xscale("log")
fig.savefig("cost.png")
def test_visualize():
#This fails if GSL is not installed
try:
design = Design()
except ImportError:
return
#Add the dimensions
design.add_parameter("Omega_m",min=0.1,max=0.9,label=r"$\Omega_m$")
design.add_parameter("w",min=-2.0,max=-0.1,label=r"$w$")
design.add_parameter("sigma8",min=0.01,max=1.6,label=r"$\sigma_8$")
#Lay down 50 points
design.put_points(50)
print(design)
#The cost function should be the diagonal one
np.testing.assert_approx_equal(design.diagonalCost(Lambda=1.0),design.cost(p=2.0,Lambda=1.0))
#Visualize the 3d diagonal configuration
design.visualize(color="blue")
design.set_title("Cost={0:.2f}".format(design.diagonalCost(Lambda=1.0)))
design.savefig("design_diagonal_3d.png")
#Visualize the 2d (Om,si8) projection
fig,ax = plt.subplots()
design.visualize(fig=fig,ax=ax,parameters=["Omega_m","sigma8"],color="red",marker=".")
design.savefig("design_diagonal_2d.png")
#Now perform the sampling of the parameter space
deltaPerc = design.sample(Lambda=1.0,p=2.0,seed=1,maxIterations=100000)
#Visualize the 3d configuration
design.visualize(color="blue")
design.set_title("Cost={0:.2f}".format(design.cost(Lambda=1.0,p=2.0)))
design.savefig("design_3d.png")
#Visualize the 2d (Om,si8) projection
fig,ax = plt.subplots()
design.visualize(fig=fig,ax=ax,parameters=["Omega_m","sigma8"],color="red",marker=".")
design.savefig("design_2d.png")
#Visualize the changes in the cost function
fig,ax = plt.subplots()
ax.plot(design.cost_values)
ax.set_xlabel(r"$N$")
ax.set_ylabel("cost")
ax.set_title("Last change={0:.1e}%".format(deltaPerc*100))
ax.set_xscale("log")
fig.savefig("cost.png")
design.add_parameter("Omega_m", min=0.1, max=0.9, label=r"$\Omega_m$")
design.add_parameter("w", min=-2.0, max=-0.1, label=r"$w$")
design.add_parameter("sigma8", min=0.01, max=1.6, label=r"$\sigma_8$")
#Lay down 50 points
design.put_points(50)
print(design)
#The cost function should be the diagonal one
np.testing.assert_approx_equal(design.diagonalCost(Lambda=1.0),
design.cost(p=2.0, Lambda=1.0))
#Visualize the 3d diagonal configuration
design.visualize(color="blue")
design.set_title("Cost={0:.2f}".format(design.diagonalCost(Lambda=1.0)))
design.savefig("design_diagonal_3d.png")
#Visualize the 2d (Om,si8) projection
fig, ax = plt.subplots()
design.visualize(fig=fig,
ax=ax,
parameters=["Omega_m", "sigma8"],
color="red",
marker=".")
design.savefig("design_diagonal_2d.png")
#Now perform the sampling of the parameter space
deltaPerc = design.sample(Lambda=1.0, p=2.0, seed=1, maxIterations=100000)
#Visualize the 3d configuration
design.visualize(color="blue")
#Add the dimensions
design.add_parameter("Omega_m",min=0.1,max=0.9,label=r"$\Omega_m$")
design.add_parameter("w",min=-2.0,max=-0.1,label=r"$w$")
design.add_parameter("sigma8",min=0.01,max=1.6,label=r"$\sigma_8$")
#Lay down 50 points
design.put_points(50)
print(design)
#The cost function should be the diagonal one
np.testing.assert_approx_equal(design.diagonalCost(Lambda=1.0),design.cost(p=2.0,Lambda=1.0))
#Visualize the 3d diagonal configuration
design.visualize(color="blue")
design.set_title("Cost={0:.2f}".format(design.diagonalCost(Lambda=1.0)))
design.savefig("design_diagonal_3d.png")
#Visualize the 2d (Om,si8) projection
fig,ax = plt.subplots()
design.visualize(fig=fig,ax=ax,parameters=["Omega_m","sigma8"],color="red",marker=".")
design.savefig("design_diagonal_2d.png")
#Now perform the sampling of the parameter space
deltaPerc = design.sample(Lambda=1.0,p=2.0,seed=1,maxIterations=100000)
#Visualize the 3d configuration
design.visualize(color="blue")
design.set_title("Cost={0:.2f}".format(design.cost(Lambda=1.0,p=2.0)))
design.savefig("design_3d.png")
#Visualize the 2d (Om,si8) projection
