def design(cmd_args):
#Load the design
design = Design.load(design_points,[r"$\Omega_m$",r"$w$",r"$\sigma_8$"])
#Create the figure
fig,ax = plt.subplots(1,2,figsize=(16,8))
#Visualize the design
design.visualize(fig,ax[0],parameters=[r"$\Omega_m$",r"$w$"])
design.visualize(fig,ax[1],parameters=[r"$\Omega_m$",r"$\sigma_8$"])
#Show also the fiducial point
ax[0].scatter(0.26,-1.0,color="red")
ax[1].scatter(0.26,0.8,color="red")
#Save the figure
fig.savefig("design.{0}".format(cmd_args.type))
def design(cmd_args):
#Load the design
design = Design.read(design_points)
design.columns = [r"$\Omega_m$",r"$w_0$",r"$\sigma_8$"]
design._labels = [r"$\Omega_m$",r"$w_0$",r"$\sigma_8$"]
design._pmin = design.values.min(0)
design._pmax = design.values.max(0)
#Create the figure
fig,ax = plt.subplots(1,2,figsize=(16,8))
#Visualize the design
design.visualize(fig,ax[0],parameters=[r"$\Omega_m$",r"$w_0$"],color=sns.xkcd_rgb["dark grey"])
design.visualize(fig,ax[1],parameters=[r"$\Omega_m$",r"$\sigma_8$"],color=sns.xkcd_rgb["dark grey"])
#Show also the fiducial point
ax[0].scatter(0.26,-1.0,marker="x",s=100,lw=3,color=sns.xkcd_rgb["pale red"])
ax[1].scatter(0.26,0.8,marker="x",s=100,lw=3,color=sns.xkcd_rgb["pale red"])
#Save the figure
fig.savefig("{0}/cfht_design.{0}".format(cmd_args.type))
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")
#!/usr/bin/env python
from lenstools.simulations import Design
import numpy as np
#Create design
d = Design()
#Add parameters
d.add_parameter("Om",0.2,0.5,"Om")
d.add_parameter("w",-1.5,-0.5,"w")
d.add_parameter("si8",0.5,1.2,"si8")
#Lay down points
d.put_points(100)
#Sample
d.sample(Lambda=1.0,p=2.0,seed=123,maxIterations=1000000)
p = d.points
#Save in increasing sigma8 order
np.save("../data/design.npy",p[p[:,2].argsort()])
from __future__ import print_function
import sys
from lenstools.simulations import Design
import numpy as np
import matplotlib.pyplot as plt
#This fails if GSL is not installed
try:
design = Design.from_specs(npoints=50,parameters=[("Om",r"$\Omega_m$",0.1,0.9),("w",r"$w$",-2.0,-1.0),("si8",r"$\sigma_8$",0.01,1.6)])
except ImportError:
sys.exit(1)
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=["Om","si8"],color="red",marker=".")
design.savefig("design_diagonal_2d.png")
#Now perform the sampling of the parameter space
from __future__ import print_function
import sys
from lenstools.simulations import Design
import numpy as np
import matplotlib.pyplot as plt
#This fails if GSL is not installed
try:
design = Design()
except ImportError:
sys.exit(1)
#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)))
from __future__ import print_function
import sys
from lenstools.simulations import Design
import numpy as np
import matplotlib.pyplot as plt
#This fails if GSL is not installed
try:
design = Design()
except ImportError:
sys.exit(1)
#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")
