def __init__(self):
Function.__init__(self)
#physical distance for each model
self.d = dDGP()
self.dcdm = dsfq()
self.dcdm.update(factor=0)
import numpy as np
from functions.sfq import dL as dsfq
import matplotlib.pyplot as plt
from functions.parent import Function
from functions.dL_PartIII import dL as dDGP
#create luminosity distance functions
d_DGP = dDGP()
d_CDM = dsfq()
#redshifts at which to calculate aggregate accuracy
z1 = 0.5
z2 = 1.32
#range of z values
rangz = np.arange(0., 2., 0.1)
#create figure and axes, set figure size
fig, ax = plt.subplots(figsize=(4, 3))
#create legend
#legend = ([])
#set x and y axis limits
ax.set_xlim(left=0.0, right=2.0)
plt.xlabel('Redshift z') #label x axis
plt.ylabel('$\\Delta d_{L}/d_{L}$') #label y axis
#create a function to calculate the aggregate accuracy
class agg_accuracy(Function):
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from functions.sfq import dsfq
from data.getdata import obtaindata
import math
cosmo_const = dsfq()
# results = obtaindata('data.csv')
# for datapoint in results:
# z = datapoint[0]
# d_z = datapoint[1]
cosmo_const.update(factor = 0)
fig, ax = plt.subplots()
cosmo_const.plot(ax, [0.2,1.7], 0.1)
cosmo_const.update(factor=1, at=0.50, tau=0.33, wp=0, wm=0, wf=-1)
cosmo_const.plot(ax, [0,1.7], 0.1)
cosmo_const.update(factor = 1, at=0.23, tau=0.33, wp=0, wm=0, wf=-1)
cosmo_const.plot(ax, [0, 1.7], 0.1)
ax.set_xlabel(r'$z$')
ax.set_ylabel(r'$d_L$') #to Alice: find out the units
fig.show()
input()
import numpy as np
from functions.sfq import dsfq
import matplotlib.pyplot as plt
from functions.parent import Function
#create luminosity distance functions
dsfq1 = dsfq()
dcdm = dsfq()
#redshifts at which to calculate aggregate accuracy
z1 = 0.5
z2 = 1.32
#range of z values
rangz = np.arange(0., 2., 0.1)
#create figure and axes, set figure size
fig, ax = plt.subplots(figsize=(4, 3))
#create legend
#dL_legend = ([])
#set x and y axis limits
ax.set_xlim(left=0.0, right=2.0)
plt.xlabel('Redshift z') #label x axis
plt.ylabel('$\\Delta d_{L}/d_{L}$') #label y axis
#create a function to calculate the aggregate accuracy
class agg_accuracy(Function):
def __init__(self):
import numpy as np
from functions.sfq import dsfq
import matplotlib.pyplot as plt
from functions.parent import Function
#create luminosity distance functions
dsfq2 = dsfq()
dcdm = dsfq()
#redshifts at which to calculate aggregate accuracy
z1 = 0.5
z2 = 1.32
#range of z values
rangz = np.arange(0., 2., 0.1)
#create figure and axes, set figure size
fig, ax = plt.subplots(figsize=(4, 3))
#create legend
#dL_legend = ([])
#set x and y axis limits
ax.set_xlim(left=0.0, right=2.0)
plt.xlabel('Redshift z') #label x axis
plt.ylabel('$\\Delta d_{L}/d_{L}$') #label y axis
#create a function to calculate the aggregate accuracy
class agg_accuracy(Function):
def __init__(self):
import math
from functions.sfq import dsfq
#create luminosity distance functions
dsfq1 = dsfq()
dsfq2 = dsfq()
dcdm = dsfq()
#redshifts at which to calculate aggregate accuracy
z1 = 0.5
z2 = 1.32
#assign appropriate values in luminosity distance functions for SFQ-I, SFQ-II and Lambda-CDM models
dsfq1.update(factor=1, at=0.50, tau=0.33, wp=0, wm=0, wf=-1)
dsfq2.update(factor=1, at=0.23, tau=0.33, wp=0, wm=0, wf=-1)
dcdm.update(factor=0)
#calculate d_L for each model at redshift z=0.5
d_L1 = dsfq1.cal(z1)
d_L2 = dsfq2.cal(z1)
d_lamcdm = dcdm.cal(z1)
#find aggregate accuracy for SFQ-I model
aggy1 = abs((d_L1 - d_lamcdm) / d_L1)
print('sfq1 (z = ' + str(z1) + '):' + str(aggy1))
#find aggregate accuracy for SFQ-I model
aggy2 = abs((d_L2 - d_lamcdm) / d_L2)
print('sfq2 (z = ' + str(z1) + '):' + str(aggy2))
#calculate d_L for each model at redshift z=1.32
d_L1 = dsfq1.cal(z2)
d_L2 = dsfq2.cal(z2)