def __init__(self,
k,
nu=None,
to_do=None,
param_mat=None,
low_extrap=None,
high_extrap=None,
n_pad=None,
verbose=False):
""" inputs:
* k grid
* the to_do list: e.g. one_loop density density , bias terms, ...
* low_extrap is the call to extrapolate the power spectrum to lower k-values,
this helps with edge effects
* n_pad is the number of zeros to add to both ends of the array. This helps with
edge effects.
* verbose is to turn on verbose settings.
"""
# if no to_do list is given, default to fastpt_simple SPT case
if to_do is None:
if verbose:
print(
'Note: You are using an earlier call structure for FASTPT. Your code will still run correctly, calling FASTPT_simple. See user manual.'
)
if nu is None: # give a warning if nu=None that a default value is being used.
print(
'WARNING: No value for nu is given. FASTPT_simple is being called with a default of nu=-2'
)
nu = -2 # this is the default value for P22+P13 and bias calculation
self.pt_simple = fastpt_simple.FASTPT(k,
nu,
param_mat=param_mat,
low_extrap=low_extrap,
high_extrap=high_extrap,
n_pad=n_pad,
verbose=verbose)
return None
# Exit initialization here, since fastpt_simple performs the various checks on the k grid and does extrapolation.
# check for log spacing
print('Initializing k-grid quantities...')
dk = np.diff(np.log(k))
# dk_test=np.ones_like(dk)*dk[0]
delta_L = (log(k[-1]) - log(k[0])) / (k.size - 1)
dk_test = np.ones_like(dk) * delta_L
log_sample_test = 'ERROR! FASTPT will not work if your in put (k,Pk) values are not sampled evenly in log space!'
np.testing.assert_array_almost_equal(dk,
dk_test,
decimal=4,
err_msg=log_sample_test,
verbose=False)
if verbose:
print('the minumum and maximum inputed log10(k) are :',
np.min(np.log10(k)), np.max(np.log10(k)))
print('the grid spacing Delta log (k) is',
(log(np.max(k)) - log(np.min(k))) / (k.size - 1))
print('number of input k points are', k.size)
print('the power spectrum is extraplated to log10(k_min)=',
low_extrap)
print('the power spectrum is extraplated to log10(k_max)=',
high_extrap)
print('the power spectrum has ', n_pad,
' zeros added to both ends of the power spectrum')
self.k_original = k
self.extrap = False
if low_extrap is not None or high_extrap is not None:
self.EK = k_extend(k, low_extrap, high_extrap)
k = self.EK.extrap_k()
self.extrap = True
self.low_extrap = low_extrap
self.high_extrap = high_extrap
self.k_old = k
# print(self.k_old.size, 'k size')
# size of input array must be an even number
if k.size % 2 != 0:
raise ValueError(
'Input array must contain an even number of elements.')
# can we just force the extrapolation to add an element if we need one more? how do we prevent the extrapolation from giving us an odd number of elements? is that hard coded into extrap? or just trim the lowest k value if there is an odd numebr and no extrapolation is requested.
if n_pad != None:
self.id_pad = np.arange(k.size) + n_pad
d_logk = delta_L
k_pad = np.log(k[0]) - np.arange(1, n_pad + 1) * d_logk
k_pad = np.exp(k_pad)
k_left = k_pad[::-1]
k_pad = np.log(k[-1]) + np.arange(1, n_pad + 1) * d_logk
k_right = np.exp(k_pad)
k = np.hstack((k_left, k, k_right))
n_pad_check = int(np.log(2) / delta_L) + 1
if n_pad < n_pad_check:
print('*** Warning ***')
print(
'You should consider increasing your zero padding to at least ',
n_pad_check)
print(
'to ensure that the minimum k_output is > 2k_min in the FASTPT universe.'
)
print('k_min in the FASTPT universe is ', k[0],
' while k_min_input is ', self.k_old[0])
self.k = k
self.k_size = k.size
# self.scalar_nu=-2
self.N = k.size
# define eta_m and eta_n=eta_m
omega = 2 * pi / (float(self.N) * delta_L)
self.m = np.arange(-self.N // 2, self.N // 2 + 1)
self.eta_m = omega * self.m
self.verbose = verbose
self.n_pad = n_pad
# define l and tau_l
self.n_l = self.m.size + self.m.size - 1
self.l = np.arange(-self.n_l // 2 + 1, self.n_l // 2 + 1)
self.tau_l = omega * self.l
self.dd_do = False
self.dd_bias_do = False
self.IA_tt_do = False
self.IA_ta_do = False
self.IA_mix_do = False
self.OV_do = False
self.kPol_do = False
self.RSD_do = False
for entry in to_do: # convert to_do list to instructions for FAST-PT initialization
if entry == 'one_loop_dd':
self.dd_do = True
continue
elif entry == 'dd_bias':
self.dd_do = True
self.dd_bias_do = True
continue
elif entry == 'IA_all' or entry == 'IA':
self.IA_tt_do = True
self.IA_ta_do = True
self.IA_mix_do = True
continue
elif entry == 'IA_tt':
self.IA_tt_do = True
continue
elif entry == 'IA_ta':
self.IA_ta_do = True
continue
elif entry == 'IA_mix':
self.IA_mix_do = True
continue
elif entry == 'OV':
self.OV_do = True
continue
elif entry == 'kPol':
self.kPol_do = True
continue
elif entry == 'RSD':
self.RSD_do = True
continue
elif entry == 'IRres':
self.dd_do = True
continue
elif entry == 'all' or entry == 'everything':
self.dd_do = True
self.dd_bias_do = True
self.IA_tt_do = True
self.IA_ta_do = True
self.IA_mix_do = True
self.OV_do = True
self.kPol_do = True
self.RSD_do = True
continue
else:
raise ValueError('FAST-PT does not recognize "' + entry +
'" in the to_do list.')
### INITIALIZATION of k-grid quantities ###
if self.dd_do:
nu = -2
# parameter matrix for 1-loop calculations
p_mat = np.array([[0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 4, 0],
[2, -2, 2, 0], [1, -1, 1, 0], [1, -1, 3, 0],
[2, -2, 0, 1]])
p_mat_lpt = np.array([[0, 0, 0, 0], [0, 0, 2, 0], [2, -2, 2, 0],
[1, -1, 1, 0], [1, -1, 3, 0], [0, 0, 4, 0],
[2, -2, 0, 1]])
self.X_spt = scalar_stuff(p_mat, nu, self.N, self.m, self.eta_m,
self.l, self.tau_l)
self.X_lpt = scalar_stuff(p_mat_lpt, nu, self.N, self.m,
self.eta_m, self.l, self.tau_l)
if self.IA_tt_do:
hE_tab, hB_tab = IA_tt()
p_mat_E = hE_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_B = hB_tab[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_IA_E = tensor_stuff(p_mat_E, self.N, self.m, self.eta_m,
self.l, self.tau_l)
self.X_IA_B = tensor_stuff(p_mat_B, self.N, self.m, self.eta_m,
self.l, self.tau_l)
if self.IA_mix_do:
IA_A_tab = IA_A()
IA_DEE_tab = IA_DEE()
IA_DBB_tab = IA_DBB()
p_mat_A = IA_A_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_DEE = IA_DEE_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_DBB = IA_DBB_tab[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_IA_A = tensor_stuff(p_mat_A, self.N, self.m, self.eta_m,
self.l, self.tau_l)
self.X_IA_DEE = tensor_stuff(p_mat_DEE, self.N, self.m, self.eta_m,
self.l, self.tau_l)
self.X_IA_DBB = tensor_stuff(p_mat_DBB, self.N, self.m, self.eta_m,
self.l, self.tau_l)
if self.IA_ta_do:
IA_deltaE1_tab = IA_deltaE1()
IA_0E0E_tab = IA_0E0E()
IA_0B0B_tab = IA_0B0B()
p_mat_deltaE1 = IA_deltaE1_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_0E0E = IA_0E0E_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_0B0B = IA_0B0B_tab[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_IA_deltaE1 = tensor_stuff(p_mat_deltaE1, self.N, self.m,
self.eta_m, self.l, self.tau_l)
self.X_IA_0E0E = tensor_stuff(p_mat_0E0E, self.N, self.m,
self.eta_m, self.l, self.tau_l)
self.X_IA_0B0B = tensor_stuff(p_mat_0B0B, self.N, self.m,
self.eta_m, self.l, self.tau_l)
if self.OV_do:
# For OV, we can use two different values for
# nu1=0 and nu2=-2
OV_tab = OV()
p_mat = OV_tab[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_OV = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l,
self.tau_l)
if self.kPol_do:
tab1, tab2, tab3 = kPol()
p_mat = tab1[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_kP1 = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
p_mat = tab2[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_kP2 = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
p_mat = tab3[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_kP3 = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
if self.RSD_do:
tabA, self.A_coeff = RSDA()
p_mat = tabA[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_RSDA = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
tabB, self.B_coeff = RSDB()
p_mat = tabB[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_RSDB = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
sig_ff = filtfilt(B, A, P_in, padlen=200)
return sig_ff
if __name__=="__main__":
d=np.loadtxt('Pk_Planck15.dat')
k=d[:,0]; P0=d[:,1]
import copy
test=copy.deepcopy(P0)
low_extrap=-4
high_extrap=5
EK=k_extend(k,low_extrap,high_extrap)
k=EK.extrap_k()
P0=EK.extrap_P_low(P0)
P0=EK.extrap_P_high(P0)
P1=filter_highk(k,P0,1,5)
P2=filter_lowk(k,P0,.01,.05)
k,P1=EK.PK_orginal(P1)
k,P2=EK.PK_orginal(P2)
k,P0=EK.PK_orginal(P0)
import matplotlib.pyplot as plt
def __init__(self,k,nu=None,to_do=None,param_mat=None,low_extrap=None,high_extrap=None,n_pad=None,verbose=False):
''' inputs:
* k grid
* the to_do list: e.g. one_loop density density , bias terms, ...
* low_extrap is the call to extrapolate the power spectrum to lower k-values,
this helps with edge effects
* n_pad is the number of zeros to add to both ends of the array. This helps with
edge effects.
* verbose is to turn on verbose settings.
'''
# if no to_do list is given, default to fastpt_simple SPT case
if (to_do is None):
if (verbose):
print('Note: You are using an earlier call structure for FASTPT. Your code will still run correctly, calling FASTPT_simple. See user manual.')
if (nu is None):# give a warning if nu=None that a default value is being used.
print('WARNING: No value for nu is given. FASTPT_simple is being called with a default of nu=-2')
nu=-2 #this is the default value for P22+P13 and bias calculation
self.pt_simple=fastpt_simple.FASTPT(k,nu,param_mat=param_mat,low_extrap=low_extrap,high_extrap=high_extrap,n_pad=n_pad,verbose=verbose)
return None
# Exit initialization here, since fastpt_simple performs the various checks on the k grid and does extrapolation.
# check for log spacing
print('Initializing k-grid quantities...')
dk=np.diff(np.log(k))
#dk_test=np.ones_like(dk)*dk[0]
delta_L=(log(k[-1])-log(k[0]))/(k.size-1)
dk_test=np.ones_like(dk)*delta_L
log_sample_test='ERROR! FASTPT will not work if your in put (k,Pk) values are not sampled evenly in log space!'
np.testing.assert_array_almost_equal(dk, dk_test, decimal=4, err_msg=log_sample_test, verbose=False)
if (verbose):
print('the minumum and maximum inputed log10(k) are :', np.min(np.log10(k)),np.max(np.log10(k)))
print('the grid spacing Delta log (k) is', (log(np.max(k))-log(np.min(k)))/(k.size-1))
print('number of input k points are', k.size)
print('the power spectrum is extraplated to log10(k_min)=', low_extrap)
print('the power spectrum is extraplated to log10(k_max)=', high_extrap)
print('the power spectrum has ', n_pad,' zeros added to both ends of the power spectrum')
self.k_original=k
self.extrap=False
if (low_extrap is not None or high_extrap is not None):
self.EK=k_extend(k,low_extrap,high_extrap)
k=self.EK.extrap_k()
self.extrap=True
self.low_extrap=low_extrap
self.high_extrap=high_extrap
self.k_old=k
#print(self.k_old.size, 'k size')
# size of input array must be an even number
if (k.size % 2 != 0):
raise ValueError('Input array must contain an even number of elements.')
if(n_pad != None):
self.id_pad=np.arange(k.size)+n_pad
d_logk=delta_L
k_pad=np.log(k[0])-np.arange(1,n_pad+1)*d_logk
k_pad=np.exp(k_pad)
k_left=k_pad[::-1]
k_pad=np.log(k[-1])+np.arange(1,n_pad+1)*d_logk
k_right=np.exp(k_pad)
k=np.hstack((k_left,k,k_right))
n_pad_check=int(np.log(2)/delta_L) +1
if (n_pad < n_pad_check):
print('*** Warning ***')
print('You should consider increasing your zero padding to at least ', n_pad_check)
print('to ensure that the minimum k_output is > 2k_min in the FASTPT universe.')
print('k_min in the FASTPT universe is ', k[0], ' while k_min_input is ', self.k_old[0])
self.k=k
self.k_size=k.size
#self.scalar_nu=-2
self.N=k.size
# define eta_m and eta_n=eta_m
omega=2*pi/(float(self.N)*delta_L)
self.m=np.arange(-self.N//2,self.N//2+1)
self.eta_m=omega*self.m
self.verbose=verbose
self.n_pad=n_pad
# define l and tau_l
self.n_l=self.m.size + self.m.size - 1
self.l=np.arange(-self.n_l//2+1,self.n_l//2+1)
self.tau_l=omega*self.l
self.dd_do=False
self.dd_bias_do=False
self.IA_tt_do=False
self.IA_ta_do=False
self.IA_mix_do=False
self.OV_do=False
self.kPol_do=False
self.RSD_do=False
for entry in to_do: #convert to_do list to instructions for FAST-PT initialization
if entry=='one_loop_dd':
self.dd_do=True
continue
elif entry=='dd_bias':
self.dd_do=True
self.dd_bias_do=True
continue
elif entry=='IA_all' or entry=='IA':
self.IA_tt_do=True
self.IA_ta_do=True
self.IA_mix_do=True
continue
elif entry=='IA_tt':
self.IA_tt_do=True
continue
elif entry=='IA_ta':
self.IA_ta_do=True
continue
elif entry=='IA_mix':
self.IA_mix_do=True
continue
elif entry=='OV':
self.OV_do=True
continue
elif entry=='kPol':
self.kPol_do=True
continue
elif entry=='RSD':
self.RSD_do=True
continue
elif entry=='sig4':
self.dd_do=True
continue
elif entry=='all' or entry=='everything':
self.dd_do=True
self.dd_bias_do=True
self.IA_tt_do=True
self.IA_ta_do=True
self.IA_mix_do=True
self.OV_do=True
self.kPol_do=True
self.RSD_do=True
continue
else:
raise ValueError('FAST-PT does not recognize "'+entry+'" in the to_do list.')
### INITIALIZATION of k-grid quantities ###
if self.dd_do:
nu=-2
# parameter matrix for 1-loop calculations
p_mat=np.array([[0,0,0,0],[0,0,2,0],[0,0,4,0],[2,-2,2,0],\
[1,-1,1,0],[1,-1,3,0],[2,-2,0,1] ])
self.X_spt=scalar_stuff(p_mat,nu,self.N,self.m,self.eta_m,self.l,self.tau_l)
if self.IA_tt_do:
hE_tab,hB_tab=IA_tt()
p_mat_E=hE_tab[:,[0,1,5,6,7,8,9]]
p_mat_B=hB_tab[:,[0,1,5,6,7,8,9]]
self.X_IA_E=tensor_stuff(p_mat_E,self.N,self.m,self.eta_m,self.l,self.tau_l)
self.X_IA_B=tensor_stuff(p_mat_B,self.N,self.m,self.eta_m,self.l,self.tau_l)
if self.IA_mix_do:
IA_A_tab = IA_A()
IA_DEE_tab = IA_DEE()
IA_DBB_tab = IA_DBB()
p_mat_A=IA_A_tab[:,[0,1,5,6,7,8,9]]
p_mat_DEE=IA_DEE_tab[:,[0,1,5,6,7,8,9]]
p_mat_DBB=IA_DBB_tab[:,[0,1,5,6,7,8,9]]
self.X_IA_A=tensor_stuff(p_mat_A,self.N,self.m,self.eta_m,self.l,self.tau_l)
self.X_IA_DEE=tensor_stuff(p_mat_DEE,self.N,self.m,self.eta_m,self.l,self.tau_l)
self.X_IA_DBB=tensor_stuff(p_mat_DBB,self.N,self.m,self.eta_m,self.l,self.tau_l)
if self.IA_ta_do:
IA_deltaE1_tab = IA_deltaE1()
IA_0E0E_tab = IA_0E0E()
IA_0B0B_tab = IA_0B0B()
p_mat_deltaE1=IA_deltaE1_tab[:,[0,1,5,6,7,8,9]]
p_mat_0E0E=IA_0E0E_tab[:,[0,1,5,6,7,8,9]]
p_mat_0B0B=IA_0B0B_tab[:,[0,1,5,6,7,8,9]]
self.X_IA_deltaE1=tensor_stuff(p_mat_deltaE1,self.N,self.m,self.eta_m,self.l,self.tau_l)
self.X_IA_0E0E=tensor_stuff(p_mat_0E0E,self.N,self.m,self.eta_m,self.l,self.tau_l)
self.X_IA_0B0B=tensor_stuff(p_mat_0B0B,self.N,self.m,self.eta_m,self.l,self.tau_l)
if self.OV_do:
# For OV, we can use two different values for
# nu1=0 and nu2=-2
OV_tab=OV()
p_mat=OV_tab[:,[0,1,5,6,7,8,9]]
self.X_OV=tensor_stuff(p_mat,self.N,self.m,self.eta_m,self.l,self.tau_l)
if self.kPol_do:
tab1,tab2,tab3=kPol()
p_mat=tab1[:,[0,1,5,6,7,8,9]]
self.X_kP1=tensor_stuff(p_mat,self.N,self.m,self.eta_m,self.l,self.tau_l)
p_mat=tab2[:,[0,1,5,6,7,8,9]]
self.X_kP2=tensor_stuff(p_mat,self.N,self.m,self.eta_m,self.l,self.tau_l)
p_mat=tab3[:,[0,1,5,6,7,8,9]]
self.X_kP3=tensor_stuff(p_mat,self.N,self.m,self.eta_m,self.l,self.tau_l)
if self.RSD_do:
tabA,self.A_coeff=RSDA()
p_mat=tabA[:,[0,1,5,6,7,8,9]]
self.X_RSDA=tensor_stuff(p_mat,self.N,self.m,self.eta_m,self.l,self.tau_l)
tabB,self.B_coeff=RSDB()
p_mat=tabB[:,[0,1,5,6,7,8,9]]
self.X_RSDB=tensor_stuff(p_mat,self.N,self.m,self.eta_m,self.l,self.tau_l)
def __init__(self,
k,
nu=None,
to_do=None,
param_mat=None,
low_extrap=None,
high_extrap=None,
n_pad=None,
verbose=False):
''' inputs:
* k grid
* the to_do list: e.g. one_loop density density , bias terms, ...
* low_extrap is the call to extrapolate the power spectrum to lower k-values,
this helps with edge effects
* n_pad is the number of zeros to add to both ends of the array. This helps with
edge effects.
* verbose is to turn on verbose settings.
'''
# if no to_do list is given, default to fastpt_simple SPT case
if (to_do is None):
# set the to_do list to nothing
# raise an error is nu=None
if (nu is None):
raise ValueError(
'nu is set to None, you need to specify a numerical value for FASTPT_simple.'
)
to_do = []
if (verbose):
print(
'Note: You are using an earlier call structure for FASTPT. Your code will still run correctly, calling FASTPT_simple. See user manual.'
)
self.pt_simple = fastpt_simple.FASTPT(k,
nu,
param_mat=param_mat,
low_extrap=low_extrap,
high_extrap=high_extrap,
n_pad=n_pad,
verbose=verbose)
# check for log spacing
dk = np.diff(np.log(k))
dk_test = np.ones_like(dk) * dk[0]
log_sample_test = 'ERROR! FASTPT will not work if your k vector is not sampled evenly in log space! \
You could use the included interpolation routine if you like.'
np.testing.assert_array_almost_equal(dk,
dk_test,
decimal=4,
err_msg=log_sample_test,
verbose=False)
if (verbose):
print('the minumum and maximum inputed log10(k) are :',
np.min(np.log10(k)), np.max(np.log10(k)))
print('the grid spacing Delta log (k) is',
(log(np.max(k)) - log(np.min(k))) / (k.size - 1))
print('number of input k points are', k.size)
print('the power spectrum is extraplated to log10(k_min)=',
low_extrap)
print('the power spectrum is extraplated to log10(k_max)=',
high_extrap)
print('the power spectrum has ', n_pad,
' zeros added to both ends of the power spectrum')
self.k_original = k
self.extrap = False
if (low_extrap is not None or high_extrap is not None):
self.EK = k_extend(k, low_extrap, high_extrap)
k = self.EK.extrap_k()
self.extrap = True
self.low_extrap = low_extrap
self.high_extrap = high_extrap
self.k_old = k
#print(self.k_old.size, 'k size')
# size of input array must be an even number
if (k.size % 2 != 0):
raise ValueError(
'Input array must contain an even number of elements.')
delta_L = (log(np.max(k)) - log(np.min(k))) / (
k.size - 1
) # need to put in a check to make sure that it is log sampled
if (n_pad != None):
self.id_pad = np.arange(k.size) + n_pad
d_logk = delta_L
k_pad = np.log(k[0]) - np.arange(1, n_pad + 1) * d_logk
k_pad = np.exp(k_pad)
k_left = k_pad[::-1]
k_pad = np.log(k[-1]) + np.arange(1, n_pad + 1) * d_logk
k_right = np.exp(k_pad)
k = np.hstack((k_left, k, k_right))
n_pad_check = int(np.log(2) / delta_L) + 1
if (n_pad < n_pad_check):
print(
'Warning, you should consider increasing your zero padding to at least ',
n_pad_check, ' .')
print('So, that you ensure that k > 2k_min.')
print(' k min in the FASTPT universe is ', k[0],
' while k min input is ', self.k_old[0])
self.k = k
self.k_size = k.size
#self.scalar_nu=-2
self.N = k.size
# define eta_m and eta_n=eta_m
omega = 2 * pi / (float(self.N) * delta_L)
self.m = np.arange(-self.N // 2, self.N // 2 + 1)
self.eta_m = omega * self.m
self.verbose = verbose
self.n_pad = n_pad
# define l and tau_l
self.n_l = self.m.size + self.m.size - 1
self.l = np.arange(-self.n_l // 2 + 1, self.n_l // 2 + 1)
self.tau_l = omega * self.l
self.dd_do = False
self.dd_bias_do = False
self.IA_tt_do = False
self.IA_ta_do = False
self.IA_mix_do = False
self.OV_do = False
self.kPol_do = False
self.RSD_do = False
for entry in to_do: #convert to_do list to instructions for FAST-PT initialization
if entry == 'one_loop_dd':
self.dd_do = True
continue
elif entry == 'dd_bias':
self.dd_do = True
self.dd_bias_do = True
continue
elif entry == 'IA_all' or entry == 'IA':
self.IA_tt_do = True
self.IA_ta_do = True
self.IA_mix_do = True
continue
elif entry == 'IA_tt':
self.IA_tt_do = True
continue
elif entry == 'IA_ta':
self.IA_ta_do = True
continue
elif entry == 'IA_mix':
self.IA_mix_do = True
continue
elif entry == 'OV':
self.OV_do = True
continue
elif entry == 'kPol':
self.kPol_do = True
continue
elif entry == 'RSD':
self.RSD_do = True
continue
elif entry == 'all' or entry == 'everything':
self.dd_do = True
self.dd_bias_do = True
self.IA_tt_do = True
self.IA_ta_do = True
self.IA_mix_do = True
self.OV_do = True
self.kPol_do = True
self.RSD_do = True
continue
else:
raise ValueError('FAST-PT does not recognize "' + entry +
'" in the to_do list.')
### INITIALIZATION of k-grid quantities ###
if self.dd_do:
nu = -2
# parameter matrix for 1-loop calculations
p_mat=np.array([[0,0,0,0],[0,0,2,0],[0,0,4,0],[2,-2,2,0],\
[1,-1,1,0],[1,-1,3,0],[2,-2,0,1] ])
self.X_spt = scalar_stuff(p_mat, nu, self.N, self.m, self.eta_m,
self.l, self.tau_l)
if self.IA_tt_do:
hE_tab, hB_tab = IA_tt()
p_mat_E = hE_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_B = hB_tab[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_IA_E = tensor_stuff(p_mat_E, self.N, self.m, self.eta_m,
self.l, self.tau_l)
self.X_IA_B = tensor_stuff(p_mat_B, self.N, self.m, self.eta_m,
self.l, self.tau_l)
if self.IA_mix_do:
IA_A_tab = IA_A()
IA_DEE_tab = IA_DEE()
IA_DBB_tab = IA_DBB()
p_mat_A = IA_A_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_DEE = IA_DEE_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_DBB = IA_DBB_tab[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_IA_A = tensor_stuff(p_mat_A, self.N, self.m, self.eta_m,
self.l, self.tau_l)
self.X_IA_DEE = tensor_stuff(p_mat_DEE, self.N, self.m, self.eta_m,
self.l, self.tau_l)
self.X_IA_DBB = tensor_stuff(p_mat_DBB, self.N, self.m, self.eta_m,
self.l, self.tau_l)
if self.IA_ta_do:
IA_deltaE1_tab = IA_deltaE1()
IA_0E0E_tab = IA_0E0E()
IA_0B0B_tab = IA_0B0B()
p_mat_deltaE1 = IA_deltaE1_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_0E0E = IA_0E0E_tab[:, [0, 1, 5, 6, 7, 8, 9]]
p_mat_0B0B = IA_0B0B_tab[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_IA_deltaE1 = tensor_stuff(p_mat_deltaE1, self.N, self.m,
self.eta_m, self.l, self.tau_l)
self.X_IA_0E0E = tensor_stuff(p_mat_0E0E, self.N, self.m,
self.eta_m, self.l, self.tau_l)
self.X_IA_0B0B = tensor_stuff(p_mat_0B0B, self.N, self.m,
self.eta_m, self.l, self.tau_l)
if self.OV_do:
# For OV, we can use two different values for
# nu1=0 and nu2=-2
OV_tab = OV()
p_mat = OV_tab[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_OV = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l,
self.tau_l)
if self.kPol_do:
tab1, tab2, tab3 = kPol()
p_mat = tab1[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_kP1 = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
p_mat = tab2[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_kP2 = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
p_mat = tab3[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_kP3 = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
if self.RSD_do:
tabA, self.A_coeff = RSDA()
p_mat = tabA[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_RSDA = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
tabB, self.B_coeff = RSDB()
p_mat = tabB[:, [0, 1, 5, 6, 7, 8, 9]]
self.X_RSDB = tensor_stuff(p_mat, self.N, self.m, self.eta_m,
self.l, self.tau_l)
def __init__(self,k,nu,param_mat=None,low_extrap=None,high_extrap=None,n_pad=None,verbose=False):
# check for log spacing
dk=np.diff(np.log(k))
dk_test=np.ones_like(dk)*dk[0]
log_sample_test='ERROR! FASTPT will not work if your k vector is not sampled evenly in log space!'
np.testing.assert_array_almost_equal(dk, dk_test, decimal=4, err_msg=log_sample_test, verbose=False)
# size of input array must be an even number
if (k.size % 2 != 0):
raise ValueError('Input array must contain an even number of elements.')
self.extrap=False
if (low_extrap is not None or high_extrap is not None):
self.EK=k_extend(k,low_extrap,high_extrap)
k=self.EK.extrap_k()
self.extrap=True
self.low_extrap=low_extrap
self.high_extrap=high_extrap
self.k_old=k
delta_L=(log(np.max(k))-log(np.min(k)))/(k.size-1)
if(n_pad !=None):
self.id_pad=np.arange(k.size)+n_pad
d_logk=delta_L
k_pad=np.log(k[0])-np.arange(1,n_pad+1)*d_logk
k_pad=np.exp(k_pad)
k_left=k_pad[::-1]
k_pad=np.log(k[-1])+np.arange(1,n_pad+1)*d_logk
k_right=np.exp(k_pad)
k=np.hstack((k_left,k,k_right))
# check to make sure that the n padding sufficient to keep the
# FASTPT k_min less than 1/2 of the input k_min (added a plus 1 to be safe)
n_pad_check=int(np.log(2)/delta_L) +1
if (n_pad < n_pad_check):
print('Warning, you should consider increasing your zero padding to at least ', n_pad_check, ' .')
print('So, that you ensure that k > 2k_min.')
print(' k min in the FASTPT universe is ', k[0], ' while k min input is ', self.k_old[0])
if(n_pad == None):
print('Your results are only good for k > 2k_min')
# default parameters for standard P_22_reg
if param_mat is None:
param_mat=np.array([[0,0,0,0],[0,0,2,0],[0,0,4,0],[2,-2,2,0],\
[1,-1,1,0],[1,-1,3,0],[2,-2,0,1] ])
self.k=k
self.k_size=k.size
self.nu=nu
self.p_mat=param_mat
self.p_size=param_mat.shape[0]
self.verbose=verbose
self.n_pad=n_pad
alpha=self.p_mat[:,0]
beta=self.p_mat[:,1]
l_Bessel=self.p_mat[:,2]
type=self.p_mat[:,3]
self.N=k.size
# define eta_m and eta_n=eta_m
omega=2*pi/(float(self.N)*delta_L)
self.m=np.arange(-self.N//2,self.N//2+1)
self.eta_m=omega*self.m
# define l and tau_l
self.n_l=self.m.size + self.m.size - 1
self.l=np.arange(-self.n_l//2+1,self.n_l//2+1)
self.tau_l=omega*self.l
#Q_m=np.zeros((param_mat.shape[0],self.N+1), dtype=complex)
self.pf=np.zeros((param_mat.shape[0]))
self.two_part_l=np.zeros((param_mat.shape[0],self.l.size), dtype=complex)
#Q_n=Q_m
self.g_m=np.zeros((param_mat.shape[0],self.N+1), dtype=complex)
self.g_n=np.zeros((param_mat.shape[0],self.N+1), dtype=complex)
self.h_l=np.zeros((param_mat.shape[0],self.l.size),dtype=complex)
self.p=-5-2*self.nu-alpha-beta
for i in range(param_mat.shape[0]):
sigma=l_Bessel[i]+1/2.
# Define Q_m and Q_n and p
# use eta_m for Q_n, the value is the same
Q_m=3/2.+ nu + alpha[i] + 1j*self.eta_m
Q_n=3/2.+ nu + beta[i] + 1j*self.eta_m
p=-5-2*nu-alpha[i]-beta[i]
self.g_m[i,:]=g_m_vals(sigma,Q_m)
if (type[i]==1):
# this is the special case, Corresponding to the regularized version of
# J_{2,-2,0,reg}(k)
# get values for g_n
# again use eta_m.
s=2+nu + beta[i]
Q_n=s+ 1j*self.eta_m
self.g_n[i,:]=gamsn(Q_n)
#two_part_m=2**Q_m
self.g_m[i,:]=self.g_m[i,:]*2.**Q_m
# prefactor
self.pf[i]=(-1)**l_Bessel[i]/pi**3*np.sqrt(pi/2.)
self.two_part_l[i,:]=np.ones(self.l.size)
else:
self.g_n[i,:]=g_m_vals(sigma,Q_n)
# pre factor
self.pf[i]=(-1)**l_Bessel[i]/pi**2*2.**(2+2*nu+alpha[i]+beta[i])
self.two_part_l[i,:]=exp(1j*self.tau_l*log2)
# calculate h_l
#arg=(p+1-1j*tau_l)
self.h_l[i,:]=gamsn(self.p[i]+1-1j*self.tau_l)
return sig_ff
if __name__ == "__main__":
d = np.loadtxt('Pk_Planck15.dat')
k = d[:, 0]
P0 = d[:, 1]
import copy
test = copy.deepcopy(P0)
low_extrap = -4
high_extrap = 5
EK = k_extend(k, low_extrap, high_extrap)
k = EK.extrap_k()
P0 = EK.extrap_P_low(P0)
P0 = EK.extrap_P_high(P0)
P1 = filter_highk(k, P0, 1, 5)
P2 = filter_lowk(k, P0, .01, .05)
k, P1 = EK.PK_orginal(P1)
k, P2 = EK.PK_orginal(P2)
k, P0 = EK.PK_orginal(P0)
import matplotlib.pyplot as plt
ax = plt.subplot(141)
