def make_args(i, testing=False): #i is the box number
#Redshifts and such
sfs = AD.scale_factors()
zs = 1./sfs - 1
x = sfs - 0.5
#Pull out the precomputed quantities from the extras
extras = ae.Extras(i, testing=testing)
M = extras.M
lM = np.log(M)
nus = extras.nu[:]
dndlnms = extras.dndlM[:]
#Get the bin-averaged quantities and the data.
Ms = [] #data masses
bs = [] #data biases
bes = [] #data bias errors
icovs = [] #inverse covariances
n_bins = [] #integrated mass functions
lMbins = [] #log bin edges
for j in range(0, len(zs)):
#Read in data
if not testing:
Mdata, Mlo, Mhigh, b, be = np.loadtxt("/Users/tmcclintock/Data/linear_bias/Box%03d_Z%d_DS50_linearbias.txt"%(i,j)).T
cov = np.loadtxt("/Users/tmcclintock/Data/linear_bias/Box%03d_Z%d_DS50_linearbias_cov.txt"%(i,j))
else:
Mdata, Mlo, Mhigh, b, be = np.loadtxt("/Users/tmcclintock/Data/linear_bias_test/TestBox%03d-combined_Z%d_DS50_linearbias.txt"%(i,j)).T
cov = np.loadtxt("/Users/tmcclintock/Data/linear_bias_test/TestBox%03d-combined_Z%d_linearbias_cov.txt"%(i,j))
#Append everything
Ms.append(Mdata)
bs.append(b)
bes.append(be)
icov = np.linalg.inv(cov)
icovs.append(icov)
#Assemble bins
Mlo = np.ascontiguousarray(Mlo)
Mhigh = np.ascontiguousarray(Mhigh)
inds = Mhigh > 1e99
Mhigh[inds] = 1e16
Mbins = np.array([Mlo, Mhigh]).T
lMbins.append(np.log(Mbins))
#Integrate the mass functions
dndM = dndlnms[j]/M
nbin = np.array([massfunction.n_in_bin(Mlo[k], Mhigh[k], M, dndM) for k in range(len(Mlo))])
n_bins.append(nbin)
continue
#Assemble the args and pickle it
args = {'biases':bs, 'icovs':icovs, 'berrs':bes, 'Ms':Ms, 'x_arr':x, 'lM_arr':lM, 'nu_arr':nus, 'nbins':n_bins, 'dndlnM_arr':dndlnms, 'lMbins':lMbins, 'sfs':sfs, 'zs':zs}
if testing: bname = 'testbox'
else: bname = 'box'
pickle.dump(args, open("./args/args_%s%d.p"%(bname, i), 'wb'))
print "Args for %s%d pickled"%(bname, i)
return
import aemulus_data as AD
from classy import Class
import cluster_toolkit as ct
from cluster_toolkit import bias
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as op
import pickle
#import aemHMF
import emcee, os, sys, itertools
from scipy.interpolate import InterpolatedUnivariateSpline as IUS
from scipy.integrate import quad
sfs = AD.scale_factors()
zs = 1./sfs - 1
x = sfs - 0.5
def model_swap(params, args, xi):
y = np.log10(200)
a1,a2 = 1+.24*y*np.exp(-(4/y)**4), 0.44*y-0.88
b1,b2 = 0.183, 1.5
c1 = 0.019+0.107*y+0.19*np.exp(-(4/y)**4)
c2 = 2.4
dropped = args['dropped']
kept = args['kept']
pars = np.ones((12))
pars[kept] = params
if len(kept) != 12:
defaults = args['defaults']
pars[dropped] = defaults[dropped]
a1,a2,b1,b2,c1,c2 = pars[:6] + xi*pars[6:]
