def write_ini(self, R0, nR0, jk=0, threads=3, action=0):
full_name = self.fname + str(R0) + self.name_root
if self.jackknife:
full_name += '_jk{0:d}'.format(jk)
print 'Ini', full_name
with open('INI_{}.ini'.format(full_name), 'w') as f:
f.write(lf.text_ini_file(threads = threads, action = action))
f.write(lf.params_upsilon())
f.write('use_upsilon= 98\n')
f.write('samples = 10000000\n')
f.write('best_fit = {0:s}best_{1:s}.dat\n'.format(self.dir_bf, full_name))
f.write('aver = {0:1.1f}\n'.format(self.aver if self.full_cov in self.bin_type else 0))
f.write(lf.params_cosmo(self.data_type) + '\n\n')
f.write('z_gg = {} \n'.format(self.z_mean))
f.write('z_gm = {} \n'.format(self.z_mean))
f.write(lf.R0_params(R0, nR0) + '\n')
f.write('use_diag = {0:s}\n\n'.format('F' if self.full_cov in self.bin_type else 'T'))
f.write('file_root = ' + self.dir_chains + full_name + '\n')
f.write('mock_file = ' + self.dir_data + full_name + '.dat' + '\n')
f.write('mock_cov = ' + self.dir_data + full_name + self.name_cov + '\n')
time.sleep(2.)
def write_bf(self, R0, run_bf=False):
full_name = self.fname + str(R0) + self.name_root
file_bf = self.dir_stats + full_name + '.margestats'
names = ['param','mean','sddev','lower1',
'upper1','limit1','lower2','upper2','limit2','other']
#best_fit= pd.read_csv(file_bf, nrows=1, header=None)
#title= best_fit.ix[0]
#log_ind = str(title).split().index('=')
#self.loglik = (str(title).split()[log_ind+1])[:5]
lines = pd.read_csv(file_bf, names= names, sep='\s+', skiprows=[0,1,2], index_col='param')
print 'bf', lines
DS = float(lines.ix['hola']['mean'])
b1_bf = float(lines.ix['LRGa']['mean'])
b2_bf = float(lines.ix['LRGb']['mean'])
lna_bf = float(lines.ix['logA']['mean'])
with open('bf_INI_{0:s}.ini'.format(full_name), 'w') as f:
f.write('param[hola] = {0:2.3f} {1:2.3f} {2:2.3f} 0.001 0.001\n'.format(DS, DS- 0.001, DS+ 0.001))
f.write('param[LRGa] = {0:2.3f} {1:2.3f} {2:2.3f} 0.001 0.001\n'.format(b1_bf, b1_bf-0.001, b1_bf+0.001))
f.write('param[LRGb] = {0:2.3f} {1:2.3f} {2:2.3f} 0.001 0.001\n'.format(b2_bf, b2_bf-0.001, b2_bf+0.001))
f.write('param[logA] = {0:2.3f} {1:2.3f} {2:2.3f} 0.001 0.001\n'.format(lna_bf,lna_bf-0.001,lna_bf+0.001))
f.write('use_upsilon = 99\n')
f.write('samples = 8\n')
f.write(lf.text_ini_file())
f.write('best_fit = {0:s}best_{1:s}.dat\n'.format(self.dir_bf, full_name))
f.write('aver = {0:1.2f}\n'.format(self.aver if self.full_cov in bin_type else 0))
f.write(lf.params_cosmo(self.data_type) + '\n\n')
f.write('z_gg = {} \n'.format(self.z_mean))
f.write('z_gm = {} \n'.format(self.z_mean))
f.write(lf.R0_params(R0, nR0) + '\n')
f.write('use_diag = {}\n\n'.format('F' if self.full_cov in self.bin_type else 'T'))
f.write('file_root = ' + self.dir_chains + 'bf_'+ full_name + '\n')
f.write('mock_file = ' + self.dir_data + full_name + '.dat' + '\n')
f.write('mock_cov = ' + self.dir_data + full_name + self.name_cov + '\n')
if run_bf:
commd = './cosmomc bf_INI_{}.ini'.format(full_name)
os.system(commd)
time.sleep(3.)
