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.)