#! /usr/bin/env python # def six_j_values ( n_data ): #*****************************************************************************80 # ## SIX_J_VALUES returns some values of the Wigner 6J function. # # Discussion: # # In Mathematica, the function can be evaluated by: # # SixJSymbol[{j1,j2,j3},{j4,j5,j6}] # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 21 February 2015 # # Author: # # John Burkardt # # Reference: # # Milton Abramowitz, Irene Stegun, # Handbook of Mathematical Functions, # National Bureau of Standards, 1964, # ISBN: 0-486-61272-4, # LC: QA47.A34. # # Stephen Wolfram, # The Mathematica Book, # Fourth Edition, # Cambridge University Press, 1999, # ISBN: 0-521-64314-7, # LC: QA76.95.W65. # # Parameters: # # Input, integer N_DATA. The user sets N_DATA to 0 before the # first call. On each subsequent call, the input value should be # the output value from the previous call. # # Output, integer N_DATA. The routine increments N_DATA by 1, and # returns the corresponding data; when there is no more data, the # output value of N_DATA will be 0 again. # # Output, real J1, J2, J3, J4, J5, J6, the arguments # of the function. # # Output, real F, the value of the function. # import numpy as np n_max = 15 f_vec = np.array ( ( \ 0.03490905138373300, \ -0.03743025039659792, \ 0.01890866390959560, \ 0.007342448254928643, \ -0.02358935185081794, \ 0.01913476955215437, \ 0.001288017397724172, \ -0.01930018366290527, \ 0.01677305949382889, \ 0.005501147274850949, \ -0.02135439790896831, \ 0.003460364451435387, \ 0.02520950054795585, \ 0.01483990561221713, \ 0.002708577680633186 )) j1_vec = np.array ( ( \ 1.0, \ 2.0, \ 3.0, \ 4.0, \ 5.0, \ 6.0, \ 7.0, \ 8.0, \ 9.0, \ 10.0, \ 11.0, \ 12.0, \ 13.0, \ 14.0, \ 15.0 )) j2_vec = np.array ( ( \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0, \ 8.0 )) j3_vec = np.array ( ( \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0, \ 7.0 )) j4_vec = np.array ( ( \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5, \ 6.5 )) j5_vec = np.array ( ( \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5 )) j6_vec = np.array ( ( \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5, \ 7.5 )) if ( n_data < 0 ): n_data = 0 if ( n_max <= n_data ): n_data = 0 j1 = 0.0 j2 = 0.0 j3 = 0.0 j4 = 0.0 j5 = 0.0 j6 = 0.0 f = 0.0 else: j1 = j1_vec[n_data] j2 = j2_vec[n_data] j3 = j3_vec[n_data] j4 = j4_vec[n_data] j5 = j5_vec[n_data] j6 = j6_vec[n_data] f = f_vec[n_data] n_data = n_data + 1 return n_data, j1, j2, j3, j4, j5, j6, f def six_j_values_test ( ): #*****************************************************************************80 # ## SIX_J_VALUES_TEST demonstrates the use of SIX_J_VALUES. # # Licensing: # # This code is distributed under the GNU LGPL license. # # Modified: # # 21 February 2015 # # Author: # # John Burkardt # import platform print ( '' ) print ( 'SIX_J_VALUES_TEST:' ) print ( ' Python version: %s' % ( platform.python_version ( ) ) ) print ( ' SIX_J_VALUES stores values of the SIX_J function.' ) print ( '' ) print ( ' J1 J2 J3 J4 J5 J6 SIX_J(X)' ) print ( '' ) n_data = 0 while ( True ): n_data, j1, j2, j3, j4, j5, j6, f = six_j_values ( n_data ) if ( n_data == 0 ): break print ( ' %4.1f %4.1f %4.1f %4.1f %4.1f %4.1f %24.16f' \ % ( j1, j2, j3, j4, j5, j6, f ) ) # # Terminate. # print ( '' ) print ( 'SIX_J_VALUES_TEST:' ) print ( ' Normal end of execution.' ) return if ( __name__ == '__main__' ): from timestamp import timestamp timestamp ( ) six_j_values_test ( ) timestamp ( )