|
| | csv_plot.pi = np.pi |
| |
| float | csv_plot.G = 6.67408*1e-11 |
| |
| float | csv_plot.M = 0.15 |
| |
| int | csv_plot.rho_c_ini = 1 |
| |
| float | csv_plot.rho_c0_value = 1.8788*10**(-26) |
| |
| int | csv_plot.a_i = 1e-3 |
| |
| | csv_plot.parser = argparse.ArgumentParser(description='Tracer_conversion') |
| |
| | csv_plot.dest |
| |
| | csv_plot.required |
| |
| | csv_plot.type |
| |
| | csv_plot.str |
| |
| | csv_plot.help |
| |
| | csv_plot.default |
| |
| | csv_plot.choices |
| |
| | csv_plot.action |
| |
| | csv_plot.False |
| |
| | csv_plot.args = parser.parse_args() |
| |
| | csv_plot.file_name = args.file_name |
| |
| | csv_plot.IfXunitInCode = args.IfXunitInCode |
| |
| | csv_plot.f = open(file_name) |
| | main code
|
| |
| | csv_plot.dat = f.readlines()[1:] |
| |
| int | csv_plot.tstep = 0 |
| |
| int | csv_plot.told = 1e6 |
| |
| | csv_plot.tem = list(map(float,line.split(', '))) |
| |
| | csv_plot.tnew = tem[0] |
| |
| int | csv_plot.number_of_data = len(tem)-6 |
| |
| int | csv_plot.N_tracer = 0 |
| |
| int | csv_plot.ID1 = -100 |
| |
| list | csv_plot.coors = [] |
| |
| list | csv_plot.coor = [tem[3],tem[4],tem[5]] |
| |
| | csv_plot.data_set = np.zeros((N_tracer,tstep,4+number_of_data)) |
| |
| int | csv_plot.t_count = 0 |
| |
| int | csv_plot.N_count = 0 |
| |
| dict | csv_plot.q_dir = {"rho":4,"v_x":5,"v_y":6,"v_z":7,"E_t":8} |
| |
| int | csv_plot.t_id = 100 |
| |
| | csv_plot.q_id = args.pq |
| |
| int | csv_plot.t_r_id = tstep-t_id-1 |
| |
| | csv_plot.t_code = data_set[0,t_r_id,0] |
| |
| | csv_plot.data_at_t = data_set[:,t_r_id,:] |
| |
| | csv_plot.sort_data = data_at_t[np.argsort(data_at_t[:,1])] |
| |
| | csv_plot.coor_data = sort_data[:,1] |
| |
| | csv_plot.e_data = np.array(sort_data[:,q_dir["E_t"]]) |
| |
| | csv_plot.m1_data = np.array(sort_data[:,q_dir["v_x"]]) |
| |
| | csv_plot.m2_data = np.array(sort_data[:,q_dir["v_y"]]) |
| |
| | csv_plot.m3_data = np.array(sort_data[:,q_dir["v_z"]]) |
| |
| | csv_plot.rho_data = np.array(sort_data[:,q_dir["rho"]]) |
| |
| | csv_plot.q_data = sort_data[:,q_dir[q_id]] |
| |
| tuple | csv_plot.t_real = (0.1694*a_i**(-0.5)-t_code/11.8)**(-3)*10**15 |
| |
| float | csv_plot.a_scale = 0.0287*(0.1694*a_i**(-0.5)-t_code/11.8)**(-2) |
| |
| int | csv_plot.coor_data_physical = 100*a_scale*coor_data |
| |
| str | csv_plot.q_id_show = "$"+q_id+"/(km/s)$" |
| |
| int | csv_plot.v_hubble = 100*(2.057*10**17/t_real)*coor_data_physical |
| |
| int | csv_plot.rho_scaling = 1/(6*pi*G*t_real**2) |
| |
| float | csv_plot.r_ta = a_scale*100*3.086*1e19*((3*M)/(4*pi*rho_c_ini))**(1/3)*(4/3/pi)**(8/9)*(t_real/6.505/1e12)**(8/9) |
| |
| float | csv_plot.v_scaling = r_ta/t_real |
| |
| int | csv_plot.P_scaling = rho_scaling*r_ta**2*1e6/(t_real**2) |
| |
| | csv_plot.label |
| |
1.9.8