ax.scatter(y[step, 0, 0:nsources, 0], y[step, 0, 0:nsources, 1], y[step, 0, 0:nsources, 2]) plt.show() return #----------------------------------------------------------- # Run code #----------------------------------------------------------- # the input directory the directory for input data input_dir = '/home/namdi/Documents/School/UNC/Parallel_Time/Data/test_fmm/t_0' # read parameters fname = input_dir + str("/info.txt") nsources, ntargets, distribution, dt, nsteps = mu.load_parameters(fname) N = nsources + ntargets # create data array y = np.zeros( (nsteps+1, 2, N, 3) ) # load initial data y0, charge_src, charge_targ, dipstr_src, dipvec_src = mu.load_initial_data(input_dir) y[0,:] = y0[:] # plot the data plot3D_timestep(y, nsources, 0) uu =1
"""----------------------------------------------------------""" # run #------------------------------------------- # this only works if one ran $ source my_activate x = commands.getstatusoutput("echo $MY_DATA_DIR") data_path = x[1] #fpath = data_path + "/kd/test_fe" fpath = data_path + "/Results/2014_10/14/least_sq_dt" fname = "/t_0/info.txt" # read in the parameters nsources, ntargets, distribution, dt, nsteps = my_utils.load_parameters(fpath + fname) # get the times t = np.linspace(start=0.0, stop=dt*nsteps, num=nsteps+1, endpoint = True) save = False doTarget = False # this is the actual data if doTarget: pot_far = lu.load_cplex_potential(fpath, "/pot_far_target.txt", ntargets, nsteps) pot_near = lu.load_cplex_potential(fpath, "/pot_near_target.txt", ntargets, nsteps) else: pot_far = lu.load_cplex_potential(fpath, "/pot_far_source.txt", nsources, nsteps) pot_near = lu.load_cplex_potential(fpath, "/pot_near_source.txt", nsources, nsteps)