sys.path.append('../../plotting_scripts')
from jupyterplots import JupyterPlots
fig_x, fig_y = JupyterPlots()
prefix = 'data/'
roundoff = 5e-6
fp = int(sys.argv[1])
fdump = prefix + f'dump_{fp}.lammpstrj'
if os.path.isfile(fdump + '.pkl'):
dumpdata = load_obj(fdump, pstatus=True)
else:
duf = DumpFile(fdump, voronoi_flag=False, cg_flag=False)
dumpdata = duf.read_dump()
save_obj(dumpdata, fdump, pstatus=True)
fig, axarr = plt.subplots(6, sharex=True, figsize=[fig_x, fig_y * 6])
N = len(dumpdata)
ncount = N
atom_num = 2
fxs = np.empty([ncount], float)
fys = np.empty([ncount], float)
xs = np.empty([ncount], float)
ys = np.empty([ncount], float)
for i in range(Nsamples):
fname = 'corr_files/'
fname += f'g_{fp}_{rho}_{i}.rdf'
data = np.loadtxt(fname)
print(i)
rbins = data[:, 1]
gs[:, i] = data[:, 2]
gcorrs = np.mean(gs, axis=1)
gstd = np.std(gs, axis=1) / np.sqrt(Nsamples)
prefix = '../../2020_03_19/raw_data_processing/pickled_data/'
dc_name = prefix + f'ret_o_{fp}_{rho}'
ret_o = load_obj(dc_name)
gmatrix = ret_o['sum_g']
N_ft_samples = ret_o['g_cnt']
rs = np.array(gmatrix[:, 0] / N_ft_samples).flatten()
g_fts = np.array(gmatrix[:, 1] / N_ft_samples).flatten()
fig, axarr = plt.subplots(2)
axarr[0].plot(rbins, gcorrs, 'o-') #yerr=gstd,fmt='o-')
axarr[0].plot(rs, g_fts, 'ko-')
axarr[0].plot(rbins, savgol_filter(gcorrs, 9, 2), 'r-')
print(rbins[110])
axarr[1].hist(gs[130, :], bins=5)
prefix = 'data/'
fps = np.array([0, 1, 100], int)
rhos = np.array([0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], float)
tcut = 1000000
fig, ax = plt.subplots(figsize=[fig_x, fig_y])
for i, fp in enumerate(fps):
pkl_name = prefix + f'P_stuff_for_fp_equals_{fp}'
if os.path.isfile(pkl_name + '.pkl'):
Ps, Perrs = load_obj(pkl_name, pstatus=True)
else:
Ps = np.empty([len(rhos)], float)
Perrs = np.empty([len(rhos)], float)
for j, rho in enumerate(rhos):
flog = prefix + f'log_{fp}_{rho}.lammps.log'
ll = LogLoader(flog, remove_chunk=0, merge_data=True)
ts = ll.data['Step']
Press = ll.data['c_press']
P_cuts = Press[ts > tcut]
Ps[j] = np.mean(P_cuts)
fig,axarr = plt.subplots(2,sharex=True,figsize=[fig_x,2*fig_y])
rhos_naive = np.array([0.05,0.1,0.2,0.3,0.4,0.5,0.6],float)
mask_naive = (np.isin(rhos_naive,rhos_winkler))
print(mask_naive)
for i,fp in enumerate(fps):
pkl_press_naive = prefix1 + f'P_stuff_for_fp_equals_{fp}'
pkl_diff_naive = prefix1 + f'Deff_stuff_for_fp_equals_{fp}'
pkl_press_winkler = prefix2 + f'P_stuff_for_fp_equals_{fp}'
pkl_diff_winkler = prefix2 + f'Deff_stuff_for_fp_equals_{fp}'
Ps_naive,Perrs_naive = load_obj(pkl_press_naive,pstatus=True)
Ps_winkler,Perrs_winkler = load_obj(pkl_press_winkler,pstatus=True)
Deffs_naive,Derrs_naive = load_obj(pkl_diff_naive,pstatus=True)
Deffs_winkler,Derrs_winkler = load_obj(pkl_diff_winkler,pstatus=True)
Ps_winkler += swim_add(fp,rhos_winkler,2)
Ps_naive = Ps_naive[mask_naive]
Deffs_naive = Deffs_naive[mask_naive]
Derrs_naive = Derrs_naive[mask_naive]
print(Deffs_naive)
print(Deffs_winkler)