sys.path.append('../../scripts')
from kirkwood import Kirkwood
sys.path.append('../../analysis_scripts')
from rdfloader import RdfLoader
from logloader import LogLoader
import thermodata_analysis as thermo
sys.path.append('../../plotting_scripts')
from jupyterplots import JupyterPlots
def gexps(rexps,g2spline):
return (np.max(g2spline)-1)*np.exp(-(rexps-1)*3.6)+1
figsize = JupyterPlots()
Nsamples = 20
prefix1 = '../../2020_04_08/winkler_pressure/data/'
prefix2 = '../../2020_04_08/winkler_pressure/rdf_splits/'
fps = np.array(['0','0.25','0.5','0.75','1','2','4','8'],
str)
try:
rho = sys.argv[1]
except:
rho = '0.05'
# load in radial distribution functions for data set
rdf = RdfLoader(prefix2,Nsamples)
sys.path.append('../../analysis_scripts')
from pickle_dump import save_obj, load_obj
from logloader import LogLoader
import seaborn as sns
import thermodata_analysis as thermo
sys.path.append('../../plotting_scripts')
from jupyterplots import JupyterPlots
colors = sns.color_palette()
fig_x, fig_y = JupyterPlots()
prefix = 'data/'
fp = int(sys.argv[1])
rho = sys.argv[2]
label = rf'$f^P={fp},\rho={rho}$'
flog = prefix + f'log_{fp}_{rho}.lammps.log'
ll = LogLoader(flog, remove_chunk=0, merge_data=True)
fig, axarr = plt.subplots(4, sharex=False, figsize=[fig_x, 4.5 * fig_y])
ts = ll.data['Step']
Press = ll.data['c_press']
def plot_correlations_and_pressure(fp,
rhostrings,
Nsamples,
prefix1,
prefix2,
tcut,
Kirkwood,
RdfLoader,
potential='W_2'):
"""
Utility plotting function of local and global simulation quantities.
Figure 1 shows local radial distribution function g_2(r) as a
function of pair separation r, as well as g_2(r)*W_2'(r), where
W_2'(r) is the effective two-body interaction of ABPs (equal to
the WCA potential when activity is turned off, fp = 0). This
figure has two columns and four rows, for eight subplots total.
Column 1 is the g_2(r) (raw data and Savitzky-Golay filtered),
column 2 is the g_2(r)W_2'(r). Rows correspond to the four
different number densities that these quantities were measured
at.
Figure 2 shows global thermodynamic properties, namely the
diffusion coefficient, D, defined via D = <r^2>'(t)/4 (row 1);
the non-ideal pressure correction, P, measured directly from
simulation and the pressure correction calculated via
g_2(r)W'(r)r^2, P_{calc} (row2); the fractional error between P
and P_{calc} (row 3).
"""
# make nice plots by changing matplotlib defaults
figsize_default = JupyterPlots()
# convert to WCA (r_min) units
rhos = np.array(rhostrings, float) * 2**(1. / 3.)
# first figure will plot the product of W_2'(r) and
# g(r), where g(r) is the rdf
# it will also plot the bare g(r) and its smoothed
# version (smoothed using savgol_filter from scipy)
fig1size = [figsize_default[0] * 1.5, figsize_default[1] * 1.5]
fig1, axarr1 = plt.subplots(len(rhos) // 2,
ncols=4,
sharex=True,
figsize=fig1size)
# define arrays to store averaged thermodynamic data,
# which will be the diffusion coefficient and the pressure
# note that diffusion coefficient is determined via
# the relationship derivative(<R^2>)/(4*dt), and so is
# not the same as the translational or rotational
# diffusivity.
Davs = np.empty([len(rhos)], float)
Pavs = np.empty([len(rhos)], float)
# also include error bars
Derrs = np.copy(Davs)
Perrs = np.copy(Pavs)
# load in class to compute kirkwood stress via rdf.
# l0 = fp_d*rmin*beta, where fp_d is the activity in
# real units.
# However, our analytic calculations (e.g. potentials)
# are defined in a length scale rmin, where as
# the simulations are defined in terms of sigma.
# so fp = fp_d*sigma*beta in the simulations. Therefore,
# l0/fp = rmin/sigma = 2**(1./6.), so
l0 = fp * 2**(1. / 6.)
# Pi = D^r*rmin**2/(2D^t) in analytic calculations, but
# Pi_s = D^r*sigma**2/(2D^t) in the simulations. So
# Pi/Pi_s = (rmin/sigma)**2 = 2**(1./3.). In the
# simulations, we hold D^r = 3D^t/sigma**2. Therefore,
# Pi_s = 3./2. and
Pi = 3. / 2. * 2**(1. / 3.)
kw = Kirkwood(l0=l0, Pi=Pi, potential=potential)
# finally, write an array for storing the pressure values
# calculated via the rdf.
Pcalcs = np.empty([len(rhos)], float)
for i, rho in enumerate(rhostrings):
data = np.loadtxt(prefix1 + f'pressure_{fp}_{rho}.txt')
# load in observables as functions of simulation time
ts = data[:, 0] # timesteps
Ts = data[:, 1] # ABP mean kinetic energy
Ds = data[ts > tcut, 2] # diffusion coefficient
Ps = data[ts > tcut, 3] # pressure
# convert to WCA (r_min) units
Davs[i] = np.mean(Ds)
Derrs[i] = np.std(Ds) / np.sqrt(len(Ds))
Pavs[i] = 2**(1. / 3.) * np.mean(Ps)
Perrs[i] = 2**(1. / 3.) * np.std(Ps) / np.sqrt(len(Ps))
dens = float(rho) * 2**(1. / 3.)
rdf = RdfLoader(prefix2, Nsamples)
rs, g2data = rdf.read_gmeans(fp, rho, savgol=True)
axarr1[0][i].plot(rs * 2**(-1. / 6.), g2data, 'o-', ms=1)
rxs = np.linspace(0.5, 5, num=1000)
g2spline = kw.g2_spline(rxs, g2data, rs)
g2timesW2 = kw.potential.prime(rxs) * g2spline
axarr1[0][i].plot(rxs, g2spline, 'k-')
axarr1[1][i].plot(rxs, g2timesW2, 'k-')
if i == 0:
axarr1[1][i].set_xlabel(r'$\tilde{r}\equiv r/r_{min}$')
else:
axarr1[1][i].set_xlabel(r'$\tilde{r}$')
Pcalcs[i] = kw.pressure_correct(dens, g2data, rs)
axarr1[1][i].set_xlim(0, 4)
axarr1[0][0].set_ylabel(r'$g_2(\tilde{r})$')
axarr1[1][0].set_ylabel(r'$g_2(\tilde{r})\tilde{W}_2^{\prime}(\tilde{r})$')
fig1.subplots_adjust(hspace=0.25, wspace=0.5)
fig2size = [figsize_default[0], figsize_default[1] * 2]
# now plot the global thermodynamic quantities
fig2, axarr2 = plt.subplots(3, sharex=True, figsize=fig2size)
axarr2[0].errorbar(rhos, Davs, yerr=Derrs, fmt='o-')
axarr2[1].errorbar(rhos, Pavs, yerr=Perrs, fmt='o', ms=2)
axarr2[1].plot(rhos, Pcalcs, 'k-')
axarr2[2].plot(rhos, np.abs(Pcalcs - Pavs) / Pavs, 'o-')
axarr2[2].set_yscale('log')
axarr2[0].set_ylabel(r'$\tilde{D}\equiv D/D^t$')
axarr2[1].set_ylabel(r'$\tilde{P}\equiv P\beta (r_{min})^2$')
axarr2[2].set_ylabel(r'$\frac{|\tilde{P}-\tilde{P}_{calc}|}{\tilde{P}}$')
axarr2[2].set_xlabel(r'$\tilde{\rho}\equiv\rho (r_{min})^2$')
axarr2[0].legend(frameon=False)
plt.show()
return
