def plot(args):
import os, os.path
import h5py
from matplotlib import pyplot as plt
import h5md._plot.label
from numpy import *
ax = plt.axes()
label = None
ax.axhline(y=1, color="black", lw=0.5)
ax.set_color_cycle(args.colors)
for (i, fn) in enumerate(args.input):
try:
f = h5py.File(fn, "r")
except IOError:
raise SystemExit("failed to open HDF5 file: %s" % fn)
try:
param = f["halmd" in f.keys() and "halmd" or "parameters"] # backwards compatibility
# determine file type, prefer precomputed static structure factor data
if "structure" in f.keys() and "ssf" in f["structure"].keys():
import filon
import h5md._plot.ssf as ssf
from scipy.constants import pi
# load static structure factor from file
H5 = f["structure/ssf/" + "/".join(args.flavour)]
q = f["structure/ssf/wavenumber"].__array__() # convert to NumPy array
S_q, S_q_err = ssf.load_ssf(H5, args)
# read some parameters
dim = param["box"].attrs["dimension"]
density = param["box"].attrs["density"]
length = param["box"].attrs["length"]
# compute pair distribution function
xlim = args.xlim or (0, min(length) / 2)
r = linspace(xlim[0], xlim[1], num=args.bins)
if r[0] == 0:
r = r[1:]
if dim == 3:
# convert 3-dim Fourier transform F[S_q - 1] / (2π)³ to 1-dim Fourier integral
pdf = filon.filon(q * (S_q - 1), q, r).imag / (2 * pi * pi * r)
pdf_err = filon.filon(q * S_q_err, q, r).imag / (2 * pi * pi * r)
pdf = 1 + pdf / density # add δ-contribution
pdf_err = pdf_err / density
elif "trajectory" in f.keys():
# compute SSF from trajectory data
H5 = f["trajectory/" + args.flavour[0]]
r, pdf, pdf_err = pdf_from_trajectory(H5["position"], param, args)
else:
raise SystemExit("Input file provides neither data for the static structure factor nor a trajectory")
# before closing the file, store attributes for later use
attrs = h5md._plot.label.attributes(param)
except IndexError:
raise SystemExit("invalid phase space sample offset")
except KeyError as what:
raise SystemExit(str(what) + "\nmissing simulation data in file: %s" % fn)
finally:
f.close()
if args.label:
label = args.label[i % len(args.label)] % attrs
elif args.legend or not args.small:
basename = os.path.splitext(os.path.basename(fn))[0]
label = r"%s" % basename.replace("_", r"\_")
if args.title:
title = args.title % attrs
c = args.colors[i % len(args.colors)]
ax.plot(r, pdf, "-", color=c, label=label)
if "pdf_err" in locals():
ax.errorbar(
r, pdf, pdf_err, fmt="o", color=c, markerfacecolor=c, markeredgecolor=c, markersize=2, linewidth=0.5
)
else:
ax.plot(r, pdf, "o", markerfacecolor=c, markeredgecolor=c, markersize=2)
# write plot data to file
if args.dump:
f = open(args.dump, "a")
print >> f, "# %s, sample %s" % (label.replace(r"\_", "_"), args.sample)
if "pdf_err" in locals():
print >> f, "# r g(r) g_err(r)"
savetxt(f, array((r, pdf, pdf_err)).T)
else:
print >> f, "# r g(r)"
savetxt(f, array((r, pdf)).T)
print >> f, "\n"
f.close()
# adjust axis ranges
ax.axis("tight")
if args.xlim:
plt.setp(ax, xlim=args.xlim)
if args.ylim:
plt.setp(ax, ylim=args.ylim)
else:
plt.setp(ax, ylim=(0, plt.ylim()[1]))
# optionally plot with logarithmic scale(s)
if args.axes == "xlog":
ax.set_xscale("log")
if args.axes == "ylog":
ax.set_yscale("log")
if args.axes == "loglog":
ax.set_xscale("log")
ax.set_yscale("log")
if args.legend or not args.small:
l = ax.legend(loc=args.legend)
l.legendPatch.set_alpha(0.7)
plt.xlabel(args.xlabel or r"distance $r / \sigma$")
plt.ylabel(args.ylabel or r"pair distribution function $g(r)$")
if args.output is None:
plt.show()
else:
plt.savefig(args.output, dpi=args.dpi)
def plot(args):
import os, os.path
import h5py
from matplotlib import pyplot as plt
import h5md._plot.label
from numpy import *
#from matplotlib import ticker
# import ssf
# import pycuda only if required
if args.cuda:
import pycuda.autoinit
make_cuda_kernels()
ax = plt.axes()
label = None
ax.axhline(y=1, color='black', lw=0.5)
ax.set_color_cycle(args.colors)
for (i, fn) in enumerate(args.input):
try:
f = h5py.File(fn, 'r')
except IOError:
raise SystemExit('failed to open HDF5 file: %s' % fn)
try:
try:
param = f['halmd' in f.keys() and 'halmd' or 'parameters'] # backwards compatibility
except KeyError:
param = None
# determine file type, prefer precomputed SSF data
ssf_path = 'structure/' + '/'.join(args.flavour) + '/static_structure_factor'
if ssf_path in f:
# load SSF from file
H5 = f[ssf_path]
q = H5['wavenumber'].__array__() # store in memory by conversion to NumPy array
S_q, S_q_err = load_ssf(H5, args)
elif 'structure/ssf/' + '/'.join(args.flavour) in f: # backwards compatibility
# load SSF from file
H5 = f['structure/ssf/' + '/'.join(args.flavour)]
q = f['structure/ssf/wavenumber'].__array__() # store in memory by conversion to NumPy array
S_q, S_q_err = load_ssf(H5, args)
elif 'trajectory' in f.keys() and param:
# compute SSF from trajectory data
H5 = f['trajectory/' + args.flavour[0]]
q, S_q = ssf_from_trajectory(H5['position'], param, args)
else:
raise SystemExit('Input file provides neither SSF data nor a trajectory')
# before closing the file, store attributes for later use
if param:
attrs = h5md._plot.label.attributes(param)
else:
attrs = {}
except IndexError:
raise SystemExit('invalid phase space sample offset')
except KeyError as what:
raise SystemExit(str(what) + '\nmissing simulation data in file: %s' % fn)
finally:
f.close()
if args.label:
label = args.label[i % len(args.label)] % attrs
elif args.legend or not args.small:
basename = os.path.splitext(os.path.basename(fn))[0]
label = r'%s' % basename.replace('_', r'\_')
if args.title:
title = args.title % attrs
c = args.colors[i % len(args.colors)]
ax.plot(q, S_q, '-', color=c, label=label)
if 'S_q_err' in locals():
ax.errorbar(q, S_q, S_q_err, fmt='o', color=c, markerfacecolor=c, markeredgecolor=c, markersize=2, linewidth=.5)
else:
ax.plot(q, S_q, 'o', markerfacecolor=c, markeredgecolor=c, markersize=2)
# optionally fit Ornstein-Zernike form
if args.fit_ornstein_zernike:
import scipy.odr as odr
density = attrs['density']
temperature = attrs['temperature']
idx, = where(q <= args.fit_limit)
kappa = mean(S_q[idx]) / density / temperature # initial guess
# result is a tuple (param, param_err, covariance_matrix)
param, param_err = odr.odr(
ornstein_zernike_log # fit model
, (kappa, 1) # initial parameter values (kappa, xi)
, S_q[idx], log(q[idx]) # data (y, x)
, extra_args=(density, temperature,), full_output=0
)[:2]
kappa, xi = abs(param)
kappa_err, xi_err = param_err
if args.verbose:
print 'Density: {0:g}'.format(density)
print 'Temperature: {0:g}'.format(temperature)
print 'Compressibility: {0:g} ± {1:g}'.format(kappa, kappa_err)
print 'Correlation length: {0:g} ± {1:g}'.format(xi, xi_err)
if args.axes == 'loglog' or args.axes == 'xlog':
xmin = args.xlim and args.xlim[0] or 0.01
x = logspace(log10(xmin), log10(3 * args.fit_limit), num=20)
else:
x = linspace(0, 3 * args.fit_limit, num=20)
y = ornstein_zernike((kappa, xi), x, density, temperature)
ax.plot(x, y, ':', color=c, linewidth=.8)
# write plot data to file
if args.dump:
f = open(args.dump, 'a')
print >>f, '# %s, sample %s' % (label.replace(r'\_', '_'), args.sample)
if 'S_q_err' in locals():
print >>f, '# q S_q S_q_err'
savetxt(f, array((q, S_q, S_q_err)).T)
else:
print >>f, '# q S_q'
savetxt(f, array((q, S_q)).T)
print >>f, '\n'
f.close()
# optionally plot power laws
if args.power_law:
p = reshape(args.power_law, (-1, 4))
for (pow_exp, pow_coeff, pow_xmin, pow_xmax) in p:
px = logspace(log10(pow_xmin), log10(pow_xmax), num=100)
py = pow_coeff * pow(px, pow_exp)
ax.plot(px, py, 'k--')
# adjust axis ranges
ax.axis('tight')
if args.xlim:
plt.setp(ax, xlim=args.xlim)
if args.ylim:
plt.setp(ax, ylim=args.ylim)
else:
plt.setp(ax, ylim=(0, plt.ylim()[1]))
# optionally plot with logarithmic scale(s)
if args.axes == 'xlog':
ax.set_xscale('log')
if args.axes == 'ylog':
ax.set_yscale('log')
if args.axes == 'loglog':
ax.set_xscale('log')
ax.set_yscale('log')
if args.legend or not args.small:
l = ax.legend(loc=args.legend)
l.legendPatch.set_alpha(0.7)
plt.xlabel(args.xlabel or r'Wavenumber $q\sigma$')
plt.ylabel(args.ylabel or r'Static structure factor $S(q)$')
if args.output is None:
plt.show()
else:
plt.savefig(args.output, dpi=args.dpi)