def func_wrapper(data, sep, savepdf):
### generate the save folder path to store the figure
savefolder = sep.savefolderbase + \
"SINGLE_SIM_INSTANCE/"
os.system("mkdir -p " + savefolder)
### plot the figure
updated_fig = plot_specific(data, sep, savepdf, fig, ax0)
### generate the save file path address to store the figure
data_separator.assign_physicalvalues(sep.fixed_param, data.sim)
savefilepath = (savefolder + data_separator.gen_path(sep.fixed_param))
if savepdf:
savefilepath += ".pdf"
else:
savefilepath += ".png"
print "Saving the figure: ", savefilepath
### store the figure
plt.savefig(savefilepath,
dpi=300,
bbox_inches='tight',
pad_inches=0.08)
fig.clf()
return
def func_wrapper(data, sep, savepdf):
### generate the save folder path to store the figure
savefolder = sep.savefolderbase + \
sep.control_param[0].physicalparamname.upper() + "/"
os.system("mkdir -p " + savefolder)
### plot the figure
updated_fig = plot_specific(data, sep, savepdf, fig, ax0)
### generate the save file path address to store the figure
data_separator.assign_physicalvalues(sep.fixed_param,
data[data.keys()[0]].sim)
savefilepath = savefolder + \
sep.control_param[1].physicalparamname.upper() + "_" + data_separator.gen_path(sep.fixed_param)
if savepdf:
savefilepath += ".pdf"
else:
savefilepath += ".png"
print "Saving the figure: ", savefilepath
### store the figure
plt.savefig(savefilepath,
dpi=300,
bbox_inches='tight',
pad_inches=0.08)
fig.clf()
return
def plot_analysis(data, sep, savepdf, *args):
""" plot the analysis data specific for this analysis"""
fig = args[0] # figure handle
ax0 = args[1] # axes handle
### normalize or select data
keys = np.sort(data.keys())
xd, yd, simd = select_data(data, keys)
gsim = simd[simd.keys()[0]]
avg_radius = gsim.avg_radius
data_separator.assign_physicalvalues(sep.fixed_param, gsim)
### plot the data
down = 35
up = -10
for key in keys:
x = xd[key]
y = yd[key]
sim = simd[key]
### curve fitting
popt, pcov = curve_fit(power_law, x[down:up], y[down:up])
yfit = power_law(x[down:up], popt[0], popt[1])
print popt[1]
sep.legend_param.assign_physicalvalue(sim)
label = data_separator.gen_label(sep.legend_param)
line0 = ax0.plot(x[1:], y[1:], label=label, \
linewidth=3.0)
line1 = ax0.plot(x[down:up], yfit, '--', label='_nolegend_', \
linewidth=2.0, c='grey')
### scales
ax0.set_xscale('log')
ax0.set_yscale('log')
### title
title = data_separator.gen_title(sep.fixed_param)
ax0.set_title(title, fontsize=20)
### side labels
ax0.set_xlabel(r'$k/k_{2R}$', fontsize=30)
ax0.set_ylabel(r'$E(k)$', fontsize=30)
### limits
#ax0.set_xlim((0, gsim.lx/2./avg_radius))
#ax0.set_ylim(())
### ticks
#ax0.xaxis.set_ticks(np.linspace(0, 15, num_ticks, endpoint=True))
#ax0.yaxis.set_ticks(np.linspace(0, uplim, num_ticks, endpoint=True))
ax0.tick_params(axis='both', which='major', labelsize=20)
### legend
ax0.legend(bbox_to_anchor=(0.65, 0., 0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
return fig
def plot_analysis(data, sep, savepdf, *args):
""" plot the analysis data specific for this analysis"""
fig = args[0] # figure handle
ax0 = args[1] # axes handle
### normalize or select data
keys = np.sort(data.keys())
xd, yd, simd = select_data(data, keys)
gsim = simd[simd.keys()[0]]
avg_radius = gsim.avg_radius
data_separator.assign_physicalvalues(sep.fixed_param, gsim)
### perform extra analysis
#extract_vortex_size(xd, yd, simd, keys)
### plot the data
for key in keys:
x = xd[key]
y = yd[key]
sim = simd[key]
### curve fitting
#popt, pcov = curve_fit(two_thirds, x[10:-300], y[10:-300])
#yfit = two_thirds(x[10:-300], popt[0])
#print popt[0]
sep.legend_param.assign_physicalvalue(sim)
label = data_separator.gen_label(sep.legend_param)
line0 = ax0.plot(x, y, label=label, \
linewidth=3.0)
#line1 = ax0.plot(x[10:-300], yfit, "--", c='grey', \
# label="_nolegend_", linewidth=2.0)
### scales
#ax0.set_xscale('log')
#ax0.set_yscale('log')
### title
title = data_separator.gen_title(sep.fixed_param)
ax0.set_title(title, fontsize=20)
### side labels
ax0.set_xlabel(r'r/R', fontsize=30)
ax0.set_ylabel(r'g(r)', fontsize=30)
### limits
ax0.set_xlim((0, 15.0))
#ax0.set_xlim((0, gsim.lx/2./avg_radius))
#ax0.set_ylim(())
### ticks
#ax0.xaxis.set_ticks(np.linspace(0, 15, num_ticks, endpoint=True))
#ax0.yaxis.set_ticks(np.linspace(0, uplim, num_ticks, endpoint=True))
ax0.tick_params(axis='both', which='major', labelsize=20)
### legend
ax0.legend(bbox_to_anchor=(0.65, 0., 0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
return fig