def plot_png(xpbc, ypbc, nbpc, ncells, lx, ly, frame):
""" plot the frame as colored png image"""
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
patches = []
k = 0
for n in range(ncells):
r = np.vstack((xpbc[k:k + nbpc[n]] / 0.5, ypbc[k:k + nbpc[n]] / 0.5)).T
k += nbpc[n]
polygon = Polygon(r, True)
patches.append(polygon)
p = PatchCollection(patches, facecolor='black', alpha=1.0)
ax0.add_collection(p)
ax0.autoscale_view()
ax0.scatter(xpbc / 0.5, ypbc / 0.5, s=1.0, c='black', alpha=1.0)
ax0.axis('equal')
ax0.set_xlim((-10, lx / 0.5 + 10))
ax0.set_ylim((-10, ly / 0.5 + 10))
ax0.axis('off')
savepath = "area_polygons_" + str(frame) + ".png"
plt.savefig(savepath, dpi=300, bbox_inches='tight', pad_inches=0.0)
plt.clf()
plt.close(fig)
return savepath
def plot_data(x, data, param_choice, sims, savebase, savefolder):
""" plot the data as a function of the chosen parameter"""
### set general plot properties
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
for p in data.keys():
sim = sims[p] # simulation information
y = data[p] # analysis data
label = r'$\epsilon=$' + str(sim.eps) + '$,f_{m}=$' + str(sim.fp) + \
'$,\kappa_{A}=$' + str(sim.areak) + '$,\kappa_{B}=$' + str(sim.kappa)
line0 = ax0.semilogx(x/sim.tau_D, y, \
linewidth=2.0, label=label)
### labels
ax0.set_xlabel(r"$\Delta t/\tau_{D}$", fontsize=30)
ax0.set_ylabel(r"$F_{s}(q)$", fontsize=30)
### limits
ax0.set_xlim((1e-1, 5e2))
ax0.set_ylim((-0.1, 1.0))
### ticks
ax0.xaxis.set_ticks([1e-1, 1e0, 1e1, 1e2, 5e2])
ax0.yaxis.set_ticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
ax0.tick_params(axis='both', which='major', labelsize=30)
### legend
ax0.legend(bbox_to_anchor=(0.15, 0.,0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
### save
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
savepath1 = base + savefolder + "_transition_per_" + param_choice + ".png"
savepath2 = base + savefolder + "_transition_per_" + param_choice + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_frames(wor, beads, cells, sim, ti, tf, savebase, save_eps):
""" plot frames within the specified time window"""
### normalize variables for plotting purposes
lx = sim.lx / sim.r_avg
ly = sim.ly / sim.r_avg
### vorticity information
steps, xbins, ybins, wx, wy, w, vx, vy = wor
nwbins = max(xbins) + 1
xlin = np.linspace(0., lx, nwbins)
ylin = np.linspace(0., ly, nwbins)
xgrid, ygrid = np.meshgrid(xlin, ylin)
### set general plot properties
savebase += 'eps_' + str(sim.eps) + '_fp_' + str(sim.fp) + \
'_areak_' + str(sim.areak) + '/'
os.system("mkdir -p " + savebase)
quant_steps = 2056
# limits
full_box_downlim = -2
full_box_uplim = lx + 2
full_box_ticks = [0, 35, 70, 105, 135]
half_box_downlim = 43
half_box_uplim = 92
half_box_ticks = [45, 90]
num_ticks = 5
ax_len = 2.2 # Length of one subplot square box
ax_b = 0.01 # Beginning/offset of the subplot in the box
ax_sep = 0.15 # Separation length between two subplots
total_subplots_in_x = 2 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
multi = True
ax1 = subp.addSubplot(multi)
ax3 = subp.addSubplot(multi)
ax0 = subp.addSubplot(multi)
ax2 = subp.addSubplot(multi)
### set subplot properties
nslices = sim.ncells
# nslices = 100 # Number of slices to take in the y dir
norm_ax0 = mpl.colors.Normalize(vmin=0, vmax=nslices)
# vertical_pos_colors_per_bead = cell2bead_vertical_pos(beads, cells, sim, nslices, ti)
# cmap_ax1 = sns.dark_palette((260, 75, 60), n_colors=nslices, input="husl", as_cmap=True)
# cmap_ax1 = sns.diverging_palette(255, 133, l=60, n=nslices, center="dark", as_cmap=True)
cmap_ax0 = plt.cm.get_cmap('jet', quant_steps)
#norm_ax3 = MidPointNorm(midpoint=0)
### plot the frames
comx = deque()
comy = deque()
ntrace = 6
delta = 6
if tf + delta > sim.nsteps:
tf -= delta
for step in range(ti, tf):
time = step * sim.dt
print 'Step / Total : ', step, tf
### normalize variables for plotting purposes
beads.xi[step, 0, :] /= sim.r_avg
beads.xi[step, 1, :] /= sim.r_avg
cells.xi[step, 0, :] /= sim.r_avg
cells.xi[step, 1, :] /= sim.r_avg
### calculate the displacement magnitudes
dx, dy, dr = calc_displacement_magnitudes(cells, step, delta, sim)
dr /= sim.r_avg
norm_ax1 = mpl.colors.Normalize(vmin=min(dr), vmax=max(dr))
### prepare for streamline plot
# ndim = sim.ncells/10
# pts = np.vstack((cells.xi[step, 0, :], cells.xi[step, 1, :])).T
# vals = np.vstack((dx, dy)).T
# xli = np.linspace(cells.xi[step, 0, :].min(), cells.xi[step, 0, :].max(), ndim)
# yli = np.linspace(cells.xi[step, 1, :].min(), cells.xi[step, 1, :].max(), ndim)
# ipts = np.vstack(a.ravel() for a in np.meshgrid(yli, xli)[::-1]).T
# ivals = interpolate.griddata(pts, vals, ipts, method='cubic')
# uli, vli = ivals.T
# uli.shape = vli.shape = (ndim, ndim)
### keep the center of mass trajectory
comx.append(cells.xi[step, 0, :])
comy.append(cells.xi[step, 1, :])
if step > ti + ntrace:
comx.popleft()
comy.popleft()
### get the vorticity information
ws = w[steps == step]
# wxs = wx[steps==step]
# wys = wy[steps==step]
# wmean = np.mean(np.sqrt(wxs**2 + wys**2))
wmean = np.mean(np.abs(ws))
wn = np.zeros((nwbins, nwbins), dtype=np.float32)
for xi, yi in zip(xbins, ybins):
wn[xi, yi] = ws[xi * nwbins + yi]
wn /= wmean
#norm_ax3 = mpl.colors.Normalize(vmin=min(wn), vmax=max(wn))
### get the velocity information
vxs = vx[steps == step]
vys = vy[steps == step]
vmean = np.mean(np.sqrt(vxs**2 + vys**2))
vn = np.zeros((nwbins, nwbins), dtype=np.float32)
vxg = np.zeros((nwbins, nwbins), dtype=np.float32)
vyg = np.zeros((nwbins, nwbins), dtype=np.float32)
for xi, yi in zip(xbins, ybins):
vn[xi, yi] = np.sqrt(vxs[xi * nwbins + yi]**2 +
vys[xi * nwbins + yi]**2)
vxg[xi, yi] = vxs[xi * nwbins + yi]
vyg[xi, yi] = vys[xi * nwbins + yi]
vn /= vmean
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b,
total_subplots_in_x)
ax1 = subp.addSubplot(multi)
ax3 = subp.addSubplot(multi)
ax0 = subp.addSubplot(multi)
ax2 = subp.addSubplot(multi)
text = r"$t/\tau_{D}$ = " + "{0:.2f}".format( time/sim.tau_D) + \
r", $t/\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A)
### AX0
line0 = ax0.scatter(beads.xi[step, 0, :], beads.xi[step, 1, :], s=4.0, \
c=beads.cid, \
cmap=cmap_ax0, \
edgecolors='None', alpha=1.0, vmin=0, vmax=nslices, \
norm=norm_ax0, rasterized=True)
ax0.axis('scaled')
### labels
#ax0.set_xlabel(r"$x/R$", fontsize=40)
ax0.set_ylabel(r"$y/R$", fontsize=40)
### limits
ax0.set_xlim((full_box_downlim, full_box_uplim))
ax0.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax0.xaxis.set_ticks(full_box_ticks)
ax0.yaxis.set_ticks(full_box_ticks)
plt.setp(ax0.get_xticklabels(), visible=False)
ax0.tick_params(axis='both', which='major', labelsize=40)
### AX1
# line1 = ax1.scatter(cells.xi[step, 0, :], cells.xi[step, 1, :], s=6.0, \
# c=np.arange(nslices), \
# #c=dr,
# cmap=cmap_ax0, \
# edgecolors='None', alpha=1.0, vmin=0, vmax=nslices, \
# norm=norm_ax0, rasterized=True)
#
# line2 = ax1.scatter(list(comx), list(comy), s=5.0, \
# c=np.ones(np.shape(list(comx)))*np.arange(nslices), \
# #c=np.ones(np.shape(list(comx)))*dr,
# cmap=cmap_ax0, \
# edgecolors='None', alpha=0.5, vmin=0, vmax=nslices, \
# norm=norm_ax0, rasterized=True)
#
# ax1.axis('scaled')
#
# ### labels
#
# ax1.set_xlabel(r"$x/R$", fontsize=40)
# ax1.set_ylabel(r"$y/R$", fontsize=40)
#
# ### limits
#
# ax1.set_xlim((full_box_downlim, full_box_uplim))
# ax1.set_ylim((full_box_downlim, full_box_uplim))
#
# ### ticks
#
# ax1.xaxis.set_ticks(full_box_ticks)
# ax1.yaxis.set_ticks(full_box_ticks)
# #plt.setp(ax1.get_yticklabels(),visible=False)
# ax1.tick_params(axis='both', which='major', labelsize=40)
#line1 = ax1.pcolor(xgrid, ygrid, vn.transpose(), cmap=cmap_ax0)
line1 = ax1.contourf(xgrid, ygrid, vn.transpose(), cmap=cmap_ax0)
line2 = ax1.quiver(cells.xi[step, 0, :], cells.xi[step, 1, :], \
dx, dy, \
#dr, cmap=cmap_ax0, norm=norm_ax1, \
headwidth=5, headlength=6, headaxislength=3.5, alpha=1.0)
# line7 = ax3.streamplot(xli, yli, \
# uli, vli, \
# linewidth=1.0, density=2, arrowstyle='->', arrowsize=1.5)
#dr, cmap=cmap_ax0, norm=norm_ax1)
ax1.axis('scaled')
### labels
ax1.set_xlabel(r"$x/R$", fontsize=40)
ax1.set_ylabel(r"$y/R$", fontsize=40)
### limits
ax1.set_xlim((full_box_downlim, full_box_uplim))
ax1.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax1.xaxis.set_ticks(full_box_ticks)
ax1.yaxis.set_ticks(full_box_ticks)
#plt.setp(ax1.get_yticklabels(),visible=False)
ax1.tick_params(axis='both', which='major', labelsize=40)
### AX2
line3 = ax2.scatter(cells.xi[step, 0, :], cells.xi[step, 1, :], s=5.0, \
c=dr,
cmap=cmap_ax0, \
edgecolors='None', alpha=0.8, vmin=min(dr), vmax=max(dr), \
norm=norm_ax1, rasterized=True)
line4 = ax2.quiver(cells.xi[step, 0, :], cells.xi[step, 1, :], \
np.cos(cells.pol[step]), np.sin(cells.pol[step]), \
#dr, cmap=cmap_ax0, norm=norm_ax1, \
headwidth=5, headlength=6, headaxislength=3.5, alpha=1.0)
line5 = ax2.quiver(cells.xi[step, 0, :], cells.xi[step, 1, :], \
dx, dy, \
dr, cmap=cmap_ax0, norm=norm_ax1, \
headwidth=5, headlength=6, headaxislength=3.5, alpha=1.0)
# line8 = ax2.streamplot(xli, yli, \
# uli, vli, \
# linewidth=1.0, density=2, arrowstyle='->', arrowsize=1.5)
ax2.axis('scaled')
cax2 = plt.colorbar(line5, ax=ax2)
#plt.colorbar(line5, cax=cax3, ticks=[])
#cax3.set_yticks([0, 0.8])
#cax3.set_yticklabels(['0', '0.7'])
cax2.ax.tick_params(labelsize=40)
cax2.ax.set_title(r"$|d_{r}|/R$", fontsize=40)
### labels
#ax2.set_xlabel(r"$x/R$", fontsize=40)
### limits
ax2.set_xlim((full_box_downlim, full_box_uplim))
ax2.set_ylim((full_box_downlim, full_box_uplim))
### ticks
#ax2.xaxis.set_ticks(full_box_ticks)
ax2.xaxis.set_ticks(full_box_ticks)
ax2.yaxis.set_ticks(full_box_ticks)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.tick_params(axis='both', which='major', labelsize=40)
### AX3
#line6 = ax3.pcolor(xgrid, ygrid, wn.transpose(), cmap=cmap_ax0)
#line6 = ax3.contourf(xgrid, ygrid, wn.transpose(), cmap=cmap_ax0, norm=norm_ax3)
line6 = ax3.contourf(xgrid, ygrid, wn.transpose(), cmap=cmap_ax0)
line7 = ax3.quiver(cells.xi[step, 0, :], cells.xi[step, 1, :], \
dx, dy, \
#dr, cmap=cmap_ax0, norm=norm_ax1, \
headwidth=5, headlength=6, headaxislength=3.5, alpha=1.0)
line7 = ax3.streamplot(xgrid, ygrid, \
vxg, vyg, \
linewidth=1.0, density=2, arrowstyle='->', arrowsize=1.5)
#dr, cmap=cmap_ax0, norm=norm_ax1)
ax3.axis('scaled')
cax3 = plt.colorbar(line6, ax=ax3)
#plt.colorbar(line5, cax=cax3, ticks=[])
#cax3.set_yticks([0, 0.8])
#cax3.set_yticklabels(['0', '0.7'])
cax3.ax.tick_params(labelsize=40)
cax3.ax.set_title(r"$\omega/<|\omega|>$", fontsize=40)
### labels
ax3.set_xlabel(r"$x/R$", fontsize=40)
### limits
ax3.set_xlim((full_box_downlim, full_box_uplim))
ax3.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax3.xaxis.set_ticks(full_box_ticks)
ax3.yaxis.set_ticks(full_box_ticks)
plt.setp(ax3.get_yticklabels(), visible=False)
ax3.tick_params(axis='both', which='major', labelsize=40)
### text
plt.figtext(subp.xbeg - 0.9 * ax_sep,
subp.ybeg + ax_len + 0.1 * ax_sep,
text,
fontsize=40)
### save
savepath1 = savebase + "frame-" + "{0:05d}".format(int(step)) + ".png"
if save_eps:
savepath2 = savebase + "frame-" + "{0:05d}".format(
int(step)) + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2,
dpi=300,
bbox_inches='tight',
pad_inches=0.08)
fig.clf()
return
def plot_data(x, data, param_choice, sims, savebase, savefolder, save_eps):
""" plot the data as a function of the chosen parameter"""
### set general plot properties
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### save properties
name, pname = misc_tools.gen_save_props(param_choice, sims[data.keys()[0]])
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
base = savebase + savefolder + '/' + name + '/'
os.system("mkdir -p " + base)
### plot
for a in data.keys():
sim = sims[a] # simulation information
y = data[a] # analysis data
label = '$\\epsilon=$' + str(sim.eps) + \
'$,f=$' + str(sim.fp) + '$,\\kappa_{A}=$' + str(sim.areak) + \
'$,\\kappa=$' + str(sim.kappa)
line0 = ax0.plot(x/sim.r_avg, y, \
linewidth=2.0, label=label)
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel("$r/R$", fontsize=40)
ax0.set_ylabel("$g(r)$", fontsize=40)
### limits
ax0.set_xlim((-1, 15))
#ax0.set_ylim((downlim, uplim))
### 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=30)
### legend
ax0.legend(bbox_to_anchor=(1.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
savepath1 = base + savefolder + "_per_" + pname + ".png"
if save_eps:
savepath2 = base + savefolder + "_per_" + name + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_data(x, y, sim, savebase, savefolder, save_eps):
""" plot the data as a function of the chosen parameter"""
### set general plot properties
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
xcut = x[70:]
ycut = y[70:]
label = '$\\epsilon=$' + str(sim.eps) + '$,f=$' + str(sim.fp) \
+ '$,\\kappa_{A}=$' + str(sim.areak)
line0 = ax0.loglog(xcut/sim.tau_D, ycut/sim.r_avg**2, \
linewidth=2.0, label=label)
line1 = ax0.loglog(xcut/sim.tau_D, xcut/sim.tau_D, \
linewidth=1.0, label=label)
line2 = ax0.loglog(xcut/sim.tau_D, (xcut/sim.tau_D)**2, '--', \
linewidth=1.0, label=label)
# print "Linear x = ", xcut
# print "Linear y = ", ycut
# s = interpolate.UnivariateSpline(xcut, ycut)
# xnew = np.linspace(np.min(xcut), np.max(xcut), num=100*len(xcut))
# #xnew = np.logspace(np.min(xcut), np.max(xcut), num=10*len(xcut), base=10.0)
# ynew = s(xnew)
# print "Smooth splined y = ", ynew
# intp = interpolate.interp1d(xnew, ynew)
# ynew = intp(xnew)
# print "Interpolated y = ", ynew
#
# line1 = ax0.loglog(xnew/sim.tau_D, ynew/sim.r_avg**2, \
# linewidth=2.0, label=label)
# line2 = ax0.loglog(xnew/sim.tau_D, (xnew/sim.tau_D)**2, \
# linewidth=1.0, label=label)
# line3 = ax0.loglog(xnew/sim.tau_D, xnew/sim.tau_D, '--',\
# linewidth=1.0, label=label)
#
# xcut = np.log10(xcut)
# ycut = np.log10(ycut)
# print (ycut[-1]-ycut[0])/(xcut[-1]-xcut[0])
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel("$t/\\tau_{D}$", fontsize=40)
ax0.set_ylabel("$\\Delta r^{2}/R^{2}$", fontsize=40)
### limits
#ax0.set_xlim((-1, 15))
#ax0.set_ylim((downlim, uplim))
### 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=30)
### legend
# ax0.legend(bbox_to_anchor=(1.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \
# prop={'size': 20}, mode="expand", frameon=False)
### save
savepath1 = base + savefolder + "_eps_" + str(sim.eps) + "_fp_" + str(sim.fp) + \
'_areak_' + str(sim.areak) + ".png"
if save_eps:
savepath2 = base + savefolder + "_eps_" + str(sim.eps) + "_fp_" + str(sim.fp) + \
'_areak_' + str(sim.areak) + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_data(x, data, param_choice, sims, savebase, savefolder, save_eps):
""" plot the data as a function of the chosen parameter"""
### set general plot properties
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### save properties
name, pname = misc_tools.gen_save_props(param_choice, sims[data.keys()[0]])
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
base = savebase + savefolder + '/' + name + '/'
os.system("mkdir -p " + base)
### plot
keys = np.sort(data.keys())
for p in keys:
sim = sims[p] # simulation information
y = data[p] # analysis data
### calculate the histogram
yh, xh = np.histogram(y, bins=50, normed=True)
xh = (xh[1:] + xh[:-1]) / 2.
label = '$\\epsilon=$' + str(sim.eps) + \
'$,f=$' + str(sim.fp) + '$,\\kappa_{A}=$' + str(sim.areak) + \
'$,\\kappa=$' + str(sim.kappa)
line0 = ax0.plot(xh, yh, \
linewidth=2.0, label=label)
### check normalization of the probability distribution
print np.sum(np.diff(xh) * yh[:-1])
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
# ax0.set_xlabel(r"$\phi$", fontsize=40)
# ax0.set_ylabel(r"$P(\phi)$", fontsize=40)
ax0.set_xlabel(r"$\rho\sigma^2$", fontsize=40)
ax0.set_ylabel(r"$P(\rho\sigma^2)$", fontsize=40)
### limits
#ax0.set_xlim((-1, 15))
#ax0.set_ylim((downlim, uplim))
### 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=30)
### legend
ax0.legend(bbox_to_anchor=(0.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
savepath1 = base + savefolder + "_per_" + pname + ".png"
if save_eps:
savepath2 = base + savefolder + "_per_" + pname + ".pdf"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_data(x, y, sim, savebase, savefolder, save_eps):
""" plot the data as a function of the chosen parameter"""
### set normalization parameter
knorm = np.pi / sim.r_avg
### pick out the relevant portion of the data
xd = 2. * np.pi / ((sim.lx - 4) / 2.)
dc = np.argmin(np.abs(x - xd))
xu = 2. * np.pi / (2. * sim.r_avg + 4)
uc = np.argmin(np.abs(x - xu))
print dc, uc
dcf = 54
ucf = 260
dc = 0
uc = len(x) - 55
print dc, uc
### normalization and appropriate data range selection
xnf = x[dcf:ucf] / knorm
ynf = y[dcf:ucf] / x[dcf:ucf]
xn = x[dc:uc] / knorm
yn = y[dc:uc] / x[dc:uc]
### curve fitting
popt, pcov = curve_fit(five_thirds, xnf, ynf)
yfit = five_thirds(xnf, popt[0])
print popt[0]
### set general plot properties
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
label = '$\\epsilon=$' + str(sim.eps) + '$,f=$' + str(
sim.fp) + '$,\\kappa_{A}=$' + str(sim.areak)
line0 = ax0.loglog(xn, yn, \
linewidth=2.0, label=label)
ax0.axvline(2 * np.pi / (sim.lx * knorm / 2.), color='black')
ax0.axvline(2 * np.pi / (sim.r_avg * 2. * knorm))
line1 = ax0.loglog(xnf, yfit, '--', \
linewidth=2.0, label=label, color='red')
# line1 = ax0.loglog(xn, xn**-5./3., '--', \
# linewidth=2.0, label=label)
# line0 = ax0.loglog(x[:cutp]/knorm, (x[:cutp]/knorm)**-8./3., '--', \
# linewidth=2.0, label=label)
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel("$q/q_{2R}$", fontsize=40)
ax0.set_ylabel("$E_{q}$", fontsize=40)
### limits
#ax0.set_xlim((-1, 15))
#ax0.set_ylim((downlim, uplim))
### 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=30)
### legend
# ax0.legend(bbox_to_anchor=(1.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \
# prop={'size': 20}, mode="expand", frameon=False)
### save
savepath1 = base + savefolder + "_eps_" + str(sim.eps) + "_fp_" + str(sim.fp) + \
'_areak_' + str(sim.areak) + ".png"
if save_eps:
savepath2 = base + savefolder + "_eps_" + str(sim.eps) + "_fp_" + str(sim.fp) + \
'_areak_' + str(sim.areak) + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_data(x, data, param_choice, sims, savebase, savefolder, save_eps):
""" plot the data as a function of the chosen parameter"""
### set general plot properties
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### save properties
name, pname, xlab, lab = \
misc_tools.gen_save_props(param_choice, sims[data.keys()[0]])
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
base = savebase + savefolder + '/' + name + '/'
os.system("mkdir -p " + base)
### plot
keys = np.sort(data.keys())
y = np.zeros((len(keys)), dtype=np.float32)
for j, p in enumerate(keys):
sim = sims[p] # simulation information
y[j] = data[p] # analysis data
line0 = ax0.scatter(x, y, label=lab, s=80)
line1 = ax0.plot(x, y, '--', label='_nolegend_')
ax0.set_xscale('log')
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel(xlab, fontsize=40)
ax0.set_ylabel(r"$\langle \Delta A \rangle/\sigma^2$", fontsize=40)
### limits
#ax0.set_xlim((-1, 15))
#ax0.set_ylim((downlim, uplim))
### 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=30)
### legend
ax0.legend(bbox_to_anchor=(0.3, 0.,0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
savepath1 = base + savefolder + "_per_" + pname + ".png"
if save_eps:
savepath2 = base + savefolder + "_per_" + pname + ".pdf"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_data(x, data, param_choice, sims, savebase, savefolder):
""" plot the data as a function of the chosen parameter"""
### set normalization parameter
knorm = np.pi / sims[data.keys()[0]].r_avg
### set general plot properties
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
xd = 2. * np.pi / ((sims[data.keys()[0]].lx - 4) / 2.)
dc = np.argmin(np.abs(x - xd))
xu = 2. * np.pi / (2. * sims[data.keys()[0]].r_avg + 4)
uc = np.argmin(np.abs(x - xu))
print dc, uc
dc = 54
uc = 260
# dc = 0
# uc = len(x)
print dc, uc
for p in data.keys():
sim = sims[p] # simulation information
y = data[p] # analysis data
### normalization and appropriate data range selection
xn = x[dc:uc] / knorm
yn = y[dc:uc] / x[dc:uc]
### curve fitting
popt, pcov = curve_fit(power_law, xn, yn)
yfit = power_law(xn, popt[0], popt[1])
print "key = ", p, ", fit param = ", popt[1]
popt, pcov = curve_fit(five_thirds, xn, yn)
yf_five_thirds = five_thirds(xn, popt[0])
popt, pcov = curve_fit(thirds, xn, yn)
yf_thirds = thirds(xn, popt[0])
label = r'$\epsilon=$' + str(sim.eps) + '$,f_{m}=$' + str(sim.fp) + \
'$,\kappa_{A}=$' + str(sim.areak)
line0 = ax0.loglog(xn, yn, \
linewidth=2.0, label=label)
# ax0.loglog(xn, yfit, '--', \
# linewidth=1.0, label='_nolegend_', color='black')
ax0.loglog(xn, yf_five_thirds, '--', \
linewidth=1.0, label='_nolegend_', color='gray')
# ax0.loglog(xn, yf_thirds, '--', \
# linewidth=1.0, label='_nolegend_', color='gray')
ax0.axvline(2 * np.pi / (sim.lx * knorm / 2.))
ax0.axvline(2 * np.pi / (sim.r_avg * 2. * knorm))
# ax0.loglog(np.ones_like(yn)*2*np.pi/(sim.lx*knorm/2.), yn, '--', \
# label='_nolegend_', linewidth=1.0, color='k')
# ax0.loglog(np.ones_like(yn)*2*np.pi/(sim.r_avg*2.*knorm), yn, '--', \
# label='_nolegend_', linewidth=1.0, color='k')
### labels
ax0.set_xlabel(r"$q/q_{2R}$", fontsize=30)
ax0.set_ylabel(r"$E(q)$", fontsize=30)
### limits
ax0.set_xlim((0.03, 1.0))
# ax0.set_ylim((0.0, 1.9))
### ticks
# ax0.xaxis.set_ticks([0, 10, 20])
ax0.yaxis.set_ticks([1e-5, 1e-3, 1e-1, 1e1])
ax0.tick_params(axis='both', which='major', labelsize=30)
### legend
ax0.legend(bbox_to_anchor=(0.35, 0.,0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
### save
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
savepath1 = base + savefolder + "_transition_per_" + param_choice + ".png"
savepath2 = base + savefolder + "_transition_per_" + param_choice + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_traj(x, sim, ti, tf, savebase, savefolder, save_eps):
""" plot trajectory within the specified time window"""
### normalize variables for plotting purposes
x /= sim.bl
lx = sim.lx / sim.bl
ly = sim.ly / sim.bl
### set general plot properties
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
downlim = -2
uplim = lx + 2
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
label = '$\\phi=$' + str(sim.density) + '$,k_{m}=$' + str(sim.km)
line0 = ax0.plot(x[ti:tf, 0, :], x[ti:tf, 1, :], \
linewidth=2.0, label=label)
ax0.axis('scaled')
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel("$x/r_{0}$", fontsize=40)
ax0.set_ylabel("$y/r_{0}$", fontsize=40)
### limits
#ax0.set_xlim((downlim, uplim))
#ax0.set_ylim((downlim, uplim))
### ticks
#ax0.xaxis.set_ticks(np.linspace(0, uplim, num_ticks, endpoint=True))
#ax0.yaxis.set_ticks(np.linspace(0, uplim, num_ticks, endpoint=True))
ax0.tick_params(axis='both', which='major', labelsize=30)
### save
savepath1 = base + savefolder + "_density_" + str(sim.density) + "_kappa_" + str(sim.kappa) + \
"_km_" + str(sim.km) + '_panti_' + str(sim.pa) + ".png"
if save_eps:
savepath2 = base + savefolder + "_density_" + str(sim.density) + "_kappa_" + str(sim.kappa) + \
"_km_" + str(sim.km) + '_panti_' + str(sim.pa) + ".eps"
plt.savefig(savepath1, dpi=200, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2, dpi=200, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_frames(beads, cells, sim, ti, tf, savebase, save_eps):
""" plot frames within the specified time window"""
### normalize variables for plotting purposes
lx = sim.lx / sim.r_avg
ly = sim.ly / sim.r_avg
### set general plot properties
savebase += 'eps_' + str(sim.eps) + '_fp_' + str(sim.fp) + \
'_areak_' + str(sim.areak) + '_kappa_' + str(sim.kappa) + '/'
os.system("mkdir -p " + savebase)
quant_steps = 2056
# limits
full_box_downlim = -2
full_box_uplim = lx + 2
full_box_ticks = [0, 35, 70, 105, 135]
half_box_downlim = 43
half_box_uplim = 92
half_box_ticks = [45, 90]
num_ticks = 5
ax_len = 2.0 # Length of one subplot square box
ax_b = 0.05 # Beginning/offset of the subplot in the box
ax_sep = -0.4 # Separation length between two subplots
total_subplots_in_x = 3 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
ax1 = subp.addSubplot()
ax2 = subp.addSubplot()
### set subplot properties
nslices = sim.ncells
# nslices = 100 # Number of slices to take in the y dir
norm_ax0 = mpl.colors.Normalize(vmin=0, vmax=nslices)
# vertical_pos_colors_per_bead = cell2bead_vertical_pos(beads, cells, sim, nslices, ti)
# cmap_ax1 = sns.dark_palette((260, 75, 60), n_colors=nslices, input="husl", as_cmap=True)
# cmap_ax1 = sns.diverging_palette(255, 133, l=60, n=nslices, center="dark", as_cmap=True)
cmap_ax0 = plt.cm.get_cmap('jet', quant_steps)
### plot the frames
comx = deque()
comy = deque()
ntrace = 6
delta = 20
if tf + delta > sim.nsteps:
tf -= delta
for step in range(ti, tf):
time = step * sim.dt
print 'Step / Total : ', step, tf
### normalize variables for plotting purposes
beads.xi[step, 0, :] /= sim.r_avg
beads.xi[step, 1, :] /= sim.r_avg
cells.xi[step, 0, :] /= sim.r_avg
cells.xi[step, 1, :] /= sim.r_avg
### calculate the displacement magnitudes
dr = calc_displacement_magnitudes(cells, step, delta, sim)
norm_ax1 = mpl.colors.Normalize(vmin=min(dr), vmax=max(dr))
### keep the center of mass trajectory
comx.append(cells.xi[step, 0, :])
comy.append(cells.xi[step, 1, :])
if step > ti + ntrace:
comx.popleft()
comy.popleft()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b,
total_subplots_in_x)
ax0 = subp.addSubplot()
ax1 = subp.addSubplot()
ax2 = subp.addSubplot()
text = r"$t/\tau_{D}$ = " + "{0:.2f}".format( time/sim.tau_D) + \
r", $t/\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A)
### AX0
line0 = ax0.scatter(beads.xi[step, 0, :], beads.xi[step, 1, :], s=4.0, \
c=beads.cid, \
cmap=cmap_ax0, \
edgecolors='None', alpha=1.0, vmin=0, vmax=nslices, \
norm=norm_ax0, rasterized=True)
ax0.axis('scaled')
### labels
ax0.set_xlabel(r"$x/R$", fontsize=40)
ax0.set_ylabel(r"$y/R$", fontsize=40)
### limits
ax0.set_xlim((full_box_downlim, full_box_uplim))
ax0.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax0.xaxis.set_ticks(full_box_ticks)
ax0.yaxis.set_ticks(full_box_ticks)
ax0.tick_params(axis='both', which='major', labelsize=40)
### AX1
line1 = ax1.scatter(cells.xi[step, 0, :], cells.xi[step, 1, :], s=6.0, \
c=np.arange(nslices), \
#c=dr,
cmap=cmap_ax0, \
edgecolors='None', alpha=1.0, vmin=0, vmax=nslices, \
norm=norm_ax0, rasterized=True)
line2 = ax1.scatter(list(comx), list(comy), s=5.0, \
c=np.ones(np.shape(list(comx)))*np.arange(nslices), \
#c=np.ones(np.shape(list(comx)))*dr,
cmap=cmap_ax0, \
edgecolors='None', alpha=0.5, vmin=0, vmax=nslices, \
norm=norm_ax0, rasterized=True)
ax1.axis('scaled')
### labels
ax1.set_xlabel(r"$x/R$", fontsize=40)
### limits
ax1.set_xlim((full_box_downlim, full_box_uplim))
ax1.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax1.xaxis.set_ticks(full_box_ticks)
plt.setp(ax1.get_yticklabels(), visible=False)
ax1.tick_params(axis='both', which='major', labelsize=40)
### AX2
line3 = ax2.scatter(cells.xi[step, 0, :], cells.xi[step, 1, :], s=5.0, \
c=dr,
cmap=cmap_ax0, \
edgecolors='None', alpha=0.8, vmin=min(dr), vmax=max(dr), \
norm=norm_ax1, rasterized=True)
line4 = ax2.quiver(cells.xi[step, 0, :], cells.xi[step, 1, :], \
np.cos(cells.pol[step]), np.sin(cells.pol[step]), \
dr, cmap=cmap_ax0, norm=norm_ax1, \
headwidth=5, headlength=6, headaxislength=3.5, alpha=1.0)
ax2.axis('scaled')
### labels
ax2.set_xlabel(r"$x/R$", fontsize=40)
### limits
ax2.set_xlim((full_box_downlim, full_box_uplim))
ax2.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax2.xaxis.set_ticks(full_box_ticks)
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.tick_params(axis='both', which='major', labelsize=40)
plt.figtext(subp.beg + ax_len - 0.4 * ax_sep,
subp.ybeg + ax_len - 0.2 * ax_sep,
text,
fontsize=40)
### save
savepath1 = savebase + "frame-" + "{0:05d}".format(int(step)) + ".png"
if save_eps:
savepath2 = savebase + "frame-" + "{0:05d}".format(
int(step)) + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2,
dpi=300,
bbox_inches='tight',
pad_inches=0.08)
fig.clf()
return
def plot_frames(beads, sim, ti, tf, savebase, save_eps):
""" plot frames within the specified time window"""
### normalize variables for plotting purposes
lx = sim.lx / sim.bl
ly = sim.ly / sim.bl
### set general plot properties
savebase += 'eps_' + str(sim.eps) + '_fp_' + str(sim.fp) + '_areak_' + str(
sim.areak) + '/'
os.system("mkdir -p " + savebase)
quant_steps = 2056
norm = mpl.colors.Normalize(vmin=0, vmax=sim.ncells)
downlim = -2
uplim = lx + 2
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot the frames
for step in range(ti, tf):
### normalize variables for plotting purposes
beads.xi[step, 0, :] /= sim.bl
beads.xi[step, 1, :] /= sim.bl
time = step * sim.dt
print 'Step / Total : ', step, tf
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b,
total_subplots_in_x)
ax0 = subp.addSubplot()
line0 = ax0.scatter(beads.xi[step, 0, :], beads.xi[step, 1, :], s=1, c=beads.cid, \
cmap=plt.cm.get_cmap('jet',quant_steps), \
edgecolors='None', alpha=0.7, vmin=0, vmax=sim.ncells, \
norm=norm, rasterized=True)
ax0.axis('scaled')
### title
ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel("$x/r_{0}$", fontsize=40)
ax0.set_ylabel("$y/r_{0}$", fontsize=40)
### limits
ax0.set_xlim((downlim, uplim))
ax0.set_ylim((downlim, uplim))
### ticks
ax0.xaxis.set_ticks(np.linspace(0, uplim, num_ticks, endpoint=True))
ax0.yaxis.set_ticks(np.linspace(0, uplim, num_ticks, endpoint=True))
ax0.tick_params(axis='both', which='major', labelsize=30)
### save
savepath1 = savebase + "frame-" + "{0:05d}".format(int(step)) + ".png"
if save_eps:
savepath2 = savebase + "frame-" + "{0:05d}".format(
int(step)) + ".eps"
plt.savefig(savepath1, dpi=200, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2,
dpi=200,
bbox_inches='tight',
pad_inches=0.08)
fig.clf()
return
def plot_frames(data, beads, cells, sim, ti, tf, savebase, save_eps):
""" plot frames within the specified time window"""
### normalize variables for plotting purposes
lx = sim.lx / sim.r_avg
ly = sim.ly / sim.r_avg
### vorticity information
steps, xbins, ybins, w, vx, vy, v = data
nwbins = int(max(xbins) + 1)
xlin = np.linspace(0., lx, nwbins)
ylin = np.linspace(0., ly, nwbins)
xgrid, ygrid = np.meshgrid(xlin, ylin)
### set general plot properties
savebase += 'eps_' + str(sim.eps) + '_fp_' + str(sim.fp) + \
'_areak_' + str(sim.areak) + '/'
os.system("mkdir -p " + savebase)
quant_steps = 2056
# limits
full_box_downlim = -2
full_box_uplim = lx + 2
full_box_ticks = [0, 35, 70, 105, 135]
half_box_downlim = 43
half_box_uplim = 92
half_box_ticks = [45, 90]
num_ticks = 5
ax_len = 2.2 # Length of one subplot square box
ax_b = 0.01 # Beginning/offset of the subplot in the box
ax_sep = 0.15 # Separation length between two subplots
total_subplots_in_x = 2 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
multi = True
ax1 = subp.addSubplot(multi)
ax3 = subp.addSubplot(multi)
ax0 = subp.addSubplot(multi)
ax2 = subp.addSubplot(multi)
### set subplot properties
nslices = sim.ncells
norm_ax0 = mpl.colors.Normalize(vmin=0, vmax=nslices)
cmap_ax0 = plt.cm.get_cmap('jet', quant_steps)
### plot the frames
comx = deque()
comy = deque()
ntrace = 6
delta = 4
### normalize central box positions for plotting purposes
beads.xi /= sim.r_avg
cells.xi /= sim.r_avg
if tf + delta > sim.nsteps:
tf -= delta
for step in range(ti, tf):
time = step * sim.dt
print 'Step / Total : ', step, tf
### calculate the displacement magnitudes
dx, dy, dr = calc_displacement_magnitudes(cells, step, delta, sim)
dx /= sim.r_avg
dy /= sim.r_avg
dr /= sim.r_avg
#norm_ax1 = mpl.colors.Normalize(vmin=min(dr), vmax=max(dr))
### keep the center of mass trajectory
comx.append(cells.xi[step, 0, :])
comy.append(cells.xi[step, 1, :])
if step > ti + ntrace:
comx.popleft()
comy.popleft()
### get the vorticity and velocity information
vs = v[steps == step]
vmean = np.mean(vs)
vn = np.zeros((nwbins, nwbins), dtype=np.float32)
ws = w[steps == step]
wmean = np.mean(np.abs(ws))
wn = np.zeros((nwbins, nwbins), dtype=np.float32)
for xi, yi in zip(xbins, ybins):
xi = int(xi)
yi = int(yi)
wn[xi, yi] = ws[xi * nwbins + yi]
vn[xi, yi] = vs[xi * nwbins + yi]
vn /= vmean
vmin = np.min(vn)
vmax = np.max(vn)
norm_ax2 = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
wn /= wmean
wmin = np.min(wn)
wmax = np.max(wn)
norm_ax3 = mpl.colors.Normalize(vmin=wmin, vmax=wmax)
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b,
total_subplots_in_x)
ax1 = subp.addSubplot(multi)
ax3 = subp.addSubplot(multi)
ax0 = subp.addSubplot(multi)
ax2 = subp.addSubplot(multi)
text = r"$t/\tau_{D}$ = " + "{0:.2f}".format( time/sim.tau_D) + \
r", $t/\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A)
### AX0
line0 = ax0.scatter(beads.xi[step, 0, :], beads.xi[step, 1, :], s=4.0, \
c=beads.cid, \
cmap=cmap_ax0, \
edgecolors='None', alpha=1.0, vmin=0, vmax=nslices, \
norm=norm_ax0, rasterized=True)
ax0.axis('scaled')
### labels
ax0.set_ylabel(r"$y/R$", fontsize=40)
### limits
ax0.set_xlim((full_box_downlim, full_box_uplim))
ax0.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax0.xaxis.set_ticks(full_box_ticks)
ax0.yaxis.set_ticks(full_box_ticks)
plt.setp(ax0.get_xticklabels(), visible=False)
ax0.tick_params(axis='both', which='major', labelsize=40)
### AX1
line1 = ax1.scatter(cells.xi[step, 0, :], cells.xi[step, 1, :], s=6.0, \
c=np.arange(nslices), \
#c=dr,
cmap=cmap_ax0, \
edgecolors='None', alpha=1.0, vmin=0, vmax=nslices, \
norm=norm_ax0, rasterized=True)
line2 = ax1.scatter(list(comx), list(comy), s=5.0, \
c=np.ones(np.shape(list(comx)))*np.arange(nslices), \
#c=np.ones(np.shape(list(comx)))*dr,
cmap=cmap_ax0, \
edgecolors='None', alpha=0.5, vmin=0, vmax=nslices, \
norm=norm_ax0, rasterized=True)
ax1.axis('scaled')
### labels
ax1.set_xlabel(r"$x/R$", fontsize=40)
ax1.set_ylabel(r"$y/R$", fontsize=40)
### limits
ax1.set_xlim((full_box_downlim, full_box_uplim))
ax1.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax1.xaxis.set_ticks(full_box_ticks)
ax1.yaxis.set_ticks(full_box_ticks)
#plt.setp(ax1.get_yticklabels(),visible=False)
ax1.tick_params(axis='both', which='major', labelsize=40)
### AX2
line3 = ax2.contourf(
xgrid,
ygrid,
vn.transpose(),
cmap=cmap_ax0,
#norm=norm_ax2, vmin=vmin, vmax=vmax)
)
line4 = ax2.quiver(cells.xi[step, 0, :], cells.xi[step, 1, :], \
dx, dy, \
headwidth=5, headlength=6, headaxislength=3.5, alpha=0.7)
ax2.axis('scaled')
cax2 = plt.colorbar(line3, ax=ax2)
#plt.colorbar(line5, cax=cax3, ticks=[])
#cax2.ax.set_yticks([0.0, 0.3, 0.6, 0.9, 1.2 ,1.5])
#cax3.set_yticklabels(['0', '0.7'])
cax2.ax.tick_params(labelsize=40)
cax2.ax.set_title(r"$|v|/<|v|>$", fontsize=40)
### labels
#ax2.set_xlabel(r"$x/R$", fontsize=40)
### limits
ax2.set_xlim((full_box_downlim, full_box_uplim))
ax2.set_ylim((full_box_downlim, full_box_uplim))
### ticks
#ax2.xaxis.set_ticks(full_box_ticks)
ax2.xaxis.set_ticks(full_box_ticks)
ax2.yaxis.set_ticks(full_box_ticks)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.tick_params(axis='both', which='major', labelsize=40)
### AX3
line6 = ax3.contourf(
xgrid,
ygrid,
wn.transpose(),
cmap=cmap_ax0,
#norm=norm_ax3, vmin=wmin, vmax=wmax)
)
line7 = ax3.quiver(cells.xi[step, 0, :], cells.xi[step, 1, :], \
dx, dy, \
headwidth=5, headlength=6, headaxislength=3.5, alpha=1.0)
ax3.axis('scaled')
cax3 = plt.colorbar(line6, ax=ax3)
#plt.colorbar(line5, cax=cax3, ticks=[])
#cax3.ax.set_yticks([-6.0, -3.0, 0.0 , 3.0, 6.0])
#cax3.set_yticklabels(['0', '0.7'])
cax3.ax.tick_params(labelsize=40)
cax3.ax.set_title(r"$\omega/<|\omega|>$", fontsize=40)
### labels
ax3.set_xlabel(r"$x/R$", fontsize=40)
### limits
ax3.set_xlim((full_box_downlim, full_box_uplim))
ax3.set_ylim((full_box_downlim, full_box_uplim))
### ticks
ax3.xaxis.set_ticks(full_box_ticks)
ax3.yaxis.set_ticks(full_box_ticks)
plt.setp(ax3.get_yticklabels(), visible=False)
ax3.tick_params(axis='both', which='major', labelsize=40)
### text
plt.figtext(subp.xbeg - 1.1 * ax_sep,
subp.ybeg + ax_len + 0.1 * ax_sep,
text,
fontsize=40)
### save
savepath1 = savebase + "frame-" + "{0:05d}".format(int(step)) + ".png"
if save_eps:
savepath2 = savebase + "frame-" + "{0:05d}".format(
int(step)) + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2,
dpi=300,
bbox_inches='tight',
pad_inches=0.08)
fig.clf()
return
def plot_data(x, y, sim, savebase, savefolder, save_eps):
""" plot the data as a function of the chosen parameter"""
### set general plot properties
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
label = '$\\phi=$' + str(sim.density) + '$,km=$' + str(sim.km)
line0 = ax0.plot(x/sim.tau_D, y, \
linewidth=2.0, label=label)
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel("$\\Delta t/\\tau_{D}$", fontsize=40)
ax0.set_ylabel("$Q(\\Delta t)$", fontsize=40)
### limits
#ax0.set_xlim((-1, 15))
#ax0.set_ylim((downlim, uplim))
### 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=30)
### legend
# ax0.legend(bbox_to_anchor=(1.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \
# prop={'size': 20}, mode="expand", frameon=False)
### save
savepath1 = base + savefolder + "_density_" + str(sim.density) + "_kappa_" + str(sim.kappa) + \
"_km_" + str(sim.km) + '_panti_' + str(sim.pa) + ".png"
if save_eps:
savepath2 = base + savefolder + "_density_" + str(sim.density) + "_kappa_" + str(sim.kappa) + \
"_km_" + str(sim.km) + '_panti_' + str(sim.pa) + ".eps"
plt.savefig(savepath1, dpi=200, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2, dpi=200, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_data(x, data, param_choice, sims, savebase, savefolder):
""" plot the data as a function of the chosen parameter"""
### set general plot properties
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
cnt = 0
for p in data.keys():
sim = sims[p] # simulation information
y = data[p] # analysis data
yth = th_msd(x, sim) # theoretical result for MSD
label = r'$\epsilon=$' + str(sim.eps) + '$,f_{m}=$' + str(sim.fp) + \
'$,\kappa_{A}=$' + str(sim.areak) + '$,\kappa_{B}=$' + str(sim.kappa)
color = list_from_cycle(ax0._get_lines.prop_cycler)[cnt]['color']
line0 = ax0.loglog(x/sim.tau_D, y/sim.r_avg**2, \
linewidth=2.0, label=label, color=color)
# line1 = ax0.loglog(x/sim.tau_D, yth/sim.r_avg**2, '--', \
# linewidth=1.0, label='_nolegend_', color=color)
cnt += 1
ax0.loglog(x / sim.tau_D, x / sim.tau_D, '--', linewidth=1.0, color='grey')
ax0.loglog(x / sim.tau_D, (x / sim.tau_D)**2,
'--',
linewidth=1.0,
color='grey')
# ax0.axvline(1./sim.Dr/sim.tau_D, color='black', alpha=0.5)
# ax0.axhline(1.0, color='black', alpha=0.5)
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel(r"$t/\tau_{D}$", fontsize=30)
ax0.set_ylabel(r"$\Delta r^{2}/R^{2}$", fontsize=30)
### limits
# ax0.set_xlim((1e-1, 5e2))
ax0.set_ylim((1e-1, 1e6))
ax0.set_ylim((1e-1, 1e10))
### ticks
# ax0.xaxis.set_ticks([1e-1, 1e0, 1e1, 1e2, 5e2])
ax0.yaxis.set_ticks([1e-2, 1e0, 1e2, 1e4, 1e6])
ax0.yaxis.set_ticks([1e-2, 1e0, 1e2, 1e4, 1e6, 1e8, 1e10])
ax0.tick_params(axis='both', which='major', labelsize=30)
### legend
ax0.legend(bbox_to_anchor=(0.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
### save
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
savepath1 = base + savefolder + "_transition_per_" + param_choice + ".png"
savepath2 = base + savefolder + "_transition_per_" + param_choice + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_data(data, a, savebase, savefolder, save_eps):
""" plot the data as a function of the chosen parameter
z encapsulates the data and sim info as a function of two parameters"""
### set general plot properties
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### collect the values to be plotted in a numpy array
x = np.zeros((len(data)), dtype=np.float32)
y = np.zeros((len(data)), dtype=np.float32)
z = np.zeros((len(data)), dtype=np.float32)
for j, key in enumerate(data.keys()):
#e, f = key # parameters
#sim = data[key].sim # simulation info for this parameter set
z[j] = data[key].data # analysis data
x[j] = data[key].p1 # x value in the plot
y[j] = data[key].p2 # y value in the plot
### get the size and color code
print z
size = 1e+8 * np.pi * z**2
quant_steps = 2056
norm = mpl.colors.Normalize(vmin=0.0, vmax=max(z))
line0 = ax0.scatter(x, y, c=z, \
cmap=plt.cm.get_cmap('jet',quant_steps), \
edgecolors='None', alpha=1.0, vmin=0.0, vmax=max(z), \
norm=norm, rasterized=True)
fig.colorbar(line0, ax=ax0)
### title
# ax0.set_title("$t/\\tau_{D}$ = " + "{0:.2f}".format(time/sim.tau_D) + \
# ", $t/\\tau_{A}$ = " + "{0:.2f}".format(time/sim.tau_A), fontsize=30)
### labels
ax0.set_xlabel("$\\epsilon$", fontsize=40)
ax0.set_ylabel("$f_{m}$", fontsize=40)
### limits
#ax0.set_xlim((-1, 15))
#ax0.set_ylim((downlim, uplim))
### 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=30)
### legend
ax0.legend(bbox_to_anchor=(1.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
### save
savepath1 = base + savefolder + "_per_AREAK_" + str(a) + ".png"
if save_eps:
savepath2 = base + savefolder + "_per_AREAK_" + str(a) + ".eps"
plt.savefig(savepath1, dpi=200, bbox_inches='tight', pad_inches=0.08)
if save_eps:
plt.savefig(savepath2, dpi=200, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_data(x, data, param_choice, sims, savebase, savefolder):
""" plot the data as a function of the chosen parameter"""
### set normalization parameter
#knorm = np.pi/sims[data.keys()[0]].r_avg
### set general plot properties
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
for p in data.keys():
sim = sims[p] # simulation information
y = data[p] # analysis data
### normalisation and appropriate data range selection
print y
histy, edges = np.histogram(y,
bins=np.logspace(np.log10(min(y)),
np.log10(max(y))))
print edges, histy
### curve fitting
# popt, pcov = curve_fit(power_law, xn, yn)
# yfit = power_law(xn, popt[0], popt[1])
# print "key = ", p,", fit param = ", popt[1]
label = r'$\epsilon=$' + str(sim.eps) + '$,f_{m}=$' + str(sim.fp) + \
'$,\kappa_{A}=$' + str(sim.areak)
line0 = ax0.plot(edges[:-1], histy, \
linewidth=2.0, label=label)
#ax0.set_xscale('log')
### labels
ax0.set_xlabel(r"$q$", fontsize=30)
ax0.set_ylabel(r"$D(q)$", fontsize=30)
### limits
# ax0.set_xlim((0, 22))
# ax0.set_ylim((0.0, 1.9))
### ticks
# ax0.xaxis.set_ticks([0, 10, 20])
# ax0.yaxis.set_ticks([0, 0.5, 1.0, 1.5])
ax0.tick_params(axis='both', which='major', labelsize=30)
### legend
ax0.legend(bbox_to_anchor=(0.35, 0.,0.65, 1.), loc=2, borderaxespad=0., \
prop={'size': 20}, mode="expand", frameon=False)
### save
base = savebase + savefolder + '/'
os.system("mkdir -p " + base)
savepath1 = base + savefolder + "_transition_per_" + param_choice + ".png"
savepath2 = base + savefolder + "_transition_per_" + param_choice + ".eps"
plt.savefig(savepath1, dpi=300, bbox_inches='tight', pad_inches=0.08)
plt.savefig(savepath2, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
def plot_slices(phases, slicer):
""" plot phase diagram by slices in 2D"""
npoints = len(phases) # total number of data points
### load the data
idx = []
x = []
y = []
z = []
markers = []
colors = []
for j in xrange(npoints):
idx.append(j)
x.append(phases[j].eps)
y.append(phases[j].fm)
z.append(phases[j].data)
markers.append(phases[j].marker)
colors.append(phases[j].color)
### convert the lists into numpy arrays
x = np.array(x, dtype=np.float32)
y = np.array(y, dtype=np.float32)
z = np.array(z, dtype=np.float32)
idx = np.array(idx, dtype=np.int32)
### set plot properties
savefolder = "/usr/users/iff_th2/duman/Cells_in_LAMMPS/PLOTS/PHASE_DIAGRAM/"
os.system("mkdir -p " + savefolder)
sfile = savefolder + "phase_diagram_from_corr_len_per_AREAK_" + \
str(slicer)
fig = plt.figure()
ax_len = 0.9 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots in the x direction
tick_num = 5 # Number of ticks
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
downlim_x = 0.03
uplim_x = 35.0
downlim_y = 0.3
uplim_y = 15.0
quant_steps = 2056
norm = mpl.colors.Normalize(vmin=0, vmax=30)
### make these smaller to increase the resolution
ax = subp.addSubplot()
for xi, yi, zi, mi, ci in zip(x, y, z, markers, colors):
line0 = ax.scatter(xi,
yi,
marker=mi,
c=zi,
s=30,
cmap=plt.cm.get_cmap('jet', quant_steps),
edgecolors='None',
alpha=1.0,
norm=norm,
vmin=0,
vmax=30)
#ax.grid("on")
cax = plt.colorbar(line0, ax=ax)
cax.ax.set_title(r'$\xi_{r}$', fontsize=30)
ax.set_title(r'$\kappa_{A} = $' + str(slicer), fontsize=30)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel(r'$\epsilon$', fontsize=30)
ax.set_ylabel(r'$f_{m}$', fontsize=30)
ax.set_xlim((downlim_x, uplim_x))
ax.set_ylim((downlim_y, uplim_y))
#ax.xaxis.set_ticks( np.linspace(downlim, uplim, num=tick_num, endpoint=True) )
#ax.yaxis.set_ticks( np.linspace(downlim, uplim, num=tick_num, endpoint=True) )
ax.tick_params(axis='both', which='major', labelsize=30)
### save the figure
plt.savefig(sfile + '.eps', dpi=300, bbox_inches='tight', pad_inches=0.08)
plt.savefig(sfile + '.png', dpi=300, bbox_inches='tight', pad_inches=0.08)
plt.clf()
return
def plot_data(data, param_choice, args):
""" plot the frames per parameter"""
### set general plot properties
savebase = '/usr/users/iff_th2/duman/Cells_in_LAMMPS/POVRAY/'
#downlim = -1
#uplim = sim.lx/4.
num_ticks = 5
ax_len = 1.0 # Length of one subplot square box
ax_b = 0.0 # Beginning/offset of the subplot in the box
ax_sep = 0.0 # Separation length between two subplots
total_subplots_in_x = 1 # Total number of subplots
fig = plt.figure()
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
name = ''
pname = ''
if param_choice == 'areak':
name = 'AREAK'
pname = name + '_eps_' + str(args.eps) + '_fp_' + str(args.fp) + \
'_kappa_' + str(args.kappa)
xlab = '$\kappa_A$'
tit = '$\epsilon=$' + str(args.eps) + ',$f_m=$' + str(args.fp) + \
',$\kappa=$' + str(args.kappa)
elif param_choice == 'eps':
name = 'EPS'
pname = name + '_fp_' + str(args.fp) + '_areak_' + str(args.areak) + \
'_kappa_' + str(args.kappa)
xlab = '$\epsilon$'
tit = '$f_m=$' + str(args.fp) + ',$\kappa_A=$' + str(args.areak) + \
',$\kappa=$' + str(args.kappa)
elif param_choice == 'fp':
name = 'FP'
pname = name + '_eps_' + str(args.eps) + '_areak_' + str(args.areak) + \
'_kappa_' + str(args.kappa)
xlab = '$f_{m}$'
tit = '$\epsilon=$' + str(args.eps) + ',$\kappa_A=$' + str(args.areak) + \
',$\kappa=$' + str(args.kappa)
elif param_choice == 'kappa':
name = 'KAPPA'
pname = name + '_eps_' + str(args.eps) + '_fp_' + str(args.fp) + \
'_areak_' + str(args.areak)
xlab = '$\kappa$'
tit = '$\epsilon=$' + str(args.eps) + ',$f_m=$' + str(args.fp) + \
',$\kappa_A=$' + str(args.areak)
base = savebase + name + '/'
os.system("mkdir -p " + base)
### plot
subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x)
ax0 = subp.addSubplot()
x = data.keys()
y = [1 for j in range(len(data.keys()))]
print x
ax0.scatter(x, y)
ax0.set_xscale('log')
ax0.set_yscale('log')
for j, p in enumerate(data.keys()):
fname = data[p]
if os.path.exists(fname):
arr_hand = read_png(fname)
zoom = 0.099
imagebox = OffsetImage(arr_hand, zoom=zoom)
xy = [x[j], y[j]] # coordinates to position this image
ab = AnnotationBbox(imagebox,
xy,
xybox=(0., -0.),
xycoords='data',
boxcoords="offset points",
frameon=1,
pad=.1)
ax0.add_artist(ab)
### title
ax0.set_title(tit, fontsize=30)
### labels
ax0.set_xlabel(xlab, fontsize=30)
#ax0.set_ylabel("$F_{s}(q,\\Delta t)$", fontsize=40)
### limits
#ax0.set_xlim((-1, 15))
ax0.set_ylim((0.9999, 1.0001))
ax0.grid(1, color='#cccccc', linestyle='--')
ax0.set_frame_on(False)
ax0.get_xaxis().tick_bottom()
ax0.axes.get_yaxis().set_visible(False)
xmin, xmax = ax0.get_xaxis().get_view_interval()
ymin, ymax = ax0.get_yaxis().get_view_interval()
ax0.add_artist(
Line2D((xmin, xmax), (ymin, ymin), color='black', linewidth=2))
### 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))
plt.setp(ax0.get_yticklabels(), visible=False)
ax0.tick_params(axis='both', which='major', labelsize=30)
### legend
# ax0.legend(bbox_to_anchor=(1.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \
# prop={'size': 20}, mode="expand", frameon=False)
### save
savepath = base + "images_per_" + pname + ".pdf"
print savepath
plt.savefig(savepath, dpi=300, bbox_inches='tight', pad_inches=0.08)
fig.clf()
return
