def makeAnimFrame(self, n):
print('saving anim frame for n = ' + str(n))
fig = plt.figure(figsize=(8.03, 10.5), dpi=80)
ax = []
ax.append(plt.subplot2grid((7, 2), (0, 0), rowspan=2))
ax.append(plt.subplot2grid((7, 2), (0, 1), rowspan=2))
ax.append(plt.subplot2grid((7, 2), (2, 0), rowspan=2))
ax.append(plt.subplot2grid((7, 2), (2, 1), rowspan=2))
ax.append(plt.subplot2grid((7, 2), (4, 0), colspan=2))
ax.append(plt.subplot2grid((7, 2), (5, 0), colspan=2))
ax.append(plt.subplot2grid((7, 2), (6, 0), colspan=2))
tPlot = doPlan.time
# Nclumps
axNum = 6
ax[axNum].plot(tPlot[n:], doPlan.nClumpsList[n:], 'gray', linewidth=1)
ax[axNum].plot(tPlot[:n], doPlan.nClumpsList[:n], 'k', linewidth=2)
ax[axNum].plot(tPlot[n], doPlan.nClumpsList[n], 'ro', markersize=5)
ax[axNum].set_ylabel(r'$N_{clumps}$')
ax[axNum].set_xlabel(r'$t \Omega$')
# mass frac
axNum = 5
mFracList = []
for n1 in range(0, len(doPlan.peakArrayList)):
mNow = np.sum(doPlan.peakArrayList[n1][:, 2])
mFracNow = mNow / doPlan.mParTot
mFracList.append(mFracNow)
ax[axNum].plot(doPlan.timeList[n:], mFracList[n:], 'gray', linewidth=1)
ax[axNum].plot(doPlan.timeList[:n], mFracList[:n], 'k', linewidth=2)
ax[axNum].plot(doPlan.timeList[n], mFracList[n], 'ro', markersize=5)
#ax[axNum].get_xaxis().set_visible(False)
ax[axNum].set_ylim(-0.05, 1.02)
ax[axNum].set_ylabel(r'$M_{plan} / M_{par}$')
# p
axNum = 4
#ax[axNum].plot(tPlot[n:], pArr[n:], 'gray', linewidth=1)
ax[axNum].plot(tPlot[:n], pArr[:n], 'k', linewidth=2)
ax[axNum].plot(tPlot[n], pArr[n], 'ro', markersize=5)
ax[axNum].fill_between(tPlot,
pArr - errArr,
pArr + errArr,
color='gray',
alpha=0.5)
ax[axNum].set_ylabel(r'$p$')
ax[axNum].set_ylim(0.5, 3.0)
pMean = np.round(np.mean(pArr[max(n - 50, 0):n]), 2)
errMean = np.round(np.mean(errArr[max(n - 50, 0):n]), 2)
if str(pMean) != 'nan':
fig.text(0.45, 0.39, r'$p_5=$' + str(pMean) + r'$\pm$' + str(errMean))
#ax[axNum].axhline(y=1.6, linestyle='--')
# cumulative hist
axNum = 2
bins, hist = readerPlan.getCumMassHist(doPlan, n)
if not isinstance(bins, int):
ax[axNum].loglog(bins, hist, 'ko', markersize=3)
ax[axNum].set_xlabel(r'$M_p$')
ax[axNum].set_ylabel(r'$N(>M_p)$')
ax[axNum].set_yscale('log')
ax[axNum].set_xscale('log')
ax[axNum].set_ylim(8.e-1, 1.1 * max(doPlan.nClumpsList))
ax[axNum].set_xlim(1.e-4, 1.e1)
# differential hist
axNum = 3
bins, hist = readerPlan.getDiffMassHist(doPlan, n)
if not isinstance(bins, int) and np.sum(hist > 0) > 4:
ax[axNum].loglog(bins, hist, 'ko', markersize=3)
p, err = readerPlan.get_p(doPlan, n)
err2best = 1.e100
for i in np.arange(-10, 0, 0.01):
scale = np.power(10.0, i)
model = scale * np.power(bins, -p)
logModel = np.log10(model)
logHist = np.log10(hist)
for i in range(len(logHist)):
if logHist[i] < -1.e-10:
logHist[i] = 10.0
err2 = np.square(logModel - logHist)
err2 = err2 * (hist > 0)
err2 = np.sum(err2)
if err2 < err2best:
err2best = err2
bestModel = model
ax[axNum].loglog(bins, bestModel, 'gray', linestyle='--')
fig.text(0.865, 0.665, r'$p=$' + str(np.round(p, 2)))
ax[axNum].set_xlabel(r'$M_p$')
ax[axNum].set_ylabel(r'$dN/dM_p$')
ax[axNum].set_yscale('log')
ax[axNum].set_xscale('log')
ax[axNum].set_ylim(1.e-2, 1.e4)
ax[axNum].set_xlim(1.e-4, 1.e1)
# xy and xz scatters
axNum = 0
data3d = do3d.get3d('dpar', n)
data2d = np.mean(data3d, axis=2)
extent = [-do3d.xmax, do3d.xmax, -do3d.ymax, do3d.ymax]
aspect = 0.77
plotData = np.transpose(np.fliplr(data2d))
cmapType = 'viridis'
plotData = np.clip(plotData, 0.01, 2)
#norm = colors.LogNorm()
#ax[axNum].imshow(plotData, extent=extent, aspect=aspect, cmap=plt.get_cmap(cmapType), norm=norm)
ax[axNum].imshow(plotData,
extent=extent,
aspect=aspect,
cmap=plt.get_cmap(cmapType))
axNum = 1
data3d = do3d.get3d('dpar', n)
data2d = np.mean(data3d, axis=1)
extent = [-do3d.xmax, do3d.xmax, -do3d.zmax, do3d.zmax]
aspect = 0.77
plotData = np.transpose(np.fliplr(data2d))
plotData = np.clip(plotData, 0.01, 10)
cmapType = 'viridis'
#norm = colors.LogNorm()
#ax[axNum].imshow(plotData, extent=extent, aspect=aspect, cmap=plt.get_cmap(cmapType), norm=norm)
ax[axNum].imshow(plotData,
extent=extent,
aspect=aspect,
cmap=plt.get_cmap(cmapType))
masses = doPlan.peakArrayList[n][:, 2]
xs = doPlan.peakArrayList[n][:, 4]
ys = doPlan.peakArrayList[n][:, 5]
zs = doPlan.peakArrayList[n][:, 6]
sizes = [np.power(1.e5 * mass, 1. / 2.) for mass in masses]
axNum = 0
ax[axNum].scatter(xs, ys, s=sizes, facecolors='none', edgecolors='r')
ax[axNum].set_xlabel(r'$r/h$')
ax[axNum].set_ylabel(r'y/h')
ax[axNum].set_ylim(-0.1, 0.1)
ax[axNum].set_xlim(-0.1, 0.1)
axNum = 1
ax[axNum].scatter(xs, zs, s=sizes, facecolors='none', edgecolors='r')
ax[axNum].set_xlabel(r'$r/h$')
ax[axNum].set_ylabel(r'$z/h$')
ax[axNum].set_ylim(-0.1, 0.1)
ax[axNum].set_xlim(-0.1, 0.1)
plt.tight_layout()
plt.savefig(pathSave + "anim_" + str(n) + ".png", bbox_inches='tight')
plt.close('all')
doPlan = readerPlan.DataPlan(pathPlan, G=G, dt=0.1)
#try:
#doPlan = readerPlan.DataPlan(pathPlan, G=G, dt=0.1)
#except:
#planPlots = False
if np.sum(do3d.get3d('dpar', 0)) > 1.e-20:
dparPlots = True
else:
dparPlots = False
################################################################################
if planPlots:
pList = []
errList = []
for n in range(0, doPlan.nTot):
try:
p, err = readerPlan.get_p(doPlan, n)
except:
p=0.0; err=0.0;
pList.append(p)
errList.append(err)
pArr = np.asarray(pList)
errArr = np.asarray(errList)
################################################################################
def prepPlotData(plotData, lim1, lim2):
plotData = np.transpose(np.fliplr(plotData))
plotData = np.clip(plotData, lim1, lim2)
plotData[0,0] = lim1
plotData[0,1] = lim2
return plotData
################################################################################
plotDataDict = {}
def makeAnimFrame(self, n):
print('saving anim frame for n = ' + str(n))
fig = plt.figure(figsize=(8.03, 10.5), dpi=80)
ax = []
ax.append(plt.subplot2grid((7, 2), (0, 0), rowspan=2))
ax.append(plt.subplot2grid((7, 2), (0, 1), rowspan=2))
ax.append(plt.subplot2grid((7, 2), (2, 0), rowspan=2))
ax.append(plt.subplot2grid((7, 2), (2, 1), rowspan=2))
ax.append(plt.subplot2grid((7, 2), (4, 0), colspan=2))
ax.append(plt.subplot2grid((7, 2), (5, 0), colspan=2))
ax.append(plt.subplot2grid((7, 2), (6, 0), colspan=2))
tPlot = doPlan.time
# Nclumps
axNum = 6
ax[axNum].plot(tPlot[n:], doPlan.nClumpsList[n:], 'gray', linewidth=1)
ax[axNum].plot(tPlot[:n], doPlan.nClumpsList[:n], 'k', linewidth=2)
ax[axNum].plot(tPlot[n], doPlan.nClumpsList[n], 'ro', markersize=5)
ax[axNum].set_ylabel(r'$N_{clumps}$')
ax[axNum].set_xlabel(r'$t \Omega$')
ax[axNum].set_xlim(0, doPlan.timeList[-1])
# mass frac
axNum = 5
mFracList = []
for n1 in range(0, len(doPlan.peakArrayList)):
mNow = np.sum(doPlan.peakArrayList[n1][:, 2])
mFracNow = mNow / doPlan.mParTot
mFracList.append(mFracNow)
ax[axNum].plot(doPlan.timeList[n:], mFracList[n:], 'gray', linewidth=1)
ax[axNum].plot(doPlan.timeList[:n], mFracList[:n], 'k', linewidth=2)
ax[axNum].plot(doPlan.timeList[n], mFracList[n], 'ro', markersize=5)
#ax[axNum].get_xaxis().set_visible(False)
#ax[axNum].set_ylim(-0.05, 1.02)
ax[axNum].set_ylabel(r'$M_{plan} / M_{par}$')
ax[axNum].set_xlim(0, doPlan.timeList[-1])
# p
axNum = 4
#ax[axNum].plot(tPlot[n:], pArr[n:], 'gray', linewidth=1)
ax[axNum].plot(tPlot[:n], pArr[:n], 'k', linewidth=2)
ax[axNum].plot(tPlot[n], pArr[n], 'ro', markersize=5)
ax[axNum].fill_between(tPlot,
pArr - errArr,
pArr + errArr,
color='gray',
alpha=0.5)
ax[axNum].set_ylabel(r'$p$')
lim1 = np.amin(pArr - errArr)
lim2 = np.amax(pArr + errArr)
if lim2 >= 5.0: lim2 = 5.0
if lim1 == 0.0: lim1 = -lim2 * 0.06
ax[axNum].set_ylim(lim1, lim2)
pMean = np.round(np.mean(pArr[max(n - 50, 0):n]), 2)
errMean = np.round(np.mean(errArr[max(n - 50, 0):n]), 2)
if str(pMean) != 'nan':
fig.text(0.45, 0.425, r'$p_5=$' + str(pMean) + r'$\pm$' + str(errMean))
#ax[axNum].axhline(y=1.6, linestyle='--')
ax[axNum].set_xlim(0, doPlan.timeList[-1])
# cumulative hist
axNum = 2
bins, hist = readerPlan.getCumMassHist(doPlan, n)
if not isinstance(bins, int):
ax[axNum].loglog(bins, hist, 'ko', markersize=3)
ax[axNum].set_xlabel(r'$M_p$')
ax[axNum].set_ylabel(r'$N(>M_p)$')
ax[axNum].set_yscale('log')
ax[axNum].set_xscale('log')
ax[axNum].set_ylim(8.e-1, 1.1 * max(doPlan.nClumpsList))
ax[axNum].set_xlim(1.e-4, 1.e1)
# differential hist
axNum = 3
bins, hist = readerPlan.getDiffMassHist(doPlan, n)
if not isinstance(bins, int) and np.sum(hist > 0) > 2:
ax[axNum].loglog(bins, hist, 'ko', markersize=3)
p, err = readerPlan.get_p(doPlan, n)
err2best = 1.e100
for i in np.arange(-10, 0, 0.01):
scale = np.power(10.0, i)
model = scale * np.power(bins, -p)
logModel = np.log10(model)
logHist = np.log10(hist)
for i in range(len(logHist)):
if logHist[i] < -1.e-10:
logHist[i] = 10.0
err2 = np.square(logModel - logHist)
err2 = err2 * (hist > 0)
err2 = np.sum(err2)
if err2 < err2best:
err2best = err2
bestModel = model
ax[axNum].loglog(bins, bestModel, 'gray', linestyle='--')
fig.text(0.865, 0.665, r'$p=$' + str(np.round(p, 2)))
ax[axNum].set_xlabel(r'$M_p$')
ax[axNum].set_ylabel(r'$dN/dM_p$')
ax[axNum].set_yscale('log')
ax[axNum].set_xscale('log')
ax[axNum].set_ylim(1.e-2, 1.e4)
ax[axNum].set_xlim(1.e-4, 1.e1)
# xy and xz scatters
masses = doPlan.peakArrayList[n][:, 2]
xs = doPlan.peakArrayList[n][:, 4]
ys = doPlan.peakArrayList[n][:, 5]
zs = doPlan.peakArrayList[n][:, 6]
sizes = [np.power(3.e3 * mass, 1. / 2.) for mass in masses]
axNum = 0
ax[axNum].scatter(xs, ys, s=sizes)
ax[axNum].set_xlabel(r'$r/h$')
ax[axNum].set_ylabel(r'y/h')
ax[axNum].set_ylim(-0.1, 0.1)
ax[axNum].set_xlim(-0.1, 0.1)
axNum = 1
ax[axNum].scatter(xs, zs, s=sizes)
ax[axNum].set_xlabel(r'$r/h$')
ax[axNum].set_ylabel(r'$z/h$')
ax[axNum].set_ylim(-0.025, 0.025)
ax[axNum].set_xlim(-0.1, 0.1)
plt.tight_layout()
plt.savefig(pathSave + "anim_" + str(n) + ".png", bbox_inches='tight')
plt.close('all')