Cloud mass CDF

cloudMassCDF.py

# --- Cloud mass distribution, of clouds ID across all snapshots -----

fig = plt.figure()
ax = fig.add_subplot(111)

allmasses = []
for snap in ss.iterkeys():
    for snapleafs in ss[snap].iterkeys():
        allmasses.append(ss[snap][snapleafs].mass_Msun)
allmasses = np.array(allmasses)

# unbinned CDF
X2 = np.sort(allmasses)
F2 = np.array(range(len(allmasses)))/float(len(allmasses))
ax.plot(X2, F2, label='unbinned CDF', lw=2, alpha=1, zorder=2)

# binned CDF
H, X1 = np.histogram(allmasses, bins=10, normed=True)
dx = X1[1] - X1[0]
F1 = np.cumsum(H) * dx
ax.plot(X1[1:], F1, label='binned CDF, bins=10', lw=2.0, alpha=0.9, zorder=3)

H, X1 = np.histogram(allmasses, bins=50, normed=True)
dx = X1[1] - X1[0]
F1 = np.cumsum(H) * dx
ax.plot(X1[1:], F1, label='binned CDF, bins=50', lw=2.0, alpha=0.9, zorder=3)

H, X1 = np.histogram(allmasses, bins=100, normed=True)
dx = X1[1] - X1[0]
F1 = np.cumsum(H) * dx
ax.plot(X1[1:], F1, label='binned CDF, bins=100', lw=2.0, alpha=0.9, zorder=3)

H, X1 = np.histogram(allmasses, bins=200, normed=True)
dx = X1[1] - X1[0]
F1 = np.cumsum(H) * dx
ax.plot(X1[1:], F1, label='binned CDF, bins=200', lw=2.0, alpha=0.9, zorder=3)

ax.set_xscale("log")
ax.set_yscale("log")

ax.set_xlabel(r"$M_{\rm cl}$ [M$_\odot$]")
ax.set_ylabel("CDF")

ax.set_xlim(allmasses.min(), allmasses.max())

ax.legend(loc="best")
plt.tight_layout()
plt.show()
fig.savefig(leafdir_out + 'MassDistribution.png', bbox_inches="tight")