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- # --- 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")
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