Calculate cluster evolution

Compute rotation and activity evolution tracks for a cluster of stars, then inspect per-star tracks, evaluate cluster distributions at a given age, and save the result to avoid recomputation.

Prerequisites

Install MORS and download stellar evolution data:

pip install fwl-mors
mors download all

Step 1: Create a cluster from arrays

Create arrays of masses and rotation rates of the same length. If Age is omitted, Omega is interpreted as the initial (~1 Myr) rotation rate.

import numpy as np
import mors

Mstar = np.array([1.0, 0.5, 0.75])
Omega = np.array([10.0, 10.0, 10.0])

cluster = mors.Cluster(Mstar=Mstar, Omega=Omega)

To print progress while tracks are computed, enable verbose:

cluster = mors.Cluster(Mstar=Mstar, Omega=Omega, verbose=True)

Step 2: Fit tracks through a specified age (optional)

If you provide Age, MORS fits each track so that the star passes through the given rotation rate at that age.

One age applied to all stars:

cluster = mors.Cluster(Mstar=Mstar, Omega=Omega, Age=100.0)

A different age for each star:

Age = np.array([80.0, 120.0, 100.0])  # same length as Mstar and Omega
cluster = mors.Cluster(Mstar=Mstar, Omega=Omega, Age=Age)

Step 3: Access per-star tracks

Each star is a mors.Star instance stored in cluster.stars. Stars are also accessible as attributes cluster.star0, cluster.star1, etc.:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
for i in range(cluster.nStars):
    ax.plot(cluster.stars[i].AgeTrack, cluster.stars[i].LxTrack)

ax.set_xlabel("Age [Myr]")
ax.set_ylabel("Lx [erg/s]")
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()

Step 4: Get cluster values at a fixed age

Use cluster.Values(Age=..., Quantity=...) to retrieve an array across all stars:

Lx = cluster.Values(Age=100.0, Quantity='Lx')

Or use the quantity accessor directly:

Lx = cluster.Lx(100.0)

For example, to plot \(L_X\) versus mass at a given age:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.scatter(cluster.Mstar, cluster.Lx(100.0))
ax.set_xlabel(r"$M_\star$ [$M_\odot$]")
ax.set_ylabel(r"$L_X$ [erg s$^{-1}$]")
ax.set_yscale('log')
plt.show()

Step 5: Save and reload

Cluster calculations can be expensive for large numbers of stars. Save and reload with:

import mors

cluster.Save(filename="cluster.pickle")

# Always use mors.Load rather than pickle.load directly,
# as it re-attaches the quantity accessor functions
cluster2 = mors.Load("cluster.pickle")

Step 6: Use the built-in model cluster (optional)

MORS includes a model cluster distribution derived from observed young cluster rotation measurements. Load and evolve it like any other cluster:

import mors

Mstar, Omega = mors.ModelCluster()
cluster = mors.Cluster(Mstar=Mstar, Omega=Omega)

Common pitfalls

• Mstar, Omega, and Age (if provided) must all be the same length.
• Age is always in Myr; Omega is in Ω☉, not rad s\(^{-1}\).
• Do not use pickle.load directly to reload a saved cluster — use mors.Load instead.
• Per-star attributes are named cluster.star0, cluster.star1, etc. (not cluster.stars0).