Tutorial: First run

By the end of this tutorial you will have installed MORS, computed your first stellar evolutionary track, plotted a quantity, queried values at specific ages, and saved the result for reuse.

Prerequisites

You need a Python 3 environment with pip and a working internet connection for the one-time data download.

Optionally, create and activate an isolated environment first. With Conda:

conda create -n mors python=3.11 -y
conda activate mors

Or with a standard virtual environment:

python -m venv .venv
source .venv/bin/activate

Step 1: Install MORS

pip install fwl-mors

Verify the installation:

python -c "import mors; print('ok')"

Step 2: Download stellar evolution data

MORS requires stellar evolution tracks, downloaded once:

mors download all

To check where the data are stored:

mors env

If you want to store data somewhere specific, set the FWL_DATA environment variable before downloading:

export FWL_DATA=/path/to/your/data
mors download all

Step 3: Create your first star

Create a 1 Msun star with an initial rotation period of 2.7 days. When Age is not specified, MORS treats the rotation as the initial value at AgeMinDefault = 1 Myr and evolves the track forward from there:

import mors

star = mors.Star(Mstar=1.0, Prot=2.7)

You can also specify the initial rotation rate as a multiple of the solar rotation rate:

star = mors.Star(Mstar=1.0, Omega=10.0)

Step 4: Inspect available tracks

Print all available track names and their units:

star.PrintAvailableTracks()
print(star.Units['Lx'])

from mors.logs import setup_logger
setup_logger("INFO")
star.PrintAvailableTracks()

Track arrays are accessible via the Tracks dictionary or as <Quantity>Track attributes directly on the star:

age = star.Tracks['Age']   # or: star.AgeTrack
lx  = star.Tracks['Lx']   # or: star.LxTrack

Step 5: Plot a track

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(star.AgeTrack, star.LxTrack)
ax.set_xlabel(f"Age [{star.Units['Age']}]")
ax.set_ylabel(f"Lx [{star.Units['Lx']}]")
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()

If you see a plot with no errors, your installation and data download are working correctly.

Step 6: Query values at specific ages

Use the generic Value method:

lx = star.Value(Age=150.0, Quantity='Lx')
print(lx)

Or the quantity accessor directly:

lx = star.Lx(150.0)
print(lx)

Both return a linearly interpolated scalar at the requested age.

Step 7: Compare slow, medium, and fast rotators (optional)

MORS includes a built-in 1 Myr rotation distribution. You can create stars at the 5th, 50th, and 95th percentiles using string shortcuts:

slow   = mors.Star(Mstar=1.0, percentile='slow')    # 5th percentile
medium = mors.Star(Mstar=1.0, percentile='medium')  # 50th percentile
fast   = mors.Star(Mstar=1.0, percentile='fast')    # 95th percentile

print(slow.percentile, medium.percentile, fast.percentile)

Step 8: Save and reload

Saving avoids recomputing the tracks next time:

star.Save(filename='star.pickle')

Reload with mors.Load rather than pickle.load directly, as it re-attaches the quantity accessor functions:

star2 = mors.Load('star.pickle')
print(star2.Lx(150.0))