Common Python Errors in Space Software — and How to Handle Them

Introduction

Software is as mission-critical in space as the hardware it controls. A small bug in how we handle errors can ripple into large consequences — from lost data to incorrect orbital predictions. In scientific programming, and especially in space systems, we don’t just write code that “works.” We write code that fails safely and predictably.

Here are some Python errors you’ll encounter often in scientific/space software, with examples drawn from orbital mechanics, ground operations, and spacecraft systems.

---

ValueError — When numbers don’t make sense

A ValueError crops up when the type is correct, but the value is invalid.

Example (orbital mechanics):

If you try to compute orbital elements with a negative semi-major axis for an Earth orbit, that’s not physically valid.

if a <= 0:
    raise ValueError("Semi-major axis must be positive for Earth orbits.")

TypeError — When the wrong kind of thing is passed

A TypeError means the input isn’t the right type.

Example (ground station scheduling):

Say your function expects a datetime object, but you pass a string instead.

def schedule_pass(pass_time):
    if not isinstance(pass_time, datetime):
        raise TypeError("Expected a datetime object for pass_time.")

Catching this early avoids subtle time conversion errors.

IndexError — When you go beyond the array

In space software, arrays are everywhere — ephemerides, sensor readings, power budgets. An IndexError means you asked for an element outside the array.

Example (power subsystem data):

voltages = [3.3, 5.0, 12.0]
print(voltages[3])  # IndexError: list index out of range

If you’re processing telemetry batches, careful index checking prevents crashes during anomaly analysis.

RuntimeError — When the system state is wrong

A RuntimeError signals that something about the current program state makes the operation impossible.

Example (attitude dynamics):

if not self.coe_calculated:
    raise RuntimeError("Orbital elements not calculated before attitude propagation.")

Here, the inputs might be valid, but the sequence of operations is wrong. This enforces logical order in mission pipelines.

AssertionError — When assumptions fail

Assertions are like sanity checks. They confirm that conditions you believe to be true really hold.

Example (ADCS quaternion):

A unit quaternion must have norm = 1.

assert np.isclose(np.linalg.norm(q), 1.0), "Quaternion not normalized!"

In simulation, this catches numerical drift before it contaminates control outputs.

IOError / FileNotFoundError — When external data is missing

Space software often relies on ephemeris files, TLE data, or config YAMLs. If a file is missing, the program must fail clearly.

try:
    tle = open("orbit.tle").read()
except FileNotFoundError:
    raise FileNotFoundError("TLE file not found. Did you fetch the latest orbital data?")

This is far better than letting downstream functions fail mysteriously.

OverflowError — When numbers blow up

Numerical instability is a fact of life in orbital mechanics. An OverflowError occurs when calculations exceed representable values.

Example (thermal subsystem):

import math
try:
    flux = math.exp(1000)
except OverflowError:
    flux = float('inf')

Here, defensive coding avoids silent wraparounds in calculations involving exponential growth (like radiative heat flux).

Wrapping Up

In space systems, we don’t always control the environment — but we do control how our software responds to unexpected conditions. By using Python’s error types thoughtfully, we make our code more transparent, robust, and trustworthy.

Error handling isn’t just programming hygiene. It’s part of the engineering discipline that keeps spacecraft (and careers) in orbit.

---

AI Attribution: This post was created in collaboration with AI. I asked it to draft a piece explaining Python error messages using examples from space software, building on a discussion we had about error handling in mission-critical code. The result is the post above.