How I Model Aspects and Orbs in a Python Astrology Engine
I like working on aspect calculations because the astrology terminology disappears pretty quickly once you get into the code. At the calculation level, an aspect is basically: How close are two points on a circle to a configured angle? That turns the problem into geometry, tolerances and a few interesting edge cases. Start with angular distance Suppose two planets have longitudes: 12° 102° Their…
Calculating the relationships between celestial bodies is a fascinating task in astrology. To do this, you first need to determine how close two points on a circle are to a specific angle, which essentially defines an aspect. This problem can be broken down into geometry, tolerances, and a few edge cases.
To calculate the angular distance between two planets, you take the absolute difference between their longitudes, wrap it around 360 degrees, and return the smaller of the two possible distances. This normalization ensures that the angular distance is always within the range of 0 to 180 degrees.
Next, you define target angles for major aspects such as conjunction (0°), sextile (60°), square (90°), trine (120°), and opposition (180°). However, astrology uses orbs to allow for some flexibility in these aspects. An orb represents the allowable deviation from the exact target angle. For instance, a square aspect might be considered valid if the actual angular separation is within a specified orb of 90°.
The orb rules are stored in a profile, which makes the implementation more transparent and easier to modify. This approach avoids hidden constants and allows for a clear understanding of the aspect calculation.
The engine focuses on reporting geometry rather than sentiment. It calculates the type of aspect, the orb, and the phase (whether the aspect is applying, exact, or separating). By keeping the interpretation-layer concepts separate, the engine output becomes more usable for different interpretation systems later on.
Additionally, the engine tracks the motion of the planets to determine whether an aspect is applying, exact, or separating. This information, combined with the angular separation and orb, provides a structured result that can be interpreted by downstream systems.
The code includes various tests to ensure the accuracy and robustness of the aspect calculations. For example, the distance between two planets is commutative, meaning distance(a, b) should always equal distance(b, a). The distance is always within the range of 0° to 180°, and wraparound behavior is tested directly.
Overall, this implementation focuses on separating the calculation of aspects from the interpretation of their meanings. By starting with the smallest deterministic representation of the domain logic and building up from there, the code remains simple and focused on the core aspect calculations. You can find this implementation in the open-source GetBirthChart Python engine (https://github.com/getbirthchart-com/gbc-astro-engine).
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