SpectralGrid Flux Shape Bug With Interpolation

Hey folks! 👋 Let's dive into a pesky little bug found in speclib's SpectralGrid class. Specifically, we're talking about an issue where the get_flux(interp=True) method, when used with certain model grids, returns a flux with an incorrect shape. This can be a real headache for anyone using this library, so let's break down the problem, how to reproduce it, and what we can do to fix it. This is important to help you understand how to use the spectral grid, especially when dealing with interpolation. This bug is a good example of why we need to be careful with the shape of the flux we get back. Getting the correct shape is critical for your data analysis, so keep reading, and we'll walk through it.

The Problem: Shape Shifting Flux

Alright, here's the deal: When you use SpectralGrid.get_flux(..., interp=True) (or its older alias, get_spectrum(..., interp=True)) with NewEra grids (like newera_jwst, newera_gaia, and newera_lowres), you might get back a flux array that's 2-D with a shape of (1, N) instead of the expected 1-D shape of (N,). This is a big problem because the downstream code often expects a 1-D flux array that perfectly aligns with the wavelength grid. Imagine trying to fit a curve to data, but your data points are in the wrong format! This breaks the assumed alignment between the wavelength and flux. Luckily, this issue doesn't appear when interp=False is used, and the Spectrum.from_grid(...) method seems to behave correctly in both cases.

For some context, the PHOENIX grids handle this just fine, whether interp is set to True or False. The bug is specific to the interpolation of NewEra grids. This is an important detail for understanding the root cause of the bug.

Why is this important?

This bug can cause significant problems with your data analysis pipelines. If your code relies on the flux having a 1-D shape, it will break. It's like your code expects a list of numbers, and it gets a list of lists. This is a common problem in scientific computing, where array shapes matter immensely. You might need to add extra steps like np.squeeze() or indexing, [0], to correct the shape manually, which can make your code harder to read and maintain. The goal is that the library should give you what you expect, so the shape should be 1-D for interpolated data.

Reproducing the Bug: A Step-by-Step Guide

To make this problem crystal clear, let's look at a script that audits the shapes of the wavelength and flux across different grids and methods. Here's a simplified version:

import numpy as np
from speclib import SpectralGrid, Spectrum

# Grids to check
GRID_NAMES = [
    "phoenix",
    "newera_jwst",
    "newera_gaia",
    "newera_lowres",
]

# Test parameters
Teff, LOGG, FEH = 2750.0, 5.0, 0.0

def _arr_info(x):
    a = np.asarray(getattr(x, "value", x))
    return a.ndim, a.shape, (a.shape[0] if a.ndim == 1 else None)

def _safe(callable_, *args, **kwargs):
    try:
        return "ok", callable_(*args, **kwargs), ""
    except Exception as e:
        return "err", None, repr(e)

rows = []

for name in GRID_NAMES:
    status, grid, err = _safe(
        SpectralGrid,
        (2600.0, 2900.0), # teff_bds
        (4.5, 5.5),  # logg_bds
        (-0.5, 0.5), # feh_bds
        wavelength=None,
        spectral_resolution=None,
        model_grid=name,
    )
    if status != "ok":
        rows.append({"grid": name, "method": "SpectralGrid(load)", "status": "err", "error": err})
        continue

    wl = getattr(grid, "wavelength", None)

    # get_flux(interp=True)
    s, f, e = _safe(grid.get_flux, Teff, LOGG, FEH, True)
    rows.append({"grid": name, "method": "get_flux(interp=True)", "status": s, "wavelength_shape": getattr(wl, 'shape', None), "flux_shape": getattr(f, 'shape', None), "error": e})

    # get_flux(interp=False)
    s, f, e = _safe(grid.get_flux, Teff, LOGG, FEH, False)
    rows.append({"grid": name, "method": "get_flux(interp=False)", "status": s, "wavelength_shape": getattr(wl, 'shape', None), "flux_shape": getattr(f, 'shape', None), "error": e})

    # Spectrum.from_grid(interp=True)
    s, spec, e = _safe(Spectrum.from_grid, Teff, LOGG, FEH, wavelength=None, model_grid=name)
    if s == "ok":
        wl_s = np.asarray(getattr(spec, "spectral_axis"))
        fx_s = getattr(spec, "flux", getattr(spec, "flux_density", None))
        rows.append({"grid": name, "method": "Spectrum.from_grid(interp=True)", "status": "ok", "wavelength_shape": wl_s.shape, "flux_shape": fx_s.shape})
    else:
        rows.append({"grid": name, "method": "Spectrum.from_grid(interp=True)", "status": "err", "error": e})

# Print the results
for row in rows:
    print(row)

This script is designed to check the shapes of the wavelength and flux arrays. By running this code, you'll clearly see the shape differences that occur. The script tests several key aspects, including SpectralGrid.get_flux(interp=True), SpectralGrid.get_flux(interp=False), and Spectrum.from_grid(interp=True). The results from the script will look something like this:

{'grid': 'phoenix', 'method': 'get_flux(interp=True)', 'status': 'ok', 'wavelength_shape': (1569128,), 'flux_shape': (1569128,), 'error': ''}
{'grid': 'newera_jwst', 'method': 'get_flux(interp=True)', 'status': 'ok', 'wavelength_shape': (139501,), 'flux_shape': (1, 139501,), 'error': ''}  # <-- WRONG
{'grid': 'newera_jwst', 'method': 'get_flux(interp=False)', 'status': 'ok', 'wavelength_shape': (139501,), 'flux_shape': (139501,), 'error': ''}
{'grid': 'newera_gaia', 'method': 'get_flux(interp=True)', 'status': 'ok', 'wavelength_shape': (8001,), 'flux_shape': (1, 8001,), 'error': ''}  # <-- WRONG

As you can see, the newera_jwst and newera_gaia grids with get_flux(interp=True) return the incorrect (1, N) shape.

How to run the script

1. Install speclib: Make sure you have the speclib library installed. You can install it using pip install speclib.
2. Copy the code: Copy the Python script provided above into a file (e.g., shape_checker.py).
3. Run the script: Execute the script from your terminal using python shape_checker.py.
4. Observe the output: The output will show you the shape of the flux for each grid and method. Look for the (1, N) shape when interp=True for NewEra grids. This is the bug. This script is a great tool, showing how the bug can cause problems.

Expected vs. Actual Behavior: A Clear Contrast

The expected behavior is that all get_flux(...) calls, regardless of the interp setting, should return a 1-D array with a shape of (N,). This 1-D array should be aligned with the grid's wavelength data. This means that for every wavelength value in your grid, you get one corresponding flux value.

However, the actual behavior for NewEra grids with interp=True is different. Instead of a (N,) shape, you get a (1, N) shape. This extra leading dimension is what causes the problems.

This difference leads to the primary issue: shape mismatches in downstream code. The code using the spectral data expects a 1-D array, but it receives a 2-D array. This discrepancy leads to errors or requires extra processing to correct.

Diving into the Root Cause: Where the Problem Lies

So, what's causing this? The most likely culprit is the interpolation path used for NewEra grids. The interpolation process, when interp=True, seems to return a