PyFES `load_config` Error: Bounding Box Issue & Solution

Aug 7, 2025 by Omar Yusuf 57 views

Troubleshooting `load_config` Error with Bounding Box in PyFES

Hey guys! Running into the dreaded ValueError when trying to load your FES tide model with a bounding box? You're not alone! This article dives deep into a common issue encountered while using the pyfes library, specifically the ValueError: the axis values must be evenly spaced from each other error that pops up when applying a bounding box during configuration loading. We'll break down the bug, provide a reproducible example, discuss the expected behavior, and offer potential solutions to get you back on track with your tidal predictions. Let's get started!

Understanding the `load_config` Error in PyFES

When working with PyFES, a powerful tool for tidal predictions, you might encounter a frustrating error related to the load_config function and bounding boxes. Specifically, the error message ValueError: the axis values must be evenly spaced from each other often arises when attempting to load a tidal model configuration with a specified bounding box. This error indicates that the underlying data structure PyFES expects an evenly spaced grid for its calculations, and the bounding box selection might be interfering with this requirement. The root cause typically lies in how the library handles subsetting the data based on the provided bounding box. If the resulting grid within the bounding box doesn't maintain uniform spacing between grid points, this error is triggered. This can occur due to various reasons, such as the original dataset having irregular spacing or the bounding box dimensions not aligning perfectly with the grid structure. Properly understanding the error message is half the battle, guys. It points us directly to the gridded nature of the data PyFES uses and the importance of maintaining even spacing when subsetting.

Diving Deeper into Evenly Spaced Data

To truly grasp this error, let's delve deeper into the concept of evenly spaced data in the context of PyFES. The library relies on regularly gridded data, meaning that the points along each axis (latitude and longitude in this case) are spaced at consistent intervals. This uniformity is crucial for the mathematical operations and interpolations performed by the tidal prediction models. Imagine trying to draw a smooth curve through points that are scattered haphazardly – it's a challenge! Similarly, PyFES needs this regularity to accurately calculate tidal components. When a bounding box is introduced, PyFES attempts to extract a subset of this gridded data. However, if the bounding box's boundaries don't perfectly align with the original grid spacing, the resulting subset might lose its even spacing. For instance, if your grid has points every 0.1 degrees, and your bounding box starts at a coordinate that isn't a multiple of 0.1, the extracted grid might have slightly different intervals, triggering the error. Therefore, ensuring that your bounding box coordinates are compatible with the underlying grid's spacing is paramount. Furthermore, it is important to consider the resolution of the original data. High-resolution datasets are more prone to these issues if the bounding box dimensions are not carefully chosen. So, next time you're specifying a bounding box, think about the grid beneath and whether you're maintaining that crucial even spacing.

Reproducing the `ValueError` with a Simple Code Example

To illustrate this issue, let's walk through a practical example. Imagine you're trying to predict tides for a specific region using PyFES and you want to speed up the process by focusing only on the area within a bounding box. You follow the documentation, create a configuration file, and write a script like the one below. But, alas, you're greeted by the dreaded ValueError. Don't worry, we'll figure this out together!

Here's the code snippet that reproduces the problem:

import pathlib
import pyfes

# This line throws out a ValueError
handlers = pyfes.load_config(pathlib.Path().absolute() / 'test_fes2022b.yml', bbox=(-10, 40, 10, 60))

And here's the content of the test_fes2022b.yml file:

radial:
 cartesian:
 paths:
 K1: /path/to/aviso-fes/data/fes2022b/load_tide/k1_fes2022.nc
 M2: /path/to/aviso-fes/data/fes2022b/load_tide/m2_fes2022.nc
 S2: /path/to/aviso-fes/data/fes2022b/load_tide/s2_fes2022.nc
tide:
 cartesian:
 paths:
 K1: /path/to/aviso-fes/data/fes2022b/ocean_tide_extrapolated/k1_fes2022.nc
 M2: /path/to/aviso-fes/data/fes2022b/ocean_tide_extrapolated/m2_fes2022.nc
 S2: /path/to/aviso-fes/data/fes2022b/ocean_tide_extrapolated/s2_fes2022.nc

You can easily run this code by saving it as myproblem.py and executing python myproblem.py in your terminal. You'll likely see the ValueError we've been discussing. This setup, while seemingly straightforward, highlights the core issue. The load_config function, when provided with the bbox argument, struggles to handle the subsetting of the data while maintaining the required even spacing. The specific bounding box coordinates (-10, 40, 10, 60) might not align perfectly with the grid structure of the underlying data files (specified in the YAML configuration), leading to the error. This reproducible example allows you to experiment with different bounding box values and observe how they affect the outcome. By tweaking the coordinates, you can gain a better understanding of the sensitivity of PyFES to grid alignment and spacing.

Breaking Down the Code

Let's dissect the code snippet to understand each part's role in triggering the error. The script starts by importing the necessary libraries: pathlib for handling file paths and pyfes for the tidal prediction functionalities. The crucial line is:

handlers = pyfes.load_config(pathlib.Path().absolute() / 'test_fes2022b.yml', bbox=(-10, 40, 10, 60))

This line attempts to load the PyFES configuration from the test_fes2022b.yml file. The pathlib.Path().absolute() part ensures that the path to the YAML file is absolute, making the script more robust regardless of the current working directory. The bbox=(-10, 40, 10, 60) argument is where the problem lies. This argument specifies the bounding box for which the tidal model should be loaded. The tuple represents (minimum longitude, minimum latitude, maximum longitude, maximum latitude). As we've discussed, this bounding box can cause issues if it doesn't align with the underlying data grid. The test_fes2022b.yml file itself defines the paths to the NetCDF files containing the tidal data. It specifies different tidal constituents (K1, M2, S2) for both radial and tide components. The paths in this file are placeholders (/path/to/aviso-fes/data/fes2022b/...) and would need to be replaced with the actual locations of your data files for the code to run successfully beyond this error. However, even with valid data paths, the ValueError will still occur if the bounding box is problematic. This detailed breakdown highlights the interplay between the configuration file, the bounding box specification, and the load_config function in triggering the error. Understanding these components is essential for debugging and finding a solution.

Expected Behavior vs. Actual Outcome

Based on the PyFES documentation, providing a bounding box during configuration loading should ideally lead to a more efficient model loading process. By specifying the bbox argument in load_config, the expectation is that PyFES would load only the data relevant to the specified geographic region. This would reduce memory consumption and speed up computations, especially when dealing with large global datasets. Think of it as ordering a pizza – you only want the slices you're going to eat, not the whole pie if you're just having a snack! This selective loading is a powerful feature for optimizing performance and focusing on specific areas of interest.

However, the actual outcome, as demonstrated by the reproducible example, deviates significantly from this expected behavior. Instead of selectively loading the data, the load_config function throws a ValueError, halting the process. This discrepancy between the expected and actual behavior highlights a bug or limitation in the current implementation of PyFES. The error message itself, the axis values must be evenly spaced from each other, suggests that the bounding box implementation is not correctly handling the gridded structure of the tidal data. It implies that the subsetting operation performed based on the bounding box is disrupting the required uniform spacing between grid points, leading to the error. This unexpected outcome can be frustrating for users who are trying to leverage the bounding box feature for performance optimization. It underscores the importance of thorough testing and bug reporting in software development. Understanding this gap between expectation and reality is crucial for guiding debugging efforts and finding a workaround or a permanent fix.

Analyzing the PyFES/Numpy/Python Version Information

When troubleshooting issues like this, providing version information is crucial for context. In this case, the user has helpfully supplied the versions of PyFES, NumPy, and Python:

pyfes.__version__: 2025.3.0
numpy.__version__: 2.2.6
sys.version: 3.10.18 | packaged by conda-forge | (main, Jun 4 2025, 14:45:41) [GCC 13.3.0]

This information can be invaluable for several reasons. First, it allows developers to reproduce the issue in the same environment. Specific versions of libraries can have different behaviors or known bugs. Knowing the exact versions helps narrow down the potential causes. For example, if the error is specific to PyFES 2025.3.0, developers can focus their attention on changes introduced in that version. Second, version information helps identify potential compatibility issues. PyFES relies heavily on NumPy for numerical operations. If there's a compatibility problem between the PyFES version and the NumPy version, it could lead to unexpected errors. Similarly, the Python version itself can play a role. While PyFES 2025.3.0 seems to be compatible with Python 3.10, there might be subtle differences in behavior compared to other Python versions. Finally, the fact that the Python distribution is