Linopy Incompatible With Xarray 2025.7.0 A Comprehensive Guide
Introduction
This article addresses a critical incompatibility issue encountered when using Linopy with Xarray version 2025.7.0. The issue manifests as a ValueError, specifically related to non-customized dimension names when the force_dim_names setting is enabled. This problem impacts users who rely on Linopy in conjunction with libraries like PyPSA, as demonstrated by the error trace originating from the assume framework. This comprehensive guide not only details the error but also provides a reproducible example, discusses the expected behavior, and outlines the installed versions that trigger the issue. Understanding and resolving this incompatibility is crucial for maintaining the stability and functionality of workflows that depend on these libraries. We will delve into the specifics of the error, its context within the Linopy and PyPSA ecosystems, and potential avenues for resolution or workarounds. This issue highlights the importance of version compatibility in software development and the challenges that can arise from updates in interdependent libraries. By documenting this problem and its resolution, we aim to assist other users facing similar issues and contribute to the ongoing development and refinement of Linopy and its related packages. This article will serve as a valuable resource for developers and researchers working in the fields of energy systems modeling, optimization, and data analysis, ensuring they can navigate this incompatibility effectively.
Problem Description
This section elaborates on the specific error encountered when Linopy is used with Xarray 2025.7.0, providing a detailed breakdown of the issue and its context. The core problem is a ValueError that arises due to incompatible handling of dimension names between Linopy and the newer version of Xarray. Specifically, the error message, "ValueError: Added data contains non-customized dimension names. This is not allowed when setting force_dim_names to True," indicates a mismatch in how Linopy expects dimension names to be formatted compared to the structure Xarray 2025.7.0 provides. This error is triggered when Linopy’s force_dim_names setting is enabled, which mandates that all dimensions have explicitly defined names. The introduction of stricter dimension name enforcement in Xarray 2025.7.0 appears to be the root cause of the incompatibility. When Linopy attempts to add constraints to the model, it checks if the added data adheres to the force_dim_names requirement. If the data contains dimensions without customized names, the ValueError is raised, halting the execution. This issue commonly surfaces within the broader context of using Linopy with PyPSA, a Python toolbox for power system analysis. The error trace provided in the original issue highlights this, with the call stack originating from PyPSA’s optimization routines. The specific function calls, such as define_nodal_balance_constraints within PyPSA, demonstrate how this incompatibility affects complex modeling workflows. Understanding the precise nature of this error is crucial for developers and researchers who rely on these tools for energy system modeling and optimization. By detailing the error message, the conditions under which it arises, and its manifestation within practical applications, this section sets the stage for exploring potential solutions and workarounds to address the Linopy-Xarray compatibility issue.
Detailed Error Stacktrace
The following stacktrace provides a comprehensive view of the error encountered when using Linopy with Xarray 2025.7.0. This detailed trace is crucial for understanding the sequence of function calls that lead to the error, allowing developers to pinpoint the exact location and cause of the issue. The error originates from within the Linopy library but is triggered by interactions with PyPSA, a Python toolbox for power system analysis. The stacktrace begins with a call to assume -s example_01d, a command from the assume framework, which likely initiates a simulation or optimization process. The first relevant function call in the trace occurs in /home/maurer/fh-gitlab/projects/ASSUME/assume/markets/base_market.py, within the clear_market method. This suggests the issue is related to market clearing operations within the assume framework. The trace then progresses through several function calls, including self.clear in redispatch.py and redispatch_network.optimize. These calls indicate that the error arises during an optimization process involving a redispatch network, a common operation in power system modeling. Further down the stacktrace, calls to functions within PyPSA are visible, specifically in /home/maurer/.conda/envs/assume-joss3/lib/python3.12/site-packages/pypsa/optimization/optimize.py. The functions optimize and create_model suggest that the error occurs during the setup or execution of an optimization model within PyPSA. The key function call that triggers the error is define_nodal_balance_constraints in pypsa/optimization/constraints.py. This function is responsible for defining the constraints related to nodal balance in the power system model. The call to n.model.add_constraints within this function indicates that the error is triggered when Linopy attempts to add constraints to the optimization model. Finally, the error is raised in linopy/model.py, specifically in the check_force_dim_names method. This confirms that the issue is related to Linopy’s handling of dimension names and the force_dim_names setting. The ValueError itself, “Added data contains non-customized dimension names. This is not allowed when setting force_dim_names to True,” provides the final confirmation of the root cause. By carefully examining this stacktrace, developers can gain a deep understanding of the sequence of events leading to the error, enabling them to devise targeted solutions or workarounds.
File "/home/maurer/fh-gitlab/projects/ASSUME/assume/markets/base_market.py", line 633, in clear_market
(accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(
^^^^^^^^^^^^^
File "/home/maurer/fh-gitlab/projects/ASSUME/assume/markets/clearing_algorithms/redispatch.py", line 195, in clear
status, termination_condition = redispatch_network.optimize(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/maurer/.conda/envs/assume-joss3/lib/python3.12/site-packages/pypsa/optimization/optimize.py", line 628, in __call__
return optimize(self.n, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/maurer/.conda/envs/assume-joss3/lib/python3.12/site-packages/pypsa/optimization/optimize.py", line 590, in optimize
m = create_model(
^^^^^^^^^^^^^
File "/home/maurer/.conda/envs/assume-joss3/lib/python3.12/site-packages/pypsa/optimization/optimize.py", line 293, in create_model
define_nodal_balance_constraints(
File "/home/maurer/.conda/envs/assume-joss3/lib/python3.12/site-packages/pypsa/optimization/constraints.py", line 622, in define_nodal_balance_constraints
n.model.add_constraints(lhs, "=", rhs, name=f"Bus{suffix}-nodal_balance", mask=mask)
File "/home/maurer/.conda/envs/assume-joss3/lib/python3.12/site-packages/linopy/model.py", line 666, in add_constraints
self.check_force_dim_names(data)
File "/home/maurer/.conda/envs/assume-joss3/lib/python3.12/site-packages/linopy/model.py", line 384, in check_force_dim_names
raise ValueError(
ValueError: Added data contains non-customized dimension names. This is not allowed when setting `force_dim_names` to True.
Reproducible Example and Expected Behavior
To effectively address the incompatibility between Linopy and Xarray 2025.7.0, it is crucial to have a reproducible example that consistently triggers the error. The provided example, assume -s example_01d from https://github.com/assume-framework/assume, serves this purpose. This command, when executed within the assume framework, initiates a simulation that ultimately leads to the ValueError described earlier. The assume framework, used for market simulations, leverages PyPSA and Linopy to model and optimize energy systems. The example_01d scenario likely involves setting up a market environment, defining network constraints, and solving an optimization problem. This process inherently utilizes Linopy’s capabilities for linear optimization and constraint management. By using this specific example, developers and researchers can reliably replicate the error, ensuring that any proposed solutions are tested under the same conditions. This reproducibility is paramount for debugging and verifying fixes. The expected behavior, in contrast to the error, is that the simulation should run without raising the ValueError. Prior to Xarray version 2025.7.0, the same command executed successfully, indicating that the issue is directly related to the changes introduced in the newer Xarray version. The successful execution would involve setting up the market model, defining the optimization problem, and solving it using Linopy, without encountering any dimension name-related errors. The system should correctly handle the dimension names in the data passed to Linopy, adhering to the force_dim_names setting without raising an exception. This expected behavior is crucial for maintaining the functionality of the assume framework and other systems that rely on Linopy and PyPSA for optimization tasks. Understanding the expected behavior helps in identifying deviations caused by the incompatibility and in validating potential solutions.
Installed Versions
To accurately diagnose and address the incompatibility between Linopy and Xarray 2025.7.0, it is essential to document the installed versions of the relevant libraries. The provided list of installed versions offers a comprehensive snapshot of the software environment in which the error was encountered. The key packages of interest include Linopy, Xarray, PyPSA, and their dependencies. Specifically, the versions listed are: argcomplete3.6.2, babel2.16.0, Bottleneck1.5.0, certifi2025.6.15, cftime1.6.4.post1, click8.2.1, cloudpickle3.1.1, codespell2.3.0, contourpy1.3.2, cycler0.12.1, dask2025.5.1, deprecation2.1.0, dill0.4.0, docutils0.21.2, fonttools4.58.5, fsspec2025.5.1, geopandas1.1.1, greenlet3.2.3, highspy1.11.0, imagesize1.4.1, kiwisolver1.4.8, linopy0.5.5, locket1.0.0, mango-agents2.1.5, MarkupSafe3.0.2, matplotlib3.10.3, nest-asyncio1.6.0, netCDF41.7.2, networkx3.5, numexpr2.11.0, numpy2.3.1, packaging25.0, paho-mqtt2.1.0, pandas2.3.0, partd1.4.2, pillow11.3.0, ply3.11, polars1.31.0, protobuf5.27.2, psycopg2-binary2.9.10, Pygments2.18.0, pyogrio0.11.0, pyomo6.9.2, pyparsing3.2.3, pyproj3.7.1, pypsa0.32.2, python-dateutil2.9.0.post0, pytz2025.2, PyYAML6.0.2, tqdm4.67.1, typing_extensions4.14.1, tzdata2025.2, validators0.35.0, wheel0.45.1, xarray==2025.7.0. The key versions that contribute to the issue are linopy==0.5.5 and xarray==2025.7.0. The problem arises specifically with Xarray 2025.7.0, as the code functioned correctly with Xarray 2025.6.1. This narrows down the issue to changes introduced in the 2025.7.0 release of Xarray. PyPSA version 0.32.2 is also relevant, as it interacts with both Linopy and Xarray in the optimization process. The other listed packages provide context for the overall environment but are less directly implicated in the incompatibility. This detailed version information is crucial for developers attempting to reproduce the issue, test potential fixes, and ensure that solutions are compatible with the existing ecosystem of libraries. Understanding the specific versions involved is a fundamental step in resolving software compatibility issues.
Potential Solutions and Workarounds
Addressing the incompatibility between Linopy and Xarray 2025.7.0 requires exploring potential solutions and workarounds to mitigate the ValueError. One immediate workaround is to downgrade Xarray to version 2025.6.1, as this version was reported to work correctly with Linopy 0.5.5. This can be achieved using pip, the Python package installer, with the command pip install xarray==2025.6.1. While this workaround provides a temporary fix, it is not a long-term solution, as it prevents users from leveraging the latest features and improvements in Xarray. A more sustainable solution involves identifying the specific changes in Xarray 2025.7.0 that cause the incompatibility and adapting Linopy to accommodate these changes. This may involve modifying how Linopy handles dimension names or how it interacts with Xarray’s internal data structures. The error message, “Added data contains non-customized dimension names. This is not allowed when setting force_dim_names to True,” suggests that the issue is related to how Linopy enforces dimension name constraints. Therefore, examining the check_force_dim_names method in Linopy’s model.py is a crucial step. Potential solutions might involve updating this method to correctly handle the dimension name format introduced in Xarray 2025.7.0. Another approach is to investigate the data passed to Linopy from PyPSA. If the data contains dimensions without explicit names, modifying PyPSA to ensure all dimensions have customized names could resolve the issue. This would involve inspecting the define_nodal_balance_constraints function in PyPSA, where the constraints are defined and passed to Linopy. Collaborating with the Linopy and Xarray development teams is essential for a comprehensive solution. Reporting the issue with a detailed description, a reproducible example, and version information helps the developers understand the problem and prioritize a fix. They may provide guidance on how to address the incompatibility or release an updated version of Linopy that is compatible with Xarray 2025.7.0. In summary, addressing this incompatibility requires a combination of short-term workarounds, in-depth code analysis, and collaboration with the relevant development teams to ensure a robust and long-lasting solution.
Conclusion
In conclusion, the incompatibility between Linopy and Xarray 2025.7.0, manifesting as a ValueError related to dimension names, poses a significant challenge for users relying on these libraries for optimization tasks. The error, triggered when force_dim_names is set to True in Linopy, highlights the importance of version compatibility in complex software ecosystems. By providing a detailed description of the issue, a reproducible example using the assume framework, and a comprehensive list of installed versions, this article aims to assist developers and researchers in understanding and addressing the problem. The immediate workaround of downgrading Xarray to version 2025.6.1 offers a temporary solution, but a more sustainable approach involves modifying Linopy to accommodate the changes introduced in Xarray 2025.7.0. This requires careful examination of Linopy’s check_force_dim_names method and potential adjustments to how data is passed from libraries like PyPSA. Collaboration with the Linopy and Xarray development teams is crucial for a comprehensive and long-term solution. By reporting the issue with detailed information, users can contribute to the ongoing refinement of these libraries and ensure their continued compatibility. Ultimately, resolving this incompatibility not only restores functionality but also strengthens the robustness and reliability of the tools used for energy systems modeling, optimization, and data analysis. This case underscores the importance of thorough testing and version management in software development, as well as the value of community collaboration in addressing and resolving complex technical challenges. The information presented in this article serves as a valuable resource for navigating this specific incompatibility and for understanding the broader implications of version dependencies in scientific computing.
