Merge branch 'master' into pre-commit-ci-update-config

Author: FabianHofmann

.pre-commit-config.yaml

@@ -24,6 +24,7 @@ repos:
     rev: v0.4.3
     hooks:
     -   id: blackdoc
+        additional_dependencies: ['black==24.8.0']
 -   repo: https://github.com/codespell-project/codespell
     rev: v2.4.1
     hooks:

doc/release_notes.rst

@@ -8,6 +8,9 @@ Upcoming Version
 * Reduced memory usage and faster file I/O operations when exporting models to LP format
 * Improved constraint equality check in `linopy.testing.assert_conequal` to less strict optionally
 * Minor bugfix for multiplying variables with numpy type constants
+* Harmonize dtypes before concatenation in lp file writing to avoid dtype mismatch errors. This error occurred when creating and storing models in netcdf format using windows machines and loading and solving them on linux machines.
+* Add option to use polars series as constant input
+* Fix expression merge to explicitly use outer join when combining expressions with disjoint coordinates for consistent behavior across xarray versions
 
 Version 0.5.6
 --------------

linopy/common.py

@@ -254,6 +254,8 @@ def as_dataarray(
         arr = pandas_to_dataarray(arr, coords=coords, dims=dims, **kwargs)
     elif isinstance(arr, np.ndarray):
         arr = numpy_to_dataarray(arr, coords=coords, dims=dims, **kwargs)
+    elif isinstance(arr, pl.Series):
+        arr = numpy_to_dataarray(arr.to_numpy(), coords=coords, dims=dims, **kwargs)
     elif isinstance(arr, np.number):
         arr = DataArray(float(arr), coords=coords, dims=dims, **kwargs)
     elif isinstance(arr, int | float | str | bool | list):
@@ -269,6 +271,7 @@ def as_dataarray(
             pd.DataFrame,
             np.ndarray,
             DataArray,
+            pl.Series,
         ]
         supported_types_str = ", ".join([t.__name__ for t in supported_types])
         raise TypeError(

linopy/constraints.py

@@ -631,7 +631,7 @@ def to_polars(self) -> pl.DataFrame:
         short = filter_nulls_polars(short)
         check_has_nulls_polars(short, name=f"{self.type} {self.name}")
 
-        df = pl.concat([short, long], how="diagonal").sort(["labels", "rhs"])
+        df = pl.concat([short, long], how="diagonal_relaxed").sort(["labels", "rhs"])
         # delete subsequent non-null rhs (happens is all vars per label are -1)
         is_non_null = df["rhs"].is_not_null()
         prev_non_is_null = is_non_null.shift(1).fill_null(False)

linopy/expressions.py

@@ -98,6 +98,7 @@
     pd.Series,
     pd.DataFrame,
     np.ndarray,
+    pl.Series,
 )
 
 
@@ -1999,6 +2000,8 @@ def merge(
 
         if override:
             kwargs["join"] = "override"
+        else:
+            kwargs.setdefault("join", "outer")
 
     if dim == TERM_DIM:
         ds = xr.concat([d[["coeffs", "vars"]] for d in data], dim, **kwargs)

linopy/remote/oetc.py

@@ -268,6 +268,47 @@ def _submit_job_to_compute_service(self, input_file_name: str) -> str:
         except Exception as e:
             raise Exception(f"Error submitting job to compute service: {e}")
 
+    def _get_job_logs(self, job_uuid: str) -> str:
+        """
+        Fetch logs for a compute job.
+
+        Args:
+            job_uuid: UUID of the job to fetch logs for
+
+        Returns:
+            str: The job logs content as a string
+
+        Raises:
+            Exception: If fetching logs fails
+        """
+        try:
+            logger.info(f"OETC - Fetching logs for job {job_uuid}...")
+
+            response = requests.get(
+                f"{self.settings.orchestrator_server_url}/compute-job/{job_uuid}/get-logs",
+                headers={
+                    "Authorization": f"{self.jwt.token_type} {self.jwt.token}",
+                    "Content-Type": "application/json",
+                },
+                timeout=30,
+            )
+
+            response.raise_for_status()
+            logs_data = response.json()
+
+            # Extract content from the response structure
+            logs_content = logs_data.get("content", "")
+
+            logger.info(f"OETC - Successfully fetched logs for job {job_uuid}")
+            return logs_content
+
+        except RequestException as e:
+            logger.warning(f"OETC - Failed to fetch logs for job {job_uuid}: {e}")
+            return f"[Unable to fetch logs: {e}]"
+        except Exception as e:
+            logger.warning(f"OETC - Error fetching logs for job {job_uuid}: {e}")
+            return f"[Error fetching logs: {e}]"
+
     def wait_and_get_job_data(
         self,
         job_uuid: str,
@@ -342,7 +383,14 @@ def wait_and_get_job_data(
                     raise Exception(error_msg)
 
                 elif job_result.status == "RUNTIME_ERROR":
-                    error_msg = f"Job failed during execution (status: {job_result.status}). Please check the OETC logs for details."
+                    # Fetch and display logs
+                    logs = self._get_job_logs(job_uuid)
+                    logger.error(f"OETC - Job {job_uuid} logs:\n{logs}")
+
+                    error_msg = (
+                        f"Job failed during execution (status: {job_result.status}).\n"
+                        f"Logs:\n{logs}"
+                    )
                     logger.error(f"OETC Error: {error_msg}")
                     raise Exception(error_msg)
 

linopy/types.py

@@ -5,7 +5,7 @@
 from typing import TYPE_CHECKING, Union
 
 import numpy
-import numpy.typing
+import polars as pl
 from pandas import DataFrame, Index, Series
 from xarray import DataArray
 from xarray.core.coordinates import DataArrayCoordinates, DatasetCoordinates
@@ -37,6 +37,7 @@
     DataArray,
     Series,
     DataFrame,
+    pl.Series,
 ]
 SignLike = Union[str, numpy.ndarray, DataArray, Series, DataFrame]  # noqa: UP007
 VariableLike = Union["ScalarVariable", "Variable"]

linopy/variables.py

@@ -287,12 +287,7 @@ def to_pandas(self) -> pd.Series:
 
     def to_linexpr(
         self,
-        coefficient: int
-        | float
-        | pd.Series
-        | pd.DataFrame
-        | np.ndarray
-        | DataArray = 1,
+        coefficient: ConstantLike = 1,
     ) -> expressions.LinearExpression:
         """
         Create a linear expression from the variables.