Bug-fix extraction from generation-cleanout-v2

CHANGELOG.rst

@@ -17,6 +17,15 @@ The changes listed in this file are categorised as follows:
 [Unreleased]
 ---------------------
 
+### Changed
+
+- Now possibly to send variable length temperature scaling timeseries, fixed noise generator for wrong ordering of base data dimensions
+
+### Fixed
+
+- Variable length timeseries now works also when don't have "year" as time dimension.
+- Fixes to generate annual and monthly gridded ensembles with unified noise and preserving more of the variance.
+
 
 [Version 1.6.0]
 -----------------------------

src/meteor/ensemble_output.py

@@ -5,6 +5,8 @@
 climate variables, spatial aggregations, and impact metrics.
 """
 
+from pathlib import Path
+
 import xarray as xr
 
 
@@ -177,6 +179,8 @@ def to_netcdf(self, path, include_impacts=True):
                 combined.attrs[key] = value
 
         # Save to netCDF
+        path = Path(path)
+        path.parent.mkdir(exist_ok=True, parents=True)
         combined.to_netcdf(path)
         print(f"✅ Saved ensemble to {path}")
 

src/meteor/geo_data_utils.py

@@ -1,4 +1,21 @@
-"""Utility functions for handling geographic data."""
+"""
+Geographic data utilities for METEOR's gridded climate fields.
+
+This module groups the helpers METEOR uses to navigate, weight, and
+spatially aggregate xarray datasets on (lat, lon, time) grids:
+
+- Coordinate discovery: :func:`get_time_name`, :func:`get_lat_name`,
+  :func:`get_lon_name`, :func:`get_year_series`.
+- Area weighting: :func:`get_weights_for_ds`,
+  :func:`apply_weights_and_do_spatial_mean`.
+- Spatial aggregation: :func:`global_mean`, :func:`regional_mean` (AR6
+  reference regions via ``regionmask``), :func:`extract_point`,
+  :func:`create_region_mask` (custom bounding boxes or precomputed masks),
+  :func:`list_ar6_regions`.
+- Time-axis helpers used by the pattern-scaling pipeline:
+  :func:`extend_temeperature_anomaly_timeseries_for_scaling`,
+  :func:`find_time_dim_and_cut`.
+"""
 
 import logging
 
@@ -35,6 +52,18 @@ def get_time_name(ds):
     raise RuntimeError("Couldn't find a time coordinate")
 
 
+def get_year_series(ds: xr.Dataset) -> np.ndarray:
+    """Extract a DataFrame of just the year columns from a larger DataFrame."""
+    time_name = get_time_name(ds)
+    if time_name == "year":
+        return ds[time_name].values
+    if time_name == "time":
+        return ds[time_name].dt.year.values
+    if time_name == "month":
+        return ds[time_name].dt.year.values + (ds[time_name].dt.month.values - 1) / 12.0
+    raise ValueError(f"Unexpected time dimension name '{time_name}'")
+
+
 def get_lat_name(ds):
     """
     Get name of latitude dimension
@@ -509,23 +538,56 @@ def extend_temeperature_anomaly_timeseries_for_scaling(
     temperature_input_anomaly : xarray.DataArray
         Temperature anomaly time series used for scaling (relative to base year)
     """
-    years_prediction = annual_temp_prediction_anomaly_gm[
-        get_time_name(annual_temp_prediction_anomaly_gm)
-    ].values
-    years_input = temperature_input_anomaly[
-        get_time_name(temperature_input_anomaly)
-    ].values
+    years_prediction = get_year_series(annual_temp_prediction_anomaly_gm)
+    years_input = get_year_series(temperature_input_anomaly)
     temperature_input_anomaly_extended = np.copy(
         annual_temp_prediction_anomaly_gm.values
     )
+    input_time_name = get_time_name(temperature_input_anomaly)
+    if input_time_name != "year":
+        temperature_input_anomaly = temperature_input_anomaly.rename(
+            {input_time_name: "year"}
+        )
+        temperature_input_anomaly.coords["year"] = years_input
     for i, year in enumerate(years_prediction):
         if year in years_input:
             temperature_input_anomaly_extended[i] = temperature_input_anomaly.sel(
                 {get_time_name(temperature_input_anomaly): year}
             ).values
+
     temperature_input_anomaly_extended = xr.DataArray(
         data=temperature_input_anomaly_extended,
         coords={get_time_name(annual_temp_prediction_anomaly_gm): years_prediction},
         dims=annual_temp_prediction_anomaly_gm.dims,
     )
+
     return temperature_input_anomaly_extended
+
+
+def find_time_dim_and_cut(base_array, n_time, n_lat, n_lon):
+    """
+    Base_array should have dimensions (time, lat, lon), but the ordering can be off
+    """
+    base_shape = base_array.shape
+    lat_dim = None
+    lon_dim = None
+    time_dim = 0
+    for i, dim_size in enumerate(base_shape):
+        if dim_size == n_lat and lat_dim is None:
+            lat_dim = i
+        elif dim_size == n_lon:
+            lon_dim = i
+        else:
+            time_dim = i
+    if lat_dim is None or lon_dim is None:
+        raise ValueError(
+            f"Couldn't find lat/lon dimensions in base array with shape {base_shape}"
+        )
+    if base_shape[time_dim] != n_time:
+        if time_dim == 0:
+            base_array = base_array[:n_time, :, :]
+        elif time_dim == 1:
+            base_array = base_array[:, :n_time, :]
+        elif time_dim == 2:
+            base_array = base_array[:, :, :n_time]
+    return base_array.transpose(time_dim, lat_dim, lon_dim)