doc: add quarto doc for cpm interporation

_quarto.yml

@@ -12,6 +12,7 @@ project:
     - "R/README.md"
     - "R/misc/Identifying_Runs.md"
     - "R/comparing-r-and-python/HADs-reprojection/WIP-Comparing-HADs-grids.md"
+    - "docs/plot-cpm-time-series.qmd"
     - "docs/reference"
     # - "docs/pipeline.qmd"
     - "docs/contributing.md"

docs/cpm-projection.qdm

@@ -0,0 +1,52 @@
+---
+title: "UKCP 2.2 Temporal Interpolation"
+format:
+  html:
+    code-fold: true
+execute:
+  cache: true
+jupyter: python3
+---
+
+To align climate projections with measured records, we interpolate five or six extra time points---standard or leap years respectively---per year.
+
+```{python}
+#| label: fig-time-series
+#| fig-cap: "Raw CPM projection 360 day years"
+
+from typing import Final
+from pathlib import Path
+
+from xarray.typing import T_Dataset
+
+from clim_recal.utils.xarray import (
+    annual_group_xr_time_series,
+    join_xr_time_series_var,
+    plot_xarray,
+)
+from clim_recal.utils.core import climate_data_mount_path
+# import numpy as np
+# import matplotlib.pyplot as plt
+
+raw_tasmax_run_01: Final[Path] = climate_data_mount_path() / 'Raw/UKCPM2.2/tasmax/01/latest'
+tasmax_var
+
+cpm_annual_ts: T_Dataset = join_xr_time_series_var(raw_tasmax_run_01, 'tas')
+
+plot_xarray(
+    getattr(summarised_year, variable_name),
+    path=plot_path,
+    time_stamp=time_stamp,
+    **kwargs,
+)
+
+# r = np.arange(0, 2, 0.01)
+# theta = 2 * np.pi * r
+# fig, ax = plt.subplots(
+#   subplot_kw = {'projection': 'polar'}
+# )
+# ax.plot(theta, r)
+# ax.set_rticks([0.5, 1, 1.5, 2])
+# ax.grid(True)
+# plt.show()
+```

python/clim_recal/utils/xarray.py

@@ -621,7 +621,7 @@ def join_xr_time_series_var(
 
 
 def annual_group_xr_time_series(
-    path: PathLike,
+    joined_xr_time_series: T_Dataset | PathLike,
     variable_name: str,
     groupby_method: str = "time.dayofyear",
     method_name: str = "median",
@@ -639,6 +639,8 @@ def annual_group_xr_time_series(
 
     Parameters
     ----------
+    xr_time_series
+        Provide existing `Dataset` to aggregate and plot (otherwise check `path`).
     path
         Path to collect files to process from, filtered via `regex`.
     variable_name
@@ -672,17 +674,18 @@ def annual_group_xr_time_series(
     Data variables:
         tasmax     (dayofyear) float64 ... 9.2 8.95 8.408 8.747 ... 6.387 8.15 9.132
     """
-    full_ts: T_DataArray = join_xr_time_series_var(
-        path=path,
-        variable_name=variable_name,
-        method_name=method_name,
-        time_dim_name=time_dim_name,
-        regex=regex,
-        start=start,
-        stop=stop,
-        step=step,
-    )
-    summarised_year_groups: T_Dataset = full_ts.groupby(groupby_method)
+    if isinstance(joined_xr_time_series, PathLike):
+        joined_xr_time_series = join_xr_time_series_var(
+            path=joined_xr_time_series,
+            variable_name=variable_name,
+            method_name=method_name,
+            time_dim_name=time_dim_name,
+            regex=regex,
+            start=start,
+            stop=stop,
+            step=step,
+        )
+    summarised_year_groups: T_Dataset = joined_xr_time_series.groupby(groupby_method)
     summarised_year: T_Dataset = getattr(summarised_year_groups, method_name)()
     try:
         assert 360 <= summarised_year.sizes["time"] <= 366
@@ -691,7 +694,7 @@ def annual_group_xr_time_series(
     if plot_path:
         plot_xarray(
             getattr(summarised_year, variable_name),
-            path=path,
+            path=plot_path,
             time_stamp=time_stamp,
             **kwargs,
         )