ADD: run workflow options and incomplete html generations (#14)

* fixes to region restriction for sites

* ported workflow to run in the package

* incomplete

ilamb3/__init__.py

@@ -43,5 +43,13 @@ def ilamb_catalog() -> pooch.Pooch:
     return registry
 
 
-__all__ = ["dataset", "compare", "analysis", "regions", "ilamb_catalog,", "conf"]
+__all__ = [
+    "dataset",
+    "compare",
+    "analysis",
+    "regions",
+    "output",
+    "ilamb_catalog,",
+    "conf",
+]
 xr.set_options(keep_attrs=True)

ilamb3/analysis/bias.py

@@ -14,6 +14,7 @@
 import xarray as xr
 
 import ilamb3.plot as plt
+import ilamb3.regions as ilr
 from ilamb3 import compare as cmp
 from ilamb3 import dataset as dset
 from ilamb3.analysis.base import ILAMBAnalysis
@@ -226,9 +227,8 @@ def _scalar(
                     weight=ref_ if (mass_weighting and weight) else None,
                 )
             elif dset.is_site(da):
-                site_dim = dset.get_dim_name(da, "site")
-                da = da.pint.dequantify()
-                da = da.mean(dim=site_dim)
+                da = ilr.Regions().restrict_to_region(da, region)
+                da = da.mean(dim=dset.get_dim_name(da, "site"))
             else:
                 raise ValueError(f"Input is neither spatial nor site: {da}")
             da = da.pint.quantify()
@@ -305,7 +305,6 @@ def plots(
         ref: xr.Dataset,
         com: dict[str, xr.Dataset],
     ) -> pd.DataFrame:
-
         # Some initialization
         regions = [None if r == "None" else r for r in df["region"].unique()]
         com["Reference"] = ref

ilamb3/compare.py

@@ -257,6 +257,10 @@ def extract_sites(
         + (ds_site[lon_site] - ds_spatial[lon_spatial]) ** 2
     )
     assert (dist < model_res).all()
+
+    # Set these are coordinates though so that we can tell this is site data
+    ds_spatial = ds_spatial.set_coords([lat_spatial, lon_spatial])
+
     return ds_spatial
 
 

ilamb3/regions.py

@@ -18,21 +18,33 @@ def restrict_to_bbox(
     work well on unsorted indices.
     """
     assert isinstance(da, xr.DataArray)
-    lat_name = dset.get_dim_name(da, "lat")
-    lon_name = dset.get_dim_name(da, "lon")
-    da = da.sortby(list(da.dims))
-    da = da.sel(
-        {
-            lat_name: slice(
-                da[lat_name].sel({lat_name: lat0}, method="nearest"),
-                da[lat_name].sel({lat_name: latf}, method="nearest"),
-            ),
-            lon_name: slice(
-                da[lon_name].sel({lon_name: lon0}, method="nearest"),
-                da[lon_name].sel({lon_name: lonf}, method="nearest"),
-            ),
-        }
-    )
+    lat_name = dset.get_coord_name(da, "lat")
+    lon_name = dset.get_coord_name(da, "lon")
+    if dset.is_site(da):
+        site_name = dset.get_dim_name(da, "site")
+        da = da.sel(
+            {
+                site_name: (
+                    (da[lat_name] >= lat0)
+                    & (da[lat_name] <= latf)
+                    & (da[lon_name] >= lon0)
+                    & (da[lon_name] <= lonf)
+                )
+            }
+        )
+    else:
+        da = da.sel(
+            {
+                lat_name: slice(
+                    da[lat_name].sel({lat_name: lat0}, method="nearest"),
+                    da[lat_name].sel({lat_name: latf}, method="nearest"),
+                ),
+                lon_name: slice(
+                    da[lon_name].sel({lon_name: lon0}, method="nearest"),
+                    da[lon_name].sel({lon_name: lonf}, method="nearest"),
+                ),
+            }
+        )
     return da
 
 

ilamb3/run.py

@@ -0,0 +1,103 @@
+"""Functions for rendering ilamb3 output."""
+
+import importlib
+
+import pandas as pd
+import xarray as xr
+from jinja2 import Template
+
+import ilamb3
+import ilamb3.regions as ilr
+from ilamb3.analysis.base import ILAMBAnalysis
+
+
+def run_analyses(
+    ref: xr.Dataset, com: xr.Dataset, analyses: dict[str, ILAMBAnalysis]
+) -> None:
+    dfs = []
+    ds_refs = []
+    ds_coms = []
+    for _, a in analyses.items():
+        df, ds_ref, ds_com = a(ref, com, regions=ilamb3.conf["regions"])
+        dfs.append(df)
+        ds_refs.append(ds_ref)
+        ds_coms.append(ds_com)
+    dfs = pd.concat(dfs)
+    dfs["name"] = dfs["name"] + " [" + df["units"] + "]"
+    ds_ref = xr.merge(ds_refs)
+    ds_com = xr.merge(ds_coms)
+    return dfs, ds_ref, ds_com
+
+
+def plot_analyses(
+    df: pd.DataFrame,
+    ref: xr.Dataset,
+    com: dict[str, xr.Dataset],
+    analyses: dict[str, ILAMBAnalysis],
+) -> pd.DataFrame:
+    df_plots = []
+    for name, a in analyses.items():
+        dfp = a.plots(df, ref, com)
+        dfp["analysis"] = name
+        df_plots.append(dfp)
+    df_plots = pd.concat(df_plots)
+    for _, row in df_plots.iterrows():
+        row["axis"].get_figure().savefig(
+            f"{row['source']}_{row['region']}_{row['name']}.png"
+        )
+    return df_plots
+
+
+def generate_html_page(
+    df: pd.DataFrame,
+    ref: xr.Dataset,
+    com: dict[str, xr.Dataset],
+    df_plots: pd.DataFrame,
+) -> str:
+    """."""
+    ilamb_regions = ilr.Regions()
+
+    # Setup template analyses and plots
+    analyses = {analysis: {} for analysis in df["analysis"].unique()}
+    for (aname, pname), df_grp in df_plots.groupby(["analysis", "name"], sort=False):
+        analyses[aname][pname] = []
+        if "Reference" in df_grp["source"].unique():
+            analyses[aname][pname] += [{"Reference": f"Reference_RNAME_{pname}.png"}]
+        analyses[aname][pname] += [{"Model": f"MNAME_RNAME_{pname}.png"}]
+    ref_plots = list(df_plots[df_plots["source"] == "Reference"]["name"].unique())
+    mod_plots = list(df_plots[df_plots["source"] != "Reference"]["name"].unique())
+    all_plots = list(set(ref_plots) | set(mod_plots))
+
+    # Setup template dictionary
+    df = df.reset_index(drop=True)  # ?
+    df["id"] = df.index
+    data = {
+        "page_header": ref.attrs["header"] if "header" in ref.attrs else "",
+        "analysis_list": list(analyses.keys()),
+        "model_names": [m for m in df["source"].unique() if m != "Reference"],
+        "ref_plots": ref_plots,
+        "mod_plots": mod_plots,
+        "all_plots": all_plots,
+        "regions": {
+            (None if key == "None" else key): (
+                "All Data" if key == "None" else ilamb_regions.get_name(key)
+            )
+            for key in df["region"].unique()
+        },
+        "analyses": analyses,
+        "data_information": {
+            key.capitalize(): ref.attrs[key]
+            for key in ["title", "institutions", "version"]
+            if key in ref.attrs
+        },
+        "table_data": str(
+            [row.to_dict() for _, row in df.drop(columns="units").iterrows()]
+        ),
+    }
+
+    # Generate the html from the template
+    template = importlib.resources.open_text(
+        "ilamb3.templates", "dataset_page.html"
+    ).read()
+    html = Template(template).render(data)
+    return html

ilamb3/templates/dataset_page.html

@@ -306,6 +306,15 @@ <h6 class="sidebar-heading d-flex justify-content-between align-items-center px-
 <!--- All models mode -->
 <div id="divAllModels">
   <br><h2>All Models</h2>
+  <select id="SelectPlot" class="form-select" aria-label="PlotSelect" onclick="updateImages()">
+{% for plot in all_plots %}
+    <option value="{{plot}}">{{plot}}</option>
+{% endfor %}
+  </select>
+  <img id="divReference" src="Reference_None_mean.png" width="32%">
+{% for mname in model_names %}
+  <img id="div{{mname}}" src="{{mname}}_None_mean.png" width="32%">
+{% endfor %}
 </div>
 
 <script>

pyproject.toml

@@ -34,6 +34,7 @@ dependencies = [
     "matplotlib>=3.10.0",
     "cartopy>=0.24.1",
     "pyyaml>=6.0.2",
+    "jinja2>=3.1.5",
 ]
 
 [dependency-groups]