add some of the notebook

forecasttools/__init__.py

@@ -4,6 +4,7 @@
 import polars as pl
 
 from forecasttools.daily_to_epiweekly import df_aggregate_to_epiweekly
+from forecasttools.idata_to_tidy import convert_idata_forecast_to_tidydraws
 from forecasttools.idata_w_dates_to_df import (
     add_time_coords_to_idata_dimension,
     add_time_coords_to_idata_dimensions,
@@ -95,4 +96,5 @@
     "generate_time_range_for_dim",
     "validate_iter_has_expected_types",
     "ensure_listlike",
+    "convert_idata_forecast_to_tidydraws"
 ]

forecasttools/idata_to_tidy.py

@@ -60,12 +60,12 @@ def convert_idata_forecast_to_tidydraws(
             value_name="value"
         )
         group_pols_df = group_pols_df.with_columns(
-            pl.col("variable").map_elements(lambda x: re.sub(r"\[.*\]", "", x)).alias("variable")
+            pl.col("variable").map_elements(
+                lambda x: re.sub(r"\[.*\]", "", x)).alias("variable")
         )
         group_pols_df = group_pols_df.with_columns(
             ((pl.col("draw") - 1) % group_pols_df["draw"].n_unique() + 1).alias(".iteration")
         )
         group_pols_df = group_pols_df.rename({"chain": ".chain", "draw": ".draw"})
         tidy_dfs[group] = group_pols_df.select([".chain", ".draw", ".iteration", "variable", "value"])
-
     return tidy_dfs

notebooks/pyrenew_dates_to_tidy.qmd

@@ -0,0 +1,41 @@
+---
+title: Add Dates To Pyrenew Idata And Use In Tidy-Verse
+format: gfm
+engine: jupyter
+---
+
+_The following notebook illustrates the addition of dates to an external `idata` object before demonstrating the tidy-usable capabilities in R._
+
+__Load In Packages And External Pyrenew InferenceData Object__
+
+```{python}
+#| echo: true
+import forecasttools
+import arviz as az
+import xarray as xr
+
+xr.set_options(display_expand_data=False, display_expand_attrs=False)
+
+
+pyrenew_idata_path = "../assets/external/inference_data_1.nc"
+pyrenew_idata = az.from_netcdf(pyrenew_idata_path)
+pyrenew_idata
+```
+
+__Define Groups To Save And Convert__
+
+```{python}
+#| echo: true
+
+pyrenew_groups = ["posterior_predictive"]
+tidy_usable_groups = forecasttools.convert_idata_forecast_to_tidydraws(
+    idata=pyrenew_idata,
+    groups=pyrenew_groups
+)
+
+# show variables
+print(tidy_usable_groups["posterior_predictive"]["variable"].unique())
+
+# show output
+tidy_usable_groups
+```