Move general datetime indexing functions from pyrenew-hew to PyRenew

pyrenew_hew/pyrenew_hew_data.py

@@ -1,4 +1,3 @@
-import datetime as dt
 import json
 from pathlib import Path
 from typing import Self
@@ -7,6 +6,14 @@
 import numpy as np
 import polars as pl
 from jax.typing import ArrayLike
+from pyrenew.time import (
+    align_observation_times,
+    convert_date,
+    create_date_time_spine,
+    get_end_date,
+    get_n_data_days,
+    validate_mmwr_dates,
+)
 
 
 class PyrenewHEWData:
@@ -48,15 +55,7 @@ def __init__(
         self.right_truncation_offset = right_truncation_offset
         self.population_size = population_size
 
-        if (
-            first_hospital_admissions_date is not None
-            and not first_hospital_admissions_date.astype(dt.datetime).weekday() == 5
-        ):
-            raise ValueError(
-                "Dates for hospital admissions timeseries must "
-                "be Saturdays (MMWR epiweek end "
-                "days)."
-            )
+        validate_mmwr_dates([first_hospital_admissions_date])
 
         self.first_ed_visits_date_ = first_ed_visits_date
         self.first_hospital_admissions_date_ = first_hospital_admissions_date
@@ -146,23 +145,23 @@ def from_json(
 
     @property
     def n_ed_visits_data_days(self):
-        return self.get_n_data_days(
-            n_datapoints=self.n_ed_visits_data_days_,
+        return get_n_data_days(
+            n_points=self.n_ed_visits_data_days_,
             date_array=self.dates_observed_ed_visits,
         )
 
     @property
     def n_hospital_admissions_data_days(self):
-        return self.get_n_data_days(
-            n_datapoints=self.n_hospital_admissions_data_days_,
+        return get_n_data_days(
+            n_points=self.n_hospital_admissions_data_days_,
             date_array=self.dates_observed_hospital_admissions,
             timestep_days=7,
         )
 
     @property
     def n_wastewater_data_days(self):
-        return self.get_n_data_days(
-            n_datapoints=self.n_wastewater_data_days_,
+        return get_n_data_days(
+            n_points=self.n_wastewater_data_days_,
             date_array=self.dates_observed_disease_wastewater,
         )
 
@@ -197,29 +196,29 @@ def first_ed_visits_date(self):
 
     @property
     def first_hospital_admissions_date(self):
-        if self.data_observed_disease_hospital_admissions is not None:
+        if self.dates_observed_hospital_admissions is not None:
             return self.dates_observed_hospital_admissions.min()
         return self.first_hospital_admissions_date_
 
     @property
     def last_wastewater_date(self):
-        return self.get_end_date(
+        return get_end_date(
             self.first_wastewater_date,
             self.n_wastewater_data_days,
             timestep_days=1,
         )
 
     @property
     def last_ed_visits_date(self):
-        return self.get_end_date(
+        return get_end_date(
             self.first_ed_visits_date,
             self.n_ed_visits_data_days,
             timestep_days=1,
         )
 
     @property
     def last_hospital_admissions_date(self):
-        return self.get_end_date(
+        return get_end_date(
             self.first_hospital_admissions_date,
             self.n_hospital_admissions_data_days,
             timestep_days=7,
@@ -314,21 +313,9 @@ def site_subpop_spine(self):
 
     @property
     def date_time_spine(self):
-        date_time_spine = (
-            pl.DataFrame(
-                {
-                    "date": pl.date_range(
-                        start=self.first_data_date_overall,
-                        end=self.last_data_date_overall,
-                        interval="1d",
-                        eager=True,
-                    )
-                }
-            )
-            .with_row_index("t")
-            .with_columns(pl.col("t").cast(pl.Int64))
+        return create_date_time_spine(
+            self.first_data_date_overall, self.last_data_date_overall
         )
-        return date_time_spine
 
     @property
     def wastewater_data_extended(self):
@@ -374,33 +361,37 @@ def ww_uncensored(self):
     @property
     def model_t_obs_wastewater(self):
         if self.nwss_training_data is not None:
-            return self.wastewater_data_extended.get_column("t").to_numpy()
+            observed_dates = self.nwss_training_data.get_column("date").to_numpy()
+            return align_observation_times(
+                observed_dates,
+                self.first_data_date_overall,
+                aggregation_freq="daily",
+            )
+        return None
 
     @property
     def model_t_obs_ed_visits(self):
         if self.nssp_training_data is not None:
-            return (
-                self.nssp_training_data.join(
-                    self.date_time_spine, on="date", how="left"
-                )
-                .get_column("t")
-                .unique()
-                .to_numpy()
+            observed_dates = (
+                self.nssp_training_data.get_column("date").unique().to_numpy()
+            )
+            return align_observation_times(
+                observed_dates,
+                self.first_data_date_overall,
+                aggregation_freq="daily",
             )
         return None
 
     @property
     def model_t_obs_hospital_admissions(self):
         if self.nhsn_training_data is not None:
-            return (
-                self.nhsn_training_data.join(
-                    self.date_time_spine,
-                    left_on="weekendingdate",
-                    right_on="date",
-                    how="left",
-                )
-                .get_column("t")
-                .to_numpy()
+            observed_dates = (
+                self.nhsn_training_data.get_column("weekendingdate").unique().to_numpy()
+            )
+            return align_observation_times(
+                observed_dates,
+                self.first_data_date_overall,
+                aggregation_freq="daily",
             )
         return None
 
@@ -439,61 +430,16 @@ def lab_site_to_subpop_map(self):
             )
         return self.lab_site_to_subpop_map_
 
-    def get_end_date(
-        self,
-        first_date: np.datetime64,
-        n_datapoints: int,
-        timestep_days: int = 1,
-    ) -> np.datetime64:
-        """
-        Get end date from a first date and a number of datapoints,
-        with handling of None values and non-daily timeseries
-        """
-        if first_date is None:
-            if n_datapoints != 0:
-                raise ValueError(
-                    "Must provide an initial date if "
-                    "n_datapoints is non-zero. "
-                    f"Got n_datapoints = {n_datapoints} "
-                    "but first_date was `None`"
-                )
-            result = None
-        else:
-            result = first_date + np.timedelta64(
-                (n_datapoints - 1) * timestep_days, "D"
-            )
-        return result
-
-    def get_n_data_days(
-        self,
-        n_datapoints: int = None,
-        date_array: ArrayLike = None,
-        timestep_days: int = 1,
-    ) -> int:
-        if n_datapoints is None and date_array is None:
-            return 0
-        elif date_array is not None and n_datapoints is not None:
-            raise ValueError(
-                "Must provide at most one out of a "
-                "number of datapoints to simulate and "
-                "an array of dates data is observed."
-            )
-        elif date_array is not None:
-            return (
-                (max(date_array) - min(date_array))
-                // np.timedelta64((timestep_days), "D")
-                + 1
-            ).item()
-        else:
-            return n_datapoints
-
     def to_forecast_data(self, n_forecast_points: int) -> Self:
         n_days = self.n_days_post_init + n_forecast_points
         n_weeks = n_days // 7
-        first_dow = self.first_data_date_overall.astype(dt.datetime).weekday()
-        to_first_sat = (5 - first_dow) % 7
+        start_date = convert_date(self.first_data_date_overall)
+        first_dow = start_date.weekday()
+        days_to_first_saturday = (5 - first_dow) % 7
+        if days_to_first_saturday == 0:
+            days_to_first_saturday = 7
         first_mmwr_ending_date = self.first_data_date_overall + np.timedelta64(
-            to_first_sat, "D"
+            days_to_first_saturday, "D"
         )
         return PyrenewHEWData(
             n_ed_visits_data_days=n_days,

pyrenew_hew/pyrenew_hew_model.py

@@ -24,7 +24,7 @@
 from pyrenew.observation import NegativeBinomialObservation
 from pyrenew.process import ARProcess, DifferencedProcess
 from pyrenew.randomvariable import DistributionalVariable, TransformedVariable
-from pyrenew.time import daily_to_mmwr_epiweekly
+from pyrenew.time import daily_to_mmwr_epiweekly, get_first_week_on_or_after_t0
 
 from pyrenew_hew.pyrenew_hew_data import PyrenewHEWData
 
@@ -533,12 +533,10 @@ def calculate_weekly_hosp_indices(
             ) // 7
         else:
             which_obs_weekly_hosp_admissions = jnp.arange(n_datapoints)
-            if model_t_first_pred_admissions < 0:
-                which_obs_weekly_hosp_admissions = which_obs_weekly_hosp_admissions[
-                    (-model_t_first_pred_admissions - 1) // 7 + 1 :
-                ]
-                # Truncate to include only the epiweek ending after
-                # model t0 for posterior prediction
+            skip_weeks = get_first_week_on_or_after_t0(model_t_first_pred_admissions)
+            which_obs_weekly_hosp_admissions = which_obs_weekly_hosp_admissions[
+                skip_weeks:
+            ]
 
         return which_obs_weekly_hosp_admissions