Refactoring the periodic broadcaster and added warn + error + test

docs/source/tutorials/periodic_effects.qmd

@@ -57,7 +57,7 @@ for i in range(0, 30, 7):
 plt.show()
 ```
 
-The implementation of the `RtWeeklyDiffProcess` (which is an instance of `RtPeriodicDiffProcess`), uses `PeriodicBroadcaster` to repeating values: `PeriodicBroadcaster(..., period_size=7, broadcast_type="repeat")`. Setting the `broadcast_type` to `"repeat"` repeats each vector element for the specified period size. The `RtWeeklyDiff` class is a particular case of `RtPeriodicDiff` with a period size of seven.
+The implementation of the `RtWeeklyDiffProcess` (which is an instance of `RtPeriodicDiffProcess`), uses `repeat_until_n` to repeating values: `repeat_until_n(..., period_size=7)`. The `RtWeeklyDiff` class is a particular case of `RtPeriodicDiff` with a period size of seven.
 
 ## Repeated sequences (tiling)
 

docs/source/tutorials/time.qmd

@@ -32,7 +32,7 @@ The `t_unit, t_start` pair can encode different types of time series data. For e
 
 ## How it relates to periodicity
 
-The `PeriodicBroadcaster()` class provides a way of tiling and repeating data accounting starting time, but it does not encode the time unit, only the period length and starting point. Furthermore, samples returned from `PeriodicEffect()` and `RtPeriodicDiffProcess()` both currently return daily values shifted so that the first entry of their arrays matches day 0 in the model.
+The `tile_until_n()` and `repeat_until_n()` functions provide a way of tiling and repeating data accounting starting time, but it does not encode the time unit, only the period length and starting point. Furthermore, samples returned from `PeriodicEffect()` and `RtPeriodicDiffProcess()` both currently return daily values shifted so that the first entry of their arrays matches day 0 in the model.
 
 ## Unimplemented features
 

model/src/pyrenew/arrayutils.py

@@ -112,138 +112,111 @@ def __repr__(self):
         return f"PeriodicProcessSample(value={self.value})"
 
 
-class PeriodicBroadcaster:
-    r"""
-    Broadcast arrays periodically using either repeat or tile,
-    considering period size and starting point.
+def tile_until_n(
+    data: ArrayLike,
+    n_timepoints: int,
+    offset: int | None = 0,
+) -> ArrayLike:
     """
+    Tile the data until it reaches `n_timepoints`.
 
-    def __init__(
-        self,
-        offset: int,
-        period_size: int,
-        broadcast_type: str,
-    ) -> None:
-        """
-        Default constructor for PeriodicBroadcaster class.
-
-        Parameters
-        ----------
-        offset : int
-            Relative point at which data starts, must be between 0 and
-            period_size - 1.
-        period_size : int
-            Size of the period.
-        broadcast_type : str
-            Type of broadcasting to use, either "repeat" or "tile".
-
-        Notes
-        -----
-        See the sample method for more information on the broadcasting types.
-
-        Returns
-        -------
-        None
-        """
-
-        self.validate(
-            offset=offset,
-            period_size=period_size,
-            broadcast_type=broadcast_type,
-        )
+    Parameters
+    ----------
+    data : ArrayLike
+        Data to broadcast.
+    n_timepoints : int
+        Duration of the sequence.
+    offset : int, optional
+        Relative point at which data starts, must be a non-negative integer.
+
+    Notes
+    -----
+    Using the `offset` parameter, the function will start the broadcast
+    from the `offset`-th element of the data. If the data is shorter than
+    `n_timepoints`, the function will repeat or tile the data until it
+    reaches `n_timepoints`.
 
-        self.period_size = period_size
-        self.offset = offset
-        self.broadcast_type = broadcast_type
-
-        return None
-
-    @staticmethod
-    def validate(offset: int, period_size: int, broadcast_type: str) -> None:
-        """
-        Validate the input parameters.
-
-        Parameters
-        ----------
-        offset : int
-            Relative point at which data starts, must be between 0 and
-            period_size - 1.
-        period_size : int
-            Size of the period.
-        broadcast_type : str
-            Type of broadcasting to use, either "repeat" or "tile".
-
-        Returns
-        -------
-        None
-        """
-
-        # Period size should be a positive integer
-        assert isinstance(
-            period_size, int
-        ), f"period_size should be an integer. It is {type(period_size)}."
-
-        assert (
-            period_size > 0
-        ), f"period_size should be a positive integer. It is {period_size}."
-
-        # Data starts should be a positive integer
-        assert isinstance(
-            offset, int
-        ), f"offset should be an integer. It is {type(offset)}."
-
-        assert (
-            0 <= offset
-        ), f"offset should be a positive integer. It is {offset}."
-
-        assert offset <= period_size - 1, (
-            "offset should be less than or equal to period_size - 1."
-            f"It is {offset}. It should be less than or equal "
-            f"to {period_size - 1}."
-        )
+    Returns
+    -------
+    ArrayLike
+        Tiled data.
+    """
+
+    # Data starts should be a positive integer
+    assert isinstance(
+        offset, int
+    ), f"offset should be an integer. It is {type(offset)}."
+
+    assert 0 <= offset, f"offset should be a positive integer. It is {offset}."
+
+    return jnp.tile(data, (n_timepoints // data.size) + 1)[
+        offset : (offset + n_timepoints)
+    ]
+
+
+def repeat_until_n(
+    data: ArrayLike,
+    n_timepoints: int,
+    period_size: int,
+    offset: int | None = 0,
+):
+    """
+    Repeat the data until it reaches `n_timepoints`.
+
+    Notes
+    -----
+    Using the `offset` parameter, the function will start the broadcast
+    from the `offset`-th element of the data. If the data is shorter than
+    `n_timepoints`, the function will repeat or tile the data until it
+    reaches `n_timepoints`.
 
-        # Broadcast type should be either "repeat" or "tile"
-        assert broadcast_type in ["repeat", "tile"], (
-            "broadcast_type should be either 'repeat' or 'tile'. "
-            f"It is {broadcast_type}."
+    Parameters
+    ----------
+    data : ArrayLike
+        Data to broadcast.
+    n_timepoints : int
+        Duration of the sequence.
+    period_size : int
+        Size of the period for the repeat broadcast.
+    offset : int, optional
+        Relative point at which data starts, must be between 0 and
+        period_size - 1. By default 0.
+
+    Returns
+    -------
+    ArrayLike
+        Repeated data.
+    """
+
+    # Data starts should be a positive integer
+    assert isinstance(
+        offset, int
+    ), f"offset should be an integer. It is {type(offset)}."
+
+    assert 0 <= offset, f"offset should be a positive integer. It is {offset}."
+
+    # Period size should be a positive integer
+    assert isinstance(
+        period_size, int
+    ), f"period_size should be an integer. It is {type(period_size)}."
+
+    assert (
+        period_size > 0
+    ), f"period_size should be a positive integer. It is {period_size}."
+
+    assert offset <= period_size - 1, (
+        "offset should be less than or equal to period_size - 1."
+        f"It is {offset}. It should be less than or equal "
+        f"to {period_size - 1}."
+    )
+
+    if (data.size * period_size) < n_timepoints:
+        raise ValueError(
+            "The data is too short to broadcast to "
+            f"the given number of timepoints ({n_timepoints}). The "
+            "repeated data would have a size of data.size * "
+            f"period_size = {data.size} * {period_size} = "
+            f"{data.size * period_size}."
         )
 
-        return None
-
-    def __call__(
-        self,
-        data: ArrayLike,
-        n_timepoints: int,
-    ) -> ArrayLike:
-        """
-        Broadcast the data to the given number of timepoints
-        considering the period size and starting point.
-
-        Parameters
-        ----------
-        data: ArrayLike
-            Data to broadcast.
-        n_timepoints : int
-            Duration of the sequence.
-
-        Notes
-        -----
-        The broadcasting is done by repeating or tiling the data. When
-        self.broadcast_type = "repeat", the function will repeat each value of the data `self.period_size` times until it reaches `n_timepoints`. When self.broadcast_type = "tile", the function will tile the data until it reaches `n_timepoints`.
-
-        Using the `offset` parameter, the function will start the broadcast from the `offset`-th element of the data. If the data is shorter than `n_timepoints`, the function will repeat or tile the data until it reaches `n_timepoints`.
-
-        Returns
-        -------
-        ArrayLike
-            Broadcasted array.
-        """
-
-        if self.broadcast_type == "repeat":
-            return jnp.repeat(data, self.period_size)[
-                self.offset : (self.offset + n_timepoints)
-            ]
-        elif self.broadcast_type == "tile":
-            return jnp.tile(
-                data, int(jnp.ceil(n_timepoints / self.period_size))
-            )[self.offset : (self.offset + n_timepoints)]
+    return jnp.repeat(data, period_size)[offset : (offset + n_timepoints)]

model/src/pyrenew/process/periodiceffect.py

@@ -35,7 +35,6 @@ class PeriodicEffect(RandomVariable):
     def __init__(
         self,
         offset: int,
-        period_size: int,
         quantity_to_broadcast: RandomVariable,
         t_start: int,
         t_unit: int,
@@ -48,8 +47,6 @@ def __init__(
         offset : int
             Relative point at which data starts, must be between 0 and
             period_size - 1.
-        period_size : int
-            Size of the period.
         quantity_to_broadcast : RandomVariable
             Values to be broadcasted (repeated or tiled).
         t_start : int
@@ -64,11 +61,7 @@ def __init__(
 
         PeriodicEffect.validate(quantity_to_broadcast)
 
-        self.broadcaster = au.PeriodicBroadcaster(
-            offset=offset,
-            period_size=period_size,
-            broadcast_type="tile",
-        )
+        self.offset = offset
 
         self.set_timeseries(
             t_start=t_start,
@@ -114,9 +107,10 @@ def sample(self, duration: int, **kwargs):
 
         return PeriodicEffectSample(
             value=SampledValue(
-                self.broadcaster(
+                au.tile_until_n(
                     data=self.quantity_to_broadcast.sample(**kwargs)[0].value,
                     n_timepoints=duration,
+                    offset=self.offset,
                 ),
                 t_start=self.t_start,
                 t_unit=self.t_unit,
@@ -157,7 +151,6 @@ def __init__(
 
         super().__init__(
             offset=offset,
-            period_size=7,
             quantity_to_broadcast=quantity_to_broadcast,
             t_start=t_start,
             t_unit=1,

model/src/pyrenew/process/rtperiodicdiff.py

@@ -2,8 +2,8 @@
 from typing import NamedTuple
 
 import jax.numpy as jnp
+import pyrenew.arrayutils as au
 from jax.typing import ArrayLike
-from pyrenew.arrayutils import PeriodicBroadcaster
 from pyrenew.metaclass import (
     RandomVariable,
     SampledValue,
@@ -77,19 +77,14 @@ def __init__(
         -------
         None
         """
-        self.name = name
-        self.broadcaster = PeriodicBroadcaster(
-            offset=offset,
-            period_size=period_size,
-            broadcast_type="repeat",
-        )
 
         self.validate(
             log_rt_rv=log_rt_rv,
             autoreg_rv=autoreg_rv,
             periodic_diff_sd_rv=periodic_diff_sd_rv,
         )
 
+        self.name = name
         self.period_size = period_size
         self.offset = offset
         self.log_rt_rv = log_rt_rv
@@ -192,7 +187,12 @@ def sample(
 
         return RtPeriodicDiffProcessSample(
             rt=SampledValue(
-                self.broadcaster(jnp.exp(log_rt.value.flatten()), duration),
+                au.repeat_until_n(
+                    data=jnp.exp(log_rt.value.flatten()),
+                    n_timepoints=duration,
+                    offset=self.offset,
+                    period_size=self.period_size,
+                ),
                 t_start=self.t_start,
                 t_unit=self.t_unit,
             ),

model/src/test/test_broadcaster.py

@@ -0,0 +1,40 @@
+"""
+Test the broadcaster utility
+"""
+
+import jax.numpy as jnp
+import numpy.testing as testing
+import pytest
+from pyrenew.arrayutils import repeat_until_n, tile_until_n
+
+
+def test_broadcaster() -> None:
+    """
+    Test the PeriodicBroadcaster utility.
+    """
+    base_array = jnp.array([1, 2, 3])
+
+    testing.assert_array_equal(
+        tile_until_n(base_array, 10),
+        jnp.array([1, 2, 3, 1, 2, 3, 1, 2, 3, 1]),
+    )
+
+    testing.assert_array_equal(
+        repeat_until_n(
+            data=base_array,
+            n_timepoints=10,
+            offset=0,
+            period_size=7,
+        ),
+        jnp.array([1, 1, 1, 1, 1, 1, 1, 2, 2, 2]),
+    )
+
+    with pytest.raises(ValueError, match="The data is too short to broadcast"):
+        repeat_until_n(
+            data=base_array,
+            n_timepoints=100,
+            offset=0,
+            period_size=7,
+        )
+
+    return None