Adding docs and fixing some tests

docs/src/reference/temporian/operators/window/moving_quantile.md

@@ -0,0 +1 @@
+::: temporian.EventSet.moving_quantile

temporian/core/event_set_ops.py

@@ -3224,7 +3224,58 @@ def moving_quantile(
         quantile: float,
         sampling: Optional[EventSetOrNode] = None,
     ) -> EventSetOrNode:
-        """ """
+        """Computes the quantile in a sliding window over an
+        [`EventSet`][temporian.EventSet].
+
+        For each t in sampling, and for each feature independently, returns at
+        time t the appropiated quantile for the feature in the window
+        (t - window_length, t].
+
+        `sampling` can't be  specified if a variable `window_length` is
+        specified (i.e. if `window_length` is an EventSet).
+
+        If `sampling` is specified or `window_length` is an EventSet, the moving
+        window is sampled at each timestamp in them, else it is sampled on the
+        input's.
+
+        Missing values (such as NaNs) are ignored.
+
+        If the window does not contain any values (e.g., all the values are
+        missing, or the window does not contain any sampling), outputs missing
+        values.
+
+        The quantile calculated in each window is equivalent to numpy's
+        `"averaged_inverted_cdf"` method.
+
+        Example:
+            ```python
+            >>> a = tp.event_set(
+            ...     timestamps=[0, 1, 2, 5, 6, 7],
+            ...     features={"value": [np.nan, 1, 5, 10, 15, 20]},
+            ... )
+
+            >>> a.moving_quantile(tp.duration.seconds(4), quantile=0.5)
+            indexes: ...
+                (6 events):
+                    timestamps: [0. 1. 2. 5. 6. 7.]
+                    'value': [ nan  1.   3.   7.5 12.5 15. ]
+            ...
+
+            ```
+
+        See [`EventSet.moving_count()`][temporian.EventSet.moving_count] for
+        examples of moving window operations with external sampling and indices.
+
+        Args:
+            window_length: Sliding window's length.
+            quantile: the desired quantile defined in the range (0, 1).
+            sampling: Timestamps to sample the sliding window's value at. If not
+                provided, timestamps in the input are used.
+
+        Returns:
+            EventSet containing the moving standard deviation of each feature in
+                the input.
+        """
         from temporian.core.operators.window.moving_quantile import (
             moving_quantile,
         )

temporian/core/test/registered_operators_test.py

@@ -81,6 +81,7 @@ def test_base(self):
             "MOVING_MAX",
             "MOVING_MIN",
             "MOVING_PRODUCT",
+            "MOVING_QUANTILE",
             "MOVING_STANDARD_DEVIATION",
             "MOVING_SUM",
             "MULTIPLICATION",

temporian/implementation/numpy_cc/operators/window.cc

@@ -174,7 +174,7 @@ py::array_t<OUTPUT> accumulate(const ArrayD &evset_timestamps,
   auto v_window_length = window_length.unchecked<1>();
 
   assert(v_timestamps.shape(0) == v_window_length.shape(0));
-  assert(v_timestamps.shape(0) == v_values.shape(0));
+  assert(v_timestamps.shape(0) == evset_values.shape(0));
 
   // Index of the first value in the window.
   size_t begin_idx = 0;
@@ -247,7 +247,7 @@ py::array_t<OUTPUT> accumulate(const ArrayD &evset_timestamps,
   auto v_sampling = sampling_timestamps.unchecked<1>();
   auto v_window_length = window_length.unchecked<1>();
 
-  assert(v_timestamps.shape(0) == v_values.shape(0));
+  assert(v_timestamps.shape(0) == evset_values.shape(0));
   assert(v_sampling.shape(0) == v_window_length.shape(0));
 
   size_t begin_idx = 0;