From 80b4c6a3f9271a1fddd69100d12782867717b58c Mon Sep 17 00:00:00 2001
From: Maximilian Roos <m@maxroos.com>
Date: Sun, 22 Sep 2024 17:56:23 -0700
Subject: [PATCH] add named array generations

---
 xarray/namedarray/_aggregations.py | 80 ++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)

diff --git a/xarray/namedarray/_aggregations.py b/xarray/namedarray/_aggregations.py
index 0b7b99baa8c..10006c97a32 100644
--- a/xarray/namedarray/_aggregations.py
+++ b/xarray/namedarray/_aggregations.py
@@ -65,8 +65,12 @@ def count(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.count()
+        <xarray.NamedArray ()> Size: 8B
+        array(5)
         """
         return self.reduce(
             duck_array_ops.count,
@@ -115,8 +119,12 @@ def all(
         ...     np.array([True, True, True, True, True, False], dtype=bool),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 6B
+        array([ True,  True,  True,  True,  True, False])
 
         >>> na.all()
+        <xarray.NamedArray ()> Size: 1B
+        array(False)
         """
         return self.reduce(
             duck_array_ops.array_all,
@@ -165,8 +173,12 @@ def any(
         ...     np.array([True, True, True, True, True, False], dtype=bool),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 6B
+        array([ True,  True,  True,  True,  True, False])
 
         >>> na.any()
+        <xarray.NamedArray ()> Size: 1B
+        array(True)
         """
         return self.reduce(
             duck_array_ops.array_any,
@@ -222,12 +234,18 @@ def max(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.max()
+        <xarray.NamedArray ()> Size: 8B
+        array(3.)
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.max(skipna=False)
+        <xarray.NamedArray ()> Size: 8B
+        array(nan)
         """
         return self.reduce(
             duck_array_ops.max,
@@ -284,12 +302,18 @@ def min(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.min()
+        <xarray.NamedArray ()> Size: 8B
+        array(0.)
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.min(skipna=False)
+        <xarray.NamedArray ()> Size: 8B
+        array(nan)
         """
         return self.reduce(
             duck_array_ops.min,
@@ -350,12 +374,18 @@ def mean(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.mean()
+        <xarray.NamedArray ()> Size: 8B
+        array(1.6)
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.mean(skipna=False)
+        <xarray.NamedArray ()> Size: 8B
+        array(nan)
         """
         return self.reduce(
             duck_array_ops.mean,
@@ -423,16 +453,24 @@ def prod(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.prod()
+        <xarray.NamedArray ()> Size: 8B
+        array(0.)
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.prod(skipna=False)
+        <xarray.NamedArray ()> Size: 8B
+        array(nan)
 
         Specify ``min_count`` for finer control over when NaNs are ignored.
 
         >>> na.prod(skipna=True, min_count=2)
+        <xarray.NamedArray ()> Size: 8B
+        array(0.)
         """
         return self.reduce(
             duck_array_ops.prod,
@@ -501,16 +539,24 @@ def sum(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.sum()
+        <xarray.NamedArray ()> Size: 8B
+        array(8.)
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.sum(skipna=False)
+        <xarray.NamedArray ()> Size: 8B
+        array(nan)
 
         Specify ``min_count`` for finer control over when NaNs are ignored.
 
         >>> na.sum(skipna=True, min_count=2)
+        <xarray.NamedArray ()> Size: 8B
+        array(8.)
         """
         return self.reduce(
             duck_array_ops.sum,
@@ -576,16 +622,24 @@ def std(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.std()
+        <xarray.NamedArray ()> Size: 8B
+        array(1.0198039)
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.std(skipna=False)
+        <xarray.NamedArray ()> Size: 8B
+        array(nan)
 
         Specify ``ddof=1`` for an unbiased estimate.
 
         >>> na.std(skipna=True, ddof=1)
+        <xarray.NamedArray ()> Size: 8B
+        array(1.14017543)
         """
         return self.reduce(
             duck_array_ops.std,
@@ -651,16 +705,24 @@ def var(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.var()
+        <xarray.NamedArray ()> Size: 8B
+        array(1.04)
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.var(skipna=False)
+        <xarray.NamedArray ()> Size: 8B
+        array(nan)
 
         Specify ``ddof=1`` for an unbiased estimate.
 
         >>> na.var(skipna=True, ddof=1)
+        <xarray.NamedArray ()> Size: 8B
+        array(1.3)
         """
         return self.reduce(
             duck_array_ops.var,
@@ -722,12 +784,18 @@ def median(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.median()
+        <xarray.NamedArray ()> Size: 8B
+        array(2.)
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.median(skipna=False)
+        <xarray.NamedArray ()> Size: 8B
+        array(nan)
         """
         return self.reduce(
             duck_array_ops.median,
@@ -793,12 +861,18 @@ def cumsum(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.cumsum()
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([1., 3., 6., 6., 8., 8.])
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.cumsum(skipna=False)
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  3.,  6.,  6.,  8., nan])
         """
         return self.reduce(
             duck_array_ops.cumsum,
@@ -864,12 +938,18 @@ def cumprod(
         ...     np.array([1, 2, 3, 0, 2, np.nan]),
         ... )
         >>> na
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  3.,  0.,  2., nan])
 
         >>> na.cumprod()
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([1., 2., 6., 0., 0., 0.])
 
         Use ``skipna`` to control whether NaNs are ignored.
 
         >>> na.cumprod(skipna=False)
+        <xarray.NamedArray (x: 6)> Size: 48B
+        array([ 1.,  2.,  6.,  0.,  0., nan])
         """
         return self.reduce(
             duck_array_ops.cumprod,