From 58c18d9b8ca62af99e9d20dfe3644d796b5bc3d8 Mon Sep 17 00:00:00 2001
From: Sven Augustin <sven.augustin@psi.ch>
Date: Mon, 5 Aug 2024 18:04:26 +0200
Subject: [PATCH] added algos/utils folder and npmemo

---
 dap/algos/utils/__init__.py |  4 ++
 dap/algos/utils/npmemo.py   | 77 +++++++++++++++++++++++++++++++++++++
 2 files changed, 81 insertions(+)
 create mode 100644 dap/algos/utils/__init__.py
 create mode 100644 dap/algos/utils/npmemo.py

diff --git a/dap/algos/utils/__init__.py b/dap/algos/utils/__init__.py
new file mode 100644
index 0000000..9efc8a4
--- /dev/null
+++ b/dap/algos/utils/__init__.py
@@ -0,0 +1,4 @@
+
+from .npmemo import npmemo
+
+
diff --git a/dap/algos/utils/npmemo.py b/dap/algos/utils/npmemo.py
new file mode 100644
index 0000000..5ea6670
--- /dev/null
+++ b/dap/algos/utils/npmemo.py
@@ -0,0 +1,77 @@
+import functools
+#import hashlib
+import numpy as np
+
+
+def npmemo(func):
+    """
+    numpy array aware memoizer
+    """
+    cache = {}
+
+    @functools.wraps(func)
+    def wrapper(*args):
+        key = make_key(args)
+        try:
+            return cache[key]
+        except KeyError:
+            cache[key] = res = func(*args)
+            return res
+
+#    wrapper.cache = cache
+    return wrapper
+
+
+def make_key(args):
+    return tuple(make_key_entry(i) for i in args)
+
+def make_key_entry(x):
+    if isinstance(x, np.ndarray):
+        return np_array_hash(x)
+    return x
+
+def np_array_hash(arr):
+#    return id(arr) # this has been used so far
+    res = arr.tobytes()
+#    res = hashlib.sha256(res).hexdigest() # if tobytes was too large, we could hash it
+#    res = (arr.shape, res) # tobytes does not take shape into account
+    return res
+
+
+
+
+
+if __name__ == "__main__":
+    @npmemo
+    def expensive(arr, offset):
+        print("recalc", arr, offset)
+        return np.dot(arr, arr) + offset
+
+    def test(arr, offset):
+        print("first")
+        res1 = expensive(arr, offset)
+        print("second")
+        res2 = expensive(arr, offset)
+        print()
+        assert np.array_equal(res1, res2)
+
+    arrays = (
+        [1, 2, 3],
+        [1, 2, 3, 4],
+        [1, 2, 3, 4]
+    )
+
+    offsets = (
+        2,
+        2,
+        5
+    )
+
+    for a, o in zip(arrays, offsets):
+        a = np.array(a)
+        test(a, o)
+
+#    print(expensive.cache)
+
+
+