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From: Colin Watson <cjwatson@debian.org>
Date: Mon, 3 Feb 2025 15:28:10 +0000
Subject: Support numpy 2.0
See: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword
Forwarded: https://github.com/telegraphic/hickle/pull/186
Bug-Debian: https://bugs.debian.org/1094905
Last-Update: 2025-02-03
---
hickle/legacy_v3/loaders/load_numpy.py | 2 +-
hickle/loaders/load_builtins.py | 4 ++--
hickle/loaders/load_numpy.py | 2 +-
hickle/tests/test_02_hickle_lookup.py | 6 +++---
4 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/hickle/legacy_v3/loaders/load_numpy.py b/hickle/legacy_v3/loaders/load_numpy.py
index 0fd6c0a..d17044b 100644
--- a/hickle/legacy_v3/loaders/load_numpy.py
+++ b/hickle/legacy_v3/loaders/load_numpy.py
@@ -115,7 +115,7 @@ def load_np_scalar_dataset(h_node):
def load_ndarray_dataset(h_node):
py_type, data = get_type_and_data(h_node)
- return np.array(data, copy=False)
+ return np.asarray(data)
def load_ndarray_masked_dataset(h_node):
py_type, data = get_type_and_data(h_node)
diff --git a/hickle/loaders/load_builtins.py b/hickle/loaders/load_builtins.py
index 7cfbf28..53b7442 100644
--- a/hickle/loaders/load_builtins.py
+++ b/hickle/loaders/load_builtins.py
@@ -170,7 +170,7 @@ def create_listlike_dataset(py_obj, h_group, name,list_len = -1,item_dtype = Non
# strings and bytes are stored as array of bytes with strings encoded
# using utf8 encoding
string_data = bytearray(py_obj,"utf8") if isinstance(py_obj,str) else memoryview(py_obj)
- string_data = np.array(string_data,copy=False)
+ string_data = np.asarray(string_data)
string_data.dtype = 'S1'
dataset = h_group.create_dataset( name, data = string_data,shape = (1,string_data.size), **kwargs)
dataset.attrs["str_type"] = py_obj.__class__.__name__.encode("ascii")
@@ -385,7 +385,7 @@ def load_list_dataset(h_node,base_type,py_obj_type):
if h_node.dtype.itemsize > 1 and 'bytes' in h_node.dtype.name:
# string dataset 4.0.x style convert it back to python string
- content = np.array(content, copy=False, dtype=str).tolist()
+ content = np.asarray(content, dtype=str).tolist()
else:
# decode bytes representing python string before final conversion
diff --git a/hickle/loaders/load_numpy.py b/hickle/loaders/load_numpy.py
index a4c76e9..bff9818 100644
--- a/hickle/loaders/load_numpy.py
+++ b/hickle/loaders/load_numpy.py
@@ -232,7 +232,7 @@ def load_ndarray_dataset(h_node,base_type,py_obj_type):
# not converted to list of string but saved as ar consequently
# itemsize of dtype is > 1
string_data = bytes(string_data).decode("utf8")
- return np.array(string_data,copy=False,dtype=dtype)
+ return np.asarray(string_data,dtype=dtype)
if issubclass(py_obj_type,np.matrix):
return py_obj_type(data=h_node[()],dtype=dtype)
# TODO how to restore other ndarray derived object_types
diff --git a/hickle/tests/test_02_hickle_lookup.py b/hickle/tests/test_02_hickle_lookup.py
index 628a2b1..dd91ffd 100644
--- a/hickle/tests/test_02_hickle_lookup.py
+++ b/hickle/tests/test_02_hickle_lookup.py
@@ -816,7 +816,7 @@ def test_ReferenceManager_get_root(h5_data):
content = data_group.create_dataset('mydata',data=12)
type_table = root_group.create_group('hickle_types_table')
int_pickle_string = bytearray(pickle.dumps(int))
- int_np_entry = np.array(int_pickle_string,copy=False)
+ int_np_entry = np.asarray(int_pickle_string)
int_np_entry.dtype = 'S1'
int_entry = type_table.create_dataset(str(len(type_table)),data = int_np_entry,shape =(1,int_np_entry.size))
int_base_type = b'int'
@@ -878,7 +878,7 @@ def test_ReferenceManager(h5_data):
with pytest.raises(lookup.ReferenceError):
reference_manager = lookup.ReferenceManager(false_root)
int_pickle_string = bytearray(pickle.dumps(int))
- int_np_entry = np.array(int_pickle_string,copy=False)
+ int_np_entry = np.asarray(int_pickle_string)
int_np_entry.dtype = 'S1'
int_entry = type_table.create_dataset(str(len(type_table)),data = int_np_entry,shape =(1,int_np_entry.size))
int_base_type = b'int'
@@ -1052,7 +1052,7 @@ def test_ReferenceManager_store_type(h5_data,compression_kwargs):
@pytest.mark.no_compression
def test_ReferenceManager_get_manager(h5_data):
h_node = h5_data.create_group('some_list')
- item_data = np.array(memoryview(b'hallo welt lore grueszet dich ipsum aus der lore von ipsum gelort in ipsum'),copy=False)
+ item_data = np.asarray(memoryview(b'hallo welt lore grueszet dich ipsum aus der lore von ipsum gelort in ipsum'))
item_data.dtype = 'S1'
h_item = h_node.create_dataset('0',data=item_data,shape=(1,item_data.size))
with lookup.ReferenceManager.create_manager(h5_data) as memo:
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