Merge pull request #712 from sezelt/np2

Fix numpy 2 deprecated aliases

Author: smribet

py4DSTEM/io/legacy/legacy13/v13_emd_classes/io.py

@@ -127,61 +127,61 @@ def Metadata_to_h5(metadata, group):
         # None
         if v is None:
             v = "_None"
-            v = np.string_(v)  # convert to byte string
+            v = np.bytes_(v)  # convert to byte string
             dset = grp.create_dataset(k, data=v)
-            dset.attrs["type"] = np.string_("None")
+            dset.attrs["type"] = np.bytes_("None")
 
         # strings
         elif isinstance(v, str):
-            v = np.string_(v)  # convert to byte string
+            v = np.bytes_(v)  # convert to byte string
             dset = grp.create_dataset(k, data=v)
-            dset.attrs["type"] = np.string_("string")
+            dset.attrs["type"] = np.bytes_("string")
 
         # bools
         elif isinstance(v, bool):
             dset = grp.create_dataset(k, data=v, dtype=bool)
-            dset.attrs["type"] = np.string_("bool")
+            dset.attrs["type"] = np.bytes_("bool")
 
         # numbers
         elif isinstance(v, Number):
             dset = grp.create_dataset(k, data=v, dtype=type(v))
-            dset.attrs["type"] = np.string_("number")
+            dset.attrs["type"] = np.bytes_("number")
 
         # arrays
         elif isinstance(v, np.ndarray):
             dset = grp.create_dataset(k, data=v, dtype=v.dtype)
-            dset.attrs["type"] = np.string_("array")
+            dset.attrs["type"] = np.bytes_("array")
 
         # tuples
         elif isinstance(v, tuple):
             # of numbers
             if isinstance(v[0], Number):
                 dset = grp.create_dataset(k, data=v)
-                dset.attrs["type"] = np.string_("tuple")
+                dset.attrs["type"] = np.bytes_("tuple")
 
             # of tuples
             elif any([isinstance(v[i], tuple) for i in range(len(v))]):
                 dset_grp = grp.create_group(k)
-                dset_grp.attrs["type"] = np.string_("tuple_of_tuples")
+                dset_grp.attrs["type"] = np.bytes_("tuple_of_tuples")
                 dset_grp.attrs["length"] = len(v)
                 for i, x in enumerate(v):
                     dset_grp.create_dataset(str(i), data=x)
 
             # of arrays
             elif isinstance(v[0], np.ndarray):
                 dset_grp = grp.create_group(k)
-                dset_grp.attrs["type"] = np.string_("tuple_of_arrays")
+                dset_grp.attrs["type"] = np.bytes_("tuple_of_arrays")
                 dset_grp.attrs["length"] = len(v)
                 for i, ar in enumerate(v):
                     dset_grp.create_dataset(str(i), data=ar, dtype=ar.dtype)
 
             # of strings
             elif isinstance(v[0], str):
                 dset_grp = grp.create_group(k)
-                dset_grp.attrs["type"] = np.string_("tuple_of_strings")
+                dset_grp.attrs["type"] = np.bytes_("tuple_of_strings")
                 dset_grp.attrs["length"] = len(v)
                 for i, s in enumerate(v):
-                    dset_grp.create_dataset(str(i), data=np.string_(s))
+                    dset_grp.create_dataset(str(i), data=np.bytes_(s))
 
             else:
                 er = f"Metadata only supports writing tuples with numeric and array-like arguments; found type {type(v[0])}"
@@ -192,23 +192,23 @@ def Metadata_to_h5(metadata, group):
             # of numbers
             if isinstance(v[0], Number):
                 dset = grp.create_dataset(k, data=v)
-                dset.attrs["type"] = np.string_("list")
+                dset.attrs["type"] = np.bytes_("list")
 
             # of arrays
             elif isinstance(v[0], np.ndarray):
                 dset_grp = grp.create_group(k)
-                dset_grp.attrs["type"] = np.string_("list_of_arrays")
+                dset_grp.attrs["type"] = np.bytes_("list_of_arrays")
                 dset_grp.attrs["length"] = len(v)
                 for i, ar in enumerate(v):
                     dset_grp.create_dataset(str(i), data=ar, dtype=ar.dtype)
 
             # of strings
             elif isinstance(v[0], str):
                 dset_grp = grp.create_group(k)
-                dset_grp.attrs["type"] = np.string_("list_of_strings")
+                dset_grp.attrs["type"] = np.bytes_("list_of_strings")
                 dset_grp.attrs["length"] = len(v)
                 for i, s in enumerate(v):
-                    dset_grp.create_dataset(str(i), data=np.string_(s))
+                    dset_grp.create_dataset(str(i), data=np.bytes_(s))
 
             else:
                 er = f"Metadata only supports writing lists with numeric and array-like arguments; found type {type(v[0])}"
@@ -490,7 +490,7 @@ def PointList_to_h5(pointlist, group):
     # Add data
     for f, t in zip(pointlist.fields, pointlist.types):
         group_current_field = grp.create_dataset(f, data=pointlist.data[f])
-        group_current_field.attrs.create("dtype", np.string_(t))
+        group_current_field.attrs.create("dtype", np.bytes_(t))
         # group_current_field.create_dataset(
         #    "data",
         #    data = pointlist.data[f]

py4DSTEM/process/classification/featurization.py

@@ -967,7 +967,7 @@ def _gmm_single(x, cv, components, num_models, random_seed=None, return_all=True
         gmm_list = []
         gmm_labels = []
         gmm_proba = []
-    lowest_bic = np.infty
+    lowest_bic = np.inf
     bic_temp = 0
     if random_seed is None:
         rng = np.random.RandomState(seed=42)

py4DSTEM/process/utils/elliptical_coords.py

@@ -320,8 +320,8 @@ def elliptical_resample(
 
     # Get (qx,qy) corresponding to the coordinates distorted by the ellipse
     xr, yr = np.mgrid[0:Nx, 0:Ny]
-    xr0 = xr.astype(np.float_) - qx0
-    yr0 = yr.astype(np.float_) - qy0
+    xr0 = xr.astype(np.float64) - qx0
+    yr0 = yr.astype(np.float64) - qy0
     xr = xr0 * np.cos(-theta) - yr0 * np.sin(-theta)
     yr = xr0 * np.sin(-theta) + yr0 * np.cos(-theta)
     qx = qx0 + xr * np.cos(theta) - yr * (b / a) * np.sin(theta)

setup.py

@@ -23,11 +23,11 @@
     keywords="STEM,4DSTEM",
     python_requires=">=3.10",
     install_requires=[
-        "numpy >= 1.19, < 2.0",
+        "numpy >= 1.19",
         "scipy >= 1.5.2",
         "h5py >= 3.2.0",
         "hdf5plugin >= 4.1.3",
-        "ncempy >= 1.8.1, <= 1.11.2",
+        "ncempy >= 1.8.1",
         "matplotlib >= 3.2.2",
         "scikit-image >= 0.17.2",
         "scikit-learn >= 0.23.2, < 1.5",