Remove support for int32, uint32 for the attributes schema

Rust serde Value are 64, and I was accepting only those and casting
to 64bits in the background so better to just say that all attributes
are 64bits (except bool and String)

demo/add_normals.py

@@ -1,14 +1,14 @@
+# -- add the normals, as extra attributes (nx, ny, nz), for all the vertices in the DT
 
-#-- add the normals, as extra attributes (nx, ny, nz), for all the vertices in the DT
+import json
 
-import startinpy
-import numpy as np
 import laspy
-import json
+import numpy as np
+import startinpy
 
 las = laspy.read("../data/small.laz")
 dt = startinpy.DT()
-dt.set_attributes_schema(np.dtype([("nx", np.float32), ("ny", float), ("nz", float)]))
+dt.set_attributes_schema(np.dtype([("nx", np.float64), ("ny", float), ("nz", float)]))
 dt.duplicates_handling = "Highest"
 
 dt.insert(las.xyz)
@@ -32,8 +32,3 @@
 # # together = np.stack((dt.points, a))
 # # together = np.concatenate((dt.points, a), axis=1)
 # print(together)
-
-
-
-
-

docs/attributes.md

@@ -8,21 +8,21 @@
 
 It is possible to store extra attributes with each vertex (besides the xyz-coordinates).
 
-Those attributes are stored as a JSON object/dictionary, a key-value pair where the key is a string and the value is one of the following [NumPy data types](https://numpy.org/doc/stable/user/basics.types.html): numpy.bool_, numpy.int32, numpy.int64, numpy.uint32, numpy.uint64, unicode (string), numpy.float32, numpy.float64.
+Those attributes are stored as a JSON object/dictionary, a key-value pair where the key is a string and the value is one of the following [NumPy data types](https://numpy.org/doc/stable/user/basics.types.html): `numpy.bool_`,  `numpy.int64`, `numpy.uint64`, unicode (string), and `numpy.float64`.
 
 To attach extra attributes, you first need to define a *schema*, it a list of the attribute names and their data types.
 [NumPy data types](https://numpy.org/doc/stable/reference/arrays.dtypes.html#arrays-dtypes) must be used to create the schema.
 
 ```python
 dt = startinpy.DT()
-myschema = np.dtype([('classification', np.uint32), ('intensity', float)])
+myschema = np.dtype([('classification', np.uint64), ('intensity', float)])
 dt.set_attributes_schema(myschema)
 ```
 
 or
 
 ```python
-dt = startinpy.DT(np.dtype([('classification', np.uint32), ('intensity', float)]))
+dt = startinpy.DT(np.dtype([('classification', np.uint64), ('intensity', float)]))
 ```
 
 Adding attributes to a triangulation that has no schema defined will result in no extra attributes being stored; only those compliant with the schema are stored.

src/lib.rs

@@ -342,14 +342,13 @@ impl DT {
     /// schema are stored.
     ///
     /// Only the following data types for each attribute are allowed:
-    /// 'numpy.bool_', 'numpy.int32', 'numpy.int64', 'numpy.uint32', 'numpy.uint64', unicode (string),
-    /// 'numpy.float32', 'numpy.float64'.
+    /// 'numpy.bool_', 'numpy.int64', 'numpy.uint64', unicode (string), 'numpy.float64'.
     ///
     /// :param dtype: a `NumPy Data type object (dtype) <https://numpy.org/doc/stable/reference/arrays.dtypes.html#arrays-dtypes>`_
     /// :return: True if the schema is valid, otherwise an error is thrown.
     ///
     /// >>> dt = startinpy.DT()
-    /// >>> myschema = np.dtype([('classification', np.uint32), ('intensity', float)])
+    /// >>> myschema = np.dtype([('classification', np.uint64), ('intensity', float)])
     /// >>> dt.set_attributes_schema(myschema)
     /// >>> dt.insert_one_pt([85000.0, 444003.2, 2.2], classification=2, intensity=111.1)
     #[pyo3(signature = (dtype))]
@@ -389,7 +388,7 @@ impl DT {
                     self.dtype.push((name.to_string(), "<u4".to_string()));
                 }
                 "uint64" => {
-                    v.push((name.to_string(), "i64".to_string()));
+                    v.push((name.to_string(), "u64".to_string()));
                     self.dtype.push((name.to_string(), "<u8".to_string()));
                 }
                 other if other.starts_with("<U") => {
@@ -414,11 +413,11 @@ impl DT {
     ///
     /// :return: a NumPy Data type object (dtype)
     ///
-    /// >>> d = np.dtype([('classification', np.float32), ('name', '<U8')])
+    /// >>> d = np.dtype([('classification', np.float64), ('name', '<U8')])
     /// >>> dt.set_attributes_schema(d)
     /// True
     /// >>> dt.get_attributes_schema()
-    /// [('classification', '<f4'), ('name', '<U8')]
+    /// [('classification', '<f8'), ('name', '<U8')]
     fn get_attributes_schema(&self) -> Vec<(String, String)> {
         self.dtype.clone()
     }
@@ -432,7 +431,7 @@ impl DT {
     ///          vertex). The array is empty if the extra attributes don't exist.
     ///
     /// >>> dt = startinpy.DT()
-    /// >>> dt.add_attribute_map(np.dtype([("classification", np.uint32)]))
+    /// >>> dt.add_attribute_map(np.dtype([("classification", np.uint64)]))
     /// >>> dt.insert_one_pt([85000.0, 444003.2, 2.2], classification=6)
     /// >>> ...
     /// >>> dt.attributes[1:]