CHANGELOG.md

@@ -9,6 +9,38 @@
   tests imported `rustyhdf5`, so they failed at collection. Distribution,
   module and tests now all say `clawhdf5`, and the module has
   `__version__`.
+- **h5py-style reads that read only what is selected.** `ds[...]` used to
+  read the whole dataset and slice it in numpy, and knew six dtypes. Now
+  integers (negative from the end), slices with positive steps, `...`, one
+  increasing list of integers per key and compound field names map onto the
+  facade's hyperslab selection (a list becomes one hyperslab per run of
+  consecutive indices), with h5py's results (numpy scalar for an all-integer
+  key, 0-d array for `scalar[...]`) and h5py's errors for everything else
+  (negative steps, `None`, boolean masks, out-of-range indices).
+  `Dataset.dtype` is the numpy dtype h5py reports, for every integer and
+  IEEE float width (incl. `float16`) in either byte order, `bool`, enums
+  (base integer with `metadata['enum']`), complex (`r`/`i` compounds),
+  fixed strings (`S<n>`), variable-length strings (`object` of `bytes`, as
+  h5py), variable-length sequences (`object` of arrays), opaque (`V<n>`),
+  HDF5 array types and compounds (numpy structured, offsets and padding
+  kept, nested). The bytes the library returns become the numpy array's
+  buffer without a copy. Types the mapping cannot describe exactly
+  (references, bitfields, time, non-IEEE floats, integers with padding
+  bits, variable-length members inside compounds) raise `TypeError` rather
+  than return guessed data. Attributes come back as h5py returns them
+  (numpy scalars and arrays with the stored dtype, `str` for
+  variable-length strings, `numpy.bytes_` for fixed ones — **a change**:
+  string attributes written by this package are fixed-length and used to
+  come back as `str` — and `clawhdf5.Empty` for a null dataspace, which
+  datasets return too). `Group`/`File` gain `get`, `values`, `items`,
+  iteration, `len`, `name`, absolute and relative paths (`g['/a/b']`,
+  `g['c/d']`, `f['/']`); `Dataset` gains `ndim`, `size`, `maxshape`,
+  `name`, `len()` and `numpy.asarray(ds)`. File access and decoding run
+  with the GIL released, so Python threads read in parallel.
+  `crates/clawhdf5-py/tests/test_read_vs_h5py.py` compares every read with
+  h5py 3.16 (HDF5 2.0) on a file h5py writes. One difference is h5py's:
+  it returns variable-length sequences of big-endian floats unswapped; this
+  package returns the stored values.
 
 ### Plugin filters (2026-09-26)
 - **LZF, bitshuffle, bzip2 and Blosc read and write, in pure Rust.** Files

crates/clawhdf5-py/src/attrs.rs

@@ -1,24 +1,31 @@
 //! PyAttrs — dict-like access to HDF5 attributes.
 
-use std::collections::HashMap;
 use std::sync::{Arc, Mutex};
 
+use clawhdf5_format::attribute::AttributeMessage;
+use pyo3::exceptions::{PyKeyError, PyTypeError, PyValueError};
 use pyo3::prelude::*;
-use pyo3::types::PyList;
+use pyo3::types::{PyList, PyTuple};
 
-use crate::{OwnedAttrValue, attr_value_to_py, py_to_attr_value};
+use crate::convert::{Converter, Elements, resolve_vl};
+use crate::{OwnedAttrValue, PyEmpty, attr_value_to_py, node, py_to_attr_value};
 
 /// Backing storage for attributes.
 enum AttrsInner {
-    /// Read-only attributes from an existing HDF5 object.
-    Read(HashMap<String, clawhdf5_rs::AttrValue>),
+    /// Attributes of an object in a file opened for reading, sorted by name.
+    Read {
+        file: Arc<clawhdf5_rs::File>,
+        attrs: Vec<AttributeMessage>,
+    },
     /// Writable attribute list shared with a parent (PyFile or PyGroup).
     Write(Arc<Mutex<Vec<(String, OwnedAttrValue)>>>),
 }
 
 /// Dict-like access to HDF5 attributes.
 ///
-/// In read mode, provides immutable access to attribute key/value pairs.
+/// In read mode, values are what h5py returns: numpy scalars for scalar
+/// attributes, numpy arrays otherwise, `str` for variable-length strings,
+/// `numpy.bytes_` for fixed-length ones, and `Empty` for a null dataspace.
 /// In write mode, attributes set here are accumulated and written when
 /// the parent file is closed.
 #[pyclass(name = "Attrs")]
@@ -27,11 +34,12 @@ pub struct PyAttrs {
 }
 
 impl PyAttrs {
-    /// Create a read-only attrs from an existing attribute map.
-    pub(crate) fn from_read(map: HashMap<String, clawhdf5_rs::AttrValue>) -> Self {
-        Self {
-            inner: AttrsInner::Read(map),
-        }
+    /// The attributes of the object at `path` in a file opened for reading.
+    pub(crate) fn read(file: Arc<clawhdf5_rs::File>, path: &str) -> PyResult<Self> {
+        let attrs = node::attributes(&file, path)?;
+        Ok(Self {
+            inner: AttrsInner::Read { file, attrs },
+        })
     }
 
     /// Create a writable attrs that shares storage with a parent object.
@@ -46,11 +54,11 @@ impl PyAttrs {
 impl PyAttrs {
     fn __getitem__(&self, py: Python<'_>, key: &str) -> PyResult<Py<PyAny>> {
         match &self.inner {
-            AttrsInner::Read(map) => match map.get(key) {
-                Some(val) => Ok(attr_value_to_py(py, val)),
-                None => Err(PyErr::new::<pyo3::exceptions::PyKeyError, _>(
-                    key.to_string(),
-                )),
+            AttrsInner::Read { file, attrs } => match attrs.iter().find(|a| a.name == key) {
+                Some(attr) => Ok(attr_to_py(py, file, attr)?.unbind()),
+                None => Err(PyKeyError::new_err(format!(
+                    "Can't open attribute (can't locate attribute: '{key}')"
+                ))),
             },
             AttrsInner::Write(store) => {
                 let guard = store.lock().unwrap();
@@ -60,16 +68,14 @@ impl PyAttrs {
                         return Ok(attr_value_to_py(py, &attr_val));
                     }
                 }
-                Err(PyErr::new::<pyo3::exceptions::PyKeyError, _>(
-                    key.to_string(),
-                ))
+                Err(PyKeyError::new_err(key.to_string()))
             }
         }
     }
 
     fn __setitem__(&self, key: &str, value: &Bound<'_, PyAny>) -> PyResult<()> {
         match &self.inner {
-            AttrsInner::Read(_) => Err(PyErr::new::<pyo3::exceptions::PyIOError, _>(
+            AttrsInner::Read { .. } => Err(PyErr::new::<pyo3::exceptions::PyIOError, _>(
                 "cannot set attributes on a read-only file",
             )),
             AttrsInner::Write(store) => {
@@ -88,14 +94,14 @@ impl PyAttrs {
 
     fn __len__(&self) -> usize {
         match &self.inner {
-            AttrsInner::Read(map) => map.len(),
+            AttrsInner::Read { attrs, .. } => attrs.len(),
             AttrsInner::Write(store) => store.lock().unwrap().len(),
         }
     }
 
     fn __contains__(&self, key: &str) -> bool {
         match &self.inner {
-            AttrsInner::Read(map) => map.contains_key(key),
+            AttrsInner::Read { attrs, .. } => attrs.iter().any(|a| a.name == key),
             AttrsInner::Write(store) => store.lock().unwrap().iter().any(|(k, _)| k == key),
         }
     }
@@ -111,10 +117,20 @@ impl PyAttrs {
         format!("<HDF5 Attrs ({n} members)>")
     }
 
+    /// The value of `key`, or `default` if there is no such attribute.
+    #[pyo3(signature = (key, default=None))]
+    fn get(&self, py: Python<'_>, key: &str, default: Option<Py<PyAny>>) -> PyResult<Py<PyAny>> {
+        if self.__contains__(key) {
+            self.__getitem__(py, key)
+        } else {
+            Ok(default.unwrap_or_else(|| py.None()))
+        }
+    }
+
     /// Return attribute names as a list.
     fn keys(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
         let names: Vec<String> = match &self.inner {
-            AttrsInner::Read(map) => map.keys().cloned().collect(),
+            AttrsInner::Read { attrs, .. } => attrs.iter().map(|a| a.name.clone()).collect(),
             AttrsInner::Write(store) => store
                 .lock()
                 .unwrap()
@@ -129,7 +145,10 @@ impl PyAttrs {
     /// Return attribute values as a list.
     fn values(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
         let vals: Vec<Py<PyAny>> = match &self.inner {
-            AttrsInner::Read(map) => map.values().map(|v| attr_value_to_py(py, v)).collect(),
+            AttrsInner::Read { file, attrs } => attrs
+                .iter()
+                .map(|a| attr_to_py(py, file, a).map(Bound::unbind))
+                .collect::<PyResult<_>>()?,
             AttrsInner::Write(store) => store
                 .lock()
                 .unwrap()
@@ -147,10 +166,10 @@ impl PyAttrs {
     /// Return attribute (key, value) pairs as a list of tuples.
     fn items(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
         let pairs: Vec<(String, Py<PyAny>)> = match &self.inner {
-            AttrsInner::Read(map) => map
+            AttrsInner::Read { file, attrs } => attrs
                 .iter()
-                .map(|(k, v)| (k.clone(), attr_value_to_py(py, v)))
-                .collect(),
+                .map(|a| Ok((a.name.clone(), attr_to_py(py, file, a)?.unbind())))
+                .collect::<PyResult<_>>()?,
             AttrsInner::Write(store) => store
                 .lock()
                 .unwrap()
@@ -166,28 +185,67 @@ impl PyAttrs {
     }
 }
 
+/// An attribute's value as h5py returns it.
+fn attr_to_py<'py>(
+    py: Python<'py>,
+    file: &clawhdf5_rs::File,
+    attr: &AttributeMessage,
+) -> PyResult<Bound<'py, PyAny>> {
+    let sb = file.superblock();
+    let conv = Converter::new(py, &attr.datatype, sb.offset_size)
+        .map_err(|e| prefix_err(py, &attr.name, e))?;
+    if node::is_null(&attr.dataspace) {
+        return Ok(PyEmpty::new(conv.dtype).into_pyobject(py)?.into_any());
+    }
+    let shape: Vec<usize> = attr
+        .dataspace
+        .dimensions
+        .iter()
+        .map(|&d| d as usize)
+        .collect();
+    let n: usize = shape.iter().product();
+    let data = if conv.is_vl() {
+        let want = n * conv.elem_size;
+        if attr.raw_data.len() < want {
+            return Err(PyValueError::new_err(format!(
+                "attribute {}: {} bytes of variable-length references, expected {want}",
+                attr.name,
+                attr.raw_data.len(),
+            )));
+        }
+        let raw = &attr.raw_data[..want];
+        let file_data = file.as_bytes();
+        let (osz, lsz, unit) = (sb.offset_size, sb.length_size, conv.vl_unit);
+        Elements::Vl(
+            py.detach(|| resolve_vl(file_data, raw, n, osz, lsz, unit))
+                .map_err(|e| PyValueError::new_err(format!("attribute {}: {e}", attr.name)))?,
+        )
+    } else {
+        Elements::Bytes(attr.raw_data.clone())
+    };
+    let arr = conv
+        .to_array(py, data, &shape, true)
+        .map_err(|e| prefix_err(py, &attr.name, e))?;
+    if shape.is_empty() {
+        // A scalar dataspace: h5py returns the element itself.
+        return arr.get_item(PyTuple::empty(py));
+    }
+    Ok(arr)
+}
+
+fn prefix_err(py: Python<'_>, name: &str, e: PyErr) -> PyErr {
+    let msg = format!("attribute {name}: {}", e.value(py));
+    if e.is_instance_of::<PyTypeError>(py) {
+        PyTypeError::new_err(msg)
+    } else {
+        PyValueError::new_err(msg)
+    }
+}
+
 #[cfg(test)]
 mod tests {
     use super::*;
 
-    #[test]
-    fn read_attrs_len() {
-        let mut map = HashMap::new();
-        map.insert("a".into(), clawhdf5_rs::AttrValue::I64(1));
-        map.insert("b".into(), clawhdf5_rs::AttrValue::F64(2.0));
-        let attrs = PyAttrs::from_read(map);
-        assert_eq!(attrs.__len__(), 2);
-    }
-
-    #[test]
-    fn read_attrs_contains() {
-        let mut map = HashMap::new();
-        map.insert("x".into(), clawhdf5_rs::AttrValue::String("hello".into()));
-        let attrs = PyAttrs::from_read(map);
-        assert!(attrs.__contains__("x"));
-        assert!(!attrs.__contains__("y"));
-    }
-
     #[test]
     fn write_attrs_len() {
         let store = Arc::new(Mutex::new(Vec::new()));

crates/clawhdf5-py/src/convert.rs

@@ -0,0 +1,600 @@
+//! HDF5 datatypes as numpy dtypes, and element bytes as numpy arrays.
+//!
+//! The dtype a file's datatype maps to is the one h5py reports for it
+//! (byte order kept, compound offsets and padding kept, `r`/`i` compounds as
+//! complex, the `FALSE`/`TRUE` enum as `bool`, fixed strings as `S<n>`,
+//! variable-length data as `object`). For every fixed-size type that dtype
+//! describes the file's element bytes exactly, so the bytes the library
+//! returns become the array's buffer as they are: the `Vec<u8>` is handed to
+//! numpy without a copy and viewed as the dtype.
+//!
+//! Anything this mapping cannot describe exactly — non-IEEE floats, integers
+//! with padding bits, VAX byte order, references, bitfields, time, and
+//! variable-length members inside compounds or arrays — is a `TypeError`,
+//! never a best-effort guess.
+
+use std::collections::HashMap;
+
+use clawhdf5_format::datatype::{CharacterSet, Datatype, DatatypeByteOrder};
+use clawhdf5_format::global_heap::GlobalHeapCollection;
+use numpy::PyArray1;
+use pyo3::exceptions::{PyTypeError, PyValueError};
+use pyo3::prelude::*;
+use pyo3::types::{PyBytes, PyDict, PyList, PyString, PyTuple};
+
+/// How the elements of a datatype become Python values.
+#[derive(Clone, Debug, PartialEq)]
+pub(crate) enum Layout {
+    /// Fixed-size elements numpy reads as they are.
+    Fixed,
+    /// A top-level HDF5 array type: elements are viewed as the base dtype and
+    /// the array's dimensions are appended to the shape (as h5py does).
+    Subarray(Vec<usize>),
+    /// Variable-length string: a global heap reference per element.
+    VlString { utf8: bool },
+    /// Variable-length sequence of a fixed-size base type.
+    VlSequence,
+}
+
+/// Everything needed to turn a dataset's or attribute's bytes into numpy.
+pub(crate) struct Converter {
+    /// The dtype reported to Python (`Dataset.dtype`).
+    pub dtype: Py<PyAny>,
+    /// The dtype the element bytes are viewed as: `dtype` itself, the base
+    /// of a subarray, or the base of a variable-length sequence.
+    pub view: Py<PyAny>,
+    pub layout: Layout,
+    /// Bytes per element in the raw buffer the library returns.
+    pub elem_size: usize,
+    /// For variable-length data, bytes per unit of an element's stored
+    /// length: 1 for strings, the base type's size for sequences.
+    pub vl_unit: usize,
+}
+
+fn unsupported(what: impl std::fmt::Display) -> PyErr {
+    PyTypeError::new_err(format!(
+        "clawhdf5 cannot read this datatype into numpy: {what}"
+    ))
+}
+
+fn byte_order_char(order: &DatatypeByteOrder, size: u32) -> PyResult<&'static str> {
+    if size == 1 {
+        return Ok("|");
+    }
+    match order {
+        DatatypeByteOrder::LittleEndian => Ok("<"),
+        DatatypeByteOrder::BigEndian => Ok(">"),
+        DatatypeByteOrder::Vax => Err(unsupported("VAX byte order")),
+    }
+}
+
+/// The numpy format string of an integer type, if it is a plain one.
+fn int_format(dt: &Datatype) -> PyResult<String> {
+    match dt {
+        Datatype::FixedPoint {
+            size,
+            byte_order,
+            signed,
+            bit_offset,
+            bit_precision,
+        } => {
+            if !matches!(size, 1 | 2 | 4 | 8) {
+                return Err(unsupported(format!("{size}-byte integer")));
+            }
+            if *bit_offset != 0 || u32::from(*bit_precision) != size * 8 {
+                return Err(unsupported(format!(
+                    "integer with {bit_precision} significant bits at offset {bit_offset} in {size} bytes"
+                )));
+            }
+            let kind = if *signed { 'i' } else { 'u' };
+            Ok(format!(
+                "{}{kind}{size}",
+                byte_order_char(byte_order, *size)?
+            ))
+        }
+        other => Err(unsupported(format!("{other:?} is not an integer"))),
+    }
+}
+
+/// The numpy format string of an IEEE 754 binary16/32/64 type.
+fn float_format(dt: &Datatype) -> PyResult<String> {
+    let Datatype::FloatingPoint {
+        size,
+        byte_order,
+        bit_offset,
+        bit_precision,
+        exponent_location,
+        exponent_size,
+        mantissa_location,
+        mantissa_size,
+        exponent_bias,
+    } = dt
+    else {
+        return Err(unsupported(format!("{dt:?} is not a float")));
+    };
+    // (exponent location, exponent size, mantissa size, bias) of IEEE 754.
+    let ieee = match size {
+        2 => (10, 5, 10, 15),
+        4 => (23, 8, 23, 127),
+        8 => (52, 11, 52, 1023),
+        _ => return Err(unsupported(format!("{size}-byte float"))),
+    };
+    let layout = (
+        *exponent_location,
+        *exponent_size,
+        *mantissa_size,
+        *exponent_bias,
+    );
+    if *bit_offset != 0
+        || u32::from(*bit_precision) != size * 8
+        || *mantissa_location != 0
+        || layout != ieee
+    {
+        return Err(unsupported(format!(
+            "non-IEEE {size}-byte float (exponent {exponent_size} bits at {exponent_location}, \
+             mantissa {mantissa_size} bits at {mantissa_location}, bias {exponent_bias})"
+        )));
+    }
+    Ok(format!("{}f{size}", byte_order_char(byte_order, *size)?))
+}
+
+/// `r`/`i` compounds of two identical IEEE floats are complex numbers in h5py.
+fn complex_format(
+    size: u32,
+    members: &[clawhdf5_format::datatype::CompoundMember],
+) -> Option<String> {
+    let [re, im] = members else { return None };
+    if re.name != "r" || im.name != "i" || re.datatype != im.datatype {
+        return None;
+    }
+    let Datatype::FloatingPoint {
+        size: fsize,
+        byte_order,
+        ..
+    } = &re.datatype
+    else {
+        return None;
+    };
+    if !matches!(fsize, 4 | 8)
+        || re.byte_offset != 0
+        || im.byte_offset != u64::from(*fsize)
+        || size != 2 * fsize
+    {
+        return None;
+    }
+    float_format(&re.datatype).ok()?;
+    let order = byte_order_char(byte_order, *fsize).ok()?;
+    Some(format!("{order}c{}", 2 * fsize))
+}
+
+/// The members of an enum as `{name: value}`.
+fn enum_members<'py>(
+    py: Python<'py>,
+    base: &Datatype,
+    members: &[clawhdf5_format::datatype::EnumMember],
+) -> PyResult<Bound<'py, PyDict>> {
+    let signed = matches!(base, Datatype::FixedPoint { signed: true, .. });
+    let dict = PyDict::new(py);
+    for m in members {
+        let value: Py<PyAny> = if signed {
+            let v = clawhdf5_format::data_read::read_as_i64(&m.value, base)
+                .map_err(|e| PyValueError::new_err(format!("enum member {}: {e}", m.name)))?;
+            let v = *v.first().ok_or_else(|| {
+                PyValueError::new_err(format!("enum member {} has no value", m.name))
+            })?;
+            v.into_pyobject(py)?.into_any().unbind()
+        } else {
+            let v = clawhdf5_format::data_read::read_as_u64(&m.value, base)
+                .map_err(|e| PyValueError::new_err(format!("enum member {}: {e}", m.name)))?;
+            let v = *v.first().ok_or_else(|| {
+                PyValueError::new_err(format!("enum member {} has no value", m.name))
+            })?;
+            v.into_pyobject(py)?.into_any().unbind()
+        };
+        dict.set_item(&m.name, value)?;
+    }
+    Ok(dict)
+}
+
+/// Whether an enum is h5py's boolean: a one-byte integer with exactly the
+/// members `FALSE` = 0 and `TRUE` = 1.
+fn is_h5py_bool(base: &Datatype, members: &[clawhdf5_format::datatype::EnumMember]) -> bool {
+    if base.type_size() != 1 || members.len() != 2 {
+        return false;
+    }
+    let value = |name: &str| {
+        members
+            .iter()
+            .find(|m| m.name == name)
+            .and_then(|m| m.value.first().copied())
+    };
+    value("FALSE") == Some(0) && value("TRUE") == Some(1)
+}
+
+fn np_dtype<'py>(py: Python<'py>, spec: impl IntoPyObject<'py>) -> PyResult<Bound<'py, PyAny>> {
+    py.import("numpy")?.getattr("dtype")?.call1((spec,))
+}
+
+fn np_dtype_with_metadata<'py>(
+    py: Python<'py>,
+    spec: impl IntoPyObject<'py>,
+    metadata: Bound<'py, PyDict>,
+) -> PyResult<Bound<'py, PyAny>> {
+    let kwargs = PyDict::new(py);
+    kwargs.set_item("metadata", metadata)?;
+    py.import("numpy")?
+        .getattr("dtype")?
+        .call((spec,), Some(&kwargs))
+}
+
+/// The numpy dtype of a fixed-size datatype, whose element bytes numpy can
+/// read as they are.
+pub(crate) fn fixed_dtype<'py>(py: Python<'py>, dt: &Datatype) -> PyResult<Bound<'py, PyAny>> {
+    match dt {
+        Datatype::FixedPoint { .. } => np_dtype(py, int_format(dt)?),
+        Datatype::FloatingPoint { .. } => np_dtype(py, float_format(dt)?),
+        Datatype::String { size, charset, .. } => {
+            if *size == 0 {
+                return Err(unsupported("zero-length fixed string"));
+            }
+            let meta = PyDict::new(py);
+            let enc = match charset {
+                CharacterSet::Ascii => "ascii",
+                CharacterSet::Utf8 => "utf-8",
+            };
+            meta.set_item("h5py_encoding", enc)?;
+            np_dtype_with_metadata(py, format!("S{size}"), meta)
+        }
+        Datatype::Opaque { size, .. } => {
+            if *size == 0 {
+                return Err(unsupported("zero-length opaque type"));
+            }
+            np_dtype(py, format!("V{size}"))
+        }
+        Datatype::Enumeration {
+            base_type, members, ..
+        } => {
+            let base = int_format(base_type)?;
+            if is_h5py_bool(base_type, members) {
+                return np_dtype(py, "?");
+            }
+            let meta = PyDict::new(py);
+            meta.set_item("enum", enum_members(py, base_type, members)?)?;
+            np_dtype_with_metadata(py, base, meta)
+        }
+        Datatype::Compound { size, members } => {
+            if let Some(c) = complex_format(*size, members) {
+                return np_dtype(py, c);
+            }
+            let names = PyList::empty(py);
+            let formats = PyList::empty(py);
+            let offsets = PyList::empty(py);
+            for m in members {
+                let end = m.byte_offset.checked_add(u64::from(m.datatype.type_size()));
+                if end.is_none_or(|end| end > u64::from(*size)) {
+                    return Err(PyValueError::new_err(format!(
+                        "compound member {} lies outside the {size}-byte compound",
+                        m.name
+                    )));
+                }
+                names.append(&m.name)?;
+                formats.append(fixed_dtype(py, &m.datatype).map_err(|e| {
+                    unsupported(format!("compound member {}: {}", m.name, e.value(py)))
+                })?)?;
+                offsets.append(m.byte_offset)?;
+            }
+            let spec = PyDict::new(py);
+            spec.set_item("names", names)?;
+            spec.set_item("formats", formats)?;
+            spec.set_item("offsets", offsets)?;
+            spec.set_item("itemsize", size)?;
+            np_dtype(py, spec)
+        }
+        Datatype::Array {
+            base_type,
+            dimensions,
+        } => {
+            let base = fixed_dtype(py, base_type)?;
+            let dims = PyTuple::new(py, dimensions)?;
+            np_dtype(py, (base, dims))
+        }
+        Datatype::VariableLength { is_string, .. } => Err(unsupported(if *is_string {
+            "variable-length string inside a compound or array type"
+        } else {
+            "variable-length sequence inside a compound or array type"
+        })),
+        Datatype::Reference { .. } => Err(unsupported("object/region references")),
+        Datatype::BitField { .. } => Err(unsupported("bitfield")),
+        Datatype::Time { .. } => Err(unsupported("time")),
+    }
+}
+
+impl Converter {
+    /// The converter for a dataset's or attribute's datatype.
+    pub(crate) fn new(py: Python<'_>, dt: &Datatype, offset_size: u8) -> PyResult<Self> {
+        match dt {
+            Datatype::VariableLength {
+                is_string: true,
+                charset,
+                ..
+            } => {
+                let utf8 = matches!(charset, Some(CharacterSet::Utf8));
+                let meta = PyDict::new(py);
+                if utf8 {
+                    meta.set_item("vlen", py.get_type::<PyString>())?;
+                } else {
+                    meta.set_item("vlen", py.get_type::<PyBytes>())?;
+                }
+                let dtype = np_dtype_with_metadata(py, "O", meta)?;
+                Ok(Self {
+                    view: dtype.clone().unbind(),
+                    dtype: dtype.unbind(),
+                    layout: Layout::VlString { utf8 },
+                    elem_size: vl_ref_size(offset_size)?,
+                    vl_unit: 1,
+                })
+            }
+            Datatype::VariableLength {
+                is_string: false,
+                base_type,
+                ..
+            } => {
+                let base = fixed_dtype(py, base_type)?;
+                let meta = PyDict::new(py);
+                meta.set_item("vlen", &base)?;
+                let dtype = np_dtype_with_metadata(py, "O", meta)?;
+                Ok(Self {
+                    dtype: dtype.unbind(),
+                    view: base.unbind(),
+                    layout: Layout::VlSequence,
+                    elem_size: vl_ref_size(offset_size)?,
+                    vl_unit: base_type.type_size() as usize,
+                })
+            }
+            Datatype::Array {
+                base_type,
+                dimensions,
+            } => {
+                let dtype = fixed_dtype(py, dt)?;
+                let base = fixed_dtype(py, base_type)?;
+                Ok(Self {
+                    dtype: dtype.unbind(),
+                    view: base.unbind(),
+                    layout: Layout::Subarray(dimensions.iter().map(|&d| d as usize).collect()),
+                    elem_size: dt.type_size() as usize,
+                    vl_unit: 0,
+                })
+            }
+            _ => {
+                let dtype = fixed_dtype(py, dt)?;
+                Ok(Self {
+                    view: dtype.clone().unbind(),
+                    dtype: dtype.unbind(),
+                    layout: Layout::Fixed,
+                    elem_size: dt.type_size() as usize,
+                    vl_unit: 0,
+                })
+            }
+        }
+    }
+
+    pub(crate) fn is_vl(&self) -> bool {
+        matches!(self.layout, Layout::VlString { .. } | Layout::VlSequence)
+    }
+
+    /// An empty array of `shape` (some dimension is zero).
+    pub(crate) fn empty<'py>(
+        &self,
+        py: Python<'py>,
+        shape: &[usize],
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let np = py.import("numpy")?;
+        match &self.layout {
+            Layout::Subarray(dims) => {
+                let mut full = shape.to_vec();
+                full.extend_from_slice(dims);
+                np.call_method1("empty", (PyTuple::new(py, full)?, self.view.bind(py)))
+            }
+            _ => np.call_method1("empty", (PyTuple::new(py, shape)?, self.dtype.bind(py))),
+        }
+    }
+
+    /// Turn decoded element data into a numpy array of `shape`.
+    ///
+    /// `str_values` decodes variable-length strings to `str` (what h5py
+    /// does for attributes) instead of `bytes` (what it does for datasets).
+    pub(crate) fn to_array<'py>(
+        &self,
+        py: Python<'py>,
+        data: Elements,
+        shape: &[usize],
+        str_values: bool,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let n: usize = shape.iter().product();
+        match (data, &self.layout) {
+            (Elements::Bytes(bytes), Layout::Fixed) => {
+                bytes_as_array(py, bytes, self.view.bind(py), shape)
+            }
+            (Elements::Bytes(bytes), Layout::Subarray(dims)) => {
+                let mut full = shape.to_vec();
+                full.extend_from_slice(dims);
+                bytes_as_array(py, bytes, self.view.bind(py), &full)
+            }
+            (Elements::Vl(items), Layout::VlString { .. }) => {
+                check_count(items.len(), n)?;
+                let mut objs: Vec<Py<PyAny>> = Vec::with_capacity(items.len());
+                for item in items {
+                    let obj = if str_values {
+                        PyBytes::new(py, &item)
+                            .call_method1("decode", ("utf-8", "surrogateescape"))?
+                            .unbind()
+                    } else {
+                        PyBytes::new(py, &item).into_any().unbind()
+                    };
+                    objs.push(obj);
+                }
+                object_array(py, objs, shape)
+            }
+            (Elements::Vl(items), Layout::VlSequence) => {
+                check_count(items.len(), n)?;
+                let base = self.view.bind(py);
+                let itemsize: usize = base.getattr("itemsize")?.extract()?;
+                let mut objs: Vec<Py<PyAny>> = Vec::with_capacity(items.len());
+                for item in items {
+                    if item.len() % itemsize != 0 {
+                        return Err(PyValueError::new_err(format!(
+                            "variable-length element of {} bytes is not a whole number of {itemsize}-byte values",
+                            item.len()
+                        )));
+                    }
+                    let len = item.len() / itemsize;
+                    objs.push(bytes_as_array(py, item, base, &[len])?.unbind());
+                }
+                object_array(py, objs, shape)
+            }
+            _ => Err(PyValueError::new_err(
+                "internal error: element data does not match the datatype",
+            )),
+        }
+    }
+}
+
+/// Element data as read, before it becomes numpy.
+pub(crate) enum Elements {
+    /// The elements' bytes, back to back.
+    Bytes(Vec<u8>),
+    /// Each variable-length element's bytes, resolved from the global heap.
+    Vl(Vec<Vec<u8>>),
+}
+
+fn vl_ref_size(offset_size: u8) -> PyResult<usize> {
+    // The library sizes a variable-length element as 16 bytes (a length, an
+    // 8-byte heap address and an index) whatever the file's offset size.
+    // Refuse the other sizes rather than read misaligned references.
+    if offset_size != 8 {
+        return Err(unsupported(format!(
+            "variable-length data in a file with {offset_size}-byte offsets"
+        )));
+    }
+    Ok(4 + usize::from(offset_size) + 4)
+}
+
+fn check_count(got: usize, want: usize) -> PyResult<()> {
+    if got != want {
+        return Err(PyValueError::new_err(format!(
+            "read {got} elements, expected {want}"
+        )));
+    }
+    Ok(())
+}
+
+/// A numpy array over `bytes` without copying them: the `Vec` becomes the
+/// array's buffer and is viewed as `dtype` with `shape`.
+pub(crate) fn bytes_as_array<'py>(
+    py: Python<'py>,
+    bytes: Vec<u8>,
+    dtype: &Bound<'py, PyAny>,
+    shape: &[usize],
+) -> PyResult<Bound<'py, PyAny>> {
+    let itemsize: usize = dtype.getattr("itemsize")?.extract()?;
+    let n: usize = shape.iter().product();
+    if n.checked_mul(itemsize) != Some(bytes.len()) {
+        return Err(PyValueError::new_err(format!(
+            "read {} bytes, expected {n} elements of {itemsize} bytes",
+            bytes.len()
+        )));
+    }
+    let shape = PyTuple::new(py, shape)?;
+    if n == 0 {
+        return py.import("numpy")?.call_method1("empty", (shape, dtype));
+    }
+    let raw = PyArray1::from_vec(py, bytes);
+    let arr = raw
+        .call_method1("view", (dtype,))?
+        .call_method1("reshape", (shape,))?;
+    // A `Vec<u8>` carries no alignment promise. numpy copes with unaligned
+    // arrays, but slowly and not in every routine, so hand out an aligned
+    // copy in the (allocator-dependent, rare) case the buffer is not.
+    if !arr
+        .getattr("flags")?
+        .getattr("aligned")?
+        .extract::<bool>()?
+    {
+        return arr.call_method0("copy");
+    }
+    Ok(arr)
+}
+
+fn object_array<'py>(
+    py: Python<'py>,
+    objs: Vec<Py<PyAny>>,
+    shape: &[usize],
+) -> PyResult<Bound<'py, PyAny>> {
+    let arr = PyArray1::from_vec(py, objs);
+    arr.call_method1("reshape", (PyTuple::new(py, shape)?,))
+}
+
+/// Resolve variable-length elements (global heap references in `raw`) to
+/// their bytes: each element's stored length times `unit` (1 for strings,
+/// the base type's size for sequences). Pure Rust, so it runs without the
+/// GIL.
+pub(crate) fn resolve_vl(
+    file_data: &[u8],
+    raw: &[u8],
+    count: usize,
+    offset_size: u8,
+    length_size: u8,
+    unit: usize,
+) -> Result<Vec<Vec<u8>>, String> {
+    let refs = clawhdf5_format::vl_data::parse_vl_references(raw, count as u64, offset_size)
+        .map_err(|e| e.to_string())?;
+    let undefined = match offset_size {
+        2 => 0xFFFF,
+        4 => 0xFFFF_FFFF,
+        _ => u64::MAX,
+    };
+    let mut collections: HashMap<u64, GlobalHeapCollection> = HashMap::new();
+    let mut out = Vec::with_capacity(refs.len());
+    for vl in &refs {
+        if vl.collection_address == 0 || vl.collection_address == undefined {
+            if vl.length != 0 {
+                return Err(format!(
+                    "variable-length element of length {} has no heap address",
+                    vl.length
+                ));
+            }
+            out.push(Vec::new());
+            continue;
+        }
+        let coll = match collections.entry(vl.collection_address) {
+            std::collections::hash_map::Entry::Occupied(e) => e.into_mut(),
+            std::collections::hash_map::Entry::Vacant(e) => {
+                let addr = usize::try_from(vl.collection_address)
+                    .map_err(|_| "global heap address out of range".to_string())?;
+                e.insert(
+                    GlobalHeapCollection::parse(file_data, addr, length_size)
+                        .map_err(|e| e.to_string())?,
+                )
+            }
+        };
+        let index = u16::try_from(vl.object_index)
+            .map_err(|_| format!("global heap object index {} out of range", vl.object_index))?;
+        let obj = coll.get_object(index).ok_or_else(|| {
+            format!(
+                "global heap object {index} not found in the collection at {}",
+                vl.collection_address
+            )
+        })?;
+        let need = (vl.length as usize)
+            .checked_mul(unit)
+            .ok_or("variable-length element too long")?;
+        if need > obj.data.len() {
+            return Err(format!(
+                "variable-length element of {need} bytes in a {}-byte heap object",
+                obj.data.len()
+            ));
+        }
+        out.push(obj.data[..need].to_vec());
+    }
+    Ok(out)
+}

crates/clawhdf5-py/src/dataset.rs

@@ -1,241 +1,341 @@
-//! PyDataset — read access to HDF5 datasets with numpy integration.
+//! PyDataset — h5py-style read access to HDF5 datasets.
+//!
+//! `ds[key]` parses the key into hyperslab selections (see `select`) and
+//! reads only those elements through the facade's `read_selection`; the
+//! bytes it returns become the numpy array's buffer without a copy (see
+//! `convert`). All file access and decoding runs with the GIL released, so
+//! Python threads reading the same or different datasets run in parallel.
 
 use std::sync::Arc;
 
-use numpy::PyArrayDyn;
-use numpy::ndarray::{ArrayD, IxDyn};
+use clawhdf5_format::datatype::Datatype;
+use pyo3::exceptions::{PyTypeError, PyValueError};
 use pyo3::prelude::*;
-use pyo3::types::PyList;
-
-use clawhdf5_rs::DType;
+use pyo3::types::{PyList, PyTuple};
 
 use crate::attrs::PyAttrs;
-use crate::to_py_err;
+use crate::convert::{Converter, Elements, resolve_vl};
+use crate::select::{self, Plan};
+use crate::{PyEmpty, node, to_py_err};
 
-/// A handle to an HDF5 dataset (read mode).
+/// A dataset in a file opened for reading.
 ///
-/// Supports numpy-style indexing via `__getitem__`:
 /// ```python
-/// ds = f['dataset_name']
-/// data = ds[:]           # read all data as numpy array
-/// shape = ds.shape
-/// dtype = ds.dtype
+/// ds = f['group/dataset']
+/// ds.shape, ds.dtype, ds.attrs['units']
+/// block = ds[10:20, ::2]      # reads only the selected elements
 /// ```
 #[pyclass(name = "Dataset")]
 pub struct PyDataset {
     file: Arc<clawhdf5_rs::File>,
     path: String,
-    cached_shape: Vec<u64>,
-    cached_dtype: DType,
+    /// `None` for a dataset with a null dataspace (h5py's `Empty`).
+    shape: Option<Vec<u64>>,
+    datatype: Datatype,
+    /// Why the datatype cannot be read into numpy, if it cannot.
+    conv: Result<Converter, String>,
 }
 
 impl PyDataset {
-    pub fn new(file: Arc<clawhdf5_rs::File>, path: String) -> PyResult<Self> {
+    pub(crate) fn open(
+        py: Python<'_>,
+        file: Arc<clawhdf5_rs::File>,
+        path: String,
+    ) -> PyResult<Self> {
+        let hdr = node::header(&file, &path)?;
+        let null = node::is_null(&node::dataspace(&file, &hdr)?);
+        let (shape, datatype) = {
             let ds = file.dataset(&path).map_err(to_py_err)?;
-        let cached_shape = ds.shape().map_err(to_py_err)?;
-        let cached_dtype = ds.dtype().map_err(to_py_err)?;
+            let shape = if null {
+                None
+            } else {
+                Some(ds.shape().map_err(to_py_err)?)
+            };
+            (shape, ds.raw_datatype().map_err(to_py_err)?)
+        };
+        let conv = Converter::new(py, &datatype, file.superblock().offset_size)
+            .map_err(|e| e.value(py).to_string());
         Ok(Self {
             file,
             path,
-            cached_shape,
-            cached_dtype,
+            shape,
+            datatype,
+            conv,
         })
     }
+
+    fn converter(&self) -> PyResult<&Converter> {
+        self.conv
+            .as_ref()
+            .map_err(|msg| PyTypeError::new_err(format!("{}: {msg}", node::name(&self.path))))
+    }
+
+    /// Read the selection described by `plan` into a numpy array.
+    fn read_plan<'py>(&self, py: Python<'py>, plan: &Plan) -> PyResult<Bound<'py, PyAny>> {
+        let conv = self.converter()?;
+        let dims = self.shape.as_deref().unwrap_or(&[]);
+        let out_shape = plan.out_shape();
+
+        let arr = if plan.is_empty() {
+            conv.empty(py, &out_shape)?
+        } else {
+            let (reads, list_axis) = plan.reads(dims);
+            let file = &*self.file;
+            let path = self.path.as_str();
+            let (vl, elem_size, unit) = (conv.is_vl(), conv.elem_size, conv.vl_unit);
+            // Everything below touches only Rust data: release the GIL.
+            let blocks: Vec<(Elements, Vec<usize>)> = py
+                .detach(|| -> Result<_, ReadError> {
+                    let ds = file.dataset(path)?;
+                    let sb = file.superblock();
+                    let mut blocks = Vec::with_capacity(reads.len());
+                    for (sel, shape) in reads {
+                        let raw = ds.read_selection(&sel)?;
+                        let n: usize = shape.iter().product();
+                        let data = if vl {
+                            if raw.len() != n * elem_size {
+                                return Err(ReadError::Other(format!(
+                                    "read {} bytes of variable-length references, expected {}",
+                                    raw.len(),
+                                    n * elem_size
+                                )));
+                            }
+                            Elements::Vl(
+                                resolve_vl(
+                                    file.as_bytes(),
+                                    &raw,
+                                    n,
+                                    sb.offset_size,
+                                    sb.length_size,
+                                    unit,
+                                )
+                                .map_err(ReadError::Other)?,
+                            )
+                        } else {
+                            Elements::Bytes(raw)
+                        };
+                        blocks.push((data, shape));
+                    }
+                    Ok(blocks)
+                })
+                .map_err(|e| e.into_py(&self.path))?;
+
+            let mut arrays = Vec::with_capacity(blocks.len());
+            for (data, shape) in blocks {
+                arrays.push(conv.to_array(py, data, &shape, false)?);
+            }
+            let joined = if arrays.len() == 1 {
+                arrays.pop().expect("one block")
+            } else {
+                let axis = list_axis.expect("several reads only for a list index");
+                // Name the dtype: left to itself numpy canonicalises a
+                // structured dtype here (drops padding, native byte order).
+                let kwargs = pyo3::types::PyDict::new(py);
+                kwargs.set_item("axis", axis)?;
+                kwargs.set_item("dtype", arrays[0].getattr("dtype")?)?;
+                kwargs.set_item("casting", "no")?;
+                py.import("numpy")?.call_method(
+                    "concatenate",
+                    (PyList::new(py, arrays)?,),
+                    Some(&kwargs),
+                )?
+            };
+            // Drop the axes indexed by an integer (length 1 in the blocks).
+            let mut shape = out_shape.clone();
+            if let crate::convert::Layout::Subarray(sub) = &conv.layout {
+                shape.extend_from_slice(sub);
+            }
+            joined.call_method1("reshape", (PyTuple::new(py, shape)?,))?
+        };
+
+        let arr = select_fields(py, arr, &plan.fields)?;
+        if plan.scalar {
+            return arr.get_item(PyTuple::empty(py));
+        }
+        Ok(arr)
+    }
 }
 
-/// Map a `DType` to a numpy dtype string.
-fn dtype_to_numpy_str(dt: &DType) -> &'static str {
-    match dt {
-        DType::F64 => "float64",
-        DType::F32 => "float32",
-        DType::I64 => "int64",
-        DType::I32 => "int32",
-        DType::I16 => "int16",
-        DType::I8 => "int8",
-        DType::U64 => "uint64",
-        DType::U32 => "uint32",
-        DType::U16 => "uint16",
-        DType::U8 => "uint8",
-        DType::String | DType::VariableLengthString => "object",
-        _ => "object",
+/// An error from the read closure, turned into a Python error with the GIL.
+enum ReadError {
+    Lib(clawhdf5_rs::Error),
+    Other(String),
 }
+
+impl From<clawhdf5_rs::Error> for ReadError {
+    fn from(e: clawhdf5_rs::Error) -> Self {
+        ReadError::Lib(e)
+    }
+}
+
+impl ReadError {
+    fn into_py(self, path: &str) -> PyErr {
+        match self {
+            ReadError::Lib(e) => to_py_err(e),
+            ReadError::Other(msg) => PyValueError::new_err(format!("{}: {msg}", node::name(path))),
+        }
+    }
+}
+
+/// Keep only the named compound fields, as h5py's `ds['x']` / `ds['x', 'y']`.
+fn select_fields<'py>(
+    py: Python<'py>,
+    arr: Bound<'py, PyAny>,
+    fields: &[String],
+) -> PyResult<Bound<'py, PyAny>> {
+    if fields.is_empty() {
+        return Ok(arr);
+    }
+    let names = arr.getattr("dtype")?.getattr("names")?;
+    if names.is_none() {
+        return Err(PyValueError::new_err(
+            "Field names only allowed for compound types",
+        ));
+    }
+    let names: Vec<String> = names.extract()?;
+    for f in fields {
+        if !names.contains(f) {
+            return Err(PyValueError::new_err(format!(
+                "Field {f} does not appear in this type."
+            )));
+        }
+    }
+    let np = py.import("numpy")?;
+    if let [one] = fields {
+        return np.call_method1("ascontiguousarray", (arr.get_item(one)?,));
+    }
+    let picked = arr.get_item(PyList::new(py, fields)?)?;
+    py.import("numpy.lib.recfunctions")?
+        .call_method1("repack_fields", (picked,))
 }
 
 #[pymethods]
 impl PyDataset {
-    /// The shape of the dataset as a tuple.
+    /// The shape of the dataset (`None` for an empty/null dataspace).
     #[getter]
-    fn shape(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
-        let tuple = pyo3::types::PyTuple::new(py, self.cached_shape.iter().map(|&d| d as usize))?;
-        Ok(tuple.into_any().unbind())
+    fn shape<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyAny>> {
+        match &self.shape {
+            Some(s) => Ok(PyTuple::new(py, s)?.into_any()),
+            None => Ok(py.None().into_bound(py)),
+        }
     }
 
-    /// The numpy dtype string of the dataset.
+    /// The maximum shape (`None` per unlimited dimension), like h5py.
     #[getter]
-    fn dtype(&self) -> &'static str {
-        dtype_to_numpy_str(&self.cached_dtype)
+    fn maxshape<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyAny>> {
+        let Some(shape) = &self.shape else {
+            return Ok(py.None().into_bound(py));
+        };
+        let max = self
+            .file
+            .dataset(&self.path)
+            .and_then(|ds| ds.max_dimensions())
+            .map_err(to_py_err)?
+            .unwrap_or_else(|| shape.clone());
+        let items: Vec<Option<u64>> = max
+            .into_iter()
+            .map(|d| (d != u64::MAX).then_some(d))
+            .collect();
+        Ok(PyTuple::new(py, items)?.into_any())
     }
 
-    /// Attribute access (read-only).
+    /// The dataset's numpy dtype, as h5py reports it.
+    #[getter]
+    fn dtype<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyAny>> {
+        Ok(self.converter()?.dtype.bind(py).clone())
+    }
+
+    #[getter]
+    fn ndim(&self) -> usize {
+        self.shape.as_ref().map_or(0, Vec::len)
+    }
+
+    /// Number of elements (`None` for an empty/null dataspace, as h5py).
+    #[getter]
+    fn size(&self) -> Option<u64> {
+        self.shape.as_ref().map(|s| s.iter().product())
+    }
+
+    /// The dataset's full name, e.g. `/group/data`.
+    #[getter]
+    fn name(&self) -> String {
+        node::name(&self.path)
+    }
+
+    /// The dataset's attributes (read-only, dict-like).
     #[getter]
     fn attrs(&self) -> PyResult<PyAttrs> {
-        let ds = self.file.dataset(&self.path).map_err(to_py_err)?;
-        let map = ds.attrs().map_err(to_py_err)?;
-        Ok(PyAttrs::from_read(map))
+        PyAttrs::read(Arc::clone(&self.file), &self.path)
     }
 
-    /// Read data via indexing. Supports `ds[:]`, `ds[0]`, `ds[0:5]`, etc.
-    ///
-    /// The full dataset is always read from the underlying file; the index
-    /// is then applied on the resulting numpy array.
-    fn __getitem__<'py>(&self, py: Python<'py>, key: &Bound<'py, PyAny>) -> PyResult<Py<PyAny>> {
-        let arr = self.read_as_numpy(py)?;
-        let indexed = arr.get_item(key)?;
-        Ok(indexed.unbind())
+    /// Read with h5py indexing: integers, slices with positive steps,
+    /// `...`, one increasing list of integers, and compound field names.
+    /// Only the selected elements are read from the file.
+    fn __getitem__<'py>(
+        &self,
+        py: Python<'py>,
+        key: &Bound<'py, PyAny>,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let Some(dims) = &self.shape else {
+            let is_empty_tuple = key.cast::<PyTuple>().is_ok_and(|t| t.is_empty());
+            let is_ellipsis = key.is_instance_of::<pyo3::types::PyEllipsis>();
+            if is_empty_tuple || is_ellipsis {
+                let empty = PyEmpty::new(self.converter()?.dtype.clone_ref(py));
+                return Ok(empty.into_pyobject(py)?.into_any());
+            }
+            return Err(PyValueError::new_err("Empty datasets cannot be sliced"));
+        };
+        let plan = select::parse(key, dims)?;
+        self.read_plan(py, &plan)
     }
 
-    fn __repr__(&self) -> String {
+    /// `numpy.asarray(ds)` reads the whole dataset.
+    #[pyo3(signature = (dtype=None, copy=None))]
+    fn __array__<'py>(
+        &self,
+        py: Python<'py>,
+        dtype: Option<&Bound<'py, PyAny>>,
+        copy: Option<bool>,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let _ = copy; // every read is a fresh array
+        let Some(dims) = &self.shape else {
+            return Err(PyValueError::new_err("an empty dataset has no array value"));
+        };
+        let ellipsis = pyo3::types::PyEllipsis::get(py).to_owned().into_any();
+        let plan = select::parse(&ellipsis, dims)?;
+        let arr = self.read_plan(py, &plan)?;
+        match dtype {
+            Some(dt) => arr.call_method1("astype", (dt,)),
+            None => Ok(arr),
+        }
+    }
+
+    fn __len__(&self) -> PyResult<usize> {
+        match self.shape.as_deref() {
+            Some([first, ..]) => Ok(*first as usize),
+            _ => Err(PyTypeError::new_err(
+                "Attempt to take len() of scalar dataset",
+            )),
+        }
+    }
+
+    fn __repr__(&self, py: Python<'_>) -> String {
+        let dtype = match &self.conv {
+            Ok(c) => c
+                .dtype
+                .bind(py)
+                .str()
+                .map(|s| s.to_string())
+                .unwrap_or_default(),
+            Err(_) => format!("{:?}", self.datatype),
+        };
+        let shape = match &self.shape {
+            Some(s) => format!("{s:?}"),
+            None => "None".to_string(),
+        };
         format!(
-            "<HDF5 Dataset \"{}\": shape {:?}, dtype {}>",
-            self.path,
-            self.cached_shape,
-            dtype_to_numpy_str(&self.cached_dtype),
+            "<HDF5 dataset \"{}\": shape {shape}, type \"{dtype}\">",
+            node::name(&self.path)
         )
     }
-
-    fn __len__(&self) -> usize {
-        self.cached_shape.first().copied().unwrap_or(0) as usize
-    }
-}
-
-impl PyDataset {
-    /// Read the full dataset and return it as a numpy array (or list for strings).
-    ///
-    /// For numeric types, the Rust I/O (file reading + decompression) is
-    /// performed inside `py.detach()` so that the GIL is released
-    /// during the potentially expensive operation.  The numpy array
-    /// construction still happens with the GIL held.
-    fn read_as_numpy<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyAny>> {
-        let file = &self.file;
-        let path = &self.path;
-        let shape: Vec<usize> = self.cached_shape.iter().map(|&d| d as usize).collect();
-
-        match &self.cached_dtype {
-            DType::F64 => {
-                let data = py
-                    .detach(|| file.dataset(path).and_then(|ds| ds.read_f64()))
-                    .map_err(to_py_err)?;
-                let nd = ArrayD::from_shape_vec(IxDyn(&shape), data)
-                    .map_err(|e| PyErr::new::<pyo3::exceptions::PyValueError, _>(e.to_string()))?;
-                let arr = PyArrayDyn::from_owned_array(py, nd);
-                Ok(arr.into_any())
-            }
-            DType::F32 => {
-                let data = py
-                    .detach(|| file.dataset(path).and_then(|ds| ds.read_f32()))
-                    .map_err(to_py_err)?;
-                let nd = ArrayD::from_shape_vec(IxDyn(&shape), data)
-                    .map_err(|e| PyErr::new::<pyo3::exceptions::PyValueError, _>(e.to_string()))?;
-                let arr = PyArrayDyn::from_owned_array(py, nd);
-                Ok(arr.into_any())
-            }
-            DType::I32 => {
-                let data = py
-                    .detach(|| file.dataset(path).and_then(|ds| ds.read_i32()))
-                    .map_err(to_py_err)?;
-                let nd = ArrayD::from_shape_vec(IxDyn(&shape), data)
-                    .map_err(|e| PyErr::new::<pyo3::exceptions::PyValueError, _>(e.to_string()))?;
-                let arr = PyArrayDyn::from_owned_array(py, nd);
-                Ok(arr.into_any())
-            }
-            DType::I64 => {
-                let data = py
-                    .detach(|| file.dataset(path).and_then(|ds| ds.read_i64()))
-                    .map_err(to_py_err)?;
-                let nd = ArrayD::from_shape_vec(IxDyn(&shape), data)
-                    .map_err(|e| PyErr::new::<pyo3::exceptions::PyValueError, _>(e.to_string()))?;
-                let arr = PyArrayDyn::from_owned_array(py, nd);
-                Ok(arr.into_any())
-            }
-            DType::U8 => {
-                // Try zero-copy first (contiguous layout), fall back to
-                // read_u64 + cast for chunked/compact datasets.
-                let data: Vec<u8> = py
-                    .detach(|| {
-                        let ds = file.dataset(path)?;
-                        match ds.read_u8_zerocopy() {
-                            Ok(slice) => Ok(slice.to_vec()),
-                            Err(_) => {
-                                let raw = ds.read_u64()?;
-                                Ok(raw.iter().map(|&v| v as u8).collect())
-                            }
-                        }
-                    })
-                    .map_err(to_py_err)?;
-                let nd = ArrayD::from_shape_vec(IxDyn(&shape), data)
-                    .map_err(|e| PyErr::new::<pyo3::exceptions::PyValueError, _>(e.to_string()))?;
-                let arr = PyArrayDyn::from_owned_array(py, nd);
-                Ok(arr.into_any())
-            }
-            DType::U64 => {
-                let data = py
-                    .detach(|| file.dataset(path).and_then(|ds| ds.read_u64()))
-                    .map_err(to_py_err)?;
-                let nd = ArrayD::from_shape_vec(IxDyn(&shape), data)
-                    .map_err(|e| PyErr::new::<pyo3::exceptions::PyValueError, _>(e.to_string()))?;
-                let arr = PyArrayDyn::from_owned_array(py, nd);
-                Ok(arr.into_any())
-            }
-            DType::String | DType::VariableLengthString => {
-                // String reads need the GIL for PyList construction, but we
-                // release it during the Rust I/O portion.
-                let data = py
-                    .detach(|| file.dataset(path).and_then(|ds| ds.read_string()))
-                    .map_err(to_py_err)?;
-                let list = PyList::new(py, &data)?;
-                Ok(list.into_any())
-            }
-            other => Err(PyErr::new::<pyo3::exceptions::PyTypeError, _>(format!(
-                "unsupported dataset dtype for reading: {other}"
-            ))),
-        }
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn dtype_mapping() {
-        assert_eq!(dtype_to_numpy_str(&DType::F64), "float64");
-        assert_eq!(dtype_to_numpy_str(&DType::F32), "float32");
-        assert_eq!(dtype_to_numpy_str(&DType::I32), "int32");
-        assert_eq!(dtype_to_numpy_str(&DType::I64), "int64");
-        assert_eq!(dtype_to_numpy_str(&DType::U8), "uint8");
-        assert_eq!(dtype_to_numpy_str(&DType::String), "object");
-    }
-
-    #[test]
-    fn dataset_from_file() {
-        let mut b = clawhdf5_rs::FileBuilder::new();
-        b.create_dataset("vals").with_f64_data(&[1.0, 2.0, 3.0]);
-        let bytes = b.finish().unwrap();
-        let file = Arc::new(clawhdf5_rs::File::from_bytes(bytes).unwrap());
-        let ds = PyDataset::new(file, "vals".into()).unwrap();
-        assert_eq!(ds.cached_shape, vec![3]);
-        assert_eq!(ds.cached_dtype, DType::F64);
-    }
-
-    #[test]
-    fn dataset_len() {
-        let mut b = clawhdf5_rs::FileBuilder::new();
-        b.create_dataset("data")
-            .with_i32_data(&[10, 20, 30, 40])
-            .with_shape(&[2, 2]);
-        let bytes = b.finish().unwrap();
-        let file = Arc::new(clawhdf5_rs::File::from_bytes(bytes).unwrap());
-        let ds = PyDataset::new(file, "data".into()).unwrap();
-        assert_eq!(ds.__len__(), 2);
-    }
 }

crates/clawhdf5-py/src/file.rs

@@ -3,11 +3,12 @@
 use std::path::PathBuf;
 use std::sync::{Arc, Mutex};
 
+use pyo3::exceptions::PyKeyError;
 use pyo3::prelude::*;
+use pyo3::types::PyList;
 
 use crate::attrs::PyAttrs;
-use crate::dataset::PyDataset;
-use crate::group::{PyGroup, WriteGroupState, finalize_write_group};
+use crate::group::{self, PyGroup, WriteGroupState, finalize_write_group};
 use crate::{DatasetSpec, OwnedAttrValue, apply_dataset_spec, extract_numpy_data, to_py_err};
 
 /// Internal state for write mode.
@@ -35,6 +36,7 @@ struct WriteState {
 #[pyclass(name = "File")]
 pub struct PyFile {
     inner: Option<FileInner>,
+    filename: String,
 }
 
 enum FileInner {
@@ -51,15 +53,20 @@ impl PyFile {
     ///   mode: 'r' for read (default), 'w' for write
     #[new]
     #[pyo3(signature = (path, mode="r"))]
-    fn new(path: &str, mode: &str) -> PyResult<Self> {
+    fn new(py: Python<'_>, path: &str, mode: &str) -> PyResult<Self> {
+        let filename = path.to_string();
         match mode {
             "r" => {
-                let file = clawhdf5_rs::File::open(path).map_err(to_py_err)?;
+                let file = py
+                    .detach(|| clawhdf5_rs::File::open(path))
+                    .map_err(to_py_err)?;
                 Ok(Self {
                     inner: Some(FileInner::Read(Arc::new(file))),
+                    filename,
                 })
             }
             "w" => Ok(Self {
+                filename,
                 inner: Some(FileInner::Write(WriteState {
                     path: PathBuf::from(path),
                     root_datasets: Vec::new(),
@@ -101,44 +108,56 @@ impl PyFile {
         Ok(false) // don't suppress exceptions
     }
 
-    /// Get a child object (dataset or group) by path.
+    /// Get a child object (dataset or group) by path; `f['/']` is the root.
     fn __getitem__(&self, py: Python<'_>, key: &str) -> PyResult<Py<PyAny>> {
-        let file = self.read_file()?;
-        // Try dataset first
-        match file.dataset(key) {
-            Ok(_) => {
-                let ds = PyDataset::new(Arc::clone(file), key.to_string())?;
-                Ok(ds.into_pyobject(py)?.into_any().unbind())
-            }
-            Err(clawhdf5_rs::Error::NotADataset(_)) => {
-                let grp = PyGroup::from_read(Arc::clone(file), key.to_string());
-                Ok(grp.into_pyobject(py)?.into_any().unbind())
-            }
-            Err(_) => {
-                // Could be a group (no DataLayout message, no error)
-                match file.group(key) {
-                    Ok(_) => {
-                        let grp = PyGroup::from_read(Arc::clone(file), key.to_string());
-                        Ok(grp.into_pyobject(py)?.into_any().unbind())
-                    }
-                    Err(e) => Err(PyErr::new::<pyo3::exceptions::PyKeyError, _>(format!(
-                        "{key}: {e}"
-                    ))),
+        group::get_item(py, self.read_file()?, "", key)
     }
+
+    /// `f.get(key, default=None)`.
+    #[pyo3(signature = (key, default=None))]
+    fn get(&self, py: Python<'_>, key: &str, default: Option<Py<PyAny>>) -> PyResult<Py<PyAny>> {
+        match group::get_item(py, self.read_file()?, "", key) {
+            Err(e) if e.is_instance_of::<PyKeyError>(py) => {
+                Ok(default.unwrap_or_else(|| py.None()))
             }
+            other => other,
         }
     }
 
     /// List the names of all children in the root group.
     fn keys(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
-        let file = self.read_file()?;
-        let root = file.root();
-        let mut names = root.datasets().map_err(to_py_err)?;
-        let groups = root.groups().map_err(to_py_err)?;
-        names.extend(groups);
-        names.sort();
-        let list = pyo3::types::PyList::new(py, &names)?;
-        Ok(list.into_any().unbind())
+        let names = group::member_names(self.read_file()?, "")?;
+        Ok(PyList::new(py, names)?.into_any().unbind())
+    }
+
+    fn values(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
+        let vals = group::values(py, self.read_file()?, "")?;
+        Ok(PyList::new(py, vals)?.into_any().unbind())
+    }
+
+    fn items(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
+        let items = group::items(py, self.read_file()?, "")?;
+        Ok(PyList::new(py, items)?.into_any().unbind())
+    }
+
+    fn __iter__(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
+        self.keys(py)?.call_method0(py, "__iter__")
+    }
+
+    fn __len__(&self) -> PyResult<usize> {
+        Ok(group::member_names(self.read_file()?, "")?.len())
+    }
+
+    /// The root group's name, `/`.
+    #[getter]
+    fn name(&self) -> &'static str {
+        "/"
+    }
+
+    /// The path the file was opened with.
+    #[getter]
+    fn filename(&self) -> &str {
+        &self.filename
     }
 
     /// Create a dataset in the root group (write mode only).
@@ -192,10 +211,7 @@ impl PyFile {
     #[getter]
     fn attrs(&self) -> PyResult<PyAttrs> {
         match self.inner.as_ref() {
-            Some(FileInner::Read(file)) => {
-                let map = file.root().attrs().map_err(to_py_err)?;
-                Ok(PyAttrs::from_read(map))
-            }
+            Some(FileInner::Read(file)) => PyAttrs::read(Arc::clone(file), ""),
             Some(FileInner::Write(state)) => Ok(PyAttrs::from_write(Arc::clone(&state.root_attrs))),
             None => Err(PyErr::new::<pyo3::exceptions::PyIOError, _>(
                 "file is closed",
@@ -216,8 +232,7 @@ impl PyFile {
     }
 
     fn __contains__(&self, key: &str) -> PyResult<bool> {
-        let file = self.read_file()?;
-        Ok(file.dataset(key).is_ok() || file.group(key).is_ok())
+        Ok(group::contains(self.read_file()?, "", key))
     }
 }
 

crates/clawhdf5-py/src/group.rs

@@ -2,12 +2,12 @@
 
 use std::sync::{Arc, Mutex};
 
+use pyo3::exceptions::{PyIOError, PyKeyError};
 use pyo3::prelude::*;
 use pyo3::types::PyList;
 
 use crate::attrs::PyAttrs;
-use crate::dataset::PyDataset;
-use crate::{DatasetSpec, OwnedAttrValue, apply_dataset_spec, extract_numpy_data, to_py_err};
+use crate::{DatasetSpec, OwnedAttrValue, apply_dataset_spec, extract_numpy_data, node, to_py_err};
 
 /// Shared state for a group being written.
 pub(crate) struct WriteGroupState {
@@ -18,15 +18,10 @@ pub(crate) struct WriteGroupState {
 
 /// An HDF5 group.
 ///
-/// In read mode, provides `__getitem__` navigation and child listing.
-/// In write mode, supports `create_dataset` and `create_group` and
-/// attribute setting.
-///
-/// ```python
-/// grp = f['group_name']
-/// grp.keys()
-/// ds = grp['dataset']
-/// ```
+/// In read mode it behaves like an h5py group: `grp['name']`,
+/// `grp['sub/path']` and `grp['/absolute/path']`, `keys()`, `values()`,
+/// `items()`, iteration, `len()`, `in`, `get()`, `name` and `attrs`.
+/// In write mode, supports `create_dataset` and attribute setting.
 #[pyclass(name = "Group")]
 pub struct PyGroup {
     inner: GroupInner,
@@ -52,46 +47,90 @@ impl PyGroup {
             inner: GroupInner::Write(state),
         }
     }
+
+    fn read_parts(&self, what: &str) -> PyResult<(&Arc<clawhdf5_rs::File>, &str)> {
+        match &self.inner {
+            GroupInner::Read { file, path } => Ok((file, path)),
+            GroupInner::Write(_) => Err(PyIOError::new_err(format!(
+                "cannot {what} a group opened for writing"
+            ))),
+        }
+    }
+}
+
+// Read-mode operations shared by `Group` and `File` (a file is its root
+// group, as in h5py).
+
+/// `group[key]`.
+pub(crate) fn get_item(
+    py: Python<'_>,
+    file: &Arc<clawhdf5_rs::File>,
+    path: &str,
+    key: &str,
+) -> PyResult<Py<PyAny>> {
+    node::open(py, file, node::join(path, key))
+}
+
+/// Names of the group's datasets and subgroups, sorted (h5py's order).
+pub(crate) fn member_names(file: &clawhdf5_rs::File, path: &str) -> PyResult<Vec<String>> {
+    let group = if path.is_empty() {
+        file.root()
+    } else {
+        file.group(path).map_err(to_py_err)?
+    };
+    let mut names = group.datasets().map_err(to_py_err)?;
+    names.extend(group.groups().map_err(to_py_err)?);
+    names.sort_by(|a, b| a.as_bytes().cmp(b.as_bytes()));
+    names.dedup();
+    Ok(names)
+}
+
+pub(crate) fn contains(file: &clawhdf5_rs::File, path: &str, key: &str) -> bool {
+    node::exists(file, &node::join(path, key))
+}
+
+pub(crate) fn values(
+    py: Python<'_>,
+    file: &Arc<clawhdf5_rs::File>,
+    path: &str,
+) -> PyResult<Vec<Py<PyAny>>> {
+    member_names(file, path)?
+        .iter()
+        .map(|n| get_item(py, file, path, n))
+        .collect()
+}
+
+pub(crate) fn items(
+    py: Python<'_>,
+    file: &Arc<clawhdf5_rs::File>,
+    path: &str,
+) -> PyResult<Vec<(String, Py<PyAny>)>> {
+    member_names(file, path)?
+        .into_iter()
+        .map(|n| {
+            let v = get_item(py, file, path, &n)?;
+            Ok((n, v))
+        })
+        .collect()
 }
 
 #[pymethods]
 impl PyGroup {
     /// Get a child object (dataset or subgroup) by name or path.
     fn __getitem__(&self, py: Python<'_>, key: &str) -> PyResult<Py<PyAny>> {
-        match &self.inner {
-            GroupInner::Read { file, path } => {
-                let full_path = if path.is_empty() {
-                    key.to_string()
-                } else {
-                    format!("{path}/{key}")
-                };
-                // Try dataset first
-                match file.dataset(&full_path) {
-                    Ok(_) => {
-                        let ds = PyDataset::new(Arc::clone(file), full_path)?;
-                        Ok(ds.into_pyobject(py)?.into_any().unbind())
+        let (file, path) = self.read_parts("read children from")?;
+        get_item(py, file, path, key)
     }
-                    Err(clawhdf5_rs::Error::NotADataset(_)) => {
-                        let grp = PyGroup::from_read(Arc::clone(file), full_path);
-                        Ok(grp.into_pyobject(py)?.into_any().unbind())
+
+    /// `group.get(key, default=None)`.
+    #[pyo3(signature = (key, default=None))]
+    fn get(&self, py: Python<'_>, key: &str, default: Option<Py<PyAny>>) -> PyResult<Py<PyAny>> {
+        let (file, path) = self.read_parts("read children from")?;
+        match get_item(py, file, path, key) {
+            Err(e) if e.is_instance_of::<PyKeyError>(py) => {
+                Ok(default.unwrap_or_else(|| py.None()))
             }
-                    Err(e) => {
-                        // Could be a group without a DataLayout message
-                        match file.group(&full_path) {
-                            Ok(_) => {
-                                let grp = PyGroup::from_read(Arc::clone(file), full_path);
-                                Ok(grp.into_pyobject(py)?.into_any().unbind())
-                            }
-                            Err(_) => Err(PyErr::new::<pyo3::exceptions::PyKeyError, _>(format!(
-                                "{key}: {e}"
-                            ))),
-                        }
-                    }
-                }
-            }
-            GroupInner::Write(_) => Err(PyErr::new::<pyo3::exceptions::PyIOError, _>(
-                "cannot read children from a group opened for writing",
-            )),
+            other => other,
         }
     }
 
@@ -99,16 +138,7 @@ impl PyGroup {
     fn keys(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
         match &self.inner {
             GroupInner::Read { file, path } => {
-                let group = if path.is_empty() {
-                    file.root()
-                } else {
-                    file.group(path).map_err(to_py_err)?
-                };
-                let mut names = group.datasets().map_err(to_py_err)?;
-                let groups = group.groups().map_err(to_py_err)?;
-                names.extend(groups);
-                names.sort();
-                let list = PyList::new(py, &names)?;
+                let list = PyList::new(py, member_names(file, path)?)?;
                 Ok(list.into_any().unbind())
             }
             GroupInner::Write(state) => {
@@ -120,6 +150,38 @@ impl PyGroup {
         }
     }
 
+    fn values(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
+        let (file, path) = self.read_parts("read children from")?;
+        Ok(PyList::new(py, values(py, file, path)?)?
+            .into_any()
+            .unbind())
+    }
+
+    fn items(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
+        let (file, path) = self.read_parts("read children from")?;
+        Ok(PyList::new(py, items(py, file, path)?)?.into_any().unbind())
+    }
+
+    fn __iter__(&self, py: Python<'_>) -> PyResult<Py<PyAny>> {
+        self.keys(py)?.call_method0(py, "__iter__")
+    }
+
+    fn __len__(&self) -> PyResult<usize> {
+        match &self.inner {
+            GroupInner::Read { file, path } => Ok(member_names(file, path)?.len()),
+            GroupInner::Write(state) => Ok(state.lock().unwrap().datasets.len()),
+        }
+    }
+
+    /// The group's full name, e.g. `/sensors`.
+    #[getter]
+    fn name(&self) -> String {
+        match &self.inner {
+            GroupInner::Read { path, .. } => node::name(path),
+            GroupInner::Write(state) => node::name(&state.lock().unwrap().name),
+        }
+    }
+
     /// Create a dataset inside this group (write mode only).
     ///
     /// Parameters:
@@ -161,7 +223,7 @@ impl PyGroup {
                 state.lock().unwrap().datasets.push(spec);
                 Ok(())
             }
-            GroupInner::Read { .. } => Err(PyErr::new::<pyo3::exceptions::PyIOError, _>(
+            GroupInner::Read { .. } => Err(PyIOError::new_err(
                 "cannot create datasets on a read-only group",
             )),
         }
@@ -171,15 +233,7 @@ impl PyGroup {
     #[getter]
     fn attrs(&self) -> PyResult<PyAttrs> {
         match &self.inner {
-            GroupInner::Read { file, path } => {
-                let group = if path.is_empty() {
-                    file.root()
-                } else {
-                    file.group(path).map_err(to_py_err)?
-                };
-                let map = group.attrs().map_err(to_py_err)?;
-                Ok(PyAttrs::from_read(map))
-            }
+            GroupInner::Read { file, path } => PyAttrs::read(Arc::clone(file), path),
             GroupInner::Write(state) => {
                 let store = Arc::clone(&state.lock().unwrap().attrs);
                 Ok(PyAttrs::from_write(store))
@@ -189,12 +243,9 @@ impl PyGroup {
 
     fn __repr__(&self) -> String {
         match &self.inner {
-            GroupInner::Read { path, .. } => {
-                if path.is_empty() {
-                    "<HDF5 Group \"/\" (root)>".to_string()
-                } else {
-                    format!("<HDF5 Group \"/{path}\">")
-                }
+            GroupInner::Read { file, path } => {
+                let n = member_names(file, path).map_or(0, |m| m.len());
+                format!("<HDF5 group \"{}\" ({n} members)>", node::name(path))
             }
             GroupInner::Write(state) => {
                 let name = &state.lock().unwrap().name;
@@ -205,14 +256,7 @@ impl PyGroup {
 
     fn __contains__(&self, key: &str) -> PyResult<bool> {
         match &self.inner {
-            GroupInner::Read { file, path } => {
-                let full_path = if path.is_empty() {
-                    key.to_string()
-                } else {
-                    format!("{path}/{key}")
-                };
-                Ok(file.dataset(&full_path).is_ok() || file.group(&full_path).is_ok())
-            }
+            GroupInner::Read { file, path } => Ok(contains(file, path, key)),
             GroupInner::Write(state) => {
                 let guard = state.lock().unwrap();
                 Ok(guard.datasets.iter().any(|d| d.name == key))
@@ -244,26 +288,24 @@ mod tests {
     use super::*;
 
     #[test]
-    fn read_group_construction() {
+    fn member_names_are_sorted() {
         let mut b = clawhdf5_rs::FileBuilder::new();
-        let mut g = b.create_group("grp");
+        b.create_dataset("zeta").with_f64_data(&[1.0]);
+        b.create_dataset("alpha").with_f64_data(&[1.0]);
+        let mut g = b.create_group("mid");
         g.create_dataset("x").with_f64_data(&[1.0]);
         let finished = g.finish();
         b.add_group(finished);
         let bytes = b.finish().unwrap();
-        let file = Arc::new(clawhdf5_rs::File::from_bytes(bytes).unwrap());
-        let _grp = PyGroup::from_read(file, "grp".into());
-    }
-
-    #[test]
-    fn write_group_state() {
-        let state = WriteGroupState {
-            name: "test".into(),
-            datasets: vec![],
-            attrs: Arc::new(Mutex::new(vec![])),
-        };
-        let arc = Arc::new(Mutex::new(state));
-        let _grp = PyGroup::from_write(arc);
+        let file = clawhdf5_rs::File::from_bytes(bytes).unwrap();
+        assert_eq!(
+            member_names(&file, "").unwrap(),
+            vec!["alpha", "mid", "zeta"]
+        );
+        assert_eq!(member_names(&file, "mid").unwrap(), vec!["x"]);
+        assert!(contains(&file, "", "mid/x"));
+        assert!(contains(&file, "mid", "/alpha"));
+        assert!(!contains(&file, "", "nope"));
     }
 
     #[test]

crates/clawhdf5-py/src/lib.rs

@@ -10,9 +10,12 @@
 //! ```
 
 mod attrs;
+mod convert;
 mod dataset;
 mod file;
 mod group;
+mod node;
+mod select;
 
 use pyo3::prelude::*;
 
@@ -46,6 +49,54 @@ pub(crate) fn to_py_err(e: clawhdf5_rs::Error) -> PyErr {
     }
 }
 
+/// The value of a dataset or attribute with a null dataspace: a type but no
+/// data. Mirrors `h5py.Empty`.
+#[pyclass(name = "Empty", frozen)]
+pub struct PyEmpty {
+    dtype: Py<PyAny>,
+}
+
+impl PyEmpty {
+    pub(crate) fn new(dtype: Py<PyAny>) -> Self {
+        Self { dtype }
+    }
+}
+
+#[pymethods]
+impl PyEmpty {
+    #[new]
+    fn py_new(py: Python<'_>, dtype: &Bound<'_, PyAny>) -> PyResult<Self> {
+        let dtype = py.import("numpy")?.getattr("dtype")?.call1((dtype,))?;
+        Ok(Self::new(dtype.unbind()))
+    }
+
+    #[getter]
+    fn dtype(&self, py: Python<'_>) -> Py<PyAny> {
+        self.dtype.clone_ref(py)
+    }
+
+    #[getter]
+    fn shape(&self, py: Python<'_>) -> Py<PyAny> {
+        py.None()
+    }
+
+    #[getter]
+    fn size(&self, py: Python<'_>) -> Py<PyAny> {
+        py.None()
+    }
+
+    fn __eq__(&self, py: Python<'_>, other: &Bound<'_, PyAny>) -> PyResult<bool> {
+        match other.cast::<PyEmpty>() {
+            Ok(o) => self.dtype.bind(py).eq(o.get().dtype.bind(py)),
+            Err(_) => Ok(false),
+        }
+    }
+
+    fn __repr__(&self, py: Python<'_>) -> PyResult<String> {
+        Ok(format!("Empty(dtype={})", self.dtype.bind(py).repr()?))
+    }
+}
+
 /// The data payload for a dataset being written.
 #[derive(Clone)]
 pub(crate) enum DatasetData {
@@ -224,6 +275,7 @@ fn clawhdf5(m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<PyDataset>()?;
     m.add_class::<PyGroup>()?;
     m.add_class::<PyAttrs>()?;
+    m.add_class::<PyEmpty>()?;
     Ok(())
 }
 
@@ -233,9 +285,9 @@ mod tests {
 
     #[test]
     fn owned_attr_value_roundtrip() {
-        let val = OwnedAttrValue::F64(3.14);
+        let val = OwnedAttrValue::F64(2.5);
         let attr: clawhdf5_rs::AttrValue = val.into();
-        assert!(matches!(attr, clawhdf5_rs::AttrValue::F64(v) if (v - 3.14).abs() < 1e-10));
+        assert!(matches!(attr, clawhdf5_rs::AttrValue::F64(v) if (v - 2.5).abs() < 1e-10));
     }
 
     #[test]

crates/clawhdf5-py/src/node.rs

@@ -0,0 +1,168 @@
+//! Resolving paths to objects in a file opened for reading.
+
+use std::sync::Arc;
+
+use clawhdf5_format::attribute::AttributeMessage;
+use clawhdf5_format::dataspace::{Dataspace, DataspaceType};
+use clawhdf5_format::message_type::MessageType;
+use clawhdf5_format::object_header::ObjectHeader;
+use pyo3::exceptions::{PyKeyError, PyTypeError, PyValueError};
+use pyo3::prelude::*;
+
+use crate::dataset::PyDataset;
+use crate::group::PyGroup;
+
+/// Join `key` onto the group path `base` the way h5py does: an absolute key
+/// starts from the root, a relative one from `base`. Paths are kept without
+/// a leading `/`; the root is `""`.
+pub(crate) fn join(base: &str, key: &str) -> String {
+    let parts = if key.starts_with('/') {
+        key.split('/').collect::<Vec<_>>()
+    } else {
+        base.split('/').chain(key.split('/')).collect()
+    };
+    parts
+        .into_iter()
+        .filter(|p| !p.is_empty() && *p != ".")
+        .collect::<Vec<_>>()
+        .join("/")
+}
+
+/// The HDF5 name (`/a/b`) of a path.
+pub(crate) fn name(path: &str) -> String {
+    format!("/{path}")
+}
+
+/// The object header of the object at `path`.
+pub(crate) fn header(file: &clawhdf5_rs::File, path: &str) -> PyResult<ObjectHeader> {
+    let sb = file.superblock();
+    let data = file.as_bytes();
+    let addr = if path.is_empty() {
+        sb.root_group_address
+    } else {
+        clawhdf5_format::group_v2::resolve_path_any(data, sb, path).map_err(|e| {
+            PyKeyError::new_err(format!(
+                "Unable to open object (object '{}' doesn't exist): {e}",
+                name(path)
+            ))
+        })?
+    };
+    let addr = usize::try_from(addr)
+        .map_err(|_| PyValueError::new_err(format!("{}: address out of range", name(path))))?;
+    ObjectHeader::parse(data, addr, sb.offset_size, sb.length_size)
+        .map_err(|e| PyValueError::new_err(format!("{}: {e}", name(path))))
+}
+
+/// What an object header describes.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub(crate) enum Kind {
+    Dataset,
+    Group,
+    Datatype,
+}
+
+pub(crate) fn kind(hdr: &ObjectHeader) -> Option<Kind> {
+    let has = |t: MessageType| hdr.messages.iter().any(|m| m.msg_type == t);
+    if has(MessageType::DataLayout) {
+        Some(Kind::Dataset)
+    } else if has(MessageType::LinkInfo)
+        || has(MessageType::Link)
+        || has(MessageType::SymbolTable)
+        || has(MessageType::GroupInfo)
+    {
+        Some(Kind::Group)
+    } else if has(MessageType::Datatype) {
+        Some(Kind::Datatype)
+    } else {
+        None
+    }
+}
+
+/// Open the object at `path` as a `Dataset` or `Group`.
+pub(crate) fn open(
+    py: Python<'_>,
+    file: &Arc<clawhdf5_rs::File>,
+    path: String,
+) -> PyResult<Py<PyAny>> {
+    let hdr = header(file, &path)?;
+    match kind(&hdr) {
+        Some(Kind::Dataset) => Ok(PyDataset::open(py, Arc::clone(file), path)?
+            .into_pyobject(py)?
+            .into_any()
+            .unbind()),
+        Some(Kind::Group) => Ok(PyGroup::from_read(Arc::clone(file), path)
+            .into_pyobject(py)?
+            .into_any()
+            .unbind()),
+        Some(Kind::Datatype) => Err(PyTypeError::new_err(format!(
+            "{}: committed (named) datatypes are not supported by clawhdf5",
+            name(&path)
+        ))),
+        None => Err(PyValueError::new_err(format!(
+            "{}: not a dataset, group or datatype",
+            name(&path)
+        ))),
+    }
+}
+
+/// Whether `path` names a dataset or group.
+pub(crate) fn exists(file: &clawhdf5_rs::File, path: &str) -> bool {
+    header(file, path)
+        .ok()
+        .and_then(|h| kind(&h))
+        .is_some_and(|k| k != Kind::Datatype)
+}
+
+/// The dataspace message of an object header.
+pub(crate) fn dataspace(file: &clawhdf5_rs::File, hdr: &ObjectHeader) -> PyResult<Dataspace> {
+    let sb = file.superblock();
+    let msg = hdr
+        .messages
+        .iter()
+        .find(|m| m.msg_type == MessageType::Dataspace)
+        .ok_or_else(|| PyValueError::new_err("object has no dataspace message"))?;
+    let data = clawhdf5_format::shared_message::message_data(
+        file.as_bytes(),
+        msg,
+        sb.offset_size,
+        sb.length_size,
+    )
+    .map_err(|e| PyValueError::new_err(e.to_string()))?;
+    Dataspace::parse(&data, sb.length_size).map_err(|e| PyValueError::new_err(e.to_string()))
+}
+
+pub(crate) fn is_null(space: &Dataspace) -> bool {
+    space.space_type == DataspaceType::Null
+}
+
+/// The attributes of the object at `path`, sorted by name (h5py's order).
+/// Attributes whose messages cannot be parsed are left out, as the facade's
+/// `attrs()` does.
+pub(crate) fn attributes(file: &clawhdf5_rs::File, path: &str) -> PyResult<Vec<AttributeMessage>> {
+    let hdr = header(file, path)?;
+    let sb = file.superblock();
+    let (mut attrs, _errors) = clawhdf5_format::attribute::extract_attributes_tolerant(
+        file.as_bytes(),
+        &hdr,
+        sb.offset_size,
+        sb.length_size,
+    )
+    .map_err(|e| PyValueError::new_err(format!("{}: {e}", name(path))))?;
+    attrs.sort_by(|a, b| a.name.as_bytes().cmp(b.name.as_bytes()));
+    Ok(attrs)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn join_paths() {
+        assert_eq!(join("", "a"), "a");
+        assert_eq!(join("a", "b/c"), "a/b/c");
+        assert_eq!(join("a/b", "/x"), "x");
+        assert_eq!(join("a", "/"), "");
+        assert_eq!(join("", "/a//b/"), "a/b");
+        assert_eq!(join("a", "./b"), "a/b");
+    }
+}

crates/clawhdf5-py/src/select.rs

@@ -0,0 +1,366 @@
+//! h5py-style indexing (`ds[1, 2:10:3, ...]`) mapped onto hyperslab
+//! selections, so only the selected elements are read.
+//!
+//! The rules and error messages follow h5py's `selections.py`: integers
+//! (negative from the end) drop their axis, slices must have a positive
+//! step, one `Ellipsis` fills the unmentioned axes, a single increasing list
+//! of integers may index one axis, and strings name compound fields.
+//! Everything else (`None`/`np.newaxis`, boolean masks, several index lists)
+//! is refused with the error h5py gives.
+
+use clawhdf5_format::selection::Selection;
+use pyo3::exceptions::{PyIndexError, PyTypeError, PyValueError};
+use pyo3::prelude::*;
+use pyo3::types::{PyEllipsis, PySlice, PyString, PyTuple};
+
+/// The selection along one axis.
+#[derive(Clone, Debug, PartialEq)]
+pub(crate) enum Axis {
+    /// A single index: the axis is dropped from the result.
+    Index(u64),
+    /// `start, start + step, ...`, `count` of them.
+    Slice { start: u64, step: u64, count: u64 },
+    /// Increasing, distinct indices.
+    List(Vec<u64>),
+}
+
+impl Axis {
+    fn len(&self) -> u64 {
+        match self {
+            Axis::Index(_) => 1,
+            Axis::Slice { count, .. } => *count,
+            Axis::List(v) => v.len() as u64,
+        }
+    }
+}
+
+/// A parsed index expression.
+#[derive(Clone, Debug, PartialEq)]
+pub(crate) struct Plan {
+    /// One entry per dataset axis.
+    pub axes: Vec<Axis>,
+    /// Compound field names to keep (empty: all).
+    pub fields: Vec<String>,
+    /// For a scalar dataset: `ds[()]` gives a scalar, `ds[...]` a 0-d array.
+    /// For other datasets: every axis was an integer, so h5py gives a scalar.
+    pub scalar: bool,
+}
+
+impl Plan {
+    /// The shape of the result.
+    pub fn out_shape(&self) -> Vec<usize> {
+        self.axes
+            .iter()
+            .filter(|a| !matches!(a, Axis::Index(_)))
+            .map(|a| a.len() as usize)
+            .collect()
+    }
+
+    /// Whether the selection is empty.
+    pub fn is_empty(&self) -> bool {
+        self.axes.iter().any(|a| a.len() == 0)
+    }
+
+    /// The hyperslab reads that make up this selection, each with the shape
+    /// of its block (index axes kept at length 1). More than one only when an
+    /// axis is indexed by a list: one read per run of consecutive indices,
+    /// concatenated along `list_axis` afterwards.
+    pub fn reads(&self, dims: &[u64]) -> (Vec<(Selection, Vec<usize>)>, Option<usize>) {
+        let list_axis = self.axes.iter().position(|a| matches!(a, Axis::List(_)));
+        let runs: Vec<(u64, u64)> = match list_axis.map(|i| &self.axes[i]) {
+            Some(Axis::List(idx)) => consecutive_runs(idx),
+            _ => vec![(0, 0)],
+        };
+        let mut out = Vec::with_capacity(runs.len());
+        for (run_start, run_len) in runs {
+            let mut start = Vec::with_capacity(dims.len());
+            let mut stride = Vec::with_capacity(dims.len());
+            let mut count = Vec::with_capacity(dims.len());
+            for axis in &self.axes {
+                let (s, st, c) = match axis {
+                    Axis::Index(i) => (*i, 1, 1),
+                    Axis::Slice { start, step, count } => (*start, *step, *count),
+                    Axis::List(_) => (run_start, 1, run_len),
+                };
+                start.push(s);
+                // A stride only matters between blocks; keep it >= 1.
+                stride.push(if c <= 1 { 1 } else { st });
+                count.push(c);
+            }
+            let block_shape: Vec<usize> = count.iter().map(|&c| c as usize).collect();
+            let whole = start.iter().all(|&s| s == 0)
+                && stride.iter().all(|&s| s == 1)
+                && count.as_slice() == dims;
+            let sel = if whole {
+                Selection::All
+            } else {
+                let block = vec![1; dims.len()];
+                Selection::Hyperslab {
+                    start,
+                    stride,
+                    count,
+                    block,
+                }
+            };
+            out.push((sel, block_shape));
+        }
+        (out, list_axis)
+    }
+}
+
+fn consecutive_runs(idx: &[u64]) -> Vec<(u64, u64)> {
+    let mut runs: Vec<(u64, u64)> = Vec::new();
+    for &i in idx {
+        match runs.last_mut() {
+            Some((s, n)) if *s + *n == i => *n += 1,
+            _ => runs.push((i, 1)),
+        }
+    }
+    runs
+}
+
+/// Parse `key` for a dataset of shape `dims`.
+pub(crate) fn parse(key: &Bound<'_, PyAny>, dims: &[u64]) -> PyResult<Plan> {
+    let items: Vec<Bound<'_, PyAny>> = match key.cast::<PyTuple>() {
+        Ok(t) => t.iter().collect(),
+        Err(_) => vec![key.clone()],
+    };
+    let mut fields = Vec::new();
+    let mut args = Vec::new();
+    for item in items {
+        if let Ok(s) = item.cast::<PyString>() {
+            fields.push(s.to_str()?.to_owned());
+        } else {
+            args.push(item);
+        }
+    }
+
+    if args.iter().any(|a| a.is_none()) {
+        return Err(PyTypeError::new_err(
+            "Indexing with None (or np.newaxis) is not supported",
+        ));
+    }
+
+    let rank = dims.len();
+    if rank == 0 {
+        return match args.as_slice() {
+            [] => Ok(Plan {
+                axes: vec![],
+                fields,
+                scalar: true,
+            }),
+            [a] if a.is_instance_of::<PyEllipsis>() => Ok(Plan {
+                axes: vec![],
+                fields,
+                scalar: false,
+            }),
+            _ => Err(PyValueError::new_err(
+                "Illegal slicing argument for scalar dataspace",
+            )),
+        };
+    }
+
+    // Expand the ellipsis (at most one) to full slices.
+    let n_ellipsis = args
+        .iter()
+        .filter(|a| a.is_instance_of::<PyEllipsis>())
+        .count();
+    if n_ellipsis > 1 {
+        return Err(PyValueError::new_err("Only one ellipsis may be used."));
+    }
+    let explicit = args.len() - n_ellipsis;
+    if explicit > rank {
+        return Err(PyValueError::new_err(format!(
+            "{explicit} indexing arguments for {rank} dimensions"
+        )));
+    }
+    let py = key.py();
+    let mut expanded: Vec<Option<Bound<'_, PyAny>>> = Vec::with_capacity(rank);
+    for a in args {
+        if a.is_instance_of::<PyEllipsis>() {
+            for _ in 0..(rank - explicit) {
+                expanded.push(None);
+            }
+        } else {
+            expanded.push(Some(a));
+        }
+    }
+    while expanded.len() < rank {
+        expanded.push(None);
+    }
+
+    let mut axes = Vec::with_capacity(rank);
+    for (arg, &n) in expanded.iter().zip(dims) {
+        axes.push(match arg {
+            None => Axis::Slice {
+                start: 0,
+                step: 1,
+                count: n,
+            },
+            Some(a) => parse_axis(py, a, n)?,
+        });
+    }
+    if axes.iter().filter(|a| matches!(a, Axis::List(_))).count() > 1 {
+        return Err(PyTypeError::new_err(
+            "Only one indexing vector or array is currently allowed for fancy indexing",
+        ));
+    }
+    let scalar = axes.iter().all(|a| matches!(a, Axis::Index(_)));
+    Ok(Plan {
+        axes,
+        fields,
+        scalar,
+    })
+}
+
+fn parse_axis(py: Python<'_>, a: &Bound<'_, PyAny>, n: u64) -> PyResult<Axis> {
+    if a.is_none() {
+        return Err(PyTypeError::new_err(
+            "Indexing with None (or np.newaxis) is not supported",
+        ));
+    }
+    if let Ok(s) = a.cast::<PySlice>() {
+        let n_isize = isize::try_from(n)
+            .map_err(|_| PyValueError::new_err("dimension too large to slice"))?;
+        let ind = s.indices(n_isize)?;
+        if ind.step < 1 {
+            return Err(PyValueError::new_err(format!(
+                "Step must be >= 1 (got {})",
+                ind.step
+            )));
+        }
+        // `slicelength` is the number of elements selected, >= 0.
+        let count = ind.slicelength as u64;
+        let start = if count == 0 { 0 } else { ind.start as u64 };
+        return Ok(Axis::Slice {
+            start,
+            step: ind.step as u64,
+            count,
+        });
+    }
+    let np = py.import("numpy")?;
+    let is_bool =
+        a.is_instance_of::<pyo3::types::PyBool>() || a.is_instance(&np.getattr("bool_")?)?;
+    let is_array_like = a.is_instance(&np.getattr("ndarray")?)?
+        || a.is_instance_of::<pyo3::types::PyList>()
+        || a.is_instance_of::<PyTuple>();
+    if !is_bool && !is_array_like && a.hasattr("__index__")? {
+        let i: i128 = a.call_method0("__index__")?.extract()?;
+        return Ok(Axis::Index(normalize(i, n)?));
+    }
+    if is_array_like {
+        let arr = np.call_method1("asarray", (a,))?;
+        let kind: String = arr.getattr("dtype")?.getattr("kind")?.extract()?;
+        if kind == "b" {
+            return Err(PyTypeError::new_err(
+                "Boolean mask indexing is not supported by clawhdf5",
+            ));
+        }
+        let ndim: usize = arr.getattr("ndim")?.extract()?;
+        let size: usize = arr.getattr("size")?.extract()?;
+        if size > 0 && kind != "i" && kind != "u" {
+            return Err(PyTypeError::new_err(
+                "Indexing arrays must have integer dtypes",
+            ));
+        }
+        if ndim > 1 {
+            return Err(PyTypeError::new_err(
+                "Only 1-D integer lists or arrays can be used for fancy indexing",
+            ));
+        }
+        let vals: Vec<i128> = arr.call_method0("tolist")?.extract()?;
+        let mut idx = Vec::with_capacity(vals.len());
+        for v in vals {
+            idx.push(normalize(v, n)?);
+        }
+        if idx.windows(2).any(|w| w[0] >= w[1]) {
+            return Err(PyTypeError::new_err(
+                "Indexing elements must be in increasing order",
+            ));
+        }
+        return Ok(Axis::List(idx));
+    }
+    Err(PyTypeError::new_err(format!(
+        "Illegal index type for clawhdf5 datasets: {}",
+        a.get_type().name()?
+    )))
+}
+
+fn normalize(i: i128, n: u64) -> PyResult<u64> {
+    let n_i = i128::from(n);
+    let j = if i < 0 { i + n_i } else { i };
+    if j < 0 || j >= n_i {
+        let hi = n_i - 1;
+        return Err(PyIndexError::new_err(format!(
+            "Index ({i}) out of range for (0-{hi})"
+        )));
+    }
+    Ok(j as u64)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn runs_group_consecutive_indices() {
+        assert_eq!(
+            consecutive_runs(&[1, 2, 3, 7, 9, 10]),
+            vec![(1, 3), (7, 1), (9, 2)]
+        );
+        assert_eq!(consecutive_runs(&[]), vec![]);
+    }
+
+    #[test]
+    fn full_selection_reads_everything() {
+        let plan = Plan {
+            axes: vec![
+                Axis::Slice {
+                    start: 0,
+                    step: 1,
+                    count: 4,
+                },
+                Axis::Slice {
+                    start: 0,
+                    step: 1,
+                    count: 3,
+                },
+            ],
+            fields: vec![],
+            scalar: false,
+        };
+        let (reads, list) = plan.reads(&[4, 3]);
+        assert_eq!(list, None);
+        assert_eq!(reads, vec![(Selection::All, vec![4, 3])]);
+    }
+
+    #[test]
+    fn index_and_step_map_to_a_hyperslab() {
+        let plan = Plan {
+            axes: vec![
+                Axis::Index(2),
+                Axis::Slice {
+                    start: 1,
+                    step: 3,
+                    count: 2,
+                },
+            ],
+            fields: vec![],
+            scalar: false,
+        };
+        let (reads, _) = plan.reads(&[4, 8]);
+        assert_eq!(
+            reads,
+            vec![(
+                Selection::Hyperslab {
+                    start: vec![2, 1],
+                    stride: vec![1, 3],
+                    count: vec![1, 2],
+                    block: vec![1, 1],
+                },
+                vec![1, 2]
+            )]
+        );
+        assert_eq!(plan.out_shape(), vec![2]);
+    }
+}

crates/clawhdf5-py/tests/conftest.py

@@ -0,0 +1,18 @@
+"""Shared fixtures for the clawhdf5 Python binding tests."""
+
+import os
+
+import pytest
+
+
+@pytest.fixture(scope="session")
+def h5py():
+    """h5py, or a skip — unless CLAWHDF5_REQUIRE_INTEROP=1, which makes a
+    missing h5py a failure (as for the Rust interop suites)."""
+    try:
+        import h5py as mod
+    except ImportError:
+        if os.environ.get("CLAWHDF5_REQUIRE_INTEROP") == "1":
+            pytest.fail("h5py is required (CLAWHDF5_REQUIRE_INTEROP=1) but not importable")
+        pytest.skip("h5py not installed")
+    return mod

crates/clawhdf5-py/tests/test_read_vs_h5py.py

@@ -0,0 +1,469 @@
+"""Every read through clawhdf5 compared against h5py (libhdf5) on files h5py
+writes: dtypes, shapes, values and the type of what comes back (array,
+numpy scalar, bytes, str, Empty), for every datatype the bindings map and a
+spread of index expressions; plus the errors h5py gives for the same keys."""
+
+import threading
+from concurrent.futures import ThreadPoolExecutor
+
+import numpy as np
+import pytest
+
+import clawhdf5
+
+# ---------------------------------------------------------------------------
+# The generated file
+# ---------------------------------------------------------------------------
+
+NUMERIC = [
+    "<i1", "<i2", "<i4", "<i8", "<u1", "<u2", "<u4", "<u8",
+    ">i2", ">i4", ">i8", ">u2", ">u4", ">u8",
+    "<f2", "<f4", "<f8", ">f2", ">f4", ">f8",
+]
+
+
+def _values(dtype, shape, seed):
+    rng = np.random.default_rng(seed)
+    dt = np.dtype(dtype)
+    n = int(np.prod(shape))
+    if dt.kind == "f":
+        return rng.standard_normal(n).astype(dt).reshape(shape)
+    info = np.iinfo(dt)
+    return rng.integers(info.min, info.max, size=n, dtype=dt.newbyteorder("=")).astype(dt).reshape(shape)
+
+
+def _compound_dtype():
+    return np.dtype(
+        {
+            "names": ["id", "pos", "label", "flag", "vec"],
+            "formats": ["<i4", ">f8", "S6", "u1", ("<f4", (3,))],
+            "offsets": [0, 8, 16, 22, 24],
+            "itemsize": 40,
+        }
+    )
+
+
+def _nested_dtype():
+    inner = np.dtype([("a", "<i2"), ("b", "<f4")])
+    return np.dtype([("x", "<u8"), ("inner", inner), ("c", "<c8")])
+
+
+def _write_fixture(h5py, path):
+    str_dt = h5py.string_dtype()
+    ascii_dt = h5py.string_dtype("ascii")
+    with h5py.File(path, "w") as f:
+        # Numeric types, both byte orders, 1-D contiguous and 2-D chunked+gzip.
+        for i, dt in enumerate(NUMERIC):
+            name = dt.replace("<", "le_").replace(">", "be_")
+            f.create_dataset(f"num/{name}_1d", data=_values(dt, (37,), i))
+            f.create_dataset(
+                f"num/{name}_2d_gzip",
+                data=_values(dt, (13, 11), 100 + i),
+                chunks=(4, 5),
+                compression="gzip",
+            )
+        f.create_dataset("num/f8_3d", data=_values("<f8", (6, 7, 5), 7), chunks=(2, 3, 5))
+        f.create_dataset(
+            "num/i4_3d_shuffle",
+            data=_values("<i4", (5, 9, 4), 8),
+            chunks=(3, 4, 2),
+            shuffle=True,
+            fletcher32=True,
+            compression="gzip",
+        )
+        f.create_dataset("num/scalar_f8", data=np.float64(3.25))
+        f.create_dataset("num/scalar_i2_be", data=np.array(-7, dtype=">i2"))
+        f.create_dataset("num/zero_size", shape=(0, 3), dtype="<f4")
+        f.create_dataset("num/empty", data=h5py.Empty("<f8"))
+        sparse = f.create_dataset("num/sparse_fill", shape=(40,), chunks=(8,), dtype="<i4", fillvalue=-3)
+        sparse[10:14] = [1, 2, 3, 4]
+        f.create_dataset("num/resizable", data=np.arange(12.0), maxshape=(None,), chunks=(5,))
+        # Compact layout (low level: h5py's create_dataset cannot ask for it).
+        dcpl = h5py.h5p.create(h5py.h5p.DATASET_CREATE)
+        dcpl.set_layout(h5py.h5d.COMPACT)
+        space = h5py.h5s.create_simple((9,))
+        dsid = h5py.h5d.create(f.id, b"num/compact_u2", h5py.h5t.py_create(np.dtype("<u2")), space, dcpl=dcpl)
+        dsid.write(h5py.h5s.ALL, h5py.h5s.ALL, np.arange(9, dtype="<u2") * 7)
+
+        # bool, enum, complex.
+        f.create_dataset("misc/bool", data=np.array([True, False, True, True, False]))
+        enum_dt = h5py.enum_dtype({"RED": 0, "GREEN": 1, "BLUE": 42}, basetype="i2")
+        f.create_dataset("misc/enum", data=np.array([0, 42, 1, 1, 0], dtype="<i2"), dtype=enum_dt)
+        uenum_dt = h5py.enum_dtype({"LO": 0, "HI": 2**40}, basetype="u8")
+        f.create_dataset("misc/enum_u8", data=np.array([0, 2**40], dtype="<u8"), dtype=uenum_dt)
+        f.create_dataset("misc/c8", data=(np.arange(6) + 1j * np.arange(6)).astype("<c8"))
+        f.create_dataset("misc/c16_2d", data=(np.arange(12) - 2j).reshape(3, 4).astype("<c16"))
+        f.create_dataset("misc/opaque", data=np.array([b"\x00\x01\x02", b"\xff\xfe\xfd"], dtype="V3"))
+
+        # Strings.
+        f.create_dataset("str/fixed", data=np.array([b"alpha", b"be", b"", b"gamma!"], dtype="S6"))
+        utf8_4 = h5py.string_dtype("utf-8", 4)
+        f.create_dataset(
+            "str/fixed_2d_utf8",
+            data=np.array([["é".encode(), b"b"], [b"c", b"dd"]], dtype=utf8_4),
+        )
+        f.create_dataset("str/vlen", data=["", "one", "twø", "a" * 300, "ünïcödé"], dtype=str_dt)
+        f.create_dataset("str/vlen_ascii", data=[b"x", b"yy", b"zzz"], dtype=ascii_dt)
+        f.create_dataset(
+            "str/vlen_2d_gzip",
+            data=np.array([[f"r{r}c{c}" * (r + c) for c in range(5)] for r in range(6)], dtype=object),
+            dtype=str_dt,
+            chunks=(2, 2),
+            compression="gzip",
+        )
+        f.create_dataset("str/vlen_scalar", data="just one", dtype=str_dt)
+
+        # Variable-length sequences.
+        vl_i = h5py.vlen_dtype(np.dtype("<i4"))
+        seqs = np.empty(4, dtype=object)
+        seqs[:] = [np.arange(3, dtype="<i4"), np.array([], dtype="<i4"), np.arange(10, dtype="<i4") * -1, np.array([7], dtype="<i4")]
+        f.create_dataset("vlen/i4", data=seqs, dtype=vl_i)
+        vl_f = h5py.vlen_dtype(np.dtype(">f8"))
+        fseqs = np.empty(3, dtype=object)
+        fseqs[:] = [np.linspace(0, 1, 5).astype(">f8"), np.array([2.5], dtype=">f8"), np.array([], dtype=">f8")]
+        f.create_dataset("vlen/f8_be", data=fseqs, dtype=vl_f)
+
+        # Compounds.
+        cdt = _compound_dtype()
+        rec = np.zeros(10, dtype=cdt)
+        rec["id"] = np.arange(10) * 3
+        rec["pos"] = np.linspace(-1, 1, 10)
+        rec["label"] = [f"n{i}".encode() for i in range(10)]
+        rec["flag"] = np.arange(10) % 2
+        rec["vec"] = np.arange(30, dtype="<f4").reshape(10, 3)
+        f.create_dataset("cmp/padded", data=rec)
+        f.create_dataset("cmp/padded_chunked", data=rec, chunks=(3,), compression="gzip")
+        ndt = _nested_dtype()
+        nrec = np.zeros((4, 3), dtype=ndt)
+        nrec["x"] = np.arange(12).reshape(4, 3)
+        nrec["inner"]["a"] = -np.arange(12).reshape(4, 3)
+        nrec["inner"]["b"] = np.arange(12).reshape(4, 3) / 4
+        nrec["c"] = np.arange(12).reshape(4, 3) * (1 + 1j)
+        f.create_dataset("cmp/nested_2d", data=nrec)
+        vdt = np.dtype([("n", "<i4"), ("s", h5py.string_dtype())])
+        vrec = np.array([(1, "a"), (2, "bb")], dtype=vdt)
+        f.create_dataset("cmp/with_vlen", data=vrec)
+
+        # A true HDF5 array datatype (h5py's high level would widen the shape).
+        tid = h5py.h5t.array_create(h5py.h5t.py_create(np.dtype("<i4")), (2, 3))
+        space = h5py.h5s.create_simple((4,))
+        dsid = h5py.h5d.create(f.id, b"cmp/array_type", tid, space)
+        dsid.write(h5py.h5s.ALL, h5py.h5s.ALL, np.arange(24, dtype="<i4").reshape(4, 2, 3), mtype=tid)
+
+        # Unsupported: references.
+        f.create_dataset("unsupported/refs", data=[f["num"].ref, f["misc"].ref], dtype=h5py.ref_dtype)
+
+        # Groups and attributes.
+        g = f.create_group("deep/er/est")
+        g.create_dataset("leaf", data=np.arange(5))
+        f.create_group("empty_group")
+        f.attrs["i4"] = np.int32(-5)
+        f.attrs["u8"] = np.uint64(2**63 + 1)
+        f.attrs["f2"] = np.float16(1.5)
+        f.attrs["f8_arr"] = np.array([1.0, 2.5, -3.0])
+        f.attrs["f8_one"] = np.array([4.0])
+        f.attrs["i2_2d_be"] = np.arange(6, dtype=">i2").reshape(2, 3)
+        f.attrs["bool"] = True
+        f.attrs["bool_arr"] = np.array([True, False])
+        f.attrs["vstr"] = "héllo"
+        f.attrs["vstr_arr"] = ["a", "bcd", ""]
+        f.attrs["fstr"] = np.bytes_(b"fixed")
+        f.attrs["fstr_arr"] = np.array([b"x", b"yz"], dtype="S2")
+        f.attrs["empty"] = h5py.Empty("<i4")
+        f.attrs["complex"] = np.complex128(1 - 2j)
+        f.attrs["compound"] = np.array((7, 2.5), dtype=[("a", "<i2"), ("b", "<f8")])
+        f.attrs["enum"] = np.array(42, dtype=enum_dt)
+        f["num/le_f8_1d"].attrs["units"] = "m/s"
+        f["num/le_f8_1d"].attrs["scale"] = np.float32(0.5)
+        g.attrs["depth"] = np.int8(3)
+
+
+@pytest.fixture(scope="module")
+def pair(h5py, tmp_path_factory):
+    path = str(tmp_path_factory.mktemp("h5") / "fixture.h5")
+    _write_fixture(h5py, path)
+    theirs = h5py.File(path, "r")
+    ours = clawhdf5.File(path, "r")
+    yield ours, theirs, path
+    theirs.close()
+    ours.close()
+
+
+def _all_datasets(h5py, f):
+    names = []
+    f.visititems(lambda n, o: names.append(n) if isinstance(o, h5py.Dataset) else None)
+    return sorted(names)
+
+
+# ---------------------------------------------------------------------------
+# Comparison helpers
+# ---------------------------------------------------------------------------
+
+
+def assert_same(ours, theirs, what=""):
+    if type(theirs).__name__ == "Empty":
+        assert isinstance(ours, clawhdf5.Empty), what
+        assert ours.dtype == theirs.dtype, what
+        return
+    assert type(ours) is type(theirs), f"{what}: {type(ours)} vs {type(theirs)}"
+    if isinstance(theirs, np.ndarray):
+        assert ours.shape == theirs.shape, what
+        assert ours.dtype == theirs.dtype, f"{what}: {ours.dtype} vs {theirs.dtype}"
+        if theirs.dtype == object:
+            for a, b in zip(ours.ravel(), theirs.ravel()):
+                assert_same(a, b, what)
+        elif theirs.dtype.names is None and theirs.dtype.kind == "V":
+            assert ours.tobytes() == theirs.tobytes(), what
+        else:
+            np.testing.assert_array_equal(ours, theirs, err_msg=what)
+    elif isinstance(theirs, np.generic):
+        assert ours.dtype == theirs.dtype, what
+        if theirs.dtype.names is not None:
+            np.testing.assert_array_equal(np.asarray(ours), np.asarray(theirs), err_msg=what)
+        else:
+            assert ours == theirs or (ours != ours and theirs != theirs), what
+    else:
+        assert ours == theirs, what
+
+
+def keys_for(shape):
+    if shape == ():
+        return [(), Ellipsis]
+    keys = [(), Ellipsis, 0, -1, slice(None), slice(None, None, 2), slice(1, None, 3), slice(0, 0), np.int64(0)]
+    n0 = shape[0]
+    if n0 == 0:
+        return [(), Ellipsis, slice(None), slice(None, None, 2), slice(0, 0)]
+    keys += [slice(n0 // 2, None), slice(-3, None), [0, n0 - 1] if n0 > 1 else [0], (Ellipsis,)]
+    if n0 > 5:
+        keys += [[1, 2, 3, 5], [0, 4, 5]]
+    if len(shape) >= 2:
+        n1 = shape[1]
+        keys += [
+            (0, 0),
+            (-1, -1),
+            (slice(1, 3), slice(None, None, 2)),
+            (Ellipsis, 1),
+            (1, Ellipsis),
+            (slice(None), [0, n1 - 1] if n1 > 1 else [0]),
+            (slice(None, None, 2), 1),
+            (slice(0, 2), slice(3, 1)),
+        ]
+    if len(shape) >= 3:
+        keys += [(0, slice(None), -1), (slice(1, None, 2), 2, slice(None, None, 3)), (Ellipsis, 0, 0), (0, Ellipsis, 1)]
+    return keys
+
+
+# h5py 3.16 (HDF5 2.0) returns the elements of a variable-length sequence of
+# big-endian floats unswapped (0.25 comes back as 2.6e-319), so these are
+# checked against the values written instead (test_vlen_big_endian).
+H5PY_MISREADS = {"vlen/f8_be"}
+
+ERROR_KEYS_1D = [slice(None, None, -1), 10**6, -(10**6), None, (0, 0, 0, 0, 0), [3, 1], (Ellipsis, Ellipsis), 1.5, "nope"]
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+
+def test_every_dataset_matches_h5py(h5py, pair):
+    ours, theirs, _ = pair
+    checked = 0
+    for name in _all_datasets(h5py, theirs):
+        if name.startswith("unsupported/") or name in H5PY_MISREADS or name == "cmp/with_vlen":
+            continue
+        t = theirs[name]
+        o = ours[name]
+        assert o.shape == t.shape, name
+        assert o.dtype == t.dtype, f"{name}: {o.dtype} vs {t.dtype}"
+        assert dict(o.dtype.metadata or {}) == dict(t.dtype.metadata or {}), name
+        assert o.ndim == t.ndim and o.size == t.size, name
+        assert o.maxshape == t.maxshape, name
+        assert o.name == t.name, name
+        if t.shape is None:
+            assert_same(o[()], t[()], name)
+            continue
+        for key in keys_for(t.shape):
+            what = f"{name}[{key!r}]"
+            try:
+                expected = t[key]
+            except Exception as e:  # noqa: BLE001 - h5py refuses: so must we
+                with pytest.raises(type(e)):
+                    o[key]
+                continue
+            assert_same(o[key], expected, what)
+            checked += 1
+    assert checked > 500
+
+
+def test_vlen_big_endian(pair):
+    ours, _, _ = pair
+    ds = ours["vlen/f8_be"]
+    assert ds.dtype.metadata["vlen"] == np.dtype(">f8")
+    got = ds[()]
+    expected = [np.linspace(0, 1, 5), np.array([2.5]), np.array([])]
+    assert got.shape == (3,)
+    for g, e in zip(got, expected):
+        assert g.dtype == np.dtype(">f8")
+        np.testing.assert_array_equal(g, e)
+    np.testing.assert_array_equal(ds[1], [2.5])
+
+
+def test_errors_match_h5py(h5py, pair):
+    ours, theirs, _ = pair
+    for name in ["num/le_i4_1d", "num/be_f8_2d_gzip", "str/vlen", "num/scalar_f8"]:
+        for key in ERROR_KEYS_1D:
+            try:
+                theirs[name][key]
+            except Exception as e:  # noqa: BLE001
+                with pytest.raises(type(e)):
+                    ours[name][key]
+            else:
+                pass  # valid for this shape; covered by the value test
+
+
+def test_compound_fields_match_h5py(pair):
+    ours, theirs, _ = pair
+    for name in ["cmp/padded", "cmp/padded_chunked", "cmp/nested_2d"]:
+        t, o = theirs[name], ours[name]
+        for field in t.dtype.names:
+            assert_same(o[field], t[field], f"{name}[{field}]")
+            assert_same(o[field, 1:3], t[field, 1:3], f"{name}[{field}, 1:3]")
+        two = list(t.dtype.names[:2])
+        expected = t[tuple(two)]
+        got = o[tuple(two)]
+        assert got.dtype.names == expected.dtype.names
+        for field in two:
+            np.testing.assert_array_equal(got[field], expected[field])
+    with pytest.raises(ValueError):
+        ours["cmp/padded"]["no_such_field"]
+    with pytest.raises(ValueError):
+        ours["num/le_i4_1d"]["id"]
+
+
+def test_numpy_asarray_and_len(pair):
+    ours, theirs, _ = pair
+    assert_same(np.asarray(ours["num/f8_3d"]), np.asarray(theirs["num/f8_3d"]))
+    assert len(ours["num/f8_3d"]) == len(theirs["num/f8_3d"])
+    with pytest.raises(TypeError):
+        len(ours["num/scalar_f8"])
+
+
+def test_attributes_match_h5py(h5py, pair):
+    ours, theirs, _ = pair
+    for path in ["/", "num/le_f8_1d", "deep/er/est"]:
+        t = theirs[path].attrs
+        o = ours[path].attrs
+        assert list(o.keys()) == sorted(t.keys()), path
+        assert len(o) == len(t)
+        for k in t.keys():
+            assert k in o
+            assert_same(o[k], t[k], f"{path}.attrs[{k}]")
+        assert o.get("missing", 5) == 5
+        with pytest.raises(KeyError):
+            o["missing"]
+        assert [k for k, _ in o.items()] == list(o.keys())
+
+
+def test_groups_match_h5py(h5py, pair):
+    ours, theirs, _ = pair
+    assert list(ours.keys()) == list(theirs.keys())
+    assert len(ours) == len(theirs)
+    for path in ["num", "deep", "deep/er", "deep/er/est", "empty_group"]:
+        o, t = ours[path], theirs[path]
+        assert isinstance(o, clawhdf5.Group)
+        assert list(o.keys()) == list(t.keys()), path
+        assert list(o) == list(t), path
+        assert len(o) == len(t), path
+        assert o.name == t.name
+    g = ours["deep/er"]
+    assert isinstance(g["est"], clawhdf5.Group)
+    assert isinstance(g["est/leaf"], clawhdf5.Dataset)
+    assert g["/deep/er/est/leaf"].name == "/deep/er/est/leaf"
+    assert "est/leaf" in g and "/num" in g and "nope" not in g
+    assert ours["/"].name == "/"
+    assert "deep/er/est/leaf" in ours
+    assert ours.get("nope") is None
+    with pytest.raises(KeyError):
+        ours["deep/nope"]
+    names = [k for k, v in ours["deep/er/est"].items()]
+    assert names == ["leaf"]
+    assert isinstance(ours["deep/er/est"].values()[0], clawhdf5.Dataset)
+
+
+def test_unsupported_types_are_errors_not_data(pair):
+    ours, _, _ = pair
+    ds = ours["unsupported/refs"]  # opening works
+    with pytest.raises(TypeError):
+        ds.dtype
+    with pytest.raises(TypeError):
+        ds[()]
+    with pytest.raises(TypeError):
+        ours["cmp/with_vlen"][()]
+
+
+def test_boolean_masks_are_refused(pair):
+    ours, _, _ = pair
+    with pytest.raises(TypeError):
+        ours["num/le_i4_1d"][np.ones(37, dtype=bool)]
+
+
+def test_reads_hand_numpy_the_rust_buffer(pair):
+    """A fixed-size read is a view over the buffer the library filled, not a
+    copy of it."""
+    ours, _, _ = pair
+    arr = ours["num/le_f8_2d_gzip"][2:9, 1:4]
+    assert not arr.flags.owndata
+    assert arr.base is not None
+    assert arr.flags.aligned and arr.flags.c_contiguous
+
+
+def test_only_the_selected_chunks_are_read(h5py, tmp_path):
+    """Damage one chunk: a selection that avoids it still reads, one that
+    touches it fails. Reading everything and slicing afterwards (what the
+    bindings did before) failed both. (The library reads the whole dataset
+    anyway when a selection's bounding box covers more than half of it, so
+    the selections here stay below that.)"""
+    path = str(tmp_path / "damaged.h5")
+    data = np.arange(1000, dtype="<f8")
+    with h5py.File(path, "w") as f:
+        f.create_dataset("d", data=data, chunks=(100,), compression="gzip")
+    with h5py.File(path, "r") as f:
+        info = f["d"].id.get_chunk_info(9)  # the last chunk
+    with open(path, "r+b") as fh:
+        fh.seek(info.byte_offset)
+        fh.write(b"\xff" * info.size)
+    with clawhdf5.File(path, "r") as f:
+        ds = f["d"]
+        np.testing.assert_array_equal(ds[0:400], data[0:400])
+        np.testing.assert_array_equal(ds[805:900], data[805:900])
+        np.testing.assert_array_equal(ds[5:450:7], data[5:450:7])
+        np.testing.assert_array_equal(ds[[3, 450, 899]], data[[3, 450, 899]])
+        with pytest.raises(Exception):
+            ds[950]
+        with pytest.raises(Exception):
+            ds[:]
+
+
+def test_threads_read_the_same_file(pair):
+    """Reads from many threads at once (the GIL is released during each
+    read) return exactly what h5py returns."""
+    ours, theirs, _ = pair
+    names = ["num/le_f8_2d_gzip", "num/i4_3d_shuffle", "str/vlen_2d_gzip", "cmp/padded_chunked", "num/be_i8_1d"]
+    expected = {n: theirs[n][()] for n in names}
+    errors = []
+    barrier = threading.Barrier(8)
+
+    def work(i):
+        barrier.wait()
+        for r in range(40):
+            n = names[(i + r) % len(names)]
+            got = ours[n][()]
+            try:
+                assert_same(got, expected[n], n)
+            except AssertionError as e:
+                errors.append(e)
+
+    with ThreadPoolExecutor(8) as pool:
+        list(pool.map(work, range(8)))
+    assert not errors, errors[:3]

crates/clawhdf5-py/tests/test_write_read.py

@@ -87,7 +87,7 @@ def test_dataset_dtype(sample_read_file):
 def test_read_root_attrs(sample_read_file):
     with clawhdf5.File(sample_read_file, "r") as f:
         assert f.attrs["version"] == 1
-        assert f.attrs["description"] == "test file"
+        assert f.attrs["description"] == b"test file"  # fixed-length string: numpy.bytes_, as in h5py
 
 
 def test_attrs_len(sample_read_file):
@@ -132,7 +132,7 @@ def test_read_group_dataset(grouped_read_file):
 def test_read_group_attrs(grouped_read_file):
     with clawhdf5.File(grouped_read_file, "r") as f:
         grp = f["sensors"]
-        assert grp.attrs["location"] == "lab"
+        assert grp.attrs["location"] == b"lab"
 
 
 def test_nested_path_access(grouped_read_file):
@@ -176,7 +176,7 @@ def test_write_with_attrs(tmp_h5):
         f.attrs["author"] = "test"
         f.attrs["count"] = 42
     with clawhdf5.File(tmp_h5, "r") as f:
-        assert f.attrs["author"] == "test"
+        assert f.attrs["author"] == b"test"
         assert f.attrs["count"] == 42