add AsdfFile.blocks API

[braingram]

Description

Add AsdfFile.blocks to provide information about ASDF blocks when an AsdfFile is created from an ASDF file.

blocks is an immutable sequence of read-only views of the block data.

>> af = asdf.open("foo.asdf")
>> af.info(max_rows=None)
root (AsdfObject)
├─asdf_library (Software)
│ ├─author (str): The ASDF Developers
│ ├─homepage (str): http://github.com/asdf-format/asdf
│ ├─name (str): asdf
│ └─version (str): 4.3.1.dev5+g2dc5555d.d20250722
├─history (dict)
│ └─extensions (list)
│   └─[0] (ExtensionMetadata)
│     ├─extension_class (str): asdf.extension._manifest.ManifestExtension
│     ├─extension_uri (str): asdf://asdf-format.org/core/extensions/core-1.6.0
│     ├─manifest_software (Software)
│     │ ├─name (str): asdf_standard
│     │ └─version (str): 1.1.1
│     └─software (Software)
│       ├─name (str): asdf
│       └─version (str): 4.3.1.dev5+g2dc5555d.d20250722
└─arrs (list)
  ├─[0] (NDArrayType)
  │ ├─shape (tuple)
  │ │ └─[0] (int): 3
  │ └─dtype (Float64DType): float64
  ├─[1] (NDArrayType)
  │ ├─shape (tuple)
  │ │ └─[0] (int): 3
  │ └─dtype (Float64DType): float64
  ├─[2] (NDArrayType)
  │ ├─shape (tuple)
  │ │ └─[0] (int): 3
  │ └─dtype (Float64DType): float64
  ├─[3] (NDArrayType)
  │ ├─shape (tuple)
  │ │ └─[0] (int): 3
  │ └─dtype (Float64DType): float64
  └─[4] (NDArrayType)
    ├─shape (tuple)
    │ └─[0] (int): 3
    └─dtype (Float64DType): float64
█ Block 0: 24 bytes
█ Block 1: 24 bytes
█ Block 2: 24 bytes
█ Block 3: 24 bytes
█ Block 4: 24 bytes
>> len(af.blocks)
5
>> af.blocks[0].header
mappingproxy({'flags': 0,
              'compression': b'\x00\x00\x00\x00',
              'allocated_size': 24,
              'used_size': 24,
              'data_size': 24,
              'checksum': b'\x16\x81\xff\xc6\xe0F\xc7\xaf\x98\xc9\xe6\xc22\xa3\xfe\n'})
>> af.blocks[0].offset
1070
>> af.blocks[0].data_offset
1120
>> af.blocks[0].loaded()
False
>> arr = af.blocks[0].load()
>> arr
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0], dtype=uint8)
>> af.blocks[0].loaded()
True
>> af.blocks[0].load() is arr  # returns the exact same array
True
>> af.blocks[0].load() is af['arrs'][0].base  # data is shared with the tree
True

TODO:

• load into array (and via NDArrayType)

Tasks

• run pre-commit on your machine
• run pytest on your machine
• Does this PR add new features and / or change user-facing code / API? (if not, label with no-changelog-entry-needed)
  • write news fragment(s) in changes/: echo "changed something" > changes/<PR#>.<changetype>.rst (see below for change types)
  • update relevant docstrings and / or docs/ page
  • for any new features, add unit tests

news fragment change types...

• changes/<PR#>.feature.rst: new feature
• changes/<PR#>.bugfix.rst: bug fix
• changes/<PR#>.doc.rst: documentation change
• changes/<PR#>.removal.rst: deprecation or removal of public API
• changes/<PR#>.general.rst: infrastructure or miscellaneous change