trimesh.caching

caching.py

Functions and classes that help with tracking changes in ndarrays and clearing cached values based on those changes.

class trimesh.caching.Cache(id_function, force_immutable=False)

Bases: object

Class to cache values which will be stored until the result of an ID function changes.

__init__(id_function, force_immutable=False)

Create a cache object.

Parameters

id_function (function) – Returns hashable value
force_immutable (bool) – If set will make all numpy arrays read-only

clear(exclude=None)

Remove elements in the cache.

Parameters: exclude (list) – List of keys in cache to not clear.

delete(key): Remove a key from the cache.

id_set(): Set the current ID to the value of the ID function.

update(items): Update the cache with a set of key, value pairs without checking id_function.

verify(): Verify that the cached values are still for the same value of id_function and delete all stored items if the value of id_function has changed.

class trimesh.caching.DataStore

Bases: collections.abc.Mapping

A class to store multiple numpy arrays and track them all for changes.

Operates like a dict that only stores numpy.ndarray

__init__()

clear(): Remove all data from the DataStore.

crc()

Get a CRC reflecting everything in the DataStore.

Returns: crc – CRC of data
Return type: int

fast_hash()

Get a CRC32 or xxhash.xxh64 reflecting the DataStore.

Returns: hashed – Checksum of data
Return type: int

is_empty()

Is the current DataStore empty or not.

Returns: empty – False if there are items in the DataStore
Return type: bool

md5()

Get an MD5 reflecting everything in the DataStore.

Returns: md5 – MD5 of data in hexadecimal
Return type: str

property mutable

Is data allowed to be altered or not.

Returns: is_mutable – Can data be altered in the DataStore
Return type: bool

pop(key)

update(values)

class trimesh.caching.TrackedArray

Bases: numpy.ndarray

Subclass of numpy.ndarray that provides hash methods to track changes.

General method is to aggressively set ‘modified’ flags on operations which might (but don’t necessarily) alter the array, ideally we sometimes compute hashes when we don’t need to, but we don’t return wrong hashes ever.

We store boolean modified flag for each hash type to make checks fast even for queries of different hashes.

md5 : str, hexadecimal MD5 of array

crc : int, zlib crc32/adler32 checksum

fast_hash : int, CRC or xxhash.xx64

crc()

A zlib.crc32 or zlib.adler32 checksum of the current data.

Returns: crc – Checksum from zlib.crc32 or zlib.adler32
Return type: int

fast_hash()

An xxhash.b64 hash of the array.

Returns: xx – xxhash.xxh64 hash of array.
Return type: int

md5()

Return an MD5 hash of the current array.

Returns: md5 – Hexadecimal MD5 of the array
Return type: str

property mutable

trimesh.caching.cache_decorator(function)

A decorator for class methods, replaces @property but will store and retrieve function return values in object cache.

Parameters

function (method) –

This is used as a decorator: ``` @cache_decorator def foo(self, things):

return ‘happy days’

```

trimesh.caching.tracked_array(array, dtype=None)

Properly subclass a numpy ndarray to track changes.

Avoids some pitfalls of subclassing by forcing contiguous arrays and does a view into a TrackedArray.

Parameters

array (array- like object) – To be turned into a TrackedArray
dtype (np.dtype) – Which dtype to use for the array

Returns

tracked – Contains input array data

Return type

TrackedArray