Classes | |
| class | PersistenceError |
Functions | |
| dict[str, Any] | numpy_to_json (np.ndarray arr) |
| np.ndarray | json_to_numpy (dict[str, Any] encoded_dict) |
| dict[str, Any]|None | encode_metadata (dict[str, Any]|None metadata) |
| dict[str, Any]|None | decode_metadata (dict[str, Any]|None metadata) |
| Any | extract_from_metadata (Any data, dict[str, Any]|None metadata, str key) |
Function Documentation
◆ decode_metadata()
| dict[str, Any] | None lsst.scarlet.lite.io.utils.decode_metadata | ( | dict[str, Any] | None | metadata | ) |
Unpack metadata from a JSON compatible format.
Parameters
----------
metadata :
The metadata to be unpacked.
Returns
-------
result :
The unpacked metadata.
Definition at line 98 of file utils.py.
98def decode_metadata(metadata: dict[str, Any] | None) -> dict[str, Any] | None:
99 """Unpack metadata from a JSON compatible format.
100
101 Parameters
102 ----------
103 metadata :
104 The metadata to be unpacked.
105
106 Returns
107 -------
108 result :
109 The unpacked metadata.
110 """
111 if metadata is None:
112 return None
113 if "array_keys" in metadata:
114 for key in metadata["array_keys"]:
115 # Default dtype is float32 to support legacy models
116 dtype = metadata.pop(f"{key}_dtype", "float32")
117 shape = metadata.pop(f"{key}_shape", None)
118 if shape is None and f"{key}Shape" in metadata:
119 # Support legacy models that use `keyShape`
120 shape = metadata[f"{key}Shape"]
121 decoded = json_to_numpy({"dtype": dtype, "shape": shape, "data": metadata[key]})
122 metadata[key] = decoded
123 # Remove the array keys after decoding
124 del metadata["array_keys"]
125 return metadata
126
127
◆ encode_metadata()
| dict[str, Any] | None lsst.scarlet.lite.io.utils.encode_metadata | ( | dict[str, Any] | None | metadata | ) |
Pack metadata into a JSON compatible format.
Parameters
----------
metadata :
The metadata to be packed.
Returns
-------
result :
The packed metadata.
Definition at line 67 of file utils.py.
67def encode_metadata(metadata: dict[str, Any] | None) -> dict[str, Any] | None:
68 """Pack metadata into a JSON compatible format.
69
70 Parameters
71 ----------
72 metadata :
73 The metadata to be packed.
74
75 Returns
76 -------
77 result :
78 The packed metadata.
79 """
80 if metadata is None:
81 return None
82 encoded = {}
83 array_keys = []
84 for key, value in metadata.items():
85 if isinstance(value, np.ndarray):
86 _encoded = numpy_to_json(value)
87 encoded[key] = _encoded["data"]
88 encoded[f"{key}_shape"] = _encoded["shape"]
89 encoded[f"{key}_dtype"] = _encoded["dtype"]
90 array_keys.append(key)
91 else:
92 encoded[key] = value
93 if len(array_keys) > 0:
94 encoded["array_keys"] = array_keys
95 return encoded
96
97
◆ extract_from_metadata()
| Any lsst.scarlet.lite.io.utils.extract_from_metadata | ( | Any | data, |
| dict[str, Any] | None | metadata, | ||
| str | key ) |
Extract relevant information from the metadata.
Parameters
----------
data :
The data to extract information from.
metadata :
The metadata to extract information from.
key :
The key to extract from the metadata.
Returns
-------
result :
A tuple containing the extracted data and metadata.
Definition at line 128 of file utils.py.
132) -> Any:
133 """Extract relevant information from the metadata.
134
135 Parameters
136 ----------
137 data :
138 The data to extract information from.
139 metadata :
140 The metadata to extract information from.
141 key :
142 The key to extract from the metadata.
143
144 Returns
145 -------
146 result :
147 A tuple containing the extracted data and metadata.
148 """
149 if data is not None:
150 return data
151 if metadata is None:
152 raise ValueError("Both data and metadata cannot be None")
153 if key not in metadata:
154 raise ValueError(f"'{key}' not found in metadata")
155 return metadata[key]
◆ json_to_numpy()
| np.ndarray lsst.scarlet.lite.io.utils.json_to_numpy | ( | dict[str, Any] | encoded_dict | ) |
Decode a JSON dictionary back to a numpy array.
Parameters
----------
encoded_dict :
Dictionary with 'dtype', 'shape', and 'data' keys.
Returns
-------
result :
The reconstructed numpy array.
Definition at line 48 of file utils.py.
48def json_to_numpy(encoded_dict: dict[str, Any]) -> np.ndarray:
49 """
50 Decode a JSON dictionary back to a numpy array.
51
52 Parameters
53 ----------
54 encoded_dict :
55 Dictionary with 'dtype', 'shape', and 'data' keys.
56
57 Returns
58 -------
59 result :
60 The reconstructed numpy array.
61 """
62 if "dtype" not in encoded_dict or "shape" not in encoded_dict or "data" not in encoded_dict:
63 raise ValueError("Encoded dictionary must contain 'dtype', 'shape', and 'data' keys.")
64 return np.array(encoded_dict["data"], dtype=encoded_dict["dtype"]).reshape(encoded_dict["shape"])
65
66
◆ numpy_to_json()
| dict[str, Any] lsst.scarlet.lite.io.utils.numpy_to_json | ( | np.ndarray | arr | ) |
Encode a numpy array as JSON-serializable dictionary.
Parameters
----------
arr :
The numpy array to encode
Returns
-------
result :
A JSON formatted dictionary containing the dtype, shape,
and data of the array.
Definition at line 14 of file utils.py.
14def numpy_to_json(arr: np.ndarray) -> dict[str, Any]:
15 """
16 Encode a numpy array as JSON-serializable dictionary.
17
18 Parameters
19 ----------
20 arr :
21 The numpy array to encode
22
23 Returns
24 -------
25 result :
26 A JSON formatted dictionary containing the dtype, shape,
27 and data of the array.
28 """
29 # Convert to native Python types for JSON serialization
30 flattened = arr.flatten()
31
32 # Convert numpy scalars to native Python types
33 if np.issubdtype(arr.dtype, np.integer):
34 data: list = [int(x) for x in flattened]
35 elif np.issubdtype(arr.dtype, np.floating):
36 data = [float(x) for x in flattened]
37 elif np.issubdtype(arr.dtype, np.complexfloating):
38 data = [complex(x) for x in flattened]
39 elif np.issubdtype(arr.dtype, np.bool_):
40 data = [bool(x) for x in flattened]
41 else:
42 # For other types (strings, objects, etc.), convert to string
43 data = [str(x) for x in flattened]
44
45 return {"dtype": str(arr.dtype), "shape": tuple(arr.shape), "data": data}
46
47
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