# What is this?
A simple python tool for hashing objects that python normally treats as unhashable. There are also a few other tools such as ways of deeply hashing functions, and an system for extending this hashability to work with other existing classes, such as dataframes or neural networks.
# How do I use this?
`pip install super_hash`
```python
from super_hash import super_hash, function_hashers, FrozenDict, helpers
normally_unhashable = {
frozenset({
frozenset({
"key-deep-deep": 10
}.items()): "key-deep",
}.items()): "first_value",
"second_value": [
{"a": 10},
]
}
a_hash = super_hash(normally_unhashable)
#
# extend what can be hashed
#
# example1:
import pandas as pd
# tell super_hash that pandas dataframes should be converted to csv, then hashed
super_hash.conversion_table[pd.DataFrame] = lambda data_frame : super_hash(data_frame.to_csv())
# example2:
import torch
# create a custom checker function
is_non_scalar_pytorch_tensor = lambda value: isinstance(value, torch.Tensor) and len(value.shape) > 0
# create a custom converter
super_hash.conversion_table[is_non_scalar_pytorch_tensor] = lambda non_scalar_tensor: super_hash(non_scalar_tensor.tolist())
# example3:
class Thing:
def __super_hash__(self):
return self.file_path
```
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"description": "# What is this?\n\nA simple python tool for hashing objects that python normally treats as unhashable. There are also a few other tools such as ways of deeply hashing functions, and an system for extending this hashability to work with other existing classes, such as dataframes or neural networks.\n\n# How do I use this?\n\n`pip install super_hash`\n\n\n```python\nfrom super_hash import super_hash, function_hashers, FrozenDict, helpers\n\nnormally_unhashable = {\n frozenset({\n frozenset({\n \"key-deep-deep\": 10\n }.items()): \"key-deep\",\n }.items()): \"first_value\",\n \"second_value\": [\n {\"a\": 10},\n ]\n}\na_hash = super_hash(normally_unhashable)\n\n# \n# extend what can be hashed\n# \n\n# example1:\nimport pandas as pd\n# tell super_hash that pandas dataframes should be converted to csv, then hashed\nsuper_hash.conversion_table[pd.DataFrame] = lambda data_frame : super_hash(data_frame.to_csv())\n\n# example2:\nimport torch\n# create a custom checker function\nis_non_scalar_pytorch_tensor = lambda value: isinstance(value, torch.Tensor) and len(value.shape) > 0\n# create a custom converter\nsuper_hash.conversion_table[is_non_scalar_pytorch_tensor] = lambda non_scalar_tensor: super_hash(non_scalar_tensor.tolist())\n\n# example3:\nclass Thing:\n def __super_hash__(self):\n return self.file_path\n```\n\n\n",
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