https://github.com/ultrafunkamsterdam/d1ct

hybrid object/mapping. d1ct is : attribute-accessible, eg: d.x == d['x'], recursive, nested dicts will become d1cts too, so you can access d.x.a[0].b and have autocompletion too!, json serializable, eg: json.dumps(d), ** unpackable, ior-able, eg: d | otherdict, d |= otherdict , subclassable, metaclassable, interchangable with dict and back.
https://github.com/ultrafunkamsterdam/d1ct

Last synced: 6 months ago
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hybrid object/mapping. d1ct is : attribute-accessible, eg: d.x == d['x'], recursive, nested dicts will become d1cts too, so you can access d.x.a[0].b and have autocompletion too!, json serializable, eg: json.dumps(d), ** unpackable, ior-able, eg: d | otherdict, d |= otherdict , subclassable, metaclassable, interchangable with dict and back.

• Host: GitHub
• URL: https://github.com/ultrafunkamsterdam/d1ct
• Owner: ultrafunkamsterdam
• License: other
• Created: 2022-05-05T14:15:21.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-05-06T12:33:27.000Z (over 3 years ago)
• Last Synced: 2025-03-28T07:46:29.340Z (7 months ago)
• Language: Python
• Size: 8.79 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
d1ct - a hybrid object/mapping

===

FEATURES

---

 - directly inherited from dict

 - accessible by attribute.   o.x == o['x'] 

 - This works also for all corner cases.

 - json.dumps json.loads

 - vars() works on it!

 - hashable! (should not yet trust on it, though)

 - autocomplete works even if the objects comes from a list

 - best of all, it works recursively. So all inner dicts will become d1cts too.

 

   

  ![d1ct screenshot](https://i.imgur.com/AKStFpo_d.webp?maxwidth=780&fidelity=grand)  

  

>  ehhh, no downsides?   

>>**oh hell yeah it got downsides**  

  

   the performance which is a magnitude slower (~6x) than builtin dict  

   to give you an impression of timeit results:

   

   **d1ct**

   -----

  0.26289230000111274  

  0.2543108999961987  

  0.25743720002355985  

  0.2560539000260178  

  0.2556736999831628  

  0.25478869999642484  

  0.2541010999993887  

  0.2552008999919053  

  0.2651131000020541  

  0.25846979999914765  

   

  **dict**

  -----

  0.04928750000544824  

  0.04902900001616217  

  0.049049399996874854  

  0.050644999981159344  

  0.0496991999971214  

  0.04940899999928661  

  0.0497540999785997  

  0.04959690000396222  

  0.04939249999006279  

  0.04959330000565387   

      

  **So... this implementation might have side effects which I did not notice, because of different use cases or whatever.**

   

 

 Usage / Testing 

 ----

   

    import d1ct

    

    d = d1ct({"a": 1, "b": {"b1": "c1"}}, kw1=1, kw2=2)

    d["z1"] = {}

    d.z1.update({"yyyy": 2323232})

    

    assert d.a == d['a'] == 1

    assert d.z1 == d['z1'] == {'yyyy': 2323232}

    assert d == {'a': 1, 'b': {'b1': 'c1'}, 'kw1': 1, 'kw2': 2, 'z1': {'yyyy': 2323232}}

    assert type(d.z1) is d1ct

    

    d.z1.update({"yyyy": 2323232})

    assert type(d.z1.yyyy) is int

    

    d.z1.update({"yyyy": {"xbb": [1, 2, 23]}})

    assert type(d.z1.yyyy) is d1ct

    

    import json

    json.dumps(d)

    

    d |= {'more_keys': {'really': 'yeah!'}}

    

    assert d == {'a': 1,'b': {'b1': 'c1'}, 'kw1': 1,'kw2': 2, 'z1': {'yyyy': {'xbb': [1, 2, 23] } },'more_keys': {'really': 'yeah!'} }