dirty-models 0.4.0

Dirty models for python 3

dirty-models

Dirty models for python 3

Documentation

http://dirty-models.readthedocs.org

Features

• Python 3 package.

• Easy to create a model.

• Non destructive modifications.

• Non false positive modifications.

• Able to restore original data for each field or whole model.

• Access to original data.

• Read only fields.

• Alias for fields.

• Custom getters and setters for each fields.

• Automatic cast value.

• Easy import from/export to dict.

• Basic field type implemented.

• HashMap model. It could be used instead of DynamicModel.

• FastDynamicModel. It could be used instead of DynamicModel. Same behavior better performance.

• Pickable models.

• Datetime fields can use any datetime format using parser and formatter functions.

• No database dependent.

Installation

$ pip install dirty-models

Issues

• Getter and setter feature needs refactor to be able to use as decorators.

• DynamicModel is too strange. I don’t trust in it. Try to use HashMapModel.

Basic usage

from dirty_models.models import BaseModel
from dirty_models.fields import StringField, IntegerField

class FooBarModel(BaseModel):
    foo = IntegerField()
    bar = StringField(name="real_bar")
    alias_field = IntegerField(alias=['alias1', 'alias2'])



fb = FooBarModel()

fb.foo = 2
assert fb.foo is 2

fb.bar = 'wow'
assert fb.bar is 'wow'
assert fb.real_bar is 'wow'

fb.alias_field = 3
assert fb.alias_field is 3
assert fb.alias1 is fb.alias_field
assert fb.alias2 is fb.alias_field

Note

Look at tests for more examples

Performance Tests

$ python3 performancerunner.py
DynamicModel start
DynamicModel: iteration no. 0 start
DynamicModel: iteration no. 0 => 0:00:02.528166
DynamicModel: iteration no. 1 start
DynamicModel: iteration no. 1 => 0:00:03.415274
DynamicModel: iteration no. 2 start
DynamicModel: iteration no. 2 => 0:00:03.115128
DynamicModel: iteration no. 3 start
DynamicModel: iteration no. 3 => 0:00:04.091488
DynamicModel: iteration no. 4 start
DynamicModel: iteration no. 4 => 0:00:05.275302
DynamicModel => 0:00:18.425358
FastDynamicModel start
FastDynamicModel: iteration no. 0 start
FastDynamicModel: iteration no. 0 => 0:00:01.351796
FastDynamicModel: iteration no. 1 start
FastDynamicModel: iteration no. 1 => 0:00:01.265681
FastDynamicModel: iteration no. 2 start
FastDynamicModel: iteration no. 2 => 0:00:01.270142
FastDynamicModel: iteration no. 3 start
FastDynamicModel: iteration no. 3 => 0:00:01.273443
FastDynamicModel: iteration no. 4 start
FastDynamicModel: iteration no. 4 => 0:00:01.280512
FastDynamicModel => 0:00:06.441574
BlobField start
BlobField: iteration no. 0 start
BlobField: iteration no. 0 => 0:00:00.000082
BlobField: iteration no. 1 start
BlobField: iteration no. 1 => 0:00:00.000027
BlobField: iteration no. 2 start
BlobField: iteration no. 2 => 0:00:00.000025
BlobField: iteration no. 3 start
BlobField: iteration no. 3 => 0:00:00.000024
BlobField: iteration no. 4 start
BlobField: iteration no. 4 => 0:00:00.000023
BlobField => 0:00:00.000181
{'DynamicModel': {'results': [datetime.timedelta(0, 2, 528166), datetime.timedelta(0, 3, 415274), datetime.timedelta(0, 3, 115128), datetime.timedelta(0, 4, 91488), datetime.timedelta(0, 5, 275302)], 'total': datetime.timedelta(0, 18, 425358)}, 'FastDynamicModel': {'results': [datetime.timedelta(0, 1, 351796), datetime.timedelta(0, 1, 265681), datetime.timedelta(0, 1, 270142), datetime.timedelta(0, 1, 273443), datetime.timedelta(0, 1, 280512)], 'total': datetime.timedelta(0, 6, 441574)}, 'BlobField': {'results': [datetime.timedelta(0, 0, 82), datetime.timedelta(0, 0, 27), datetime.timedelta(0, 0, 25), datetime.timedelta(0, 0, 24), datetime.timedelta(0, 0, 23)], 'total': datetime.timedelta(0, 0, 181)}}