9900d508f5
- Refactored REST client to use requests library for Python 3 compatibility. - Added docstrings to all public methods & classes. - Fleshed out CRUD APIs for data, feed, group. - Wrote full suite of integration tests to verify changes. - Updated setup.py to bump version to 0.9 & depend on requests and paho-mqtt modules. - Updated and tested code to work with both Python 2 and 3. - Broke out errors into separate file, and added explicit data model classes. - General cleanup and preparation for public release.
117 lines
4.5 KiB
Python
117 lines
4.5 KiB
Python
# Copyright (c) 2014 Adafruit Industries
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# Author: Tony DiCola
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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from collections import namedtuple
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# Handle python 2 and 3 (where map functions like itertools.imap)
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try:
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from itertools import imap as map
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except ImportError:
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# Ignore import error on python 3 since map already behaves as expected.
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pass
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# List of fields/properties that are present on a data object from IO.
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DATA_FIELDS = [ 'created_epoch',
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'created_at',
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'updated_at',
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'value',
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'completed_at',
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'feed_id',
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'expiration',
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'position',
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'id' ]
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STREAM_FIELDS = [ 'completed_at',
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'created_at',
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'id',
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'value' ]
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FEED_FIELDS = [ 'last_value_at',
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'name',
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'stream',
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'created_at',
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'updated_at',
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'unit_type',
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'mode',
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'key',
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'unit_symbol',
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'fixed',
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'last_value',
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'id' ]
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GROUP_FIELDS = [ 'description',
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'source_keys',
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'id',
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'source',
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'key',
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'feeds',
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'properties',
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'name' ]
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# These are very simple data model classes that are based on namedtuple. This is
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# to keep the classes simple and prevent any confusion around updating data
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# locally and forgetting to send those updates back up to the IO service (since
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# tuples are immutable you can't change them!). Depending on how people use the
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# client it might be prudent to revisit this decision and consider making these
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# full fledged classes that are mutable.
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Data = namedtuple('Data', DATA_FIELDS)
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Stream = namedtuple('Stream', STREAM_FIELDS)
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Feed = namedtuple('Feed', FEED_FIELDS)
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Group = namedtuple('Group', GROUP_FIELDS)
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# Magic incantation to make all parameters to the initializers optional with a
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# default value of None.
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Data.__new__.__defaults__ = tuple(None for x in DATA_FIELDS)
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Stream.__new__.__defaults__ = tuple(None for x in STREAM_FIELDS)
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Feed.__new__.__defaults__ = tuple(None for x in FEED_FIELDS)
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Group.__new__.__defaults__ = tuple(None for x in GROUP_FIELDS)
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# Define methods to convert from dicts to the data types.
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def _from_dict(cls, data):
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# Convert dict to call to class initializer (to work with the data types
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# base on namedtuple). However be very careful to preserve forwards
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# compatibility by ignoring any attributes in the dict which are unknown
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# by the data type.
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params = {x: data.get(x, None) for x in cls._fields}
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return cls(**params)
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def _feed_from_dict(cls, data):
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params = {x: data.get(x, None) for x in cls._fields}
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# Parse the stream if provided and generate a stream instance.
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params['stream'] = Stream.from_dict(data.get('stream', {}))
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return cls(**params)
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def _group_from_dict(cls, data):
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params = {x: data.get(x, None) for x in cls._fields}
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# Parse the feeds if they're provided and generate feed instances.
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params['feeds'] = tuple(map(Feed.from_dict, data.get('feeds', [])))
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return cls(**params)
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# Now add the from_dict class methods defined above to the data types.
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Data.from_dict = classmethod(_from_dict)
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Stream.from_dict = classmethod(_from_dict)
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Feed.from_dict = classmethod(_feed_from_dict)
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Group.from_dict = classmethod(_group_from_dict)
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