Dataset.__init__() - Code Metrics - Inspection of "Fixed tests for new dataset setup" - connectordb/connectordb-python - Measure and Improve Code Quality continuously with Scrutinizer

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by Daniel

created 2016-05-26 11:02 UTC

Dataset.init() B

↳ Parent: Dataset

Complexity

Conditions

Size

Total Lines

Duplication

Lines	29
Ratio	100 %

Importance

Changes	1
Bugs	0	Features	0

Metric	Value
cc	4
c	1
b	0
f	0
dl	29
loc	29
rs	8.5806

from __future__ import absolute_import

from .._stream import Stream, query_maker
from .merge import Merge, get_stream
import six


# param_stream adds the stream correctly into the query (depending on what stream parameter was given)
def param_stream(cdb, params, stream):
    if isinstance(stream, Merge):
        params["merge"] = stream.query
    else:
        params["stream"] = get_stream(cdb, stream)


class Dataset(object):
    """ConnectorDB is capable of taking several separate unrelated streams, and based upon
    the chosen interpolation method, putting them all together to generate tabular data centered about
    either another stream's datapoints, or based upon time intervals.

    The underlying issue that Datasets solve is that in ConnectorDB, streams are inherently unrelated.
    In most data stores, such as standard relational (SQL) databases, and even excel spreadsheets, data is in tabular
    form. That is, if we have measurements of temperature in our house and our mood, we have a table:

        +--------------+----------------------+
        | Mood Rating  | Room Temperature (F) |
        +==============+======================+
        | 7            | 73                   |
        +--------------+----------------------+
        | 3            | 84                   |
        +--------------+----------------------+
        | 5            | 79                   |
        +--------------+----------------------+

    The benefit of having such a table is that it is easy to perform data analysis. You know which temperature
    value corresponds to which mood rating. The downside of having such tables
    is that Mood Rating and Room Temperature must be directly related - a temperature measurement must be made
    each time a mood rating is given. ConnectorDB has no such restrictions. Mood Rating and Room Temperature
    can be entirely separate sensors, which update data at their own rate. In ConnectorDB, each stream
    can be inserted with any timestamp, and without regard for any other streams.

    This separation of Streams makes data require some preprocessing and interpolation before it can be used
    for analysis. This is the purpose of the Dataset query. ConnectorDB can put several streams together based
    upon chosen transforms and interpolators, returning a tabular structure which can readily be used for ML
    and statistical applications.

    There are two types of dataset queries

    :T-Dataset:

        T-Dataset: A dataset query which is generated based upon a time range. That is, you choose a time range and a
        time difference between elements of the dataset, and that is used to generate your dataset.

            +--------------+----------------------+
            | Timestamp    | Room Temperature (F) |
            +==============+======================+
            | 1pm          | 73                   |
            +--------------+----------------------+
            | 4pm          | 84                   |
            +--------------+----------------------+
            | 8pm          | 79                   |
            +--------------+----------------------+

        If I were to generate a T-dataset from 12pm to 8pm with dt=2 hours, using the interpolator "closest",
        I would get the following result:

            +--------------+----------------------+
            | Timestamp    | Room Temperature (F) |
            +==============+======================+
            | 12pm         | 73                   |
            +--------------+----------------------+
            | 2pm          | 73                   |
            +--------------+----------------------+
            | 4pm          | 84                   |
            +--------------+----------------------+
            | 6pm          | 84                   |
            +--------------+----------------------+
            | 8pm          | 79                   |
            +--------------+----------------------+

        The "closest" interpolator happens to return the datapoint closest to the given timestamp. There are many
        interpolators to choose from (described later).

        Hint: T-Datasets can be useful for plotting data (such as daily or weekly averages).

    :X-Dataset:
        X-datasets allow to generate datasets based not on evenly spaced timestamps, but based upon a stream's values

        Suppose you have the following data:

            +-----------+--------------+---+-----------+----------------------+
            | Timestamp | Mood Rating  |   | Timestamp | Room Temperature (F) |
            +===========+==============+===+===========+======================+
            | 1pm       | 7            |   | 2pm       | 73                   |
            +-----------+--------------+---+-----------+----------------------+
            | 4pm       | 3            |   | 5pm       | 84                   |
            +-----------+--------------+---+-----------+----------------------+
            | 11pm      | 5            |   | 8pm       | 81                   |
            +-----------+--------------+---+-----------+----------------------+
            |           |              |   | 11pm      | 79                   |
            +-----------+--------------+---+-----------+----------------------+

        An X-dataset with X=Mood Rating, and the interpolator "closest" on Room Temperature would generate:

            +--------------+----------------------+
            | Mood Rating  | Room Temperature (F) |
            +==============+======================+
            | 7            | 73                   |
            +--------------+----------------------+
            | 3            | 84                   |
            +--------------+----------------------+
            | 5            | 79                   |
            +--------------+----------------------+

    :Interpolators:

        Interpolators are special functions which specify how exactly the data is supposed to be combined
        into a dataset. There are several interpolators, such as "before", "after", "closest" which work
        on any type of datapoint, and there are more advanced interpolators which require a certain datatype
        such as the "sum" or "average" interpolator (which require numerical type).

        In order to get detailed documentation on the exact interpolators that the version of ConnectorDB you are
        are connected to supports, you can do the following::

            cdb = connectordb.ConnectorDB(apikey)
            info = cdb.info()
            # Prints out all the supported interpolators and their associated documentation
            print info["interpolators"]

    """

    def __init__(self, cdb, x=None, t1=None, t2=None, dt=None, limit=None, i1=None, i2=None, transform=None, itransform=None):

        """In order to begin dataset generation, you need to specify the reference time range or stream.

        To generate a T-dataset::
            d = Dataset(cdb, t1=start, t2=end, dt=tchange)
        To generate a Y-dataset::
            d = Dataset(cdb,"mystream", i1=start, i2=end)

        Note that everywhere you insert a stream name, you are also free to insert Stream objects
        or even Merge queries. The Dataset query in ConnectorDB supports merges natively for each field.

        The only "special" field in this query is the "itransform". This is a special transform to run on the
        entire row of data after the all of the interpolations complete.
        """
        self.cdb = cdb
        self.query = query_maker(t1, t2, limit, i1, i2, transform)

        if x is not None:
            if dt is not None:
                raise Exception(
                    "Can't do both T-dataset and X-dataset at the same time")
            # Add the stream to the query as the X-dataset
            param_stream(self.cdb, self.query, x)
        elif dt is not None:
            self.query["dt"] = dt
        else:
            raise Exception("Dataset must have either x or dt parameter")

        self.query["dataset"] = {}

    def addStream(self, stream, interpolator="closest", t1=None, t2=None, dt=None, limit=None, i1=None, i2=None, transform=None,colname=None):

        """Adds the given stream to the query construction. Additionally, you can choose the interpolator to use for this stream, as well as a special name
        for the column in the returned dataset. If no column name is given, the full stream path will be used.

        addStream also supports Merge queries. You can insert a merge query instead of a stream, but be sure to name the column::

            d = Dataset(cdb, t1=time.time()-1000,t2=time.time(),dt=10.)
            d.addStream("temperature","average")
            d.addStream("steps","sum")

            m = Merge(cdb)
            m.addStream("mystream")
            m.addStream("mystream2")
            d.addStream(m,colname="mycolumn")

            result = d.run()
        """

        streamquery = query_maker(t1, t2, limit, i1, i2, transform)
        param_stream(self.cdb, streamquery, stream)

        streamquery["interpolator"] = interpolator

        if colname is None:
            # What do we call this column?
            if isinstance(stream, six.string_types):
                colname = stream
            elif isinstance(stream, Stream):
                colname = stream.path
            else:
                raise Exception(
                    "Could not find a name for the column! use the 'colname' parameter.")

        if colname in self.query["dataset"] or colname is "x":
            raise Exception(
                "The column name either exists, or is labeled 'x'. Use the colname parameter to change the column name.")

        self.query["dataset"][colname] = streamquery

    def run(self):
        """Runs the dataset query, and returns the result"""
        return self.cdb.db.query("dataset", self.query)


1		from __future__ import absolute_import
2
3		from .._stream import Stream, query_maker
4		from .merge import Merge, get_stream
5		import six
6
7
8		# param_stream adds the stream correctly into the query (depending on what stream parameter was given)
9		def param_stream(cdb, params, stream):
10		if isinstance(stream, Merge):
11		params["merge"] = stream.query
12		else:
13		params["stream"] = get_stream(cdb, stream)
14
15
16		class Dataset(object):
17		"""ConnectorDB is capable of taking several separate unrelated streams, and based upon
18		the chosen interpolation method, putting them all together to generate tabular data centered about
19		either another stream's datapoints, or based upon time intervals.
20
21		The underlying issue that Datasets solve is that in ConnectorDB, streams are inherently unrelated.
22		In most data stores, such as standard relational (SQL) databases, and even excel spreadsheets, data is in tabular
23		form. That is, if we have measurements of temperature in our house and our mood, we have a table:
24
25		+--------------+----------------------+
26		\| Mood Rating \| Room Temperature (F) \|
27		+==============+======================+
28		\| 7 \| 73 \|
29		+--------------+----------------------+
30		\| 3 \| 84 \|
31		+--------------+----------------------+
32		\| 5 \| 79 \|
33		+--------------+----------------------+
34
35		The benefit of having such a table is that it is easy to perform data analysis. You know which temperature
36		value corresponds to which mood rating. The downside of having such tables
37		is that Mood Rating and Room Temperature must be directly related - a temperature measurement must be made
38		each time a mood rating is given. ConnectorDB has no such restrictions. Mood Rating and Room Temperature
39		can be entirely separate sensors, which update data at their own rate. In ConnectorDB, each stream
40		can be inserted with any timestamp, and without regard for any other streams.
41
42		This separation of Streams makes data require some preprocessing and interpolation before it can be used
43		for analysis. This is the purpose of the Dataset query. ConnectorDB can put several streams together based
44		upon chosen transforms and interpolators, returning a tabular structure which can readily be used for ML
45		and statistical applications.
46
47		There are two types of dataset queries
48
49		:T-Dataset:
50
51		T-Dataset: A dataset query which is generated based upon a time range. That is, you choose a time range and a
52		time difference between elements of the dataset, and that is used to generate your dataset.
53
54		+--------------+----------------------+
55		\| Timestamp \| Room Temperature (F) \|
56		+==============+======================+
57		\| 1pm \| 73 \|
58		+--------------+----------------------+
59		\| 4pm \| 84 \|
60		+--------------+----------------------+
61		\| 8pm \| 79 \|
62		+--------------+----------------------+
63
64		If I were to generate a T-dataset from 12pm to 8pm with dt=2 hours, using the interpolator "closest",
65		I would get the following result:
66
67		+--------------+----------------------+
68		\| Timestamp \| Room Temperature (F) \|
69		+==============+======================+
70		\| 12pm \| 73 \|
71		+--------------+----------------------+
72		\| 2pm \| 73 \|
73		+--------------+----------------------+
74		\| 4pm \| 84 \|
75		+--------------+----------------------+
76		\| 6pm \| 84 \|
77		+--------------+----------------------+
78		\| 8pm \| 79 \|
79		+--------------+----------------------+
80
81		The "closest" interpolator happens to return the datapoint closest to the given timestamp. There are many
82		interpolators to choose from (described later).
83
84		Hint: T-Datasets can be useful for plotting data (such as daily or weekly averages).
85
86		:X-Dataset:
87		X-datasets allow to generate datasets based not on evenly spaced timestamps, but based upon a stream's values
88
89		Suppose you have the following data:
90
91		+-----------+--------------+---+-----------+----------------------+
92		\| Timestamp \| Mood Rating \| \| Timestamp \| Room Temperature (F) \|
93		+===========+==============+===+===========+======================+
94		\| 1pm \| 7 \| \| 2pm \| 73 \|
95		+-----------+--------------+---+-----------+----------------------+
96		\| 4pm \| 3 \| \| 5pm \| 84 \|
97		+-----------+--------------+---+-----------+----------------------+
98		\| 11pm \| 5 \| \| 8pm \| 81 \|
99		+-----------+--------------+---+-----------+----------------------+
100		\| \| \| \| 11pm \| 79 \|
101		+-----------+--------------+---+-----------+----------------------+
102
103		An X-dataset with X=Mood Rating, and the interpolator "closest" on Room Temperature would generate:
104
105		+--------------+----------------------+
106		\| Mood Rating \| Room Temperature (F) \|
107		+==============+======================+
108		\| 7 \| 73 \|
109		+--------------+----------------------+
110		\| 3 \| 84 \|
111		+--------------+----------------------+
112		\| 5 \| 79 \|
113		+--------------+----------------------+
114
115		:Interpolators:
116
117		Interpolators are special functions which specify how exactly the data is supposed to be combined
118		into a dataset. There are several interpolators, such as "before", "after", "closest" which work
119		on any type of datapoint, and there are more advanced interpolators which require a certain datatype
120		such as the "sum" or "average" interpolator (which require numerical type).
121
122		In order to get detailed documentation on the exact interpolators that the version of ConnectorDB you are
123		are connected to supports, you can do the following::
124
125		cdb = connectordb.ConnectorDB(apikey)
126		info = cdb.info()
127		# Prints out all the supported interpolators and their associated documentation
128		print info["interpolators"]
129
130		"""
131
132	View Code Duplication	def __init__(self, cdb, x=None, t1=None, t2=None, dt=None, limit=None, i1=None, i2=None, transform=None, itransform=None):
		0 ignored issues – show Duplication introduced 2016-05-26 11:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
133		"""In order to begin dataset generation, you need to specify the reference time range or stream.
134
135		To generate a T-dataset::
136		d = Dataset(cdb, t1=start, t2=end, dt=tchange)
137		To generate a Y-dataset::
138		d = Dataset(cdb,"mystream", i1=start, i2=end)
139
140		Note that everywhere you insert a stream name, you are also free to insert Stream objects
141		or even Merge queries. The Dataset query in ConnectorDB supports merges natively for each field.
142
143		The only "special" field in this query is the "itransform". This is a special transform to run on the
144		entire row of data after the all of the interpolations complete.
145		"""
146		self.cdb = cdb
147		self.query = query_maker(t1, t2, limit, i1, i2, transform)
148
149		if x is not None:
150		if dt is not None:
151		raise Exception(
152		"Can't do both T-dataset and X-dataset at the same time")
153		# Add the stream to the query as the X-dataset
154		param_stream(self.cdb, self.query, x)
155		elif dt is not None:
156		self.query["dt"] = dt
157		else:
158		raise Exception("Dataset must have either x or dt parameter")
159
160		self.query["dataset"] = {}
161
162	View Code Duplication	def addStream(self, stream, interpolator="closest", t1=None, t2=None, dt=None, limit=None, i1=None, i2=None, transform=None,colname=None):
		0 ignored issues – show Duplication introduced 2016-05-26 11:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
163		"""Adds the given stream to the query construction. Additionally, you can choose the interpolator to use for this stream, as well as a special name
164		for the column in the returned dataset. If no column name is given, the full stream path will be used.
165
166		addStream also supports Merge queries. You can insert a merge query instead of a stream, but be sure to name the column::
167
168		d = Dataset(cdb, t1=time.time()-1000,t2=time.time(),dt=10.)
169		d.addStream("temperature","average")
170		d.addStream("steps","sum")
171
172		m = Merge(cdb)
173		m.addStream("mystream")
174		m.addStream("mystream2")
175		d.addStream(m,colname="mycolumn")
176
177		result = d.run()
178		"""
179
180		streamquery = query_maker(t1, t2, limit, i1, i2, transform)
181		param_stream(self.cdb, streamquery, stream)
182
183		streamquery["interpolator"] = interpolator
184
185		if colname is None:
186		# What do we call this column?
187		if isinstance(stream, six.string_types):
188		colname = stream
189		elif isinstance(stream, Stream):
190		colname = stream.path
191		else:
192		raise Exception(
193		"Could not find a name for the column! use the 'colname' parameter.")
194
195		if colname in self.query["dataset"] or colname is "x":
196		raise Exception(
197		"The column name either exists, or is labeled 'x'. Use the colname parameter to change the column name.")
198
199		self.query["dataset"][colname] = streamquery
200
201		def run(self):
202		"""Runs the dataset query, and returns the result"""
203		return self.cdb.db.query("dataset", self.query)
204

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