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from __future__ import absolute_import |
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import datetime |
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import json |
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import os.path |
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class DatapointArray(list): |
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""" Sometimes you might want to generate a stream by combining multiple disparate |
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data sources. Since ConnectorDB streams currently only permit appending, |
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a stream's data must be ordered correctly. |
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The DatapointArray allows you to load all the data you'd need into a single object, |
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and it merges the data together and formats it to be compatible with ConnectorDB |
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""" |
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def __init__(self, data=[]): |
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"""The data |
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""" |
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list.__init__(self, data) |
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def __add__(self, other): |
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return DatapointArray(self).merge(other) |
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def __radd__(self, other): |
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return DatapointArray(self).merge(other) |
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def __getitem__(self, key): |
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if (key == "t"): |
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return self.t() |
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if (key == "d"): |
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return self.d() |
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d = list.__getitem__(self, key) |
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if isinstance(key, slice): |
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d = DatapointArray(d) |
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d._d = self._d[key] |
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d._t = self._t[key] |
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return d |
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def sort(self, f=lambda d: d["t"]): |
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"""Sort here works by sorting by timestamp by default""" |
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list.sort(self, key=f) |
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return self |
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def d(self): |
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"""Returns just the data portion of the datapoints as a list""" |
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return list(map(lambda x: x["d"], self.raw())) |
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def t(self): |
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"""Returns just the timestamp portion of the datapoints as a list. |
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The timestamps are in python datetime's date format.""" |
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return list(map(lambda x: datetime.datetime.fromtimestamp(x["t"]), self.raw())) |
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def merge(self, array): |
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"""Adds the given array of datapoints to the generator. |
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It assumes that the datapoints are formatted correctly for ConnectorDB, meaning |
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that they are in the format:: |
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[{"t": unix timestamp, "d": data}] |
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The data does NOT need to be sorted by timestamp - this function sorts it for you |
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""" |
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self.extend(array) |
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self.sort() |
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return self |
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def raw(self): |
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"""Returns array as a raw python array. For cases where for some reason |
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the DatapointArray wrapper does not work for you |
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""" |
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return list.__getitem__(self, slice(None, None)) |
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def writeJSON(self, filename): |
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"""Writes the data to the given file:: |
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DatapointArray([{"t": unix timestamp, "d": data}]).writeJSON("myfile.json") |
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The data can later be loaded using loadJSON. |
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""" |
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with open(filename, "w") as f: |
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json.dump(self, f) |
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def loadJSON(self, filename): |
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"""Adds the data from a JSON file. The file is expected to be in datapoint format:: |
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d = DatapointArray().loadJSON("myfile.json") |
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""" |
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with open(filename, "r") as f: |
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self.merge(json.load(f)) |
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return self |
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def loadExport(self, folder): |
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"""Adds the data from a ConnectorDB export. If it is a stream export, then the folder |
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is the location of the export. If it is a device export, then the folder is the export folder |
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with the stream name as a subdirectory |
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If it is a user export, you will use the path of the export folder, with the user/device/stream |
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appended to the end:: |
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myuser.export("./exportdir") |
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DatapointArray().loadExport("./exportdir/username/devicename/streamname") |
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""" |
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self.loadJSON(os.path.join(folder, "data.json")) |
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return self |
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def tshift(self, t): |
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"""Shifts all timestamps in the datapoint array by the given number of seconds. |
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It is the same as the 'tshift' pipescript transform. |
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Warning: The shift is performed in-place! This means that it modifies the underlying array:: |
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d = DatapointArray([{"t":56,"d":1}]) |
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d.tshift(20) |
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print(d) # [{"t":76,"d":1}] |
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""" |
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raw = self.raw() |
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for i in range(len(raw)): |
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raw[i]["t"] += t |
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return self |
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def sum(self): |
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"""Gets the sum of the data portions of all datapoints within""" |
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raw = self.raw() |
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s = 0 |
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for i in range(len(raw)): |
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s += raw[i]["d"] |
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return s |
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def mean(self): |
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"""Gets the mean of the data portions of all datapoints within""" |
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return self.sum() / float(len(self)) |
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connectordb /
connectordb-python
