def parse(self, sineslist):
doc = DataObjectCollection()
for sines in sineslist:
do = DataObject()
for key, sine in sines.items():
ts = TimeSeries(sine)
ts.sample_rate = 1
do[key] = ts
doc.append(do)
return doc
def parse(self, sineslist):
doc = DataObjectCollection()
for sines in sineslist:
do = DataObject()
for key, sine in sines.items():
ts = TimeSeries(sine)
ts.sample_rate = 1
do[key] = ts
doc.append(do)
return doc
def parse(self, sines):
doc = DataObjectCollection()
do = DataObject()
for key, sine in sines.items():
ts = TimeSeries(sine)
ts.sample_rate = 1
# ts.rangex = (-1,1)
do[key] = ts
doc.append(do)
return doc
def parse(self, sines):
doc = DataObjectCollection()
do = DataObject()
for key, sine in sines.items():
ts = TimeSeries(sine)
ts.sample_rate = 1
# ts.rangex = (-1,1)
do[key] = ts
doc.append(do)
return doc
def parse(self, stations, years, fields):
''' Pass in some stations and years. For convenience (and to match CRN's data output)
we'll only deal with data in complete years. '''
# First make sure we've got the data locally:
self._download(stations, years)
doc = DataObjectCollection()
for station in stations:
do = DataObject()
for field in self.fields:
# Skip some fields we know we don't care about
useless_fields = [
'WBANNO', 'UTC_DATE', 'UTC_TIME', 'LST_DATE', 'LST_TIME',
'CRX_VN', 'SUR_TEMP_TYPE'
]
if (fields and field not in fields) or (not fields and field
in useless_fields):
continue
do[field] = TimeSeries([])
for year in years:
f = open(self.storage_dir + self._filename(station, year))
for line in f:
values = line.split()
do.append(values, self.fields)
for ts in do.values():
ts.replace_data(
interpolate_forward_backward(ts, missing_values))
doc.append(do)
return doc
def parse(self):
d = eval(self.file.readline().strip())
doc = DataObjectCollection(sample_rate=1 / 3.0)
for i, octant in enumerate(d):
do = DataObject()
for varname, values in octant.items():
ts = TimeSeries(values)
do[varname] = ts
doc.append(do)
return doc
def test_do_imposes_sample_rate():
# create a TimeSeries
ts1 = TimeSeries(['datapoint'])
# create a DO and put the TS in it
do1 = DataObject(sample_rate=60)
do1['somed'] = ts1
# test the sample_rate of the TimeSeries (which it should derive from the DO)
assert(do1['somed'].sample_rate == 60)
# while we're at it, make sure that it percolates up to the DOC
doc = DataObjectCollection([do1])
assert doc.sample_rate == 60, str(doc.sample_rate) + ' is not 60.'
def test_datamapper_1():
# create a TimeSeries and stick something in it
ts1 = TimeSeries(['datapoint'], sample_rate=60)
# create a DO and put the TS in it
do1 = DataObject()
do1['somedata'] = ts1
assert do1.keys() == ['somedata']
# create a DOC and put the DO in it
doc = DataObjectCollection()
doc.append(do1)
# dig down through the levels and get the datapoint we originally inserted
timeseries = doc[0]['somedata']
datapoint = timeseries[0]
assert(datapoint == 'datapoint')
def test_combine_range():
''' Make some sines, modify them to have different ranges, and combine the ranges. '''
parser = MultiSineDictParser()
sinelist = []
for i in range(3):
sines = generate_sines(3, 3)
sinelist.append(sines)
doc = parser.parse(sinelist)
for i, do in enumerate(doc):
do[0] = TimeSeries([(i + 1) * v for v in do[0]])
old_ranges = [do[0].ts_range for do in doc]
assert old_ranges[0] == (0.2871614310423001, 0.4665948234153722), old_ranges[0]
assert len(set(old_ranges)) == 3 # All identical
doc.combine_range(0)
new_ranges = [do[0].ts_range for do in doc]
assert new_ranges[0] == (0.2871614310423001, 1.3997844702461166), new_ranges[0]
assert len(set(new_ranges)) == 1 # All identical
def _getDataObject():
''' Convenience method to return a DataObject initialized to fit the buoy data. '''
do = DataObject(metadata={'buoy_id': id})
for key in ['LAT', 'LON', 'TEMP']:
do[key] = TimeSeries([])
return do
def test_remap_range():
inlist = TimeSeries([0, .5, 1])
# original_range = (0, 1)
desired_range = (0, 10)
outlist = inlist.remap_range(desired_range)
assert outlist == TimeSeries([0.0, 5.0, 10.0]), 'outlist is ' + str(outlist) + ': ' + str(type(outlist))
inlist = TimeSeries([1.0, 1.5, 2])
original_range = (1, 2)
desired_range = (100, 110)
outlist = inlist.remap_range(desired_range)
assert outlist == TimeSeries([100.0, 105.0, 110.0]), 'outlist is ' + str(outlist)
# same thing without explicitly setting the original range
inlist = TimeSeries([1.0, 1.5, 2])
desired_range = (100, 110)
outlist = inlist.remap_range(desired_range)
assert outlist == TimeSeries([100.0, 105.0, 110.0]), 'outlist is ' + str(outlist)
def test_resample():
ts = TimeSeries([0, 1, 2, 3, 4, 5, 6])
ts.resample(.4)
assert str(ts) == 'TimeSeries([0.0, 0.4, 0.8, 1.2000000000000002, 1.6, 2.0, 2.4000000000000004, 2.8000000000000003, 3.2, 3.6, 4.0, 4.4, 4.800000000000001, 5.2, 5.6000000000000005], sample_rate=None, ts_range=(0.0, 5.6000000000000005))'
ts.resample(1 / .4) # Reversible? Should be except that we lose some off the end (we have to)
assert str(ts) == 'TimeSeries([0.0, 1.0, 2.0, 3.0, 4.0, 5.0], sample_rate=None, ts_range=(0.0, 5.0))'
def test_ts_range():
ts = TimeSeries([2, 3, 1, 5, 4], ts_range=(0, 5))
assert(ts.ts_range == (0, 5))
ts = TimeSeries([2, 3, 1, 5, 4])
assert(ts.ts_range == (1, 5))
