def test_merge_rts_2d_intersect(self):
""" Test merging of two rts of 2-d data and has intersection."""
d1=[[1.0,2.0]]*int(spy.math.pow(2,10))
d2=[[3.0,4.0]]*int(spy.math.pow(2,11))
st1=datetime.datetime(year=1990,month=2,day=3,hour=11, minute=15)
st2=datetime.datetime(year=1990,month=3,day=3,hour=11, minute=15)
ts1=rts(d1,st1,time_interval(hours=1))
ts2=rts(d2,st2,time_interval(hours=1))
## Intentionally put some nan into ts1 to see effect.
ii=ts1.index_after(st2)
num_nan=10
ts1.data[ii+1:ii+num_nan+2,]=spy.nan
nt=merge(ts1,ts2)
self.assertEqual(nt.start,ts1.start)
self.assertEqual(nt.end,ts2.end)
total_n=number_intervals(ts1.start,ts2.end,ts1.interval)+1
self.assertEqual(len(nt.data),total_n)
s1=nt.index_after(ts1.end)
s2=nt.index_after(ts2.start)
s3=ts2.index_after(ts1.end)
self.assert_(spy.allclose(nt.data[0:ii+1,],ts1.data[0:ii+1,]))
self.assert_(spy.allclose(nt.data[ii+1:ii+num_nan+2,],ts2.data[0:num_nan+1,]))
self.assert_(spy.allclose(nt.data[ii+num_nan+2:s1+1,],ts1.data[ii+num_nan+2:s1+1,]))
self.assert_(spy.allclose(nt.data[s1+1:len(nt.data),],ts2.data[s3+1:len(ts2.data),]))
def test_merge_rts_no_intersect(self):
""" Test merging two rts without intersection."""
d1=[1]*int(spy.math.pow(2,10))
d2=[2]*int(spy.math.pow(2,11))
st1=datetime.datetime(year=1990,month=2,day=3,hour=11, minute=15)
st2=datetime.datetime(year=1990,month=5,day=3,hour=11, minute=15)
ts1=rts(d1,st1,time_interval(hours=1))
ts2=rts(d2,st2,time_interval(hours=1))
nt=merge(ts1,ts2)
self.assertEqual(nt.start,ts1.start)
self.assertEqual(nt.end,ts2.end)
total_n=number_intervals(ts1.start,ts2.end,ts1.interval)+1
self.assertEqual(len(nt.data),total_n)
s1=nt.index_after(ts1.end)
s2=nt.index_after(ts2.start)
self.assert_(spy.alltrue(spy.isnan(nt.data[s1+1:s2])))
self.assert_(spy.allclose(nt.data[0:s1+1],ts1.data))
self.assert_(spy.allclose(nt.data[s2:len(nt.data)],ts2.data))
def test_merge_large_rts(self):
largenum=250000
st1=datetime.datetime(1920,1,2)
data1=range(0,largenum)
dt=datetime.timedelta(minutes=15)
rt1=rts(data1,st1,dt,{})
st2=rt1.end+dt
data2=range(largenum,2*largenum)
rt2=rts(data2,st2,dt,{})
st3=rt2.end+dt
data3=range(2*largenum,3*largenum)
rt3=rts(data3,st3,dt,{})
st4=rt3.end+dt
data4=range(3*largenum,4*largenum)
rt4=rts(data4,st4,dt,{})
ts=merge(rt1,rt2,rt3,rt4)
self.assertEqual(len(ts),largenum*4)
self.assertEqual(ts.data[largenum],data2[0])
self.assertEqual(ts.data[2*largenum],data3[0])
self.assertEqual(ts.data[3*largenum],data4[0])
def test_multidimension_tsdata(self):
""" Test interploation methods on multi-dimensional data set."""
msgstr = "on test_multidimension_tsdata"
num = 1000
data = [sin(2.0 * pi * k / 250.0) for k in range(num)]
data = sciarray(data).reshape(num / 4, 4)
id = 1
ts=rts(data,datetime(year=1990,month=1,day=2),\
parse_interval("1hour"),{})
ts_single_dimension=rts(data[:,id],datetime(year=1990,month=1,day=2),\
parse_interval("1hour"),{})
times=time_sequence(datetime(year=1990,month=1,day=3),\
parse_interval("1hour"),50)
self.assertEqual(abs(ts_single_dimension.data-ts.\
data[:,id]).max(),0.0)
##rhsit won't pass this test
function_to_test = [linear, spline, monotonic_spline]
for funcs in function_to_test:
nts = funcs(ts, times, filter_nan=False)
nts_single_dimension=funcs(ts_single_dimension,times,\
filter_nan=False)
self.assertEqual(len(nts),len(times),\
msg="test of %s fail %s."%(funcs.__name__,msgstr))
self.assertEqual(nts.data.shape[1],ts.data.shape[1],\
msg="test of %s fail %s."%(funcs.__name__,msgstr))
self.assertEqual(abs(nts_single_dimension.data-nts.\
data[:,id]).max(),0.0)
def _retrieve_rts_all_time(book, selection, **args):
""" return ts when all times are given to the left of each data col"""
rvar = _parse_range(selection)
sheet = book.Worksheets(rvar[0])
if "header_labels" in args.keys():
header_labels = args["header_labels"]
else:
header_labels = None
(header_lst,data_start_row)=_retrieve_headers_by_tryerror\
(sheet,rvar,header_labels)
data_range = sheet.Range(rvar[1] + str(data_start_row) + ":" + rvar[3] +
str(data_start_row))
ts_time_data = data_range.Value
num_ts = len(ts_time_data[0]) / 2
tsl = []
t1 = sheet.Range(rvar[1] + str(data_start_row))
col = t1.Columns[0].Column
t2 = sheet.Cells(data_start_row + 3, col)
d1 = sheet.Cells(data_start_row, col + 1)
d2 = sheet.Cells(rvar[4], col + 1)
for i in range(0, num_ts):
ts_time = sheet.Range(t1, t2).Value
tt0 = ts_time[0][0]
tt1 = ts_time[1][0]
tt0 = datetime(tt0.year, tt0.month, tt0.day, tt0.hour, tt0.minute)
tt1 = datetime(tt1.year, tt1.month, tt1.day, tt1.hour, tt1.minute)
data = sheet.Range(d1, d2).Value
data = array(data)
if data.shape[1] == 1:
data = data.flatten()
if header_lst:
ts = rts(data, tt0, tt1 - tt0, header_lst[i])
else:
ts = rts(data, tt0, tt1 - tt0)
tsl.append(ts)
col = col + 2
t1 = sheet.Cells(data_start_row, col)
t2 = sheet.Cells(data_start_row + 3, col)
d1 = sheet.Cells(data_start_row, col + 1)
d2 = sheet.Cells(rvar[4], col + 1)
if len(tsl) == 1:
return tsl[0]
else:
return tsl
def ts_split(ts, shared=True):
""" Splits a 2D multivariate series into constituent univariate series.
Parameters
----------
ts : :class:`~vtools.data.timeseries.TimeSeries`
shared :: Boolean
Return time sereis share or copy data array of input one
Returns
-------
out1,out2 : :class:`~vtools.data.timeseries.TimeSeries`
Two comonent time series.
"""
if ts.data.ndim > 2: raise ValueError("Only 2D arrays can be split")
if shared:
dsource = ts.data
else:
dsource = ts.data.copy()
ncol = dsource.shape[1]
out = []
for jcol in range(ncol):
if ts.is_regular():
colts = rts(dsource[:, jcol], ts.start, ts.interval)
else:
colts = its(ts.ticks, dsource[:, jcol])
out.append(colts)
return tuple(out)
def test_butterworth(self):
""" Butterworth filter on isa series of 1hour interval with four
frequencies.
"""
# Test operations on ts of varied values.
test_ts=[(pd.Timestamp(1990,2,3,11,15),\
self.two_to_ten,hours(1))]
f1 = 0.76
f2 = 0.44
f3 = 0.95
f4 = 1.23
pi = np.pi
av1 = f1 * pi / 12.
av2 = f2 * pi / 12.
av3 = f3 * pi / 12.
av4 = f4 * pi / 12.
for (st, num, delta) in test_ts:
## this day contains components of with frequecies of 0.76/day,
## 0.44/day, 0.95/day, 1.23/day
data = [
np.sin(av1 * k) + 0.7 * np.cos(av2 * k) +
2.4 * np.sin(av3 * k) + 0.1 * np.sin(av4 * k)
for k in np.arange(num)
]
# This ts is the orignial one.
ts0 = rts(data, st, delta)
ts = butterworth(ts0, cutoff_period=hours(40))
self.assertTrue(ts.index.freq == ts0.index.freq)
def read_csv_from_dss(fpath):
""" Read CSV file from dss
Assume LST (no daylight saving)
Parameters
----------
fpath: str
file path
Returns
-------
array of vtools.data.timeseries.TimeSeries
regular time series.
"""
with open(fpath, 'rU') as f:
reader = csv.reader(f)
data = []
times = []
for i, row in enumerate(reader):
if i == 1:
stations = row[2:]
elif i > 6:
t = datetime(*strptime(row[1], r'%d%b%Y %H%M')[:5])
times.append(t)
row_data = list(map(convert_string_to_float, row[2:]))
data.append(row_data)
data = np.array(data)
return [rts(data[:, i], times[0], times[1] - times[0],
props={'unit': 'ec', 'name': stations[i]})
for i in range(len(stations))]
def test_period_op3(self):
""" Test period operation on time series with 2-dimensional data."""
st = datetime.datetime(year=1990, month=2, day=3, hour=11, minute=15)
num = 3005
dimension2 = 3
delta = time_interval(minutes=5)
interval = '1 hour'
op_delta = time_interval(hours=1)
aligned_start = datetime.datetime(year=1990,
month=2,
day=3,
hour=12,
minute=0)
data=[[random.uniform(self.min_val,self.max_val) for i in range(dimension2)] \
for k in range(num)]
data = sciarray(data)
# Reformalize raw data, insert known mini_val and max_val
# and calcuate hourly mean to use later.
i0 = 9 # this is the first index with aligned calendar
num_interval = (num - i0 + 1) // 12
for k in range(num_interval):
index = i0 + k * 12 + 1
data[index, ] = self.min_val
index = index + 1
data[index, ] = self.max_val
nt_data = data[i0:12 * num_interval + i0, ]
nt_data = sciarray(nt_data)
nt_data.shape = (num_interval, 12, -1)
nt_mean = sciadd.reduce(
nt_data,
1,
) / 12
nt_sum = sciadd.reduce(
nt_data,
1,
)
nt_min = sciminimum.reduce(
nt_data,
1,
)
nt_max = scimaximum.reduce(
nt_data,
1,
)
ts = rts(data, st, delta, {})
for (op, op_data) in [(MIN, nt_min), (MAX, nt_max), (MEAN, nt_mean),
(SUM, nt_sum)]:
nt = period_op(ts, interval, op)
assert_array_equal(nt.data,op_data,\
err_msg="two array not equal in average" \
" by %s"%(op))
def test_period_op_uncompatible_interval(self):
""" Test behaviour of period operation on TS with interval uncompatible
with operation time interval
"""
test_input = [
(datetime.datetime(year=1990, month=2, day=3,
hour=11, minute=15), 3005,
time_interval(minutes=45), "1hour", time_interval(hours=1)),
(datetime.datetime(year=1990, month=2,
day=3, hour=11, minute=15), 3301,
time_interval(days=1), "1hour", time_interval(hours=1)),
(datetime.datetime(year=1990, month=1,
day=1, hour=00, minute=00), 10957,
time_interval(days=2), "1 month", time_interval(months=1)),
(datetime.datetime(year=1990, month=1, day=1,
hour=00, minute=00), 10957,
time_interval(minutes=35), "3 days", time_interval(days=3)),
(datetime.datetime(year=1990, month=1,
day=1, hour=00, minute=00), 59,
time_interval(months=5), "3 years", time_interval(years=3)),
]
for (st, num, delta, interval, op_delta) in test_input:
data=[random.uniform(self.min_val,self.max_val) \
for k in range(num)]
ts = rts(data, st, delta, {})
for op in [MIN, MAX, MEAN, SUM]:
self.assertRaises(ValueError, period_op, ts, interval, op)
def test_save_data(self):
## save some ts into dss file, ts may contain
## header.
## save rts first.
data = range(1000)
start = "12/21/2000 2:00"
interval = "1hour"
prop = {}
prop[TIMESTAMP] = PERIOD_START
prop[AGGREGATION] = MEAN
prop["datum"] = "NGVD88"
prop["manager"] = "John Doe"
prop["model"] = "hydro 7.5"
rt1 = rts(data, start, interval, prop)
id = "vtools.datastore.dss.DssService"
path = "/TEST/DOWNSTREAM/EC//1HOUR/STAGE/"
source = self.test_file_path
data_ref = DataReference(id, source=source, selector=path)
self.dss_service.add_data(data_ref, rt1)
dssc = self.dss_service.get_catalog(source)
path = "/TEST/DOWNSTREAM/EC//1HOUR/STAGE/"
data_ref = dssc.data_references(path).next()
rtt = self.dss_service.get_data(data_ref)
self.assertTrue(len(rtt) == len(data))
self.assertTrue(rtt.props[TIMESTAMP] == PERIOD_START)
self.assertTrue(rtt.props[AGGREGATION] == MEAN)
self.assertTrue(rtt.times[0], dtm.datetime(2000, 12, 21, 2))
extent = "time_window=(12/21/2000 02:00,01/31/2001 18:00)"
data_ref = DataReference(id, source, None, path, extent)
rtt2 = self.dss_service.get_data(data_ref)
self.assertTrue(rtt.start == rtt2.start)
self.assertTrue(rtt.end == rtt2.end)
## then its.
path = "/HERE/IS/ITS//IR-YEAR/TEST/"
data = range(20)
data_ref = DataReference(id, source=source, selector=path)
prop[AGGREGATION] = INDIVIDUAL
times=["01/15/1997","02/17/1997","03/5/1997",\
"04/25/1997","05/1/1997","06/15/1997",\
"07/25/1997","08/14/1997","09/17/1997",\
"10/15/1997","11/21/1997","12/3/1997",\
"01/9/1998","02/15/1998","03/19/1998",\
"04/15/1998","05/19/1998","06/30/1998",\
"07/15/1998","08/24/1998"]
times = map(parse_time, times)
itt = its(times, data, prop)
self.dss_service.add_data(data_ref, itt)
extent = "time_window=(1/10/1997 02:00,09/30/1998 18:00)"
data_ref = DataReference(id, source, None, path, extent)
rtt3 = self.dss_service.get_data(data_ref)
self.assertTrue(parse_time("01/15/1997") == rtt3.start)
self.assertTrue(parse_time("08/24/1998") == rtt3.end)
def test_butterworth(self):
""" Butterworth filter on a series of 1hour interval with four
frequencies.
"""
# Test operations on ts of varied values.
test_ts=[(datetime.datetime(year=1990,month=2,day=3,hour=11, minute=15),\
int(numpy.math.pow(2,10)),time_interval(hours=1)),]
f1 = 0.76
f2 = 0.44
f3 = 0.95
f4 = 1.23
av1 = f1 * pi / 12
av2 = f2 * pi / 12
av3 = f3 * pi / 12
av4 = f4 * pi / 12
import pylab as plt
for (st, num, delta) in test_ts:
## this day contains components of with frequecies of 0.76/day,
## 0.44/day, 0.95/day, 1.23/day
data = [
numpy.math.sin(av1 * k) + 0.7 * numpy.math.cos(av2 * k) +
2.4 * numpy.math.sin(av3 * k) + 0.1 * numpy.math.sin(av4 * k)
for k in range(num)
]
# This ts is the orignial one.
ts0 = rts(data, st, delta, {})
ts = butterworth(ts0)
self.assert_(ts.is_regular())
def test_save2newf(self):
""" try to save ts to a non exist file."""
## save rts first.
data = range(1000)
start = "12/21/2000 2:00"
interval = "1hour"
prop = {}
prop[TIMESTAMP] = PERIOD_START
prop[AGGREGATION] = MEAN
prop["datum"] = "NGVD88"
prop["manager"] = "John Doe"
prop["model"] = "hydro 7.5"
rt1 = rts(data, start, interval, prop)
id = "vtools.datastore.dss.DssService"
path = "/TEST/DOWNSTREAM/EC//1HOUR/STAGE/"
source = 'newdss.dss'
data_ref = DataReference(id, source=source, selector=path)
self.dss_service.add_data(data_ref, rt1)
self.assertTrue(os.path.exists(source))
def _bind(ts1, ts2):
""" bind data from timeseries ts1 and ts2.
Parameters
----------
ts1,ts2 : :class:`~vtools.data.timeseries.TimeSeries`
Two timeseries
Returns
-------
merged : :class:`~vtools.data.timeseries.TimeSeries`
A new binded time series if success.
"""
if (not ((ts1.data.ndim == 1) and (ts2.data.ndim == 1))):
raise ValueError("bind only support time series of univariate")
ts = None
ts_is_regular = False
new_ts_time_sequence = []
new_start = None
new_interval = None
if ((ts1.is_regular()) and (ts2.is_regular())):
ts1_start = ts1.times[0]
ts1_end = ts1.times[-1]
ts2_start = ts2.times[0]
ts2_end = ts2.times[-1]
new_start = ts1_start
if new_start > ts2_start:
new_start = ts2_start
new_end = ts1_end
if new_end < ts2_end:
new_end = ts2_end
new_interval = ts1.interval
ts2_interval = ts2.interval
if new_interval > ts2_interval:
new_interval = ts2_interval
num_data = number_intervals(new_start, new_end, new_interval) + 1
new_ts_time_sequence = time_sequence(new_start, new_interval, num_data)
ts_is_regular = True
else:
new_ts_time_sequence = np.union1d(ts1.ticks, ts2.ticks)
new_ts_len = len(new_ts_time_sequence)
new_data = np.array([[np.nan] * new_ts_len, [np.nan] * new_ts_len])
ts1_data_id = np.searchsorted(new_ts_time_sequence, ts1.ticks)
ts2_data_id = np.searchsorted(new_ts_time_sequence, ts2.ticks)
new_data[0, ts1_data_id] = ts1.data
new_data[1, ts2_data_id] = ts2.data
new_data = new_data.transpose()
if ts_is_regular:
ts = rts(new_data, new_start, new_interval)
else:
ts = its(new_ts_time_sequence, new_data)
return ts
def test_ts_accumulate(self):
ts_start = datetime(year=1990, month=2, day=3, hour=11, minute=45)
ts_len = 100
ts_intvl = days(1)
data = range(ts_len)
ts0 = rts(data, ts_start, ts_intvl)
accadd_ts = ts_accumulate(ts0, numpyadd)
self.assertEqual(len(accadd_ts), ts_len)
self.assertEqual(accadd_ts.data[-1], sum(data))
def test_butterworth_noevenorder(self):
""" test a butterworth with non even order input
"""
st = datetime.datetime(year=2000, month=2, day=3)
delta = time_interval(hours=1)
data = arange(100)
order = 7
ts0 = rts(data, st, delta, {})
self.assertRaises(ValueError, butterworth, ts0, order)
def test_butterworth_noevenorder(self):
""" test a butterworth with non even order input
"""
start = pd.Timestamp(2000, 2, 3)
freq = hours(1)
data = np.arange(100)
order = 7
ts0 = rts(data, start, freq)
self.assertRaises(ValueError, butterworth, ts0, order)
def test_ts_minimum(self):
ts_start = datetime(year=1990, month=2, day=3, hour=11, minute=45)
ts_len = 100
ts_intvl = days(1)
data = range(ts_len)
ts0 = rts(data, ts_start, ts_intvl)
tsmin = ts_maximum(ts0, 50)
self.assertEqual(tsmin[49].value, 50.)
self.assertEqual(tsmin[51].value, 51.)
def test_merge_rts_intersect2(self):
""" Test merging two rts with intersection."""
d1=[1.0]*4
d2=[2.0]*4
d1[2]=spy.nan
st1=datetime.datetime(year=1990,month=2,day=1)
st2=datetime.datetime(year=1990,month=3,day=1)
ts1=rts(d1,st1,time_interval(months=1))
ts2=rts(d2,st2,time_interval(months=1))
nt=merge(ts1,ts2)
## test ts1 data take priority on valid data
self.assertEqual(nt.data[1],ts1.data[1])
## test ts2 data take prioirty on invalid data of ts1
self.assertEqual(nt.data[2],ts2.data[2])
def create_rts(self, delta, num):
""" Only create a regular time series for usage of testing.
"""
## This is a ten years long time series aproximately.
st = datetime(year=1990, month=1, day=1, hour=00, minute=00)
data=[0.3*sin(k*pi/1200+pi/15)+0.4*sin(k*pi/1100+pi/6)+1.1*sin(k*pi/990+pi/18) \
for k in range(num)]
ts = rts(data, st, delta, {})
return ts
def test_resample_rts_aligned(self):
# test resampling regular time series with aligned start
data=range(100)
st=datetime.datetime(year=2000,month=2,day=1,hour=2,minute=15)
delta=time_interval(minutes=15)
ts=rts(data,st,delta,{})
resample_interval=time_interval(hours=1)
nts=resample(ts,resample_interval,aligned=True)
rstart=datetime.datetime(year=2000,month=2,day=1,hour=3)
self.assertEqual(rstart,nts.start)
def ts_accumulate(ts, ufunc):
"""Apply ufunc.accumulate to the data and produce a neatened time series
The function will be applied cumulatively to the data.
So... ts_apply(ts, add) will produce a time series, where each entry
is the cumulative sum up to that index.
"""
if ts.is_regular():
return rts(ufunc.accumulate(ts.data), ts.start, ts.interval, ts.props)
else:
return its(ts.ticks, ufunc.accumulate(ts.data), ts.props)
def ts_gaussian_filter(ts, sigma, order=0, mode="reflect", cval=0.0):
""" wrapper of scipy gaussian_filter.
Parameters
-----------
ts: :class:`~vtools.data.timeseries.TimeSeries`
Must has data of one dimension, and regular.
sigma: int or :ref:`time_interval<time_intervals>`
Standard deviation for Gaussian kernel presented as number of
samples, or time interval.
order: int,optional
Order of the gaussian filter. Must be one of 0,1,2,3. Default 0.
mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
This input determines how the array borders are handled,
where cval is the value when mode is 'constant'.
Default is 'reflect'
cval : scalar, optional
Value to fill past edges of input if mode is 'constant'.
Default is 0.0
Returns
-------
result : :class:`~vtools.data.timeseries.TimeSeries`
A new regular time series with the same interval of ts.
Raise
--------
ValueError
If input timeseries is not regular,
or input order is not 0,1,2,3.
"""
if not ts.is_regular():
raise ValueError("Only regular time series are supported by\
guassian filter")
sigma_int = 1
if is_interval(sigma):
ticks_sigma = ticks(sigma)
ticks_interval = ticks(ts.inteval)
sigma_int = int(ticks_sigma / ticks_interval)
elif type(sigma) == type(1):
sigma_int = sigma
else:
raise ValueError("sigma must be int or timeinterval")
filtered_data=gaussian_filter(ts.data,sigma_int,order=order,\
mode=mode,cval=cval)
filtered_ts = rts(filtered_data, ts.start, ts.interval)
return filtered_ts
def test_add_data(self):
def rand_gen():
while True:
yield random()
tss = []
## create several ts
# 1
st = parse_time("1/2/1987 10:30")
dt = parse_interval("1hour")
prop={"agency":"dwr","interval":"1hour","station":"rsac045",\
"datum":"NGVD88","var":"flow"}
n = 13470
data = list(islice(rand_gen(), n))
ts = rts(data, st, dt, prop)
tss.append(ts)
# 2
st = parse_time("3/20/1997 10:30")
dt = parse_interval("1day")
prop={"bearu":"usgs","interval":"1day","lat":70.90,\
"long":34.45,"datum":"NGVD88","var":"stage"}
n = 40960
data = list(islice(rand_gen(), n))
ts = rts(data, st, dt, prop)
tss.append(ts)
# 3
st = parse_time("1/2/1967 4:30")
dt = parse_interval("15min")
prop={"place":"uml","interval":"15min","station":"rsac045",\
"datum":"NGVD88","var":"bod"}
n = 20000
data = list(islice(rand_gen(), n))
ts = rts(data, st, dt, prop)
tss.append(ts)
#
ref=DataReferenceFactory(EXCEL_DATA_SOURCE,"store.xls",\
selector="dss2excel$B5")
self.excel_service.batch_add(ref, tss)
def test_daily_average(self):
""" Test godin filter on 2-dimensional data set."""
d1 = [1.0] * 800 + [2.0] * 400 + [1.0] * 400
d2 = [1.0] * 800 + [2.0] * 400 + [1.0] * 400
data = numpy.array([d1, d2])
data = numpy.transpose(data)
data[336, :] = numpy.nan
st = datetime.datetime(year=1990, month=2, day=3, hour=11, minute=15)
delta = time_interval(minutes=15)
test_ts = rts(data, st, delta, {})
nt3 = daily_average(test_ts)
def test_norm_Linf(self):
# Test operations on ts of varied values.
# ts_start, ts_len, ts_interval, shift_interval_str, shift_interval
ts_start = datetime(year=1990, month=2, day=3, hour=11, minute=00)
ts_len = 10
ts_intvl = hours(1)
data = range(ts_len)
# This ts is the orignial one
ts0 = rts(data, ts_start, ts_intvl)
ts1 = rts(data, ts_start, ts_intvl)
l1norm = norm_diff_linf(ts0, ts1)
self.assertEqual(l1norm, 0.0)
d2 = [1., 2., 0.]
d3 = [0., 0., 1.]
ts0 = rts(d2, ts_start, ts_intvl)
ts1 = rts(d3, ts_start, ts_intvl)
l1norm2 = norm_diff_linf(ts0, ts1)
self.assertEqual(l1norm2, 2.0)
def test_merge_rts_intersect(self):
""" Test merging two rts with intersection."""
d1=[1.0]*int(spy.math.pow(2,10))
d2=[2.0]*int(spy.math.pow(2,11))
st1=datetime.datetime(year=1990,month=2,day=3,hour=11, minute=15)
st2=datetime.datetime(year=1990,month=3,day=3,hour=11, minute=15)
ts1=rts(d1,st1,time_interval(hours=1))
ts2=rts(d2,st2,time_interval(hours=1))
## Intentionally put some nan into ts1 to see effect.
ii=ts1.index_after(st2)
num_nan=10
ts1.data[ii+1:ii+num_nan+2]=spy.nan
nt=merge(ts1,ts2)
self.assertEqual(nt.start,ts1.start)
self.assertEqual(nt.end,ts2.end)
total_n=number_intervals(ts1.start,ts2.end,ts1.interval)+1
self.assertEqual(len(nt.data),total_n)
s1=nt.index_after(ts1.end)
s2=nt.index_after(ts2.start)
s3=ts2.index_after(ts1.end)
self.assert_(spy.allclose(nt.data[0:ii+1],ts1.data[0:ii+1]))
self.assert_(spy.allclose(nt.data[ii+1:ii+num_nan+2],ts2.data[0:num_nan+1]))
self.assert_(spy.allclose(nt.data[ii+num_nan+2:s1+1],ts1.data[ii+num_nan+2:s1+1]))
self.assert_(spy.allclose(nt.data[s1+1:len(nt.data)],ts2.data[s3+1:len(ts2.data)]))
## a small test
d1=[1.0]*4
d2=[2.0]*2
st1=datetime.datetime(year=1990,month=2,day=1)
st2=datetime.datetime(year=1990,month=3,day=1)
ts1=rts(d1,st1,time_interval(months=1))
ts2=rts(d2,st2,time_interval(months=1))
nt=merge(ts1,ts2)
self.assertEqual(len(nt),len(ts1))
self.assertEqual(nt.data[-1],ts1.data[-1])
d1=[1.0,1.0,1.0,spy.nan]
d2=[2.0,3.0,4.0]
st1=datetime.datetime(year=1990,month=2,day=1)
st2=datetime.datetime(year=1990,month=3,day=1)
ts1=rts(d1,st1,time_interval(months=1))
ts2=rts(d2,st2,time_interval(months=1))
nt=merge(ts1,ts2)
self.assertEqual(len(nt),len(ts1))
self.assertEqual(nt.data[-1],ts2.data[-1])
def test_bind_multivar(self):
""" test behaviour of bind on multvariate ts"""
start = parse_time("1996-2-1")
interval = parse_interval("1hour")
data1=sciarray([1.0,1.0,1.0,1.0,1.0,1.0,2.0,3.0,3.0,3.0])
data2t=sciarray([[1.0,1.0,1.0,1.0,1.0,1.0,2.0,3.0,3.0,3.0],
[2.0,2.1,2.8,9.1,3.2,0.5,0.1,8.1,1.2,1.1]])
data2=data2t.transpose()
data_temp= sciarray([data1[:],data2t[0,:],data2t[1,:]]).transpose()
ts1=rts(data1,start,interval,{})
ts2=rts(data2,start,interval,{})
new_ts = ts_bind(ts1,ts2)
self.assertEqual(len(new_ts),len(ts1))
self.assertEqual(new_ts.start,ts1.start)
self.assertEqual(new_ts.interval,interval)
for (d1,d2,d3),(dt1,dt2,dt3) in zip(new_ts.data,data_temp):
self.assertEqual(d1,dt1)
self.assertEqual(d2,dt2)
self.assertEqual(d3,dt3)
def test_ts_maximum(self):
ts_start = datetime(year=1990, month=2, day=3, hour=11, minute=45)
ts_len = 100
ts_intvl = days(1)
data = range(ts_len)
ts0 = rts(data, ts_start, ts_intvl)
tsmax = ts_maximum(ts0, 0)
self.assertEqual(tsmax, data[-1])
time_window = (ts0.times[5], ts0.times[30])
truemax = data[30]
tsmax = ts_max(ts0.window(ts0.times[5], ts0.times[30]))
self.assertEqual(tsmax, truemax)
def test_ts_sum(self):
ts_start = datetime(year=1990, month=2, day=3, hour=11, minute=45)
ts_len = 100
ts_intvl = days(1)
data = range(ts_len)
ts0 = rts(data, ts_start, ts_intvl)
tssum = ts_sum(ts0)
self.assertEqual(tssum, sum(data))
time_window = (ts0.times[5], ts0.times[30])
truesum = sum(data[5:31])
tssum = ts_sum(ts0.window(time_window[0], time_window[1]))
self.assertEqual(tssum, truesum)