def test_aggregate_ninterval_groupby():
"""Test API ValueError, ninterval and groupby."""
with pytest.raises(ValueError):
_ = tstoolbox.aggregate(statistic="sum",
groupby="7D",
ninterval=7,
input_ts="tests/data_flat.csv")
def test_aggregate_ninterval_groupby():
"""Test API ValueError, ninterval and groupby."""
with pytest.raises(ValueError):
_ = tstoolbox.aggregate(statistic="sum",
groupby="7D",
ninterval=7,
input_ts="tests/data_flat.csv")
def test_aggregate_groupby():
"""Test API ValueError, groupby and agg_interval."""
with pytest.raises(ValueError):
_ = tstoolbox.aggregate(statistic="sum",
groupby="D",
agg_interval="D",
input_ts="tests/data_flat.csv")
def test_aggregate_direct_sum(self):
''' Test daily mean summation
'''
out = tstoolbox.aggregate(statistic='sum',
agg_interval='daily',
input_ts='tests/data_flat.csv')
assert_frame_equal(out, self.aggregate_direct_sum)
def daily_to_daytime_hourly_trapezoid(
latitude,
statistic='mean',
input_ts='-',
start_date=None,
end_date=None,
float_format='%g',
print_input=''):
'''
Daily to hourly disaggregation based on a trapezoidal shape.
'''
from tstoolbox import tstoolbox
tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts),
start_date=start_date,
end_date=end_date,
pick=None)
lrad = latitude*np.pi/180.0
ad = 0.40928*np.cos(0.0172141*(172 - tsd.index.dayofyear))
ss = np.sin(lrad)*np.sin(ad)
cs = np.cos(lrad)*np.cos(ad)
x2 = -ss/cs
delt = 7.6394*(np.pi/2.0 - np.arctan(x2/np.square(1 - x2**2)))
sunr = 12.0 - delt/2.0
#develop hourly distribution given sunrise,
#sunset and length of day (DELT)
dtr2 = delt / 2.0
dtr4 = delt / 4.0
crad = 2.0/3.0/dtr2/60 # using minutes...
tr2 = sunr + dtr4
tr3 = tr2 + dtr2
tr4 = tr3 + dtr4
sdate = datetime.datetime(tsd.index[0].year, tsd.index[0].month,
tsd.index[0].day)
edate = datetime.datetime(tsd.index[-1].year, tsd.index[-1].month,
tsd.index[-1].day) + datetime.timedelta(days=1) - datetime.timedelta(hours=1)
datevalue = pandas.DatetimeIndex(start=sdate, end=edate,
freq='MIN')
fdata = pandas.Series([np.nan]*(len(datevalue)), index=datevalue)
fdata[0] = 0.0
fdata[-1] = 0.0
for index in range(len(sunr)):
cdate = tsd.index[index]
fdata[datetime.datetime(cdate.year, cdate.month, cdate.day, int(sunr[index]), int((sunr[index] - int(sunr[index]))*60))] = 0.0
fdata[datetime.datetime(cdate.year, cdate.month, cdate.day, int(tr4[index]), int((tr4[index] - int(tr4[index]))*60))] = 0.0
fdata[datetime.datetime(cdate.year, cdate.month, cdate.day, int(tr2[index]), int((tr2[index] - int(tr2[index]))*60))] = crad[index]
fdata[datetime.datetime(cdate.year, cdate.month, cdate.day, int(tr3[index]), int((tr3[index] - int(tr3[index]))*60))] = crad[index]
fdata = fdata.interpolate('linear')
fdata = fdata.fillna(0.0)
fdata = tstoolbox.aggregate(statistic=statistic,
agg_interval='H',
input_ts=fdata
)
return tsutils.print_input(print_input, tsd, fdata, None,
float_format=float_format)
def test_aggregate_groupby():
"""Test API ValueError, groupby and agg_interval."""
with pytest.raises(ValueError):
_ = tstoolbox.aggregate(
statistic="sum",
groupby="D",
agg_interval="D",
input_ts="tests/data_flat.csv",
)
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import pytest
from tstoolbox import tstoolbox
# Pull this in once.
df = tstoolbox.aggregate(agg_interval="D",
clean=True,
input_ts="tests/02234500_65_65.csv")
# Pull this in once.
dfa = tstoolbox.aggregate(agg_interval="A",
clean=True,
input_ts="tests/02234500_65_65.csv")
@pytest.mark.mpl_image_compare(tolerance=6)
def test_time_plot():
plt.close("all")
return tstoolbox.plot(
type="time",
columns=1,
clean=True,
input_ts="tests/02234500_65_65.csv",
ofilename=None,
plot_styles="classic",
)
def test_aggregate_agg_interval():
"""Test API agg_interval."""
with pytest.warns(UserWarning):
_ = tstoolbox.aggregate(statistic="mean",
agg_interval="D",
input_ts="tests/data_flat.csv")
def test_aggregate_bad_statistic():
"""Test API statistic name."""
with pytest.raises(ValueError):
_ = tstoolbox.aggregate(statistic="camel",
groupby="D",
input_ts="tests/data_flat.csv")
def test_aggregate_direct_sum(self):
"""Test daily mean summation."""
out = tstoolbox.aggregate(statistic='sum',
groupby='daily',
input_ts='tests/data_flat.csv')
assert_frame_equal(out, self.aggregate_direct_sum)
import matplotlib
matplotlib.use('Agg')
import pytest
from tstoolbox import tstoolbox
# Pull this in once.
df = tstoolbox.aggregate(agg_interval='D',
clean=True,
input_ts='tests/02234500_65_65.csv')
# Pull this in once.
dfa = tstoolbox.aggregate(agg_interval='A',
clean=True,
input_ts='tests/02234500_65_65.csv')
@pytest.mark.mpl_image_compare
def test_time_plot():
return tstoolbox.plot(type='time',
columns=1,
clean=True,
input_ts='tests/02234500_65_65.csv',
ofilename=None)
@pytest.mark.mpl_image_compare
def test_time_multiple_traces_plot():
return tstoolbox.plot(type='time',
columns=[2,3],
style='b-,r*',
input_ts='tests/data_daily_sample.csv',
ofilename=None)
def test_aggregate_bad_statistic():
"""Test API statistic name."""
with pytest.raises(ValueError):
_ = tstoolbox.aggregate(statistic="camel",
groupby="D",
input_ts="tests/data_flat.csv")
def test_aggregate_direct_sum(self):
"""Test daily mean summation."""
out = tstoolbox.aggregate(statistic="sum",
groupby="daily",
input_ts="tests/data_flat.csv")
assert_frame_equal(out, self.aggregate_direct_sum)
def test_aggregate_ninterval():
"""Test API ninterval."""
with pytest.warns(UserWarning):
_ = tstoolbox.aggregate(statistic="mean",
ninterval=7,
input_ts="tests/data_flat.csv")
