def test_dtss_partition_by_average(self):
"""
This test illustrates use of partition_by client and server-side.
The main point here is to ensure that the evaluate period covers
both the historical and evaluation peri
"""
with tempfile.TemporaryDirectory() as c_dir:
# setup data to be calculated
utc = Calendar()
d = deltahours(1)
t = utc.time(2000, 1, 1)
n = utc.diff_units(t, utc.add(t, Calendar.YEAR, 10), d)
ta = TimeAxis(t, d, n)
td = TimeAxis(t, d * 24, n // 24)
n_ts = 1
store_tsv = TsVector() # something we store at server side
for i in range(n_ts):
pts = TimeSeries(
ta,
np.sin(
np.linspace(start=0, stop=1.0 * (i + 1),
num=ta.size())),
point_fx.POINT_AVERAGE_VALUE)
ts_id = shyft_store_url(f"{i}")
store_tsv.append(TimeSeries(
ts_id, pts)) # generate a bound pts to store
# start dtss server
dtss = DtsServer()
cache_on_write = True
port_no = find_free_port()
host_port = 'localhost:{0}'.format(port_no)
dtss.set_auto_cache(True)
dtss.set_listening_port(port_no)
dtss.set_container(
"test", c_dir
) # notice we set container 'test' to point to c_dir directory
dtss.start_async(
) # the internal shyft time-series will be stored to that container
# create dts client
c = DtsClient(
host_port,
auto_connect=False) # demonstrate object life-time connection
c.store_ts(store_tsv,
overwrite_on_write=True,
cache_on_write=cache_on_write)
t_0 = utc.time(2018, 1, 1)
tax = TimeAxis(t_0, Calendar.DAY, 365)
ts_h1 = TimeSeries(shyft_store_url(f'{0}'))
ts_h2 = store_tsv[0]
ts_p1 = ts_h1.partition_by(utc, t, Calendar.YEAR, 10,
t_0).average(tax)
ts_p2 = ts_h2.partition_by(utc, t, Calendar.YEAR, 10,
t_0).average(tax)
def test_dtss_remove_series(self):
with tempfile.TemporaryDirectory() as c_dir:
# start the server
dtss = DtsServer()
port_no = find_free_port()
host_port = 'localhost:{0}'.format(port_no)
dtss.set_listening_port(port_no)
dtss.set_container(
"test", c_dir
) # notice we set container 'test' to point to c_dir directory
dtss.start_async(
) # the internal shyft time-series will be stored to that container
# setup some data
utc = Calendar()
d = deltahours(1)
n = 365 * 24 // 3
t = utc.time(2016, 1, 1)
ta = TimeAxis(t, d, n)
tsv = TsVector()
pts = TimeSeries(ta, np.linspace(start=0, stop=1.0, num=ta.size()),
point_fx.POINT_AVERAGE_VALUE)
tsv.append(TimeSeries("cache://test/foo", pts))
# get a client
client = DtsClient(host_port)
client.store_ts(tsv)
# start with no removing
dtss.set_can_remove(False)
# we should be disallowed to remove now
try:
client.remove("shyft://test/foo")
except Exception as err:
self.assertEqual(
str(err), "dtss::server: server does not support removing")
# then try with allowing remove
dtss.set_can_remove(True)
# we only support removing shyft-url style data
try:
client.remove("protocol://test/foo")
except Exception as err:
self.assertEqual(
str(err),
"dtss::server: server does not allow removing for non shyft-url type data"
)
# now it should work
client.remove("shyft://test/foo")
def test_failures(self):
"""
Verify that dtss client server connections are auto-magically
restored and fixed
"""
with tempfile.TemporaryDirectory() as c_dir:
# start the server
dtss = DtsServer()
port_no = find_free_port()
host_port = 'localhost:{0}'.format(port_no)
dtss.set_listening_port(port_no)
dtss.set_container(
"test", c_dir
) # notice we set container 'test' to point to c_dir directory
dtss.start_async(
) # the internal shyft time-series will be stored to that container
# setup some data
utc = Calendar()
d = deltahours(1)
n = 365 * 24 // 3
t = utc.time(2016, 1, 1)
ta = TimeAxis(t, d, n)
tsv = TsVector()
pts = TimeSeries(ta, np.linspace(start=0, stop=1.0, num=ta.size()),
point_fx.POINT_AVERAGE_VALUE)
tsv.append(TimeSeries("cache://test/foo", pts))
# get a client
client = DtsClient(host_port, auto_connect=False)
client.store_ts(tsv)
client.close()
client.store_ts(tsv) # should just work, it re-open automagically
dtss.clear(
) # the server is out and away, no chance this would work
try:
client.store_ts(tsv)
self.assertTrue(
False,
'This should throw, because there is no dtss server to help you'
)
except Exception as ee:
self.assertFalse(False, f'expected {ee} here')
dtss.set_listening_port(port_no)
dtss.start_async()
client.store_ts(
tsv) # this should just work, automagically reconnect
def test_get_ts_info(self):
"""
Verify we can get specific TsInfo objects for time-series from the server backend.
"""
with tempfile.TemporaryDirectory() as c_dir:
# start the server
dtss = DtsServer()
port_no = find_free_port()
host_adr = 'localhost:{0}'.format(port_no)
dtss.set_listening_port(port_no)
dtss.set_container(
"testing", c_dir
) # notice we set container 'test' to point to c_dir directory
dtss.start_async(
) # the internal shyft time-series will be stored to that container
# get a client
client = DtsClient(host_adr)
try:
client.get_ts_info(r'shyft://testing/data')
except Exception as e:
pass
else:
# only end up here if no exceptions
self.fail('Could fetch info for non-existing ts info')
# setup some data
utc = Calendar()
d = deltahours(1)
n = 365 * 24 // 3
t = utc.time(2016, 1, 1)
ta = TimeAxis(t, d, n)
tsv = TsVector()
pts = TimeSeries(ta, np.linspace(start=0, stop=1.0, num=ta.size()),
point_fx.POINT_AVERAGE_VALUE)
tsv.append(TimeSeries(r'shyft://testing/data', pts))
client.store_ts(tsv)
info: TsInfo = client.get_ts_info(r'shyft://testing/data')
self.assertEqual(info.name, r'data')
self.assertEqual(info.point_fx, point_fx.POINT_AVERAGE_VALUE)
self.assertEqual(info.data_period, ta.total_period())
def test_can_create_cf_compliant_file(self):
# create files
test_file = path.join(path.abspath(os.curdir), 'shyft_test.nc')
if path.exists(test_file):
os.remove(test_file)
# create meta info
epsg_id = 32633
x0 = 100000
x1 = 200000
y0 = 100000
y1 = 200000
x = 101000
y = 101000
z = 1200
temperature = TimeSeriesMetaInfo('temperature',
'/observed/at_stn_abc/temperature',
'observed air temperature', x, y, z,
epsg_id)
# create time axis
utc = Calendar()
ta = TimeAxis(utc.time(2016, 1, 1), deltahours(1), 24)
data = np.arange(0, ta.size(), dtype=np.float64)
ts = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the first batch
t_ds = TimeSeriesStore(test_file, temperature)
t_ds.create_new_file()
t_ds.append_ts_data(ts)
# expected result
ts_exp = ts
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# Append data
print("\n\n append at the end data")
# create time axis
ta = TimeAxis(utc.time(2016, 1, 2), deltahours(1), 48)
ts = TimeSeries(ta,
dv.from_numpy(np.arange(0, ta.size(),
dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the data
t_ds.append_ts_data(ts)
# expected result
ta = TimeAxis(utc.time(2016, 1, 1), deltahours(1), 72)
data = np.empty(72)
data[:24] = np.arange(0, 24, dtype=np.float64)
data[24:72] = np.arange(0, 48, dtype=np.float64) # <-- new data
ts_exp = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# Append with overlap
print("\n\n append with overlap")
# create time axis
ta = TimeAxis(utc.time(2016, 1, 3), deltahours(1), 48)
ts = TimeSeries(ta,
dv.from_numpy(np.arange(0, ta.size(),
dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the data
t_ds.append_ts_data(ts)
# expected result
ta = TimeAxis(utc.time(2016, 1, 1), deltahours(1), 96)
data = np.empty(96)
data[:24] = np.arange(0, 24, dtype=np.float64)
data[24:48] = np.arange(0, 24, dtype=np.float64) # <-- new data
data[48:96] = np.arange(0, 48, dtype=np.float64) # <-- new data
ts_exp = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# Append with gap in time axis
print("\n\n Append with gap in time axis")
# create time axis
ta = TimeAxis(utc.time(2016, 1, 6), deltahours(1), 24)
ts = TimeSeries(ta,
dv.from_numpy(np.arange(0, ta.size(),
dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the data
t_ds.append_ts_data(ts)
# expected result
time_vals = np.append(
TimeAxis(utc.time(2016, 1, 1), deltahours(1), 96).time_points[:-1],
ta.time_points)
# print(time_vals)
ta = TimeAxis(UtcTimeVector.from_numpy(time_vals.astype(np.int64)))
data = np.empty(120)
data[:24] = np.arange(0, 24, dtype=np.float64)
data[24:48] = np.arange(0, 24, dtype=np.float64)
data[48:96] = np.arange(0, 48, dtype=np.float64)
data[96:120] = np.arange(0, 24, dtype=np.float64) # <-- new data
ts_exp = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
# print(ts_exp.total_period())
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
# print(geo_temperature[0].ts.time_axis.time_points - ts_exp.time_axis.time_points)
# print(geo_temperature[0].ts.time_axis.time_points - time_vals)
# print(ts_exp.time_axis.time_points - time_vals)
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# Add new data in the middle where nothing was defined (no moving)
print(
"\n\n Add new data in the middle where nothing was defined (no moving)"
)
# create time axis
ta = TimeAxis(utc.time(2016, 1, 2), deltahours(1), 24)
ts = TimeSeries(ta,
dv.from_numpy(
np.arange(100, 100 + ta.size(), dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the data
t_ds.append_ts_data(ts)
# expected result
time_vals = np.append(
TimeAxis(utc.time(2016, 1, 1), deltahours(1), 96).time_points[:-1],
TimeAxis(utc.time(2016, 1, 6), deltahours(1), 24).time_points)
ta = TimeAxis(UtcTimeVector.from_numpy(time_vals.astype(np.int64)))
data = np.empty(120)
data[:24] = np.arange(0, 24, dtype=np.float64)
data[24:48] = np.arange(100, 124, dtype=np.float64) # <-- new data
data[48:96] = np.arange(0, 48, dtype=np.float64)
data[96:120] = np.arange(0, 24, dtype=np.float64)
ts_exp = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# print(ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# Insert new data in the middle and move rest
print("\n\n insert new data and move rest")
# create time axis
ta = TimeAxis(utc.time(2016, 1, 5), deltahours(1), 36)
ts = TimeSeries(ta,
dv.from_numpy(
np.arange(200, 200 + ta.size(), dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the data
t_ds.append_ts_data(ts)
# expected result
ta = TimeAxis(utc.time(2016, 1, 1), deltahours(1), 144)
data = np.empty(144)
data[:24] = np.arange(0, 24, dtype=np.float64)
data[24:48] = np.arange(100, 124, dtype=np.float64)
data[48:96] = np.arange(0, 48, dtype=np.float64)
data[96:132] = np.arange(200, 236, dtype=np.float64) # <-- new data
data[132:144] = np.arange(12, 24, dtype=np.float64)
ts_exp = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# Add new data before existing data without overlap
print("\n\n add new data before existing data without overlap")
# create time axis
ta = TimeAxis(utc.time(2015, 12, 31), deltahours(1), 24)
ts = TimeSeries(ta,
dv.from_numpy(
np.arange(300, 300 + ta.size(), dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the first batch
t_ds.append_ts_data(ts)
# expected result
ta = TimeAxis(utc.time(2015, 12, 31), deltahours(1), 168)
data = np.empty(168)
data[:24] = np.arange(300, 324, dtype=np.float64) # <-- new data
data[24:48] = np.arange(0, 24, dtype=np.float64)
data[48:72] = np.arange(100, 124, dtype=np.float64)
data[72:120] = np.arange(0, 48, dtype=np.float64)
data[120:156] = np.arange(200, 236, dtype=np.float64)
data[156:168] = np.arange(12, 24, dtype=np.float64)
ts_exp = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# add new data before existing data with overlap
print("\n\n add new data before existing data with overlap")
# create time axis
ta = TimeAxis(utc.time(2015, 12, 30), deltahours(1), 36)
ts = TimeSeries(ta,
dv.from_numpy(
np.arange(400, 400 + ta.size(), dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the first batch
# t_ds = TimeSeriesStore(test_file, temperature)
t_ds.append_ts_data(ts)
# expected result
ta = TimeAxis(utc.time(2015, 12, 30), deltahours(1), 192)
data = np.empty(192)
data[:36] = np.arange(400, 436, dtype=np.float64) # <-- new data
data[36:48] = np.arange(312, 324, dtype=np.float64)
data[48:72] = np.arange(0, 24, dtype=np.float64)
data[72:96] = np.arange(100, 124, dtype=np.float64)
data[96:144] = np.arange(0, 48, dtype=np.float64)
data[144:180] = np.arange(200, 236, dtype=np.float64)
data[180:192] = np.arange(12, 24, dtype=np.float64)
ts_exp = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# Overwrite everything with less data points
# create time axis
print('\n\n Overwrite everything with less data points')
ta = TimeAxis(utc.time(2015, 12, 30), deltahours(24), 9)
ts = TimeSeries(ta,
dv.from_numpy(
np.arange(1000, 1000 + ta.size(),
dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# write the time series
t_ds.append_ts_data(ts)
# expected result
ts_exp = ts
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# Insert data with different dt
# create time axis
print('\n\n Insert data with different dt')
ta = TimeAxis(utc.time(2016, 1, 1), deltahours(1), 24)
ts = TimeSeries(ta,
dv.from_numpy(np.arange(0, 24, dtype=np.float64)),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# write the time series
t_ds.append_ts_data(ts)
# expected result
time_points = np.empty(33, dtype=np.int)
time_points[0:2] = TimeAxis(utc.time(2015, 12, 30), deltahours(24),
1).time_points
time_points[2:26] = TimeAxis(utc.time(2016, 1, 1), deltahours(1),
23).time_points
time_points[26:] = TimeAxis(utc.time(2016, 1, 2), deltahours(24),
6).time_points
ta = TimeAxis(UtcTimeVector.from_numpy(time_points))
data = np.empty(32)
data[0:2] = np.array([1000, 1001])
data[2:26] = np.arange(0, 24) # <-- new data
data[26:] = np.arange(1003, 1009)
ts_exp = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# delete data with range UtcPeriod in the middle
print('\n\n delete data with range UtcPeriod')
tp = UtcPeriod(utc.time(2015, 12, 31), utc.time(2016, 1, 1, 12))
# ta = TimeAxis(utc.time(2016, 1, 1), deltahours(1), 24)
# ts = TimeSeries(ta, dv.from_numpy(np.arange(0, 24, dtype=np.float64)), point_fx=point_fx.POINT_AVERAGE_VALUE)
# write the time series
t_ds.remove_tp_data(tp)
# expected result
time_points = np.array([
1451433600, 1451653200, 1451656800, 1451660400, 1451664000,
1451667600, 1451671200, 1451674800, 1451678400, 1451682000,
1451685600, 1451689200, 1451692800, 1451779200, 1451865600,
1451952000, 1452038400, 1452124800, 1452211200
])
ta = TimeAxis(UtcTimeVector.from_numpy(time_points))
data = np.array([
1000, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 1003, 1004, 1005,
1006, 1007, 1008
])
ts_exp = TimeSeries(ta, dv.from_numpy(data),
point_fx.POINT_INSTANT_VALUE
) # TODO: is this correct policy to use
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# delete data with range UtcPeriod at the start
print('\n\n delete data with range UtcPeriod at the start')
tp = UtcPeriod(1451433600, 1451667600)
# ta = TimeAxis(utc.time(2016, 1, 1), deltahours(1), 24)
# ts = TimeSeries(ta, dv.from_numpy(np.arange(0, 24, dtype=np.float64)), point_fx=point_fx.POINT_AVERAGE_VALUE)
# write the time series
t_ds.remove_tp_data(tp)
# expected result
time_points = np.array([
1451671200, 1451674800, 1451678400, 1451682000, 1451685600,
1451689200, 1451692800, 1451779200, 1451865600, 1451952000,
1452038400, 1452124800, 1452211200
])
ta = TimeAxis(UtcTimeVector.from_numpy(time_points))
data = np.array(
[18, 19, 20, 21, 22, 23, 1003, 1004, 1005, 1006, 1007, 1008])
ts_exp = TimeSeries(
ta, dv.from_numpy(data), point_fx.POINT_INSTANT_VALUE
) # TODO: is this correct policy to use for this test
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# delete data with range UtcPeriod at the end
print('\n\n delete data with range UtcPeriod at the end')
tp = UtcPeriod(1451952000, utc.time(2016, 1, 10))
# ta = TimeAxis(utc.time(2016, 1, 1), deltahours(1), 24)
# ts = TimeSeries(ta, dv.from_numpy(np.arange(0, 24, dtype=np.float64)), point_fx=point_fx.POINT_AVERAGE_VALUE)
# write the time series
t_ds.remove_tp_data(tp)
# expected result
time_points = np.array([
1451671200, 1451674800, 1451678400, 1451682000, 1451685600,
1451689200, 1451692800, 1451779200, 1451865600, 1451952000
])
ta = TimeAxis(UtcTimeVector.from_numpy(time_points))
data = np.array([18, 19, 20, 21, 22, 23, 1003, 1004, 1005])
ts_exp = TimeSeries(ta, dv.from_numpy(data),
point_fx.POINT_INSTANT_VALUE)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
try:
rts_map = ts_dr.get_timeseries(['temperature'],
ts_exp.total_period())
except CFDataRepositoryError:
pass
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# delete data with range UtcPeriod everything
print('\n\n delete data with range UtcPeriod everything')
tp = UtcPeriod(utc.time(2016, 1, 1), utc.time(2016, 1, 10))
# write the time series
t_ds.remove_tp_data(tp)
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
self.assertRaises(CFDataRepositoryError, ts_dr.get_timeseries,
['temperature'], tp)
# --------------------------------------
# insert data in between time saved data points
print('\n\n insert data in between time saved data points')
# insert first data in which we want to insert the second batch
utc = Calendar()
ta = TimeAxis(utc.time(2016, 1, 1), deltahours(24), 2)
data = np.arange(0, ta.size(), dtype=np.float64)
ts = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the first batch
t_ds.append_ts_data(ts)
# insert first data for every hour in between
utc = Calendar()
ta = TimeAxis(utc.time(2016, 1, 1) + deltahours(1), deltahours(1), 23)
data = np.arange(10, 10 + ta.size(), dtype=np.float64)
ts = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the first batch
t_ds.append_ts_data(ts)
# expected result
time_points = np.array([
1451606400, 1451610000, 1451613600, 1451617200, 1451620800,
1451624400, 1451628000, 1451631600, 1451635200, 1451638800,
1451642400, 1451646000, 1451649600, 1451653200, 1451656800,
1451660400, 1451664000, 1451667600, 1451671200, 1451674800,
1451678400, 1451682000, 1451685600, 1451689200, 1451692800, 0
])
time_points[-1] = 2 * time_points[-2] - time_points[
-3] # last time point calc
data = np.array([
0, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
26, 27, 28, 29, 30, 31, 32, 1
])
ta = TimeAxis(UtcTimeVector.from_numpy(time_points))
ts_exp = TimeSeries(
ta, dv.from_numpy(data), point_fx.POINT_INSTANT_VALUE
) # TODO: is this correct policy value for this case
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy()))
# --------------------------------------
# insert data including nan
print('\n\n insert data including nan')
utc = Calendar()
ta = TimeAxis(utc.time(2016, 1, 1) + deltahours(1), deltahours(1), 23)
data = np.arange(10, 10 + ta.size(), dtype=np.float64)
data[4] = np.nan
data[
6] = np.nan # np.inf, but trouble getting inf trough all version of numpy/netcdf
data[8] = np.nan # -np.inf, --"--
ts = TimeSeries(ta,
dv.from_numpy(data),
point_fx=point_fx.POINT_AVERAGE_VALUE)
# save the first batch
t_ds.append_ts_data(ts)
# expected result
time_points = np.array([
1451606400, 1451610000, 1451613600, 1451617200, 1451620800,
1451624400, 1451628000, 1451631600, 1451635200, 1451638800,
1451642400, 1451646000, 1451649600, 1451653200, 1451656800,
1451660400, 1451664000, 1451667600, 1451671200, 1451674800,
1451678400, 1451682000, 1451685600, 1451689200, 1451692800, 0
])
time_points[-1] = 2 * time_points[-2] - time_points[
-3] # last time point calc
data = np.array([
0,
10,
11,
12,
13,
np.nan,
15,
# np.inf,
np.
nan, # TODO: figure out how to unmask restoring 'used' mask-values
17,
#-np.inf,
np.nan,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
1
])
ta = TimeAxis(UtcTimeVector.from_numpy(time_points))
ts_exp = TimeSeries(
ta, dv.from_numpy(data),
point_fx.POINT_INSTANT_VALUE) # TODO: policy right ?
# now read back the result using a *standard* shyft cf geo repository
selection_criteria = {'bbox': [[x0, x1, x1, x0], [y0, y0, y1, y1]]}
ts_dr = CFDataRepository(epsg_id, test_file, selection_criteria)
# now read back 'temperature' that we know should be there
rts_map = ts_dr.get_timeseries(['temperature'], ts_exp.total_period())
# and verify that we get exactly back what we wanted.
self.assertIsNotNone(rts_map)
self.assertTrue('temperature' in rts_map)
geo_temperature = rts_map['temperature']
self.assertEqual(len(geo_temperature), 1)
self.assertLessEqual(
GeoPoint.distance2(geo_temperature[0].mid_point(),
GeoPoint(x, y, z)), 1.0)
# check if time axis is as expected
self.assertEqual(geo_temperature[0].ts.time_axis, ts_exp.time_axis)
self.assertTrue(
np.allclose(geo_temperature[0].ts.time_axis.time_points,
ts_exp.time_axis.time_points))
self.assertEqual(geo_temperature[0].ts.point_interpretation(),
point_fx.POINT_AVERAGE_VALUE)
# check if variable data is as expected
self.assertTrue(
np.allclose(geo_temperature[0].ts.values.to_numpy(),
ts_exp.values.to_numpy(),
equal_nan=True))
if path.exists(test_file):
os.remove(test_file)
def test_ts_cache(self):
""" Verify dtss ts-cache functions exposed to python """
with tempfile.TemporaryDirectory() as c_dir:
# setup data to be calculated
utc = Calendar()
d = deltahours(1)
n = 100
t = utc.time(2016, 1, 1)
ta = TimeAxis(t, d, n)
n_ts = 10
store_tsv = TsVector() # something we store at server side
tsv = TsVector(
) # something we put an expression into, refering to stored ts-symbols
for i in range(n_ts):
pts = TimeSeries(
ta,
np.sin(np.linspace(start=0, stop=1.0 * i, num=ta.size())),
point_fx.POINT_AVERAGE_VALUE)
ts_id = shyft_store_url("{0}".format(i))
tsv.append(float(1.0) * TimeSeries(ts_id)
) # make an expression that returns what we store
store_tsv.append(TimeSeries(
ts_id, pts)) # generate a bound pts to store
# add one external ts
tsv.append(TimeSeries(fake_store_url("_any_ts_id_will_do")))
# then start the server
dtss = DtsServer()
dtss.cb = self.dtss_read_callback # rig external callbacks as well.
self.callback_count = 0
self.rd_throws = False
cache_on_write = True
port_no = find_free_port()
host_port = 'localhost:{0}'.format(port_no)
dtss.set_auto_cache(True)
dtss.set_listening_port(port_no)
dtss.set_container(
"test", c_dir
) # notice we set container 'test' to point to c_dir directory
dtss.start_async(
) # the internal shyft time-series will be stored to that container
dts = DtsClient(
host_port,
auto_connect=False) # demonstrate object life-time connection
cs0 = dtss.cache_stats
dts.store_ts(store_tsv,
overwrite_on_write=True,
cache_on_write=cache_on_write)
r1 = dts.evaluate(tsv,
ta.total_period(),
use_ts_cached_read=True,
update_ts_cache=True)
cs1 = dtss.cache_stats
ccs1 = dts.cache_stats # client can also provide cahce-stats
dtss.flush_cache_all() # force the cache empty
dtss.clear_cache_stats()
cs2 = dtss.cache_stats # just to ensure clear did work
r1 = dts.evaluate(
tsv,
ta.total_period(),
use_ts_cached_read=True,
update_ts_cache=True
) # second evaluation, cache is empty, will force read(misses)
cs3 = dtss.cache_stats
r1 = dts.evaluate(
tsv,
ta.total_period(),
use_ts_cached_read=True,
update_ts_cache=True
) # third evaluation, cache is now filled, all hits
cs4 = dtss.cache_stats
# now verify explicit caching performed by the python callback
self.cache_dtss = dtss
self.cache_reads = True
dts.cache_flush() # is the equivalent of
# dtss.flush_cache_all()
# dtss.clear_cache_stats()
# use explicit cache-control instead of global
dtss.set_auto_cache(
False
) # turn off auto caching, we want to test the explicit caching
r1 = dts.evaluate(
tsv,
ta.total_period(),
use_ts_cached_read=True,
update_ts_cache=False
) # evaluation, just misses, but we cache explict the external
cs5 = dtss.cache_stats # ok base line a lots of misses
r1 = dts.evaluate(tsv,
ta.total_period(),
use_ts_cached_read=True,
update_ts_cache=False)
cs6 = dtss.cache_stats # should be one hit here
dts.close() # close connection (will use context manager later)
dtss.clear() # close server
# now the moment of truth:
self.assertEqual(len(r1), len(tsv))
for i in range(n_ts - 1):
self.assertEqual(r1[i].time_axis, store_tsv[i].time_axis)
assert_array_almost_equal(r1[i].values.to_numpy(),
store_tsv[i].values.to_numpy(),
decimal=4)
self.assertEqual(cs0.hits, 0)
self.assertEqual(cs0.misses, 0)
self.assertEqual(cs0.coverage_misses, 0)
self.assertEqual(cs0.id_count, 0)
self.assertEqual(cs0.point_count, 0)
self.assertEqual(cs0.fragment_count, 0)
self.assertEqual(cs1.hits, n_ts)
self.assertEqual(
cs1.misses, 1
) # because we cache on store, so 10 cached, 1 external with miss
self.assertEqual(cs1.coverage_misses, 0)
self.assertEqual(cs1.id_count, n_ts + 1)
self.assertEqual(cs1.point_count, (n_ts + 1) * n)
self.assertEqual(cs1.fragment_count, n_ts + 1)
# verify client side cache_stats
self.assertEqual(ccs1.hits, n_ts)
self.assertEqual(
ccs1.misses, 1
) # because we cache on store, so 10 cached, 1 external with miss
self.assertEqual(ccs1.coverage_misses, 0)
self.assertEqual(ccs1.id_count, n_ts + 1)
self.assertEqual(ccs1.point_count, (n_ts + 1) * n)
self.assertEqual(ccs1.fragment_count, n_ts + 1)
self.assertEqual(cs2.hits, 0)
self.assertEqual(cs2.misses, 0)
self.assertEqual(cs2.coverage_misses, 0)
self.assertEqual(cs2.id_count, 0)
self.assertEqual(cs2.point_count, 0)
self.assertEqual(cs2.fragment_count, 0)
self.assertEqual(cs3.hits, 0)
self.assertEqual(
cs3.misses, n_ts +
1) # because we cache on store, we don't even miss one time
self.assertEqual(cs3.coverage_misses, 0)
self.assertEqual(cs3.id_count, n_ts + 1)
self.assertEqual(cs3.point_count, (n_ts + 1) * n)
self.assertEqual(cs3.fragment_count, n_ts + 1)
self.assertEqual(cs4.hits,
n_ts + 1) # because previous read filled cache
self.assertEqual(cs4.misses,
n_ts + 1) # remembers previous misses.
self.assertEqual(cs4.coverage_misses, 0)
self.assertEqual(cs4.id_count, n_ts + 1)
self.assertEqual(cs4.point_count, (n_ts + 1) * n)
self.assertEqual(cs4.fragment_count, n_ts + 1)
self.assertEqual(cs6.hits, 1) # because previous read filled cache
self.assertEqual(cs6.misses,
n_ts * 2 + 1) # remembers previous misses.
self.assertEqual(cs6.coverage_misses, 0)
self.assertEqual(cs6.id_count, 1)
self.assertEqual(cs6.point_count, 1 * n)
self.assertEqual(cs6.fragment_count, 1)
def test_ts_store(self):
"""
This test verifies the shyft internal time-series store,
that allow identified time-series to be stored
in the backend using a directory container specified for the
location.
All time-series of the form shyft://<container>/<ts-name>
is mapped to the configured <container> (aka a directory on the server)
This applies to expressions, as well as the new
.store_ts(ts_vector) function that allows the user to
stash away time-series into the configured back-end container.
All find-operations of the form shyft://<container>/<regular-expression>
is mapped to a search in the corresponding directory for the <container>
:return:
"""
with tempfile.TemporaryDirectory() as c_dir:
# setup data to be calculated
utc = Calendar()
d = deltahours(1)
n = 365 * 24 // 3
t = utc.time(2016, 1, 1)
ta = TimeAxis(t, d, n)
n_ts = 10
store_tsv = TsVector() # something we store at server side
tsv = TsVector(
) # something we put an expression into, refering to stored ts-symbols
for i in range(n_ts):
pts = TimeSeries(
ta,
np.sin(np.linspace(start=0, stop=1.0 * i, num=ta.size())),
point_fx.POINT_AVERAGE_VALUE)
ts_id = shyft_store_url("{0}".format(i))
tsv.append(float(1.0) * TimeSeries(ts_id)
) # make an expression that returns what we store
store_tsv.append(TimeSeries(
ts_id, pts)) # generate a bound pts to store
# krls with some extra challenges related to serialization
tsv_krls = TsVector()
krls_ts = TimeSeries(shyft_store_url("9")).krls_interpolation(
dt=d, gamma=1e-3, tolerance=0.001, size=ta.size())
tsv_krls.append(krls_ts)
# min_max_check_ts_fill also needs a serial check
# create a trivial-case
ts9 = TimeSeries(shyft_store_url("9"))
ts_qac = ts9.min_max_check_linear_fill(v_min=-10.0 * n_ts,
v_max=10.0 * n_ts)
tsv_krls.append(ts_qac)
tsv_krls.append(ts9)
tsv_krls.append(ts9.inside(min_v=-0.5, max_v=0.5))
# then start the server
dtss = DtsServer()
port_no = find_free_port()
host_port = 'localhost:{0}'.format(port_no)
dtss.set_auto_cache(True)
std_max_items = dtss.cache_max_items
dtss.cache_max_items = 3000
tst_max_items = dtss.cache_max_items
dtss.set_listening_port(port_no)
dtss.set_container(
"test", c_dir
) # notice we set container 'test' to point to c_dir directory
dtss.start_async(
) # the internal shyft time-series will be stored to that container
# also notice that we dont have to setup callbacks in this case (but we could, and they would work)
#
# finally start the action
dts = DtsClient(host_port)
# then try something that should work
dts.store_ts(store_tsv)
r1 = dts.evaluate(tsv, ta.total_period())
f1 = dts.find(
r"shyft://test/\d") # find all ts with one digit, 0..9
r2 = dts.evaluate(tsv_krls, ta.total_period())
url_x = shyft_store_url(r'does not exists')
tsvx = TsVector()
tsvx.append(TimeSeries(url_x))
try:
rx = dts.evaluate(tsvx, ta.total_period())
self.assertFalse(True, 'This did not work out')
except RuntimeError as rex:
self.assertIsNotNone(rex)
dts.close() # close connection (will use context manager later)
dtss.clear() # close server
# now the moment of truth:
self.assertEqual(len(r1), len(tsv))
for i in range(n_ts - 1):
self.assertEqual(r1[i].time_axis, store_tsv[i].time_axis)
assert_array_almost_equal(r1[i].values.to_numpy(),
store_tsv[i].values.to_numpy(),
decimal=4)
self.assertEqual(len(f1), 10)
self.assertEqual(len(r2), len(tsv_krls))
assert_array_almost_equal(r2[1].values.to_numpy(),
r2[2].values.to_numpy(),
decimal=4)
self.assertEqual(1000000, std_max_items)
self.assertEqual(3000, tst_max_items)
def test_functionality_hosting_localhost(self):
# setup data to be calculated
utc = Calendar()
d = deltahours(1)
d24 = deltahours(24)
n = 240
n24 = 10
t = utc.time(2016, 1, 1)
ta = TimeAxis(t, d, n)
ta24 = TimeAxis(t, d24, n24)
n_ts = 100
percentile_list = IntVector([0, 35, 50, 65, 100])
tsv = TsVector()
store_tsv = TsVector() # something we store at server side
for i in range(n_ts):
pts = TimeSeries(ta, np.linspace(start=0, stop=1.0, num=ta.size()),
point_fx.POINT_AVERAGE_VALUE)
tsv.append(float(1 + i / 10) * pts)
store_tsv.append(TimeSeries("cache://test/{0}".format(i),
pts)) # generate a bound pts to store
dummy_ts = TimeSeries('dummy://a')
tsv.append(dummy_ts.integral(ta))
self.assertGreater(len(ts_stringify(tsv[0])),
10) # just ensure ts_stringify work on expr.
# then start the server
dtss = DtsServer()
port_no = find_free_port()
host_port = 'localhost:{0}'.format(port_no)
dtss.set_listening_port(port_no)
dtss.cb = self.dtss_read_callback
dtss.find_cb = self.dtss_find_callback
dtss.store_ts_cb = self.dtss_store_callback
dtss.start_async()
dts = DtsClient(StringVector([host_port]), True,
1000) # as number of hosts
# then try something that should work
dts.store_ts(store_tsv)
r1 = dts.evaluate(tsv, ta.total_period())
tsv1x = tsv.inside(-0.5, 0.5)
tsv1x.append(tsv1x[-1].decode(
start_bit=1, n_bits=1)) # just to verify serialization/bind
tsv1x.append(store_tsv[1].derivative())
tsv1x.append(store_tsv[1].pow(
2.0)) # just for verify pow serialization(well, it's a bin-op..)
r1x = dts.evaluate(tsv1x, ta.total_period())
r2 = dts.percentiles(tsv, ta.total_period(), ta24, percentile_list)
r3 = dts.find('netcdf://dummy\.nc/ts\d')
self.rd_throws = True
ex_count = 0
try:
rx = dts.evaluate(tsv, ta.total_period())
except RuntimeError as e:
ex_count = 1
pass
self.rd_throws = True
try:
fx = dts.find('should throw')
except RuntimeError as e:
ex_count += 1
pass
dts.close() # close connection (will use context manager later)
dtss.clear() # close server
self.assertEqual(ex_count, 2)
self.assertEqual(len(r1), len(tsv))
self.assertEqual(self.callback_count, 4)
for i in range(n_ts - 1):
self.assertEqual(r1[i].time_axis, tsv[i].time_axis)
assert_array_almost_equal(r1[i].values.to_numpy(),
tsv[i].values.to_numpy(),
decimal=4)
self.assertEqual(len(r2), len(percentile_list))
dummy_ts.bind(
TimeSeries(ta,
fill_value=1.0,
point_fx=point_fx.POINT_AVERAGE_VALUE))
p2 = tsv.percentiles(ta24, percentile_list)
# r2 = tsv.percentiles(ta24,percentile_list)
for i in range(len(p2)):
self.assertEqual(r2[i].time_axis, p2[i].time_axis)
assert_array_almost_equal(r2[i].values.to_numpy(),
p2[i].values.to_numpy(),
decimal=1)
self.assertEqual(self.find_count, 2)
self.assertEqual(len(r3), 10) # 0..9
for i in range(len(r3)):
self.assertEqual(r3[i], self.ts_infos[i])
self.assertIsNotNone(r1x)
self.assertEqual(1, len(self.stored_tsv))
self.assertEqual(len(store_tsv), len(self.stored_tsv[0]))
for i in range(len(store_tsv)):
self.assertEqual(self.stored_tsv[0][i].ts_id(),
store_tsv[i].ts_id())
def test_forecast(self):
fx_avg = ts_point_fx.POINT_AVERAGE_VALUE
utc = Calendar()
ta = TimeAxis(utc.time(2017, 1, 1, 0, 0, 0), deltahours(24), 4)
historical_data = TsVector()
forecast_sets = TsVectorSet()
weight_sets = dv()
num_historical_data = 56
# Let's make three sets, one of two elements, one of three, and one of
# four.
forecasts_1 = TsVector()
forecasts_2 = TsVector()
forecasts_3 = TsVector()
forecasts_1.append(TimeSeries(ta, dv([13.4, 15.6, 17.1, 19.1]),
fx_avg))
forecasts_1.append(TimeSeries(ta, dv([34.1, 2.40, 43.9, 10.2]),
fx_avg))
forecast_sets.append(forecasts_1)
weight_sets.append(5.0)
forecasts_2.append(TimeSeries(ta, dv([83.1, -42.2, 0.4, 23.4]),
fx_avg))
forecasts_2.append(TimeSeries(ta, dv([15.1, 6.500, 4.2, 2.9]), fx_avg))
forecasts_2.append(TimeSeries(ta, dv([53.1, 87.90, 23.8, 5.6]),
fx_avg))
forecast_sets.append(forecasts_2)
weight_sets.append(9.0)
forecasts_3.append(
TimeSeries(ta, dv([1.5, -1.9, -17.2, -10.0]), fx_avg))
forecasts_3.append(
TimeSeries(ta, dv([4.7, 18.2, 15.3000, 8.9]), fx_avg))
forecasts_3.append(
TimeSeries(ta, dv([-45.2, -2.3, 80.2, 71.0]), fx_avg))
forecasts_3.append(
TimeSeries(ta, dv([45.1, -92.0, 34.4, 65.8]), fx_avg))
forecast_sets.append(forecasts_3)
weight_sets.append(3.0)
for i in range(num_historical_data):
historical_data.append(
TimeSeries(ta,
dv.from_numpy(np.random.random(ta.size()) * 50.0),
fx_avg))
# need one more exposed from core here: auto historical_order = qm::quantile_index<tsa_t>(historical_data, ta);
interpolation_start = ta.time(2)
interpolation_end = ta.time(3)
# Act
result = quantile_map_forecast(forecast_sets, weight_sets,
historical_data, ta,
interpolation_start, interpolation_end,
False)
self.assertIsNotNone(result)
self.assertEqual(len(result), num_historical_data)
for ts in result:
self.assertEqual(ts.size(), ta.size())